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b/master/.doctrees/environment.pickle differ diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index 16e1979b8..8de50c429 100644 Binary files a/master/.doctrees/index.doctree and b/master/.doctrees/index.doctree differ diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index d8eba485c..73f29c159 100644 Binary files a/master/.doctrees/migrating/migrate_v2.doctree and b/master/.doctrees/migrating/migrate_v2.doctree differ diff --git a/master/.doctrees/nbsphinx/tutorials/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/audio.ipynb index 4565d11d1..3abe77e8c 100644 --- a/master/.doctrees/nbsphinx/tutorials/audio.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:08.540462Z", - "iopub.status.busy": "2023-08-14T16:34:08.540014Z", - "iopub.status.idle": "2023-08-14T16:34:12.763425Z", - "shell.execute_reply": "2023-08-14T16:34:12.762632Z" + "iopub.execute_input": "2023-08-15T04:05:12.762164Z", + "iopub.status.busy": "2023-08-15T04:05:12.761899Z", + "iopub.status.idle": "2023-08-15T04:05:18.219217Z", + "shell.execute_reply": "2023-08-15T04:05:18.218250Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:12.768105Z", - "iopub.status.busy": "2023-08-14T16:34:12.767316Z", - "iopub.status.idle": "2023-08-14T16:34:12.771691Z", - "shell.execute_reply": "2023-08-14T16:34:12.770985Z" + "iopub.execute_input": "2023-08-15T04:05:18.224030Z", + "iopub.status.busy": "2023-08-15T04:05:18.223494Z", + "iopub.status.idle": "2023-08-15T04:05:18.230130Z", + "shell.execute_reply": "2023-08-15T04:05:18.229261Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:12.774937Z", - "iopub.status.busy": "2023-08-14T16:34:12.774410Z", - "iopub.status.idle": "2023-08-14T16:34:12.780609Z", - "shell.execute_reply": "2023-08-14T16:34:12.779966Z" + "iopub.execute_input": "2023-08-15T04:05:18.233550Z", + "iopub.status.busy": "2023-08-15T04:05:18.233291Z", + "iopub.status.idle": "2023-08-15T04:05:18.239821Z", + "shell.execute_reply": "2023-08-15T04:05:18.238808Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:12.783672Z", - "iopub.status.busy": "2023-08-14T16:34:12.783238Z", - "iopub.status.idle": "2023-08-14T16:34:14.748685Z", - "shell.execute_reply": "2023-08-14T16:34:14.747362Z" + "iopub.execute_input": "2023-08-15T04:05:18.243646Z", + "iopub.status.busy": "2023-08-15T04:05:18.243402Z", + "iopub.status.idle": "2023-08-15T04:05:20.262686Z", + "shell.execute_reply": "2023-08-15T04:05:20.261312Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:14.753317Z", - "iopub.status.busy": "2023-08-14T16:34:14.752707Z", - "iopub.status.idle": "2023-08-14T16:34:14.773571Z", - "shell.execute_reply": "2023-08-14T16:34:14.772852Z" + "iopub.execute_input": "2023-08-15T04:05:20.267169Z", + "iopub.status.busy": "2023-08-15T04:05:20.266837Z", + "iopub.status.idle": "2023-08-15T04:05:20.287552Z", + "shell.execute_reply": "2023-08-15T04:05:20.286755Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:14.814263Z", - "iopub.status.busy": "2023-08-14T16:34:14.813674Z", - "iopub.status.idle": "2023-08-14T16:34:14.820711Z", - "shell.execute_reply": "2023-08-14T16:34:14.820012Z" + "iopub.execute_input": "2023-08-15T04:05:20.345676Z", + "iopub.status.busy": "2023-08-15T04:05:20.344523Z", + "iopub.status.idle": "2023-08-15T04:05:20.353687Z", + "shell.execute_reply": "2023-08-15T04:05:20.352476Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:14.823992Z", - "iopub.status.busy": "2023-08-14T16:34:14.823548Z", - "iopub.status.idle": "2023-08-14T16:34:15.597206Z", - "shell.execute_reply": "2023-08-14T16:34:15.596453Z" + "iopub.execute_input": "2023-08-15T04:05:20.358060Z", + "iopub.status.busy": "2023-08-15T04:05:20.357310Z", + "iopub.status.idle": "2023-08-15T04:05:21.279259Z", + "shell.execute_reply": "2023-08-15T04:05:21.278116Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:15.601063Z", - "iopub.status.busy": "2023-08-14T16:34:15.600476Z", - "iopub.status.idle": "2023-08-14T16:34:17.769494Z", - "shell.execute_reply": "2023-08-14T16:34:17.768653Z" + "iopub.execute_input": "2023-08-15T04:05:21.284553Z", + "iopub.status.busy": "2023-08-15T04:05:21.283752Z", + "iopub.status.idle": "2023-08-15T04:05:23.543792Z", + "shell.execute_reply": "2023-08-15T04:05:23.542760Z" }, "id": "vL9lkiKsHvKr" }, @@ -472,10 +472,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:17.773501Z", - "iopub.status.busy": "2023-08-14T16:34:17.773223Z", - "iopub.status.idle": "2023-08-14T16:34:17.805124Z", - "shell.execute_reply": "2023-08-14T16:34:17.804409Z" + "iopub.execute_input": "2023-08-15T04:05:23.548837Z", + "iopub.status.busy": "2023-08-15T04:05:23.548044Z", + "iopub.status.idle": "2023-08-15T04:05:23.589300Z", + "shell.execute_reply": "2023-08-15T04:05:23.588291Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -555,10 +555,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:17.808280Z", - "iopub.status.busy": "2023-08-14T16:34:17.807902Z", - "iopub.status.idle": "2023-08-14T16:34:17.811889Z", - "shell.execute_reply": "2023-08-14T16:34:17.811225Z" + "iopub.execute_input": "2023-08-15T04:05:23.593624Z", + "iopub.status.busy": "2023-08-15T04:05:23.593335Z", + "iopub.status.idle": "2023-08-15T04:05:23.598636Z", + "shell.execute_reply": "2023-08-15T04:05:23.597424Z" }, "id": "I8JqhOZgi94g" }, @@ -580,10 +580,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:17.814740Z", - "iopub.status.busy": "2023-08-14T16:34:17.814382Z", - "iopub.status.idle": "2023-08-14T16:34:30.445435Z", - "shell.execute_reply": "2023-08-14T16:34:30.444681Z" + "iopub.execute_input": "2023-08-15T04:05:23.602808Z", + "iopub.status.busy": "2023-08-15T04:05:23.602522Z", + "iopub.status.idle": "2023-08-15T04:05:43.241588Z", + "shell.execute_reply": "2023-08-15T04:05:43.240516Z" }, "id": "2FSQ2GR9R_YA" }, @@ -615,10 +615,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:30.449248Z", - "iopub.status.busy": "2023-08-14T16:34:30.448815Z", - "iopub.status.idle": "2023-08-14T16:34:30.453940Z", - "shell.execute_reply": "2023-08-14T16:34:30.453354Z" + "iopub.execute_input": "2023-08-15T04:05:43.246179Z", + "iopub.status.busy": "2023-08-15T04:05:43.245794Z", + "iopub.status.idle": "2023-08-15T04:05:43.252164Z", + "shell.execute_reply": "2023-08-15T04:05:43.251414Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -628,18 +628,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-14.196308 7.319454 12.47899 ... 2.289091 2.817013\n", - " -10.892642 ]\n", - " [-24.898056 5.2561927 12.559636 ... -3.5597174 9.6206665\n", - " -10.285249 ]\n", - " [-21.709625 7.5033684 7.913807 ... -6.819826 3.1831462\n", - " -17.208761 ]\n", + "[[-14.196314 7.319463 12.478973 ... 2.289077 2.8170207\n", + " -10.892647 ]\n", + " [-24.898058 5.2561903 12.559641 ... -3.5597146 9.620667\n", + " -10.28525 ]\n", + " [-21.709623 7.503369 7.913801 ... -6.819831 3.1831489\n", + " -17.208763 ]\n", " ...\n", - " [-16.08425 6.321053 12.005463 ... 1.216175 9.478231\n", - " -10.682177 ]\n", - " [-15.053815 5.2424726 1.091422 ... -0.78335106 9.039538\n", - " -23.569181 ]\n", - " [-19.76109 1.1258249 16.75323 ... 3.3508852 11.598273\n", + " [-16.08426 6.321049 12.005462 ... 1.2161489 9.478238\n", + " -10.682178 ]\n", + " [-15.053809 5.2424674 1.0914197 ... -0.78334606 9.039537\n", + " -23.569172 ]\n", + " [-19.761091 1.1258256 16.75323 ... 3.3508904 11.598279\n", " -16.237118 ]]\n", "Shape of array: (2500, 512)\n" ] @@ -677,10 +677,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:30.457081Z", - "iopub.status.busy": "2023-08-14T16:34:30.456426Z", - "iopub.status.idle": "2023-08-14T16:34:36.144081Z", - "shell.execute_reply": "2023-08-14T16:34:36.143333Z" + "iopub.execute_input": "2023-08-15T04:05:43.257071Z", + "iopub.status.busy": "2023-08-15T04:05:43.256227Z", + "iopub.status.idle": "2023-08-15T04:05:53.661669Z", + "shell.execute_reply": "2023-08-15T04:05:53.660748Z" }, "id": "i_drkY9YOcw4" }, @@ -714,10 +714,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.148528Z", - "iopub.status.busy": "2023-08-14T16:34:36.147985Z", - "iopub.status.idle": "2023-08-14T16:34:36.155802Z", - "shell.execute_reply": "2023-08-14T16:34:36.155235Z" + "iopub.execute_input": "2023-08-15T04:05:53.666463Z", + "iopub.status.busy": "2023-08-15T04:05:53.665480Z", + "iopub.status.idle": "2023-08-15T04:05:53.677928Z", + "shell.execute_reply": "2023-08-15T04:05:53.676868Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -764,10 +764,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.160246Z", - "iopub.status.busy": "2023-08-14T16:34:36.158896Z", - "iopub.status.idle": "2023-08-14T16:34:36.268757Z", - "shell.execute_reply": "2023-08-14T16:34:36.267788Z" + "iopub.execute_input": "2023-08-15T04:05:53.685046Z", + "iopub.status.busy": "2023-08-15T04:05:53.683288Z", + "iopub.status.idle": "2023-08-15T04:05:53.842559Z", + "shell.execute_reply": "2023-08-15T04:05:53.841393Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.272961Z", - "iopub.status.busy": "2023-08-14T16:34:36.272352Z", - "iopub.status.idle": "2023-08-14T16:34:36.288028Z", - "shell.execute_reply": "2023-08-14T16:34:36.287351Z" + "iopub.execute_input": "2023-08-15T04:05:53.847371Z", + "iopub.status.busy": "2023-08-15T04:05:53.846891Z", + "iopub.status.idle": "2023-08-15T04:05:53.866752Z", + "shell.execute_reply": "2023-08-15T04:05:53.865809Z" }, "scrolled": true }, @@ -836,11 +836,11 @@ "\n", "Examples representing most severe instances of this issue:\n", " is_label_issue label_score given_label predicted_label\n", - "176 False 0.006548 7 8\n", - "2318 False 0.008296 3 6\n", - "986 False 0.010476 6 3\n", - "1946 True 0.012922 6 8\n", - "516 True 0.013263 6 8\n" + "176 False 0.006510 7 8\n", + "2318 False 0.008255 3 6\n", + "986 False 0.010404 6 3\n", + "1946 True 0.012856 6 8\n", + "469 True 0.013254 6 3\n" ] } ], @@ -862,10 +862,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.291140Z", - "iopub.status.busy": "2023-08-14T16:34:36.290665Z", - "iopub.status.idle": "2023-08-14T16:34:36.300713Z", - "shell.execute_reply": "2023-08-14T16:34:36.300006Z" + "iopub.execute_input": "2023-08-15T04:05:53.871163Z", + "iopub.status.busy": "2023-08-15T04:05:53.870826Z", + "iopub.status.idle": "2023-08-15T04:05:53.885810Z", + "shell.execute_reply": "2023-08-15T04:05:53.884701Z" } }, "outputs": [ @@ -900,35 +900,35 @@ " \n", " 0\n", " False\n", - " 0.100049\n", + " 0.100326\n", " 7\n", " 6\n", " \n", " \n", " 1\n", " False\n", - " 0.998728\n", + " 0.998723\n", " 0\n", " 0\n", " \n", " \n", " 2\n", " False\n", - " 0.998785\n", + " 0.998768\n", " 0\n", " 0\n", " \n", " \n", " 3\n", " False\n", - " 0.980984\n", + " 0.981028\n", " 8\n", " 8\n", " \n", " \n", " 4\n", " False\n", - " 0.998247\n", + " 0.998219\n", " 5\n", " 5\n", " \n", @@ -938,11 +938,11 @@ ], "text/plain": [ " is_label_issue label_score given_label predicted_label\n", - "0 False 0.100049 7 6\n", - "1 False 0.998728 0 0\n", - "2 False 0.998785 0 0\n", - "3 False 0.980984 8 8\n", - "4 False 0.998247 5 5" + "0 False 0.100326 7 6\n", + "1 False 0.998723 0 0\n", + "2 False 0.998768 0 0\n", + "3 False 0.981028 8 8\n", + "4 False 0.998219 5 5" ] }, "execution_count": 17, @@ -969,10 +969,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.304775Z", - "iopub.status.busy": "2023-08-14T16:34:36.304169Z", - "iopub.status.idle": "2023-08-14T16:34:36.309563Z", - "shell.execute_reply": "2023-08-14T16:34:36.308867Z" + "iopub.execute_input": "2023-08-15T04:05:53.891349Z", + "iopub.status.busy": "2023-08-15T04:05:53.891043Z", + "iopub.status.idle": "2023-08-15T04:05:53.898700Z", + "shell.execute_reply": "2023-08-15T04:05:53.897431Z" } }, "outputs": [ @@ -981,7 +981,7 @@ "output_type": "stream", "text": [ "Here are indices of the most likely errors: \n", - " [1946 516 469 1871 1955 2132]\n" + " [1946 469 516 1871 1955 2132]\n" ] } ], @@ -1010,10 +1010,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.312924Z", - "iopub.status.busy": "2023-08-14T16:34:36.312492Z", - "iopub.status.idle": "2023-08-14T16:34:36.321251Z", - "shell.execute_reply": "2023-08-14T16:34:36.320630Z" + "iopub.execute_input": "2023-08-15T04:05:53.904356Z", + "iopub.status.busy": "2023-08-15T04:05:53.904052Z", + "iopub.status.idle": "2023-08-15T04:05:53.915022Z", + "shell.execute_reply": "2023-08-15T04:05:53.913965Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1051,13 +1051,13 @@ " 6\n", " \n", " \n", - " 516\n", - " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav\n", + " 469\n", + " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_35.wav\n", " 6\n", " \n", " \n", - " 469\n", - " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_35.wav\n", + " 516\n", + " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav\n", " 6\n", " \n", " \n", @@ -1082,16 +1082,16 @@ "text/plain": [ " wav_audio_file_path \\\n", "1946 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_14.wav \n", - "516 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav \n", "469 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_35.wav \n", + "516 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav \n", "1871 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_theo_27.wav \n", "1955 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/4_george_31.wav \n", "2132 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_nicolas_8.wav \n", "\n", " label \n", "1946 6 \n", - "516 6 \n", "469 6 \n", + "516 6 \n", "1871 6 \n", "1955 4 \n", "2132 6 " @@ -1133,10 +1133,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.324459Z", - "iopub.status.busy": "2023-08-14T16:34:36.324114Z", - "iopub.status.idle": "2023-08-14T16:34:36.478689Z", - "shell.execute_reply": "2023-08-14T16:34:36.477918Z" + "iopub.execute_input": "2023-08-15T04:05:53.920241Z", + "iopub.status.busy": "2023-08-15T04:05:53.919956Z", + "iopub.status.idle": "2023-08-15T04:05:54.149960Z", + "shell.execute_reply": "2023-08-15T04:05:54.148803Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1190,10 +1190,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.482869Z", - "iopub.status.busy": "2023-08-14T16:34:36.482468Z", - "iopub.status.idle": "2023-08-14T16:34:36.628249Z", - "shell.execute_reply": "2023-08-14T16:34:36.627420Z" + "iopub.execute_input": "2023-08-15T04:05:54.154904Z", + "iopub.status.busy": "2023-08-15T04:05:54.154422Z", + "iopub.status.idle": "2023-08-15T04:05:54.369861Z", + "shell.execute_reply": "2023-08-15T04:05:54.368691Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1238,10 +1238,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.631458Z", - "iopub.status.busy": 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"_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index e9f6aff95..f316857e1 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:42.915488Z", - "iopub.status.busy": "2023-08-14T16:34:42.915086Z", - "iopub.status.idle": "2023-08-14T16:34:44.217392Z", - "shell.execute_reply": "2023-08-14T16:34:44.216629Z" + "iopub.execute_input": "2023-08-15T04:06:00.459386Z", + "iopub.status.busy": "2023-08-15T04:06:00.459070Z", + "iopub.status.idle": "2023-08-15T04:06:02.160285Z", + "shell.execute_reply": "2023-08-15T04:06:02.159205Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.221436Z", - "iopub.status.busy": "2023-08-14T16:34:44.220914Z", - "iopub.status.idle": "2023-08-14T16:34:44.225581Z", - "shell.execute_reply": "2023-08-14T16:34:44.224968Z" + "iopub.execute_input": "2023-08-15T04:06:02.165128Z", + "iopub.status.busy": "2023-08-15T04:06:02.164618Z", + "iopub.status.idle": "2023-08-15T04:06:02.171560Z", + "shell.execute_reply": "2023-08-15T04:06:02.170528Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.229924Z", - "iopub.status.busy": "2023-08-14T16:34:44.228919Z", - "iopub.status.idle": "2023-08-14T16:34:44.241570Z", - "shell.execute_reply": "2023-08-14T16:34:44.240961Z" + "iopub.execute_input": "2023-08-15T04:06:02.175888Z", + "iopub.status.busy": "2023-08-15T04:06:02.175604Z", + "iopub.status.idle": "2023-08-15T04:06:02.190133Z", + "shell.execute_reply": "2023-08-15T04:06:02.188944Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.244401Z", - "iopub.status.busy": "2023-08-14T16:34:44.244038Z", - "iopub.status.idle": "2023-08-14T16:34:44.251620Z", - "shell.execute_reply": "2023-08-14T16:34:44.250955Z" + "iopub.execute_input": "2023-08-15T04:06:02.194092Z", + "iopub.status.busy": "2023-08-15T04:06:02.193693Z", + "iopub.status.idle": "2023-08-15T04:06:02.203440Z", + "shell.execute_reply": "2023-08-15T04:06:02.202456Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.254956Z", - "iopub.status.busy": "2023-08-14T16:34:44.254553Z", - "iopub.status.idle": "2023-08-14T16:34:45.279737Z", - "shell.execute_reply": "2023-08-14T16:34:45.278867Z" + "iopub.execute_input": "2023-08-15T04:06:02.207966Z", + "iopub.status.busy": "2023-08-15T04:06:02.207677Z", + "iopub.status.idle": "2023-08-15T04:06:03.386462Z", + "shell.execute_reply": "2023-08-15T04:06:03.384766Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.283496Z", - "iopub.status.busy": "2023-08-14T16:34:45.282988Z", - "iopub.status.idle": "2023-08-14T16:34:45.681053Z", - "shell.execute_reply": "2023-08-14T16:34:45.680429Z" + "iopub.execute_input": "2023-08-15T04:06:03.390865Z", + "iopub.status.busy": "2023-08-15T04:06:03.390461Z", + "iopub.status.idle": "2023-08-15T04:06:04.027635Z", + "shell.execute_reply": "2023-08-15T04:06:04.026575Z" } }, "outputs": [ @@ -568,10 +568,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.684965Z", - "iopub.status.busy": "2023-08-14T16:34:45.684453Z", - "iopub.status.idle": "2023-08-14T16:34:45.711962Z", - "shell.execute_reply": "2023-08-14T16:34:45.711414Z" + "iopub.execute_input": "2023-08-15T04:06:04.031977Z", + "iopub.status.busy": "2023-08-15T04:06:04.031651Z", + "iopub.status.idle": "2023-08-15T04:06:04.082638Z", + "shell.execute_reply": "2023-08-15T04:06:04.081292Z" } }, "outputs": [], @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.715263Z", - "iopub.status.busy": "2023-08-14T16:34:45.714798Z", - "iopub.status.idle": "2023-08-14T16:34:45.731186Z", - "shell.execute_reply": "2023-08-14T16:34:45.730418Z" + "iopub.execute_input": "2023-08-15T04:06:04.087418Z", + "iopub.status.busy": "2023-08-15T04:06:04.086643Z", + "iopub.status.idle": "2023-08-15T04:06:04.108629Z", + "shell.execute_reply": "2023-08-15T04:06:04.107643Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.734368Z", - "iopub.status.busy": "2023-08-14T16:34:45.733830Z", - "iopub.status.idle": "2023-08-14T16:34:47.408298Z", - "shell.execute_reply": "2023-08-14T16:34:47.407504Z" + "iopub.execute_input": "2023-08-15T04:06:04.113569Z", + "iopub.status.busy": "2023-08-15T04:06:04.112999Z", + "iopub.status.idle": "2023-08-15T04:06:06.354211Z", + "shell.execute_reply": "2023-08-15T04:06:06.352263Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.412393Z", - "iopub.status.busy": "2023-08-14T16:34:47.411424Z", - "iopub.status.idle": "2023-08-14T16:34:47.444902Z", - "shell.execute_reply": "2023-08-14T16:34:47.444133Z" + "iopub.execute_input": "2023-08-15T04:06:06.359511Z", + "iopub.status.busy": "2023-08-15T04:06:06.358878Z", + "iopub.status.idle": "2023-08-15T04:06:06.410428Z", + "shell.execute_reply": "2023-08-15T04:06:06.409305Z" } }, "outputs": [ @@ -789,8 +789,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] } @@ -820,10 +820,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.448218Z", - "iopub.status.busy": "2023-08-14T16:34:47.447809Z", - "iopub.status.idle": "2023-08-14T16:34:47.474076Z", - "shell.execute_reply": "2023-08-14T16:34:47.473299Z" + "iopub.execute_input": "2023-08-15T04:06:06.414451Z", + "iopub.status.busy": "2023-08-15T04:06:06.414051Z", + "iopub.status.idle": "2023-08-15T04:06:06.456082Z", + "shell.execute_reply": "2023-08-15T04:06:06.453835Z" } }, "outputs": [ @@ -900,8 +900,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] }, @@ -909,7 +909,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:219: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n", + "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:219: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.\n", " warnings.warn(\n", "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:249: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.\n", " warnings.warn(\n", @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.477441Z", - "iopub.status.busy": "2023-08-14T16:34:47.476865Z", - "iopub.status.idle": "2023-08-14T16:34:47.495517Z", - "shell.execute_reply": "2023-08-14T16:34:47.494834Z" + "iopub.execute_input": "2023-08-15T04:06:06.461887Z", + "iopub.status.busy": "2023-08-15T04:06:06.461204Z", + "iopub.status.idle": "2023-08-15T04:06:06.487468Z", + "shell.execute_reply": "2023-08-15T04:06:06.486486Z" } }, "outputs": [ @@ -1024,8 +1024,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n", "52 False 3.857172e-02 [] 3.859087e-02\n", "5 False 4.085049e-02 [] 4.087324e-02\n", @@ -1068,17 +1068,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.498979Z", - "iopub.status.busy": "2023-08-14T16:34:47.498455Z", - "iopub.status.idle": "2023-08-14T16:34:47.528844Z", - "shell.execute_reply": "2023-08-14T16:34:47.528298Z" + "iopub.execute_input": "2023-08-15T04:06:06.491363Z", + "iopub.status.busy": "2023-08-15T04:06:06.490997Z", + "iopub.status.idle": "2023-08-15T04:06:06.543482Z", + "shell.execute_reply": "2023-08-15T04:06:06.542489Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b47b66d984614e9a892970ceaf1af373", + "model_id": "ad27969aed8544379888325be1f8523a", "version_major": 2, "version_minor": 0 }, @@ -1093,7 +1093,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved Datalab to folder: datalab-files\n" + "Saved Datalab to folder: datalab-files" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -1114,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.531903Z", - "iopub.status.busy": "2023-08-14T16:34:47.531204Z", - "iopub.status.idle": "2023-08-14T16:34:47.549746Z", - "shell.execute_reply": "2023-08-14T16:34:47.549202Z" + "iopub.execute_input": "2023-08-15T04:06:06.548878Z", + "iopub.status.busy": "2023-08-15T04:06:06.548341Z", + "iopub.status.idle": "2023-08-15T04:06:06.576112Z", + "shell.execute_reply": "2023-08-15T04:06:06.575247Z" } }, "outputs": [ @@ -1125,7 +1132,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Datalab loaded from folder: datalab-files\n", + "Datalab loaded from folder: datalab-files\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Here is a summary of the different kinds of issues found in the data:\n", "\n", " issue_type num_issues\n", @@ -1191,8 +1204,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] } @@ -1235,10 +1248,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.552574Z", - "iopub.status.busy": "2023-08-14T16:34:47.552133Z", - "iopub.status.idle": "2023-08-14T16:34:47.559455Z", - "shell.execute_reply": "2023-08-14T16:34:47.558906Z" + "iopub.execute_input": "2023-08-15T04:06:06.580499Z", + "iopub.status.busy": "2023-08-15T04:06:06.579988Z", + "iopub.status.idle": "2023-08-15T04:06:06.589379Z", + "shell.execute_reply": "2023-08-15T04:06:06.588523Z" } }, "outputs": [], @@ -1295,10 +1308,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.562288Z", - "iopub.status.busy": "2023-08-14T16:34:47.561852Z", - "iopub.status.idle": "2023-08-14T16:34:47.584937Z", - "shell.execute_reply": "2023-08-14T16:34:47.584392Z" + "iopub.execute_input": "2023-08-15T04:06:06.593272Z", + "iopub.status.busy": "2023-08-15T04:06:06.592675Z", + "iopub.status.idle": "2023-08-15T04:06:06.626746Z", + "shell.execute_reply": "2023-08-15T04:06:06.625887Z" } }, "outputs": [ @@ -1308,7 +1321,13 @@ "text": [ "Finding superstition issues ...\n", "\n", - "Audit complete. 32 issues found in the dataset.\n", + "Audit complete. 32 issues found in the dataset.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Here is a summary of the different kinds of issues found in the data:\n", "\n", " issue_type num_issues\n", @@ -1392,8 +1411,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] } @@ -1430,7 +1449,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1704a258aa2a41abb4ebb25e2cd9cb32": { + 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b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 2a4292ff5..19378ef8d 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:53.167076Z", - "iopub.status.busy": "2023-08-14T16:34:53.166795Z", - "iopub.status.idle": "2023-08-14T16:34:54.448005Z", - "shell.execute_reply": "2023-08-14T16:34:54.446674Z" + "iopub.execute_input": "2023-08-15T04:06:12.939155Z", + "iopub.status.busy": "2023-08-15T04:06:12.938600Z", + "iopub.status.idle": "2023-08-15T04:06:14.701973Z", + "shell.execute_reply": "2023-08-15T04:06:14.700912Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:54.452422Z", - "iopub.status.busy": "2023-08-14T16:34:54.451891Z", - "iopub.status.idle": "2023-08-14T16:34:54.457056Z", - "shell.execute_reply": "2023-08-14T16:34:54.456392Z" + "iopub.execute_input": "2023-08-15T04:06:14.707524Z", + "iopub.status.busy": "2023-08-15T04:06:14.706552Z", + "iopub.status.idle": "2023-08-15T04:06:14.711665Z", + "shell.execute_reply": "2023-08-15T04:06:14.710558Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:54.460448Z", - "iopub.status.busy": "2023-08-14T16:34:54.460210Z", - "iopub.status.idle": "2023-08-14T16:34:54.472902Z", - "shell.execute_reply": "2023-08-14T16:34:54.471509Z" + "iopub.execute_input": "2023-08-15T04:06:14.716431Z", + "iopub.status.busy": "2023-08-15T04:06:14.716139Z", + "iopub.status.idle": "2023-08-15T04:06:14.731280Z", + "shell.execute_reply": "2023-08-15T04:06:14.729815Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:54.475691Z", - "iopub.status.busy": "2023-08-14T16:34:54.475266Z", - "iopub.status.idle": "2023-08-14T16:34:54.481073Z", - "shell.execute_reply": "2023-08-14T16:34:54.480351Z" + "iopub.execute_input": "2023-08-15T04:06:14.735601Z", + "iopub.status.busy": "2023-08-15T04:06:14.734968Z", + "iopub.status.idle": "2023-08-15T04:06:14.742218Z", + "shell.execute_reply": "2023-08-15T04:06:14.741129Z" } }, "outputs": [], @@ -443,10 +443,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:54.484358Z", - "iopub.status.busy": "2023-08-14T16:34:54.483887Z", - "iopub.status.idle": "2023-08-14T16:34:54.752556Z", - "shell.execute_reply": "2023-08-14T16:34:54.751225Z" + "iopub.execute_input": "2023-08-15T04:06:14.746673Z", + "iopub.status.busy": "2023-08-15T04:06:14.746232Z", + "iopub.status.idle": "2023-08-15T04:06:15.108822Z", + "shell.execute_reply": "2023-08-15T04:06:15.107719Z" }, "nbsphinx": "hidden" }, @@ -515,10 +515,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:54.756502Z", - "iopub.status.busy": "2023-08-14T16:34:54.755718Z", - "iopub.status.idle": "2023-08-14T16:34:55.235324Z", - "shell.execute_reply": "2023-08-14T16:34:55.234493Z" + "iopub.execute_input": "2023-08-15T04:06:15.113351Z", + "iopub.status.busy": "2023-08-15T04:06:15.113038Z", + "iopub.status.idle": "2023-08-15T04:06:15.813786Z", + "shell.execute_reply": "2023-08-15T04:06:15.812732Z" } }, "outputs": [ @@ -554,10 +554,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:55.239689Z", - "iopub.status.busy": "2023-08-14T16:34:55.239064Z", - "iopub.status.idle": "2023-08-14T16:34:55.243836Z", - "shell.execute_reply": "2023-08-14T16:34:55.243068Z" + "iopub.execute_input": "2023-08-15T04:06:15.818722Z", + "iopub.status.busy": "2023-08-15T04:06:15.817854Z", + "iopub.status.idle": "2023-08-15T04:06:15.823749Z", + "shell.execute_reply": "2023-08-15T04:06:15.822806Z" } }, "outputs": [], @@ -596,10 +596,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:55.247184Z", - "iopub.status.busy": "2023-08-14T16:34:55.246640Z", - "iopub.status.idle": "2023-08-14T16:34:55.274737Z", - "shell.execute_reply": "2023-08-14T16:34:55.273592Z" + "iopub.execute_input": "2023-08-15T04:06:15.828046Z", + "iopub.status.busy": "2023-08-15T04:06:15.827537Z", + "iopub.status.idle": "2023-08-15T04:06:15.878684Z", + "shell.execute_reply": "2023-08-15T04:06:15.877570Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:55.278165Z", - "iopub.status.busy": "2023-08-14T16:34:55.277535Z", - "iopub.status.idle": "2023-08-14T16:34:56.991894Z", - "shell.execute_reply": "2023-08-14T16:34:56.990445Z" + "iopub.execute_input": "2023-08-15T04:06:15.883720Z", + "iopub.status.busy": "2023-08-15T04:06:15.883024Z", + "iopub.status.idle": "2023-08-15T04:06:18.178416Z", + "shell.execute_reply": "2023-08-15T04:06:18.176987Z" } }, "outputs": [ @@ -677,10 +677,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:56.996177Z", - "iopub.status.busy": "2023-08-14T16:34:56.995335Z", - "iopub.status.idle": "2023-08-14T16:34:57.020444Z", - "shell.execute_reply": "2023-08-14T16:34:57.019640Z" + "iopub.execute_input": "2023-08-15T04:06:18.184074Z", + "iopub.status.busy": "2023-08-15T04:06:18.182888Z", + "iopub.status.idle": "2023-08-15T04:06:18.216443Z", + "shell.execute_reply": "2023-08-15T04:06:18.215484Z" } }, "outputs": [ @@ -754,8 +754,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n", "\n", "\n", @@ -814,10 +814,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:57.024813Z", - "iopub.status.busy": "2023-08-14T16:34:57.023528Z", - "iopub.status.idle": "2023-08-14T16:34:57.034262Z", - "shell.execute_reply": "2023-08-14T16:34:57.033636Z" + "iopub.execute_input": "2023-08-15T04:06:18.220388Z", + "iopub.status.busy": "2023-08-15T04:06:18.220025Z", + "iopub.status.idle": "2023-08-15T04:06:18.233286Z", + "shell.execute_reply": "2023-08-15T04:06:18.232321Z" } }, "outputs": [ @@ -907,10 +907,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:57.037617Z", - "iopub.status.busy": "2023-08-14T16:34:57.037228Z", - "iopub.status.idle": "2023-08-14T16:34:57.047277Z", - "shell.execute_reply": "2023-08-14T16:34:57.046238Z" + "iopub.execute_input": "2023-08-15T04:06:18.237868Z", + "iopub.status.busy": "2023-08-15T04:06:18.237520Z", + "iopub.status.idle": "2023-08-15T04:06:18.247548Z", + "shell.execute_reply": "2023-08-15T04:06:18.246711Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:57.050598Z", - "iopub.status.busy": "2023-08-14T16:34:57.050050Z", - "iopub.status.idle": "2023-08-14T16:34:57.062616Z", - "shell.execute_reply": "2023-08-14T16:34:57.062011Z" + "iopub.execute_input": "2023-08-15T04:06:18.251597Z", + "iopub.status.busy": "2023-08-15T04:06:18.251034Z", + "iopub.status.idle": "2023-08-15T04:06:18.268556Z", + "shell.execute_reply": "2023-08-15T04:06:18.267428Z" } }, "outputs": [ @@ -1122,10 +1122,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:57.065762Z", - "iopub.status.busy": "2023-08-14T16:34:57.065419Z", - "iopub.status.idle": "2023-08-14T16:34:57.079294Z", - "shell.execute_reply": "2023-08-14T16:34:57.078261Z" + "iopub.execute_input": "2023-08-15T04:06:18.272738Z", + "iopub.status.busy": "2023-08-15T04:06:18.272170Z", + "iopub.status.idle": "2023-08-15T04:06:18.288119Z", + "shell.execute_reply": "2023-08-15T04:06:18.287057Z" } }, "outputs": [ @@ -1241,10 +1241,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:57.082531Z", - "iopub.status.busy": "2023-08-14T16:34:57.082190Z", - "iopub.status.idle": "2023-08-14T16:34:57.090658Z", - "shell.execute_reply": "2023-08-14T16:34:57.090081Z" + "iopub.execute_input": "2023-08-15T04:06:18.293194Z", + "iopub.status.busy": "2023-08-15T04:06:18.292601Z", + "iopub.status.idle": "2023-08-15T04:06:18.305128Z", + "shell.execute_reply": "2023-08-15T04:06:18.304009Z" }, "scrolled": true }, @@ -1357,10 +1357,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:57.093845Z", - "iopub.status.busy": "2023-08-14T16:34:57.093397Z", - "iopub.status.idle": "2023-08-14T16:34:57.107367Z", - "shell.execute_reply": "2023-08-14T16:34:57.106709Z" + "iopub.execute_input": "2023-08-15T04:06:18.309197Z", + "iopub.status.busy": "2023-08-15T04:06:18.308901Z", + "iopub.status.idle": "2023-08-15T04:06:18.326112Z", + "shell.execute_reply": "2023-08-15T04:06:18.324910Z" } }, "outputs": [ diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 86f119404..387b9504c 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:02.227177Z", - "iopub.status.busy": "2023-08-14T16:35:02.226903Z", - "iopub.status.idle": "2023-08-14T16:35:03.428497Z", - "shell.execute_reply": "2023-08-14T16:35:03.427456Z" + "iopub.execute_input": "2023-08-15T04:06:24.339018Z", + "iopub.status.busy": "2023-08-15T04:06:24.338541Z", + "iopub.status.idle": "2023-08-15T04:06:25.871822Z", + "shell.execute_reply": "2023-08-15T04:06:25.870795Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.432932Z", - "iopub.status.busy": "2023-08-14T16:35:03.432110Z", - "iopub.status.idle": "2023-08-14T16:35:03.490575Z", - "shell.execute_reply": "2023-08-14T16:35:03.489827Z" + "iopub.execute_input": "2023-08-15T04:06:25.876083Z", + "iopub.status.busy": "2023-08-15T04:06:25.875613Z", + "iopub.status.idle": "2023-08-15T04:06:25.957591Z", + "shell.execute_reply": "2023-08-15T04:06:25.956593Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.494677Z", - "iopub.status.busy": "2023-08-14T16:35:03.494257Z", - "iopub.status.idle": "2023-08-14T16:35:03.783524Z", - "shell.execute_reply": "2023-08-14T16:35:03.782784Z" + "iopub.execute_input": "2023-08-15T04:06:25.962347Z", + "iopub.status.busy": "2023-08-15T04:06:25.961851Z", + "iopub.status.idle": "2023-08-15T04:06:26.378085Z", + "shell.execute_reply": "2023-08-15T04:06:26.375960Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.786897Z", - "iopub.status.busy": "2023-08-14T16:35:03.786499Z", - "iopub.status.idle": "2023-08-14T16:35:03.792961Z", - "shell.execute_reply": "2023-08-14T16:35:03.791865Z" + "iopub.execute_input": "2023-08-15T04:06:26.382009Z", + "iopub.status.busy": "2023-08-15T04:06:26.381651Z", + "iopub.status.idle": "2023-08-15T04:06:26.387860Z", + "shell.execute_reply": "2023-08-15T04:06:26.386877Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.796289Z", - "iopub.status.busy": "2023-08-14T16:35:03.795763Z", - "iopub.status.idle": "2023-08-14T16:35:03.806352Z", - "shell.execute_reply": "2023-08-14T16:35:03.805577Z" + "iopub.execute_input": "2023-08-15T04:06:26.391990Z", + "iopub.status.busy": "2023-08-15T04:06:26.391639Z", + "iopub.status.idle": "2023-08-15T04:06:26.408482Z", + "shell.execute_reply": "2023-08-15T04:06:26.407395Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.809681Z", - "iopub.status.busy": "2023-08-14T16:35:03.809137Z", - "iopub.status.idle": "2023-08-14T16:35:03.812266Z", - "shell.execute_reply": "2023-08-14T16:35:03.811600Z" + "iopub.execute_input": "2023-08-15T04:06:26.412913Z", + "iopub.status.busy": "2023-08-15T04:06:26.412614Z", + "iopub.status.idle": "2023-08-15T04:06:26.416768Z", + "shell.execute_reply": "2023-08-15T04:06:26.415747Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.815427Z", - "iopub.status.busy": "2023-08-14T16:35:03.814817Z", - "iopub.status.idle": "2023-08-14T16:35:09.165751Z", - "shell.execute_reply": "2023-08-14T16:35:09.165025Z" + "iopub.execute_input": "2023-08-15T04:06:26.420826Z", + "iopub.status.busy": "2023-08-15T04:06:26.420503Z", + "iopub.status.idle": "2023-08-15T04:06:34.308983Z", + "shell.execute_reply": "2023-08-15T04:06:34.307885Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:09.170070Z", - "iopub.status.busy": "2023-08-14T16:35:09.169399Z", - "iopub.status.idle": "2023-08-14T16:35:09.181712Z", - "shell.execute_reply": "2023-08-14T16:35:09.181076Z" + "iopub.execute_input": "2023-08-15T04:06:34.313717Z", + "iopub.status.busy": "2023-08-15T04:06:34.312990Z", + "iopub.status.idle": "2023-08-15T04:06:34.328344Z", + "shell.execute_reply": "2023-08-15T04:06:34.327513Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:09.184943Z", - "iopub.status.busy": "2023-08-14T16:35:09.184566Z", - "iopub.status.idle": "2023-08-14T16:35:10.830327Z", - "shell.execute_reply": "2023-08-14T16:35:10.828663Z" + "iopub.execute_input": "2023-08-15T04:06:34.332988Z", + "iopub.status.busy": "2023-08-15T04:06:34.332018Z", + "iopub.status.idle": "2023-08-15T04:06:36.543615Z", + "shell.execute_reply": "2023-08-15T04:06:36.542578Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.834565Z", - "iopub.status.busy": "2023-08-14T16:35:10.833819Z", - "iopub.status.idle": "2023-08-14T16:35:10.856910Z", - "shell.execute_reply": "2023-08-14T16:35:10.856116Z" + "iopub.execute_input": "2023-08-15T04:06:36.548564Z", + "iopub.status.busy": "2023-08-15T04:06:36.547711Z", + "iopub.status.idle": "2023-08-15T04:06:36.577076Z", + "shell.execute_reply": "2023-08-15T04:06:36.576297Z" }, "scrolled": true }, @@ -551,11 +551,11 @@ "\n", "Examples representing most severe instances of this issue:\n", " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", - "915 True 0.0 [168] 0.0\n", - "294 True 0.0 [691] 0.0\n", - "12 True 0.0 [2, 1, 6, 9] 0.0\n", - "9 True 0.0 [2, 12, 1, 6] 0.0\n", - "185 True 0.0 [582] 0.0\n" + "690 True 0.0 [246] 0.0\n", + "185 True 0.0 [582] 0.0\n", + "691 True 0.0 [294] 0.0\n", + "168 True 0.0 [915] 0.0\n", + "187 True 0.0 [27] 0.0\n" ] } ], @@ -577,10 +577,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.860336Z", - "iopub.status.busy": "2023-08-14T16:35:10.859870Z", - "iopub.status.idle": "2023-08-14T16:35:10.872563Z", - "shell.execute_reply": "2023-08-14T16:35:10.871903Z" + "iopub.execute_input": "2023-08-15T04:06:36.581022Z", + "iopub.status.busy": "2023-08-15T04:06:36.580768Z", + "iopub.status.idle": "2023-08-15T04:06:36.594191Z", + "shell.execute_reply": "2023-08-15T04:06:36.593380Z" } }, "outputs": [ @@ -684,10 +684,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.875499Z", - "iopub.status.busy": "2023-08-14T16:35:10.875049Z", - "iopub.status.idle": "2023-08-14T16:35:10.886836Z", - "shell.execute_reply": "2023-08-14T16:35:10.886133Z" + "iopub.execute_input": "2023-08-15T04:06:36.597642Z", + "iopub.status.busy": "2023-08-15T04:06:36.597392Z", + "iopub.status.idle": "2023-08-15T04:06:36.614765Z", + "shell.execute_reply": "2023-08-15T04:06:36.613773Z" } }, "outputs": [ @@ -816,10 +816,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.890115Z", - "iopub.status.busy": "2023-08-14T16:35:10.889558Z", - "iopub.status.idle": "2023-08-14T16:35:10.899616Z", - "shell.execute_reply": "2023-08-14T16:35:10.898918Z" + "iopub.execute_input": "2023-08-15T04:06:36.619010Z", + "iopub.status.busy": "2023-08-15T04:06:36.618732Z", + "iopub.status.idle": "2023-08-15T04:06:36.631223Z", + "shell.execute_reply": "2023-08-15T04:06:36.630047Z" } }, "outputs": [ @@ -933,10 +933,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.902603Z", - "iopub.status.busy": "2023-08-14T16:35:10.902236Z", - "iopub.status.idle": "2023-08-14T16:35:10.913787Z", - "shell.execute_reply": "2023-08-14T16:35:10.913056Z" + "iopub.execute_input": "2023-08-15T04:06:36.635132Z", + "iopub.status.busy": "2023-08-15T04:06:36.634834Z", + "iopub.status.idle": "2023-08-15T04:06:36.647954Z", + "shell.execute_reply": "2023-08-15T04:06:36.647001Z" } }, "outputs": [ @@ -969,38 +969,38 @@ " \n", " \n", " \n", - " 915\n", + " 690\n", " True\n", " 0.0\n", - " [168]\n", + " [246]\n", " 0.0\n", " \n", " \n", - " 294\n", + " 185\n", " True\n", " 0.0\n", - " [691]\n", + " [582]\n", " 0.0\n", " \n", " \n", - " 12\n", + " 691\n", " True\n", " 0.0\n", - " [2, 1, 6, 9]\n", + " [294]\n", " 0.0\n", " \n", " \n", - " 9\n", + " 168\n", " True\n", " 0.0\n", - " [2, 12, 1, 6]\n", + " [915]\n", " 0.0\n", " \n", " \n", - " 185\n", + " 187\n", " True\n", " 0.0\n", - " [582]\n", + " [27]\n", " 0.0\n", " \n", " \n", @@ -1009,18 +1009,18 @@ ], "text/plain": [ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets \\\n", - "915 True 0.0 [168] \n", - "294 True 0.0 [691] \n", - "12 True 0.0 [2, 1, 6, 9] \n", - "9 True 0.0 [2, 12, 1, 6] \n", + "690 True 0.0 [246] \n", "185 True 0.0 [582] \n", + "691 True 0.0 [294] \n", + "168 True 0.0 [915] \n", + "187 True 0.0 [27] \n", "\n", " distance_to_nearest_neighbor \n", - "915 0.0 \n", - "294 0.0 \n", - "12 0.0 \n", - "9 0.0 \n", - "185 0.0 " + "690 0.0 \n", + "185 0.0 \n", + "691 0.0 \n", + "168 0.0 \n", + "187 0.0 " ] }, "execution_count": 14, @@ -1047,10 +1047,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.916620Z", - "iopub.status.busy": "2023-08-14T16:35:10.916256Z", - "iopub.status.idle": "2023-08-14T16:35:10.924387Z", - "shell.execute_reply": "2023-08-14T16:35:10.923680Z" + "iopub.execute_input": "2023-08-15T04:06:36.651799Z", + "iopub.status.busy": "2023-08-15T04:06:36.651522Z", + "iopub.status.idle": "2023-08-15T04:06:36.662712Z", + "shell.execute_reply": "2023-08-15T04:06:36.661634Z" } }, "outputs": [ @@ -1134,10 +1134,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.928211Z", - "iopub.status.busy": "2023-08-14T16:35:10.927783Z", - "iopub.status.idle": "2023-08-14T16:35:10.935693Z", - "shell.execute_reply": "2023-08-14T16:35:10.935004Z" + "iopub.execute_input": "2023-08-15T04:06:36.666802Z", + "iopub.status.busy": "2023-08-15T04:06:36.666383Z", + "iopub.status.idle": "2023-08-15T04:06:36.677129Z", + "shell.execute_reply": "2023-08-15T04:06:36.676261Z" } }, "outputs": [ @@ -1221,10 +1221,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.939524Z", - "iopub.status.busy": "2023-08-14T16:35:10.939096Z", - "iopub.status.idle": "2023-08-14T16:35:10.947356Z", - "shell.execute_reply": "2023-08-14T16:35:10.946744Z" + "iopub.execute_input": "2023-08-15T04:06:36.682148Z", + "iopub.status.busy": "2023-08-15T04:06:36.681858Z", + "iopub.status.idle": "2023-08-15T04:06:36.692440Z", + "shell.execute_reply": "2023-08-15T04:06:36.691471Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 504833002..fdaa7dbee 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:16.394911Z", - "iopub.status.busy": "2023-08-14T16:35:16.394459Z", - "iopub.status.idle": "2023-08-14T16:35:19.711881Z", - "shell.execute_reply": "2023-08-14T16:35:19.711099Z" + "iopub.execute_input": "2023-08-15T04:06:42.702366Z", + "iopub.status.busy": "2023-08-15T04:06:42.701728Z", + "iopub.status.idle": "2023-08-15T04:06:46.542642Z", + "shell.execute_reply": "2023-08-15T04:06:46.541476Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef7ea2d184234585b196aaa1e6dfa9c6", + "model_id": "056557d1d4234df2b477a1bed7b33975", "version_major": 2, "version_minor": 0 }, @@ -118,7 +118,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.716204Z", - "iopub.status.busy": "2023-08-14T16:35:19.715631Z", - "iopub.status.idle": "2023-08-14T16:35:19.721608Z", - "shell.execute_reply": "2023-08-14T16:35:19.720566Z" + "iopub.execute_input": "2023-08-15T04:06:46.547123Z", + "iopub.status.busy": "2023-08-15T04:06:46.546604Z", + "iopub.status.idle": "2023-08-15T04:06:46.553528Z", + "shell.execute_reply": "2023-08-15T04:06:46.552604Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.724516Z", - "iopub.status.busy": "2023-08-14T16:35:19.724158Z", - "iopub.status.idle": "2023-08-14T16:35:19.727985Z", - "shell.execute_reply": "2023-08-14T16:35:19.727287Z" + "iopub.execute_input": "2023-08-15T04:06:46.557507Z", + "iopub.status.busy": "2023-08-15T04:06:46.557071Z", + "iopub.status.idle": "2023-08-15T04:06:46.563080Z", + "shell.execute_reply": "2023-08-15T04:06:46.562193Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.731033Z", - "iopub.status.busy": "2023-08-14T16:35:19.730672Z", - "iopub.status.idle": "2023-08-14T16:35:19.786572Z", - "shell.execute_reply": "2023-08-14T16:35:19.785812Z" + "iopub.execute_input": "2023-08-15T04:06:46.567085Z", + "iopub.status.busy": "2023-08-15T04:06:46.566592Z", + "iopub.status.idle": "2023-08-15T04:06:46.702263Z", + "shell.execute_reply": "2023-08-15T04:06:46.701336Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.790288Z", - "iopub.status.busy": "2023-08-14T16:35:19.789903Z", - "iopub.status.idle": "2023-08-14T16:35:19.794671Z", - "shell.execute_reply": "2023-08-14T16:35:19.793954Z" + "iopub.execute_input": "2023-08-15T04:06:46.706673Z", + "iopub.status.busy": "2023-08-15T04:06:46.706360Z", + "iopub.status.idle": "2023-08-15T04:06:46.714530Z", + "shell.execute_reply": "2023-08-15T04:06:46.713394Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'cancel_transfer'}\n" + "Classes: {'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.798638Z", - "iopub.status.busy": "2023-08-14T16:35:19.798206Z", - "iopub.status.idle": "2023-08-14T16:35:19.802243Z", - "shell.execute_reply": "2023-08-14T16:35:19.801507Z" + "iopub.execute_input": "2023-08-15T04:06:46.717770Z", + "iopub.status.busy": "2023-08-15T04:06:46.717487Z", + "iopub.status.idle": "2023-08-15T04:06:46.722112Z", + "shell.execute_reply": "2023-08-15T04:06:46.721230Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.806022Z", - "iopub.status.busy": "2023-08-14T16:35:19.805568Z", - "iopub.status.idle": "2023-08-14T16:35:24.146211Z", - "shell.execute_reply": "2023-08-14T16:35:24.145149Z" + "iopub.execute_input": "2023-08-15T04:06:46.726330Z", + "iopub.status.busy": "2023-08-15T04:06:46.726030Z", + "iopub.status.idle": "2023-08-15T04:06:55.095272Z", + "shell.execute_reply": "2023-08-15T04:06:55.094045Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "854eceb183194d189d0644b94667a047", + "model_id": "78850cee016a4f549acb251e6907ec97", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38452b07808248799b994855f08c50f5", + "model_id": "27bea9547d554a1ebcce66dbf2f2123e", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "233135365c17440296d98899c896e852", + "model_id": "cb1e9c0f8ed84a158eb60a50605a5624", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f4215ba6842043d29bbd0b4489ad485c", + "model_id": "e670054d6e664342b8be1415cc2a08fd", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29692c2c1db748a893cfc1fb8abce2a3", + "model_id": "7ee46b182acc43ecb3825e2ce4fe7ee1", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a4cc6539e8c477da71b89a59c875743", + "model_id": "f8a45b46d98b455aa559106b59bfbb8f", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe05db40755d4e3da22eb5a28e07998a", + "model_id": "b785bc01e99945f493b4762b8ca2f014", "version_major": 2, "version_minor": 0 }, @@ -503,7 +503,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense.weight']\n", + "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight']\n", "- This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" ] @@ -544,10 +544,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:24.150734Z", - "iopub.status.busy": "2023-08-14T16:35:24.150339Z", - "iopub.status.idle": "2023-08-14T16:35:25.416439Z", - "shell.execute_reply": "2023-08-14T16:35:25.415713Z" + "iopub.execute_input": "2023-08-15T04:06:55.100814Z", + "iopub.status.busy": "2023-08-15T04:06:55.100494Z", + "iopub.status.idle": "2023-08-15T04:06:57.178018Z", + "shell.execute_reply": "2023-08-15T04:06:57.177086Z" }, "scrolled": true }, @@ -579,10 +579,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:25.421098Z", - "iopub.status.busy": "2023-08-14T16:35:25.420569Z", - "iopub.status.idle": "2023-08-14T16:35:25.425218Z", - "shell.execute_reply": "2023-08-14T16:35:25.424673Z" + "iopub.execute_input": "2023-08-15T04:06:57.182975Z", + "iopub.status.busy": "2023-08-15T04:06:57.181979Z", + "iopub.status.idle": "2023-08-15T04:06:57.186272Z", + "shell.execute_reply": "2023-08-15T04:06:57.185484Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:25.428714Z", - "iopub.status.busy": "2023-08-14T16:35:25.428300Z", - "iopub.status.idle": "2023-08-14T16:35:27.143901Z", - "shell.execute_reply": "2023-08-14T16:35:27.142970Z" + "iopub.execute_input": "2023-08-15T04:06:57.190330Z", + "iopub.status.busy": "2023-08-15T04:06:57.189507Z", + "iopub.status.idle": "2023-08-15T04:06:59.455915Z", + "shell.execute_reply": "2023-08-15T04:06:59.454479Z" }, "scrolled": true }, @@ -647,10 +647,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.148606Z", - "iopub.status.busy": "2023-08-14T16:35:27.148067Z", - "iopub.status.idle": "2023-08-14T16:35:27.174259Z", - "shell.execute_reply": "2023-08-14T16:35:27.173541Z" + "iopub.execute_input": "2023-08-15T04:06:59.460606Z", + "iopub.status.busy": "2023-08-15T04:06:59.459998Z", + "iopub.status.idle": "2023-08-15T04:06:59.496947Z", + "shell.execute_reply": "2023-08-15T04:06:59.495685Z" }, "scrolled": true }, @@ -685,8 +685,8 @@ "981 True 0.000005 card_about_to_expire card_payment_fee_charged\n", "974 True 0.000150 beneficiary_not_allowed change_pin\n", "982 True 0.000219 apple_pay_or_google_pay card_about_to_expire\n", - "990 True 0.000326 apple_pay_or_google_pay beneficiary_not_allowed\n", - "971 True 0.000512 beneficiary_not_allowed change_pin\n", + "990 True 0.000329 apple_pay_or_google_pay beneficiary_not_allowed\n", + "971 True 0.000511 beneficiary_not_allowed change_pin\n", "\n", "\n", "---------------------- outlier issues ----------------------\n", @@ -723,11 +723,11 @@ "\n", "Examples representing most severe instances of this issue:\n", " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", - "148 True 0.006237 [160] 0.006237\n", "160 True 0.006237 [148] 0.006237\n", - "514 True 0.006485 [546] 0.006485\n", + "148 True 0.006237 [160] 0.006237\n", "546 True 0.006485 [514] 0.006485\n", - "481 False 0.008165 [] 0.008165\n", + "514 True 0.006485 [546] 0.006485\n", + "481 False 0.008164 [] 0.008165\n", "\n", "\n", "---------------------- non_iid issues ----------------------\n", @@ -775,10 +775,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.177966Z", - "iopub.status.busy": "2023-08-14T16:35:27.177684Z", - "iopub.status.idle": "2023-08-14T16:35:27.190717Z", - "shell.execute_reply": "2023-08-14T16:35:27.190081Z" + "iopub.execute_input": "2023-08-15T04:06:59.500605Z", + "iopub.status.busy": "2023-08-15T04:06:59.500287Z", + "iopub.status.idle": "2023-08-15T04:06:59.515550Z", + "shell.execute_reply": "2023-08-15T04:06:59.514589Z" }, "scrolled": true }, @@ -814,35 +814,35 @@ " \n", " 0\n", " False\n", - " 0.806101\n", + " 0.806028\n", " cancel_transfer\n", " cancel_transfer\n", " \n", " \n", " 1\n", " False\n", - " 0.270940\n", + " 0.270972\n", " cancel_transfer\n", " cancel_transfer\n", " \n", " \n", " 2\n", " False\n", - " 0.695413\n", + " 0.695399\n", " cancel_transfer\n", " cancel_transfer\n", " \n", " \n", " 3\n", " False\n", - " 0.179787\n", + " 0.179756\n", " cancel_transfer\n", " apple_pay_or_google_pay\n", " \n", " \n", " 4\n", " False\n", - " 0.822664\n", + " 0.822703\n", " cancel_transfer\n", " cancel_transfer\n", " \n", @@ -852,11 +852,11 @@ ], "text/plain": [ " is_label_issue label_score given_label predicted_label\n", - "0 False 0.806101 cancel_transfer cancel_transfer\n", - "1 False 0.270940 cancel_transfer cancel_transfer\n", - "2 False 0.695413 cancel_transfer cancel_transfer\n", - "3 False 0.179787 cancel_transfer apple_pay_or_google_pay\n", - "4 False 0.822664 cancel_transfer cancel_transfer" + "0 False 0.806028 cancel_transfer cancel_transfer\n", + "1 False 0.270972 cancel_transfer cancel_transfer\n", + "2 False 0.695399 cancel_transfer cancel_transfer\n", + "3 False 0.179756 cancel_transfer apple_pay_or_google_pay\n", + "4 False 0.822703 cancel_transfer cancel_transfer" ] }, "execution_count": 12, @@ -888,10 +888,10 @@ "execution_count": 13, "metadata": { "execution": { - 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"shell.execute_reply": "2023-08-15T04:06:59.567442Z" } }, "outputs": [ @@ -1246,10 +1246,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.239722Z", - "iopub.status.busy": "2023-08-14T16:35:27.239173Z", - "iopub.status.idle": "2023-08-14T16:35:27.250441Z", - "shell.execute_reply": "2023-08-14T16:35:27.249734Z" + "iopub.execute_input": "2023-08-15T04:06:59.571892Z", + "iopub.status.busy": "2023-08-15T04:06:59.571626Z", + "iopub.status.idle": "2023-08-15T04:06:59.586680Z", + "shell.execute_reply": "2023-08-15T04:06:59.585542Z" } }, "outputs": [ @@ -1282,37 +1282,37 @@ " \n", " \n", " \n", - " 148\n", + " 160\n", " True\n", " 0.006237\n", - " [160]\n", + " [148]\n", " 0.006237\n", " \n", " \n", - " 160\n", + " 148\n", " True\n", " 0.006237\n", - " [148]\n", + " [160]\n", " 0.006237\n", " \n", " \n", - " 514\n", + " 546\n", " True\n", " 0.006485\n", - " [546]\n", + " [514]\n", " 0.006485\n", " \n", " \n", - " 546\n", + " 514\n", " True\n", " 0.006485\n", - " [514]\n", + " [546]\n", " 0.006485\n", " \n", " \n", " 481\n", " False\n", - " 0.008165\n", + " 0.008164\n", " []\n", " 0.008165\n", " \n", @@ -1322,17 +1322,17 @@ ], "text/plain": [ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets \\\n", - "148 True 0.006237 [160] \n", "160 True 0.006237 [148] \n", - "514 True 0.006485 [546] \n", + "148 True 0.006237 [160] \n", "546 True 0.006485 [514] \n", - "481 False 0.008165 [] \n", + "514 True 0.006485 [546] \n", + "481 False 0.008164 [] \n", "\n", " distance_to_nearest_neighbor \n", - "148 0.006237 \n", "160 0.006237 \n", - "514 0.006485 \n", + "148 0.006237 \n", "546 0.006485 \n", + "514 0.006485 \n", "481 0.008165 " ] }, @@ -1360,10 +1360,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.253566Z", - "iopub.status.busy": "2023-08-14T16:35:27.253019Z", - "iopub.status.idle": "2023-08-14T16:35:27.259711Z", - "shell.execute_reply": 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"right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 193713732..380bad948 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:32.902764Z", - "iopub.status.busy": "2023-08-14T16:35:32.902307Z", - "iopub.status.idle": "2023-08-14T16:35:34.113852Z", - "shell.execute_reply": "2023-08-14T16:35:34.112804Z" + "iopub.execute_input": "2023-08-15T04:07:05.139899Z", + "iopub.status.busy": "2023-08-15T04:07:05.139526Z", + "iopub.status.idle": "2023-08-15T04:07:06.732406Z", + "shell.execute_reply": "2023-08-15T04:07:06.730972Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:34.118331Z", - "iopub.status.busy": "2023-08-14T16:35:34.117836Z", - "iopub.status.idle": "2023-08-14T16:35:34.122367Z", - "shell.execute_reply": "2023-08-14T16:35:34.121609Z" + "iopub.execute_input": "2023-08-15T04:07:06.737551Z", + "iopub.status.busy": "2023-08-15T04:07:06.736932Z", + "iopub.status.idle": "2023-08-15T04:07:06.743683Z", + "shell.execute_reply": "2023-08-15T04:07:06.742320Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:34.126097Z", - "iopub.status.busy": "2023-08-14T16:35:34.125861Z", - "iopub.status.idle": "2023-08-14T16:35:34.174859Z", - "shell.execute_reply": "2023-08-14T16:35:34.174043Z" + "iopub.execute_input": "2023-08-15T04:07:06.748471Z", + "iopub.status.busy": "2023-08-15T04:07:06.748129Z", + "iopub.status.idle": "2023-08-15T04:07:06.807511Z", + "shell.execute_reply": "2023-08-15T04:07:06.806269Z" }, "nbsphinx": "hidden" }, @@ -301,10 +301,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:34.178210Z", - "iopub.status.busy": "2023-08-14T16:35:34.177706Z", - "iopub.status.idle": "2023-08-14T16:35:54.034304Z", - "shell.execute_reply": "2023-08-14T16:35:54.033593Z" + "iopub.execute_input": "2023-08-15T04:07:06.812076Z", + "iopub.status.busy": "2023-08-15T04:07:06.811768Z", + "iopub.status.idle": "2023-08-15T04:07:36.275789Z", + "shell.execute_reply": "2023-08-15T04:07:36.274906Z" }, "id": "dhTHOg8Pyv5G" }, @@ -378,29 +378,29 @@ " sunglass\n", " 836\n", " 40\n", - " 28\n", + " 27\n", " 0.80\n", - " 0.736842\n", + " 0.729730\n", " 0.20\n", " \n", " \n", " 1\n", - " tiger cat\n", - " 282\n", + " Toy Poodle\n", + " 265\n", " 38\n", - " 30\n", + " 8\n", " 0.76\n", - " 0.714286\n", + " 0.400000\n", " 0.24\n", " \n", " \n", " 2\n", - " Toy Poodle\n", - " 265\n", + " tiger cat\n", + " 282\n", " 38\n", - " 8\n", + " 29\n", " 0.76\n", - " 0.400000\n", + " 0.707317\n", " 0.24\n", " \n", " \n", @@ -415,12 +415,12 @@ " \n", " \n", " 4\n", - " laptop computer\n", - " 620\n", + " Saharan horned viper\n", + " 66\n", " 36\n", - " 14\n", + " 4\n", " 0.72\n", - " 0.500000\n", + " 0.222222\n", " 0.28\n", " \n", " \n", @@ -435,8 +435,8 @@ " \n", " \n", " 995\n", - " croquet ball\n", - " 522\n", + " ringlet\n", + " 322\n", " 0\n", " 2\n", " 0.00\n", @@ -445,42 +445,42 @@ " \n", " \n", " 996\n", - " automated teller machine\n", - " 480\n", + " monarch butterfly\n", + " 323\n", + " 0\n", " 0\n", - " 4\n", " 0.00\n", - " 0.074074\n", + " 0.000000\n", " 1.00\n", " \n", " \n", " 997\n", - " picket fence\n", - " 716\n", - " 0\n", + " magpie\n", + " 18\n", " 0\n", + " 2\n", " 0.00\n", - " 0.000000\n", + " 0.038462\n", " 1.00\n", " \n", " \n", " 998\n", - " ping-pong ball\n", - " 722\n", + " go-kart\n", + " 573\n", " 0\n", - " 2\n", + " 5\n", " 0.00\n", - " 0.038462\n", + " 0.090909\n", " 1.00\n", " \n", " \n", " 999\n", - " high-speed train\n", - " 466\n", + " fox squirrel\n", + " 335\n", " 0\n", - " 4\n", + " 5\n", " 0.00\n", - " 0.074074\n", + " 0.090909\n", " 1.00\n", " \n", " \n", @@ -489,44 +489,31 @@ "" ], "text/plain": [ - " Class Name Class Index Label Issues \\\n", - "0 sunglass 836 40 \n", - "1 tiger cat 282 38 \n", - "2 Toy Poodle 265 38 \n", - "3 CRT screen 782 37 \n", - "4 laptop computer 620 36 \n", - ".. ... ... ... \n", - "995 croquet ball 522 0 \n", - "996 automated teller machine 480 0 \n", - "997 picket fence 716 0 \n", - "998 ping-pong ball 722 0 \n", - "999 high-speed train 466 0 \n", - "\n", - " Inverse Label Issues Label Noise Inverse Label Noise \\\n", - "0 28 0.80 0.736842 \n", - "1 30 0.76 0.714286 \n", - "2 8 0.76 0.400000 \n", - "3 22 0.74 0.628571 \n", - "4 14 0.72 0.500000 \n", - ".. ... ... ... \n", - "995 2 0.00 0.038462 \n", - "996 4 0.00 0.074074 \n", - "997 0 0.00 0.000000 \n", - "998 2 0.00 0.038462 \n", - "999 4 0.00 0.074074 \n", + " Class Name Class Index Label Issues Inverse Label Issues \\\n", + "0 sunglass 836 40 27 \n", + "1 Toy Poodle 265 38 8 \n", + "2 tiger cat 282 38 29 \n", + "3 CRT screen 782 37 22 \n", + "4 Saharan horned viper 66 36 4 \n", + ".. ... ... ... ... \n", + "995 ringlet 322 0 2 \n", + "996 monarch butterfly 323 0 0 \n", + "997 magpie 18 0 2 \n", + "998 go-kart 573 0 5 \n", + "999 fox squirrel 335 0 5 \n", "\n", - " Label Quality Score \n", - "0 0.20 \n", - "1 0.24 \n", - "2 0.24 \n", - "3 0.26 \n", - "4 0.28 \n", - ".. ... \n", - "995 1.00 \n", - "996 1.00 \n", - "997 1.00 \n", - "998 1.00 \n", - "999 1.00 \n", + " Label Noise Inverse Label Noise Label Quality Score \n", + "0 0.80 0.729730 0.20 \n", + "1 0.76 0.400000 0.24 \n", + "2 0.76 0.707317 0.24 \n", + "3 0.74 0.628571 0.26 \n", + "4 0.72 0.222222 0.28 \n", + ".. ... ... ... \n", + "995 0.00 0.038462 1.00 \n", + "996 0.00 0.000000 1.00 \n", + "997 0.00 0.038462 1.00 \n", + "998 0.00 0.090909 1.00 \n", + "999 0.00 0.090909 1.00 \n", "\n", "[1000 rows x 7 columns]" ] @@ -631,45 +618,45 @@ " \n", " 499495\n", " Kerry Blue Terrier\n", - " pickup truck\n", + " pill bottle\n", " 183\n", - " 717\n", + " 720\n", " 0\n", " 0.00000\n", " \n", " \n", " 499496\n", " Kerry Blue Terrier\n", - " picket fence\n", + " piggy bank\n", " 183\n", - " 716\n", + " 719\n", " 0\n", " 0.00000\n", " \n", " \n", " 499497\n", " Kerry Blue Terrier\n", - " Pickelhaube\n", + " pier\n", " 183\n", - " 715\n", + " 718\n", " 0\n", " 0.00000\n", " \n", " \n", " 499498\n", " Kerry Blue Terrier\n", - " plectrum\n", + " pickup truck\n", " 183\n", - " 714\n", + " 717\n", " 0\n", " 0.00000\n", " \n", " \n", " 499499\n", - " Kerry Blue Terrier\n", - " plate rack\n", - " 183\n", - " 729\n", + " ear\n", + " toilet paper\n", + " 998\n", + " 999\n", " 0\n", " 0.00000\n", " \n", @@ -686,11 +673,11 @@ "3 tights tank suit 638 \n", "4 laptop computer notebook computer 620 \n", "... ... ... ... \n", - "499495 Kerry Blue Terrier pickup truck 183 \n", - "499496 Kerry Blue Terrier picket fence 183 \n", - "499497 Kerry Blue Terrier Pickelhaube 183 \n", - "499498 Kerry Blue Terrier plectrum 183 \n", - "499499 Kerry Blue Terrier plate rack 183 \n", + "499495 Kerry Blue Terrier pill bottle 183 \n", + "499496 Kerry Blue Terrier piggy bank 183 \n", + "499497 Kerry Blue Terrier pier 183 \n", + "499498 Kerry Blue Terrier pickup truck 183 \n", + "499499 ear toilet paper 998 \n", "\n", " Class Index B Num Overlapping Examples Joint Probability \n", "0 998 44 0.00088 \n", @@ -699,11 +686,11 @@ "3 639 36 0.00072 \n", "4 681 35 0.00070 \n", "... ... ... ... \n", - "499495 717 0 0.00000 \n", - "499496 716 0 0.00000 \n", - "499497 715 0 0.00000 \n", - "499498 714 0 0.00000 \n", - "499499 729 0 0.00000 \n", + "499495 720 0 0.00000 \n", + "499496 719 0 0.00000 \n", + "499497 718 0 0.00000 \n", + "499498 717 0 0.00000 \n", + "499499 999 0 0.00000 \n", "\n", "[499500 rows x 6 columns]" ] @@ -790,9 +777,9 @@ " skateboard\n", " 184\n", " 37\n", - " 26\n", + " 23\n", " 0.359223\n", - " 0.282609\n", + " 0.258427\n", " 0.640777\n", " \n", " \n", @@ -800,9 +787,9 @@ " chopsticks\n", " 38\n", " 29\n", - " 19\n", + " 20\n", " 0.341176\n", - " 0.253333\n", + " 0.263158\n", " 0.658824\n", " \n", " \n", @@ -837,8 +824,8 @@ " \n", " \n", " 251\n", - " hummingbird\n", - " 112\n", + " raccoon\n", + " 167\n", " 0\n", " 0\n", " 0.000000\n", @@ -847,8 +834,8 @@ " \n", " \n", " 252\n", - " starfish\n", - " 200\n", + " hummingbird\n", + " 112\n", " 0\n", " 0\n", " 0.000000\n", @@ -857,18 +844,18 @@ " \n", " \n", " 253\n", - " saturn\n", - " 176\n", + " hourglass\n", + " 109\n", " 0\n", - " 6\n", + " 2\n", " 0.000000\n", - " 0.058824\n", + " 0.022989\n", " 1.000000\n", " \n", " \n", " 254\n", - " raccoon\n", - " 167\n", + " starfish\n", + " 200\n", " 0\n", " 0\n", " 0.000000\n", @@ -877,12 +864,12 @@ " \n", " \n", " 255\n", - " leopards\n", - " 128\n", - " 0\n", + " saturn\n", + " 176\n", " 0\n", + " 5\n", " 0.000000\n", - " 0.000000\n", + " 0.049505\n", " 1.000000\n", " \n", " \n", @@ -893,29 +880,29 @@ "text/plain": [ " Class Name Class Index Label Issues Inverse Label Issues \\\n", "0 tennis-shoes 254 37 33 \n", - "1 skateboard 184 37 26 \n", - "2 chopsticks 38 29 19 \n", + "1 skateboard 184 37 23 \n", + "2 chopsticks 38 29 20 \n", "3 drinking-straw 58 28 18 \n", "4 yo-yo 248 33 37 \n", ".. ... ... ... ... \n", - "251 hummingbird 112 0 0 \n", - "252 starfish 200 0 0 \n", - "253 saturn 176 0 6 \n", - "254 raccoon 167 0 0 \n", - "255 leopards 128 0 0 \n", + "251 raccoon 167 0 0 \n", + "252 hummingbird 112 0 0 \n", + "253 hourglass 109 0 2 \n", + "254 starfish 200 0 0 \n", + "255 saturn 176 0 5 \n", "\n", " Label Noise Inverse Label Noise Label Quality Score \n", "0 0.359223 0.333333 0.640777 \n", - "1 0.359223 0.282609 0.640777 \n", - "2 0.341176 0.253333 0.658824 \n", + "1 0.359223 0.258427 0.640777 \n", + "2 0.341176 0.263158 0.658824 \n", "3 0.337349 0.246575 0.662651 \n", "4 0.330000 0.355769 0.670000 \n", ".. ... ... ... \n", "251 0.000000 0.000000 1.000000 \n", "252 0.000000 0.000000 1.000000 \n", - "253 0.000000 0.058824 1.000000 \n", + "253 0.000000 0.022989 1.000000 \n", "254 0.000000 0.000000 1.000000 \n", - "255 0.000000 0.000000 1.000000 \n", + "255 0.000000 0.049505 1.000000 \n", "\n", "[256 rows x 7 columns]" ] @@ -992,19 +979,19 @@ " \n", " \n", " 3\n", - " frog\n", - " toad\n", - " 79\n", - " 255\n", + " beer-mug\n", + " coffee-mug\n", + " 9\n", + " 40\n", " 22\n", " 0.000739\n", " \n", " \n", " 4\n", - " beer-mug\n", - " coffee-mug\n", - " 9\n", - " 40\n", + " frog\n", + " toad\n", + " 79\n", + " 255\n", " 22\n", " 0.000739\n", " \n", @@ -1019,46 +1006,46 @@ " \n", " \n", " 32635\n", - " conch\n", - " tennis-shoes\n", - " 47\n", - " 254\n", + " cormorant\n", + " covered-wagon\n", + " 48\n", + " 49\n", " 0\n", " 0.000000\n", " \n", " \n", " 32636\n", " conch\n", - " greyhound\n", + " toad\n", " 47\n", - " 253\n", + " 255\n", " 0\n", " 0.000000\n", " \n", " \n", " 32637\n", " conch\n", - " faces-easy\n", + " tennis-shoes\n", " 47\n", - " 252\n", + " 254\n", " 0\n", " 0.000000\n", " \n", " \n", " 32638\n", " conch\n", - " car-side\n", + " greyhound\n", " 47\n", - " 251\n", + " 253\n", " 0\n", " 0.000000\n", " \n", " \n", " 32639\n", - " cormorant\n", - " duck\n", - " 48\n", - " 59\n", + " tennis-shoes\n", + " toad\n", + " 254\n", + " 255\n", " 0\n", " 0.000000\n", " \n", @@ -1068,18 +1055,18 @@ "" ], "text/plain": [ - " Class Name A Class Name B Class Index A Class Index B \\\n", - "0 sneaker tennis-shoes 190 254 \n", - "1 frisbee yo-yo 78 248 \n", - "2 duck goose 59 88 \n", - "3 frog toad 79 255 \n", - "4 beer-mug coffee-mug 9 40 \n", - "... ... ... ... ... \n", - "32635 conch tennis-shoes 47 254 \n", - "32636 conch greyhound 47 253 \n", - "32637 conch faces-easy 47 252 \n", - "32638 conch car-side 47 251 \n", - "32639 cormorant duck 48 59 \n", + " Class Name A Class Name B Class Index A Class Index B \\\n", + "0 sneaker tennis-shoes 190 254 \n", + "1 frisbee yo-yo 78 248 \n", + "2 duck goose 59 88 \n", + "3 beer-mug coffee-mug 9 40 \n", + "4 frog toad 79 255 \n", + "... ... ... ... ... \n", + "32635 cormorant covered-wagon 48 49 \n", + "32636 conch toad 47 255 \n", + "32637 conch tennis-shoes 47 254 \n", + "32638 conch greyhound 47 253 \n", + "32639 tennis-shoes toad 254 255 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 66 0.002216 \n", @@ -1375,37 +1362,37 @@ " \n", " \n", " 4\n", - " 0\n", " 2\n", - " 0\n", + " 8\n", " 2\n", + " 8\n", " 1\n", " 0.0001\n", " \n", " \n", " 5\n", - " 3\n", - " 7\n", - " 3\n", - " 7\n", + " 4\n", + " 6\n", + " 4\n", + " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 6\n", - " 2\n", - " 8\n", - " 2\n", - " 8\n", + " 3\n", + " 7\n", + " 3\n", + " 7\n", " 1\n", " 0.0001\n", " \n", " \n", " 7\n", - " 4\n", - " 6\n", - " 4\n", - " 6\n", + " 0\n", + " 2\n", + " 0\n", + " 2\n", " 1\n", " 0.0001\n", " \n", @@ -1429,316 +1416,316 @@ " \n", " \n", " 10\n", - " 0\n", " 1\n", - " 0\n", + " 2\n", " 1\n", + " 2\n", " 0\n", " 0.0000\n", " \n", " \n", " 11\n", - " 0\n", - " 4\n", - " 0\n", - " 4\n", + " 3\n", + " 8\n", + " 3\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 12\n", - " 0\n", - " 3\n", - " 0\n", - " 3\n", + " 4\n", + " 5\n", + " 4\n", + " 5\n", " 0\n", " 0.0000\n", " \n", " \n", " 13\n", - " 1\n", + " 0\n", " 5\n", - " 1\n", + " 0\n", " 5\n", " 0\n", " 0.0000\n", " \n", " \n", " 14\n", - " 1\n", " 4\n", - " 1\n", + " 7\n", " 4\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 15\n", - " 1\n", - " 3\n", - " 1\n", - " 3\n", + " 4\n", + " 8\n", + " 4\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 16\n", - " 1\n", - " 2\n", - " 1\n", - " 2\n", + " 0\n", + " 4\n", + " 0\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 17\n", " 0\n", - " 9\n", + " 3\n", " 0\n", - " 9\n", + " 3\n", " 0\n", " 0.0000\n", " \n", " \n", " 18\n", - " 0\n", - " 8\n", - " 0\n", - " 8\n", + " 5\n", + " 7\n", + " 5\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 19\n", - " 0\n", - " 6\n", - " 0\n", - " 6\n", + " 5\n", + " 8\n", + " 5\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 20\n", - " 0\n", " 5\n", - " 0\n", + " 9\n", " 5\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 21\n", - " 2\n", " 6\n", - " 2\n", + " 7\n", " 6\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 22\n", - " 2\n", - " 5\n", - " 2\n", - " 5\n", + " 6\n", + " 8\n", + " 6\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 23\n", - " 2\n", - " 4\n", - " 2\n", - " 4\n", + " 6\n", + " 9\n", + " 6\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 24\n", - " 2\n", - " 3\n", - " 2\n", - " 3\n", + " 7\n", + " 8\n", + " 7\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 25\n", - " 1\n", - " 6\n", - " 1\n", - " 6\n", + " 7\n", + " 9\n", + " 7\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 26\n", - " 1\n", - " 7\n", - " 1\n", - " 7\n", + " 3\n", + " 9\n", + " 3\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 27\n", - " 1\n", - " 8\n", - " 1\n", - " 8\n", + " 0\n", + " 6\n", + " 0\n", + " 6\n", " 0\n", " 0.0000\n", " \n", " \n", " 28\n", " 1\n", - " 9\n", + " 3\n", " 1\n", - " 9\n", + " 3\n", " 0\n", " 0.0000\n", " \n", " \n", " 29\n", + " 2\n", " 3\n", - " 8\n", + " 2\n", " 3\n", - " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 30\n", - " 3\n", - " 6\n", - " 3\n", - " 6\n", + " 1\n", + " 4\n", + " 1\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 31\n", - " 2\n", - " 9\n", - " 2\n", - " 9\n", + " 1\n", + " 5\n", + " 1\n", + " 5\n", " 0\n", " 0.0000\n", " \n", " \n", " 32\n", - " 3\n", - " 4\n", - " 3\n", - " 4\n", + " 1\n", + " 6\n", + " 1\n", + " 6\n", " 0\n", " 0.0000\n", " \n", " \n", " 33\n", - " 4\n", + " 1\n", " 7\n", - " 4\n", + " 1\n", " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 34\n", - " 4\n", - " 5\n", - " 4\n", - " 5\n", + " 1\n", + " 8\n", + " 1\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 35\n", - " 4\n", - " 8\n", - " 4\n", - " 8\n", + " 1\n", + " 9\n", + " 1\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 36\n", - " 3\n", - " 9\n", - " 3\n", - " 9\n", + " 2\n", + " 4\n", + " 2\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 37\n", - " 5\n", - " 7\n", - " 5\n", - " 7\n", + " 3\n", + " 6\n", + " 3\n", + " 6\n", " 0\n", " 0.0000\n", " \n", " \n", " 38\n", + " 2\n", " 5\n", - " 8\n", + " 2\n", " 5\n", - " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 39\n", + " 2\n", " 6\n", - " 7\n", + " 2\n", " 6\n", - " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 40\n", - " 5\n", + " 0\n", " 9\n", - " 5\n", + " 0\n", " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 41\n", - " 6\n", + " 0\n", " 8\n", - " 6\n", + " 0\n", " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 42\n", - " 6\n", + " 2\n", " 9\n", - " 6\n", + " 2\n", " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 43\n", - " 7\n", - " 8\n", - " 7\n", - " 8\n", + " 3\n", + " 4\n", + " 3\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 44\n", - " 7\n", - " 9\n", - " 7\n", - " 9\n", + " 0\n", + " 1\n", + " 0\n", + " 1\n", " 0\n", " 0.0000\n", " \n", @@ -1752,47 +1739,47 @@ "1 5 6 5 6 \n", "2 4 9 4 9 \n", "3 3 5 3 5 \n", - "4 0 2 0 2 \n", - "5 3 7 3 7 \n", - "6 2 8 2 8 \n", - "7 4 6 4 6 \n", + "4 2 8 2 8 \n", + "5 4 6 4 6 \n", + "6 3 7 3 7 \n", + "7 0 2 0 2 \n", "8 8 9 8 9 \n", "9 0 7 0 7 \n", - "10 0 1 0 1 \n", - "11 0 4 0 4 \n", - "12 0 3 0 3 \n", - "13 1 5 1 5 \n", - "14 1 4 1 4 \n", - "15 1 3 1 3 \n", - "16 1 2 1 2 \n", - "17 0 9 0 9 \n", - "18 0 8 0 8 \n", - "19 0 6 0 6 \n", - "20 0 5 0 5 \n", - "21 2 6 2 6 \n", - "22 2 5 2 5 \n", - "23 2 4 2 4 \n", - "24 2 3 2 3 \n", - "25 1 6 1 6 \n", - "26 1 7 1 7 \n", - "27 1 8 1 8 \n", - "28 1 9 1 9 \n", - "29 3 8 3 8 \n", - "30 3 6 3 6 \n", - "31 2 9 2 9 \n", - "32 3 4 3 4 \n", - "33 4 7 4 7 \n", - "34 4 5 4 5 \n", - "35 4 8 4 8 \n", - "36 3 9 3 9 \n", - "37 5 7 5 7 \n", - "38 5 8 5 8 \n", - "39 6 7 6 7 \n", - "40 5 9 5 9 \n", - "41 6 8 6 8 \n", - "42 6 9 6 9 \n", - "43 7 8 7 8 \n", - "44 7 9 7 9 \n", + "10 1 2 1 2 \n", + "11 3 8 3 8 \n", + "12 4 5 4 5 \n", + "13 0 5 0 5 \n", + "14 4 7 4 7 \n", + "15 4 8 4 8 \n", + "16 0 4 0 4 \n", + "17 0 3 0 3 \n", + "18 5 7 5 7 \n", + "19 5 8 5 8 \n", + "20 5 9 5 9 \n", + "21 6 7 6 7 \n", + "22 6 8 6 8 \n", + "23 6 9 6 9 \n", + "24 7 8 7 8 \n", + "25 7 9 7 9 \n", + "26 3 9 3 9 \n", + "27 0 6 0 6 \n", + "28 1 3 1 3 \n", + "29 2 3 2 3 \n", + "30 1 4 1 4 \n", + "31 1 5 1 5 \n", + "32 1 6 1 6 \n", + "33 1 7 1 7 \n", + "34 1 8 1 8 \n", + "35 1 9 1 9 \n", + "36 2 4 2 4 \n", + "37 3 6 3 6 \n", + "38 2 5 2 5 \n", + "39 2 6 2 6 \n", + "40 0 9 0 9 \n", + "41 0 8 0 8 \n", + "42 2 9 2 9 \n", + "43 3 4 3 4 \n", + "44 0 1 0 1 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 3 0.0003 \n", @@ -1924,9 +1911,9 @@ " dog\n", " 5\n", " 46\n", - " 58\n", + " 59\n", " 0.046\n", - " 0.057312\n", + " 0.058243\n", " 0.954\n", " \n", " \n", @@ -1984,9 +1971,9 @@ " airplane\n", " 0\n", " 16\n", - " 32\n", + " 31\n", " 0.016\n", - " 0.031496\n", + " 0.030542\n", " 0.984\n", " \n", " \n", @@ -2016,25 +2003,25 @@ "text/plain": [ " Class Name Class Index Label Issues Inverse Label Issues Label Noise \\\n", "0 cat 3 71 67 0.071 \n", - "1 dog 5 46 58 0.046 \n", + "1 dog 5 46 59 0.046 \n", "2 bird 2 35 32 0.035 \n", "3 truck 9 31 12 0.031 \n", "4 deer 4 22 26 0.022 \n", "5 frog 6 20 13 0.020 \n", "6 automobile 1 18 13 0.018 \n", - "7 airplane 0 16 32 0.016 \n", + "7 airplane 0 16 31 0.016 \n", "8 ship 8 13 21 0.013 \n", "9 horse 7 12 10 0.012 \n", "\n", " Inverse Label Noise Label Quality Score \n", "0 0.067269 0.929 \n", - "1 0.057312 0.954 \n", + "1 0.058243 0.954 \n", "2 0.032096 0.965 \n", "3 0.012232 0.969 \n", "4 0.025896 0.978 \n", "5 0.013092 0.980 \n", "6 0.013065 0.982 \n", - "7 0.031496 0.984 \n", + "7 0.030542 0.984 \n", "8 0.020833 0.987 \n", "9 0.010020 0.988 " ] @@ -2192,39 +2179,39 @@ " \n", " \n", " 12\n", - " airplane\n", " bird\n", - " 0\n", + " dog\n", " 2\n", - " 6\n", - " 0.0006\n", + " 5\n", + " 7\n", + " 0.0007\n", " \n", " \n", " 13\n", - " cat\n", + " dog\n", " horse\n", - " 3\n", + " 5\n", " 7\n", " 6\n", " 0.0006\n", " \n", " \n", " 14\n", - " dog\n", + " cat\n", " horse\n", - " 5\n", + " 3\n", " 7\n", " 6\n", " 0.0006\n", " \n", " \n", " 15\n", + " airplane\n", " bird\n", - " dog\n", + " 0\n", " 2\n", " 5\n", - " 6\n", - " 0.0006\n", + " 0.0005\n", " \n", " \n", " 16\n", @@ -2255,33 +2242,24 @@ " \n", " \n", " 19\n", - " bird\n", " horse\n", - " 2\n", + " truck\n", " 7\n", + " 9\n", " 3\n", " 0.0003\n", " \n", " \n", " 20\n", - " bird\n", - " ship\n", - " 2\n", - " 8\n", - " 3\n", - " 0.0003\n", - " \n", - " \n", - " 21\n", + " deer\n", " horse\n", - " truck\n", + " 4\n", " 7\n", - " 9\n", " 3\n", " 0.0003\n", " \n", " \n", - " 22\n", + " 21\n", " deer\n", " frog\n", " 4\n", @@ -2290,10 +2268,19 @@ " 0.0003\n", " \n", " \n", + " 22\n", + " bird\n", + " ship\n", + " 2\n", + " 8\n", + " 3\n", + " 0.0003\n", + " \n", + " \n", " 23\n", - " deer\n", + " bird\n", " horse\n", - " 4\n", + " 2\n", " 7\n", " 3\n", " 0.0003\n", @@ -2309,9 +2296,9 @@ " \n", " \n", " 25\n", - " airplane\n", + " automobile\n", " frog\n", - " 0\n", + " 1\n", " 6\n", " 1\n", " 0.0001\n", @@ -2328,162 +2315,162 @@ " \n", " 27\n", " airplane\n", - " dog\n", + " frog\n", " 0\n", - " 5\n", + " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 28\n", - " automobile\n", - " dog\n", - " 1\n", - " 5\n", + " cat\n", + " truck\n", + " 3\n", + " 9\n", " 1\n", " 0.0001\n", " \n", " \n", " 29\n", - " frog\n", - " ship\n", - " 6\n", - " 8\n", + " airplane\n", + " dog\n", + " 0\n", + " 5\n", " 1\n", " 0.0001\n", " \n", " \n", " 30\n", + " automobile\n", " dog\n", - " frog\n", + " 1\n", " 5\n", - " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 31\n", - " automobile\n", - " frog\n", - " 1\n", - " 6\n", + " bird\n", + " truck\n", + " 2\n", + " 9\n", " 1\n", " 0.0001\n", " \n", " \n", " 32\n", - " cat\n", + " deer\n", " truck\n", - " 3\n", + " 4\n", " 9\n", " 1\n", " 0.0001\n", " \n", " \n", " 33\n", - " deer\n", - " truck\n", - " 4\n", - " 9\n", + " dog\n", + " frog\n", + " 5\n", + " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 34\n", - " bird\n", - " truck\n", - " 2\n", - " 9\n", + " frog\n", + " ship\n", + " 6\n", + " 8\n", " 1\n", " 0.0001\n", " \n", " \n", " 35\n", - " automobile\n", - " bird\n", - " 1\n", - " 2\n", + " horse\n", + " ship\n", + " 7\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 36\n", - " airplane\n", - " deer\n", - " 0\n", - " 4\n", + " frog\n", + " truck\n", + " 6\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 37\n", - " automobile\n", - " deer\n", - " 1\n", - " 4\n", + " frog\n", + " horse\n", + " 6\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 38\n", " automobile\n", - " cat\n", + " deer\n", " 1\n", - " 3\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 39\n", - " automobile\n", - " horse\n", - " 1\n", - " 7\n", + " dog\n", + " ship\n", + " 5\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 40\n", + " airplane\n", " deer\n", - " ship\n", + " 0\n", " 4\n", - " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 41\n", - " frog\n", + " automobile\n", " horse\n", - " 6\n", + " 1\n", " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 42\n", - " dog\n", - " ship\n", - " 5\n", - " 8\n", + " automobile\n", + " bird\n", + " 1\n", + " 2\n", " 0\n", " 0.0000\n", " \n", " \n", " 43\n", - " horse\n", - " ship\n", - " 7\n", - " 8\n", + " automobile\n", + " cat\n", + " 1\n", + " 3\n", " 0\n", " 0.0000\n", " \n", " \n", " 44\n", - " frog\n", - " truck\n", - " 6\n", - " 9\n", + " deer\n", + " ship\n", + " 4\n", + " 8\n", " 0\n", " 0.0000\n", " \n", @@ -2505,39 +2492,39 @@ "9 airplane cat 0 3 \n", "10 airplane truck 0 9 \n", "11 ship truck 8 9 \n", - "12 airplane bird 0 2 \n", - "13 cat horse 3 7 \n", - "14 dog horse 5 7 \n", - "15 bird dog 2 5 \n", + "12 bird dog 2 5 \n", + "13 dog horse 5 7 \n", + "14 cat horse 3 7 \n", + "15 airplane bird 0 2 \n", "16 airplane automobile 0 1 \n", "17 automobile ship 1 8 \n", "18 cat ship 3 8 \n", - "19 bird horse 2 7 \n", - "20 bird ship 2 8 \n", - "21 horse truck 7 9 \n", - "22 deer frog 4 6 \n", - "23 deer horse 4 7 \n", + "19 horse truck 7 9 \n", + "20 deer horse 4 7 \n", + "21 deer frog 4 6 \n", + "22 bird ship 2 8 \n", + "23 bird horse 2 7 \n", "24 dog truck 5 9 \n", - "25 airplane frog 0 6 \n", + "25 automobile frog 1 6 \n", "26 airplane horse 0 7 \n", - "27 airplane dog 0 5 \n", - "28 automobile dog 1 5 \n", - "29 frog ship 6 8 \n", - "30 dog frog 5 6 \n", - "31 automobile frog 1 6 \n", - "32 cat truck 3 9 \n", - "33 deer truck 4 9 \n", - "34 bird truck 2 9 \n", - "35 automobile bird 1 2 \n", - "36 airplane deer 0 4 \n", - "37 automobile deer 1 4 \n", - "38 automobile cat 1 3 \n", - "39 automobile horse 1 7 \n", - "40 deer ship 4 8 \n", - "41 frog horse 6 7 \n", - "42 dog ship 5 8 \n", - "43 horse ship 7 8 \n", - "44 frog truck 6 9 \n", + "27 airplane frog 0 6 \n", + "28 cat truck 3 9 \n", + "29 airplane dog 0 5 \n", + "30 automobile dog 1 5 \n", + "31 bird truck 2 9 \n", + "32 deer truck 4 9 \n", + "33 dog frog 5 6 \n", + "34 frog ship 6 8 \n", + "35 horse ship 7 8 \n", + "36 frog truck 6 9 \n", + "37 frog horse 6 7 \n", + "38 automobile deer 1 4 \n", + "39 dog ship 5 8 \n", + "40 airplane deer 0 4 \n", + "41 automobile horse 1 7 \n", + "42 automobile bird 1 2 \n", + "43 automobile cat 1 3 \n", + "44 deer ship 4 8 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 73 0.0073 \n", @@ -2552,10 +2539,10 @@ "9 10 0.0010 \n", "10 8 0.0008 \n", "11 7 0.0007 \n", - "12 6 0.0006 \n", + "12 7 0.0007 \n", "13 6 0.0006 \n", "14 6 0.0006 \n", - "15 6 0.0006 \n", + "15 5 0.0005 \n", "16 5 0.0005 \n", "17 4 0.0004 \n", "18 3 0.0003 \n", @@ -2679,9 +2666,9 @@ " seal\n", " 72\n", " 49\n", - " 55\n", + " 56\n", " 0.49\n", - " 0.518868\n", + " 0.523364\n", " 0.51\n", " \n", " \n", @@ -2736,22 +2723,22 @@ " \n", " \n", " 97\n", - " motorcycle\n", - " 48\n", + " orange\n", + " 53\n", " 3\n", - " 5\n", + " 12\n", " 0.03\n", - " 0.049020\n", + " 0.110092\n", " 0.97\n", " \n", " \n", " 98\n", - " orange\n", - " 53\n", + " motorcycle\n", + " 48\n", " 3\n", - " 12\n", + " 5\n", " 0.03\n", - " 0.110092\n", + " 0.049020\n", " 0.97\n", " \n", " \n", @@ -2773,27 +2760,27 @@ " Class Name Class Index Label Issues Inverse Label Issues Label Noise \\\n", "0 boy 11 54 38 0.54 \n", "1 girl 35 53 40 0.53 \n", - "2 seal 72 49 55 0.49 \n", + "2 seal 72 49 56 0.49 \n", "3 man 46 45 47 0.45 \n", "4 shark 73 43 46 0.43 \n", ".. ... ... ... ... ... \n", "95 road 68 5 11 0.05 \n", "96 skunk 75 5 3 0.05 \n", - "97 motorcycle 48 3 5 0.03 \n", - "98 orange 53 3 12 0.03 \n", + "97 orange 53 3 12 0.03 \n", + "98 motorcycle 48 3 5 0.03 \n", "99 wardrobe 94 3 5 0.03 \n", "\n", " Inverse Label Noise Label Quality Score \n", "0 0.452381 0.46 \n", "1 0.459770 0.47 \n", - "2 0.518868 0.51 \n", + "2 0.523364 0.51 \n", "3 0.460784 0.55 \n", "4 0.446602 0.57 \n", ".. ... ... \n", "95 0.103774 0.95 \n", "96 0.030612 0.95 \n", - "97 0.049020 0.97 \n", - "98 0.110092 0.97 \n", + "97 0.110092 0.97 \n", + "98 0.049020 0.97 \n", "99 0.049020 0.97 \n", "\n", "[100 rows x 7 columns]" @@ -2871,19 +2858,19 @@ " \n", " \n", " 3\n", - " baby\n", - " boy\n", - " 2\n", - " 11\n", + " maple_tree\n", + " oak_tree\n", + " 47\n", + " 52\n", " 25\n", " 0.0025\n", " \n", " \n", " 4\n", - " maple_tree\n", - " oak_tree\n", - " 47\n", - " 52\n", + " otter\n", + " seal\n", + " 55\n", + " 72\n", " 25\n", " 0.0025\n", " \n", @@ -2899,45 +2886,45 @@ " \n", " 4945\n", " cattle\n", - " telephone\n", + " whale\n", " 19\n", - " 86\n", + " 95\n", " 0\n", " 0.0000\n", " \n", " \n", " 4946\n", " cattle\n", - " tank\n", + " willow_tree\n", " 19\n", - " 85\n", + " 96\n", " 0\n", " 0.0000\n", " \n", " \n", " 4947\n", " cattle\n", - " table\n", + " woman\n", " 19\n", - " 84\n", + " 98\n", " 0\n", " 0.0000\n", " \n", " \n", " 4948\n", " cattle\n", - " sweet_pepper\n", + " worm\n", " 19\n", - " 83\n", + " 99\n", " 0\n", " 0.0000\n", " \n", " \n", " 4949\n", - " cattle\n", - " trout\n", - " 19\n", - " 91\n", + " woman\n", + " worm\n", + " 98\n", + " 99\n", " 0\n", " 0.0000\n", " \n", @@ -2947,18 +2934,18 @@ "" ], "text/plain": [ - " Class Name A Class Name B Class Index A Class Index B \\\n", - "0 girl woman 35 98 \n", - "1 boy man 11 46 \n", - "2 maple_tree willow_tree 47 96 \n", - "3 baby boy 2 11 \n", - "4 maple_tree oak_tree 47 52 \n", - "... ... ... ... ... \n", - "4945 cattle telephone 19 86 \n", - "4946 cattle tank 19 85 \n", - "4947 cattle table 19 84 \n", - "4948 cattle sweet_pepper 19 83 \n", - "4949 cattle trout 19 91 \n", + " Class Name A Class Name B Class Index A Class Index B \\\n", + "0 girl woman 35 98 \n", + "1 boy man 11 46 \n", + "2 maple_tree willow_tree 47 96 \n", + "3 maple_tree oak_tree 47 52 \n", + "4 otter seal 55 72 \n", + "... ... ... ... ... \n", + "4945 cattle whale 19 95 \n", + "4946 cattle willow_tree 19 96 \n", + "4947 cattle woman 19 98 \n", + "4948 cattle worm 19 99 \n", + "4949 woman worm 98 99 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 34 0.0034 \n", @@ -3380,18 +3367,18 @@ " \n", " \n", " 17\n", - " rec.sport.baseball\n", - " 9\n", + " soc.religion.christian\n", + " 15\n", + " 0\n", " 0\n", - " 2\n", " 0.000000\n", - " 0.005013\n", + " 0.000000\n", " 1.000000\n", " \n", " \n", " 18\n", - " soc.religion.christian\n", - " 15\n", + " talk.politics.mideast\n", + " 17\n", " 0\n", " 0\n", " 0.000000\n", @@ -3400,12 +3387,12 @@ " \n", " \n", " 19\n", - " talk.politics.mideast\n", - " 17\n", - " 0\n", + " rec.sport.baseball\n", + " 9\n", " 0\n", + " 2\n", " 0.000000\n", - " 0.000000\n", + " 0.005013\n", " 1.000000\n", " \n", " \n", @@ -3431,9 +3418,9 @@ "14 sci.med 13 2 2 \n", "15 rec.motorcycles 8 2 0 \n", "16 rec.sport.hockey 10 2 0 \n", - "17 rec.sport.baseball 9 0 2 \n", - "18 soc.religion.christian 15 0 0 \n", - "19 talk.politics.mideast 17 0 0 \n", + "17 soc.religion.christian 15 0 0 \n", + "18 talk.politics.mideast 17 0 0 \n", + "19 rec.sport.baseball 9 0 2 \n", "\n", " Label Noise Inverse Label Noise Label Quality Score \n", "0 0.034483 0.009646 0.965517 \n", @@ -3453,9 +3440,9 @@ "14 0.005051 0.005051 0.994949 \n", "15 0.005025 0.000000 0.994975 \n", "16 0.005013 0.000000 0.994987 \n", - "17 0.000000 0.005013 1.000000 \n", + "17 0.000000 0.000000 1.000000 \n", "18 0.000000 0.000000 1.000000 \n", - "19 0.000000 0.000000 1.000000 " + "19 0.000000 0.005013 1.000000 " ] }, "metadata": {}, @@ -3522,26 +3509,26 @@ " \n", " 2\n", " misc.forsale\n", - " rec.autos\n", + " sci.electronics\n", " 6\n", - " 7\n", + " 12\n", " 7\n", " 0.000929\n", " \n", " \n", " 3\n", " misc.forsale\n", - " sci.electronics\n", + " rec.autos\n", " 6\n", - " 12\n", + " 7\n", " 7\n", " 0.000929\n", " \n", " \n", " 4\n", - " comp.graphics\n", + " comp.os.ms-windows.misc\n", " comp.windows.x\n", - " 1\n", + " 2\n", " 5\n", " 5\n", " 0.000664\n", @@ -3558,45 +3545,45 @@ " \n", " 185\n", " comp.sys.mac.hardware\n", - " rec.sport.baseball\n", + " rec.motorcycles\n", " 4\n", - " 9\n", + " 8\n", " 0\n", " 0.000000\n", " \n", " \n", " 186\n", " comp.sys.mac.hardware\n", - " rec.sport.hockey\n", + " rec.sport.baseball\n", " 4\n", - " 10\n", + " 9\n", " 0\n", " 0.000000\n", " \n", " \n", " 187\n", " comp.sys.mac.hardware\n", - " sci.crypt\n", + " rec.sport.hockey\n", " 4\n", - " 11\n", + " 10\n", " 0\n", " 0.000000\n", " \n", " \n", " 188\n", " comp.sys.mac.hardware\n", - " sci.electronics\n", + " sci.crypt\n", " 4\n", - " 12\n", + " 11\n", " 0\n", " 0.000000\n", " \n", " \n", " 189\n", - " comp.sys.ibm.pc.hardware\n", - " talk.politics.guns\n", - " 3\n", - " 16\n", + " talk.politics.misc\n", + " talk.religion.misc\n", + " 18\n", + " 19\n", " 0\n", " 0.000000\n", " \n", @@ -3606,31 +3593,31 @@ "" ], "text/plain": [ - " Class Name A Class Name B Class Index A \\\n", - "0 alt.atheism talk.religion.misc 0 \n", - "1 comp.os.ms-windows.misc comp.sys.ibm.pc.hardware 2 \n", - "2 misc.forsale rec.autos 6 \n", - "3 misc.forsale sci.electronics 6 \n", - "4 comp.graphics comp.windows.x 1 \n", - ".. ... ... ... \n", - "185 comp.sys.mac.hardware rec.sport.baseball 4 \n", - "186 comp.sys.mac.hardware rec.sport.hockey 4 \n", - "187 comp.sys.mac.hardware sci.crypt 4 \n", - "188 comp.sys.mac.hardware sci.electronics 4 \n", - "189 comp.sys.ibm.pc.hardware talk.politics.guns 3 \n", + " Class Name A Class Name B Class Index A \\\n", + "0 alt.atheism talk.religion.misc 0 \n", + "1 comp.os.ms-windows.misc comp.sys.ibm.pc.hardware 2 \n", + "2 misc.forsale sci.electronics 6 \n", + "3 misc.forsale rec.autos 6 \n", + "4 comp.os.ms-windows.misc comp.windows.x 2 \n", + ".. ... ... ... \n", + "185 comp.sys.mac.hardware rec.motorcycles 4 \n", + "186 comp.sys.mac.hardware rec.sport.baseball 4 \n", + "187 comp.sys.mac.hardware rec.sport.hockey 4 \n", + "188 comp.sys.mac.hardware sci.crypt 4 \n", + "189 talk.politics.misc talk.religion.misc 18 \n", "\n", " Class Index B Num Overlapping Examples Joint Probability \n", "0 19 14 0.001859 \n", "1 3 10 0.001328 \n", - "2 7 7 0.000929 \n", - "3 12 7 0.000929 \n", + "2 12 7 0.000929 \n", + "3 7 7 0.000929 \n", "4 5 5 0.000664 \n", ".. ... ... ... \n", - "185 9 0 0.000000 \n", - "186 10 0 0.000000 \n", - "187 11 0 0.000000 \n", - "188 12 0 0.000000 \n", - "189 16 0 0.000000 \n", + "185 8 0 0.000000 \n", + "186 9 0 0.000000 \n", + "187 10 0 0.000000 \n", + "188 11 0 0.000000 \n", + "189 19 0 0.000000 \n", "\n", "[190 rows x 6 columns]" ] diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index cb42f04fd..de2d5a8f0 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:56.431503Z", - "iopub.status.busy": "2023-08-14T16:35:56.430984Z", - "iopub.status.idle": "2023-08-14T16:35:57.647255Z", - "shell.execute_reply": "2023-08-14T16:35:57.645721Z" + "iopub.execute_input": "2023-08-15T04:07:38.998037Z", + "iopub.status.busy": "2023-08-15T04:07:38.997583Z", + "iopub.status.idle": "2023-08-15T04:07:40.503084Z", + "shell.execute_reply": "2023-08-15T04:07:40.501920Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:57.651515Z", - "iopub.status.busy": "2023-08-14T16:35:57.651120Z", - "iopub.status.idle": "2023-08-14T16:35:57.657122Z", - "shell.execute_reply": "2023-08-14T16:35:57.655981Z" + "iopub.execute_input": "2023-08-15T04:07:40.508143Z", + "iopub.status.busy": "2023-08-15T04:07:40.507750Z", + "iopub.status.idle": "2023-08-15T04:07:40.514509Z", + "shell.execute_reply": "2023-08-15T04:07:40.513502Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:57.660137Z", - "iopub.status.busy": "2023-08-14T16:35:57.659721Z", - "iopub.status.idle": "2023-08-14T16:36:00.302636Z", - "shell.execute_reply": "2023-08-14T16:36:00.301614Z" + "iopub.execute_input": "2023-08-15T04:07:40.518392Z", + "iopub.status.busy": "2023-08-15T04:07:40.518152Z", + "iopub.status.idle": "2023-08-15T04:07:43.942942Z", + "shell.execute_reply": "2023-08-15T04:07:43.941718Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.307500Z", - "iopub.status.busy": "2023-08-14T16:36:00.306351Z", - "iopub.status.idle": "2023-08-14T16:36:00.348507Z", - "shell.execute_reply": "2023-08-14T16:36:00.347621Z" + "iopub.execute_input": "2023-08-15T04:07:43.948436Z", + "iopub.status.busy": "2023-08-15T04:07:43.947659Z", + "iopub.status.idle": "2023-08-15T04:07:43.998509Z", + "shell.execute_reply": "2023-08-15T04:07:43.997139Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.352674Z", - "iopub.status.busy": "2023-08-14T16:36:00.352158Z", - "iopub.status.idle": "2023-08-14T16:36:00.387042Z", - "shell.execute_reply": "2023-08-14T16:36:00.386119Z" + "iopub.execute_input": "2023-08-15T04:07:44.004072Z", + "iopub.status.busy": "2023-08-15T04:07:44.003167Z", + "iopub.status.idle": "2023-08-15T04:07:44.055136Z", + "shell.execute_reply": "2023-08-15T04:07:44.053936Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.391275Z", - "iopub.status.busy": "2023-08-14T16:36:00.390770Z", - "iopub.status.idle": "2023-08-14T16:36:00.395273Z", - "shell.execute_reply": "2023-08-14T16:36:00.394579Z" + "iopub.execute_input": "2023-08-15T04:07:44.060637Z", + "iopub.status.busy": "2023-08-15T04:07:44.059764Z", + "iopub.status.idle": "2023-08-15T04:07:44.064694Z", + "shell.execute_reply": "2023-08-15T04:07:44.063643Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.398600Z", - "iopub.status.busy": "2023-08-14T16:36:00.398140Z", - "iopub.status.idle": "2023-08-14T16:36:00.401414Z", - "shell.execute_reply": "2023-08-14T16:36:00.400716Z" + "iopub.execute_input": "2023-08-15T04:07:44.068392Z", + "iopub.status.busy": "2023-08-15T04:07:44.068117Z", + "iopub.status.idle": "2023-08-15T04:07:44.073046Z", + "shell.execute_reply": "2023-08-15T04:07:44.072247Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.404714Z", - "iopub.status.busy": "2023-08-14T16:36:00.404166Z", - "iopub.status.idle": "2023-08-14T16:36:00.438673Z", - "shell.execute_reply": "2023-08-14T16:36:00.438099Z" + "iopub.execute_input": "2023-08-15T04:07:44.076654Z", + "iopub.status.busy": 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"version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/image.ipynb b/master/.doctrees/nbsphinx/tutorials/image.ipynb index d1448e934..7c4dde612 100644 --- a/master/.doctrees/nbsphinx/tutorials/image.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/image.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:09.469287Z", - "iopub.status.busy": "2023-08-14T16:36:09.468775Z", - "iopub.status.idle": "2023-08-14T16:36:11.804719Z", - "shell.execute_reply": "2023-08-14T16:36:11.803211Z" + "iopub.execute_input": "2023-08-15T04:07:55.391429Z", + "iopub.status.busy": "2023-08-15T04:07:55.390926Z", + "iopub.status.idle": "2023-08-15T04:07:58.338831Z", + "shell.execute_reply": "2023-08-15T04:07:58.337802Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"torch\", \"torchvision\", \"skorch\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -120,10 +120,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:11.808909Z", - "iopub.status.busy": "2023-08-14T16:36:11.808498Z", - "iopub.status.idle": "2023-08-14T16:36:11.842679Z", - "shell.execute_reply": "2023-08-14T16:36:11.842008Z" + "iopub.execute_input": "2023-08-15T04:07:58.343300Z", + "iopub.status.busy": "2023-08-15T04:07:58.342795Z", + "iopub.status.idle": "2023-08-15T04:07:58.388556Z", + "shell.execute_reply": "2023-08-15T04:07:58.387569Z" } }, "outputs": [], @@ -141,10 +141,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:11.846047Z", - "iopub.status.busy": "2023-08-14T16:36:11.845806Z", - "iopub.status.idle": "2023-08-14T16:36:11.850649Z", - "shell.execute_reply": "2023-08-14T16:36:11.849965Z" + "iopub.execute_input": "2023-08-15T04:07:58.392775Z", + "iopub.status.busy": "2023-08-15T04:07:58.392129Z", + "iopub.status.idle": "2023-08-15T04:07:58.398736Z", + "shell.execute_reply": "2023-08-15T04:07:58.397802Z" }, "nbsphinx": "hidden" }, @@ -174,10 +174,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:11.853493Z", - "iopub.status.busy": "2023-08-14T16:36:11.853262Z", - "iopub.status.idle": "2023-08-14T16:36:57.542730Z", - "shell.execute_reply": "2023-08-14T16:36:57.541907Z" + "iopub.execute_input": "2023-08-15T04:07:58.402547Z", + "iopub.status.busy": "2023-08-15T04:07:58.402244Z", + "iopub.status.idle": "2023-08-15T04:08:58.354501Z", + "shell.execute_reply": "2023-08-15T04:08:58.353131Z" } }, "outputs": [ @@ -231,10 +231,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:57.547583Z", - "iopub.status.busy": "2023-08-14T16:36:57.546812Z", - "iopub.status.idle": "2023-08-14T16:36:57.552869Z", - "shell.execute_reply": "2023-08-14T16:36:57.552186Z" + "iopub.execute_input": "2023-08-15T04:08:58.359263Z", + "iopub.status.busy": "2023-08-15T04:08:58.358986Z", + "iopub.status.idle": "2023-08-15T04:08:58.366908Z", + "shell.execute_reply": "2023-08-15T04:08:58.365951Z" } }, "outputs": [], @@ -286,10 +286,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:57.555822Z", - "iopub.status.busy": "2023-08-14T16:36:57.555451Z", - "iopub.status.idle": "2023-08-14T16:36:57.558652Z", - "shell.execute_reply": "2023-08-14T16:36:57.557956Z" + "iopub.execute_input": "2023-08-15T04:08:58.379572Z", + "iopub.status.busy": "2023-08-15T04:08:58.371139Z", + "iopub.status.idle": "2023-08-15T04:08:58.384265Z", + "shell.execute_reply": "2023-08-15T04:08:58.383481Z" } }, "outputs": [], @@ -316,10 +316,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:57.561796Z", - "iopub.status.busy": "2023-08-14T16:36:57.561427Z", - "iopub.status.idle": "2023-08-14T16:38:41.836916Z", - "shell.execute_reply": "2023-08-14T16:38:41.836160Z" + "iopub.execute_input": "2023-08-15T04:08:58.387676Z", + "iopub.status.busy": "2023-08-15T04:08:58.387214Z", + "iopub.status.idle": "2023-08-15T04:11:30.677342Z", + "shell.execute_reply": "2023-08-15T04:11:30.676453Z" } }, "outputs": [ @@ -329,70 +329,70 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6908\u001b[0m \u001b[32m0.9139\u001b[0m \u001b[35m0.3099\u001b[0m 3.3471\n" + " 1 \u001b[36m0.6908\u001b[0m \u001b[32m0.9139\u001b[0m \u001b[35m0.3099\u001b[0m 5.8484\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2 \u001b[36m0.2112\u001b[0m \u001b[32m0.9412\u001b[0m \u001b[35m0.2002\u001b[0m 3.3083\n" + " 2 \u001b[36m0.2112\u001b[0m \u001b[32m0.9412\u001b[0m \u001b[35m0.2000\u001b[0m 4.9170\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3 \u001b[36m0.1521\u001b[0m \u001b[32m0.9516\u001b[0m \u001b[35m0.1574\u001b[0m 3.4646\n" + " 3 \u001b[36m0.1521\u001b[0m \u001b[32m0.9516\u001b[0m \u001b[35m0.1574\u001b[0m 4.8178\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 4 \u001b[36m0.1240\u001b[0m \u001b[32m0.9594\u001b[0m \u001b[35m0.1332\u001b[0m 3.2663\n" + " 4 \u001b[36m0.1239\u001b[0m \u001b[32m0.9595\u001b[0m \u001b[35m0.1332\u001b[0m 4.9101\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 5 \u001b[36m0.1066\u001b[0m \u001b[32m0.9633\u001b[0m \u001b[35m0.1178\u001b[0m 3.2562\n" + " 5 \u001b[36m0.1066\u001b[0m \u001b[32m0.9633\u001b[0m \u001b[35m0.1179\u001b[0m 4.8797\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6 \u001b[36m0.0948\u001b[0m \u001b[32m0.9660\u001b[0m \u001b[35m0.1072\u001b[0m 3.2716\n" + " 6 \u001b[36m0.0948\u001b[0m \u001b[32m0.9663\u001b[0m \u001b[35m0.1071\u001b[0m 5.0642\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 7 \u001b[36m0.0860\u001b[0m \u001b[32m0.9682\u001b[0m \u001b[35m0.0994\u001b[0m 3.2971\n" + " 7 \u001b[36m0.0860\u001b[0m \u001b[32m0.9683\u001b[0m \u001b[35m0.0995\u001b[0m 4.9326\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 8 \u001b[36m0.0792\u001b[0m \u001b[32m0.9708\u001b[0m \u001b[35m0.0934\u001b[0m 3.2870\n" + " 8 \u001b[36m0.0792\u001b[0m \u001b[32m0.9703\u001b[0m \u001b[35m0.0934\u001b[0m 4.8050\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 9 \u001b[36m0.0737\u001b[0m \u001b[32m0.9725\u001b[0m \u001b[35m0.0886\u001b[0m 3.4553\n" + " 9 \u001b[36m0.0736\u001b[0m \u001b[32m0.9728\u001b[0m \u001b[35m0.0886\u001b[0m 4.8635\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 10 \u001b[36m0.0690\u001b[0m \u001b[32m0.9736\u001b[0m \u001b[35m0.0847\u001b[0m 3.4006\n" + " 10 \u001b[36m0.0690\u001b[0m \u001b[32m0.9738\u001b[0m \u001b[35m0.0847\u001b[0m 4.7248\n" ] }, { @@ -401,70 +401,70 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.7043\u001b[0m \u001b[32m0.9247\u001b[0m \u001b[35m0.2786\u001b[0m 3.3475\n" + " 1 \u001b[36m0.7043\u001b[0m \u001b[32m0.9247\u001b[0m \u001b[35m0.2785\u001b[0m 4.8613\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2 \u001b[36m0.1907\u001b[0m \u001b[32m0.9465\u001b[0m \u001b[35m0.1817\u001b[0m 3.4382\n" + " 2 \u001b[36m0.1907\u001b[0m \u001b[32m0.9464\u001b[0m \u001b[35m0.1817\u001b[0m 4.8761\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3 \u001b[36m0.1355\u001b[0m \u001b[32m0.9556\u001b[0m \u001b[35m0.1477\u001b[0m 3.3651\n" + " 3 \u001b[36m0.1355\u001b[0m \u001b[32m0.9561\u001b[0m \u001b[35m0.1477\u001b[0m 4.8335\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 4 \u001b[36m0.1100\u001b[0m \u001b[32m0.9616\u001b[0m \u001b[35m0.1289\u001b[0m 3.3878\n" + " 4 \u001b[36m0.1100\u001b[0m \u001b[32m0.9613\u001b[0m \u001b[35m0.1290\u001b[0m 4.8704\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 5 \u001b[36m0.0943\u001b[0m \u001b[32m0.9648\u001b[0m \u001b[35m0.1166\u001b[0m 3.4701\n" + " 5 \u001b[36m0.0943\u001b[0m \u001b[32m0.9644\u001b[0m \u001b[35m0.1168\u001b[0m 4.8599\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6 \u001b[36m0.0834\u001b[0m \u001b[32m0.9684\u001b[0m \u001b[35m0.1079\u001b[0m 3.4018\n" + " 6 \u001b[36m0.0834\u001b[0m \u001b[32m0.9686\u001b[0m \u001b[35m0.1078\u001b[0m 4.8590\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 7 \u001b[36m0.0751\u001b[0m \u001b[32m0.9702\u001b[0m \u001b[35m0.1014\u001b[0m 3.3094\n" + " 7 \u001b[36m0.0751\u001b[0m \u001b[32m0.9702\u001b[0m \u001b[35m0.1015\u001b[0m 4.8019\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 8 \u001b[36m0.0687\u001b[0m \u001b[32m0.9713\u001b[0m \u001b[35m0.0963\u001b[0m 3.3262\n" + " 8 \u001b[36m0.0687\u001b[0m \u001b[32m0.9711\u001b[0m \u001b[35m0.0965\u001b[0m 4.8567\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 9 \u001b[36m0.0634\u001b[0m \u001b[32m0.9724\u001b[0m \u001b[35m0.0921\u001b[0m 3.3722\n" + " 9 \u001b[36m0.0634\u001b[0m \u001b[32m0.9724\u001b[0m \u001b[35m0.0921\u001b[0m 4.8372\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 10 \u001b[36m0.0589\u001b[0m \u001b[32m0.9732\u001b[0m \u001b[35m0.0887\u001b[0m 3.3797\n" + " 10 \u001b[36m0.0589\u001b[0m \u001b[32m0.9734\u001b[0m \u001b[35m0.0887\u001b[0m 4.6923\n" ] }, { @@ -473,70 +473,70 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.7931\u001b[0m \u001b[32m0.9112\u001b[0m \u001b[35m0.3372\u001b[0m 3.4436\n" + " 1 \u001b[36m0.7932\u001b[0m \u001b[32m0.9116\u001b[0m \u001b[35m0.3376\u001b[0m 5.0162\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2 \u001b[36m0.2282\u001b[0m \u001b[32m0.9486\u001b[0m \u001b[35m0.1948\u001b[0m 3.3737\n" + " 2 \u001b[36m0.2283\u001b[0m \u001b[32m0.9483\u001b[0m \u001b[35m0.1951\u001b[0m 4.8961\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3 \u001b[36m0.1533\u001b[0m \u001b[32m0.9592\u001b[0m \u001b[35m0.1501\u001b[0m 3.3492\n" + " 3 \u001b[36m0.1534\u001b[0m \u001b[32m0.9590\u001b[0m \u001b[35m0.1502\u001b[0m 4.9592\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 4 \u001b[36m0.1217\u001b[0m \u001b[32m0.9641\u001b[0m \u001b[35m0.1277\u001b[0m 3.4138\n" + " 4 \u001b[36m0.1218\u001b[0m \u001b[32m0.9643\u001b[0m \u001b[35m0.1278\u001b[0m 4.9167\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 5 \u001b[36m0.1032\u001b[0m \u001b[32m0.9678\u001b[0m \u001b[35m0.1135\u001b[0m 3.3927\n" + " 5 \u001b[36m0.1032\u001b[0m \u001b[32m0.9684\u001b[0m \u001b[35m0.1134\u001b[0m 4.8419\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6 \u001b[36m0.0903\u001b[0m \u001b[32m0.9701\u001b[0m \u001b[35m0.1037\u001b[0m 3.3229\n" + " 6 \u001b[36m0.0904\u001b[0m \u001b[32m0.9706\u001b[0m \u001b[35m0.1038\u001b[0m 4.9709\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 7 \u001b[36m0.0809\u001b[0m \u001b[32m0.9729\u001b[0m \u001b[35m0.0964\u001b[0m 3.4252\n" + " 7 \u001b[36m0.0809\u001b[0m \u001b[32m0.9731\u001b[0m \u001b[35m0.0962\u001b[0m 4.9817\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 8 \u001b[36m0.0736\u001b[0m \u001b[32m0.9747\u001b[0m \u001b[35m0.0903\u001b[0m 3.3964\n" + " 8 \u001b[36m0.0736\u001b[0m \u001b[32m0.9744\u001b[0m \u001b[35m0.0904\u001b[0m 4.9848\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 9 \u001b[36m0.0677\u001b[0m \u001b[32m0.9761\u001b[0m \u001b[35m0.0861\u001b[0m 3.3905\n" + " 9 \u001b[36m0.0678\u001b[0m \u001b[32m0.9761\u001b[0m \u001b[35m0.0860\u001b[0m 4.9951\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 10 \u001b[36m0.0630\u001b[0m \u001b[32m0.9766\u001b[0m \u001b[35m0.0825\u001b[0m 3.3361\n" + " 10 \u001b[36m0.0630\u001b[0m \u001b[32m0.9774\u001b[0m \u001b[35m0.0825\u001b[0m 4.9483\n" ] } ], @@ -563,10 +563,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:41.840983Z", - "iopub.status.busy": "2023-08-14T16:38:41.840439Z", - "iopub.status.idle": "2023-08-14T16:38:41.849447Z", - "shell.execute_reply": "2023-08-14T16:38:41.848724Z" + "iopub.execute_input": "2023-08-15T04:11:30.681804Z", + "iopub.status.busy": "2023-08-15T04:11:30.681266Z", + "iopub.status.idle": "2023-08-15T04:11:30.694199Z", + "shell.execute_reply": "2023-08-15T04:11:30.693378Z" } }, "outputs": [ @@ -574,7 +574,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cross-validated estimate of accuracy on held-out data: 0.9752428571428572\n" + "Cross-validated estimate of accuracy on held-out data: 0.9752571428571428\n" ] } ], @@ -605,10 +605,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:41.853072Z", - "iopub.status.busy": "2023-08-14T16:38:41.852507Z", - "iopub.status.idle": "2023-08-14T16:38:44.479863Z", - "shell.execute_reply": "2023-08-14T16:38:44.478936Z" + "iopub.execute_input": "2023-08-15T04:11:30.698029Z", + "iopub.status.busy": "2023-08-15T04:11:30.697509Z", + "iopub.status.idle": "2023-08-15T04:11:34.274191Z", + "shell.execute_reply": "2023-08-15T04:11:34.272479Z" } }, "outputs": [ @@ -624,7 +624,7 @@ "output_type": "stream", "text": [ "\n", - "Audit complete. 143 issues found in the dataset.\n" + "Audit complete. 144 issues found in the dataset.\n" ] } ], @@ -649,10 +649,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.483936Z", - "iopub.status.busy": "2023-08-14T16:38:44.483157Z", - "iopub.status.idle": "2023-08-14T16:38:44.521002Z", - "shell.execute_reply": "2023-08-14T16:38:44.520297Z" + "iopub.execute_input": "2023-08-15T04:11:34.278629Z", + "iopub.status.busy": "2023-08-15T04:11:34.278144Z", + "iopub.status.idle": "2023-08-15T04:11:34.323853Z", + "shell.execute_reply": "2023-08-15T04:11:34.322860Z" }, "scrolled": true }, @@ -688,35 +688,35 @@ " \n", " 0\n", " False\n", - " 0.828540\n", + " 0.822983\n", " 5\n", " 5\n", " \n", " \n", " 1\n", " False\n", - " 0.999814\n", + " 0.999811\n", " 0\n", " 0\n", " \n", " \n", " 2\n", " False\n", - " 0.992302\n", + " 0.992340\n", " 4\n", " 4\n", " \n", " \n", " 3\n", " False\n", - " 0.997823\n", + " 0.997847\n", " 1\n", " 1\n", " \n", " \n", " 4\n", " False\n", - " 0.990165\n", + " 0.989921\n", " 9\n", " 9\n", " \n", @@ -726,11 +726,11 @@ ], "text/plain": [ " is_label_issue label_score given_label predicted_label\n", - "0 False 0.828540 5 5\n", - "1 False 0.999814 0 0\n", - "2 False 0.992302 4 4\n", - "3 False 0.997823 1 1\n", - "4 False 0.990165 9 9" + "0 False 0.822983 5 5\n", + "1 False 0.999811 0 0\n", + "2 False 0.992340 4 4\n", + "3 False 0.997847 1 1\n", + "4 False 0.989921 9 9" ] }, "execution_count": 10, @@ -757,10 +757,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.524046Z", - "iopub.status.busy": "2023-08-14T16:38:44.523597Z", - "iopub.status.idle": "2023-08-14T16:38:44.533569Z", - "shell.execute_reply": "2023-08-14T16:38:44.532876Z" + "iopub.execute_input": "2023-08-15T04:11:34.328031Z", + "iopub.status.busy": "2023-08-15T04:11:34.327694Z", + "iopub.status.idle": "2023-08-15T04:11:34.347479Z", + "shell.execute_reply": "2023-08-15T04:11:34.345922Z" } }, "outputs": [ @@ -769,8 +769,8 @@ "output_type": "stream", "text": [ "Top 15 most likely label errors: \n", - " [59915 24798 19124 53216 2720 59701 8200 50340 21601 32776 56014 2676\n", - " 35480 46726 7010]\n" + " [59915 24798 19124 53216 2720 59701 8200 50340 21601 32776 2676 35480\n", + " 56014 46726 7010]\n" ] } ], @@ -815,10 +815,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.536542Z", - "iopub.status.busy": "2023-08-14T16:38:44.536126Z", - "iopub.status.idle": "2023-08-14T16:38:44.806282Z", - "shell.execute_reply": "2023-08-14T16:38:44.805072Z" + "iopub.execute_input": "2023-08-15T04:11:34.352449Z", + "iopub.status.busy": "2023-08-15T04:11:34.351333Z", + "iopub.status.idle": "2023-08-15T04:11:34.688566Z", + "shell.execute_reply": "2023-08-15T04:11:34.686739Z" }, "nbsphinx": "hidden" }, @@ -848,16 +848,16 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.810109Z", - "iopub.status.busy": "2023-08-14T16:38:44.809682Z", - "iopub.status.idle": "2023-08-14T16:38:45.578747Z", - "shell.execute_reply": "2023-08-14T16:38:45.577854Z" + "iopub.execute_input": "2023-08-15T04:11:34.693348Z", + "iopub.status.busy": "2023-08-15T04:11:34.693012Z", + "iopub.status.idle": "2023-08-15T04:11:35.922369Z", + "shell.execute_reply": "2023-08-15T04:11:35.921230Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -889,10 +889,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.583760Z", - "iopub.status.busy": "2023-08-14T16:38:45.582270Z", - "iopub.status.idle": "2023-08-14T16:38:45.681117Z", - "shell.execute_reply": "2023-08-14T16:38:45.680369Z" + "iopub.execute_input": "2023-08-15T04:11:35.927075Z", + "iopub.status.busy": "2023-08-15T04:11:35.926329Z", + "iopub.status.idle": "2023-08-15T04:11:36.070520Z", + "shell.execute_reply": "2023-08-15T04:11:36.069635Z" } }, "outputs": [ @@ -923,10 +923,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.685065Z", - "iopub.status.busy": "2023-08-14T16:38:45.684803Z", - "iopub.status.idle": "2023-08-14T16:38:45.781304Z", - "shell.execute_reply": "2023-08-14T16:38:45.780675Z" + "iopub.execute_input": "2023-08-15T04:11:36.076103Z", + "iopub.status.busy": "2023-08-15T04:11:36.074626Z", + "iopub.status.idle": "2023-08-15T04:11:36.211656Z", + "shell.execute_reply": "2023-08-15T04:11:36.210847Z" } }, "outputs": [ @@ -957,10 +957,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.786102Z", - "iopub.status.busy": "2023-08-14T16:38:45.784826Z", - "iopub.status.idle": "2023-08-14T16:38:45.880563Z", - "shell.execute_reply": "2023-08-14T16:38:45.879942Z" + "iopub.execute_input": "2023-08-15T04:11:36.217234Z", + "iopub.status.busy": "2023-08-15T04:11:36.215798Z", + "iopub.status.idle": "2023-08-15T04:11:36.353472Z", + "shell.execute_reply": "2023-08-15T04:11:36.352639Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.885148Z", - "iopub.status.busy": "2023-08-14T16:38:45.883999Z", - "iopub.status.idle": "2023-08-14T16:38:45.977970Z", - "shell.execute_reply": "2023-08-14T16:38:45.977315Z" + "iopub.execute_input": "2023-08-15T04:11:36.359304Z", + "iopub.status.busy": "2023-08-15T04:11:36.357629Z", + "iopub.status.idle": "2023-08-15T04:11:36.499605Z", + "shell.execute_reply": "2023-08-15T04:11:36.498645Z" } }, "outputs": [ @@ -1027,10 +1027,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.982623Z", - "iopub.status.busy": "2023-08-14T16:38:45.981459Z", - "iopub.status.idle": "2023-08-14T16:38:45.987703Z", - "shell.execute_reply": "2023-08-14T16:38:45.987147Z" + "iopub.execute_input": "2023-08-15T04:11:36.505724Z", + "iopub.status.busy": "2023-08-15T04:11:36.503866Z", + "iopub.status.idle": "2023-08-15T04:11:36.516157Z", + "shell.execute_reply": "2023-08-15T04:11:36.515355Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 7b343e9df..69b1f690e 100644 --- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:50.776115Z", - "iopub.status.busy": "2023-08-14T16:38:50.775572Z", - "iopub.status.idle": "2023-08-14T16:38:52.091496Z", - "shell.execute_reply": "2023-08-14T16:38:52.090378Z" + "iopub.execute_input": "2023-08-15T04:11:42.217241Z", + "iopub.status.busy": "2023-08-15T04:11:42.216782Z", + "iopub.status.idle": "2023-08-15T04:11:43.845952Z", + "shell.execute_reply": "2023-08-15T04:11:43.844795Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.095900Z", - "iopub.status.busy": "2023-08-14T16:38:52.095380Z", - "iopub.status.idle": "2023-08-14T16:38:52.358817Z", - "shell.execute_reply": "2023-08-14T16:38:52.358028Z" + "iopub.execute_input": "2023-08-15T04:11:43.851897Z", + "iopub.status.busy": "2023-08-15T04:11:43.850757Z", + "iopub.status.idle": "2023-08-15T04:11:44.197410Z", + "shell.execute_reply": "2023-08-15T04:11:44.196275Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.363975Z", - "iopub.status.busy": "2023-08-14T16:38:52.362676Z", - "iopub.status.idle": "2023-08-14T16:38:52.482210Z", - "shell.execute_reply": "2023-08-14T16:38:52.481015Z" + "iopub.execute_input": "2023-08-15T04:11:44.202209Z", + "iopub.status.busy": "2023-08-15T04:11:44.201800Z", + "iopub.status.idle": "2023-08-15T04:11:44.224652Z", + "shell.execute_reply": "2023-08-15T04:11:44.223481Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.487306Z", - "iopub.status.busy": "2023-08-14T16:38:52.485958Z", - "iopub.status.idle": "2023-08-14T16:38:52.747309Z", - "shell.execute_reply": "2023-08-14T16:38:52.746514Z" + "iopub.execute_input": "2023-08-15T04:11:44.229037Z", + "iopub.status.busy": "2023-08-15T04:11:44.228210Z", + "iopub.status.idle": "2023-08-15T04:11:44.709696Z", + "shell.execute_reply": "2023-08-15T04:11:44.708776Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.750901Z", - "iopub.status.busy": "2023-08-14T16:38:52.750445Z", - "iopub.status.idle": "2023-08-14T16:38:52.780081Z", - "shell.execute_reply": "2023-08-14T16:38:52.779432Z" + "iopub.execute_input": "2023-08-15T04:11:44.714055Z", + "iopub.status.busy": "2023-08-15T04:11:44.713709Z", + "iopub.status.idle": "2023-08-15T04:11:44.762896Z", + "shell.execute_reply": "2023-08-15T04:11:44.761726Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.783344Z", - "iopub.status.busy": "2023-08-14T16:38:52.782875Z", - "iopub.status.idle": "2023-08-14T16:38:54.527350Z", - "shell.execute_reply": "2023-08-14T16:38:54.526237Z" + "iopub.execute_input": "2023-08-15T04:11:44.767437Z", + "iopub.status.busy": "2023-08-15T04:11:44.767034Z", + "iopub.status.idle": "2023-08-15T04:11:46.952056Z", + "shell.execute_reply": "2023-08-15T04:11:46.950612Z" } }, "outputs": [ @@ -471,10 +471,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:54.531701Z", - "iopub.status.busy": "2023-08-14T16:38:54.530933Z", - "iopub.status.idle": "2023-08-14T16:38:54.554957Z", - "shell.execute_reply": "2023-08-14T16:38:54.554308Z" + "iopub.execute_input": "2023-08-15T04:11:46.956615Z", + "iopub.status.busy": "2023-08-15T04:11:46.955724Z", + "iopub.status.idle": "2023-08-15T04:11:46.987375Z", + "shell.execute_reply": "2023-08-15T04:11:46.986222Z" }, "scrolled": true }, @@ -599,10 +599,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:54.558483Z", - "iopub.status.busy": "2023-08-14T16:38:54.557866Z", - "iopub.status.idle": "2023-08-14T16:38:55.782238Z", - "shell.execute_reply": "2023-08-14T16:38:55.781335Z" + "iopub.execute_input": "2023-08-15T04:11:46.992305Z", + "iopub.status.busy": "2023-08-15T04:11:46.991994Z", + "iopub.status.idle": "2023-08-15T04:11:48.499802Z", + "shell.execute_reply": "2023-08-15T04:11:48.498502Z" }, "id": "AaHC5MRKjruT" }, @@ -721,10 +721,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:55.786400Z", - "iopub.status.busy": "2023-08-14T16:38:55.785872Z", - "iopub.status.idle": "2023-08-14T16:38:55.803350Z", - "shell.execute_reply": "2023-08-14T16:38:55.802153Z" + "iopub.execute_input": "2023-08-15T04:11:48.504507Z", + "iopub.status.busy": "2023-08-15T04:11:48.504174Z", + "iopub.status.idle": "2023-08-15T04:11:48.534371Z", + "shell.execute_reply": "2023-08-15T04:11:48.533120Z" }, "id": "Wy27rvyhjruU" }, @@ -773,10 +773,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:55.806809Z", - "iopub.status.busy": "2023-08-14T16:38:55.806293Z", - "iopub.status.idle": "2023-08-14T16:38:55.901561Z", - "shell.execute_reply": "2023-08-14T16:38:55.900194Z" + "iopub.execute_input": "2023-08-15T04:11:48.538402Z", + "iopub.status.busy": "2023-08-15T04:11:48.537800Z", + "iopub.status.idle": "2023-08-15T04:11:48.683964Z", + "shell.execute_reply": "2023-08-15T04:11:48.682786Z" }, "id": "Db8YHnyVjruU" }, @@ -883,10 +883,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:55.905422Z", - "iopub.status.busy": "2023-08-14T16:38:55.904974Z", - "iopub.status.idle": "2023-08-14T16:38:56.127150Z", - "shell.execute_reply": "2023-08-14T16:38:56.126419Z" + "iopub.execute_input": "2023-08-15T04:11:48.689235Z", + "iopub.status.busy": "2023-08-15T04:11:48.688547Z", + "iopub.status.idle": "2023-08-15T04:11:49.008958Z", + "shell.execute_reply": "2023-08-15T04:11:49.008016Z" }, "id": "iJqAHuS2jruV" }, @@ -923,10 +923,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.130936Z", - "iopub.status.busy": "2023-08-14T16:38:56.130291Z", - "iopub.status.idle": "2023-08-14T16:38:56.154267Z", - "shell.execute_reply": "2023-08-14T16:38:56.153530Z" + "iopub.execute_input": "2023-08-15T04:11:49.013905Z", + "iopub.status.busy": "2023-08-15T04:11:49.013108Z", + "iopub.status.idle": "2023-08-15T04:11:49.044766Z", + "shell.execute_reply": "2023-08-15T04:11:49.043583Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -937,13 +937,13 @@ "
CleanLearning(clf=LogisticRegression(random_state=0),\n",
        "              find_label_issues_kwargs={'confident_joint': array([[68,  0,  8,  8],\n",
        "       [ 5, 46,  3,  0],\n",
-       "       [15,  3, 32, 13],\n",
+       "       [15,  3, 31, 14],\n",
        "       [ 2,  1, 12, 34]]),\n",
        "                                        'min_examples_per_class': 10},\n",
        "              seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" @@ -952,7 +952,7 @@ "CleanLearning(clf=LogisticRegression(random_state=0),\n", " find_label_issues_kwargs={'confident_joint': array([[68, 0, 8, 8],\n", " [ 5, 46, 3, 0],\n", - " [15, 3, 32, 13],\n", + " [15, 3, 31, 14],\n", " [ 2, 1, 12, 34]]),\n", " 'min_examples_per_class': 10},\n", " seed=0)" @@ -988,10 +988,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.157383Z", - "iopub.status.busy": "2023-08-14T16:38:56.157139Z", - "iopub.status.idle": "2023-08-14T16:38:56.171139Z", - "shell.execute_reply": "2023-08-14T16:38:56.170490Z" + "iopub.execute_input": "2023-08-15T04:11:49.049055Z", + "iopub.status.busy": "2023-08-15T04:11:49.048700Z", + "iopub.status.idle": "2023-08-15T04:11:49.079401Z", + "shell.execute_reply": "2023-08-15T04:11:49.077983Z" }, "id": "0lonvOYvjruV" }, @@ -1032,8 +1032,8 @@ " yellow\n", " 2\n", " 3\n", - " 25\n", - " 0.100\n", + " 26\n", + " 0.104\n", " \n", " \n", " 1\n", @@ -1094,7 +1094,7 @@ "5 blue yellow 1 3 \n", "\n", " Num Overlapping Examples Joint Probability \n", - "0 25 0.100 \n", + "0 26 0.104 \n", "1 23 0.092 \n", "2 10 0.040 \n", "3 6 0.024 \n", @@ -1138,10 +1138,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.174321Z", - "iopub.status.busy": "2023-08-14T16:38:56.173928Z", - "iopub.status.idle": "2023-08-14T16:38:56.281499Z", - "shell.execute_reply": "2023-08-14T16:38:56.280431Z" + "iopub.execute_input": "2023-08-15T04:11:49.083644Z", + "iopub.status.busy": "2023-08-15T04:11:49.083283Z", + "iopub.status.idle": "2023-08-15T04:11:49.257347Z", + "shell.execute_reply": "2023-08-15T04:11:49.255590Z" }, "id": "MfqTCa3kjruV" }, @@ -1222,10 +1222,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.285502Z", - "iopub.status.busy": "2023-08-14T16:38:56.285141Z", - "iopub.status.idle": "2023-08-14T16:38:56.437890Z", - "shell.execute_reply": "2023-08-14T16:38:56.437102Z" + "iopub.execute_input": "2023-08-15T04:11:49.262416Z", + "iopub.status.busy": "2023-08-15T04:11:49.262054Z", + "iopub.status.idle": "2023-08-15T04:11:49.521075Z", + "shell.execute_reply": "2023-08-15T04:11:49.519953Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1235,7 +1235,7 @@ "output_type": "stream", "text": [ " Noisy Test Accuracy (on given test labels) using yourFavoriteModel: 69%\n", - " Noisy Test Accuracy (on given test labels) using yourFavoriteModel (+ CleanLearning): 70%\n", + " Noisy Test Accuracy (on given test labels) using yourFavoriteModel (+ CleanLearning): 71%\n", "Actual Test Accuracy (on corrected test labels) using yourFavoriteModel: 83%\n", "Actual Test Accuracy (on corrected test labels) using yourFavoriteModel (+ CleanLearning): 86%\n" ] @@ -1285,10 +1285,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.441660Z", - "iopub.status.busy": "2023-08-14T16:38:56.441138Z", - "iopub.status.idle": "2023-08-14T16:38:56.447352Z", - "shell.execute_reply": "2023-08-14T16:38:56.446706Z" + "iopub.execute_input": "2023-08-15T04:11:49.525640Z", + "iopub.status.busy": "2023-08-15T04:11:49.524992Z", + "iopub.status.idle": "2023-08-15T04:11:49.533471Z", + "shell.execute_reply": "2023-08-15T04:11:49.532560Z" }, "id": "0rXP3ZPWjruW" }, @@ -1297,7 +1297,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " * Overall, about 28% (70 of the 250) labels in your dataset have potential issues.\n", + " * Overall, about 28% (71 of the 250) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.72.\n" ] } @@ -1326,10 +1326,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.450663Z", - "iopub.status.busy": "2023-08-14T16:38:56.450237Z", - "iopub.status.idle": "2023-08-14T16:38:56.456650Z", - "shell.execute_reply": "2023-08-14T16:38:56.456029Z" + "iopub.execute_input": "2023-08-15T04:11:49.537658Z", + "iopub.status.busy": "2023-08-15T04:11:49.537362Z", + "iopub.status.idle": "2023-08-15T04:11:49.545727Z", + "shell.execute_reply": "2023-08-15T04:11:49.544747Z" }, "id": "-iRPe8KXjruW" }, @@ -1384,10 +1384,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.459472Z", - "iopub.status.busy": "2023-08-14T16:38:56.459242Z", - "iopub.status.idle": "2023-08-14T16:38:56.510899Z", - "shell.execute_reply": "2023-08-14T16:38:56.510176Z" + "iopub.execute_input": "2023-08-15T04:11:49.549795Z", + "iopub.status.busy": "2023-08-15T04:11:49.549438Z", + "iopub.status.idle": "2023-08-15T04:11:49.625516Z", + "shell.execute_reply": "2023-08-15T04:11:49.624411Z" }, "id": "ZpipUliyjruW" }, @@ -1438,10 +1438,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.515925Z", - "iopub.status.busy": "2023-08-14T16:38:56.514491Z", - "iopub.status.idle": "2023-08-14T16:38:56.568719Z", - "shell.execute_reply": "2023-08-14T16:38:56.567917Z" + "iopub.execute_input": "2023-08-15T04:11:49.629886Z", + "iopub.status.busy": "2023-08-15T04:11:49.629429Z", + "iopub.status.idle": "2023-08-15T04:11:49.714256Z", + "shell.execute_reply": "2023-08-15T04:11:49.713055Z" }, "id": "SLq-3q4xjruX" }, @@ -1456,7 +1456,7 @@ "\t---\t---\t---\t---\n", "s=0 |\t0.27\t0.0\t0.03\t0.03\n", "s=1 |\t0.02\t0.18\t0.01\t0.0\n", - "s=2 |\t0.06\t0.01\t0.13\t0.05\n", + "s=2 |\t0.06\t0.01\t0.12\t0.06\n", "s=3 |\t0.01\t0.0\t0.05\t0.14\n", "\tTrace(matrix) = 0.72\n", "\n", @@ -1464,11 +1464,11 @@ " Noise Matrix (aka Noisy Channel) P(given_label|true_label) of shape (4, 4)\n", " p(s|y)\ty=0\ty=1\ty=2\ty=3\n", "\t---\t---\t---\t---\n", - "s=0 |\t0.76\t0.0\t0.15\t0.15\n", - "s=1 |\t0.06\t0.92\t0.05\t0.0\n", - "s=2 |\t0.17\t0.06\t0.58\t0.24\n", - "s=3 |\t0.02\t0.02\t0.22\t0.62\n", - "\tTrace(matrix) = 2.88\n", + "s=0 |\t0.76\t0.0\t0.15\t0.14\n", + "s=1 |\t0.06\t0.92\t0.06\t0.0\n", + "s=2 |\t0.17\t0.06\t0.57\t0.25\n", + "s=3 |\t0.02\t0.02\t0.22\t0.61\n", + "\tTrace(matrix) = 2.86\n", "\n" ] } @@ -1510,10 +1510,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.572993Z", - "iopub.status.busy": "2023-08-14T16:38:56.571730Z", - "iopub.status.idle": "2023-08-14T16:38:56.675161Z", - "shell.execute_reply": "2023-08-14T16:38:56.674234Z" + "iopub.execute_input": "2023-08-15T04:11:49.718778Z", + "iopub.status.busy": "2023-08-15T04:11:49.718460Z", + "iopub.status.idle": "2023-08-15T04:11:49.885722Z", + "shell.execute_reply": "2023-08-15T04:11:49.884310Z" }, "id": "g5LHhhuqFbXK" }, @@ -1545,10 +1545,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.679666Z", - "iopub.status.busy": "2023-08-14T16:38:56.678969Z", - "iopub.status.idle": "2023-08-14T16:38:56.799687Z", - "shell.execute_reply": "2023-08-14T16:38:56.798561Z" + "iopub.execute_input": "2023-08-15T04:11:49.891585Z", + "iopub.status.busy": "2023-08-15T04:11:49.890879Z", + "iopub.status.idle": "2023-08-15T04:11:50.065254Z", + "shell.execute_reply": "2023-08-15T04:11:50.062746Z" }, "id": "p7w8F8ezBcet" }, @@ -1605,10 +1605,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.803862Z", - "iopub.status.busy": "2023-08-14T16:38:56.803408Z", - "iopub.status.idle": "2023-08-14T16:38:57.033837Z", - "shell.execute_reply": "2023-08-14T16:38:57.032393Z" + "iopub.execute_input": "2023-08-15T04:11:50.070114Z", + "iopub.status.busy": "2023-08-15T04:11:50.069403Z", + "iopub.status.idle": "2023-08-15T04:11:50.391629Z", + "shell.execute_reply": "2023-08-15T04:11:50.390582Z" }, "id": "WETRL74tE_sU" }, @@ -1643,10 +1643,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.037711Z", - "iopub.status.busy": "2023-08-14T16:38:57.037050Z", - "iopub.status.idle": "2023-08-14T16:38:57.275493Z", - "shell.execute_reply": "2023-08-14T16:38:57.274608Z" + "iopub.execute_input": "2023-08-15T04:11:50.396282Z", + "iopub.status.busy": "2023-08-15T04:11:50.395715Z", + "iopub.status.idle": "2023-08-15T04:11:50.739603Z", + "shell.execute_reply": "2023-08-15T04:11:50.738379Z" }, "id": "kCfdx2gOLmXS" }, @@ -1808,10 +1808,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.279191Z", - "iopub.status.busy": "2023-08-14T16:38:57.278660Z", - "iopub.status.idle": "2023-08-14T16:38:57.288439Z", - "shell.execute_reply": "2023-08-14T16:38:57.287435Z" + "iopub.execute_input": "2023-08-15T04:11:50.744940Z", + "iopub.status.busy": "2023-08-15T04:11:50.743946Z", + "iopub.status.idle": "2023-08-15T04:11:50.755184Z", + "shell.execute_reply": "2023-08-15T04:11:50.754133Z" }, "id": "-uogYRWFYnuu" }, @@ -1865,10 +1865,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.291592Z", - "iopub.status.busy": "2023-08-14T16:38:57.291241Z", - "iopub.status.idle": "2023-08-14T16:38:57.525140Z", - "shell.execute_reply": "2023-08-14T16:38:57.524474Z" + "iopub.execute_input": "2023-08-15T04:11:50.759456Z", + "iopub.status.busy": "2023-08-15T04:11:50.758753Z", + "iopub.status.idle": "2023-08-15T04:11:51.099946Z", + "shell.execute_reply": "2023-08-15T04:11:51.098810Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1915,10 +1915,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.528536Z", - "iopub.status.busy": "2023-08-14T16:38:57.528030Z", - "iopub.status.idle": "2023-08-14T16:38:58.998091Z", - "shell.execute_reply": "2023-08-14T16:38:58.997122Z" + "iopub.execute_input": "2023-08-15T04:11:51.104497Z", + "iopub.status.busy": "2023-08-15T04:11:51.104131Z", + "iopub.status.idle": "2023-08-15T04:11:53.261276Z", + "shell.execute_reply": "2023-08-15T04:11:53.259913Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index d2e1c73be..6bb81ae12 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -84,10 +84,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:04.566223Z", - "iopub.status.busy": "2023-08-14T16:39:04.565970Z", - "iopub.status.idle": "2023-08-14T16:39:05.857895Z", - "shell.execute_reply": "2023-08-14T16:39:05.857048Z" + "iopub.execute_input": "2023-08-15T04:12:00.220721Z", + "iopub.status.busy": "2023-08-15T04:12:00.220036Z", + "iopub.status.idle": "2023-08-15T04:12:01.751652Z", + "shell.execute_reply": "2023-08-15T04:12:01.750507Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.861837Z", - "iopub.status.busy": "2023-08-14T16:39:05.861404Z", - "iopub.status.idle": "2023-08-14T16:39:05.867112Z", - "shell.execute_reply": "2023-08-14T16:39:05.866037Z" + "iopub.execute_input": "2023-08-15T04:12:01.756794Z", + "iopub.status.busy": "2023-08-15T04:12:01.756250Z", + "iopub.status.idle": "2023-08-15T04:12:01.763030Z", + "shell.execute_reply": "2023-08-15T04:12:01.761780Z" } }, "outputs": [], @@ -259,10 +259,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.870301Z", - "iopub.status.busy": "2023-08-14T16:39:05.869753Z", - "iopub.status.idle": "2023-08-14T16:39:05.880018Z", - "shell.execute_reply": "2023-08-14T16:39:05.879342Z" + "iopub.execute_input": "2023-08-15T04:12:01.767430Z", + "iopub.status.busy": "2023-08-15T04:12:01.767137Z", + "iopub.status.idle": "2023-08-15T04:12:01.781597Z", + "shell.execute_reply": "2023-08-15T04:12:01.780069Z" }, "nbsphinx": "hidden" }, @@ -346,10 +346,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.883073Z", - "iopub.status.busy": "2023-08-14T16:39:05.882490Z", - "iopub.status.idle": "2023-08-14T16:39:05.956367Z", - "shell.execute_reply": "2023-08-14T16:39:05.955119Z" + "iopub.execute_input": "2023-08-15T04:12:01.785865Z", + "iopub.status.busy": "2023-08-15T04:12:01.785175Z", + "iopub.status.idle": "2023-08-15T04:12:01.899306Z", + "shell.execute_reply": "2023-08-15T04:12:01.898246Z" } }, "outputs": [], @@ -375,10 +375,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.959658Z", - "iopub.status.busy": "2023-08-14T16:39:05.959273Z", - "iopub.status.idle": "2023-08-14T16:39:05.985253Z", - "shell.execute_reply": "2023-08-14T16:39:05.984575Z" + "iopub.execute_input": "2023-08-15T04:12:01.904183Z", + "iopub.status.busy": "2023-08-15T04:12:01.903864Z", + "iopub.status.idle": "2023-08-15T04:12:01.940877Z", + "shell.execute_reply": "2023-08-15T04:12:01.939857Z" } }, "outputs": [ @@ -593,10 +593,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.988337Z", - "iopub.status.busy": "2023-08-14T16:39:05.987894Z", - "iopub.status.idle": "2023-08-14T16:39:05.992506Z", - "shell.execute_reply": "2023-08-14T16:39:05.991818Z" + "iopub.execute_input": "2023-08-15T04:12:01.945644Z", + "iopub.status.busy": "2023-08-15T04:12:01.945346Z", + "iopub.status.idle": "2023-08-15T04:12:01.951831Z", + "shell.execute_reply": "2023-08-15T04:12:01.950840Z" } }, "outputs": [ @@ -667,10 +667,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.996364Z", - "iopub.status.busy": "2023-08-14T16:39:05.995846Z", - "iopub.status.idle": "2023-08-14T16:39:06.928954Z", - "shell.execute_reply": "2023-08-14T16:39:06.928140Z" + "iopub.execute_input": "2023-08-15T04:12:01.957068Z", + "iopub.status.busy": "2023-08-15T04:12:01.956792Z", + "iopub.status.idle": "2023-08-15T04:12:03.300272Z", + "shell.execute_reply": "2023-08-15T04:12:03.299271Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:06.933004Z", - "iopub.status.busy": "2023-08-14T16:39:06.932570Z", - "iopub.status.idle": "2023-08-14T16:39:06.964673Z", - "shell.execute_reply": "2023-08-14T16:39:06.963364Z" + "iopub.execute_input": "2023-08-15T04:12:03.305298Z", + "iopub.status.busy": "2023-08-15T04:12:03.304980Z", + "iopub.status.idle": "2023-08-15T04:12:03.359639Z", + "shell.execute_reply": "2023-08-15T04:12:03.358392Z" } }, "outputs": [], @@ -730,10 +730,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:06.968053Z", - "iopub.status.busy": "2023-08-14T16:39:06.967652Z", - "iopub.status.idle": "2023-08-14T16:39:17.298908Z", - "shell.execute_reply": "2023-08-14T16:39:17.298066Z" + "iopub.execute_input": "2023-08-15T04:12:03.364885Z", + "iopub.status.busy": "2023-08-15T04:12:03.364581Z", + "iopub.status.idle": "2023-08-15T04:12:17.526402Z", + "shell.execute_reply": "2023-08-15T04:12:17.525125Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.303423Z", - "iopub.status.busy": "2023-08-14T16:39:17.302590Z", - "iopub.status.idle": "2023-08-14T16:39:17.313405Z", - "shell.execute_reply": "2023-08-14T16:39:17.312806Z" + "iopub.execute_input": "2023-08-15T04:12:17.531305Z", + "iopub.status.busy": "2023-08-15T04:12:17.530680Z", + "iopub.status.idle": "2023-08-15T04:12:17.544840Z", + "shell.execute_reply": "2023-08-15T04:12:17.544018Z" }, "scrolled": true }, @@ -877,10 +877,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.316536Z", - "iopub.status.busy": "2023-08-14T16:39:17.315983Z", - "iopub.status.idle": "2023-08-14T16:39:17.335724Z", - "shell.execute_reply": "2023-08-14T16:39:17.334561Z" + "iopub.execute_input": "2023-08-15T04:12:17.549081Z", + "iopub.status.busy": "2023-08-15T04:12:17.548785Z", + "iopub.status.idle": "2023-08-15T04:12:17.577758Z", + "shell.execute_reply": "2023-08-15T04:12:17.576632Z" } }, "outputs": [ @@ -1130,10 +1130,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.338775Z", - "iopub.status.busy": "2023-08-14T16:39:17.338230Z", - "iopub.status.idle": "2023-08-14T16:39:17.346421Z", - "shell.execute_reply": "2023-08-14T16:39:17.345706Z" + "iopub.execute_input": "2023-08-15T04:12:17.582009Z", + "iopub.status.busy": "2023-08-15T04:12:17.581660Z", + "iopub.status.idle": "2023-08-15T04:12:17.593134Z", + "shell.execute_reply": "2023-08-15T04:12:17.591991Z" }, "scrolled": true }, @@ -1307,10 +1307,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.350196Z", - "iopub.status.busy": "2023-08-14T16:39:17.349658Z", - "iopub.status.idle": "2023-08-14T16:39:17.352939Z", - "shell.execute_reply": "2023-08-14T16:39:17.352233Z" + "iopub.execute_input": "2023-08-15T04:12:17.598424Z", + "iopub.status.busy": "2023-08-15T04:12:17.598149Z", + "iopub.status.idle": "2023-08-15T04:12:17.602758Z", + "shell.execute_reply": "2023-08-15T04:12:17.601720Z" } }, "outputs": [], @@ -1332,10 +1332,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.355865Z", - "iopub.status.busy": "2023-08-14T16:39:17.355431Z", - "iopub.status.idle": "2023-08-14T16:39:17.362085Z", - "shell.execute_reply": "2023-08-14T16:39:17.360816Z" + "iopub.execute_input": "2023-08-15T04:12:17.606831Z", + "iopub.status.busy": "2023-08-15T04:12:17.606490Z", + "iopub.status.idle": "2023-08-15T04:12:17.614848Z", + "shell.execute_reply": "2023-08-15T04:12:17.613814Z" }, "scrolled": true }, @@ -1387,10 +1387,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.364970Z", - "iopub.status.busy": "2023-08-14T16:39:17.364525Z", - "iopub.status.idle": "2023-08-14T16:39:17.367730Z", - "shell.execute_reply": "2023-08-14T16:39:17.367023Z" + "iopub.execute_input": "2023-08-15T04:12:17.618814Z", + "iopub.status.busy": "2023-08-15T04:12:17.618549Z", + "iopub.status.idle": "2023-08-15T04:12:17.624010Z", + "shell.execute_reply": "2023-08-15T04:12:17.623099Z" } }, "outputs": [], @@ -1414,10 +1414,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.370694Z", - "iopub.status.busy": "2023-08-14T16:39:17.370263Z", - "iopub.status.idle": "2023-08-14T16:39:17.375525Z", - "shell.execute_reply": "2023-08-14T16:39:17.374820Z" + "iopub.execute_input": "2023-08-15T04:12:17.627524Z", + "iopub.status.busy": "2023-08-15T04:12:17.627268Z", + "iopub.status.idle": "2023-08-15T04:12:17.635141Z", + "shell.execute_reply": "2023-08-15T04:12:17.634326Z" } }, "outputs": [ @@ -1472,10 +1472,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.379505Z", - "iopub.status.busy": "2023-08-14T16:39:17.378891Z", - "iopub.status.idle": "2023-08-14T16:39:17.415484Z", - "shell.execute_reply": "2023-08-14T16:39:17.414820Z" + "iopub.execute_input": "2023-08-15T04:12:17.638739Z", + "iopub.status.busy": "2023-08-15T04:12:17.638403Z", + "iopub.status.idle": "2023-08-15T04:12:17.703889Z", + "shell.execute_reply": "2023-08-15T04:12:17.702915Z" } }, "outputs": [], @@ -1496,12 +1496,14 @@ "id": "e59f7d4f", "metadata": {}, "source": [ - "## Further model improvements \n", + "## Further improvements \n", "You can also repeatedly iterate this process of getting better consensus labels using the model's out-of-sample predicted probabilities and then retraining the model with the improved labels to get even better predicted probabilities!\n", "For details, see our [examples](https://github.com/cleanlab/examples) notebook on [Iterative use of Cleanlab to Improve Classification Models (and Consensus Labels) from Data Labeled by Multiple Annotators](https://github.com/cleanlab/examples/blob/master/multiannotator_cifar10/multiannotator_cifar10.ipynb).\n", "\n", "If possible, the best way to improve your model is to collect additional labels for both previously annotated data and extra not-yet-labeled examples (i.e. *active learning*). To decide which data is most informative to label next, use `cleanlab.multiannotator.get_active_learning_scores()` rather than the methods from this tutorial. This is demonstrated in our examples notebook on [Active Learning with Multiple Data Annotators via ActiveLab](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb).\n", "\n", + "While this notebook focused on analzying the labels of your data, Cleanlab can also check your data features for various issues. Learn how to do this by following our [Datalab tutorials](../tutorials/datalab/index.html), except you do not need to pass in `labels` now that you've already analyzed them with this notebook (or you can provide `labels` to Datalab as the consensus labels estimated here).\n", + "\n", "\n", "## How does cleanlab.multiannotator work?\n", "\n", @@ -1516,10 +1518,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.418568Z", - "iopub.status.busy": "2023-08-14T16:39:17.418189Z", - "iopub.status.idle": "2023-08-14T16:39:17.424432Z", - "shell.execute_reply": "2023-08-14T16:39:17.423722Z" + "iopub.execute_input": "2023-08-15T04:12:17.708894Z", + "iopub.status.busy": "2023-08-15T04:12:17.708385Z", + "iopub.status.idle": "2023-08-15T04:12:17.717233Z", + "shell.execute_reply": "2023-08-15T04:12:17.716111Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 5ec6fbb9a..405ad1669 100644 --- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb @@ -63,10 +63,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:23.132932Z", - "iopub.status.busy": "2023-08-14T16:39:23.132674Z", - "iopub.status.idle": "2023-08-14T16:39:24.423664Z", - "shell.execute_reply": "2023-08-14T16:39:24.422887Z" + "iopub.execute_input": "2023-08-15T04:12:23.679270Z", + "iopub.status.busy": "2023-08-15T04:12:23.678968Z", + "iopub.status.idle": "2023-08-15T04:12:25.352861Z", + "shell.execute_reply": "2023-08-15T04:12:25.351774Z" }, "nbsphinx": "hidden" }, @@ -78,7 +78,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -104,10 +104,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:24.427701Z", - "iopub.status.busy": "2023-08-14T16:39:24.427322Z", - "iopub.status.idle": "2023-08-14T16:39:24.819484Z", - "shell.execute_reply": "2023-08-14T16:39:24.818714Z" + "iopub.execute_input": "2023-08-15T04:12:25.359018Z", + "iopub.status.busy": "2023-08-15T04:12:25.358546Z", + "iopub.status.idle": "2023-08-15T04:12:25.832738Z", + "shell.execute_reply": "2023-08-15T04:12:25.831285Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:24.823419Z", - "iopub.status.busy": "2023-08-14T16:39:24.823156Z", - "iopub.status.idle": "2023-08-14T16:39:24.840240Z", - "shell.execute_reply": "2023-08-14T16:39:24.839632Z" + "iopub.execute_input": "2023-08-15T04:12:25.837950Z", + "iopub.status.busy": "2023-08-15T04:12:25.837590Z", + "iopub.status.idle": "2023-08-15T04:12:25.860001Z", + "shell.execute_reply": "2023-08-15T04:12:25.859033Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:24.843424Z", - "iopub.status.busy": "2023-08-14T16:39:24.842880Z", - "iopub.status.idle": "2023-08-14T16:39:27.980216Z", - "shell.execute_reply": "2023-08-14T16:39:27.979547Z" + "iopub.execute_input": "2023-08-15T04:12:25.864444Z", + "iopub.status.busy": "2023-08-15T04:12:25.863854Z", + "iopub.status.idle": "2023-08-15T04:12:31.363434Z", + "shell.execute_reply": "2023-08-15T04:12:31.362388Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:27.983940Z", - "iopub.status.busy": "2023-08-14T16:39:27.983589Z", - "iopub.status.idle": "2023-08-14T16:39:30.002008Z", - "shell.execute_reply": "2023-08-14T16:39:30.000328Z" + "iopub.execute_input": "2023-08-15T04:12:31.368176Z", + "iopub.status.busy": "2023-08-15T04:12:31.367846Z", + "iopub.status.idle": "2023-08-15T04:12:34.271660Z", + "shell.execute_reply": "2023-08-15T04:12:34.270454Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:30.006531Z", - "iopub.status.busy": "2023-08-14T16:39:30.005848Z", - "iopub.status.idle": "2023-08-14T16:39:30.024465Z", - "shell.execute_reply": "2023-08-14T16:39:30.023764Z" + "iopub.execute_input": "2023-08-15T04:12:34.276855Z", + "iopub.status.busy": "2023-08-15T04:12:34.276145Z", + "iopub.status.idle": "2023-08-15T04:12:34.297136Z", + "shell.execute_reply": "2023-08-15T04:12:34.296082Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:30.027444Z", - "iopub.status.busy": "2023-08-14T16:39:30.027004Z", - "iopub.status.idle": "2023-08-14T16:39:31.770450Z", - "shell.execute_reply": "2023-08-14T16:39:31.769590Z" + "iopub.execute_input": "2023-08-15T04:12:34.300992Z", + "iopub.status.busy": "2023-08-15T04:12:34.300717Z", + "iopub.status.idle": "2023-08-15T04:12:36.427573Z", + "shell.execute_reply": "2023-08-15T04:12:36.426108Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:31.774922Z", - "iopub.status.busy": "2023-08-14T16:39:31.773768Z", - "iopub.status.idle": "2023-08-14T16:39:34.891228Z", - "shell.execute_reply": "2023-08-14T16:39:34.890413Z" + "iopub.execute_input": "2023-08-15T04:12:36.432974Z", + "iopub.status.busy": "2023-08-15T04:12:36.432037Z", + "iopub.status.idle": "2023-08-15T04:12:41.919080Z", + "shell.execute_reply": "2023-08-15T04:12:41.917945Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:34.894830Z", - "iopub.status.busy": "2023-08-14T16:39:34.894325Z", - "iopub.status.idle": "2023-08-14T16:39:34.900277Z", - "shell.execute_reply": "2023-08-14T16:39:34.899560Z" + "iopub.execute_input": "2023-08-15T04:12:41.923694Z", + "iopub.status.busy": "2023-08-15T04:12:41.922889Z", + "iopub.status.idle": "2023-08-15T04:12:41.932487Z", + "shell.execute_reply": "2023-08-15T04:12:41.931491Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:34.903919Z", - "iopub.status.busy": "2023-08-14T16:39:34.903467Z", - "iopub.status.idle": "2023-08-14T16:39:34.908336Z", - "shell.execute_reply": "2023-08-14T16:39:34.907636Z" + "iopub.execute_input": "2023-08-15T04:12:41.936773Z", + "iopub.status.busy": "2023-08-15T04:12:41.935850Z", + "iopub.status.idle": "2023-08-15T04:12:41.943208Z", + "shell.execute_reply": "2023-08-15T04:12:41.942263Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:34.911327Z", - "iopub.status.busy": "2023-08-14T16:39:34.910960Z", - "iopub.status.idle": "2023-08-14T16:39:34.914947Z", - "shell.execute_reply": "2023-08-14T16:39:34.914235Z" + "iopub.execute_input": "2023-08-15T04:12:41.947171Z", + "iopub.status.busy": "2023-08-15T04:12:41.946865Z", + "iopub.status.idle": "2023-08-15T04:12:41.954123Z", + "shell.execute_reply": "2023-08-15T04:12:41.952819Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 7d463c144..8c89fd49e 100644 --- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:39.962599Z", - "iopub.status.busy": "2023-08-14T16:39:39.962143Z", - "iopub.status.idle": "2023-08-14T16:39:41.263291Z", - "shell.execute_reply": "2023-08-14T16:39:41.262506Z" + "iopub.execute_input": "2023-08-15T04:12:46.730680Z", + "iopub.status.busy": "2023-08-15T04:12:46.729765Z", + "iopub.status.idle": "2023-08-15T04:12:48.395501Z", + "shell.execute_reply": "2023-08-15T04:12:48.394591Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:41.267254Z", - "iopub.status.busy": "2023-08-14T16:39:41.266709Z", - "iopub.status.idle": "2023-08-14T16:39:42.609703Z", - "shell.execute_reply": "2023-08-14T16:39:42.608506Z" + "iopub.execute_input": "2023-08-15T04:12:48.400543Z", + "iopub.status.busy": "2023-08-15T04:12:48.400083Z", + "iopub.status.idle": "2023-08-15T04:12:52.439980Z", + "shell.execute_reply": "2023-08-15T04:12:52.438701Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:42.614057Z", - "iopub.status.busy": "2023-08-14T16:39:42.613581Z", - "iopub.status.idle": "2023-08-14T16:39:42.618988Z", - "shell.execute_reply": "2023-08-14T16:39:42.618047Z" + "iopub.execute_input": "2023-08-15T04:12:52.445260Z", + "iopub.status.busy": "2023-08-15T04:12:52.443674Z", + "iopub.status.idle": "2023-08-15T04:12:52.450922Z", + "shell.execute_reply": "2023-08-15T04:12:52.449696Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:42.622112Z", - "iopub.status.busy": "2023-08-14T16:39:42.621666Z", - "iopub.status.idle": "2023-08-14T16:39:42.628947Z", - "shell.execute_reply": "2023-08-14T16:39:42.628358Z" + "iopub.execute_input": "2023-08-15T04:12:52.455377Z", + "iopub.status.busy": "2023-08-15T04:12:52.455003Z", + "iopub.status.idle": "2023-08-15T04:12:52.463773Z", + "shell.execute_reply": "2023-08-15T04:12:52.462779Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:42.631948Z", - "iopub.status.busy": "2023-08-14T16:39:42.631508Z", - "iopub.status.idle": "2023-08-14T16:39:43.415293Z", - "shell.execute_reply": "2023-08-14T16:39:43.414599Z" + "iopub.execute_input": "2023-08-15T04:12:52.467789Z", + "iopub.status.busy": "2023-08-15T04:12:52.467507Z", + "iopub.status.idle": "2023-08-15T04:12:53.399657Z", + "shell.execute_reply": "2023-08-15T04:12:53.398574Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.418896Z", - "iopub.status.busy": "2023-08-14T16:39:43.418166Z", - "iopub.status.idle": "2023-08-14T16:39:43.425597Z", - "shell.execute_reply": "2023-08-14T16:39:43.424891Z" + "iopub.execute_input": "2023-08-15T04:12:53.403958Z", + "iopub.status.busy": "2023-08-15T04:12:53.403647Z", + "iopub.status.idle": "2023-08-15T04:12:53.412688Z", + "shell.execute_reply": "2023-08-15T04:12:53.411821Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.428659Z", - "iopub.status.busy": "2023-08-14T16:39:43.428209Z", - "iopub.status.idle": "2023-08-14T16:39:43.432703Z", - "shell.execute_reply": "2023-08-14T16:39:43.432169Z" + "iopub.execute_input": "2023-08-15T04:12:53.416963Z", + "iopub.status.busy": "2023-08-15T04:12:53.416686Z", + "iopub.status.idle": "2023-08-15T04:12:53.422310Z", + "shell.execute_reply": "2023-08-15T04:12:53.421300Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.435443Z", - "iopub.status.busy": "2023-08-14T16:39:43.435002Z", - "iopub.status.idle": "2023-08-14T16:39:43.666450Z", - "shell.execute_reply": "2023-08-14T16:39:43.665710Z" + "iopub.execute_input": "2023-08-15T04:12:53.427813Z", + "iopub.status.busy": "2023-08-15T04:12:53.427310Z", + "iopub.status.idle": "2023-08-15T04:12:53.700425Z", + "shell.execute_reply": "2023-08-15T04:12:53.699169Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.669988Z", - "iopub.status.busy": "2023-08-14T16:39:43.669441Z", - "iopub.status.idle": "2023-08-14T16:39:43.783457Z", - "shell.execute_reply": "2023-08-14T16:39:43.782576Z" + "iopub.execute_input": "2023-08-15T04:12:53.705133Z", + "iopub.status.busy": "2023-08-15T04:12:53.704539Z", + "iopub.status.idle": "2023-08-15T04:12:53.863686Z", + "shell.execute_reply": "2023-08-15T04:12:53.862366Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.786829Z", - "iopub.status.busy": "2023-08-14T16:39:43.786566Z", - "iopub.status.idle": "2023-08-14T16:39:43.793693Z", - "shell.execute_reply": "2023-08-14T16:39:43.793069Z" + "iopub.execute_input": "2023-08-15T04:12:53.868642Z", + "iopub.status.busy": "2023-08-15T04:12:53.868291Z", + "iopub.status.idle": "2023-08-15T04:12:53.878050Z", + "shell.execute_reply": "2023-08-15T04:12:53.876990Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.797050Z", - "iopub.status.busy": "2023-08-14T16:39:43.796488Z", - "iopub.status.idle": "2023-08-14T16:39:44.243208Z", - "shell.execute_reply": "2023-08-14T16:39:44.242500Z" + "iopub.execute_input": "2023-08-15T04:12:53.882368Z", + "iopub.status.busy": "2023-08-15T04:12:53.882082Z", + "iopub.status.idle": "2023-08-15T04:12:54.467969Z", + "shell.execute_reply": "2023-08-15T04:12:54.467053Z" } }, "outputs": [ @@ -706,7 +706,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "./example_images/000000009483.jpg | idx 50 | label quality score: 6.955697261680538e-05 | is issue: True\n" + "./example_images/000000009483.jpg | idx 50 | label quality score: 6.95569726168054e-05 | is issue: True\n" ] }, { @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:44.246825Z", - "iopub.status.busy": "2023-08-14T16:39:44.246174Z", - "iopub.status.idle": "2023-08-14T16:39:44.651342Z", - "shell.execute_reply": "2023-08-14T16:39:44.650504Z" + "iopub.execute_input": "2023-08-15T04:12:54.474238Z", + "iopub.status.busy": "2023-08-15T04:12:54.473708Z", + "iopub.status.idle": "2023-08-15T04:12:54.983491Z", + "shell.execute_reply": "2023-08-15T04:12:54.982595Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:44.655236Z", - "iopub.status.busy": "2023-08-14T16:39:44.654618Z", - "iopub.status.idle": "2023-08-14T16:39:45.115093Z", - "shell.execute_reply": "2023-08-14T16:39:45.114419Z" + "iopub.execute_input": "2023-08-15T04:12:54.989327Z", + "iopub.status.busy": "2023-08-15T04:12:54.988500Z", + "iopub.status.idle": "2023-08-15T04:12:55.630738Z", + "shell.execute_reply": "2023-08-15T04:12:55.629855Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:45.120918Z", - "iopub.status.busy": "2023-08-14T16:39:45.120404Z", - "iopub.status.idle": "2023-08-14T16:39:45.685123Z", - "shell.execute_reply": "2023-08-14T16:39:45.684468Z" + "iopub.execute_input": "2023-08-15T04:12:55.640582Z", + "iopub.status.busy": "2023-08-15T04:12:55.640012Z", + "iopub.status.idle": "2023-08-15T04:12:56.404999Z", + "shell.execute_reply": "2023-08-15T04:12:56.403866Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:45.689614Z", - "iopub.status.busy": "2023-08-14T16:39:45.688954Z", - "iopub.status.idle": "2023-08-14T16:39:46.255297Z", - "shell.execute_reply": "2023-08-14T16:39:46.254504Z" + "iopub.execute_input": "2023-08-15T04:12:56.408878Z", + "iopub.status.busy": "2023-08-15T04:12:56.408501Z", + "iopub.status.idle": "2023-08-15T04:12:57.123474Z", + "shell.execute_reply": "2023-08-15T04:12:57.122757Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:46.261200Z", - "iopub.status.busy": "2023-08-14T16:39:46.260660Z", - "iopub.status.idle": "2023-08-14T16:39:46.519231Z", - "shell.execute_reply": "2023-08-14T16:39:46.518588Z" + "iopub.execute_input": "2023-08-15T04:12:57.129895Z", + "iopub.status.busy": "2023-08-15T04:12:57.128975Z", + "iopub.status.idle": "2023-08-15T04:12:57.495427Z", + "shell.execute_reply": "2023-08-15T04:12:57.494687Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:46.522549Z", - "iopub.status.busy": "2023-08-14T16:39:46.522132Z", - "iopub.status.idle": "2023-08-14T16:39:46.751608Z", - "shell.execute_reply": "2023-08-14T16:39:46.750965Z" + "iopub.execute_input": "2023-08-15T04:12:57.499195Z", + "iopub.status.busy": "2023-08-15T04:12:57.498740Z", + "iopub.status.idle": "2023-08-15T04:12:57.780087Z", + "shell.execute_reply": "2023-08-15T04:12:57.778580Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "./example_images/000000143931.jpg | idx 2 | label quality score: 0.9876495074395957 | is issue: False\n" + "./example_images/000000143931.jpg | idx 2 | label quality score: 0.9876495074395956 | is issue: False\n" ] }, { @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:46.757727Z", - "iopub.status.busy": "2023-08-14T16:39:46.757275Z", - "iopub.status.idle": "2023-08-14T16:39:46.762434Z", - "shell.execute_reply": "2023-08-14T16:39:46.761822Z" + "iopub.execute_input": "2023-08-15T04:12:57.785129Z", + "iopub.status.busy": "2023-08-15T04:12:57.784702Z", + "iopub.status.idle": "2023-08-15T04:12:57.791156Z", + "shell.execute_reply": "2023-08-15T04:12:57.789935Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 54a230023..4ac133341 100644 --- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:49.701332Z", - "iopub.status.busy": "2023-08-14T16:39:49.700869Z", - "iopub.status.idle": "2023-08-14T16:39:52.238174Z", - "shell.execute_reply": "2023-08-14T16:39:52.237139Z" + "iopub.execute_input": "2023-08-15T04:13:01.276358Z", + "iopub.status.busy": "2023-08-15T04:13:01.276030Z", + "iopub.status.idle": "2023-08-15T04:13:04.634021Z", + "shell.execute_reply": "2023-08-15T04:13:04.633035Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:52.242802Z", - "iopub.status.busy": "2023-08-14T16:39:52.242181Z", - "iopub.status.idle": "2023-08-14T16:39:52.681185Z", - "shell.execute_reply": "2023-08-14T16:39:52.680422Z" + "iopub.execute_input": "2023-08-15T04:13:04.638878Z", + "iopub.status.busy": "2023-08-15T04:13:04.638316Z", + "iopub.status.idle": "2023-08-15T04:13:05.178752Z", + "shell.execute_reply": "2023-08-15T04:13:05.177526Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:52.685555Z", - "iopub.status.busy": "2023-08-14T16:39:52.685121Z", - "iopub.status.idle": "2023-08-14T16:39:52.691385Z", - "shell.execute_reply": "2023-08-14T16:39:52.690733Z" + "iopub.execute_input": "2023-08-15T04:13:05.183546Z", + "iopub.status.busy": "2023-08-15T04:13:05.183092Z", + "iopub.status.idle": "2023-08-15T04:13:05.190329Z", + "shell.execute_reply": "2023-08-15T04:13:05.189444Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:52.694787Z", - "iopub.status.busy": "2023-08-14T16:39:52.694359Z", - "iopub.status.idle": "2023-08-14T16:40:01.520932Z", - "shell.execute_reply": "2023-08-14T16:40:01.520157Z" + "iopub.execute_input": "2023-08-15T04:13:05.193696Z", + "iopub.status.busy": "2023-08-15T04:13:05.193209Z", + "iopub.status.idle": "2023-08-15T04:13:13.098686Z", + "shell.execute_reply": "2023-08-15T04:13:13.097701Z" } }, "outputs": [ @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31d9a8a019bb4735b52e2e20691a104b", + "model_id": "6fb54979a8f547e087b7c624f518e4ca", "version_major": 2, "version_minor": 0 }, @@ -361,10 +361,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:01.524893Z", - "iopub.status.busy": "2023-08-14T16:40:01.524269Z", - "iopub.status.idle": "2023-08-14T16:40:01.531453Z", - "shell.execute_reply": "2023-08-14T16:40:01.530560Z" + "iopub.execute_input": "2023-08-15T04:13:13.103196Z", + "iopub.status.busy": "2023-08-15T04:13:13.102368Z", + "iopub.status.idle": "2023-08-15T04:13:13.111667Z", + "shell.execute_reply": "2023-08-15T04:13:13.110700Z" }, "nbsphinx": "hidden" }, @@ -415,10 +415,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:01.534441Z", - "iopub.status.busy": "2023-08-14T16:40:01.534066Z", - "iopub.status.idle": "2023-08-14T16:40:02.157718Z", - "shell.execute_reply": "2023-08-14T16:40:02.156990Z" + "iopub.execute_input": "2023-08-15T04:13:13.116484Z", + "iopub.status.busy": "2023-08-15T04:13:13.115927Z", + "iopub.status.idle": "2023-08-15T04:13:14.057425Z", + "shell.execute_reply": "2023-08-15T04:13:14.055757Z" } }, "outputs": [ @@ -451,10 +451,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:02.161321Z", - "iopub.status.busy": "2023-08-14T16:40:02.160728Z", - "iopub.status.idle": "2023-08-14T16:40:02.751301Z", - "shell.execute_reply": "2023-08-14T16:40:02.750423Z" + "iopub.execute_input": "2023-08-15T04:13:14.062107Z", + "iopub.status.busy": "2023-08-15T04:13:14.061803Z", + "iopub.status.idle": "2023-08-15T04:13:15.011711Z", + "shell.execute_reply": "2023-08-15T04:13:15.010731Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:02.754970Z", - "iopub.status.busy": "2023-08-14T16:40:02.754525Z", - "iopub.status.idle": "2023-08-14T16:40:02.759013Z", - "shell.execute_reply": "2023-08-14T16:40:02.758309Z" + "iopub.execute_input": "2023-08-15T04:13:15.016499Z", + "iopub.status.busy": "2023-08-15T04:13:15.016048Z", + "iopub.status.idle": "2023-08-15T04:13:15.021466Z", + "shell.execute_reply": "2023-08-15T04:13:15.020513Z" } }, "outputs": [], @@ -518,10 +518,10 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:02.762364Z", - "iopub.status.busy": "2023-08-14T16:40:02.761718Z", - "iopub.status.idle": "2023-08-14T16:40:14.768823Z", - "shell.execute_reply": "2023-08-14T16:40:14.767462Z" + "iopub.execute_input": "2023-08-15T04:13:15.025852Z", + "iopub.status.busy": "2023-08-15T04:13:15.025509Z", + "iopub.status.idle": "2023-08-15T04:13:40.096129Z", + "shell.execute_reply": "2023-08-15T04:13:40.095066Z" } }, "outputs": [ @@ -580,10 +580,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:14.772170Z", - "iopub.status.busy": "2023-08-14T16:40:14.771746Z", - "iopub.status.idle": "2023-08-14T16:40:16.547615Z", - "shell.execute_reply": "2023-08-14T16:40:16.546926Z" + "iopub.execute_input": "2023-08-15T04:13:40.101450Z", + "iopub.status.busy": "2023-08-15T04:13:40.100901Z", + "iopub.status.idle": "2023-08-15T04:13:42.878160Z", + "shell.execute_reply": "2023-08-15T04:13:42.877149Z" } }, "outputs": [ @@ -627,10 +627,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:16.550942Z", - "iopub.status.busy": "2023-08-14T16:40:16.550603Z", - "iopub.status.idle": "2023-08-14T16:40:16.849171Z", - "shell.execute_reply": "2023-08-14T16:40:16.848413Z" + "iopub.execute_input": "2023-08-15T04:13:42.882148Z", + "iopub.status.busy": "2023-08-15T04:13:42.881778Z", + "iopub.status.idle": "2023-08-15T04:13:43.314573Z", + "shell.execute_reply": "2023-08-15T04:13:43.313595Z" } }, "outputs": [ @@ -666,10 +666,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:16.852465Z", - "iopub.status.busy": "2023-08-14T16:40:16.852055Z", - "iopub.status.idle": "2023-08-14T16:40:17.739634Z", - "shell.execute_reply": "2023-08-14T16:40:17.738919Z" + "iopub.execute_input": "2023-08-15T04:13:43.318603Z", + "iopub.status.busy": "2023-08-15T04:13:43.318274Z", + "iopub.status.idle": "2023-08-15T04:13:44.563996Z", + "shell.execute_reply": "2023-08-15T04:13:44.563100Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:17.744650Z", - "iopub.status.busy": "2023-08-14T16:40:17.744303Z", - "iopub.status.idle": "2023-08-14T16:40:18.100429Z", - "shell.execute_reply": "2023-08-14T16:40:18.099724Z" + "iopub.execute_input": "2023-08-15T04:13:44.567882Z", + "iopub.status.busy": "2023-08-15T04:13:44.567595Z", + "iopub.status.idle": "2023-08-15T04:13:45.113671Z", + "shell.execute_reply": "2023-08-15T04:13:45.112827Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:18.103903Z", - "iopub.status.busy": "2023-08-14T16:40:18.103360Z", - "iopub.status.idle": "2023-08-14T16:40:18.398570Z", - "shell.execute_reply": "2023-08-14T16:40:18.397824Z" + "iopub.execute_input": "2023-08-15T04:13:45.117588Z", + "iopub.status.busy": "2023-08-15T04:13:45.117283Z", + "iopub.status.idle": "2023-08-15T04:13:45.548896Z", + "shell.execute_reply": "2023-08-15T04:13:45.547850Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:18.402624Z", - "iopub.status.busy": "2023-08-14T16:40:18.402351Z", - "iopub.status.idle": "2023-08-14T16:40:18.560477Z", - "shell.execute_reply": "2023-08-14T16:40:18.559639Z" + "iopub.execute_input": "2023-08-15T04:13:45.553159Z", + "iopub.status.busy": "2023-08-15T04:13:45.552800Z", + "iopub.status.idle": "2023-08-15T04:13:45.795047Z", + "shell.execute_reply": "2023-08-15T04:13:45.793980Z" } }, "outputs": [], @@ -853,10 +853,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:18.564583Z", - "iopub.status.busy": "2023-08-14T16:40:18.564027Z", - "iopub.status.idle": "2023-08-14T16:41:19.462057Z", - "shell.execute_reply": "2023-08-14T16:41:19.460497Z" + "iopub.execute_input": "2023-08-15T04:13:45.800039Z", + "iopub.status.busy": "2023-08-15T04:13:45.799746Z", + "iopub.status.idle": "2023-08-15T04:15:27.779244Z", + "shell.execute_reply": "2023-08-15T04:15:27.778165Z" } }, "outputs": [ @@ -893,10 +893,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:19.466012Z", - "iopub.status.busy": "2023-08-14T16:41:19.465529Z", - "iopub.status.idle": "2023-08-14T16:41:21.095602Z", - "shell.execute_reply": "2023-08-14T16:41:21.094820Z" + "iopub.execute_input": "2023-08-15T04:15:27.784226Z", + "iopub.status.busy": "2023-08-15T04:15:27.783438Z", + "iopub.status.idle": "2023-08-15T04:15:29.818974Z", + "shell.execute_reply": "2023-08-15T04:15:29.817577Z" } }, "outputs": [ @@ -927,10 +927,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:21.100022Z", - "iopub.status.busy": "2023-08-14T16:41:21.098968Z", - "iopub.status.idle": "2023-08-14T16:41:21.386873Z", - "shell.execute_reply": "2023-08-14T16:41:21.385880Z" + "iopub.execute_input": "2023-08-15T04:15:29.823698Z", + "iopub.status.busy": "2023-08-15T04:15:29.822995Z", + "iopub.status.idle": "2023-08-15T04:15:30.075021Z", + "shell.execute_reply": "2023-08-15T04:15:30.073745Z" } }, "outputs": [], @@ -944,10 +944,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:21.391192Z", - "iopub.status.busy": "2023-08-14T16:41:21.390701Z", - "iopub.status.idle": "2023-08-14T16:41:21.395331Z", - "shell.execute_reply": "2023-08-14T16:41:21.393882Z" + "iopub.execute_input": "2023-08-15T04:15:30.080553Z", + "iopub.status.busy": "2023-08-15T04:15:30.080203Z", + "iopub.status.idle": "2023-08-15T04:15:30.086749Z", + "shell.execute_reply": "2023-08-15T04:15:30.085382Z" } }, "outputs": [], @@ -969,10 +969,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:21.398357Z", - "iopub.status.busy": "2023-08-14T16:41:21.397907Z", - "iopub.status.idle": "2023-08-14T16:41:21.408299Z", - "shell.execute_reply": "2023-08-14T16:41:21.407684Z" + "iopub.execute_input": "2023-08-15T04:15:30.091068Z", + "iopub.status.busy": "2023-08-15T04:15:30.090755Z", + "iopub.status.idle": "2023-08-15T04:15:30.108498Z", + "shell.execute_reply": "2023-08-15T04:15:30.106424Z" }, "nbsphinx": "hidden" }, @@ -1017,23 +1017,31 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01a6598b5036437a9937e367ba5945e0": { + "03f3e3bbbd3e4f069ce93c9f77605447": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a7952551ea73471da7d1961607d2083e", + "max": 170498071.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e49f0205b8604488943e7af8ed1685b8", + "value": 170498071.0 } }, - "057b1902f56244ae9732302f3abb5b41": { + "2158e363bdc34ca6a7e8dd7fa89930ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1048,13 +1056,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_1ba9c3edcaa243efa4e9ca63cdaa629a", + "layout": "IPY_MODEL_3c305ca54c754287b8b47129cdd318ee", "placeholder": "​", - "style": "IPY_MODEL_efedb267c81645c5a3541ab2d4f2aa47", - "value": " 170498071/170498071 [00:01<00:00, 116569130.25it/s]" + "style": "IPY_MODEL_81d32f2b0cab4c8dad9119bda5d8b6a6", + "value": " 170498071/170498071 [00:02<00:00, 66755727.40it/s]" } }, - "1ba9c3edcaa243efa4e9ca63cdaa629a": { + "3c305ca54c754287b8b47129cdd318ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1106,22 +1114,7 @@ "width": null } }, - "1e71569d7e2d4dab965bc20efd956d0f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "31d9a8a019bb4735b52e2e20691a104b": { + "6fb54979a8f547e087b7c624f518e4ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1136,35 +1129,44 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3ea582420f404b10b74d8dabc145da17", - "IPY_MODEL_fc1cb9e0f95d411ab45e896daa259b85", - "IPY_MODEL_057b1902f56244ae9732302f3abb5b41" + "IPY_MODEL_b716a96f859c42ffb9a19fc47529653d", + "IPY_MODEL_03f3e3bbbd3e4f069ce93c9f77605447", + "IPY_MODEL_2158e363bdc34ca6a7e8dd7fa89930ba" ], - "layout": "IPY_MODEL_63a124ccef8640adb49350fd4617ac47" + "layout": "IPY_MODEL_8a90f3d0d7744a5d92a1df111880bcad" } }, - "3ea582420f404b10b74d8dabc145da17": { + "81d32f2b0cab4c8dad9119bda5d8b6a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6d2934806cb04e6692ab1956bd814684", - "placeholder": "​", - "style": "IPY_MODEL_1e71569d7e2d4dab965bc20efd956d0f", - "value": "100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "63a124ccef8640adb49350fd4617ac47": { + "8a5215a042d0433d840ef0639d2c44a8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8a90f3d0d7744a5d92a1df111880bcad": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1216,7 +1218,7 @@ "width": null } }, - "6d2934806cb04e6692ab1956bd814684": { + "a2589ff80d2145158e258121b8bf0168": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1268,7 +1270,7 @@ "width": null } }, - "94051f55351942e9ac89e5ad3e39ab07": { + "a7952551ea73471da7d1961607d2083e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1320,43 +1322,41 @@ "width": null } }, - "efedb267c81645c5a3541ab2d4f2aa47": { + "b716a96f859c42ffb9a19fc47529653d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a2589ff80d2145158e258121b8bf0168", + "placeholder": "​", + "style": "IPY_MODEL_8a5215a042d0433d840ef0639d2c44a8", + "value": "100%" } }, - "fc1cb9e0f95d411ab45e896daa259b85": { + "e49f0205b8604488943e7af8ed1685b8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_94051f55351942e9ac89e5ad3e39ab07", - "max": 170498071.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_01a6598b5036437a9937e367ba5945e0", - "value": 170498071.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 3adefafc8..b2f48e7a7 100644 --- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb @@ -94,10 +94,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:26.900240Z", - "iopub.status.busy": "2023-08-14T16:41:26.899956Z", - "iopub.status.idle": "2023-08-14T16:41:28.260647Z", - "shell.execute_reply": "2023-08-14T16:41:28.259848Z" + "iopub.execute_input": "2023-08-15T04:15:35.657044Z", + "iopub.status.busy": "2023-08-15T04:15:35.656740Z", + "iopub.status.idle": "2023-08-15T04:15:37.321011Z", + "shell.execute_reply": "2023-08-15T04:15:37.319956Z" }, "nbsphinx": "hidden" }, @@ -109,7 +109,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.264674Z", - "iopub.status.busy": "2023-08-14T16:41:28.264076Z", - "iopub.status.idle": "2023-08-14T16:41:28.292674Z", - "shell.execute_reply": "2023-08-14T16:41:28.291926Z" + "iopub.execute_input": "2023-08-15T04:15:37.325382Z", + "iopub.status.busy": "2023-08-15T04:15:37.324999Z", + "iopub.status.idle": "2023-08-15T04:15:37.356577Z", + "shell.execute_reply": "2023-08-15T04:15:37.355608Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.296447Z", - "iopub.status.busy": "2023-08-14T16:41:28.296029Z", - "iopub.status.idle": "2023-08-14T16:41:28.299897Z", - "shell.execute_reply": "2023-08-14T16:41:28.299173Z" + "iopub.execute_input": "2023-08-15T04:15:37.360428Z", + "iopub.status.busy": "2023-08-15T04:15:37.359963Z", + "iopub.status.idle": "2023-08-15T04:15:37.364967Z", + "shell.execute_reply": "2023-08-15T04:15:37.364077Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.303141Z", - "iopub.status.busy": "2023-08-14T16:41:28.302712Z", - "iopub.status.idle": "2023-08-14T16:41:28.394485Z", - "shell.execute_reply": "2023-08-14T16:41:28.393600Z" + "iopub.execute_input": "2023-08-15T04:15:37.369213Z", + "iopub.status.busy": "2023-08-15T04:15:37.368895Z", + "iopub.status.idle": "2023-08-15T04:15:37.577674Z", + "shell.execute_reply": "2023-08-15T04:15:37.576613Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.398606Z", - "iopub.status.busy": "2023-08-14T16:41:28.398121Z", - "iopub.status.idle": "2023-08-14T16:41:28.784028Z", - "shell.execute_reply": "2023-08-14T16:41:28.783225Z" + "iopub.execute_input": "2023-08-15T04:15:37.582270Z", + "iopub.status.busy": "2023-08-15T04:15:37.581669Z", + "iopub.status.idle": "2023-08-15T04:15:38.001213Z", + "shell.execute_reply": "2023-08-15T04:15:38.000064Z" }, "nbsphinx": "hidden" }, @@ -410,16 +410,16 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.788461Z", - "iopub.status.busy": "2023-08-14T16:41:28.788011Z", - "iopub.status.idle": "2023-08-14T16:41:29.076080Z", - "shell.execute_reply": "2023-08-14T16:41:29.075399Z" + "iopub.execute_input": "2023-08-15T04:15:38.006301Z", + "iopub.status.busy": "2023-08-15T04:15:38.005926Z", + "iopub.status.idle": "2023-08-15T04:15:38.415839Z", + "shell.execute_reply": "2023-08-15T04:15:38.414880Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.080183Z", - "iopub.status.busy": "2023-08-14T16:41:29.079807Z", - "iopub.status.idle": "2023-08-14T16:41:29.087407Z", - "shell.execute_reply": "2023-08-14T16:41:29.086656Z" + "iopub.execute_input": "2023-08-15T04:15:38.420585Z", + "iopub.status.busy": "2023-08-15T04:15:38.420274Z", + "iopub.status.idle": "2023-08-15T04:15:38.430755Z", + "shell.execute_reply": "2023-08-15T04:15:38.429006Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.090572Z", - "iopub.status.busy": "2023-08-14T16:41:29.090320Z", - "iopub.status.idle": "2023-08-14T16:41:29.098472Z", - "shell.execute_reply": "2023-08-14T16:41:29.097846Z" + "iopub.execute_input": "2023-08-15T04:15:38.435510Z", + "iopub.status.busy": "2023-08-15T04:15:38.434699Z", + "iopub.status.idle": "2023-08-15T04:15:38.445025Z", + "shell.execute_reply": "2023-08-15T04:15:38.443909Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.101369Z", - "iopub.status.busy": "2023-08-14T16:41:29.101129Z", - "iopub.status.idle": "2023-08-14T16:41:29.104208Z", - "shell.execute_reply": "2023-08-14T16:41:29.103502Z" + "iopub.execute_input": "2023-08-15T04:15:38.449301Z", + "iopub.status.busy": "2023-08-15T04:15:38.448583Z", + "iopub.status.idle": "2023-08-15T04:15:38.452749Z", + "shell.execute_reply": "2023-08-15T04:15:38.451981Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.107094Z", - "iopub.status.busy": "2023-08-14T16:41:29.106862Z", - "iopub.status.idle": "2023-08-14T16:41:44.013721Z", - "shell.execute_reply": "2023-08-14T16:41:44.012387Z" + "iopub.execute_input": "2023-08-15T04:15:38.456395Z", + "iopub.status.busy": "2023-08-15T04:15:38.455727Z", + "iopub.status.idle": "2023-08-15T04:15:59.540378Z", + "shell.execute_reply": "2023-08-15T04:15:59.539307Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.019384Z", - "iopub.status.busy": "2023-08-14T16:41:44.017911Z", - "iopub.status.idle": "2023-08-14T16:41:44.029853Z", - "shell.execute_reply": "2023-08-14T16:41:44.028803Z" + "iopub.execute_input": "2023-08-15T04:15:59.545461Z", + "iopub.status.busy": "2023-08-15T04:15:59.544456Z", + "iopub.status.idle": "2023-08-15T04:15:59.557177Z", + "shell.execute_reply": "2023-08-15T04:15:59.556271Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.033999Z", - "iopub.status.busy": "2023-08-14T16:41:44.032987Z", - "iopub.status.idle": "2023-08-14T16:41:44.038558Z", - "shell.execute_reply": "2023-08-14T16:41:44.037893Z" + "iopub.execute_input": "2023-08-15T04:15:59.560930Z", + "iopub.status.busy": "2023-08-15T04:15:59.560387Z", + "iopub.status.idle": "2023-08-15T04:15:59.565938Z", + "shell.execute_reply": "2023-08-15T04:15:59.565117Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.041619Z", - "iopub.status.busy": "2023-08-14T16:41:44.041209Z", - "iopub.status.idle": "2023-08-14T16:41:44.046666Z", - "shell.execute_reply": "2023-08-14T16:41:44.044690Z" + "iopub.execute_input": "2023-08-15T04:15:59.569608Z", + "iopub.status.busy": "2023-08-15T04:15:59.568944Z", + "iopub.status.idle": "2023-08-15T04:15:59.574418Z", + "shell.execute_reply": "2023-08-15T04:15:59.573529Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.049816Z", - "iopub.status.busy": "2023-08-14T16:41:44.049261Z", - "iopub.status.idle": "2023-08-14T16:41:44.053012Z", - "shell.execute_reply": "2023-08-14T16:41:44.052305Z" + "iopub.execute_input": "2023-08-15T04:15:59.578453Z", + "iopub.status.busy": "2023-08-15T04:15:59.577885Z", + "iopub.status.idle": "2023-08-15T04:15:59.582894Z", + "shell.execute_reply": "2023-08-15T04:15:59.582110Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.055960Z", - "iopub.status.busy": "2023-08-14T16:41:44.055592Z", - "iopub.status.idle": "2023-08-14T16:41:44.066254Z", - "shell.execute_reply": "2023-08-14T16:41:44.065532Z" + "iopub.execute_input": "2023-08-15T04:15:59.586852Z", + "iopub.status.busy": "2023-08-15T04:15:59.586214Z", + "iopub.status.idle": "2023-08-15T04:15:59.600255Z", + "shell.execute_reply": "2023-08-15T04:15:59.598988Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.070223Z", - "iopub.status.busy": "2023-08-14T16:41:44.069665Z", - "iopub.status.idle": "2023-08-14T16:41:44.284777Z", - "shell.execute_reply": "2023-08-14T16:41:44.284027Z" + "iopub.execute_input": "2023-08-15T04:15:59.604776Z", + "iopub.status.busy": "2023-08-15T04:15:59.604025Z", + "iopub.status.idle": "2023-08-15T04:15:59.903060Z", + "shell.execute_reply": "2023-08-15T04:15:59.902022Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.289420Z", - "iopub.status.busy": "2023-08-14T16:41:44.288270Z", - "iopub.status.idle": "2023-08-14T16:41:44.503475Z", - "shell.execute_reply": "2023-08-14T16:41:44.502798Z" + "iopub.execute_input": "2023-08-15T04:15:59.907490Z", + "iopub.status.busy": "2023-08-15T04:15:59.906912Z", + "iopub.status.idle": "2023-08-15T04:16:00.176339Z", + "shell.execute_reply": "2023-08-15T04:16:00.175513Z" } }, "outputs": [ @@ -995,10 +995,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:44.506979Z", - "iopub.status.busy": "2023-08-14T16:41:44.506300Z", - "iopub.status.idle": "2023-08-14T16:41:45.384093Z", - "shell.execute_reply": "2023-08-14T16:41:45.383356Z" + "iopub.execute_input": "2023-08-15T04:16:00.180851Z", + "iopub.status.busy": "2023-08-15T04:16:00.180231Z", + "iopub.status.idle": "2023-08-15T04:16:01.483638Z", + "shell.execute_reply": "2023-08-15T04:16:01.482573Z" } }, "outputs": [], @@ -1014,10 +1014,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:45.388795Z", - "iopub.status.busy": "2023-08-14T16:41:45.387967Z", - "iopub.status.idle": "2023-08-14T16:41:45.535295Z", - "shell.execute_reply": "2023-08-14T16:41:45.534571Z" + "iopub.execute_input": "2023-08-15T04:16:01.488960Z", + "iopub.status.busy": "2023-08-15T04:16:01.488344Z", + "iopub.status.idle": "2023-08-15T04:16:01.639609Z", + "shell.execute_reply": "2023-08-15T04:16:01.638492Z" } }, "outputs": [ @@ -1056,10 +1056,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:45.538591Z", - "iopub.status.busy": "2023-08-14T16:41:45.538173Z", - "iopub.status.idle": "2023-08-14T16:41:45.551363Z", - "shell.execute_reply": "2023-08-14T16:41:45.550102Z" + "iopub.execute_input": "2023-08-15T04:16:01.643890Z", + "iopub.status.busy": "2023-08-15T04:16:01.643315Z", + "iopub.status.idle": "2023-08-15T04:16:01.659327Z", + "shell.execute_reply": "2023-08-15T04:16:01.658525Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index a95d983c2..49c3f8f36 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:50.802529Z", - "iopub.status.busy": "2023-08-14T16:41:50.801886Z", - "iopub.status.idle": "2023-08-14T16:41:52.374276Z", - "shell.execute_reply": "2023-08-14T16:41:52.372684Z" + "iopub.execute_input": "2023-08-15T04:16:06.887902Z", + "iopub.status.busy": "2023-08-15T04:16:06.887593Z", + "iopub.status.idle": "2023-08-15T04:16:10.746876Z", + "shell.execute_reply": "2023-08-15T04:16:10.745395Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:52.378948Z", - "iopub.status.busy": "2023-08-14T16:41:52.378308Z", - "iopub.status.idle": "2023-08-14T16:42:44.002414Z", - "shell.execute_reply": "2023-08-14T16:42:44.001156Z" + "iopub.execute_input": "2023-08-15T04:16:10.752135Z", + "iopub.status.busy": "2023-08-15T04:16:10.751782Z", + "iopub.status.idle": "2023-08-15T04:17:00.270162Z", + "shell.execute_reply": "2023-08-15T04:17:00.268781Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:44.007121Z", - "iopub.status.busy": "2023-08-14T16:42:44.006529Z", - "iopub.status.idle": "2023-08-14T16:42:45.243446Z", - "shell.execute_reply": "2023-08-14T16:42:45.242587Z" + "iopub.execute_input": "2023-08-15T04:17:00.275717Z", + "iopub.status.busy": "2023-08-15T04:17:00.274812Z", + "iopub.status.idle": "2023-08-15T04:17:01.948788Z", + "shell.execute_reply": "2023-08-15T04:17:01.947773Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.247465Z", - "iopub.status.busy": "2023-08-14T16:42:45.246844Z", - "iopub.status.idle": "2023-08-14T16:42:45.251006Z", - "shell.execute_reply": "2023-08-14T16:42:45.250305Z" + "iopub.execute_input": "2023-08-15T04:17:01.953074Z", + "iopub.status.busy": "2023-08-15T04:17:01.952608Z", + "iopub.status.idle": "2023-08-15T04:17:01.959803Z", + "shell.execute_reply": "2023-08-15T04:17:01.958708Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.254680Z", - "iopub.status.busy": "2023-08-14T16:42:45.254242Z", - "iopub.status.idle": "2023-08-14T16:42:45.258856Z", - "shell.execute_reply": "2023-08-14T16:42:45.258148Z" + "iopub.execute_input": "2023-08-15T04:17:01.963636Z", + "iopub.status.busy": "2023-08-15T04:17:01.963338Z", + "iopub.status.idle": "2023-08-15T04:17:01.969439Z", + "shell.execute_reply": "2023-08-15T04:17:01.968494Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.262614Z", - "iopub.status.busy": "2023-08-14T16:42:45.262183Z", - "iopub.status.idle": "2023-08-14T16:42:45.266363Z", - "shell.execute_reply": "2023-08-14T16:42:45.265636Z" + "iopub.execute_input": "2023-08-15T04:17:01.973873Z", + "iopub.status.busy": "2023-08-15T04:17:01.973393Z", + "iopub.status.idle": "2023-08-15T04:17:01.979134Z", + "shell.execute_reply": "2023-08-15T04:17:01.978143Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.270102Z", - "iopub.status.busy": "2023-08-14T16:42:45.269665Z", - "iopub.status.idle": "2023-08-14T16:42:45.273068Z", - "shell.execute_reply": "2023-08-14T16:42:45.272380Z" + "iopub.execute_input": "2023-08-15T04:17:01.983843Z", + "iopub.status.busy": "2023-08-15T04:17:01.983345Z", + "iopub.status.idle": "2023-08-15T04:17:01.988934Z", + "shell.execute_reply": "2023-08-15T04:17:01.987147Z" } }, "outputs": [], @@ -333,10 +333,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.276250Z", - "iopub.status.busy": "2023-08-14T16:42:45.275823Z", - "iopub.status.idle": "2023-08-14T16:43:49.330123Z", - "shell.execute_reply": "2023-08-14T16:43:49.328981Z" + "iopub.execute_input": "2023-08-15T04:17:01.993419Z", + "iopub.status.busy": "2023-08-15T04:17:01.992969Z", + "iopub.status.idle": "2023-08-15T04:18:23.345783Z", + "shell.execute_reply": "2023-08-15T04:18:23.344394Z" } }, "outputs": [ @@ -350,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79e22eb31bc045a5a7fc1711d7439f7c", + "model_id": "5969d9c86bfa4eb4a361b6db421fbf04", "version_major": 2, "version_minor": 0 }, @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4873d8069c004fb89cb850243aedf1bd", + "model_id": "6468ac6e511549ada6743c8ac8a6cbce", "version_major": 2, "version_minor": 0 }, @@ -407,10 +407,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:43:49.334755Z", - "iopub.status.busy": "2023-08-14T16:43:49.334182Z", - "iopub.status.idle": "2023-08-14T16:43:50.343458Z", - "shell.execute_reply": "2023-08-14T16:43:50.342661Z" + "iopub.execute_input": "2023-08-15T04:18:23.350917Z", + "iopub.status.busy": "2023-08-15T04:18:23.350569Z", + "iopub.status.idle": "2023-08-15T04:18:24.650680Z", + "shell.execute_reply": "2023-08-15T04:18:24.649513Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:43:50.347465Z", - "iopub.status.busy": "2023-08-14T16:43:50.346664Z", - "iopub.status.idle": "2023-08-14T16:43:53.283808Z", - "shell.execute_reply": "2023-08-14T16:43:53.283128Z" + "iopub.execute_input": "2023-08-15T04:18:24.654885Z", + "iopub.status.busy": "2023-08-15T04:18:24.654365Z", + "iopub.status.idle": "2023-08-15T04:18:28.499715Z", + "shell.execute_reply": "2023-08-15T04:18:28.498600Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:43:53.287368Z", - "iopub.status.busy": "2023-08-14T16:43:53.286940Z", - "iopub.status.idle": "2023-08-14T16:44:31.179688Z", - "shell.execute_reply": "2023-08-14T16:44:31.178604Z" + "iopub.execute_input": "2023-08-15T04:18:28.504223Z", + "iopub.status.busy": "2023-08-15T04:18:28.503602Z", + "iopub.status.idle": "2023-08-15T04:19:19.644095Z", + "shell.execute_reply": "2023-08-15T04:19:19.643208Z" } }, "outputs": [ @@ -546,7 +546,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 13131/4997436 [00:00<00:37, 131297.29it/s]" + " 0%| | 9043/4997436 [00:00<00:55, 90421.46it/s]" ] }, { @@ -554,7 +554,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 26448/4997436 [00:00<00:37, 132391.49it/s]" + " 0%| | 18630/4997436 [00:00<00:53, 93621.03it/s]" ] }, { @@ -562,7 +562,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 39725/4997436 [00:00<00:37, 132560.62it/s]" + " 1%| | 28336/4997436 [00:00<00:52, 95187.25it/s]" ] }, { @@ -570,7 +570,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 53044/4997436 [00:00<00:37, 132803.63it/s]" + " 1%| | 38205/4997436 [00:00<00:51, 96565.24it/s]" ] }, { @@ -578,7 +578,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 66339/4997436 [00:00<00:37, 132852.46it/s]" + " 1%| | 47862/4997436 [00:00<00:51, 96031.52it/s]" ] }, { @@ -586,7 +586,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 79652/4997436 [00:00<00:36, 132942.44it/s]" + " 1%| | 57596/4997436 [00:00<00:51, 96472.29it/s]" ] }, { @@ -594,7 +594,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 92947/4997436 [00:00<00:36, 132619.67it/s]" + " 1%|▏ | 68471/4997436 [00:00<00:49, 100472.41it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 106210/4997436 [00:00<00:36, 132276.11it/s]" + " 2%|▏ | 78520/4997436 [00:00<00:49, 100314.39it/s]" ] }, { @@ -610,7 +610,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 119458/4997436 [00:00<00:36, 132335.07it/s]" + " 2%|▏ | 88609/4997436 [00:00<00:48, 100488.27it/s]" ] }, { @@ -618,7 +618,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 132777/4997436 [00:01<00:36, 132594.50it/s]" + " 2%|▏ | 98713/4997436 [00:01<00:48, 100653.84it/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 146094/4997436 [00:01<00:36, 132768.87it/s]" + " 2%|▏ | 108779/4997436 [00:01<00:49, 99253.53it/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 159372/4997436 [00:01<00:36, 132162.88it/s]" + " 2%|▏ | 118904/4997436 [00:01<00:48, 99851.78it/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 172589/4997436 [00:01<00:36, 131996.99it/s]" + " 3%|▎ | 129279/4997436 [00:01<00:48, 101022.03it/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 185790/4997436 [00:01<00:36, 131354.76it/s]" + " 3%|▎ | 139385/4997436 [00:01<00:48, 100901.47it/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 199148/4997436 [00:01<00:36, 132018.73it/s]" + " 3%|▎ | 149478/4997436 [00:01<00:48, 99223.11it/s] " ] }, { @@ -666,7 +666,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 212584/4997436 [00:01<00:36, 132717.88it/s]" + " 3%|▎ | 159408/4997436 [00:01<00:49, 98042.56it/s]" ] }, { @@ -674,7 +674,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 225932/4997436 [00:01<00:35, 132943.28it/s]" + " 3%|▎ | 169907/4997436 [00:01<00:48, 100089.89it/s]" ] }, { @@ -682,7 +682,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 239228/4997436 [00:01<00:35, 132747.42it/s]" + " 4%|▎ | 180121/4997436 [00:01<00:47, 100683.58it/s]" ] }, { @@ -690,7 +690,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 252512/4997436 [00:01<00:35, 132770.64it/s]" + " 4%|▍ | 190197/4997436 [00:01<00:48, 98343.13it/s] " ] }, { @@ -698,7 +698,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 266008/4997436 [00:02<00:35, 133423.41it/s]" + " 4%|▍ | 200725/4997436 [00:02<00:47, 100375.50it/s]" ] }, { @@ -706,7 +706,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 279484/4997436 [00:02<00:35, 133822.03it/s]" + " 4%|▍ | 210778/4997436 [00:02<00:48, 99398.18it/s] " ] }, { @@ -714,7 +714,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 292988/4997436 [00:02<00:35, 134185.36it/s]" + " 4%|▍ | 220973/4997436 [00:02<00:47, 100008.67it/s]" ] }, { @@ -722,7 +722,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 306407/4997436 [00:02<00:35, 133753.57it/s]" + " 5%|▍ | 231084/4997436 [00:02<00:47, 100327.61it/s]" ] }, { @@ -730,7 +730,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 319783/4997436 [00:02<00:34, 133714.32it/s]" + " 5%|▍ | 241124/4997436 [00:02<00:48, 98167.25it/s] " ] }, { @@ -738,7 +738,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 333155/4997436 [00:02<00:35, 133011.63it/s]" + " 5%|▌ | 250954/4997436 [00:02<00:48, 97426.98it/s]" ] }, { @@ -746,7 +746,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 346458/4997436 [00:02<00:35, 132722.66it/s]" + " 5%|▌ | 260706/4997436 [00:02<00:48, 96895.09it/s]" ] }, { @@ -754,7 +754,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 359747/4997436 [00:02<00:34, 132741.94it/s]" + " 5%|▌ | 270402/4997436 [00:02<00:49, 94618.01it/s]" ] }, { @@ -762,7 +762,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 373054/4997436 [00:02<00:34, 132836.91it/s]" + " 6%|▌ | 280048/4997436 [00:02<00:49, 95152.63it/s]" ] }, { @@ -770,7 +770,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 386469/4997436 [00:02<00:34, 133227.46it/s]" + " 6%|▌ | 289574/4997436 [00:02<00:49, 94507.84it/s]" ] }, { @@ -778,7 +778,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 399933/4997436 [00:03<00:34, 133647.99it/s]" + " 6%|▌ | 299032/4997436 [00:03<00:49, 94221.06it/s]" ] }, { @@ -786,7 +786,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 413409/4997436 [00:03<00:34, 133979.09it/s]" + " 6%|▌ | 308547/4997436 [00:03<00:49, 94492.53it/s]" ] }, { @@ -794,7 +794,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 426849/4997436 [00:03<00:34, 134104.04it/s]" + " 6%|▋ | 318244/4997436 [00:03<00:49, 95224.35it/s]" ] }, { @@ -802,7 +802,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 440295/4997436 [00:03<00:33, 134207.24it/s]" + " 7%|▋ | 328094/4997436 [00:03<00:48, 96196.64it/s]" ] }, { @@ -810,7 +810,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 453780/4997436 [00:03<00:33, 134396.85it/s]" + " 7%|▋ | 337817/4997436 [00:03<00:48, 96501.76it/s]" ] }, { @@ -818,7 +818,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 467220/4997436 [00:03<00:33, 133967.84it/s]" + " 7%|▋ | 347598/4997436 [00:03<00:47, 96888.69it/s]" ] }, { @@ -826,7 +826,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 480618/4997436 [00:03<00:33, 133556.44it/s]" + " 7%|▋ | 357289/4997436 [00:03<00:47, 96730.76it/s]" ] }, { @@ -834,7 +834,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 493975/4997436 [00:03<00:33, 133420.22it/s]" + " 7%|▋ | 366964/4997436 [00:03<00:48, 96451.70it/s]" ] }, { @@ -842,7 +842,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 507318/4997436 [00:03<00:33, 132933.23it/s]" + " 8%|▊ | 376812/4997436 [00:03<00:47, 97038.61it/s]" ] }, { @@ -850,7 +850,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 520924/4997436 [00:03<00:33, 133841.74it/s]" + " 8%|▊ | 386686/4997436 [00:03<00:47, 97545.09it/s]" ] }, { @@ -858,7 +858,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 534309/4997436 [00:04<00:33, 133651.18it/s]" + " 8%|▊ | 396476/4997436 [00:04<00:47, 97649.68it/s]" ] }, { @@ -866,7 +866,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 547794/4997436 [00:04<00:33, 134008.35it/s]" + " 8%|▊ | 406242/4997436 [00:04<00:47, 97027.45it/s]" ] }, { @@ -874,7 +874,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 561196/4997436 [00:04<00:33, 133896.95it/s]" + " 8%|▊ | 415946/4997436 [00:04<00:47, 95960.47it/s]" ] }, { @@ -882,7 +882,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█▏ | 574587/4997436 [00:04<00:33, 133684.63it/s]" + " 9%|▊ | 425622/4997436 [00:04<00:47, 96176.35it/s]" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 587956/4997436 [00:04<00:33, 133211.39it/s]" + " 9%|▊ | 435242/4997436 [00:04<00:47, 95628.84it/s]" ] }, { @@ -898,7 +898,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 601278/4997436 [00:04<00:33, 132958.27it/s]" + " 9%|▉ | 444807/4997436 [00:04<00:48, 94227.00it/s]" ] }, { @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 614703/4997436 [00:04<00:32, 133342.11it/s]" + " 9%|▉ | 454450/4997436 [00:04<00:47, 94873.93it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 628038/4997436 [00:04<00:32, 133244.56it/s]" + " 9%|▉ | 464490/4997436 [00:04<00:46, 96509.17it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 641363/4997436 [00:04<00:32, 132797.23it/s]" + " 9%|▉ | 474466/4997436 [00:04<00:46, 97472.37it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 654644/4997436 [00:04<00:32, 132673.99it/s]" + " 10%|▉ | 485168/4997436 [00:04<00:44, 100314.21it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 668018/4997436 [00:05<00:32, 132991.05it/s]" + " 10%|▉ | 495570/4997436 [00:05<00:44, 101417.84it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 681318/4997436 [00:05<00:32, 132975.74it/s]" + " 10%|█ | 505998/4997436 [00:05<00:43, 102270.35it/s]" ] }, { @@ -954,7 +954,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 694750/4997436 [00:05<00:32, 133375.25it/s]" + " 10%|█ | 516288/4997436 [00:05<00:43, 102455.29it/s]" ] }, { @@ -962,7 +962,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 708088/4997436 [00:05<00:32, 133260.67it/s]" + " 11%|█ | 526917/4997436 [00:05<00:43, 103602.21it/s]" ] }, { @@ -970,7 +970,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 721415/4997436 [00:05<00:32, 133023.45it/s]" + " 11%|█ | 537279/4997436 [00:05<00:43, 102401.60it/s]" ] }, { @@ -978,7 +978,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 734718/4997436 [00:05<00:32, 132213.42it/s]" + " 11%|█ | 547524/4997436 [00:05<00:43, 102287.39it/s]" ] }, { @@ -986,7 +986,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 748019/4997436 [00:05<00:32, 132448.19it/s]" + " 11%|█ | 557756/4997436 [00:05<00:43, 101729.16it/s]" ] }, { @@ -994,7 +994,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 761360/4997436 [00:05<00:31, 132732.64it/s]" + " 11%|█▏ | 567932/4997436 [00:05<00:43, 101146.03it/s]" ] }, { @@ -1002,7 +1002,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 774908/4997436 [00:05<00:31, 133550.28it/s]" + " 12%|█▏ | 578049/4997436 [00:05<00:43, 100970.05it/s]" ] }, { @@ -1010,7 +1010,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 788264/4997436 [00:05<00:31, 133541.88it/s]" + " 12%|█▏ | 588148/4997436 [00:05<00:44, 99485.94it/s] " ] }, { @@ -1018,7 +1018,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 801858/4997436 [00:06<00:31, 134258.54it/s]" + " 12%|█▏ | 598102/4997436 [00:06<00:45, 96860.54it/s]" ] }, { @@ -1026,7 +1026,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 815425/4997436 [00:06<00:31, 134679.93it/s]" + " 12%|█▏ | 608118/4997436 [00:06<00:44, 97818.81it/s]" ] }, { @@ -1034,7 +1034,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 828894/4997436 [00:06<00:30, 134486.04it/s]" + " 12%|█▏ | 617914/4997436 [00:06<00:44, 97819.68it/s]" ] }, { @@ -1042,7 +1042,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 842376/4997436 [00:06<00:30, 134582.75it/s]" + " 13%|█▎ | 627994/4997436 [00:06<00:44, 98697.73it/s]" ] }, { @@ -1050,7 +1050,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 855835/4997436 [00:06<00:30, 134123.46it/s]" + " 13%|█▎ | 638085/4997436 [00:06<00:43, 99351.21it/s]" ] }, { @@ -1058,7 +1058,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 869248/4997436 [00:06<00:30, 133545.40it/s]" + " 13%|█▎ | 648983/4997436 [00:06<00:42, 102216.41it/s]" ] }, { @@ -1066,7 +1066,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 882623/4997436 [00:06<00:30, 133603.26it/s]" + " 13%|█▎ | 659212/4997436 [00:06<00:43, 100417.42it/s]" ] }, { @@ -1074,7 +1074,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 895984/4997436 [00:06<00:30, 133508.65it/s]" + " 13%|█▎ | 669265/4997436 [00:06<00:43, 99668.22it/s] " ] }, { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 909336/4997436 [00:06<00:30, 133508.85it/s]" + " 14%|█▎ | 679240/4997436 [00:06<00:43, 98703.30it/s]" ] }, { @@ -1090,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 922688/4997436 [00:06<00:30, 133480.31it/s]" + " 14%|█▍ | 689117/4997436 [00:07<00:44, 97781.21it/s]" ] }, { @@ -1098,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 936037/4997436 [00:07<00:30, 133395.34it/s]" + " 14%|█▍ | 698916/4997436 [00:07<00:43, 97840.92it/s]" ] }, { @@ -1106,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 949377/4997436 [00:07<00:30, 132766.88it/s]" + " 14%|█▍ | 708704/4997436 [00:07<00:44, 97089.19it/s]" ] }, { @@ -1114,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 962693/4997436 [00:07<00:30, 132882.06it/s]" + " 14%|█▍ | 718459/4997436 [00:07<00:44, 97222.81it/s]" ] }, { @@ -1122,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 976056/4997436 [00:07<00:30, 133103.40it/s]" + " 15%|█▍ | 728438/4997436 [00:07<00:43, 97982.69it/s]" ] }, { @@ -1130,7 +1130,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 989428/4997436 [00:07<00:30, 133285.63it/s]" + " 15%|█▍ | 738239/4997436 [00:07<00:43, 97123.22it/s]" ] }, { @@ -1138,7 +1138,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1002929/4997436 [00:07<00:29, 133800.74it/s]" + " 15%|█▍ | 747955/4997436 [00:07<00:43, 96839.67it/s]" ] }, { @@ -1146,7 +1146,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1016376/4997436 [00:07<00:29, 133999.23it/s]" + " 15%|█▌ | 757641/4997436 [00:07<00:44, 96107.36it/s]" ] }, { @@ -1154,7 +1154,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1029777/4997436 [00:07<00:29, 133778.29it/s]" + " 15%|█▌ | 767274/4997436 [00:07<00:43, 96169.61it/s]" ] }, { @@ -1162,7 +1162,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1043177/4997436 [00:07<00:29, 133841.00it/s]" + " 16%|█▌ | 776935/4997436 [00:07<00:43, 96297.32it/s]" ] }, { @@ -1170,7 +1170,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1056697/4997436 [00:07<00:29, 134245.17it/s]" + " 16%|█▌ | 786566/4997436 [00:08<00:43, 96011.42it/s]" ] }, { @@ -1178,7 +1178,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1070122/4997436 [00:08<00:29, 133811.84it/s]" + " 16%|█▌ | 796168/4997436 [00:08<00:44, 95442.98it/s]" ] }, { @@ -1186,7 +1186,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1083504/4997436 [00:08<00:29, 133337.80it/s]" + " 16%|█▌ | 805714/4997436 [00:08<00:43, 95429.66it/s]" ] }, { @@ -1194,7 +1194,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1096839/4997436 [00:08<00:29, 132545.23it/s]" + " 16%|█▋ | 815258/4997436 [00:08<00:43, 95276.06it/s]" ] }, { @@ -1202,7 +1202,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1110095/4997436 [00:08<00:29, 132537.43it/s]" + " 17%|█▋ | 824787/4997436 [00:08<00:43, 94916.41it/s]" ] }, { @@ -1210,7 +1210,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1123350/4997436 [00:08<00:29, 132496.06it/s]" + " 17%|█▋ | 834280/4997436 [00:08<00:43, 94869.72it/s]" ] }, { @@ -1218,7 +1218,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1136601/4997436 [00:08<00:29, 132242.39it/s]" + " 17%|█▋ | 843768/4997436 [00:08<00:43, 94776.90it/s]" ] }, { @@ -1226,7 +1226,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1149859/4997436 [00:08<00:29, 132338.02it/s]" + " 17%|█▋ | 853246/4997436 [00:08<00:43, 94626.68it/s]" ] }, { @@ -1234,7 +1234,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1163132/4997436 [00:08<00:28, 132452.10it/s]" + " 17%|█▋ | 862904/4997436 [00:08<00:43, 95208.23it/s]" ] }, { @@ -1242,7 +1242,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▎ | 1176381/4997436 [00:08<00:28, 132460.74it/s]" + " 17%|█▋ | 872676/4997436 [00:08<00:42, 95957.72it/s]" ] }, { @@ -1250,7 +1250,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1189628/4997436 [00:08<00:28, 132360.13it/s]" + " 18%|█▊ | 882498/4997436 [00:09<00:42, 96631.76it/s]" ] }, { @@ -1258,7 +1258,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1202865/4997436 [00:09<00:28, 132236.49it/s]" + " 18%|█▊ | 892489/4997436 [00:09<00:42, 97609.94it/s]" ] }, { @@ -1266,7 +1266,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1216089/4997436 [00:09<00:28, 131837.05it/s]" + " 18%|█▊ | 902282/4997436 [00:09<00:41, 97703.73it/s]" ] }, { @@ -1274,7 +1274,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1229273/4997436 [00:09<00:28, 131784.42it/s]" + " 18%|█▊ | 912678/4997436 [00:09<00:41, 99576.83it/s]" ] }, { @@ -1282,7 +1282,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1242452/4997436 [00:09<00:28, 131326.51it/s]" + " 18%|█▊ | 923280/4997436 [00:09<00:40, 101502.76it/s]" ] }, { @@ -1290,7 +1290,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1255586/4997436 [00:09<00:28, 130713.00it/s]" + " 19%|█▊ | 934275/4997436 [00:09<00:39, 104030.04it/s]" ] }, { @@ -1298,7 +1298,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1268658/4997436 [00:09<00:28, 130536.80it/s]" + " 19%|█▉ | 944679/4997436 [00:09<00:39, 102342.95it/s]" ] }, { @@ -1306,7 +1306,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1281789/4997436 [00:09<00:28, 130763.37it/s]" + " 19%|█▉ | 955278/4997436 [00:09<00:39, 103417.32it/s]" ] }, { @@ -1314,7 +1314,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1295435/4997436 [00:09<00:27, 132463.09it/s]" + " 19%|█▉ | 965626/4997436 [00:09<00:39, 101661.32it/s]" ] }, { @@ -1322,7 +1322,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1308825/4997436 [00:09<00:27, 132888.98it/s]" + " 20%|█▉ | 975802/4997436 [00:09<00:39, 101060.47it/s]" ] }, { @@ -1330,7 +1330,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1322115/4997436 [00:09<00:27, 132715.05it/s]" + " 20%|█▉ | 985915/4997436 [00:10<00:39, 100327.52it/s]" ] }, { @@ -1338,7 +1338,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1335569/4997436 [00:10<00:27, 133257.15it/s]" + " 20%|█▉ | 995953/4997436 [00:10<00:40, 99996.36it/s] " ] }, { @@ -1346,7 +1346,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1348896/4997436 [00:10<00:27, 132876.17it/s]" + " 20%|██ | 1005956/4997436 [00:10<00:40, 99137.28it/s]" ] }, { @@ -1354,7 +1354,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1362305/4997436 [00:10<00:27, 133237.41it/s]" + " 20%|██ | 1015873/4997436 [00:10<00:40, 98449.14it/s]" ] }, { @@ -1362,7 +1362,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1375706/4997436 [00:10<00:27, 133467.19it/s]" + " 21%|██ | 1025792/4997436 [00:10<00:40, 98664.61it/s]" ] }, { @@ -1370,7 +1370,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1389134/4997436 [00:10<00:26, 133706.58it/s]" + " 21%|██ | 1036109/4997436 [00:10<00:39, 99997.63it/s]" ] }, { @@ -1378,7 +1378,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1402505/4997436 [00:10<00:26, 133678.04it/s]" + " 21%|██ | 1046359/4997436 [00:10<00:39, 100733.72it/s]" ] }, { @@ -1386,7 +1386,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1416004/4997436 [00:10<00:26, 134069.67it/s]" + " 21%|██ | 1056435/4997436 [00:10<00:39, 100482.27it/s]" ] }, { @@ -1394,7 +1394,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▊ | 1429412/4997436 [00:10<00:26, 133896.03it/s]" + " 21%|██▏ | 1066485/4997436 [00:10<00:40, 98249.95it/s] " ] }, { @@ -1402,7 +1402,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1442802/4997436 [00:10<00:26, 133704.25it/s]" + " 22%|██▏ | 1076322/4997436 [00:10<00:40, 97548.19it/s]" ] }, { @@ -1410,7 +1410,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1456173/4997436 [00:10<00:26, 133545.57it/s]" + " 22%|██▏ | 1087632/4997436 [00:11<00:38, 102122.43it/s]" ] }, { @@ -1418,7 +1418,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1469528/4997436 [00:11<00:26, 133281.37it/s]" + " 22%|██▏ | 1097859/4997436 [00:11<00:38, 101757.72it/s]" ] }, { @@ -1426,7 +1426,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1482857/4997436 [00:11<00:26, 132945.42it/s]" + " 22%|██▏ | 1108045/4997436 [00:11<00:38, 100888.83it/s]" ] }, { @@ -1434,7 +1434,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1496152/4997436 [00:11<00:26, 132213.55it/s]" + " 22%|██▏ | 1118142/4997436 [00:11<00:39, 99247.20it/s] " ] }, { @@ -1442,7 +1442,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1509491/4997436 [00:11<00:26, 132562.23it/s]" + " 23%|██▎ | 1128076/4997436 [00:11<00:39, 96881.65it/s]" ] }, { @@ -1450,7 +1450,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1522749/4997436 [00:11<00:26, 131883.92it/s]" + " 23%|██▎ | 1137792/4997436 [00:11<00:39, 96956.31it/s]" ] }, { @@ -1458,7 +1458,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1536110/4997436 [00:11<00:26, 132396.17it/s]" + " 23%|██▎ | 1147498/4997436 [00:11<00:40, 95845.76it/s]" ] }, { @@ -1466,7 +1466,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1549499/4997436 [00:11<00:25, 132839.93it/s]" + " 23%|██▎ | 1157091/4997436 [00:11<00:40, 95382.92it/s]" ] }, { @@ -1474,7 +1474,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███▏ | 1562784/4997436 [00:11<00:25, 132628.46it/s]" + " 23%|██▎ | 1166997/4997436 [00:11<00:39, 96461.45it/s]" ] }, { @@ -1482,7 +1482,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1576160/4997436 [00:11<00:25, 132965.07it/s]" + " 24%|██▎ | 1176649/4997436 [00:11<00:40, 95301.33it/s]" ] }, { @@ -1490,7 +1490,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1589458/4997436 [00:11<00:25, 132888.79it/s]" + " 24%|██▎ | 1186259/4997436 [00:12<00:39, 95533.98it/s]" ] }, { @@ -1498,7 +1498,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1602750/4997436 [00:12<00:25, 132896.47it/s]" + " 24%|██▍ | 1195817/4997436 [00:12<00:41, 92690.97it/s]" ] }, { @@ -1506,7 +1506,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1616040/4997436 [00:12<00:25, 132876.31it/s]" + " 24%|██▍ | 1205373/4997436 [00:12<00:40, 93522.97it/s]" ] }, { @@ -1514,7 +1514,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1629328/4997436 [00:12<00:25, 132369.62it/s]" + " 24%|██▍ | 1215080/4997436 [00:12<00:39, 94560.20it/s]" ] }, { @@ -1522,7 +1522,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1642566/4997436 [00:12<00:25, 131799.72it/s]" + " 25%|██▍ | 1224700/4997436 [00:12<00:39, 95042.99it/s]" ] }, { @@ -1530,7 +1530,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1655828/4997436 [00:12<00:25, 132040.46it/s]" + " 25%|██▍ | 1234541/4997436 [00:12<00:39, 96022.79it/s]" ] }, { @@ -1538,7 +1538,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1669044/4997436 [00:12<00:25, 132072.08it/s]" + " 25%|██▍ | 1244531/4997436 [00:12<00:38, 97173.88it/s]" ] }, { @@ -1546,7 +1546,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1682303/4997436 [00:12<00:25, 132222.73it/s]" + " 25%|██▌ | 1254506/4997436 [00:12<00:38, 97940.02it/s]" ] }, { @@ -1554,7 +1554,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1695690/4997436 [00:12<00:24, 132712.58it/s]" + " 25%|██▌ | 1264305/4997436 [00:12<00:38, 97687.62it/s]" ] }, { @@ -1562,7 +1562,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1709097/4997436 [00:12<00:24, 133117.19it/s]" + " 25%|██▌ | 1274226/4997436 [00:12<00:37, 98137.95it/s]" ] }, { @@ -1570,7 +1570,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1722518/4997436 [00:12<00:24, 133442.18it/s]" + " 26%|██▌ | 1284043/4997436 [00:13<00:37, 97764.69it/s]" ] }, { @@ -1578,7 +1578,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1735903/4997436 [00:13<00:24, 133563.14it/s]" + " 26%|██▌ | 1293822/4997436 [00:13<00:38, 97041.52it/s]" ] }, { @@ -1586,7 +1586,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1749374/4997436 [00:13<00:24, 133903.53it/s]" + " 26%|██▌ | 1304078/4997436 [00:13<00:37, 98678.93it/s]" ] }, { @@ -1594,7 +1594,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1762765/4997436 [00:13<00:24, 133699.88it/s]" + " 26%|██▋ | 1314639/4997436 [00:13<00:36, 100742.11it/s]" ] }, { @@ -1602,7 +1602,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1776159/4997436 [00:13<00:24, 133767.56it/s]" + " 27%|██▋ | 1324717/4997436 [00:13<00:36, 100371.33it/s]" ] }, { @@ -1610,7 +1610,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1789580/4997436 [00:13<00:23, 133896.09it/s]" + " 27%|██▋ | 1335238/4997436 [00:13<00:35, 101797.97it/s]" ] }, { @@ -1618,7 +1618,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1802970/4997436 [00:13<00:23, 133433.50it/s]" + " 27%|██▋ | 1345448/4997436 [00:13<00:35, 101886.57it/s]" ] }, { @@ -1626,7 +1626,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 1816487/4997436 [00:13<00:23, 133948.96it/s]" + " 27%|██▋ | 1355639/4997436 [00:13<00:35, 101704.51it/s]" ] }, { @@ -1634,7 +1634,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1829899/4997436 [00:13<00:23, 133998.20it/s]" + " 27%|██▋ | 1365826/4997436 [00:13<00:35, 101752.35it/s]" ] }, { @@ -1642,7 +1642,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1843300/4997436 [00:13<00:23, 133980.91it/s]" + " 28%|██▊ | 1376047/4997436 [00:14<00:35, 101884.26it/s]" ] }, { @@ -1650,7 +1650,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1856699/4997436 [00:13<00:23, 133646.33it/s]" + " 28%|██▊ | 1386237/4997436 [00:14<00:35, 101345.60it/s]" ] }, { @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1870064/4997436 [00:14<00:23, 133355.10it/s]" + " 28%|██▊ | 1396462/4997436 [00:14<00:35, 101613.09it/s]" ] }, { @@ -1666,7 +1666,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1883400/4997436 [00:14<00:23, 133328.09it/s]" + " 28%|██▊ | 1406625/4997436 [00:14<00:35, 100502.93it/s]" ] }, { @@ -1674,7 +1674,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1896794/4997436 [00:14<00:23, 133507.44it/s]" + " 28%|██▊ | 1417196/4997436 [00:14<00:35, 102047.03it/s]" ] }, { @@ -1682,7 +1682,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1910395/4997436 [00:14<00:22, 134253.38it/s]" + " 29%|██▊ | 1428237/4997436 [00:14<00:34, 104535.71it/s]" ] }, { @@ -1690,7 +1690,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1923821/4997436 [00:14<00:22, 134242.55it/s]" + " 29%|██▉ | 1438695/4997436 [00:14<00:34, 104523.00it/s]" ] }, { @@ -1698,7 +1698,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1937246/4997436 [00:14<00:22, 134192.08it/s]" + " 29%|██▉ | 1449601/4997436 [00:14<00:33, 105872.25it/s]" ] }, { @@ -1706,7 +1706,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1950712/4997436 [00:14<00:22, 134330.35it/s]" + " 29%|██▉ | 1460191/4997436 [00:14<00:33, 105229.22it/s]" ] }, { @@ -1714,7 +1714,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1964146/4997436 [00:14<00:22, 134201.10it/s]" + " 29%|██▉ | 1470833/4997436 [00:14<00:33, 105532.22it/s]" ] }, { @@ -1722,7 +1722,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1977569/4997436 [00:14<00:22, 134206.15it/s]" + " 30%|██▉ | 1482038/4997436 [00:15<00:32, 107476.57it/s]" ] }, { @@ -1730,7 +1730,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1990990/4997436 [00:14<00:22, 133907.23it/s]" + " 30%|██▉ | 1492788/4997436 [00:15<00:33, 103580.94it/s]" ] }, { @@ -1738,7 +1738,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2004459/4997436 [00:15<00:22, 134140.16it/s]" + " 30%|███ | 1503894/4997436 [00:15<00:33, 105759.68it/s]" ] }, { @@ -1746,7 +1746,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2017874/4997436 [00:15<00:22, 133940.16it/s]" + " 30%|███ | 1514791/4997436 [00:15<00:32, 106701.46it/s]" ] }, { @@ -1754,7 +1754,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2031269/4997436 [00:15<00:22, 133504.44it/s]" + " 31%|███ | 1525483/4997436 [00:15<00:32, 105837.68it/s]" ] }, { @@ -1762,7 +1762,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2044622/4997436 [00:15<00:22, 133508.12it/s]" + " 31%|███ | 1536083/4997436 [00:15<00:33, 103665.61it/s]" ] }, { @@ -1770,7 +1770,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2057974/4997436 [00:15<00:22, 133083.27it/s]" + " 31%|███ | 1546468/4997436 [00:15<00:34, 101248.23it/s]" ] }, { @@ -1778,7 +1778,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 2071283/4997436 [00:15<00:21, 133047.13it/s]" + " 31%|███ | 1556613/4997436 [00:15<00:34, 100550.14it/s]" ] }, { @@ -1786,7 +1786,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2084588/4997436 [00:15<00:21, 132998.43it/s]" + " 31%|███▏ | 1566681/4997436 [00:15<00:34, 98941.86it/s] " ] }, { @@ -1794,7 +1794,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2097889/4997436 [00:15<00:21, 132894.30it/s]" + " 32%|███▏ | 1576586/4997436 [00:15<00:35, 97737.48it/s]" ] }, { @@ -1802,7 +1802,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2111221/4997436 [00:15<00:21, 133018.82it/s]" + " 32%|███▏ | 1586367/4997436 [00:16<00:35, 97130.25it/s]" ] }, { @@ -1810,7 +1810,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2124523/4997436 [00:15<00:21, 132567.27it/s]" + " 32%|███▏ | 1596084/4997436 [00:16<00:35, 95951.17it/s]" ] }, { @@ -1818,7 +1818,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2137785/4997436 [00:16<00:21, 132579.57it/s]" + " 32%|███▏ | 1605732/4997436 [00:16<00:35, 96101.15it/s]" ] }, { @@ -1826,7 +1826,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2151069/4997436 [00:16<00:21, 132655.75it/s]" + " 32%|███▏ | 1615575/4997436 [00:16<00:34, 96783.31it/s]" ] }, { @@ -1834,7 +1834,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2164552/4997436 [00:16<00:21, 133304.83it/s]" + " 33%|███▎ | 1625383/4997436 [00:16<00:34, 97164.84it/s]" ] }, { @@ -1842,7 +1842,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▎ | 2177948/4997436 [00:16<00:21, 133499.94it/s]" + " 33%|███▎ | 1635103/4997436 [00:16<00:35, 95987.84it/s]" ] }, { @@ -1850,7 +1850,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2191299/4997436 [00:16<00:21, 133252.55it/s]" + " 33%|███▎ | 1644706/4997436 [00:16<00:35, 95645.98it/s]" ] }, { @@ -1858,7 +1858,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2204741/4997436 [00:16<00:20, 133600.91it/s]" + " 33%|███▎ | 1654414/4997436 [00:16<00:34, 96033.68it/s]" ] }, { @@ -1866,7 +1866,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2218102/4997436 [00:16<00:20, 133432.59it/s]" + " 33%|███▎ | 1664178/4997436 [00:16<00:34, 96509.11it/s]" ] }, { @@ -1874,7 +1874,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2231498/4997436 [00:16<00:20, 133587.41it/s]" + " 33%|███▎ | 1674030/4997436 [00:16<00:34, 97104.45it/s]" ] }, { @@ -1882,7 +1882,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2244911/4997436 [00:16<00:20, 133745.91it/s]" + " 34%|███▎ | 1683761/4997436 [00:17<00:34, 97161.49it/s]" ] }, { @@ -1890,7 +1890,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2258286/4997436 [00:16<00:20, 133371.38it/s]" + " 34%|███▍ | 1693479/4997436 [00:17<00:34, 95512.19it/s]" ] }, { @@ -1898,7 +1898,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2271652/4997436 [00:17<00:20, 133454.07it/s]" + " 34%|███▍ | 1703055/4997436 [00:17<00:34, 95581.52it/s]" ] }, { @@ -1906,7 +1906,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2284998/4997436 [00:17<00:20, 133058.80it/s]" + " 34%|███▍ | 1712700/4997436 [00:17<00:34, 95836.34it/s]" ] }, { @@ -1914,7 +1914,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2298305/4997436 [00:17<00:20, 132499.45it/s]" + " 34%|███▍ | 1722288/4997436 [00:17<00:34, 95761.91it/s]" ] }, { @@ -1922,7 +1922,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2311638/4997436 [00:17<00:20, 132731.56it/s]" + " 35%|███▍ | 1731867/4997436 [00:17<00:34, 95636.18it/s]" ] }, { @@ -1930,7 +1930,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2325035/4997436 [00:17<00:20, 133097.06it/s]" + " 35%|███▍ | 1741821/4997436 [00:17<00:33, 96797.94it/s]" ] }, { @@ -1938,7 +1938,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2338461/4997436 [00:17<00:19, 133440.95it/s]" + " 35%|███▌ | 1752159/4997436 [00:17<00:32, 98757.97it/s]" ] }, { @@ -1946,7 +1946,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2351946/4997436 [00:17<00:19, 133858.30it/s]" + " 35%|███▌ | 1762189/4997436 [00:17<00:32, 99217.12it/s]" ] }, { @@ -1954,7 +1954,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2365462/4997436 [00:17<00:19, 134245.92it/s]" + " 35%|███▌ | 1772512/4997436 [00:17<00:32, 100417.10it/s]" ] }, { @@ -1962,7 +1962,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2378985/4997436 [00:17<00:19, 134537.92it/s]" + " 36%|███▌ | 1783009/4997436 [00:18<00:31, 101778.46it/s]" ] }, { @@ -1970,7 +1970,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2392440/4997436 [00:17<00:19, 134534.37it/s]" + " 36%|███▌ | 1793188/4997436 [00:18<00:31, 100781.01it/s]" ] }, { @@ -1978,7 +1978,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2405987/4997436 [00:18<00:19, 134813.79it/s]" + " 36%|███▌ | 1803269/4997436 [00:18<00:32, 99457.13it/s] " ] }, { @@ -1986,7 +1986,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2419567/4997436 [00:18<00:19, 135106.01it/s]" + " 36%|███▋ | 1813220/4997436 [00:18<00:32, 99339.05it/s]" ] }, { @@ -1994,7 +1994,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▊ | 2433078/4997436 [00:18<00:18, 135050.85it/s]" + " 36%|███▋ | 1823537/4997436 [00:18<00:31, 100468.65it/s]" ] }, { @@ -2002,7 +2002,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2446584/4997436 [00:18<00:18, 134866.58it/s]" + " 37%|███▋ | 1834006/4997436 [00:18<00:31, 101703.01it/s]" ] }, { @@ -2010,7 +2010,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2460071/4997436 [00:18<00:18, 134632.22it/s]" + " 37%|███▋ | 1844180/4997436 [00:18<00:31, 100432.47it/s]" ] }, { @@ -2018,7 +2018,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2473709/4997436 [00:18<00:18, 135151.90it/s]" + " 37%|███▋ | 1855214/4997436 [00:18<00:30, 103363.95it/s]" ] }, { @@ -2026,7 +2026,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2487311/4997436 [00:18<00:18, 135409.27it/s]" + " 37%|███▋ | 1866461/4997436 [00:18<00:29, 106062.57it/s]" ] }, { @@ -2034,7 +2034,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2500853/4997436 [00:18<00:18, 135080.90it/s]" + " 38%|███▊ | 1877075/4997436 [00:18<00:29, 106079.05it/s]" ] }, { @@ -2042,7 +2042,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2514362/4997436 [00:18<00:18, 134742.47it/s]" + " 38%|███▊ | 1888248/4997436 [00:19<00:28, 107763.01it/s]" ] }, { @@ -2050,7 +2050,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2527837/4997436 [00:18<00:18, 134260.26it/s]" + " 38%|███▊ | 1899029/4997436 [00:19<00:30, 103252.16it/s]" ] }, { @@ -2058,7 +2058,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2541316/4997436 [00:19<00:18, 134416.13it/s]" + " 38%|███▊ | 1909397/4997436 [00:19<00:30, 101240.68it/s]" ] }, { @@ -2066,7 +2066,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2554917/4997436 [00:19<00:18, 134891.12it/s]" + " 38%|███▊ | 1919554/4997436 [00:19<00:30, 100540.05it/s]" ] }, { @@ -2074,7 +2074,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 2568407/4997436 [00:19<00:18, 134659.58it/s]" + " 39%|███▊ | 1930051/4997436 [00:19<00:30, 101824.90it/s]" ] }, { @@ -2082,7 +2082,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2581897/4997436 [00:19<00:17, 134727.11it/s]" + " 39%|███▉ | 1940505/4997436 [00:19<00:29, 102619.55it/s]" ] }, { @@ -2090,7 +2090,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2595370/4997436 [00:19<00:17, 134642.48it/s]" + " 39%|███▉ | 1950783/4997436 [00:19<00:30, 101183.89it/s]" ] }, { @@ -2098,7 +2098,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2608835/4997436 [00:19<00:17, 134079.46it/s]" + " 39%|███▉ | 1961846/4997436 [00:19<00:29, 103955.15it/s]" ] }, { @@ -2106,7 +2106,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2622384/4997436 [00:19<00:17, 134497.98it/s]" + " 39%|███▉ | 1972963/4997436 [00:19<00:28, 106085.38it/s]" ] }, { @@ -2114,7 +2114,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2635835/4997436 [00:19<00:17, 133948.65it/s]" + " 40%|███▉ | 1983600/4997436 [00:20<00:28, 106157.35it/s]" ] }, { @@ -2122,7 +2122,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2649231/4997436 [00:19<00:17, 133602.93it/s]" + " 40%|███▉ | 1994226/4997436 [00:20<00:29, 103300.81it/s]" ] }, { @@ -2130,7 +2130,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2662592/4997436 [00:19<00:17, 132682.81it/s]" + " 40%|████ | 2004579/4997436 [00:20<00:29, 100599.06it/s]" ] }, { @@ -2138,7 +2138,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 2675862/4997436 [00:20<00:17, 131904.77it/s]" + " 40%|████ | 2014665/4997436 [00:20<00:30, 96250.17it/s] " ] }, { @@ -2146,7 +2146,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2689080/4997436 [00:20<00:17, 131983.83it/s]" + " 41%|████ | 2024336/4997436 [00:20<00:30, 96343.59it/s]" ] }, { @@ -2154,7 +2154,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2702491/4997436 [00:20<00:17, 132615.00it/s]" + " 41%|████ | 2034199/4997436 [00:20<00:30, 96999.90it/s]" ] }, { @@ -2162,7 +2162,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2715877/4997436 [00:20<00:17, 132982.41it/s]" + " 41%|████ | 2044041/4997436 [00:20<00:30, 97405.15it/s]" ] }, { @@ -2170,7 +2170,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2729349/4997436 [00:20<00:16, 133497.89it/s]" + " 41%|████ | 2053801/4997436 [00:20<00:30, 96578.36it/s]" ] }, { @@ -2178,7 +2178,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2742700/4997436 [00:20<00:16, 133496.60it/s]" + " 41%|████▏ | 2063528/4997436 [00:20<00:30, 96779.86it/s]" ] }, { @@ -2186,7 +2186,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2756255/4997436 [00:20<00:16, 134109.74it/s]" + " 41%|████▏ | 2073431/4997436 [00:20<00:30, 97440.90it/s]" ] }, { @@ -2194,7 +2194,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2769678/4997436 [00:20<00:16, 134143.61it/s]" + " 42%|████▏ | 2083184/4997436 [00:21<00:29, 97464.83it/s]" ] }, { @@ -2202,7 +2202,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2783093/4997436 [00:20<00:16, 134130.03it/s]" + " 42%|████▏ | 2093032/4997436 [00:21<00:29, 97763.13it/s]" ] }, { @@ -2210,7 +2210,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2796507/4997436 [00:20<00:16, 134129.03it/s]" + " 42%|████▏ | 2102813/4997436 [00:21<00:29, 97400.36it/s]" ] }, { @@ -2218,7 +2218,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2809976/4997436 [00:21<00:16, 134294.01it/s]" + " 42%|████▏ | 2112596/4997436 [00:21<00:29, 97522.37it/s]" ] }, { @@ -2226,7 +2226,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2823415/4997436 [00:21<00:16, 134319.19it/s]" + " 42%|████▏ | 2122351/4997436 [00:21<00:29, 97236.86it/s]" ] }, { @@ -2234,7 +2234,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2836848/4997436 [00:21<00:16, 134057.32it/s]" + " 43%|████▎ | 2132134/4997436 [00:21<00:29, 97410.92it/s]" ] }, { @@ -2242,7 +2242,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2850254/4997436 [00:21<00:16, 133690.98it/s]" + " 43%|████▎ | 2142029/4997436 [00:21<00:29, 97868.75it/s]" ] }, { @@ -2250,7 +2250,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2863624/4997436 [00:21<00:16, 132386.22it/s]" + " 43%|████▎ | 2151923/4997436 [00:21<00:28, 98180.70it/s]" ] }, { @@ -2258,7 +2258,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2876866/4997436 [00:21<00:16, 132177.81it/s]" + " 43%|████▎ | 2161786/4997436 [00:21<00:28, 98312.11it/s]" ] }, { @@ -2266,7 +2266,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2890167/4997436 [00:21<00:15, 132421.98it/s]" + " 43%|████▎ | 2171618/4997436 [00:21<00:28, 97497.87it/s]" ] }, { @@ -2274,7 +2274,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2903505/4997436 [00:21<00:15, 132704.89it/s]" + " 44%|████▎ | 2181549/4997436 [00:22<00:28, 98034.45it/s]" ] }, { @@ -2282,7 +2282,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2916777/4997436 [00:21<00:15, 132370.15it/s]" + " 44%|████▍ | 2191355/4997436 [00:22<00:29, 96535.66it/s]" ] }, { @@ -2290,7 +2290,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▊ | 2930027/4997436 [00:21<00:15, 132407.74it/s]" + " 44%|████▍ | 2201845/4997436 [00:22<00:28, 98999.63it/s]" ] }, { @@ -2298,7 +2298,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2943269/4997436 [00:22<00:15, 131845.77it/s]" + " 44%|████▍ | 2212070/4997436 [00:22<00:27, 99962.61it/s]" ] }, { @@ -2306,7 +2306,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2956489/4997436 [00:22<00:15, 131947.12it/s]" + " 44%|████▍ | 2222182/4997436 [00:22<00:27, 100304.21it/s]" ] }, { @@ -2314,7 +2314,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2969685/4997436 [00:22<00:15, 131859.57it/s]" + " 45%|████▍ | 2232596/4997436 [00:22<00:27, 101446.09it/s]" ] }, { @@ -2322,7 +2322,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2982900/4997436 [00:22<00:15, 131943.74it/s]" + " 45%|████▍ | 2242807/4997436 [00:22<00:27, 101642.64it/s]" ] }, { @@ -2330,7 +2330,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2996227/4997436 [00:22<00:15, 132335.71it/s]" + " 45%|████▌ | 2253202/4997436 [00:22<00:26, 102331.64it/s]" ] }, { @@ -2338,7 +2338,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3009513/4997436 [00:22<00:15, 132490.05it/s]" + " 45%|████▌ | 2263438/4997436 [00:22<00:27, 100462.44it/s]" ] }, { @@ -2346,7 +2346,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3022788/4997436 [00:22<00:14, 132565.91it/s]" + " 45%|████▌ | 2273494/4997436 [00:22<00:27, 100159.59it/s]" ] }, { @@ -2354,7 +2354,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3036183/4997436 [00:22<00:14, 132976.58it/s]" + " 46%|████▌ | 2283530/4997436 [00:23<00:27, 100217.23it/s]" ] }, { @@ -2362,7 +2362,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3049556/4997436 [00:22<00:14, 133198.33it/s]" + " 46%|████▌ | 2293556/4997436 [00:23<00:27, 98673.23it/s] " ] }, { @@ -2370,7 +2370,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████▏ | 3062876/4997436 [00:22<00:14, 133023.20it/s]" + " 46%|████▌ | 2303431/4997436 [00:23<00:27, 97093.16it/s]" ] }, { @@ -2378,7 +2378,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3076211/4997436 [00:23<00:14, 133117.83it/s]" + " 46%|████▋ | 2314120/4997436 [00:23<00:26, 99960.63it/s]" ] }, { @@ -2386,7 +2386,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3089587/4997436 [00:23<00:14, 133306.45it/s]" + " 47%|████▋ | 2325330/4997436 [00:23<00:25, 103533.89it/s]" ] }, { @@ -2394,7 +2394,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3102918/4997436 [00:23<00:14, 133284.69it/s]" + " 47%|████▋ | 2335784/4997436 [00:23<00:25, 103812.95it/s]" ] }, { @@ -2402,7 +2402,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3116247/4997436 [00:23<00:14, 133189.49it/s]" + " 47%|████▋ | 2346176/4997436 [00:23<00:25, 102037.53it/s]" ] }, { @@ -2410,7 +2410,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3129649/4997436 [00:23<00:13, 133435.98it/s]" + " 47%|████▋ | 2356416/4997436 [00:23<00:25, 102142.40it/s]" ] }, { @@ -2418,7 +2418,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3143022/4997436 [00:23<00:13, 133522.54it/s]" + " 47%|████▋ | 2366981/4997436 [00:23<00:25, 103177.28it/s]" ] }, { @@ -2426,7 +2426,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3156441/4997436 [00:23<00:13, 133719.76it/s]" + " 48%|████▊ | 2378336/4997436 [00:23<00:24, 106257.59it/s]" ] }, { @@ -2434,7 +2434,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3169814/4997436 [00:23<00:13, 133660.85it/s]" + " 48%|████▊ | 2388970/4997436 [00:24<00:25, 102909.93it/s]" ] }, { @@ -2442,7 +2442,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 3183262/4997436 [00:23<00:13, 133904.29it/s]" + " 48%|████▊ | 2399289/4997436 [00:24<00:26, 99912.33it/s] " ] }, { @@ -2450,7 +2450,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3196702/4997436 [00:23<00:13, 134050.97it/s]" + " 48%|████▊ | 2409312/4997436 [00:24<00:26, 98589.45it/s]" ] }, { @@ -2458,7 +2458,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3210108/4997436 [00:24<00:13, 134029.95it/s]" + " 48%|████▊ | 2419193/4997436 [00:24<00:26, 97717.22it/s]" ] }, { @@ -2466,7 +2466,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3223512/4997436 [00:24<00:13, 133841.70it/s]" + " 49%|████▊ | 2428979/4997436 [00:24<00:26, 97363.73it/s]" ] }, { @@ -2474,7 +2474,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3236981/4997436 [00:24<00:13, 134091.41it/s]" + " 49%|████▉ | 2438724/4997436 [00:24<00:26, 97205.79it/s]" ] }, { @@ -2482,7 +2482,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3250391/4997436 [00:24<00:13, 133748.12it/s]" + " 49%|████▉ | 2448451/4997436 [00:24<00:26, 96628.16it/s]" ] }, { @@ -2490,7 +2490,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3263907/4997436 [00:24<00:12, 134165.91it/s]" + " 49%|████▉ | 2458225/4997436 [00:24<00:26, 96951.88it/s]" ] }, { @@ -2498,7 +2498,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3277347/4997436 [00:24<00:12, 134232.89it/s]" + " 49%|████▉ | 2467924/4997436 [00:24<00:26, 95707.86it/s]" ] }, { @@ -2506,7 +2506,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3290914/4997436 [00:24<00:12, 134660.84it/s]" + " 50%|████▉ | 2477711/4997436 [00:25<00:26, 96340.51it/s]" ] }, { @@ -2514,7 +2514,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3304381/4997436 [00:24<00:12, 134044.23it/s]" + " 50%|████▉ | 2487350/4997436 [00:25<00:26, 96274.43it/s]" ] }, { @@ -2522,7 +2522,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▋ | 3317787/4997436 [00:24<00:12, 133970.14it/s]" + " 50%|████▉ | 2496981/4997436 [00:25<00:25, 96184.12it/s]" ] }, { @@ -2530,7 +2530,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3331185/4997436 [00:24<00:12, 133557.91it/s]" + " 50%|█████ | 2506602/4997436 [00:25<00:26, 95384.34it/s]" ] }, { @@ -2538,7 +2538,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3344542/4997436 [00:25<00:12, 133176.88it/s]" + " 50%|█████ | 2516498/4997436 [00:25<00:25, 96442.43it/s]" ] }, { @@ -2546,7 +2546,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3357861/4997436 [00:25<00:12, 132697.62it/s]" + " 51%|█████ | 2526146/4997436 [00:25<00:25, 96013.40it/s]" ] }, { @@ -2554,7 +2554,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3371132/4997436 [00:25<00:12, 132335.67it/s]" + " 51%|█████ | 2535934/4997436 [00:25<00:25, 96565.33it/s]" ] }, { @@ -2562,7 +2562,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3384525/4997436 [00:25<00:12, 132807.27it/s]" + " 51%|█████ | 2545644/4997436 [00:25<00:25, 96716.39it/s]" ] }, { @@ -2570,7 +2570,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3397957/4997436 [00:25<00:12, 133256.87it/s]" + " 51%|█████ | 2555318/4997436 [00:25<00:25, 96277.40it/s]" ] }, { @@ -2578,7 +2578,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3411449/4997436 [00:25<00:11, 133749.64it/s]" + " 51%|█████▏ | 2565176/4997436 [00:25<00:25, 96960.01it/s]" ] }, { @@ -2586,7 +2586,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 3424839/4997436 [00:25<00:11, 133792.78it/s]" + " 52%|█████▏ | 2574874/4997436 [00:26<00:25, 96690.60it/s]" ] }, { @@ -2594,7 +2594,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3438219/4997436 [00:25<00:11, 133714.58it/s]" + " 52%|█████▏ | 2584545/4997436 [00:26<00:25, 93995.06it/s]" ] }, { @@ -2602,7 +2602,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3451591/4997436 [00:25<00:11, 133406.11it/s]" + " 52%|█████▏ | 2594401/4997436 [00:26<00:25, 95330.54it/s]" ] }, { @@ -2610,7 +2610,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3464932/4997436 [00:25<00:11, 132435.85it/s]" + " 52%|█████▏ | 2604274/4997436 [00:26<00:24, 96330.49it/s]" ] }, { @@ -2618,7 +2618,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3478197/4997436 [00:26<00:11, 132497.90it/s]" + " 52%|█████▏ | 2613919/4997436 [00:26<00:24, 95952.49it/s]" ] }, { @@ -2626,7 +2626,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3491457/4997436 [00:26<00:11, 132526.72it/s]" + " 53%|█████▎ | 2623856/4997436 [00:26<00:24, 96963.29it/s]" ] }, { @@ -2634,7 +2634,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3504790/4997436 [00:26<00:11, 132765.02it/s]" + " 53%|█████▎ | 2634484/4997436 [00:26<00:23, 99734.04it/s]" ] }, { @@ -2642,7 +2642,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3518068/4997436 [00:26<00:11, 132642.24it/s]" + " 53%|█████▎ | 2646011/4997436 [00:26<00:22, 104366.29it/s]" ] }, { @@ -2650,7 +2650,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3531511/4997436 [00:26<00:11, 133173.63it/s]" + " 53%|█████▎ | 2656906/4997436 [00:26<00:22, 105734.13it/s]" ] }, { @@ -2658,7 +2658,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3544829/4997436 [00:26<00:10, 133154.28it/s]" + " 53%|█████▎ | 2667486/4997436 [00:26<00:22, 104688.49it/s]" ] }, { @@ -2666,7 +2666,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3558306/4997436 [00:26<00:10, 133636.50it/s]" + " 54%|█████▎ | 2677961/4997436 [00:27<00:22, 102063.08it/s]" ] }, { @@ -2674,7 +2674,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 3571864/4997436 [00:26<00:10, 134217.43it/s]" + " 54%|█████▍ | 2688425/4997436 [00:27<00:22, 102809.99it/s]" ] }, { @@ -2682,7 +2682,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3585387/4997436 [00:26<00:10, 134519.31it/s]" + " 54%|█████▍ | 2698811/4997436 [00:27<00:22, 103117.56it/s]" ] }, { @@ -2690,7 +2690,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3598908/4997436 [00:26<00:10, 134723.47it/s]" + " 54%|█████▍ | 2709133/4997436 [00:27<00:22, 101988.71it/s]" ] }, { @@ -2698,7 +2698,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3612381/4997436 [00:27<00:10, 134546.92it/s]" + " 54%|█████▍ | 2719341/4997436 [00:27<00:22, 100958.52it/s]" ] }, { @@ -2706,7 +2706,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3625836/4997436 [00:27<00:10, 133988.47it/s]" + " 55%|█████▍ | 2729444/4997436 [00:27<00:22, 100669.75it/s]" ] }, { @@ -2714,7 +2714,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3639471/4997436 [00:27<00:10, 134691.73it/s]" + " 55%|█████▍ | 2739516/4997436 [00:27<00:22, 100675.19it/s]" ] }, { @@ -2722,7 +2722,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3652941/4997436 [00:27<00:09, 134555.35it/s]" + " 55%|█████▌ | 2750369/4997436 [00:27<00:21, 103001.02it/s]" ] }, { @@ -2730,7 +2730,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3666549/4997436 [00:27<00:09, 135008.14it/s]" + " 55%|█████▌ | 2760674/4997436 [00:27<00:21, 102275.09it/s]" ] }, { @@ -2738,7 +2738,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3680051/4997436 [00:27<00:09, 134482.21it/s]" + " 55%|█████▌ | 2771747/4997436 [00:27<00:21, 104784.01it/s]" ] }, { @@ -2746,7 +2746,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3693500/4997436 [00:27<00:09, 134233.75it/s]" + " 56%|█████▌ | 2782231/4997436 [00:28<00:21, 102748.69it/s]" ] }, { @@ -2754,7 +2754,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3707000/4997436 [00:27<00:09, 134459.27it/s]" + " 56%|█████▌ | 2792518/4997436 [00:28<00:21, 101339.07it/s]" ] }, { @@ -2762,7 +2762,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3720525/4997436 [00:27<00:09, 134694.11it/s]" + " 56%|█████▌ | 2802662/4997436 [00:28<00:22, 98827.73it/s] " ] }, { @@ -2770,7 +2770,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3733995/4997436 [00:28<00:09, 134530.94it/s]" + " 56%|█████▋ | 2813189/4997436 [00:28<00:21, 100692.84it/s]" ] }, { @@ -2778,7 +2778,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3747449/4997436 [00:28<00:09, 134422.51it/s]" + " 56%|█████▋ | 2823402/4997436 [00:28<00:21, 101111.80it/s]" ] }, { @@ -2786,7 +2786,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3760892/4997436 [00:28<00:09, 133983.49it/s]" + " 57%|█████▋ | 2833556/4997436 [00:28<00:21, 101231.22it/s]" ] }, { @@ -2794,7 +2794,7 @@ "output_type": "stream", "text": [ "\r", - " 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3841883/4997436 [00:28<00:08, 134729.81it/s]" + " 58%|█████▊ | 2892568/4997436 [00:29<00:23, 89686.43it/s]" ] }, { @@ -2842,7 +2842,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3855357/4997436 [00:28<00:08, 134252.92it/s]" + " 58%|█████▊ | 2901884/4997436 [00:29<00:23, 90665.76it/s]" ] }, { @@ -2850,7 +2850,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3868783/4997436 [00:29<00:08, 133248.17it/s]" + " 58%|█████▊ | 2911728/4997436 [00:29<00:22, 92897.25it/s]" ] }, { @@ -2858,7 +2858,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3882171/4997436 [00:29<00:08, 133433.68it/s]" + " 58%|█████▊ | 2921533/4997436 [00:29<00:21, 94394.63it/s]" ] }, { @@ -2866,7 +2866,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3895633/4997436 [00:29<00:08, 133785.70it/s]" + " 59%|█████▊ | 2931362/4997436 [00:29<00:21, 95535.13it/s]" ] }, { @@ -2874,7 +2874,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3909013/4997436 [00:29<00:08, 133656.32it/s]" + " 59%|█████▉ | 2940938/4997436 [00:29<00:22, 92446.28it/s]" ] }, { @@ -2882,7 +2882,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3922562/4997436 [00:29<00:08, 134201.11it/s]" + " 59%|█████▉ | 2950588/4997436 [00:29<00:21, 93620.30it/s]" ] }, { @@ -2890,7 +2890,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3935983/4997436 [00:29<00:07, 133737.01it/s]" + " 59%|█████▉ | 2960040/4997436 [00:29<00:21, 93862.40it/s]" ] }, { @@ -2898,7 +2898,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3949443/4997436 [00:29<00:07, 133992.67it/s]" + " 59%|█████▉ | 2969715/4997436 [00:30<00:21, 94710.29it/s]" ] }, { @@ -2906,7 +2906,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3962863/4997436 [00:29<00:07, 134052.14it/s]" + " 60%|█████▉ | 2979511/4997436 [00:30<00:21, 95666.87it/s]" ] }, { @@ -2914,7 +2914,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3976271/4997436 [00:29<00:07, 134057.46it/s]" + " 60%|█████▉ | 2989090/4997436 [00:30<00:21, 95487.15it/s]" ] }, { @@ -2922,7 +2922,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3989678/4997436 [00:29<00:07, 133875.47it/s]" + " 60%|██████ | 2998647/4997436 [00:30<00:21, 95129.74it/s]" ] }, { @@ -2930,7 +2930,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4003066/4997436 [00:30<00:07, 133538.47it/s]" + " 60%|██████ | 3008233/4997436 [00:30<00:20, 95344.92it/s]" ] }, { @@ -2938,7 +2938,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4016530/4997436 [00:30<00:07, 133865.63it/s]" + " 60%|██████ | 3017781/4997436 [00:30<00:20, 95382.66it/s]" ] }, { @@ -2946,7 +2946,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4029983/4997436 [00:30<00:07, 134060.90it/s]" + " 61%|██████ | 3027652/4997436 [00:30<00:20, 96374.99it/s]" ] }, { @@ -2954,7 +2954,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4043390/4997436 [00:30<00:07, 133832.47it/s]" + " 61%|██████ | 3037489/4997436 [00:30<00:20, 96965.41it/s]" ] }, { @@ -2962,7 +2962,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4056774/4997436 [00:30<00:07, 133718.73it/s]" + " 61%|██████ | 3047303/4997436 [00:30<00:20, 97314.19it/s]" ] }, { @@ -2970,7 +2970,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4070175/4997436 [00:30<00:06, 133803.28it/s]" + " 61%|██████ | 3057872/4997436 [00:30<00:19, 99821.72it/s]" ] }, { @@ -2978,7 +2978,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4083648/4997436 [00:30<00:06, 134079.08it/s]" + " 61%|██████▏ | 3067856/4997436 [00:31<00:19, 99331.54it/s]" ] }, { @@ -2986,7 +2986,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4097057/4997436 [00:30<00:06, 133799.81it/s]" + " 62%|██████▏ | 3077791/4997436 [00:31<00:19, 97863.94it/s]" ] }, { @@ -2994,7 +2994,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4110445/4997436 [00:30<00:06, 133819.46it/s]" + " 62%|██████▏ | 3087583/4997436 [00:31<00:19, 97755.42it/s]" ] }, { @@ -3002,7 +3002,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4123846/4997436 [00:30<00:06, 133874.18it/s]" + " 62%|██████▏ | 3097363/4997436 [00:31<00:19, 97191.84it/s]" ] }, { @@ -3010,7 +3010,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4137332/4997436 [00:31<00:06, 134165.89it/s]" + " 62%|██████▏ | 3107948/4997436 [00:31<00:18, 99758.62it/s]" ] }, { @@ -3018,7 +3018,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4150749/4997436 [00:31<00:06, 133864.27it/s]" + " 62%|██████▏ | 3118208/4997436 [00:31<00:18, 100601.91it/s]" ] }, { @@ -3026,7 +3026,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4164145/4997436 [00:31<00:06, 133891.34it/s]" + " 63%|██████▎ | 3128301/4997436 [00:31<00:18, 100697.74it/s]" ] }, { @@ -3034,7 +3034,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▎ | 4177535/4997436 [00:31<00:06, 133221.69it/s]" + " 63%|██████▎ | 3138412/4997436 [00:31<00:18, 100814.49it/s]" ] }, { @@ -3042,7 +3042,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4190966/4997436 [00:31<00:06, 133543.87it/s]" + " 63%|██████▎ | 3148496/4997436 [00:31<00:18, 99669.31it/s] " ] }, { @@ -3050,7 +3050,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4204322/4997436 [00:31<00:05, 133454.35it/s]" + " 63%|██████▎ | 3158468/4997436 [00:31<00:18, 99251.74it/s]" ] }, { @@ -3058,7 +3058,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4217668/4997436 [00:31<00:05, 133120.42it/s]" + " 63%|██████▎ | 3169089/4997436 [00:32<00:18, 101309.89it/s]" ] }, { @@ -3066,7 +3066,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4230981/4997436 [00:31<00:05, 128917.03it/s]" + " 64%|██████▎ | 3179225/4997436 [00:32<00:18, 100821.92it/s]" ] }, { @@ -3074,7 +3074,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4243901/4997436 [00:31<00:05, 128670.22it/s]" + " 64%|██████▍ | 3190101/4997436 [00:32<00:17, 103180.74it/s]" ] }, { @@ -3082,7 +3082,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4257290/4997436 [00:31<00:05, 130202.47it/s]" + " 64%|██████▍ | 3200424/4997436 [00:32<00:17, 100917.96it/s]" ] }, { @@ -3090,7 +3090,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4270690/4997436 [00:32<00:05, 131325.04it/s]" + " 64%|██████▍ | 3210759/4997436 [00:32<00:17, 101628.54it/s]" ] }, { @@ -3098,7 +3098,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4284169/4997436 [00:32<00:05, 132350.86it/s]" + " 64%|██████▍ | 3221393/4997436 [00:32<00:17, 103019.09it/s]" ] }, { @@ -3106,7 +3106,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4297778/4997436 [00:32<00:05, 133462.99it/s]" + " 65%|██████▍ | 3231705/4997436 [00:32<00:17, 102060.39it/s]" ] }, { @@ -3114,7 +3114,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▋ | 4311406/4997436 [00:32<00:05, 134299.92it/s]" + " 65%|██████▍ | 3242029/4997436 [00:32<00:17, 102405.09it/s]" ] }, { @@ -3122,7 +3122,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4324898/4997436 [00:32<00:05, 134480.95it/s]" + " 65%|██████▌ | 3252276/4997436 [00:32<00:17, 100972.64it/s]" ] }, { @@ -3130,7 +3130,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4338488/4997436 [00:32<00:04, 134903.77it/s]" + " 65%|██████▌ | 3262381/4997436 [00:32<00:17, 100468.05it/s]" ] }, { @@ -3138,7 +3138,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4351982/4997436 [00:32<00:04, 134011.32it/s]" + " 65%|██████▌ | 3272433/4997436 [00:33<00:17, 100009.96it/s]" ] }, { @@ -3146,7 +3146,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4365387/4997436 [00:32<00:04, 133927.19it/s]" + " 66%|██████▌ | 3282572/4997436 [00:33<00:17, 100404.58it/s]" ] }, { @@ -3154,7 +3154,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4378912/4997436 [00:32<00:04, 134318.99it/s]" + " 66%|██████▌ | 3292616/4997436 [00:33<00:17, 98804.75it/s] " ] }, { @@ -3162,7 +3162,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4392346/4997436 [00:32<00:04, 133953.62it/s]" + " 66%|██████▌ | 3302503/4997436 [00:33<00:17, 96704.57it/s]" ] }, { @@ -3170,7 +3170,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4405743/4997436 [00:33<00:04, 133774.09it/s]" + " 66%|██████▋ | 3312185/4997436 [00:33<00:17, 95786.48it/s]" ] }, { @@ -3178,7 +3178,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4419423/4997436 [00:33<00:04, 134675.57it/s]" + " 66%|██████▋ | 3321806/4997436 [00:33<00:17, 95907.73it/s]" ] }, { @@ -3186,7 +3186,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 4432937/4997436 [00:33<00:04, 134812.44it/s]" + " 67%|██████▋ | 3331499/4997436 [00:33<00:17, 96205.53it/s]" ] }, { @@ -3194,7 +3194,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4446522/4997436 [00:33<00:04, 135120.63it/s]" + " 67%|██████▋ | 3341148/4997436 [00:33<00:17, 96286.92it/s]" ] }, { @@ -3202,7 +3202,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4460083/4997436 [00:33<00:03, 135263.41it/s]" + " 67%|██████▋ | 3350815/4997436 [00:33<00:17, 96398.89it/s]" ] }, { @@ -3210,7 +3210,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4473610/4997436 [00:33<00:03, 135034.91it/s]" + " 67%|██████▋ | 3360647/4997436 [00:33<00:16, 96967.21it/s]" ] }, { @@ -3218,7 +3218,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4487114/4997436 [00:33<00:03, 134978.66it/s]" + " 67%|██████▋ | 3370359/4997436 [00:34<00:16, 97008.44it/s]" ] }, { @@ -3226,7 +3226,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4500613/4997436 [00:33<00:03, 134900.21it/s]" + " 68%|██████▊ | 3380062/4997436 [00:34<00:16, 95768.55it/s]" ] }, { @@ -3234,7 +3234,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4514104/4997436 [00:33<00:03, 134420.04it/s]" + " 68%|██████▊ | 3389644/4997436 [00:34<00:16, 95252.24it/s]" ] }, { @@ -3242,7 +3242,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4527547/4997436 [00:33<00:03, 134096.78it/s]" + " 68%|██████▊ | 3399173/4997436 [00:34<00:16, 94917.08it/s]" ] }, { @@ -3250,7 +3250,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4541049/4997436 [00:34<00:03, 134368.50it/s]" + " 68%|██████▊ | 3408667/4997436 [00:34<00:16, 94343.66it/s]" ] }, { @@ -3258,7 +3258,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4554487/4997436 [00:34<00:03, 134346.88it/s]" + " 68%|██████▊ | 3418287/4997436 [00:34<00:16, 94890.69it/s]" ] }, { @@ -3266,7 +3266,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 4568022/4997436 [00:34<00:03, 134645.17it/s]" + " 69%|██████▊ | 3427778/4997436 [00:34<00:16, 94578.03it/s]" ] }, { @@ -3274,7 +3274,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4581487/4997436 [00:34<00:03, 134464.30it/s]" + " 69%|██████▉ | 3437401/4997436 [00:34<00:16, 95053.73it/s]" ] }, { @@ -3282,7 +3282,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4594934/4997436 [00:34<00:03, 133259.07it/s]" + " 69%|██████▉ | 3447013/4997436 [00:34<00:16, 95369.18it/s]" ] }, { @@ -3290,7 +3290,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4608263/4997436 [00:34<00:02, 133265.01it/s]" + " 69%|██████▉ | 3456556/4997436 [00:35<00:16, 95383.37it/s]" ] }, { @@ -3298,7 +3298,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4621592/4997436 [00:34<00:02, 132822.47it/s]" + " 69%|██████▉ | 3466096/4997436 [00:35<00:16, 94524.37it/s]" ] }, { @@ -3306,7 +3306,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4634876/4997436 [00:34<00:02, 132471.31it/s]" + " 70%|██████▉ | 3475564/4997436 [00:35<00:16, 94568.88it/s]" ] }, { @@ -3314,7 +3314,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4648125/4997436 [00:34<00:02, 132317.78it/s]" + " 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71%|███████ | 3534582/4997436 [00:35<00:14, 98040.58it/s]" ] }, { @@ -3362,7 +3362,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4728253/4997436 [00:35<00:02, 132774.21it/s]" + " 71%|███████ | 3544915/4997436 [00:35<00:14, 99618.09it/s]" ] }, { @@ -3370,7 +3370,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4741535/4997436 [00:35<00:01, 132668.31it/s]" + " 71%|███████ | 3554880/4997436 [00:36<00:14, 99402.17it/s]" ] }, { @@ -3378,7 +3378,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4754805/4997436 [00:35<00:01, 132253.77it/s]" + " 71%|███████▏ | 3565099/4997436 [00:36<00:14, 100233.42it/s]" ] }, { @@ -3386,7 +3386,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4768145/4997436 [00:35<00:01, 132593.87it/s]" + " 72%|███████▏ | 3575124/4997436 [00:36<00:14, 100015.22it/s]" ] }, { @@ -3394,7 +3394,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4781406/4997436 [00:35<00:01, 132464.36it/s]" + " 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+ " 73%|███████▎ | 3636964/4997436 [00:36<00:13, 101776.52it/s]" ] }, { @@ -3442,7 +3442,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4861335/4997436 [00:36<00:01, 133106.96it/s]" + " 73%|███████▎ | 3647638/4997436 [00:36<00:13, 103253.57it/s]" ] }, { @@ -3450,7 +3450,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4874717/4997436 [00:36<00:00, 133318.40it/s]" + " 73%|███████▎ | 3657967/4997436 [00:37<00:13, 100904.24it/s]" ] }, { @@ -3458,7 +3458,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4888097/4997436 [00:36<00:00, 133460.15it/s]" + " 73%|███████▎ | 3668071/4997436 [00:37<00:13, 100154.41it/s]" ] }, { @@ -3466,7 +3466,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4901444/4997436 [00:36<00:00, 133136.02it/s]" + " 74%|███████▎ | 3678206/4997436 [00:37<00:13, 100500.51it/s]" ] }, { @@ -3474,7 +3474,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4914934/4997436 [00:36<00:00, 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"2023-08-15T04:19:33.479380Z", + "shell.execute_reply": "2023-08-15T04:19:33.474545Z" } }, "outputs": [], @@ -3786,10 +4778,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:43.087336Z", - "iopub.status.busy": "2023-08-14T16:44:43.086338Z", - "iopub.status.idle": "2023-08-14T16:44:47.501499Z", - "shell.execute_reply": "2023-08-14T16:44:47.500617Z" + "iopub.execute_input": "2023-08-15T04:19:33.510002Z", + "iopub.status.busy": "2023-08-15T04:19:33.509461Z", + "iopub.status.idle": "2023-08-15T04:19:39.214389Z", + "shell.execute_reply": "2023-08-15T04:19:39.211982Z" } }, "outputs": [ @@ -3858,17 +4850,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:47.505607Z", - "iopub.status.busy": "2023-08-14T16:44:47.504971Z", - "iopub.status.idle": "2023-08-14T16:44:49.943168Z", - "shell.execute_reply": "2023-08-14T16:44:49.942102Z" + "iopub.execute_input": "2023-08-15T04:19:39.222168Z", + "iopub.status.busy": "2023-08-15T04:19:39.221577Z", + "iopub.status.idle": "2023-08-15T04:19:42.694909Z", + "shell.execute_reply": "2023-08-15T04:19:42.693802Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "517e39fd77f4484b90525fcded82b444", + "model_id": "622c4463e94d45d98eee2008d6ca938b", "version_major": 2, "version_minor": 0 }, @@ -3898,10 +4890,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:49.947215Z", - "iopub.status.busy": "2023-08-14T16:44:49.946784Z", - "iopub.status.idle": "2023-08-14T16:44:50.231957Z", - "shell.execute_reply": "2023-08-14T16:44:50.230385Z" + "iopub.execute_input": "2023-08-15T04:19:42.702534Z", + "iopub.status.busy": "2023-08-15T04:19:42.701769Z", + "iopub.status.idle": "2023-08-15T04:19:43.094484Z", + "shell.execute_reply": "2023-08-15T04:19:43.093317Z" } }, "outputs": [], @@ -3915,10 +4907,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:50.236508Z", - "iopub.status.busy": "2023-08-14T16:44:50.235999Z", - "iopub.status.idle": "2023-08-14T16:44:57.439232Z", - "shell.execute_reply": "2023-08-14T16:44:57.438425Z" + "iopub.execute_input": "2023-08-15T04:19:43.099368Z", + "iopub.status.busy": "2023-08-15T04:19:43.099052Z", + "iopub.status.idle": "2023-08-15T04:19:53.166865Z", + "shell.execute_reply": "2023-08-15T04:19:53.165809Z" } }, "outputs": [ @@ -3944,7 +4936,7 @@ }, { "data": { - "image/png": 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Oo48+moMPPpg77riDY489lilTpjBs2DDA+zlTX1/PbrvtxooVKzjnnHPo27cvDz30EK+//nqSPF77c1tYuXJlfK9jJ3V1dVx88cVxp7eOSy65hLlz57Jo0SJX53dsK5j777+f3//+92l50VgkEmH//ffnww8/5LTTTmPMmDE8/fTTzJkzJyntv//9bw4//HAmTJjANddcw4YNGzjxxBMZMGBAUlova53Vq1ez1157UVlZyW9+8xvKysr46aef4hcaKisruf322znttNM46KCDOPjggwH4xS9+AdgOsKlTpzJgwAB+85vfUFRUxCOPPMKsWbN4/PHHOeiggwC7fXbddVdCoVA83V133dXmcXT9+vVJYYFAIOkxzKuuuorc3FwuvPBCmpub43dYh0IhZsyYwU477cSf//xnCgsLEUJwwAEH8Prrr3PiiScyadIkXnzxRX7961+zbNky/vrXvyaU/dprr/HII49w5pln0qdPH4YOHcrLL7/MkUceye677851110HwDfffMM777yTNKc6CQaDfPTRR5x22mmej0G65+TYRZzy8vJ42KpVq9hhhx3izsHKykqef/55TjzxRGprazn33HMB257YfffdWbJkCWeffTb9+/fngQceSLAPncQuxr7zzjvxvtElCIMhi6ipqRGAmDVrVlLchg0bxJo1a+J/DQ0N8bjLLrtMOLv7t99+KwBxyy23JJRx+umni+Li4njet956SwBi3rx5CeleeOGFpPAhQ4YIQPz3v/+Nh61evVrk5eWJCy64IKVugDjjjDNc0zh1inHqqaeKwsJC0dTUFA+bNm2aAMSNN94YD2tubhaTJk0SVVVVoqWlRQghxAMPPCB8Pp946623Esq84447BCDeeeedBP3mzJkT/z1x4kQxc+bMlHoZDIaO5eOPPxaAePnll4UQQkQiETFw4EBxzjnnJKRbtGiRAERJSYlYvXp1Qtz+++8vCgsLxbJly+JhCxcuFIFAQMimAiDy8vLEokWL4mF33nmnAETfvn1FbW1tPPySSy4RQEJa1Th2zTXXCMuyxOLFixPCjzzySFFYWCi+++47ccMNNwhAPPXUU/F4r2PY559/LgBx+umnJ6Q76qijBCAuu+yyJJmcxI7dFVdcIdasWSNWrlwp3njjDbHVVlsJQDz++ONCCCHuvfdeAYiddtpJhEKheP66ujpRVlYmTj755IRyV65cKUpLSxPCJ02aJPr16yc2btwYD3vppZcEIIYMGZKQX5b92GOPFT6fT3z00UdJOkQiESGEEI8++qgAxOuvv56UZtq0aWLatGnx3zfddJMAxIMPPhgPa2lpEVOmTBHFxcXxto4dn969e4v169fH0z799NMCEM8++2xSXU5ef/11AYh77rlHrFmzRixfvlz8+9//FkOHDhWWZcX1ic3lRx55ZEL+WPuedNJJCeEXXnihAMRrr70WD4vN1bE2E8K2Lfr16ye22mqreFhTU5MIh8MJ5S1atEjk5eWJK6+8Mkn2AQMGJPT9Rx55RADi5ptvjofNmTMnZRvG+pDznBk3blxCu8h1x9oyEomIUaNGiRkzZsTbWwj7nBs2bJjYc88942GlpaUpbQ5D9yXWz5xjxZw5cwQgrr766njYhg0bREFBgbAsSzz88MPx8AULFiT1Xa/nzI033pg0ljc2NooxY8a0uz97ZeHChSI/P18cc8wxSXEXXnihGDZsWNyeHjJkiNLOjR0n5zmqoqGhQYwePTo+dh933HHin//8p1i1alVSWnnsddblHDMef/xxAYibbropHhYOh8Vuu+0mAHHvvffGwydMmCAGDhwo6urq4mFvvPFG0lzida3z5JNPJvUZmTVr1mjn1N13311MmDAhYb0SiUTEjjvuKEaNGhUPO/fccwUgPvjgg3jY6tWrRWlpqafjHpsnVH+jR4+Op4uNn8OHD0+yS2Jt/Jvf/CYh/KmnnhKA+OMf/5gQPnv2bGFZlvj+++/jYYDw+Xzi66+/Tkh7zjnniJKSkgQbwQvff/+9cu0ak9fZph0xJ7/wwgti5MiRwrIs8eGHH8bTnnjiiaJfv35i7dq1CWUcccQRorS0NH5sY/bEI488Ek9TX18vRo4cqbVJtthiC7HPPvu4ytrRmMcwDVlFbW0tgPLZ5enTp1NZWRn/i902rmKLLbZg0qRJ/Otf/4qHhcNhHnvsMfbff//41YtHH32U0tJS9txzT9auXRv/mzx5MsXFxUlXwcaOHRu/RRXsqyyjR49O29u8nFdV6urqWLt2LTvvvDMNDQ0sWLAgIW0gEODUU0+N/87NzeXUU09l9erVfPLJJ3H9ttxyS8aMGZOgX+zRF9VVvhhlZWV8/fXXLFy4MC26GQyG9jFv3jyqq6vjj5ZblsXhhx/Oww8/rHxE+5BDDkm4YywcDvPKK68wa9ashLtlRo4cyT777KOsc/fdd0+4oh670/eQQw6hV69eSeHOMdA5jtXX17N27Vp23HFHhBB89tlnCfXceuutlJaWMnv2bP7whz9wzDHHcOCBB8bjvY5h//nPfwB7s1onsSueXrnsssuorKykb9++TJ8+nR9++IHrrrsufgU9xsknn5yw79TLL7/Mxo0bOfLIIxPk9Pv9bL/99nE5V6xYweeff86cOXMoLS2N599zzz0ZO3asq2yRSISnnnqK/fffn2222SYpvj13NfznP/+hb9++HHnkkfGwnJwczj77bDZt2sSbb76ZkP7www9PuOIcmw+9zoEnnHAClZWV9O/fn5kzZ1JfX8/cuXOT9PnVr36VJCfA+eefnxB+wQUXAPYdFk769++fcKW6pKSEY489ls8++yz+prS8vLz4vjXhcJh169ZRXFzM6NGjlY8qHnvssQl9f/bs2fTr1y8uW2fw+eefs3DhQo466ijWrVsX72f19fXsvvvu/Pe//40/ylRWVsYHH3zQLd/IZ9g8TjrppPj3srIyRo8eTVFREYcddlg8fPTo0ZSVlSWc217PmRdeeIEBAwZwwAEHxMPy8/M5+eSTE+RoS3/2QkNDA4ceeigFBQVce+21CXHfffcdN998MzfccEPCyzVU3HfffQghUj5SXVBQwAcffMCvf/3reL4TTzyRfv36cdZZZ6Xc7kTFCy+8QE5OTsKx8vl88bu8Yyxfvpwvv/ySY489NmHNNG3aNCZMmJCQ1utaJ3ZH1nPPPZfyEVSZ9evX89prr3HYYYfF1y9r165l3bp1zJgxg4ULF7Js2TLAHs932GGHhLsGKysr449BeuXxxx/n5ZdfTvi79957k9LNmTNHe9eafBfXf/7zH/x+f5ItccEFFyCE4Pnnn08InzZtWtLcXVZW1q7HC9etWwck3tWVinTOyXvvvTc1NTU88MAD8acWhBA8/vjj7L///gghEvrPjBkzqKmpiZ/7//nPf+jXr1/CVkeFhYWccsop2vrLy8tZu3atZ307AvMYpiGriBmimzZtSoq78847qaurY9WqVQkbMOs4/PDD+e1vf8uyZcsYMGAAb7zxBqtXr07YC2fhwoXU1NRQVVWlLGP16tUJvwcPHpyUpry8POmZ//by9ddf8/vf/57XXnst7jiMUVNTk/C7f//+SRtRbrHFFoB9G+0OO+zAwoUL+eabb7SPWsn6Obnyyis58MAD2WKLLRg/fjx77703xxxzTPx2a4PB0PGEw2Eefvhhdt111/i+TmA7qW688UZeffVV9tprr4Q8sUdmYqxevZrGxkbl2zN1b9SUx7qYY8e5V6Qz3DkGLlmyhEsvvZRnnnkmaWyUx7GKigr+9re/ceihh1JdXc3f/va3hHivY9jixYvx+XyMGDEiIX706NHKfDpOOeUUDj30UHw+H2VlZfF9SGTkYxy7qBBz4snE9q5ZvHgxAKNGjUpKo3PSxFizZg21tbWMHz/emzIeWLx4MaNGjUra7Dj22GZM3hhyv4gZ6V7nwEsvvZSdd94Zv99Pnz592HLLLZUv55GPb6x95f7at29fysrKkuQcOXJkkvPQOT/27duXSCTCzTffzG233caiRYsSHM/yY6GQ3GaWZTFy5MikPfs6klg/Uz2SFaOmpoby8nKuv/565syZw6BBg5g8eTL77rsvxx57LMOHD+8scQ0ZSH5+ftJ4WlpaysCBA5POmdLS0oRz2+s5s3jxYkaMGJFUnnz+tqU/pyIcDnPEEUcwf/58nn/++aTHqM855xx23HFHDjnkkJRltYXS0lKuv/56rr/+ehYvXsyrr77Kn//85/iFoD/+8Y9tKm/x4sX069cv6RFS+djFxjzdvO6cS7yudaZNm8YhhxzCFVdcwV//+lemT5/OrFmzOOqoo1I6GL///nuEEPzhD3/gD3/4g7aeAQMGsHjxYuVWO22dr3fZZRdPG/zL80mMQCCQtP3B4sWL6d+/f8KFEdDPiaqyTz/9dB555BH22WcfBgwYwF577cVhhx3G3nvvnVJWIGlvNDfSNSdv2rSJJ598kocffjjBHlizZg0bN27krrvu4q677lKW4bTDVHOvW7sKIdLy+PLmYJxlhqyitLSUfv368dVXXyXFxQZWr4bp4YcfziWXXMKjjz7KueeeyyOPPEJpaWnCYBWJRKiqqlJulg0kGRS6N1i1ZWDTsXHjRqZNm0ZJSQlXXnklI0aMID8/n08//ZSLL764XRtfRiIRJkyYwF/+8hdlvLzwdbLLLrvwww8/8PTTT/PSSy9x991389e//pU77rgj4aqkwWDoOF577TVWrFjBww8/zMMPP5wUP2/evCRn2ebsnxRDN9alGgPD4TB77rkn69ev5+KLL2bMmDEUFRWxbNkyjjvuOOU49uKLLwK2cffzzz8n7DeyOWNYexg1ahR77LFHynTyMY7p9cADDyj3wukub2ve3DlwwoQJ7Tq+MdJpVF999dX84Q9/4IQTTuCqq66ioqICn8/Hueeem/aNptNFTK4bbriBSZMmKdPE7jI57LDD2HnnnXnyySd56aWXuOGGG7juuut44okntHeUGro/7R3bIf3nTFv6cypOPvlknnvuOebNm5d00eK1117jhRde4IknnkhYQ4RCIRobG/npp5+oqKhI2JC/PQwZMoQTTjiBgw46iOHDhzNv3ry4s8yyLOU4qXuBVzrxutaxLIvHHnuM999/n2effZYXX3yRE044gRtvvJH333/ftS1ibXnhhRcyY8YMZRrdxbmORjefOO+UTGfZVVVVfP7557z44os8//zzPP/889x7770ce+yxSS/PcRJzOLflBox0zsmzZs2ioaGBk08+mZ122olBgwbF2/WXv/yl1qm9OTdRbNiwQXnxsDPpHtaZoUcxc+ZM7r77bj788MN2bewZY9iwYWy33Xb861//4swzz+SJJ55g1qxZCVdHRowYwSuvvMLUqVPTssDcHN544w3WrVvHE088wS677BIPd95N4mT58uVJrzn+7rvvAOK3jo8YMYIvvviC3XffvV2LjIqKCo4//niOP/54Nm3axC677MLll19unGUGQycxb948qqqqlI+dP/HEEzz55JPccccdruNXVVUV+fn5fP/990lxqrDN4csvv+S7775j7ty5HHvssfFw3eMIL7zwAnfffTcXXXQR8+bNY86cOXzwwQdx55LXMWzIkCFEIhF++OGHhKuY3377bZo0cyd2R1tVVZWrMyi2IbTq8fZUslZWVlJSUqK8mOSkLWP9kCFD+N///kckEklYNMQe+4/J29XE2nfhwoUJLytYtWoVGzduTJIzdpeD81jI8+Njjz3Grrvuyj//+c+EvBs3blTerSC3mRCC77//Pi13W3tts1g/Kykp8eR07NevH6effjqnn346q1evZuutt+ZPf/qTcZYZ2oXXc2bIkCHMnz8/6RyU55u29mcdv/71r7n33nu56aabEh4pj7FkyRKApMfpwX7L37Bhw/jrX//a5sf2dZSXlzNixIiEsbq8vFz5aJx8p9KQIUN4/fXXaWhoSLi7TD52sTHPy7ze1rXODjvswA477MCf/vQnHnroIY4++mgefvhhTjrpJO1YFbtjNScnJ2VbDhkypF1zYGcwZMgQXnnlFerq6hLuLmvrnJibm8v+++/P/vvvTyQS4fTTT+fOO+/kD3/4g+sd/QUFBdp1X2dw7bXX8uSTT/KnP/2JO+64I/528XA47Kldv/rqq6TzXteuoVCIpUuXJjyu3RWYPcsMWcdFF11EYWEhJ5xwAqtWrUqKb8tdXIcffjjvv/8+99xzD2vXrk14BBPsK6/hcJirrroqKW8oFGLjxo1tlr+9xK4OOPVraWmJvxVOJhQKceeddyakvfPOO6msrGTy5MmArd+yZcv4xz/+kZS/sbEx/hY3FbFn52MUFxczcuTIdu3BYDAY2k5jYyNPPPEE++23H7Nnz076O/PMM6mrq1O+lt2J3+9njz324KmnnkrYv+j7779P2n9jc1GNY0II5SvTN27cGH/b7tVXX83dd9/Np59+ytVXXx1P43UMiy3+5cc4b7rpps3WyQszZsygpKSEq6++WrnXy5o1awDbeTFp0iTmzp2b8Ejqyy+/zPz5813r8Pl8zJo1i2effZaPP/44KT52zGMXULzMX/vuuy8rV65M2N8zFApxyy23UFxczLRp01KW0Rnsu+++QHJ7xu44lN96unz58oQ3ZNbW1nL//fczadKk+J1/fr8/yZ549NFH4/vqyNx///3U1dXFfz/22GOsWLEiLY6noqIiT+01efJkRowYwZ///GfldhWxfhYOh5Meea6qqqJ///5mDje0G6/nzIwZM1i2bFnC3NTU1JQ0jnvtz27ccMMN/PnPf+a3v/2t9k2Du+22G08++WTSX2VlJdtssw1PPvkk+++/fzz9ihUrWLBgQcp9u7744gvlfkuLFy9m/vz5CRduRowYwYIFCxJ0+uKLL3jnnXcS8s6YMYNgMJhwrCKRSNIFs/79+zN+/Hjuv//+hGP35ptv8uWXXyak9brW2bBhQ1L7xu74i40bMQeePF5VVVUxffp07rzzTlasWJFUj1Pvfffdl/fff58PP/wwIV5351tnsu+++xIOh7n11lsTwv/6179iWZan8V5eP/l8vvhFFbfxNycnh2222UY5v3cWI0aM4JBDDuG+++5j5cqV+P1+DjnkEB5//HHlhTq5XZcvX85jjz0WD2toaNA+vjl//nyamprYcccd069IGzB3lhmyjlGjRvHQQw9x5JFHMnr0aI4++mgmTpyIEIJFixbx0EMP4fP5kp4zV3HYYYdx4YUXcuGFF1JRUZHkFZ82bRqnnnoq11xzDZ9//jl77bUXOTk5LFy4kEcffZSbb745YaPCzeXjjz9W7l8wffp0dtxxR8rLy5kzZw5nn302lmXxwAMPaJ2D/fv357rrruOnn35iiy224F//+heff/45d911V/x1wccccwyPPPIIv/rVr3j99deZOnUq4XCYBQsW8Mgjj/Diiy8qN4oG+2UG06dPZ/LkyVRUVPDxxx/HX0NvMBg6nmeeeYa6ujrtVbcddtiByspK5s2bl3QhQObyyy/npZdeYurUqZx22mlxY3D8+PF8/vnnaZN5zJgxjBgxggsvvJBly5ZRUlLC448/rnys4JxzzmHdunW88sor+P1+9t57b0466ST++Mc/cuCBBzJx4kTPY9ikSZM48sgjue2226ipqWHHHXfk1VdfTfudczpKSkq4/fbbOeaYY9h666054ogjqKysZMmSJfz73/9m6tSpceP7mmuuYebMmey0006ccMIJrF+/nltuuYVx48YpF4xOrr76al566SWmTZvGKaecwpZbbsmKFSt49NFHefvttykrK2PSpEn4/X6uu+46ampqyMvLY7fddlPuV3PKKadw5513ctxxx/HJJ58wdOhQHnvsMd555x1uuummpH1buoqJEycyZ84c7rrrrviWBR9++CFz585l1qxZ8ZdfxNhiiy048cQT+eijj6iuruaee+5h1apVCZs/77ffflx55ZUcf/zx7Ljjjnz55ZfMmzdPu6dXRUUFO+20E8cffzyrVq3ipptuYuTIkUmblreHyZMnc/vtt/PHP/6RkSNHUlVVpdz/zufzcffdd7PPPvswbtw4jj/+eAYMGMCyZct4/fXXKSkp4dlnn6Wuro6BAwcye/ZsJk6cSHFxMa+88gofffQRN95442bLa+iZeD1nTj31VG699VaOPPJIzjnnHPr168e8efPIz88HWu+k9NqfdTz55JNcdNFFjBo1ii233JIHH3wwIX7PPfekurqawYMHK/ccPvfcc6murmbWrFkJ4Zdccglz585l0aJFrpv8v/zyy1x22WUccMAB7LDDDhQXF/Pjjz9yzz330NzczOWXXx5Pe8IJJ/CXv/yFGTNmcOKJJ7J69WruuOMOxo0bl7BH8axZs9huu+244IIL+P777xkzZgzPPPMM69evTzh2YM8HBx54IFOnTuX4449nw4YN8XndOZd4XevMnTuX2267jYMOOogRI0ZQV1fHP/7xD0pKSuIXLAoKChg7diz/+te/2GKLLaioqGD8+PGMHz+ev//97+y0005MmDCBk08+meHDh7Nq1Sree+89fv75Z7744gvAvinigQceYO+99+acc86hqKiIu+66K36ns1cee+wx5aOhsXZvD/vvvz+77rorv/vd7/jpp5+YOHEiL730Ek8//TTnnntu0r6oKk466STWr1/PbrvtxsCBA1m8eDG33HILkyZNSrgzWsWBBx7I7373O2prazf7seD28utf/5pHHnmEm266iWuvvZZrr72W119/ne23356TTz6ZsWPHsn79ej799FNeeeWVeN88+eSTufXWWzn22GP55JNP6NevHw888EDS/nsxXn75ZQoLC9lzzz07U71kOuelmwZD+vn+++/FaaedJkaOHCny8/NFQUGBGDNmjPjVr34lPv/884S0sdcIq5g6darylfNO7rrrLjF58mRRUFAgevXqJSZMmCAuuugisXz58nga3Sumda+DlkHzmmNAXHXVVUIIId555x2xww47iIKCAtG/f39x0UUXiRdffDHplbvTpk0T48aNEx9//LGYMmWKyM/PF0OGDBG33nprUr0tLS3iuuuuE+PGjRN5eXmivLxcTJ48WVxxxRWipqYmQb85c+bEf//xj38U2223nSgrK4sf+z/96U+ipaUlpa4Gg2Hz2X///UV+fr6or6/XpjnuuONETk6OWLt2bfxV4jfccIMy7auvviq22morkZubK0aMGCHuvvtuccEFF4j8/PyEdIA444wzEsJ0ZcdeP/7oo4/Gw+bPny/22GMPUVxcLPr06SNOPvlk8cUXXyS89j72evMbb7wxobza2loxZMgQMXHixPhY43UMa2xsFGeffbbo3bu3KCoqEvvvv79YunSp9jX3XvSTuffeewUgPvroI2X866+/LmbMmCFKS0tFfn6+GDFihDjuuOPExx9/nJDu8ccfF1tuuaXIy8sTY8eOFU888UTSq+GFEErZFy9eLI499lhRWVkp8vLyxPDhw8UZZ5whmpub42n+8Y9/iOHDhwu/358wf6jmq1WrVonjjz9e9OnTR+Tm5ooJEybE28nL8fFyfFX9REVsLl+zZk1SXDAYFFdccYUYNmyYyMnJEYMGDRKXXHKJaGpqSkgXm6tffPFF8Ytf/ELk5eWJMWPGJNXd1NQkLrjgAtGvXz9RUFAgpk6dKt57772kYxST/f/+7//EJZdcIqqqqkRBQYGYOXOmWLx4cUKZXtow1ocWLVoUD1u5cqWYOXOm6NWrlwDi9cfqds7/Qgjx2WefiYMPPlj07t1b5OXliSFDhojDDjtMvPrqq0IIIZqbm8Wvf/1rMXHiRNGrVy9RVFQkJk6cKG677TaXo2/oTqjGqjlz5oiioqKktDGbUka2e72eM0II8eOPP4qZM2eKgoICUVlZKS644ALx+OOPC0C8//77CWlT9WcdsfFC9yefN6n0izFnzpykc1TFjz/+KC699FKxww47iKqqKhEIBERlZaWYOXOmeO2115LSP/jgg2L48OEiNzdXTJo0Sbz44ovKMWPNmjXiqKOOEr169RKlpaXiuOOOE++8844AxMMPP5yQ9uGHHxZjxowReXl5Yvz48eKZZ54RhxxyiBgzZkxS/anWOp9++qk48sgjxeDBg0VeXp6oqqoS++23X9L89e6774rJkyeL3NzcpPHthx9+EMcee6zo27evyMnJEQMGDBD77befeOyxxxLK+N///iemTZsm8vPzxYABA8RVV10l/vnPf3o67l7b3W3e0Z0LQghRV1cnzjvvPNG/f3+Rk5MjRo0aJW644QYRiUQS0qlsJSGEeOyxx8Ree+0lqqqqRG5urhg8eLA49dRTxYoVK1z1EsKejwOBgHjggQeS5HX2k46ek6dPny5KSkrExo0b43KdccYZYtCgQSInJ0f07dtX7L777uKuu+5KyLd48WJxwAEHiMLCQtGnTx9xzjnniBdeeEF5Pm6//fbil7/8paucnYElRBp2HjcYDAaDwdDtmDVrFl9//bVy/xCDwWAwGNLFTTfdxHnnncfPP//MgAEDulqcrOKpp57ioIMO4u2332bq1KmuaSdNmkRlZaV2r1BDZnPiiSfy3Xff8dZbb3W1KB3G559/ztZbb82nn36qfblHZ2H2LDMYDAaDwUBjY2PC74ULF/Kf//yH6dOnd41ABoPBYOiWyPNNU1MTd955J6NGjTKOshTIxy4cDnPLLbdQUlLC1ltvHQ8PBoOEQqGEtG+88QZffPGFmdezmMsuu4yPPvooaT+77sS1117L7Nmzu9xRBmDuLDMYDAaDwUC/fv047rjjGD58OIsXL+b222+nubmZzz77rMtf3W0wGAyG7sM+++zD4MGDmTRpEjU1NTz44IN8/fXXzJs3j6OOOqqrxctoTjrpJBobG5kyZQrNzc088cQTvPvuu1x99dVccskl8XQ//fQTe+yxB7/85S/p378/CxYs4I477qC0tJSvvvqK3r17d6EWBkN2YDb4NxgMBoPBwN57783//d//sXLlSvLy8pgyZQpXX321cZQZDAaDIa3MmDGDu+++m3nz5hEOhxk7diwPP/xwypfRGOy3d954440899xzNDU1MXLkSG655ZakF2yVl5czefJk7r77btasWUNRUREzZ87k2muvNY4yg8EjGX1n2d///nduuOEGVq5cycSJE7nlllvYbrvtulosg8FgMHQjzFxjMBgMho7EzDMGg8GQfWTsnmX/+te/OP/887nsssv49NNPmThxIjNmzGD16tVdLZrBYDAYuglmrjEYDAZDR2LmGYPBYMhOMvbOsu23355tt92WW2+9FYBIJMKgQYM466yz+M1vftPF0hkMBoOhO2DmGoPBYDB0JGaeMRgMhuwkI/csa2lp4ZNPPknYpNDn87HHHnvw3nvvKfM0NzfT3Nwc/x2JRFi/fj29e/fGsqwOl9lgMBi6O0II6urq6N+/Pz5fxt6Y7Jm2zjVmnjEYDIaOxcwzZp4xGAyGjqQt80xGOsvWrl1LOBymuro6Iby6upoFCxYo81xzzTVcccUVnSGewWAw9GiWLl3KwIEDu1qMzaatc42ZZwwGg6FzMPOMwWAwGDoSL/NMRjrL2sMll1zC+eefH/9dU1PD4MGDmYC9MZsF6J43jV2nEY7fwhGOI97SfHcrT44TJMrjrEcng7MsVV65TFW9buU76zc6qsuT44yOahkyScfgWGiaDuRqBHETQCVsqgOpyqdSVncgZXQNo4r3IqeuDI86Rprhp9uhV69emoK7N7p5ZuYuEAi491EZXbN76eu4/NblSdVt2iqHW5hcj9FRLS8uv3V5jI6Zp2N9MSwdCMEcSVhZARTxzgLNPGPmGfTzzFKgpOvEMhgMXU1NTVdL0G2ora1l0KBBnuaZjHSW9enTB7/fz6pVqxLCV61aRd++fZV58vLyyMvLSwr3R/9kvNoUKhtEZ0PIcXK8m7PAq2NCV5YKo6O+Xq91q+KNjtmhY2gkBGeAL1dKoFtUtEUIXTpVHakOjkpBuRxdOreVXAfq2F0eBWnrXKObZ3IC9p9qoe/mBEh1brmdL6q1plur6LqZrly3coyOyXFGx9T1dDcdBdBUAMuHQTgXfG4TJy5hzgK9pFPVYeaZjCVd80wJxllmMPRYMnOL+azHyzyTkZsB5ObmMnnyZF599dV4WCQS4dVXX2XKlCmbXX5sjo8dHvkwWYowZ3isuzrLcdodzj+5PJ3toSpTVbcsgw6jo9Gxp+oIEB4MTXuBcF7p97KyUx0glRByOmd6S5PHGSbndcarwp3IdQhFXEfq2I1I51wjNwMkH06B+hDq+r/ctXSH33kuyHlUdejKV3UZlV7O70ZHdf1GRzXdScfmfFg8BFpyHQnNPKOW3cwzHbamMRgM3RzjKOtSMvLOMoDzzz+fOXPmsM0227Dddttx0003UV9fz/HHH9+mclQLaeccrzO45EW481O1QPdic+jsAJUdIsupKk9VvtExtUxGRz3dQcfwYGjcDyLFisJkZWKZ3Ix+lRDOMlSCpCrDbSEjpDh51aYa1HR0hI7djHTNNfL6L4bsBHDL58yjKy/2W7fId0uj6wKqc1RXpi6PTlZdmNHR6OiWJpN1BNtR9tNQaMxXZFAVaOaZZPnkMsw8YzAYDIkYR1mXk7HOssMPP5w1a9Zw6aWXsnLlSiZNmsQLL7yQtEGmF3QLf12YKlye290W/DoHgs4WUDkQBIn1udkyzu9GR/dwo2P31jE8SHKUuSnpzCwLLx8kWSGVgCpl3cLc0rjRloZU1bk5OnZD0jXX6M6NGG7nkWrdqmpGZzOrHACpuqfcxG5rcF39Rkd9WqOjWpfupKNA4Sgz80xynWaeSSCdaxqDwdBDMI6yjMASonu2RG1tLaWlpUwkcc8y2QDC8VuOV9EWo9EtzImqLl09crk6GVV1GR1T1210zE4dASJ9oOFQiPTSFJxKyfYcGK8rslSrL0hPQ3WwjpEW+PEme8PhkhKze0psnpm1m71nWQxVd5CbWoXbOac6r5zfvZy7XuJ1srvJanTU53OGyXmMjtmlowBCOfDjcGgo0Ahp5hkzz6SZ2DxTg9mzzGDoMXRP90zGEB9XPcwzGblnWbpRLcJj32VjSf7tZkQ5y5BtEqcN4AyT65XtHYtkeVW2kTOPLKfR0ejopCfoGKmAxv2jjjJVpXIhKiXlSlKFyYsQZzkWyUo460TKK8vm/K6Styt1NCShO8yq76qFfwzVoZa7krNMtzxeZJLzOPM65XXrSkZHo2NP0TEUgCWDo44ypITydzPPmHnGYDAY2oNxlGUU3d5Z5rQDkD5lQ0k2mmR7wlmWat5Hihck2zVyWTKp7BOdLkZHo2NP1TFSAY0HQrjSRUDdQkIuXGesq1Z2sXCV8PJiRaWQbrWmW8io6EodDXFUfV/1XUeq7qJb+7rlkc8tHW7NrapDzmN0TCzXTV6jY3bqKIBwAJYMgVr5goyZZ9TlmnnGYDAY2o5xlGUcGbtnWUcgOwtUcc40sqNAtiVU87nOSNPZK3I6XfkqW8WtfqNjskxGR3U5se/ZqGOCo0ylhMoYd1sUyPHywVCtopzpUxn5XhcBKjllGbpKR0MSzkOkahbd+hUSD7ET1fnp1rS6ZnUrV04n55XLVsmSqlxnHqOj0VFOI8uuK1dO15k6hgP2Wy9re5FYmJlnzDxjMBgM6cI4yjKSbn9nmRM3YykWr1u4O20D+c8i0QZwlqcywJz1yLaPbI+4GaYqWY2ORseeoqMogKYZDkdZLFJl+KfCbX6SFw0qgXRh8sFLVb4K1UovE3Q0JCCvDVXnmZwWEs9LVT5VF1Kd20jxqnW1bh2sks9NbqNjYlpnPUbH7qWj8MHy/g5HmaowM88k15NKBjPPGAwGQyvGUZaxdPs7y5yOA/m3ysiDZJvCbcEvhzvrkut1yys7OWScMsjOEFVdRkejo5uccl3ZpCOAKIDGmRAapBDEUoSlUki1MpORFXdbKTrjdcqp4lLV7fzdlToaElAdNmecLo/zU+VEkPPr1pjO9PK5KTe97OyQ6/HifDA6JocbHbufjsIHywbA+gpJAKewZp4x84zBYDBsDsZRltF0e2eZCtUc7rbwVzkGZIMu9l21yFcZZ6qyVHV5MU5VchsdEzE6dg8dgVZH2XCNIG4LCJ3wssEuCy0LI5etOphymOpg6fKq0mSSjgYtusW4Cufh9XLeupXtxVGRKszNwZGqHB1GR3UaL/UaHduez0s5OhKGOx8s6w/reoMw84yZZwwGg6EjMI6yjKfbP4apmtvlcDfjTmX8qeZ8OZ0qrfMvVZ06GVTyGh3d06pkl+XWpTM6ZpiOqRxlOgNdhbxgUC0+VAKlWnTIBzfVqlGXXl58ZJKOhiRSrfXkQ5pqsa9qXjeTStXUznNflVdOk0oHo6PR0S19d9ExEnOU9UHtKMuEMdjMMwaDwZDdGEdZVtDtnWUq48qJJaXVLeblMmNxqvQq41JlI6SSxy1etkeMjomyGx1b08Xisl7HADRPizrKdEJ4mXd0hr1clm61pcvjFFgO18mgWyHqDm4m6GhwxYvDwELdlPJh160ldcjnV1vW0M46Uq1VjY6JZRsdu4+OwoI1lVFHmTOBqkA3zDzT+t3MMwaDwWDIUrq9s0xlhHmxA1SGWaoLX3IaUOdx2g0qOdyMP4FaFqOjGqNj99CRADTtCi0TFELFFg9evHpyuGox4VTAKbSqHmf9qlWZbjWpkle1oMhEHQ1JqJrb+Sl/V+VVpXOW47bmREqnKt/NoaE633VyGh3VGB1bP7NWR8t2lK3sazvN4oVk2hhs5hmDwWDIbsxdZVlDt3eWgdomkOOcc7bKgNM5HJyfsrHoDJPToomXw1Wo5DQ6tn4aHZPJZh0JQNN0aJlEsoEtG9a6AyBXIC8e5N+6OUwpoBTnhu4gq1aH2apjD8RtfalDbibnuSB/1+WR451huvWq83tbmtPoqMbo2D10jPhgVRWs6AcRM89kvo4Gg8GQrRhHWVbRIzb49zJ3qwy6tlxYc+ZLdQrEbANBop2gckKksiNi8UbH1nC5DKNjMtmio/BHHWVbkeza96q08yC5Ge+yYKr8spJyebJ30Bmv8xzqypC/u4V1po4Gz8jNKzed6rfq8MvfVedaqjxeHAfOc9Srs8HoaHR0kzcbdBQWrKq2/0QqxXVhZp5JXbaZZwwGQ0/GOMmykm7vLJOvWqaa43VzuepKp5y+LXKoypZtCi/1qHQxOhod5TRudavKzQQdhQUtkyE4CdtR5rYikr1/bgsBucJUKy3VgVXFy7J4RT7A2aCjIQlVN0i1nkzlSFCVJ+fVnZtuTe7WZd2cEEZHdT1GR3V5ct5M1FFY9hsvV1eRuJm/ikwag808YzAYDAZDh9MjHsOUjTZnuDNMNrxU6OwHlbNAVW6qsmT5kH47HROqtEZHo6ObXNmiY8xR1rwzCL9GUJ1gOiFlAZ2CqNKqhJfj5QOnW1EKRR6drJmuoyEJt8NooT9X5fPErWy5yd0cF7qmkutylu38c8tndDQ66sg6HS3bSbZsgOPRS1w+M2UMNvOMwWAwZBfmrrKspds7y5x2hfMTkudt1eI91bwt2wgxhBSnqstySSvXLf9OlcfomPzb6Jj4GSMTdRRF0LwjCLd7X91WYSrDHynOrVxVXnl1ploYqBrGWa5qJerWaJmoo8EVt2aR08RQLfzdyol1IVU5qerWydKWJjY6qsszOiaTyTqG/bC2t71fmVaQTByDzTxjMBgM2YNxlGU13d5Z5sRtrpfn9FTOA2c++U+2W3QGni5OrltOpzJW5bxyPc5Po2NymNExs3QktnjxugprzzwkHxhdvMqQF1K8bmEhp1EdyGzT0ZCEauHvtblU563z3NSdi25N4taMct2p8stpjY5qjI7Zp6MvAj63SmNhmTYGm3nGYDAYDIZOods7y9zmcOe8LBuBOoeCpQiTy1M5E5zfVT4AlVGnS6NySoDRUa5XLsfomFxepuqYgEpIWTC5Qrly+SAJqSxnflUd8qdOSbfFgLxqVJWfDToaXPGyqNeteVXp3c5TuVxdGtW4oCpf17ztcVwYHZPjjI5tk6mzdEwKyPQx2MwzBoPBYDB0Ct1+g39Qz+dyvByeyuhTlS0U6VRhOrnktDrnhVy+qiyjY2IeVX06OY2O7nlVadOto1AFOCr3A6VhCAio80OzBRFVWlXFcri8CpQPkurTRbakulUH1a18VV63OjtTR10HMSjPiVSOA9XhdDaB7rDrzj05vyreGSaXr1sD62Q2Ohods1lH13lGLiRTxmCVcmaeMRgMBoOhQ+gRzjJ5Ho+hsg/c8ruFuZWhS6uTyxkmG5K6eoyORsdUMrilSZU2I3QUdpqt6+HU1TC2AXIFrA3Al4Xwdgk0WfB5EawPtMoQdlasM9RlRXRC68Lk787yVYp6lUF3MNxWmB2pY6oO2INJtdB3W+s6w3SLfxndmlflINCVpXNsuK29dWFGx2QZjY7qMjJFR62wcmKdQF0xBju/m3nGYDAYshtLMVCaPc4yim7vLLOkz1TpYsg2gGxTqAw1N5tCLlsVLzsa3C7AqeQ0OhoddXI406ni3MpWxXe0jklEM2zRCLf+BL2DrWWUh2BkExy0wf79TDksybWfMe8Vho0BKA7DyhxYmgff59vfmy0QsoDyd/mg6VZ9zjyqg5HqQDvDUq1W5ZWqSm7dwLe5OhrahW49qXMW6LqOM49b11U5MlI1o1x2W5vd6JhcrtFRHS6nyQQdEwTN5DHYzDMGg8HQPVA5ytzChbDjjDOtU+n2zjKvqBb6qng5zBkn2zKQbMfoHAYqe0KOQxHXFoyORsds0VFXaJMPWqSCQxa81wtCQG0Abq+Gn/LsuFwBORGoCsGIJthuExy/2n6M86sCeLUUviiEBufb0FRGvQ55BenMr1vAyPnlOuRy3FaDqo6QavGj+t5WHQ1adOeNytmgyqdKrzpvdc3spblUXVS1ztZ1O6NjYn5VPlV6o2Nm6SjkhLIwmTwGm3nGYDAYujcqp1gsTHaoGQdah9HtnWVuC/7Yb5UBpTMQUcSp0rrlEVK8m8NBlk+Vx+iYXIbRMVle1e9UebpKxyQZo5FLcuHCITB7HfQJ2XeVvVgG91VCMJom4sjTCDQGbCfa93nwUinkC9iyAfbZCNcsse88e6wCXimFVTnYd5upDppzweFlcaA7GG4LEmc6S/ouo6pLKOKc4bJO8m8vOhpc0TWJM8wtXyyP29rR7Tx2ojqvVGW55XGT1eiozyfXo8pndFSX01k6ahNn+hhs5hmDwWDIfmJ3ijl/tyVvDMtqLcc4zdJOt3eWgXrhL8elCneb8+XfstNB5QRI5UAQJNbnZq84vxsd3cONjlmmo1SAAD4qho+L7KgcYd9plvA4pUrYaFlCQKMPPi2Gz4pgbiUcvg5OWwUnr4a7q+Dpcqj1O+r3siKTD4hGfmUZnlZxivReGlJVp2qR5FVHgxbduRHD7TxSrYNVzehsZtUaNVX3lJtY1XXkPKpyjI7qtEZHtS6ZqqNw/pATuNGVY7CZZwwGg6H7kA7nlvNuM+M0Szu+rhagM7GkP1y+y3lku0BlszjTqYw02QhU1e90PKgMRpVsRkejY3fUMUEBKVJY9h1kzT6S7wRLpaRoTfZzHvylHxwzEr4qhN8sh3/+CNvUR6sSUpmx70IKUx0oOUzOq5PZyyLCo44Jn7pVant0NCSh6v9yV5CbQJfH+ak7z3DEy+G6euR4uUwv57FKXqOjOo/z0+iYeToqK8qWMdjMMwaDwWBwIoTacWbYLHqEs0w2/pzf5XnaDWceVZnOdLLDAum3rl5VGmfaVPmMjqkxOibLlrE6qgxvp9CyArIychpnfPRTWPBjHpw/BG7oB8Ob4PZFcOQ6e88z7UpQV5+skFu6TtJRK5dqhZxKR4Mrqc5TtzWsCrd1LZo4t3Wwap2rk1kXZ3Q0OqrIRh3jiWRhMnkMNvOMwWAwGHTITjPDZtHtnWXynO4Mj33Kc7gzTEhhKNJC8tzvzCunk8tw5lXVJRugKl2MjkbH7qRjQoRqnNcZ8Sg+5fxC8WfZb8e8vxLOGWpv+P+7ZXDeCsiLOMpJNefIqzIvc1Qn6hiPk8tLJYvBE6q+rTrf5PPYS7mqT1UaN1TnpmqscKZR6WN0NDrqyCYdXTOrhMiUMdjMMwaDwWBIhbnLLC2k3Vl2zTXXsO2229KrVy+qqqqYNWsW3377bUKapqYmzjjjDHr37k1xcTGHHHIIq1atSkizZMkSZs6cSWFhIVVVVfz6178mFAptlmyqRb88j+scBbEwN0eAHCbnbWseOb9QfNeVZXTUy2R0TC1jW/N0lI7ajKmMb5Wh7jwoqpUgELHgnV5w3lBYG4Dj1sC5KyBPtfLTrdhSye51YdNBOsbzWCTW46aPl9VtJ5Jp84xznahqFtUaEynO7RyQy9Wdm8588jkor2Wd6XR55bKNjsnlIsUZHbNPRyWZPAabeaZTyLR5xmAwGNqM/CIAQ5tJu7PszTff5IwzzuD999/n5ZdfJhgMstdee1FfXx9Pc9555/Hss8/y6KOP8uabb7J8+XIOPvjgeHw4HGbmzJm0tLTw7rvvMnfuXO677z4uvfTSzZItla2hW6irDC7ZoFPZJrI9oTIwLelTNgJ1xqicFkWY0TG5TKNj9uiYkEhnRMvGvspg94JI/PpZIVw50H4ZwHFr4IxV4Hdb+enCvKzOukBHVxm86tiFZOI8IzsH3NarziYVUlrVWhVFmOocdcbL56vze3ub2+ioTuusx+iYfTpm7Rhs5pkOJRPnGYPBYGgzxmG2WVhCdOzrEtasWUNVVRVvvvkmu+yyCzU1NVRWVvLQQw8xe/ZsABYsWMCWW27Je++9xw477MDzzz/Pfvvtx/Lly6murgbgjjvu4OKLL2bNmjXk5uYm1dPc3Exzc3P8d21tLYMGDWIi9is/ZRtAnqvdDoJsQ7jhTNuefLo8KptIpZOqvBhGR6OjLm2m6RjuC/VHg4i9r1dVoe6gyAKkEl5Tjg84YQ1cuNx+4+bvB8Gz5bS+UECeb2SFnXLoVpSqdJ2oY1I+WQ5Jx0gT/Hgz1NTUUFJSoimo8+nqeWbWbhAIaA+bJ+Sm0jWdrmzdOZYqv5d1tpzf6Oie31mX0VGfP5anq3QM5sC3o+3PpExOAXWCy2HOuA4ag80803V09TxTA2TO0TAYDJ7JlDdTmjdlxqmtraW0tNTTPNPhe5bV1NQAUFFRAcAnn3xCMBhkjz32iKcZM2YMgwcP5r333gPgvffeY8KECfGJBWDGjBnU1tby9ddfK+u55pprKC0tjf8NGjRIK5NwfMb+dMaUzl6xHGHO787uJ6R4S0rrLEtXl+UI15WtwuiYXL/RMXt0FDkgfFJhbsa9bpEgK6UTXj4A2G/bfKg3vNUL8iNw/goY0CKVIedXKSSHqeTvIh0TypAbzk3HDCOT5hkvC/kYuuaO/XZb06rqdeZxO39V+WR53DA6JmJ0zE4dhQWRLp5nEspoyxhs5plOJ5PmGYPBYGg35u6yNtGhzrJIJMK5557L1KlTGT9+PAArV64kNzeXsrKyhLTV1dWsXLkynsY5scTiY3EqLrnkEmpqauJ/S5cuBdwX+25hMYQiXucQUDkknGlVDg9dnToZVPIaHd3TqmSX5dalMzp2nY5JCVQrpViFslHv/K0zvnUGuyO8wQe394VNfujfAqesjj6OqSvDTWYVcgN0gY5K2VPpmEFkwjwDqRfx8iGVzxVderdm05Uvn8u6vHKaVDoYHY2ObumzTUdLgC8CWTkGm3mmU8mUecZgMBjajXkcs10EUidpP2eccQZfffUVb7/9dkdWA0BeXh55eXlJ4SrjyoklpZXtATcHga48VXws3GkYtiW/LI9cnpu8RkejY7p0FM7EfvtP+EEURON70froJGA1gtUk1dti/znlE2EgrKhDNuS9CJ4KlWEvLzYc3z8vhJfK4OB1sN8G+Fdv+LpAUb8so6o+Oa28WOgiHZWLGlX9XuruZDJhnnEinz9u57IcLzdV0rmXAmeTeTGDdKdZqrxGx+SyjY7p1VFYrZ8Rx7goLGjKj94RFsUXgfwmCPtaC/OHwYqQgBVNGyPio/WxelnJTB6DzTzT6WTaPGMwGAyGzqHDnGVnnnkmzz33HP/9738ZOHBgPLxv3760tLSwcePGhKsxq1atom/fvvE0H374YUJ5sbfLxNJ4RWWIebED5Dy6C2RuaeQw2T7ROTLc5FOFp3KE6DA6Gh11YTodQ2OgZZIdIQpA5AG+6Ce248xZoRXBfp7RSRAs6UVQVhMJTjWRLwmuWrHpDHUvB8CZx1mWYlESseDJCttRVhyGw9bB5QOjWeR88srTGZ9K3i7U0VX+tq7cO5FMmWd0TgrdmlHOq0unKkeVx60ON4eGXL7OMeIMMzqqMTqq87ZVR4C6ElhdCcJnO7RCgdZ4YUFYYbkGQg7nF1FnmTxOCsgJRusWEPZDyK8QKpPHYF0auS6dvNmgo5lnDAZDZ9AT9+4SgvhdZZbVM49BG0n7Y5hCCM4880yefPJJXnvtNYYNG5YQP3nyZHJycnj11VfjYd9++y1LlixhypQpAEyZMoUvv/yS1atXx9O8/PLLlJSUMHbs2LbLJH2Xf8c+22rs6WwDlQEqp0UTL4erUMlpdGz9NDomkw4dI9XQtAeEBkNoEIQrIVICkWIQAftPLkz4sPcfi8aLHBAF0Xy9on8ldlmxckODIFLmqFg22uUDoDLKdagMebkMxSLhywJYlGdH7VYLvUOKerzMN7qDnAE6JuXXrbQzgEybZ2QHhicdHHljv4Xmuy6PHO8MU/gJkr63pTmNjmqMjunVMZgLSwdCXS/YVAQNhdCSa/8Fc2zHWSyPsFq/B3PsO8silu00Cwai+XKif9Ey6ovtcjcVQ1OBo+LuNAabeSYtZNo8YzAY0ohxEhk8kvY7y8444wweeughnn76aXr16hV/Jr+0tJSCggJKS0s58cQTOf/886moqKCkpISzzjqLKVOmsMMOOwCw1157MXbsWI455hiuv/56Vq5cye9//3vOOOOMdt2a7GXuVhl0KjvBi+2Q6vSL2QaCRDtB5WhJZUfE4o2OreFyGUbHZNqqY6QAGveESKGLALKSsgKyV84Z78gXWAY586FxD2x3vqoM+btbmLMON+NdV7aARh+82wtGN0JlEH7RAK+VKsrzoqNqQZIBOibI63XB00Vk4jyjQm5euelUv1WHX/6uGk9S5fHiHHGOQ14dKkZHo6ObvG3RUfhgZV/bqdXR80xhI5RtgBX9SXwU001At7DOHIPNPNMpZMs8YzAY2khnOsrM3VtZjyVEelvQ0mwYd++993LccccB0NTUxAUXXMD//d//0dzczIwZM7jtttsSbklevHgxp512Gm+88QZFRUXMmTOHa6+9lkDAm38v9krQidhbK8Xli37qbAbVXC6nTeXAUOGWR2Wc6up0s1905erKNjrq5dXRI3X0QdPu0LKVS2Y3xWLxHgTyr4XChyFSAfVH0GqUO8vwInRblfSwmtxzI9z6E1gC7qiGv/Yj/shPgnxtOejycetiHVO1Y6QJfrwZT69a7kgybZ6ZtRvkSFlSrU+daeTvKuR41W9nHV6dJM68spxeZTI6Gh11aXW/ZfmwYG0fWDZAGlflglXCyRWlGIMDIRi10L4DbeGo6J5oWTIGm3mm88i0eaYG6LqjYTB0IzrLeRUbQzLRWeYc3zJRvg4mPq56mGfS7izLFJzOsgCJ9oV8UUsXBu42ie4iWaq65HLc8qg+5TpTfRodjY6bo2NwPDTNIGHjfleFVEKp0iri/SugaB6EB0jOMpc8yjplOXW4rQAddVjAvhvhz4shIGBtDvxyJPyQ13YdtTJ2sY5e2jHSAj/e1PWLmEwhNs8cuBvkBtwPtVtYDFUzei3HGe4W76zHa9dJNX54kTeG0dE9TJapJ+gI9uORi4a17k+WlCiN80xOCMYssO9gS3CWZcEYbOaZnodxlhkMaaSz7yrr7Dq9Ypxlnp1lad+zLNNwsy/kruFmR7iVjyKNkOJUdansCp3t4mbcGh2T6zU6JtMeHSPV0DwNe+N+pyByIZYUJhvBOqFVQqiEVqHK42bsI8W5lSvltYBZ68EfjSsN2Zv9J5XnRUe3NCpZOknH+O9U7Whwxa1Z5DQx5MOeqpxYF1KVk6punSxtaWKjo7o8o2MyXnQM5dh3lIU6eZ4JhLBfApBtY7CZZwwGg6Ht9ECnkBLNXbMGNd3eWebEba6X5/RUDhJnPvlPtlt0RqwuTq5bTqcyVuW8cj3OT6NjcpjRMVk+EdunrMhFGJWRK6R4ndEtp1HNYV5XYe2Z/+QDk6JubXu1VUe5ITNIR0/taEhC5dzw2lyqru88N3XjjVuTuDWjXHeq/HJao6Mao2P7dIz4YFl/aChQRHbQPOOLkPimzGwbg808YzAYDG3DOMoM7aTbO8sixfanag53zsuyEahzKFiKMLk8lcPE+d2Zxs1w1aWRbRo3O8XoaHRsj474oXkXCPeXBFYJIX+CWkk3Q1knqK5c+eDL9clly/XIuqTS0Y226uh2DLtSR6/taEgiGHBfi4L+sOryOOO05ynqJnFzpshraV039FqeThajY3Kc0VH6bcGaPrCxXKqog+eZQMh2mGmFy/Qx2MwzBoPBYDB0Ct3eWdZwEBB94YxqPpfD5TDnb53TIfapmt9lu0XOq7N75Dpl2WVnhy6dSk45TFWfHO6UV8bo2L10BAj+AoITpAjZ4FUZyjpj3JlWPiA6gZ3lCCmNLJPqAOoOnrOM2GcqHd1kc9bnVUfdsetKHb22oyGJxUNASLOps9lU44fc3KpwnYOirU0uoxp3nHWquqOzbGc5Rkejo7NMrzoKoLYEVvWN5jXzTPp1NPNM96KmpqslMBiyE3NXmWEz8PYqlixG5Nt/ltX+i1U6x4LbpzJMAM3ueeS8siGs00FXlmzI6nCTwS1NqrRGR5ts0jHcH5p2InHxr1sJealU/i4b0V6RZdDllQ+2Kk7VCG46ppKpLTq2RYbO1NFLOxp7Q0koYP8l3a3SBVgiKofQO0viaTW/3ZwzujDZqaNz0sjrfTfc1vXO+o2OenkzVcdgLqzoB2EzzyTH6cLMPGMwGAxtwzjKDJtJ93eWFUD9L8mMyTdsv+0v5zvwLwWr3g6W7RXZBtFdgEORzg2dwauzm1TGspvdJJetijc6qsvLFB1FITTtaZ83SQLK3+UCdas+uUKdwG54XcnJqziV3Jb0Kcc7v+sWAzrZYr9T6bg5q9WO0lFVt6p+QxItufDdFmTEPGMJKKmF8o1Q0Ai+6IsoUjWjM97rWJRQL+o1s87pozs9ZDmc4XIaWU6jY3boKHz2hv6N+YpI+XtPmmdUdaviY+WaeabnkWkLf7NRuCGTybTzxZCVdHtnGVZ03zKvBpObByTVdw/5I2UQHA2+TRD4EQLf2Q40q0ldnFzk5kxLqdb9bnU77SaVTDr7TXVojI6ZqSO+6D5l1SQrpev7bvOQvLpSCe/lQKgOnG4lKdfv5XyWfyt0zBVQFdTUoSvbi46qhUgX6egqo+q7IYFgDhkzz6ztA+v6QGEDlG2AogYoaLDjdWts1Tpb1+1045/KoaLKp0qvGpt06nvpkkbHRBkzRUeANZVQU6rJqPreQ+YZLWaeMWQqMWeEcZoZDIZuSrd3lhVHwO+4st7sg1D0e0rjANSGhJvVKeeV46LxkV7QMtHeG8qqhZyFUcfZKhDSolyuTmebuNksKuNXZwSjiFOldcsjpHiVDk6Mjl2vIz7wbYTcT+1+KXIUiXUFOo1xL4ZzqoMh05HnowcdfREo0D1i11YdFbpagB/Ij7RGhYEmH0RUZenq2gwdPbejIZl2tHlC+nT3a8tew9QX2n++iO0wq1gPRZsgJwRWxF1kVZwstiyCWx45zs3hEvvttrZWlaM71EZHd1k7W8f8JqhcYzt1Iz5Fgh46z6RVx66wbc0803Nx3sFjHGcGg6Eb0e2dZbvVQEOh/V0AIQuCFmzyw0Y/lIRhfcD+C1rRed2rBRtL68zjzOvBqhWAKIPmbaBlK/BtgMC3tvPMtw57xYy7veL8rhI7lQPIKZbOrpF/61SU45x1yWkFyToYHfX6dLiOIch7HyJFENwSyEGtoFxAqhWZfECcgrkp5jWNKn07z0etjqlog44W4BNQHoK+Qdsh1rfFDg8I+7MoAiOboDQEXxXCO71gZQ4I54Im3Tp6aUeDGueJ7sXz0Mn9OuKDul6wqdh+LDPmOCveZL8ZEJHc1VVF6sY4RZVJaVVrfV0dsbJU6/BUw4zcjVWHSs6jKsfoqE6bDh1LaqCoHjaUR51lXTTPWML+y5h5Jp06dvGYY+aZHoxxnBkyBcvCPIqZAcTGgSxti27vLHumHHz5joDooiBXwJBm+N0yqAxBnQ9W5ML3+fB8GXyXH12Y6lBZjKksSZXB4TAchB/Cfey/lu3BtwZyvoHAD+CrARFJtC+82EdyulR2llO8tqjoLNPNfpINeZU+Otm9pmuLvdxdddycdkxAt+JxxqUSyMsBiDobRD6ERtmfShl0pPt8lOtOhQcdLQG/qIdD1sOgFhjeBGty4OLB8F6vqF/cIasPqAza6f/5AzxZAQ/1gXqfo5506tiWdjQkojqujn6dFA5d0q+FBWE/1PaynWc5QdtpUbYRiuvAH2nN5zYGuXV3N4eOrhy37uks00s9qjw6eY2O+nQdraOwHDZWJ88zzXmwqhoCQY0MOjp6nkmjjknhzjKzRUdD21A5prp6cWocZwZD90U+p3XjjTwOdPW41Ea6vbMsjmMSF0CzBQvz4cQRsO0muHYJjG+EPWvggA1wzEhY5ryzRjYKkL6rfquMCCHFIaWPJQtAuJ/9Z00F/0r7xQCB78HaZOeVxdGprCheKaKcRw5T2VmyGqrDpapLdZq42Xfp0lEUQWgIWAICC+xPZzndQUc5T3vaMSmhmxAx49xtJScLKQsRDYuUQ8OB2I+IyencjHVVfWk8H7W0QcfSMPxxKWzRZDvGXi2F3w2COn80qTOvBREBq3Lhjmr4shCuWQKT6+G3g2G9vwN09NqOhmR0J1eqgaAL+7UAWnKgpQw2lkJu0H4hQOUa+zE5f0jd7G7i6OLcxkjVulpXlkN8bR5dnK5cL2naqqPwRffkwnZEWqL76bi57ZgU0InzTMgPK/q6yNBV80wadUwi23Q0tB3V/mGZtDA1jjNDTyNTzr2OwKujTEYIsu1Os+7vLJONAFp/C6DJgrd7waFbwIgme8+gNTn2I0/t8mRIdbimkWVUGSSAyIPQUNvJ45sK/mX2/maBRWA16G0gN5Hd7BShCFeFyWlTHa5UNpkuv5tN11YdRS9o2QEI245Hgt1Px81tR1dS9NWENDqBUxjiVsTu46FBmjq66nz0gouOdX74wyD70e9GH8wviN4llkLHCPYY9bvBcNNPcN1iuHAI1MijdxrHnIQ0htR46NfKha3zexfOM8KC5lz7r6YU8pqhtMZ+TLN4E8r9zZwOEpVabr9VY5vu1HGm86Kisywnsrxe1urt1bElB5YOsh2OJbWJ+6Z2Fx1Vn87yNkfHTplnhO0gbs7V1NHFdl9CGl09WTzmKGU0pIdsWIAax1l6MC9YMHQVmzPOxBxmmeTMd6H7O8vciI4tAlieA8tzXdK5eTcg0XhBEaYbx+R4p+XqLD+aNlIIkVEQHAW+OggsjjrOloJodrdhZMNWZSS72TSqMFVeXR43m02WWWe0O8Pao6O1KTlzd9PRGaaSQRWW0HVjDhzVSqcNfTXJSE41l0fjrTrwL406y3TpOvt81ODDijsSnPUIIIJIyBu24PMihTwe5BCW7TB7thwOXwcnrYa/9oNIJ4w57XYcGmy8HrdMmGcs+2tTHjRV2W8tzG+y36pZsd52ovlDajHkquVTX0jp5TxOEXWq6NRwK1dOpzu15fF7c3S0ol8ifgj7Wp1l3UlHnSyeddTNC501zwh7v77mLLD72qtjSjJZR0PPwTjODIaeSxY4zLq/s0yerFXh4G4x6jwQqoldtkadYbLxoCqrDeVHekHLeAiOA98qKHwUaExOLquBJkxlG6nsHeenTmTZIHezu3TlyPKp8sXCVfqowqxm+8UJ/p+T5ekuOm5OOwo/tEzG3jMsXX1VtTpTCR0Nj5RC8xSFgDoFvcjgLKO956OzWOFj32924KSft6WssTgpvsUf5OOB3/Lg5Jeoy21Uy9fGMSds2XuWHbABjlxr78e4sCDNOqZqx8yez7qObjrPRCxoKLD/1lfY+5sN/5GEO828iKsT3UmqclSi61RXyZDK16CTuz06+iL2/m++CATC+nzZrOPmtKOw7M39w34XoTt6nvFBfZEiL1K+DDof26pjkmyq+lV5ulpHM8/0XMydUm3HHCvD5pKqD3WEM8v5OGaG0/2dZZA8wctGhjzhI8XpDAI5XaqyVXLJxoXOs+ESL4BIGeBPLkqFl3g3MdzCVOqqDqtbPanyedVBGS/AagZLcaVfpY8qLON1TFG3LgwfNE+Flm00gqWhryYI7JbHqYAsQ1ecjxL+DaM4/pOTsSK62xJgp0W/YFNeIw9s/ZK6zHaMOT/k2/uXbV8HMzfCzfnR7J085hgUdPN5JgKEAq3FyGLrVMdDnDyG6Q6RTlxZJjk+5dinkbe9Osbesuhz3HXa3XRsbztiwfresLIv9t1lXTnPZPH52GN0NBgMhvaS4XcsZQ0dffdXht9d5kudJMuxHH/QajAIEo0HlaUohzknc2c5cn3yd1UeN2NCFyYbPUIRJonkrFZnOOvsEl01FmoVVCrK8sjNoVJRRiXnZukYtN8u6kzX7XRUlJNKx3A1hLYAgppMcuXt7avZeD7KxTaXuzrKwH5Ec87HezNq7cDEepzytFHHELCgwI7epdZ+q2+njzmGZLK1X7fx3HW+EMUtq2rN7RRXVks+dKpurcqjU1XOrzvkTnndDl2bdRSQ1wR91rYes26no0beVDoGc2Bdb/CFXYTpqfOM0VFdt8FgMLSVDHa+ZCVZchdYR9D9nWVuBoZsaKiMDp0162ZF6mRQGQtOZMtXZzlLslsNkP+O/ekV2Z5SieV2iGRDOlUe1SnWlkNpacLdUOkIgN9+hDVWbnfTsb3t6F8FRfdDzldSApXA7emrqQz+TD4f28ngjdWc+v4BdlFp0jE3erfKFk2wdb2m4g4ecwwS2dqv29DmOUF73zJLI4NuvHRDpZ6um7mFyXm8dFM5jdc8XnSM+KAlD+oLk+O6i45u5brpmBOEkd9Dv5WKAnr6PGN0NPOMgZ68KDekkdgG8ob00UOPZ/d/DFM1iTsnZme6VGFOA0BXjspIiKWTr56pynLmccapEGCFoPAZ8C9OzCLbIKC3heR0sohO8bw6c1RlySrJh1UVppIzFt9uHf1gNSXL2610pB3tGAFasPcj6oC+miB0tp2P7WRj/ibeHPF5YrlOWdqqI1AdtKNzI1AWJn06tqUdDYlka7/22OY+AUMWR9+MKRKzqMSTq5TjdOt41eED9zpU6unq8yqXahxvi44Rn/3IakuuOq0sL2SfjqoydN8ThlJhv/Ag/oiqmWcS8zjjVPQkHQ09jx66GDcYsoIeerde93eWxUhpwSnSqoyDVOlk40OOk/O0xTpV9VE/EHE3nN2K1+VRpdEZ0zpDOZU8brZSKll0ZabKEyNSok7THXRMRzt2SF/VVZYN52M7eWv4/3huy3ft4tOlo0xnjzkGPdnWrz22ubCI78OlqibVeKhDtVaXy9aVp4uXVUklW6oxuj06WprN/XVl68rLZB1Vadqjo5lnFGUbHQ0Gg8FgyAh6jrNMNiB03g+hiMclnbN8VZ3OvLqyZcNGl05RtwDwJdsaOieKwJvqzurcHC1uxrxOZaet5HaYVTptro4AtIB/dffVMR3t2BF9VSlAKmEy5XxsJ2ErbGdPl46p6IQxx+BCtvXrNrS5sFKL095ukmq4cJYfi/NSj9taXZcmLTpaUNAIA34mLnS309FFFq86xhOYecboqKrbYDAYDIYupvvvWQZq6zM2YXsxDOQ4Vy+Doz45r1y3Ks5Zhq5cFxnlomIqulWlqk4W0VmWKp3zzymqs2yV+LIfQK5TZxu2V0fA3rOsWF1fd9Bxc9sxSah099VsPB/bSdAfIifieE1tW3UEAgL6tcCgFvu7ki4ccwxRsrFft7HNVetgOV4eq3Tpdenc0Kkgj8UqVPWrvqdDR38Ycluw3/bYxnMnW3TUyeaFpENi5pnU5fZUHQ0Gg8Fg6EK6vbOsIBxVUmVtOidy2dvhzKPzjsiGha5cIX3XGQuykSHXK9cllaGykVSi6IxmVdVO8XR4scF0yKq42V3OsLbqKHJBFEX/CoAcEDlqWdqqR6boqJKr3e3YEX3VWV62nY8yoSKXyFZmfbUz+83fMbEeDzpaAvIjMK0OblkET38L9/wAlSFFJV045hiiZGu/bkeb64p2olJRN9bJValOPV1YLE+qsc5Zv3NMleWQ62urjlgQ9tv7ldX1gtpS4nfkdRcd092OZp4xOpp5xmAwGHoAsZcuZNnehN3+Mcw+IShphFwBGwKwPBdCkGxVxnCG6yxCN8+ELk6Xz2mAyMaQyuiw1PFeHTap0qQ6LG5lqQxznS2mUs1Zj9th1KGTSwDN20LL1tFCciA0DHwTIWcB5HxJ4ob/Ivt0lNNsTjtqBUlHX9UdwEw/H2WaKhSByeREAuy4eDxPjX/LvsskhY5+AcOaoTII66Kj80fF8L8i+G8vWJMDhRFFfpmOGnMMarK1X7dxnnFmS4V8SFIdDvm7bthQ5VEdSt34K+dR0R4dsWBVNayvAEtAcx4sGwDry6FiA5RvcGxqHxUg23TsiHb0NOb0lHnG6GjmGYPBC86N1mPfs8wBYchCLIvN2uQ/S18Q0O2dZUtzwZcHedFF6N4bYUkuzC+EkMrAwBHmhsoocTMmdAtvOd753c37IsmrKl4los7gVhm2XhxGOnGd5cqypbKddHaXKt6LjpFqCE7AvqMsligA4X4Q7mvHWY2tZfrWQP6bYLVkvo7hShABW36rCXxNifEqmXUyCPlHR/RVN8Ey/XxsB6+O/IQ/T3u4tQiNjhbQvwV2qIMFBfBxMQSB7wrgzZLWPPkCqoIpKu2kMcfgwK2/ZHq/buM842aOyyqmGvM6Al09qjkORVh7dWzJhbV9IBhoDRQW1BdDQ5HtRMttgWD0juZedVC5xn5ks610to4Rv61ffhMJL3noEHr6PGN0NPOMweCGzuGgCjcONEM6yVJn1+bS4Y9hXnvttViWxbnnnhsPa2pq4owzzqB3794UFxdzyCGHsGrVqoR8S5YsYebMmRQWFlJVVcWvf/1rQiHVM0gesKDZshehL5VCeRhOXQV9W9B7jVRGgkBvlKjS6LwfGhmVBopTJmcdUplutozKQJaLFSTaSAL94dDZOZb0J0i2g+Q8skry91SLLS86inyI9JIqcFQS7gOhQa1/LVtB454gcjNfx0g5hAdCpBJEWXraMSXt7asqY9pZlq4eWXBn2Z15PraDTwZ+y6peG1x19AHb1sPB6+HVUvi6wHaUJTSiG1005mQSGTHPxMi2ft3GecatO6rGKuefKo0KeQxziqOrV+U7UOVX1W9J8e3RsbZX1FGGFBH9qC+CDeWwqdj+W9kXfh5oP7aZ6Tqur4Alg4k/UqqrQ6at7ZgkRE+cZ4yOZp4xGNKFED3WwWFIMz24H3Wos+yjjz7izjvv5Be/+EVC+Hnnncezzz7Lo48+yptvvsny5cs5+OCD4/HhcJiZM2fS0tLCu+++y9y5c7nvvvu49NJL2y6E5B1o8dmPM/2nDM5eCZMapHnZad3pLE4VKkMm1WJXZ9Q486ryKGTQJZWzqRw2ToeK/F1OI1dvSXHyokhl4MtyyDae83sqI9urjvGCVd/lAi0IjoPGvUDkZraOvhqwQuBfCb7V6WlHJenqq6ny6Mro6vNxc3DR0xKwRw0csB7+WQkb/SQvIrwuRmLfO2nMyRQycZ5JSab063a0uW59q6rKkv6c6dzGPHlNLderW7PHynU7pDp53Q6/Fx2Virq0o7Bs59nPA+07t+TkmaRjrzooX2+PV+lsxyR6+jxjdMxYMmKeccPcQWRwowc7OgydTDccizrMWbZp0yaOPvpo/vGPf1BeXh4Pr6mp4Z///Cd/+ctf2G233Zg8eTL33nsv7777Lu+//z4AL730EvPnz+fBBx9k0qRJ7LPPPlx11VX8/e9/p6WlRVelZ4QFi/Lgzmo4YyVMrsd94o+h84bIk7ybN0WXJ0FAxW+VN0Mhr6yGzlHiJZ8bqQ6XmxwxnPaWbHD7gBzLTjOoELYqgx37QJG//TomVKxKLBmRwbH23mZp1TEAwq8WB4AciPSGSBnxO9vksuOf+eBbBzSBVQdE0tiOndBXlfUJUhvizjxynSp5030+toGdF02korFXa92Sjls0wRHr4OZ+0OBTyCDrqKMLx5yuJJPnGSC7+rXHNndGe13fymOXU1xVfFvKcuaRfVW6MGdcLH/EgoZCaMpvn47Kwp0FKNok5jCr65WYtaN0jJUlsB+rDOUkdk8ZAQgf5DVD5drWgtLRjgmVxD7NPJP42+iYEWT8PAMgRLdcpBoMhgzBy/iyuXuaZSgd5iw744wzmDlzJnvssUdC+CeffEIwGEwIHzNmDIMHD+a9994D4L333mPChAlUV1fH08yYMYPa2lq+/vprZX3Nzc3U1tYm/AGtk7tiAl+cC/+sgt8sh/5B1FfOnDiNBNUnjt+6Ra8ujy6tbCWr0qVIItsgqZxJsTQ6A1w2vFX1yUa0Kr/cJBZQlgNbl8O4UhhQCAELRhbD7MFw3DCozk+Djl7bERD+9Ooo8rHfxKlICxCpgOCW0DLOfnRUAOEhQB722Rp7zCcADbPsR0Z1Ow/K7RiwoCQAOb5klZXHqiP6qu58dArrzJNJ52M7mLJ4HCd+ONPe50fSMV/A+SvgyQpYE9vnKJWObjJ20ZjTlWT8PJMN/boNba4aszVJld91KngR11m//NcW3A5ZXTHUlnhLr4pLwGM7CiDiS/zdUTrGvgsf/DQEFg+xv4PtuBOWLYvwtTrKfhpi78UWz6spP2E+jJalI6HLm3kmOY3RMaPImHkmFcZhZshWuqGDpVviNr50U0cZdNAG/w8//DCffvopH330UVLcypUryc3NpaysLCG8urqalStXxtM4J5ZYfCxOxTXXXMMVV1yhFsg5MUuGxIfFsCAfzlwJfxgEYVU/kI0PlfdEN/ELRRqnxam7sqYqX44XyUnckjvFV4ksUqTRrdvlC5e68p1xMrkW7NAHwgK+qoHaYGva11fDf1eDz4KQaL+ObW5HKXhzdYyljxSAr9n+IXIBx8VFqwF89eBfDr4NEBwPLZPsxy1FMYgc8K+2C4tUQLAIaIZwf7BaIO8duzzVcdi5Ek4aAd/VwasrYVVTq15rmqAu5NCnI/qqylBWGfap6unq87EN1ObXs7xknVKOqXXQryW6gb/ueOl0VKV1/o7V14FjTleTTfNMQlim9us2zDOpxjoZt+pTHQ6nCKp6veT3ks4SULU6dTk6fBFJ0DbOM+nUUQDCR+Jm/MLRBgJKayEnaH/f1AvWVNovG2jJte+yK98IoQDUldjx9UVQ2GCHVa9KfjFBXBbLdjgWNNovNHBrxyShzTyTXH5P1TFDyLh5xgvdeNFqyHLkfunsq863ehrHb2bSQ8eVtDvLli5dyjnnnMPLL79Mfn5+uovXcskll3D++efHf9fW1jJo0KDkhJL1GbHsOzv+vgjmVsK3BbgbDJYU7hWllSjFuaXVGUxC+TOpWJUt5AxLld8LKjtK9VuOy7XgwIHwfR18vlGdNoztSGuvjvEfm9mOluIz1TFLOLYWtGxtO7qIALmQ/4LtGBOFIIodGSz7zrFwPwgPaC0v3M9ReHE0rD/4GiD3A/C1qLvwu2vhh02wvgXqQ63hvQIwpMj+/GIjNMvKysqofnvpqypUq0H5M1POx3Zw/+QX+L+tXrHvsnDo6Mfe0P+DYqjzKerQ6dgWOmrMyQCybZ5JCMu0fu1xnnE6XnTrZLd4Z5qEPFb0t2xDu4glJ9edognh0fneiqRIp5DRi47CSpEhRTvqouW5WVdkwulpwfL+9iOl/rAtW//lkNfUWljxJvBH54GV1fZLB5wFNhQlFr6hzP7zR6D3umi5KpkElNTaeeQhQ6mjmWf0aY2OXUrGzzMqjJPB0Nk4HShufU/laPEaZshsuvmYk3Zn2SeffMLq1avZeuut42HhcJj//ve/3Hrrrbz44ou0tLSwcePGhKsxq1atom/fvgD07duXDz/8MKHc2NtlYmlk8vLyyMvLS45wLlJVn8AP+dDsszfa/jZfk1buB0qrn1bPk5xOh87DJK8yYvGq8Gh65fASfbxLhICI2i6Rq4t9D42E4BhHFWHwrQH/CvCtt8sWARAF2I8XBuy9tvDbb2kUHh7ytQTMqoefesN7zS5+CQH+9eBbZX9aDdHwEPFjrmuqxAo1v1XtmPxVJVbrYkkRnlClD5q3h/BgCFe2ZmreFfvusjzsfcfyITgKrGaI9CG5cVSVW4nBkNxVWiKwpIEEBFATsu/m650Le/SFl3Ig8V1Omrqd4V76qtt56FXHrjof20lDTjMRSyTpWB6CX9TDfwYQd6J60tGLV6CzxpwuJBvnmYzs1x7b3BK2k0l+tC6WJOy34wLR8Vi1bpfzxKpY0wdqS+27nGJ3KxXVQ36TXSeWXXYokFh/c569x5hXQgFbzrxmfRpLQEGDfVdUTiiqT0x/EV2HOuR36ugPOw5bO+cZ2YegmkeE4ntCmAWrqmFd7+jjnaJV/7xmO94Xse8gC/ttB1pDIXjtq74I+BSOsoTsmvHCKWckldMnlsEZ3t3nGaNjRpFx84xh81Dd1dSefG54vauvuzgXZF2No6v7kqptu3Hbp91Ztvvuu/Pll18mhB1//PGMGTOGiy++mEGDBpGTk8Orr77KIYccAsC3337LkiVLmDJlCgBTpkzhT3/6E6tXr6aqqgqAl19+mZKSEsaOHdt2oVQGAq2fZSHoFYad6uxN/2OP+qktURKNgFSLazfjQl416KxjnR7R7027gtWoqNvCfnTPD75a+22J/lXgX2Onj0hXkuP4IFIZ3eB+gBQXXTRZDSAC2LfI+El0jCkWAjodB7TAkNXw0AAIqVYAjvKCUZ2tFrCaolH19vfAYrAUiyDfeods7WxHlb2pU0sV3iqMvf9YuE9iZHCkphJdX5D7jSSz20JVLsrJuhZ4fRUcOBrmRaDe+SKCNPVVz2XodOzK87G9KHQc3QiVIegb2yuxLTp6qS9GR4w5GUA2zjNAZvZrD20esWDp4FbnUQxfBCxhO1uEZTuZChptp0xes50+JL3QJEZLLoQDsKbK/u6Ud20fWvf5i8qkdNS5Dbya86Y+dseUzrFQYdftE613XuWEbKdSYYMdLlPQGH0M01mu/B1c21E1BejGb1nkhLyWfQdY3FEWzVBf6NDdUUlDga4gRYXSPCMfirb4T8Iq69PMM0ZHM8+kh2x9FLOtjqnNKbcrj49b3dniSMvG/mUwtIO0O8t69erF+PHjE8KKioro3bt3PPzEE0/k/PPPp6KigpKSEs466yymTJnCDjvsAMBee+3F2LFjOeaYY7j++utZuXIlv//97znjjDPad7VFnqyl8zsA5AgoiEQNYdlKdRoTzk9InvSd4bJF6zauuBkbcl7JqAlXk1LHSBkQvYvbCmPfkZWjlj++CFHUBdib/8YcbSo5dXoqdJxaB58XRR1lLjrG8YHIw76TTQCldnBouKbKEPg2ktimbWlHktXxYme6qiIbrG6Fq1Yjsh6aNoxnj+aJPX6kUjsWVheC/22CvWrgyfLkcpX1taGvJgnXHh274nyUyalPkcAuvjkQVOpYIKLva5AcAkn1q3TU0RljTobYcNk4z2Rkv27DudtYQEodgyVQ63j5qy+SuHm9quqkuqJrPBE7Xm46qnTd3PEJe44LCwjn2Wlacm1n0/oKtS6BsK1r0twp1+dhnpFPM9U8o1IloTlUjhG5sjTMMwmyO9Lrsjp/5wQVSsgyJFWiKCz2vTvMM0ZHM88YvJOJzpp0yJSJehkMPZgO2eA/FX/961/x+XwccsghNDc3M2PGDG677bZ4vN/v57nnnuO0005jypQpFBUVMWfOHK688sq2V6YyIrxM8M68cjlujh3ZENEZ9ao4nZGgyquS001HRx0iejeY1uPTSTpawM61trMsLToq6hU50Tu52qOjSFZDJZpKxNinZi2mLqiN7ZiUV5E0UgHNOwMRyP8vWDXq6p3yflUAU5sgV0CLvFjt5L6qzCuX09nnI0Df92DldrRsGkJIcauJQPBV3x95aYuPEuVI15gjy+6mh1tce9sxC8i4eSbT+3W65xkBYZ+kj1xONuqoqDcUoP06SuXKc4buMKQ6TMoCZNnkNG2YZ5w058OyAWBFYMDy1kdddcOWshgzzyTLIedVydnNdPRbsMVA+FGRPBPp1HnGkNlk69182YTzJQBeMe1i2Ew6xVn2xhtvJPzOz8/n73//O3//+9+1eYYMGcJ//vOfza9cNdHLcanyup1jKsO3LYtdZ1o3w0JOl8pqlvPJdajK1dFBOlrYd/OVhTTpulpHKZtcjUptIcUriWXoCB2ln+GBEBxtf8/5BnJq9EXE8kUs2KIJRjXB1wWKQntgX00ibwNM/Bv39Krmif4bkqIjlmBN0UYacqRng9Mx5sh0xZiTgWTFPJNp/bonnLuZrqNLEV5FUx6WTpxnGgpb7ygsrofK1d6qUJKt7dgT+mon6DikL1xxFPznQhfZupAunWcMmY9xzBiymZ7Ud0tLPSftkjvLOhXZUFBZozI6i1VlcOj6lapOXR6dwaGymFX1pzKEZANLNnpSldWROqaqp6t1dKBrHvkw6FTXFpROHeVqIokBsiyyvAL7UeSqIBQp3hpn+qqD/PWsr1rPonKFjG4ybM6Y40ZnjTmGZLzMM5nar3vCuZvpOtK+U0vnL7Hc6k/TPOMMzmtuTSrva6fy72gTyIVnWzv2hL7awTqWFkJ+LgaDwWDoTHqSo6yNeHhfYTfAeTXLaRjorFNnvDyZO61T2fMAyQaKs0w5jypeVZ5TFlkPOd4Zp8ovHGkyRUdnuZmko0IslVqyis5iZVG0eqRDR4UcwnFnWKSchDNebkZl05i+6o226qjK78yn01FFZ485BjXZ3K97wrmbqTpKossiyOLIYiXlFVGHVQfPM845rsExz9SW2JHOeJUOCYJ3h3bsCX21A3X0+yDgh8Wr4e/PYjAYDAZDx1FT4zlpz3CWqSw2reUm5XOmlY0LOV3su8pAkQ0FOZ/KGlbJ6GaQpNJRJUdX6piqfGeeWHhn6qgo2pldJYaqSguoGqJI0BE6OoOK7T3L8t+AvHcgNBREfrJtq7KlkwtzfO/pfVWmvTqq8KqjnKcrxhxDItnYr3vCuZvpOkpFaqITitT5OpIOTQfOM3F5fbChAirXQEkd1PVqfbupKrvrkJbN7dgT+moH6hhTZX0dvPSpokyDwWCwLMyjrobOpvs7yywSDQnnhK0711QGhNNKdeZVWbO6cuS6VcaNW5hsacqGUiodZX26WkdVmRmoowUICyJVEC5XV61atDjVHD2F1v0oO0pHBb6N4FsL/lVgBROzOw+BthjTV1PTXh1VcnrRMVacBUG5o3XGmGNIJlv7dU84d7NEx9g801Bkv3lUWHoRZXUSRLUg6HzhQAfNM3HVLPsvvwnKNtrfQ/5keeUiE+hG7dgT+mpH6BgREA5r6jYYDAaDoYvoGXuWOT+9Ln6dBkQMlYEh53GTQWWUqAwQZx5n2SqZZHnlcnTpMkDHgmAe5fWFbLFqNHtuCvLmiC9o8YcyUsfgltC0F1ghKHgWAotbk8t2rUocS1V/unWUVbLsDf4jpfYdZioVE+TOA6T96E1f9UB7ddTl8ahj0IJlOS7lOfOkqx29HpOehtd5JtP6dU84d7NIxw3lsHQQ+CIwZDGU1OpPPddTsSN1VBDxwfL+EPaDJex9L3XFKovpZu2YIG837asJ8pp5xmDILtrzVsmuxtxNZugiur+zTOUV0BkfqjzOdKpJXi5HPpedhoDO4FQZPbK8KmQjKZWOXsI6UcfxK4cxdllv/N8cww7kctCc3/Fj7xUZp6PAdjaJPBC5xPcBU9mWsniepqF06SiLboFvNfjXQLgRIn2w7yW11GueSDkQgkg10CIXppApVm8P6KueaKuOSOlQpJN1lKtUyd8ZY44hkWzt1z3h3M0SHQXQnGc7niKW7XhSiakbSlIOU2meZ2I/LQEFjVC+Ieow84E/FB2bFIT9EMyxH9dMqEeWNUvbMUHebtpXE+Q184zBkEimPyaYybK54XTsedVBCLLKIWjISLq/syyGbBioJnY5rco4SJVONj7kODmPzmCIhbsZK7o8Ktm8yO41XZp09AsflrCwAJ+w8Alfp+loCYfoOh2F7ThqHA6Rstb4SAmEe0tFNoO1KTG7s+hwUJLLjXbo6BOQJyBg2S/AFHkQHAXBcRBqsn/jh4ZZYIVRInJB9IJAAbAw8TgkyaWStxv31XbRUWOOTFeOOYZEsq1f94RzN9N1FPam+BFfdHP8aHxTPmwqhpygPV8Jy9643xdOVkU5ZHWQjsKC2FQdDsD6Ctvx1VDYulfZT8Nsh1nEn1iGP2LrFcyxHYLdqh2d8nTXvuqUx8wzBkN2ke2OI9lBlq1OP0PW0XOcZbIB4bYo1sWrjBJnHlU5zrxOY0JlKKgMHlUdznA5XqejzqDqSh1VdJSOAorCkB+BkjD0D8K0WnigDyzN0+sYGgbhPq1lWGFo3hmad0xMF/geCp5LVNdpDzZuAiu2Q2AH6FgVhONXw8t5sM4PYl9o7AtrcyFc4shTnKxjUltFXOJMX02mPTqq8nvRMRWdMeYY9GRbv+4J526m62jBhjL7EUxn3lXV9p8v0pq/fAMM/Nkhm1SU8KM/BmnSMeKz9yTzBeCH4dBYSHyfslie+sIUxyP2uzu1oyrM6KjWwcwzBkN68Xo3m1eHU1c71dpzF5nB0EH0DGeZ7LmARAPDK24GiFyfLq+zbpXh4yxDZ9C4pXczolSy6OTcTB0tS0riddztAB39ArZshGYLluTB+gAszoP5BVAbu/KtMdIivVrDrQj4mkEUg5UDuTmtWUr7QcmIxLyWD/oMgrxCmP1byC+F9eMgWARrauwNbZ20hKChKVqvsH/HRBMpdFyZA3+usjdZxg/11VDmh0n18E0BNMQWUV7aMUEJun1f9aSjG+3RMVV+Oc65yFDRFWOOIZFs7Nc94dzNMh190X9C2FFhf2uWvpVQGUlcO/gDUNYH/Dkwdios+hJy8qHesueT0iLYsAnC0YsgtQ0QiX5PmmdUMkry+iKQE7KdZs15tD4KbtpRn9dZt9HRzDMGQ7rpTIdSZ9yhlu13wRm6Fd3fWSYvMhWGQJMFzar3gjrHHpVBqCrP7cqaXJ6qLqeRIZcp1yXr5aKjVsaO0NGCylJYXZNCRxUdoOM2m2BlLizObY0TFmxQOcqk/P5CGDcExg6GfhXQt9xO5vdBZRn4ovmKciFXVsWC3HywfPb3X2/fuiBpCZJEcxAaohvsB0OwptZOvGETLF4N85fAl4uiix5FO4Qd3wW2U7ClALbbBG+VOOJTtSNSWHfuq0j5dDq60V4d5fzOep1lqOJ0+WXZO2LMMSTjYZ5JSpcJ/bonnLtZoGOOH7YcBDuNg0GV9vyypgbqoxdP+pRAfTNsNRx6FyXWZfkgJzr5WBZM2QsOjzrUwhG77GD0vTlCRJ1l0fzOeaa+yZ5nPvsB/rfITut5DPKgozJdN2tHo2MadTQYDF1HKuebudvL0IPo/s4yS/Fdmqhr/dDgdJbJCx85v2oiVxkTXvK5GUAqo8NSxHvQUStrB+hoAbnOnqXRsTGnmbAvjB8I+cKEfOG061gcfezyw1xF/hQ65vhhzh4we6eoPptpwFlWaxEFecnxBXlQVtz6e0i1I1LYzrS5r8Ljb0MwTFI7ijxo2jv6O/qWxE0+aPTBgBb7rjpZx2Qh0Ru+3bCvJqHT0Y3O1FFln3TmmGNQk639OoPPXcuCvBz7LqiWcGuc5ciXUGwW6ogFw6vh3INgRD8oyNWkbwMWEIheCIq9LNfv2DusMD8xvTzPHLQj3PQUvPN19LhLOob9sGSwvZdafE+yHt5XjY6KODPPGAyGzcE45AwZQvd3lgnNd3A3SlOdoyqjxM2Y0C28VYtgoQmTjSOhiNPpqDKSFDrG7JWcAPE7YPNzIE++bQo7PjdgO3GcBHx2eH7UUg+GWx/7EA55vqlawlcD17KpaB4PVzfyc+matOs4rsF+rEXbnJp29AFHTYfDd0lcaHQZlt0GJ+wJa2vglc9J6ifCD8GRJB3DHGE/jrnEuRBz66suMjjrS/ie5r5qRUDIDmw5eQgIOuLyonk66nx0o606OlEdQxVe7YbOGHMMiXiZZzyOwUnldqN5xvlURWyOCYXsMdbniOtTArtNgp3HQ10DvDMfQlGH2bC+0NQChXnw7c/w81r7Dqmf18KmJttR1KcE1tY67uDtgrk0AU07+i04alcYPwR1u3Y2FvQqgN8cBn9/Dp5937FeEa0ftY7tCeJ0s76qxOiorkOVxswzhmwhNjGlyzljnDwGQ7ei+zvLnDgnZN2VMmda2dBQGQUqg1HO7/ydSjaVoeIs15lGZYyk0tG5WPFD7xIYVm0/YlhZCgP62M6uqrLWq9OFeZCvcZb5rNa9UJxVbGpqdaKtr4PGZjtsTY39980SWLU+SH1hDfMr3uLl/h2go4DSsH1nWVvbceQAOHhqhjjKHAQC8Mvd4KPvoKaB5P6i0DFPQFHs0U0nur6aig7oq1YTWEHb4WeFwFcLoYGaeqJl+FcC0cdW/aujL2Po61HHtp6PbmxOX5Vl8zLmqOqXy3H+1rE57WjQ04bxqbvPMxb23WG9Cuy5ZfvR9mOGlmXPNzl+ez4oLbLTxSgtglLHJvGTpP0gY+y3HYSFfTFmbS0sXWPXNagS/v0hzH0FmuRH3jtgnmlvO1aWweRRirK7Est2ZE4bD899EFWjB/RVoyOZp6PB0BkY55bBYHCh+zvL5AndZRIuDUFBBFoCJBsDcnkyqkneaUSo8rkZNTp5dXl8foQ/gBVs1gjYSlEebDMKDpxiP/pRlB+9qp9GA6WooPX74CopUtibCm/YBFXvwP9eTo5v83FRJA/IvdtjO1oW7LONvejKRAb2gW23iN5dpjJcUXx3kqqvuuVx0t6+qsjjq4PCR7HfxCmgZZuos0xlYMcW4vVQ8Gw0TkCkwuEsS/f56EY7+6oANjjHmra2oyq+s8YcQyJtHJ/a3eZdPc+45ZGS9i6B3SfBHpNsx1hRvr0Xl5wv4THAtmLZd2f5ffYFn34VrVGzd7b3m3zpU1i4zL7zrKHZMcyl0NFn2Xt7+S17WMrxg8/n2BBfRMtqZzv6fbD3ZCgpbJfmHU5uIHoMVHtkdqO+mlCG0VGfz8wzBkNqjOPNALTrbZ7mDaAZS/d3lulQOAcKIvYja655QH11zjnJO40Ir3mcqMrT1RP9Xb/FjtRueyB9H7oEKxxM1jFq/P9iKJwww95MuMvumrLsN0lWlwMzofcAyHksug+Xi47O/H6f7fTLcfTg/Bwoje751dxipysOQVEYKophU6NjDxZn2dJnnxLYcayi3gzB54O9toY3/gehiCaRU0cnXvuqW55U5aXoq8piCoEwWA32b1+tQg6JSEk0TfQYWJtcZILNOx+9kMrYV4w5a1ONwCp5dXXH0nf0mGPwjryo9HL8Mnie0crrkKUoD6aOhTl72i9E6aoXWvl9MH6o7TALhmD5enjlM/uu3GHV9vy3eBV8s9QWv7QQth9jf85fYt/pVlYEu0yw56YBve09xdbV2unrm2D5Ovhike2My82xHWqrNkYdainaZOJwOGyXrjs+qRjez37hwBtfuiTK8r7aE87HrNXR0LOxrOxzGGyOvLqJINuOgSGZbOzLhgS6v7NMoL4C5tXgUOWxHGnkclW/VWlT1e2UQVeuw1jJ2biSlqphtFQNI2/Fd4nyAuXF9v5b+2+fnk2E04YF246H8R/BZz/ajqDqMnthUlFiL1xie6M1tdj7duX4YcvB9qKsb7m96FlXC40tifukBUPQr8B+U+V+OfYjOv/+0N7/pjHqTIvJ4PycOs52mGUy44bYe/csXE7qvmqB5bPvkIiliYho1/LSD9LcV1VpRADIaS3GVxP7IpXvzFMEIhd80TfG+dclp2lNDJbKCRj9jEe5nbupSKGjVjb5GLqNOanq1pWrS6OTQVdupowbmUZb5hlL+p5F84wyTVRef9QBdOq+MLxv5jzCblm2I2toNZw4A47ZHfKils/6OnjmA/vz0J2gfx/H3VTY85EFCfrKG+IfujPU1NsXbvx++H45PPwGfPht1I+vaZPtRrfOV5lIQZ4t45c/2cdHeOyrPit+/aK1a2VYX1XmcZbfDc5HrWzZpKPBkI2oHF9OZ4nqDiLjTDE4MQ62jKL7O8sgcWL2uvh0IueR+6/bxK+6wuc0VrxcjdPFO8KC5f0I9h5E4/DJ5C3/Li6vD3sBc9YBtpGfiVexcwOw9zbw7TLYYoBtbIfC8NVP9pX+/y2CI3aB71fAkEqobbDvDLjsAdhve3vPl3tesjd7Li6wHV2x/dZ2iMBBW8PCMju8uAD22Ao+WWgvAJqcG8Rb9uJlz60y8zg5Kci1j9nyp+w97oNWVA1HX60sgV0nwnZ9oX8JbB9oTbNivX1HxetfQF3U2aS9mpvmvqo6H0UOhAaAPzoiCT/k/g8IO8rIhUgp+Nba+awgWBGIFIIoAKvedphFetnlYYHVCHkfgH+Z/liCnb9xJog8DzpqC3HXMa1jjq5eZ9qOGnMMalK1earjngXzjE7H4nz45a5wwJSoAyhDx8/YGzZjVJTAcXs4E9gfnh19lp22wnFx5RdDYeSRcOd/7Eflm1okm9eyLwJtk2l7lSnYa2vbfvj13fadeXEcbZ+fY9+F1q8cKnrBhKH2I6+F+fbd3J//aM/lTfLLFrqoryal7YbnY1LabNTRYMhWhEheRMgOMtkZ4vzttgDR5TH0DLwuTk2/SCs9w1nmxGkU6Pqcm8FgSeFeUeXX1eelLkl+4Q+A5aO0LI/Dp9n7oDS12HdXHbWrvZjJWMPcsh/3+PR7ePOLALtMiPDDCovpvwizaCUg7H3PVm6wH2/ZYiCsrrGvehfkRh+fGQ2n72dvDJ2f0zqeWEDEgr4b7Q2gdx4HH3wLc/awX0xw81PQHGoVZfxQGDWg049A27FsXd7/EnLqgfXwftDWFWHvD7TTeHvftW0Gwgf/hRdD9ksXRg+wF4z5ubDvdvDkO/ZxtZqxnVOKlznESUNflfVwfvrWt34Gvk9MIsD2/kakU7LFdor51kNgEYhCaN4eWiZBzg+Q+2HyWkAWJ1xO611sOh3d8KJjOsacttDBY47BBVWby59ZOM/E0vgtGFhl759YUw8n7GU7VTL9IoOSdMts2Y6isw+0L+b8+0P77jUnE4fbd7plOrE7vWdNsZ1eS9fA0rXEj1m/cvtC3ORR9gUqCxKPp7DvaF/wMzz0Ory3IDEOOb0c55a2PfNMdzwf5bTdWUdDz6I7O4Pc9PKqs8rp1p2J6aq6My/Tj0N7jKN0v6XV0G66v7PM7UqXrv+p0ioWDEDy5C+kclOdH6ordU4ZkOLl8GhYJN/erOsXUwZzij96jolodBYYHgW5sNPIYvpRwXbbrMEvchg1vJaGFlvl/FwY1b81fTAMYwZGX0yAvShx07OqDPqU2gu7jxfaGz1vM8q+Er5iAyDshcGMre23gWYDfUrgTycBAn7+ApbNg6V5tpOsT4l9B960CeALwqJv4OdyGDcY9t8B3vvGfhRzTQ1M2RL++zEUPhJ9/HE80F9RYZr6qvY89IElcLeVZUdZrJhYQBCoASu6Z1Ckl/2Zaq4RJdiPgup0dM0sfW/rmKNLqxpzdDJ15phjSMZLm8vHPQvnGYT9uOGv9oXpv7AvTgTD0ccas2Ce6Uz8fvvCy9Rx8PzHrXty5gZs55MvS46Xzwezd7LfDv38R/CXJ+3uMGYg/OYwGFyJqwPH77e3Ddhrsj33BsOatI48cdI9z3TD87Hb6mgwZDNtdWQZh4g7qY6lfLyzYeFryBq6v7MM1AYC6Cd74fiU88gr+VSLazfjImnFn0I+OV30e0v1CDbsciyEg7RYefE3eGFl0frFgqlbb2Lq1rFd2hsBKMxTJ8+Rem5KPS17cVLeC07e2w5qCdmPZD74GggLBva2H/vMmoNmtT5uWl8C4xphSa59N9mhO9uPsvargJAFB/0GDrSib3SzbKcgln3n4Zoa+P5bqFsPVjP4lgB9pbrS1Fe1ZQiIlCVHO6tXFeXqWHNEqk69hLDYW/p0CwoX4jZ+Kh1VgiQUoMnjQYYEOnrMMajxckzl448mTwbOM/k59h2+u4y3L07ExuC8LLm40FVsNQKOmg4PvWE7ioZW29sNZM08A/H5c2Cl/eKEgjz7Tm5XR5nENqPsPUaXrWvdE66j+qrnMrL4fPRcRrbqaOi5dBdHR2c4wHqKk82pp07nTDoWqkdxDVlLz3CW6a66uaEyGoT0KaeT86uuzHmpz5lWlddhdEQKSlg/+7f0a/6JUf97kC1GF1AjLCqKM2jQyDSixy43B/bbzn4s84cV9lvPivO7VrQ2I+y75GpqYVQzfFNtP2LapzQxWY70aKXlOAb9e9v7zLyLw+njKD+JdvbVpDD5fMxtDVKdPjJudr6I7X1WCPiBULIqzuojhVKkxznu4E/2Y7Toz+KyVTw86VU2FG5KLjzVmKNS1G3M0dFBY44VBKsZrCYQxjGiJ1Wbxzo2tL3Nu3ieKcyFMw+A3SbazpJM2bw/Gwj44ejdYER/+62ZU7bUXwTKdEYPtF+us9fW9t1ibXH4FebBFcfAw2/CS59GAzugryaFdcPzMSmsG+gY209QkLg1hqGHYRwMBmCzHqs0fah9mMc+tXR/Z5nKiPAywTvzyuXI5emumMlX8kgR5zRsZDliSSz7MY7+ve1NdceMCzB44M0MDy6ktH9j/E4jgzcqS+Evp9j7qUzZkjYZ/5nCi5/CA8/BLyMwY3Lb3+RpWfYi6F0vxnEb+mo8nRyvOR9FdF89eZx2Vi16RZ02weTq4t+t1rvURC72Zv9OuSPEX9cWL9vpLEulo0PwMcvGM2bdBBoDLbw4+kPbWeZhzMnRjT1ex5xEMRK/p2nM8dVB7icQ+AGsBrBaIJTBb+/rUrzMMzrvb4bNM7FFq99n3wU1fog9No4faj+SZ2g7Ab+9L+dOY6MBWTjPgH1ncmWJfQd2W9cjlgVDq+z9VIG02UTxdHJ8Nzkfk+SX6892HS0oKYADdoDpv7CD166F2X9RpDd0f8xCffPJ9P27UrG5zi5zV9fmke39R8dm9Inu7yxTTfRyXKq8bn1GNibauth1ptUYFpZl3/E0eiBsOxq2Gg4D+tj7eFnWemC9+6bsBj2WfcX7qOnJj3ZmBZa9l1tsXKsopl0LsS2HWFBSQshnEekDwqojTAFhAup+m64wx/eWPiC2th1ihKJvtSyynWM5CyE00HbgWPUQHAM530C4GkQxiID99szQcAj8ZL+nIAxE8qF5bAiaA4RGQLgP+BrsMghBzvJmhM9HpAgEIWKv37QIt2+y0C0uSAwb0qzJ79WRr6szHWOOsF+WUPiU/eZRZ3Jfqv2Geipe5xnnMZfju3ieKcy33zK8y3h7nskLQO8S++JMtjp3Mo4sP45fL4HvlsPydW2/KAN216tvqSAkAoQpxU8tFs2tkaoM6QhLdW5hEaIChB8fDfjEJgR+wlRgiTAWTUSEvS+sRRNC5OGjEZ9ovZNYkE+Y1oNiiRb8bHSV06IFS7REvzuu4rQmaM0nn9/dYMzBsveIPWUf2Gfb1rVMZZGLXAaDwZ3u6OjIVlRtIb+oQPd21PaUq4vr6Wym8zQb3QNtIiIKQLgfpAgCQRNgEaSaIH5340uO0/6O4Bc1RC0p7CV4KeDTl+UIs6wII6rWsv92LWw3xt7vIxDbW8mQNupaAvj9gpxAdnoChveFyup8Nq4P0JsgQjTTGPJTGAh77isl1eX8dOyN1NYXEfCHeW39//GNNZ0l4YEdKnsCvYEdsR+btEh8O+UwbIdwSzQsF/slBDnEdbQGgMgDxkbDwoBP4Ns+SCQnt7XcstYqc/uuJZxbZA+kkWasaAUB1uCjyf5uraJF1BLmRWATKgQ+GsUvaGCQJ0dXSCwHfqZJbEFDTCAPY05ERIjwFUTHq0YxjgYK1QtDR5gQfoKiPyLV2AZYkQhV/30V39oNSWsjgwZBinkmtkoMI8ghRCUJ84BclvPTER6iNwL7GT6fqMUf7Y8+0YhFIxFRiCBfW1aIimh8mIBYTYRSLIJs2f9HztwvyPgh0X0QTWMbFGw5CP72KyguaF/+DQ0lPL3gen4OVxAhHx9NWGTCM3cWEQoRWFgE8dGMwEeEQiwiWISIRK9IWoQQBOLpYghyiJDnKDGML7r3qg6fVY+PBgD81OCnlhxrGYV8TIReWKKZfL6JOuEElhC0MNA+hz2uh/JYQAtDCItSfFaLpzEnJHpjEcbPBvxsJCgGEKGAAOu0eSIinyB9teOXj2YCrMSKRgiRQ26gifMO/JI9tmoxN4IYDIb00RbHU2fIoAp324etPfuydZWTLNMH7zTI1+2dZUsjN2NF3C27kkgjywLXkxsJszRyFWvCpWmqPYLfqsN5WS0setHqBdDj80U4dPJL/HbmPykrxCxeOpDmsJ8cX4TYXUXZRmEeHD+7kKe+GUx50VqW1q3nqe8HcdZW37K6Pp/HFw6iPM92AvksmFC5gcJAmI9WVjC6oo5f9NkIgRDhytWEAqMIhYs5r/9pCCE/0+vFdaK61Oyl8wr7tMiNpXfUYdF652Suo45cqY7YOiWQmDesPP3tNI3l5Y6g1nzNjGotQ0BLpJEw76N3luWwOnIOy8PenoNebj3FssCjLOcEloe38pQHoCDSTEicASxFEGBN5EyWh4d5zC3fiqBux5y61QxY+pIyp7lepWZ55EqsiNuGh/bGeRW+B9gkplEb2Qsv84CMcHqHCdt3QILtdLCaiIiCuDNNnT8Qr9eiBUGAwtxGLjj0HLbst9TMMwZXcgJQVpwcHhHw0k/9WNeYx9g+NTQE/QhhsXX1ev6zqD/DSuqZXL2e/JwmAkXriNT2gbBFhCKHk1l3+5KKzZhnEtIn1yHIIUxhPE4gEPEJyIqeg/a5ZKfT1ZRDmDyFXK11hkVZcrwQrOdIwIdFkFzrZ4KiMupkEoTxZkPaRAhYawmJ3tjmvtcRvLUNbOdgTvx36jxut6ElHv+q/HVsM/oscnPWepTLYDB0e9L9GKW506pj6QGPvXaIs2zZsmVcfPHFPP/88zQ0NDBy5EjuvfdettlmGwCEEFx22WX84x//YOPGjUydOpXbb7+dUaNGxctYv349Z511Fs8++yw+n49DDjmEm2++meJihaXmQthXCj69QQOw0SrloCGXYyFYm9Mr1Y1obcBHiPY53iYP/ZbLDryHorymdAlj0NCnQPdMXJZgwZSB69lhwHo2BQN8sbqclrCPHzYW89XaMm74aEvCjp3ZCwIh/JZgUzCHPYas4NfbfMPSukKae78NvT6FcAEiEoBQL7vwYAlEoovsluidkWCHhaTzMZIDEd1i3YqW43aCCbBCYGXW5BaggRyR4g4IXxD83hyucyt25emSKazOLQZfS+oMMayWhGMjfC3gb0N+DwRa1uNraUp6kifTpsJMmmeafFuAL/rskPDbfwqWh8c5HF5ej6hqgQ8QQFh+QBD2WeC8o8zXbPdHbZF+RDgfy2rhjN3/xajq5ZnXwIasoCXs44VF/fjt25Oobckh1x8GYVEQCLFFRR2frKpgy4oaztrqOxbXFvFz0bsw+HP7Kka4ECK50FJmfwo/NPex54BIfnS+cBAJ2OFJzyJizz2x80Pu+yLqKEo4L0XCR8KcI6zENPE4AUTA10F3wwlfVA97nhT4aWaEI4HumUcFlsC2Qauih8ljPmHZeYVF3DnoaT6WbymLHV/Vs6M2Ewd9R2WvDd7k6iIyaZ7xjHEOGLId+c4w06czm27ePml3lm3YsIGpU6ey66678vzzz1NZWcnChQspd9zBcf311/O3v/2NuXPnMmzYMP7whz8wY8YM5s+fT36+bfAfffTRrFixgpdffplgMMjxxx/PKaecwkMPPdQ2gQY9DgXuu1ILIaAp6pTKz+9yD6nPEvxytw8oynW/hd9gcGJZ0Cs3xE4D1jCl/xqe/mEgF/93qwRHGQIaQ62n/WtL+vLfn6sQwiIY8YG/2V5oWwBrE/IpaiTp9YjCj72jvgJhQcjDZiSBBrAiqdN1IuUNm6hYsF4KFbw5dhFrN5WTF/LTMOwbKPfm3BbBICIYhLy8tr1aMByCRbXRpzDD0P8FKClPma0t5NYuwwrbDrhMdJJBps4z0btPwnnRBa9EJBcRKoZgGUT8SXFJjmeZnNqoIxnIqQErCDl1dph8zlhBu39osRfluf4Iu4x/mYBHJ6/BILN8UwF/eGcitS12n28J+6N34+by0creAHy9rozTXtkWERtNAo3gbwQrOqYmzC+OO5TkK5fx+UVyvgggXNTq6AnUJ+YLRx3JsvMtdjFI+KPnUtAuP+jY/NPXAoFNxOc7X4stf0cQzie3bjwQsY9jsJRckU8LQfA32DLkr7KPnSsiOl609by27GPlb4aN422HphWBkvnJx9RrebHjmyRiDjO3fYZABm+EmXHzzObiZW3TEYte4+wwbA6m7xi6GEuI9PbC3/zmN7zzzju89dZbynghBP379+eCCy7gwgsvBKCmpobq6mruu+8+jjjiCL755hvGjh3LRx99FL9688ILL7Dvvvvy888/079//6Rym5ubaW5uvUOotraWQYMGMf7BqXxd36fVSANvd/DrVolOG00+cvKzSqrfct2KOqoLm/jPwa9TWZjldzwZupQ1DXns88SurG5Q7G+Spr6qfWpGVY6ODD8fETCsoZ7PX32dkmDiHQXn/WI8N48ciSUsIpbYvKeGPOhYEArzyWuvs2XdJpr8PrbbdRpflpamtR1Lv1jBiNs+UDZrGPgce8wuKWnHDt9pItPmGf/f9iGcJ91R2ZY2F6rIJK0Sy9jMNu9X1Mj5k7/hoFE/k+vPLOe0IXtoCvk46Old+HpdmZlndL+96ugsMzYmRKJ3dlnB1ogOnmdax6NYwoha/s1sxz/u9AXHjluUlKyuFsaVmnkmRmye6erjYTAYugkqx3lXOCWdcnRU/ZqLBLVAKd7mmbS/CP6ZZ55hm2224dBDD6WqqoqtttqKf/zjH/H4RYsWsXLlSvbYY494WGlpKdtvvz3vvfceAO+99x5lZWXxiQVgjz32wOfz8cEHHyjrveaaaygtLY3/DRo0CIDb9/iQLXvXtiZ0TuiOC5jxuNifilgeS/rutClUyHmElMdRX3leC6f+YiG9842jzLB5VOS3MKaipsP6ahyVcawqR0WGn49eEEDE55IpjTpaCG+D9ua0oySa/D0TyLR55rytv8WX8BgXbWtzi+iCOBqh+p7Gfl1V2MRtu3/EYaOXGEeZYbPI90fYe9gK+4eZZ9xl8zLPOOWzhP2YfeyxUrfHIdOuowArEv1Lo45uzrQMI9PmGYPBYDB0Pml3lv3444/x5/VffPFFTjvtNM4++2zmzp0LwMqVKwGorq5OyFddXR2PW7lyJVVVVQnxgUCAioqKeBqZSy65hJqamvjf0qVLASjPD7Y6DHTGhM4Ikw0MVXwM56pSNqJUV9+cRMO3rlrPw/u9zYkTfsCX9pYx9DT8lmC7vus6pK8qrzA7y5XTW1J8FpyPno36TtSxPBikT7PkSE93O2pUdInudDJtnpk1ain9ixozv18DW5TXcv8+77J19fqu3nEgcxHYFzkzpcNnMhbsMmC1/ZIcM890bx2dYXIZKplkfSQdI8JiU0vmvmcs0+YZg8FgMHQ+aZ+lIpEI22yzDVdffTUAW221FV999RV33HEHc+bMSXd1cfLy8siTH4PBnpvHlzfyREvvthXoa9Zv4mqFiO8PI6Sr8rKhIRtQOsMJ2K7fusS74AxxltQW8r81ZUnhaxvzWdvobHfBvsOXM9YcR7Bgi/I6LATCuSpWGdOx3x77qvIqshuyES+Hy4sAOY9KHtkAl9M686jkiQkvAOHD3gRaQziC8s0foSJobuPYshn4muvxC6tVpZYyaKlIbyVBe28aee2WSX6VTJtnyvKC7D9sDbd/OiF5vzJhRfcj86n7kBNL2POLv5HEffsirfuTqc6VNpy7R2/5E2MrajOrQTMEIeCnmiIeWjCUVQ35zBi6ghxfhElVG6gqaDbHTENRToiAL2LvexmjR88zUYHD0ZcORHLsfQkh+cU4/ubWPcgiOa0vKohE92bL2WR/9zepX6pjhew0KiK5iS+QsUJ2fTE5RMAuN5xPfF8xYUGohPiYE8mFUKG9f1lLeeq9FWP4WiBQlxiWs8m2raME65cQDK2C3A56YcJmkmnzjKEHIF/BMvt1GXoiXq/kdtL5kXZnWb9+/Rg7dmxC2JZbbsnjjz8OQN++fQFYtWoV/fr1i6dZtWoVkyZNiqdZvXp1QhmhUIj169fH83vGgrHFufh/PphwpA23a/mCaDcZ9zXbhoAFBKKbqObUQP5ayF1vGwhyO6uu/MlxBi3vLq/kov9upY6UDPGallyu2vF/5pgCQ0rqyfNHaAr51f1PdYy89lVdnLwo8dIOKqeWbvEky6GSG+yFQDjfNu7DeRAsjb7ZM7pocW48HMl1eYsn0LwaxMuA9Ja1jRNhyUFumqWV9ZFmzug7lFwRolekgWXrdoWNaX6j1toPgJeTmiOTTLZMm2csCwbRD5Yclrx5PxZJL8JwLUy+NSMallMLuRvsuaboJ8hf2a55pjSvxYyNGt5d3oezX9uGNY32xtzPL+rPVlUbOHrLRew+eBW1LTmU5rVQEAjbQ505jgCU57eQHwjTGAz0sHnGsueNpiporrBf3iF89pwTczLhs+eZ+MsFpPHAEkCkNS5WYcyxbkWiYZHkvPEydJvkSwpbkdZHOoXfLssXtJ1x8XIddVhhRZ2qA65rAJXXsZXbX9iOpk1PcdZu/5eRY1KmzTOGbo5qQjEvRzB0Z3R9O8P6fNqdZVOnTuXbb79NCPvuu+8YMmQIAMOGDaNv3768+uqr8cmktraWDz74gNNOOw2AKVOmsHHjRj755BMmT54MwGuvvUYkEmH77bdvs0wDyldTmNNCXVMxqa2faFzY7dAUOsqobs1rCfuNRQOetZ1mJBerNQgyq190DgIiwp4L4odJc3xcD49kizWF2vB2wW5O74JmCnJCNIWjx0R3dV2OA/e+qlqouC0wdKgWVLo7wdziY/mFBQ2DYNMIewETKk5eCLSH2NvUkuRy3pHmZcEgh3k9WHa6eivAQ2W7JeZJ99gRPVbOLtIWSTuDTJxnLCwI55LaA5DiSArFiSSEfQdjc287vG4UDHrUvisERbGZ0lCZgICQsIgIixxfxD40iuMjBPzfgqFxRxlAS9jHT7VF/Pnjsdzw0VhqW3Lond/MgOJGDhj5M4dtscQcayDHF6EwEGaDs/9163nGB/VDoHYMNFfadxgLp3BtQFgk74jiPPdj9owvOS6ePKAOlw+eoPViUYyE39IB0N5tLdej09vNYypYW1fBM5/vyi93+DflRZn3NEAmzjOGbkyGOQgMBoNN2p1l5513HjvuuCNXX301hx12GB9++CF33XUXd911FwCWZXHuuefyxz/+kVGjRsVftdy/f39mzZoF2Fdu9t57b04++WTuuOMOgsEgZ555JkcccYTyzTGpqCzeQN/SdVFnWYz2rCzcrLWoQeJrSS5PTq6yaXqIwS0E1LUE+Km2mHeWVfLhyt74LUGeP8yMoSs4YOQyZb6WsIc7M6LHcFR55hldXUVRIER5XpANTY67ptz6mte+KhR5VL+9XHzWyaBCtwCLhTcOgBV7R6/iuyX0KpQXob3eLpruMceZRiZ9Osrr30wgE+eZLfv/QE4gRDDkfAwz3W0enWN6fWdv/O3Ey7nbQxAC1jXm8e2GEj5eVcF/l1bRGPYzsqyO32w3n/7Fjco8iY/0g8BiZX1BQtjG5lx+qOlFdVGj7SwzUJwbon9xI8s2FbYGdtd5BmDDJFi3XfTCgjOhTuC2jsGqg9Ld5hlbx+9WDuX+d/fnnD3mZdx4lYnzjMFgMBg6l7Q7y7bddluefPJJLrnkEq688kqGDRvGTTfdxNFHHx1Pc9FFF1FfX88pp5zCxo0b2WmnnXjhhRfIz2+9ojtv3jzOPPNMdt99d3w+H4cccgh/+9vf2iVTQW4zI6uWsHDVENyNCHnil8OkeEuAr8ne1yF/JRT+DAXL7cc0dVdUnZ+SfRIRdJ8FjrBVaQr5Wd+Uy7JNhXyzvoQPVvThy7WlrKwvoDnsTzBKy/KCHDBiWfJFUwHz15UmlK2zJS1LMLy0vnscwzSQ649QnBvUJ9BdRU/RV5P6t7y4ULWPboHktiaQT8NUciEUV/jdFgAqVAK5oRNKddB0eZ31uF2NTyXD5uiozpcpDjInmTjPjKxaysDyVSxaM5D0zzONULASClZAwTLIW0v8zXhtPHcbgpm7oXabERAWFo0hP+uacvl2fQkfr+rNqvp8PljZm1X1BYSFFT8GX68tZcbQFfbLGByHWQhY15RnO3s8jE8+n2D6oMRHq3oyAUswqXIDH63U7N/YneYZgf3kgC9oP96vrTgVqcZgnXewO80zFkLAS1/vyK+mP0Jejout0gVk4jxjMBgMhs6lQ6zm/fbbj/32208bb1kWV155JVdeeaU2TUVFBQ899FBa5LEQjKxeAl+21RPlsMqssL0xak4t5Gy0jaW8dfYeMv5GO15lkDnR2TbR34triruFr6ymOYcnFw7kneWVLKopZmV9Pg3BAKHYo3Cqi68WLNzYi2DEIsefaFHXtQT4YEXvZINVYUvm+iMMLG7oCLWykoBPMKi4gf+tKW9bxhR9NSEdoLTTU/liXNoxSQadzS2nLVgB1W/AxgnQ3IfkvVZkYdzCnEK64eWuAK+kyqNa/aVyDG6+jpujUUeRafNMYW4jFUU1UWdZW4geXUvYd435miFQD4XLILApOt/U2I/46xxkTlKcuws39Mq8xmwHi2sL+deCIXy5toyfaotY1/j/7b15nFTlne//OVXdtfRWTXfTG7uIKAhqdESMmsnYVzHGaPR3JxjuuMRIFpnEG03USVySVzIanWsSczOYyc/tjibG5BoyiUqCICEqQUFQAWkWGxqkF6Cpqt67luf+caqqz3nqOaeqoZc6VZ/361WvOudZv59znqrv9zxn86IvUoR4UpjCzwhoWLltDhY3HEWVT392W1wA/3vraXjpwyk43OM317H4f/K5Y5hfHXL8Nhw1NKDSO5S5nIxT/UzpfqDxj8DRC/XbMOPGUHo0/4PlGb189DMCAxEP4iN5ruM4kmt+hjgcPoOMEMeRR6eYbdCAM6fshaYJCOXbyAwTYq6I/hyYop7EgUpYnxjzhAF3b+JNQvFUu2nNGOMBu1hG8V+5P1yKgZgb/iKrh7U6g1daGnH/mwvTt7UqfjJsn+1HA9jVVYEFNaHUthuKu7Bq7zQcCJept63UXrkniir/8NuWCh0NgEtTDLaTHKupOnJ7VvVUByZQLMvtZzqQSbMpDpQ3A2X7gIE6oOcU/dbMSIXh1kyVSLsfswahaYjBDaENz1dY11UJlrG7rCETdgcnqgMbKztVl16oYWiXGZcm0BA4iow/pKSfKerR3w5XHNLHra8N8B7R81xRjJWfaQmXIY70pyQ5iVhcw8+2zsXzzTMyX60EmLbP+0crcf+bC3HvBe+j2j+I7qFivNLSiA+6Aln/P1V6h1BNP5OZvPUzAHwdwJT/AgZrge7T9DdFDlYbrjY7mf/gbOoa05zoZ4bLXTD7PfiK+XsieQ7fCEOIIymMyTIAp9a2wucZ0N/W5IroZ+7d/YYrxUL6mXt3vz5ZpsUwfHAjNWZ3Ms8qcDcuq07aAWjr9aGr34Mp5enPU3ESkbgGkRSVbXAsgJ5IMb614Rxc0HAU0ID+SBF2dVVg57GAfiuNjCLJ447B73b2ZOO4cRJj1bIdYxyc6eS0ql87+5JpcjuqE+5aVL9dzf8RIIr0ybLBWqB3KjBYpz+Q2fh2MqVRw8YfLqrGNTO+i9lDh1EZ68XNx1djwcB+yQDVwYjKULurBeS0bK4cUPWXzYGR/c4d6aFVoePSBG6/7P/g9Q9PR7A/8TKZpJ/xdehXjZn8TOKK5CTj5Gf2h0oRGizGJF9u3fI0EroGPFh3sG44YQT/TwIafr9vCt7uqMLMil509PrQEi4bbkdGkTa/JoSy4ugJ25+PWJ7ky2s/E9MnuX1tekKkAhiqSpyoma2/GCb1gpnMfkY9kycbkF9+BgAWTt2dmkeIxoBwTwZTCHEivKKM5ArGschJ3IwUzGTZ5IqjmDT7t+jvcycmwyLQX8ltHDAWlTPFGZnqy3WEtJ6gJ1KMX+2aiS8u2ItyTxRu18T/sQqhm+kawW8pFtcsNZrWFW3uOFaJHUcrM8d+qraJknKP4qDuJMeq7X5Uxd928XI2v6+RljfW0aKAt0u/QrR8l/72r2iZfhXAQD0QKQcilYk3ZxpfDDBs/JBWjA2lC7ChdCEAAa8YwqK+XdjnbbTZEKqNIG8M1QYZiXAVqoOgTBtNPvgxWyovEwUaUF56BGLa/wUGvQCEfmImebmK3UQALPLGwM8cCJfi4bfn4R9Pa8XCycGc8DMjxV8UQ1lxFJ0n/P+k4XB3CQ4bH0g/gv+ntl4/InHNkdturJhe0QsNYvhEGVBYfgZCnwz3hIDSFqBqi36VWbQM6G9MPBagCBicBEQrEhNoVv/BVuJkI1WGOcXP6Ms15UFceOo2QABtx4F39gL7D2bRPSGEkJNHCE6YZaBgJsvKvf2YWfshDn802TrWsMIqzrAKxuSjS7tAzlA2LjT8bNsc/Hb3dMyvCeI7F2zHrEBv9iJPlMSEWFxo6IkUoa3HjwPdpdhxNIA9wXL8t+ntuPa0LKMXAewLlVtqzCoGy+bgkBNlWTOlrG/Ux+qE7seT/T26hwB3lz6BVr4nUbZYP6gZqNGvPBuYDAxVAnGvdFWA3thDk5daiJENshJpNfVkd6be7oAm00HXSGZozBYmv/mTy0xJcRSTSroRiojxH9dZ/nYFNDz3wSy8uGcarp1zEN9etANlqgn1sSDha6JxDcFBD/YGy7HneDk0TeCyGe2oKx3Iqo1tnZNwsLtkwv6fugY8iMRc8BXFs+ioMPAXxdIfx1OofgZI3E4d0W+19rUPl4979cmzSECfQOuvB2Il0nPP7PxAvvgZ3YbpVW2oDxzFgSPA/f8JHD4GTK20aIYQQsjow6sebSmYyTK3JnB6VRhvHp6sLiAHQVbLQHqgB5uykNJVMYWh75hw4XCPH229Pvz301pPaLJsKOZCz1ARPO44BmPpT6YZjOlvqGzv9aGzz4eD3aU43OPHkX4v9ofKcLTfi8GYK3WGeF51KOtgMyY0/QHSNhoBWAfPMiMMcj2u+Iiugst7xnCspsqq0q3aHInd4/p7jOgv6yg+nphAcwExPzAUAAZq9SvQhqoMV59BatjuQMVu48hTUZkMV4mQ+8qmvupgSShLcJIseyo8EVwx6zBWvnuaukCO+BlAv839V7tmYsnMNnxipG92FEBf1I3+qBtxoeFIvxdxocHnjqHaP4RyTwRFmoAA0BspwuEePw73+vHGR5OxN1iO4KAHreESBAc9iMRd8LjiOGtyMKvJsqG4hqd3nIJI3JVR41j9P80O9MDn8GeLjjYNpf3wuOIYiLr1BPqZdI2A/tzb0pbhduJe/bEAkQqgv2H4GZsxn6LhfPIzeluNlUdwvDuGn78EtB7Rc46FMphEyHjAh/ETQlBAk2XQEpM+ENCf0m1T1ipIsmnb9K060WbVhkWgKKBhS0cVlsxsG1HwJwTwk3fm4vldM1DmiaJ7KHlb2TDRuIbeSBFicW34zWE2tIzgLZ2dfV7sPl5hTrQLhu3IJl8yqq5kgAcxEqbtMQZjdSz2o23+ePweEdefN1XUC/gP6xmiSL9lc6ga6EvcVhMJJK4+y3ajjcQYI9kcoGTaSKqDHqv65lLGiTNijaYB59Qeh4bEy2RybVxL/cWFhuauCnxiaueI/Ex3pAi3/OkC7A2WQwigJ1KEuNBQ7Iqj3BPBKYEeNJQNYCDqwq6uAA73+jEUcyFu8TuJCg2dfV5lnszRfh/2Bsuz1jja/08aBK6dcxDFvAXTxCTfECq8EX2yjH7Gvg1jvntQ/3i79LdsCjcQ9wF9U/VHBSSvQIuWApZvjHSin9HLxeIanvozsKl5uGhoHG6oIIQQQrKhcCbLAJxdexwlRTH0RROyVSe9kutWJ/SM35DyjfVV7VutW5RZ11qH287ejcpsH8QsgH3BMvxq1wwc7ffhSL9Fn1axkIXGHUcDGIi64S/OMAklgFf2N+JYv8fcpo1GVRupPHk7q/aToY7HHUfTjHb12x8LmE+f8hF+3TwDu7sSk5hjMFbTOIn9mJu/RwH96rPE7ZtliavPoiX67ZqDtfrtNEOTgFgZEHcrjFcdfKjyVMuqI8dMBz+Zjv7s+rU+ZiX2zKjohdcdx0AsMQZyelwDL7c04n/Ma0FJpv93gw3vHanE5o4qRJNXdyXai8Rd6IsWoaPXPyI/E4eGnccq0TS9I+Mgm+wfQI1/EB8Gyybk/6m6ZAjnNxzjj0GiIjFJ2tnr0xPoZ+zXrcpoMUDrBSqaE+mafkJmqBIYnKy/PGCgDoj6AVGcOFHjRD+jrze3T0Of2w+BfsM2yNAlIeMBryojhMDZb48fMTMrenBO7XH7M5DJyyg0aT2J8QhSDtBUdTSY25TLW/UjgL3Bcjy94xT9gfmZEPptMQ+9NR9H+32jqnFvsBzbjkyybxNAe58PT28/BQJa1hqVsZgcQAqY6yoCV00T+PwZ+/GFM/fxOYUSDaUD+NzcA2M6VkdrP6aRQ79Hkx0aAMT159GUHgImvQM0vgJM/w0w7bdA/Tqgcrv+FkT3APSXiajEZZqSsjpClI/oVMvZ/BBUG8zckkD6riPWzK7swbl1XY4Z1+8drcQzO07BYDRDOCD0q5L/cqgW/2vzGfpE2WhpBLDmQD2Cg8UZ23vj8GR80FUxMf9PmsD1p+/H1LI+azsLlGKXwGUz2uhnRlOjlkh0DwD+diDwPlD3KjD9BWDGC0DjS0BgJ1ByECjqsxCZq35GF9tydDp2Hbkkff8RQgghOUBBXVnmcQn8f6e14s3DNRBWDl4VVBnT5BhBDsSsjj1VJ+nkGEYK1gQ0/GzbafAXxXDT/A/hdcdNJsSEhq4BD1pCZdjcXoUNh2qxqb0mc6AxQo2RuAsPv30G/v/LNqHaN5SmYTDmwr5QGR7dfAb2h8vM7WbQqNyGVgGmjf3Tyvtw+8d2odjNKCsNLXEr5hiO1dHajyrbc+X3mFmjAFxDgPeY/infBcCtXwEQqdSvPBtIXH0WLQFE8uqzbLAywHjkZRQpbwBZCCzqqP8XVYdaRI3HFcfFUzvxhtXzMYGcGtfRxP/7Rz1+fHvRDviLYhDQ/9eHYm4c7fdi57EKbO6oxs5jAbx7pBL90aJR/+2+d6QSj787B7efuws+dzxVNi702zybj1fgt7unY/3BOnQPSZNq4+hnbpr/IU/IqNCA+TUhuDShP96BfmbsNGoRoDgCFIeBklY9I+ZPvCygVP/un6K/jTNn/YxeRwgXjsX/B0pcG+HWwiOwlRBCCBl7CmqyDBpw8dRO1PgHcaTfpz7yk0+eyTGAHCMY2k5LUwVPqjpWgROAwagbP3xrHtYfrMOCmiA0TeBwTwm6h4rQEynCh6FyBAeLh8/yW8U0J6NRAO90VOGm1YuxuPEoNEMD/dEibOusxO7jFfrtrXKMlIVGW1vldAuNgzEXNN5+aYkwPitojMaqqaxVG8b+xmKsyvaq2hovjQCgxfSrz4p7AP8hvUDcoz/7bLBWf3nA4GT9gc5xD4affaY60LASLpe1O/qyqmPsA9AfbAsIWJ5WIFZowN/VH0OxK64/hN4B4zoWd+HZD2ZhX7AcC2qC6Oz3YcfRAHojRega8KA/6h4eCWP02xVCwy/ePxVvt1fj07M/QqV3CJ19PrzVXo3mrgp09PkwaLy1dQL8TLVvEJN8QxYNkLhqUskI/cwY+RmhX1lW/qG+Htiuv2EzWqK/mKZ7tv5ymliJPqmWE35meMJMQwSAO/O2I4QQQsaZwposA3Cs34veiCRbFVAB1kGMak7GKi5Q5VktWwRQ0bgLb3w02XylglVAJwercvkT1Cig4d3OSXj3yKQ0eWm2y31noVEZJI1AY3+0CD1DxZiU7fPdCowD4dLhlTEcqye7H03lc/T3eHIahf4wZ9cg4D0KVOwEhFs/iBmaNPw8mrSrz6wOVOyOsFQbxu4oZNj4vsmz0X7B5+E/2gLf0QPwd7WiuLcLYqAXEHyBRib0t0RKiTk+ruNxbcL9TCTuwtsd1Xi7ozpNXprtct/j8P80EHMjGtdQxIf7K3mns2r4JQ45+x8slc/R3+NJadSE/nIady/gPQKU74b+hmcP0Dct8XKaSmBgsu57LCfPxtbPJMtGxBQcjn8fXuxBhWsN3OIgAF5lRgghZBRIPn/wBG8LKKzJMgG8drBu+AH/STJtO/lkGRTLdm3IwU6mdPNJN/s45WTT5T5VtmTTxgRrHIi60S1PghIdAfO2yeH9mNE+Y58qW7JpI9c0ajHA1Q0Udw/fUhP36FebDdbot24OJK4+Sz3QOVvkgyC7M//6stBciPkrEDz1QgTnXARAQItGUDTQjeKuVuA3d42g/wJEAG+3VyMmv7mu0Ma1imw02jHBGrv6veiPFsFXxKvLVPRGiszb3Ip8GauO0SgAxICifqBiNyB265kxf+LETLnua3qn675nxLdvjtzPmPM0DIo5GMQcdMf+G7RYB4AvjKB/QgghJAPGl3aEw0AgkFW1gppdGIy7sOZAg75id+bQGKwYz9rZ+X+rdmRUgZBVeav+VGcXM9VR9ZVHGqNCQ3uvH/OqeTZSJiYSV5ap9lWO7UdL2/JorFralsoX+pVnviP6VQEVH+gHLtEy6eqzysRVAS5DI7I4uePMRvdMXYCB6umA5kJyo4siDyJlNYgU+0HsiQoNfztco69wXOeVRgHwBWlWCCA8WEw/4xSNEIC7Dyjp09PKAVQX674mUg50n6Z/RwL6J248XNAUyyPzM1blBTwQqAIhhBBy0oxC0FZQk2UfBsvwwbEK+4BHhezPk1gFKCMNZIyBmaq+VZuq9o15BaQxLjQEBz2KRkkk7kLIuG1yeD9mhcPHalbItmsxoDikf0r36xlxr34QMzA58eyzWiBSpl8ZYGmksWH5QEb/jntKMOQpkeqMxPjCpq3Xjz3BcvoZFQ7X2B0pRluvH1V+XlkmExfAwe6S4YQc3o9Z4fCxmhVpfiYCiAjgHdBP1ACJybNKYLAKiPuA3hm6vxFFgHDpnzRDMvsZdR36GUIIIblF4UyWCWBTW435Fkw5UMnGT8tn6FQTlqqgxRgnqMpYxQ1ywAVFurGuXLZQNGpAS8jwXC6SYjDmxvEBjzP2ozEvX8eqMe9ENCYTXQOAbwDwdSS0Fw0/0Dl56+ZQNRDzAfItgSZUwqyOAEkmth8J6L+3JBzXahyocSDqxistjZhfHeJPQ6I/5sbB7lJH7EdTXp6OVVPeCfsZAFps+A3PAkBgh377ZrxIv9o5fLp+xXPMp69b+hr6GUIIIc6jYCbLokLDq6311ie5AHUAZMTKz2dCriPXVbWlOtGWqR25boFp7OzzQQic6PP78paOPh9CVrfHyOTAfiyEsTomGrUo4Anrn+TVZzFf4uqzOn3ybHCyfkATL5aMsRNAsiUugNcO1kNA47i2w8EaQ0PF/KUoCA569Eli+pnsbHOsxrj+8gBAv9LZf1ivEC8CBhqA/gZ98mywRr+Nk36GEEKIgymYybKPukvwbmdluq9W+XFIy4A6gFEFGJkCIFW+3G9yXbZFZbOA2tYC1Lj+YB3aev1oLOsHSSCAP+9vSL+iMof3YyGM1THXCEB/I1o/4O4HfO2JOomrAQar9AObnlmGqwGMBsqohBGZo/1erD9Uq69wXOelxtUtDVi+YC+mVfSBJBDAutY6BAeLTWm5vB8LYayOm5+BAFxDQOkBoOSAnh4r0a9wHgoAfdMTV58ZXxxAP0MIIST3KYzJMgGsP1SL0FCxfbAhBwOqoAEwByNQpI0kEIFFnipfRg6M5PQC09jW68dvd0/D187ZnV3bBcCRfi9+s3u6OTHH96OyjsoeB49VZR2VPaOtUYsCnqD+KfsQmPRO4sqzWv3qs8HJQLQ0ceWZqmP+sCwRwHMfzERHry+39rlMPo5rmTHU2NHnw+r9Dfjign28ijlB16AH/7lzFoRx4+X4flTWUdnj4LGqrKOyZyw0FvXpPkYAmPSuPlEWLQW65+qPChgK6BNqqRcHGBvkD4sQQkhuUBCTZYd6/PjFe6fC5IBlxw9YBzKA/ZlDVSAik80ZSFVd1dlJq6BLZXshaRTAL3fNxJWnHMbsQA/jLQH84v1T9We5OWk/2tmWL2PVzrbx1FjUr39SV54VJ648qxm+dXOoUn+ZgO1zzwocAbSES/Gb3TPSJwxybZ+r6ubbuFbVHTU/o+Hft52GM2tCWNxwlH5GAC80T0dzV4Wz9qOdbfkyVu1sG1eN8WFf4z2qJ8aLh18cEAnoJ2oGahMvqJEFEEIIIRND3k+WRWLA/9p2Blq7Ew9/Vzl+lV9WnXWTMQY6qrN5cp7cv6o/+dsuoLGigDUe7inBvW8sxP++dDOqfEPpZ0ydSqb9oyAO6G9/hea4/WjZp1W+sT41nrhGVwTwHNc/ZXsAuPSHOQ8F9FtqQhUWjRU2/VEXHnhrIQ4l3wbopH1eCON6lDUeG/Di7r+ejf+zZCNmVPQ637+cBHEAb3w0Gann9AGO2Y+WfVrlG+tT44lpBABNAO4hwN0JeDuHG4iWANFy+hlCCCE5Q95Plq3Z34A/7JtqX8guGLEKFLI54ybn2Z2Bk/tQ5auWR3K20Yo80/j6R7X43sYFuOXMfXjvaCUaS/vwyemd9nVznPY+H/5z5yy0hksRF8AZ1WF89ezdyOpaH4fuRyV5NlaV5JTGxMOci3r1Bzn7ojadFi7P7pyJvySfVWaFY/a5YjnvxrVieYQaW0JluOlPF+C2s3dj4eQg6kv6UeEtwN+HAOJCc+x+VJJnY1VJzmkUQHGv/sFBm04JIY7H+AwDYTVrT0hukPf31fzb5nkYiiVkJn+PquDAuKw6WyaficsUQKjyM7Ul1zHaa2W73BY1AgBe3DMNn/39xfj262dhbfItqFkQiWnYFyzDW23V6BmauLnkSEzDQNSNaFwXX1cygC8u2IdLpnbi6IAXq/c3IBKz//lGYi4cG/A6ej8Wwlh1jEYN0B/mTGR+8f6piCd+q3m1z+U283Fcy22OQOO+4+W48y8fwzWrLsGqvdOy9jP5xEDMjcM9fkfvx0IYq47TSAjJX4QYniTjwz9JjpP3V5Z19PkAX2LF7swXDHlyUGHMk8uOBPnsm1VbdoGGyiZI+dQIaMBQTH/rUvPxCkSFhqJMB/oC+OnWuXhqxymIxl3442fXo8zTY19njHhy+2z8unkGTq8K40sL9+DMmhAmeYfw309rxWdOPYTvvH4WuoeK4S0atGwjEnchPFjs+P1YCGPVURpJGr2RYsD4krd82+eFMK5PUGM8rqFnqBgv7pmGz809AG9RPKPtcSTOVE7kMYIAPgyVYcexAACg3BPBmdUhRIWGWFzDlPLs3iotAESFy/H7sRDGqqM0EkJOnuRE1ERfvaVpahuEACfLSK6T95NlJicOxbIRDZmDihPpS/U/YNVWpqBIKMpSY3r7hvLNXRU43OPH9Io+C4N0goPF+OOHUxAa8MBfHJ3Q45ij/V7sDZZjb7Acrx2swxWzDmPF2bsxK9ADj0s/GDvS70VNifVk2ZF+L7oGvOpMB+7HQhirjtFIzBTCPqfG9PYN5feFytDW68dMu2eYCf2W+sffnYMFNUHMCvRgdmUPyj2RUZ08S52wN5iYXIkDOD7gwaa2Gjz01jzsD5cBANyaQKV3CHEAn5vbim+c+wGE0OAvitnaFYvrk2tWep22HwthrDpGIyEkP5AnynJlEo+QLBj1ybJYLIYHHngAzz77LNrb29HY2IibbroJ3/nOd6AlfhxCCNx///34xS9+gWAwiI9//ONYuXIl5syZk2qnq6sL//zP/4w//OEPcLlcuO666/CTn/wEZWVlIzNI5XRtAlllmobMjt6qDU2xLAcumQIOY4CiqkuNtvYFB4vx/K4ZuOO8XXC71H/MsbiGJ7fPxr6gPr6icRdeaWnAZMVk1Hl1xzDVcNY9JjT85WAtwkPF8LpjuHxmG3xFcUTjGtYcqEf3ULGFodY0dw0/4LY3UoTf7p6Oda11+OyphzCvOoQ1B+pxQcNRnFEVVm4HIYC32qrRG3HnzX5MpeXxWE2l5bLGHIB+xqYvjmvrdsZQY2jQg3/dNB8//uQWlBTHlOU3d1ThX14/C7u6AgAEil0CjaX9OL06hKllfZha3o9yTwRedxyaJnB6VRgBTwTH+r2Y5BvEoZ4SeN1x1JUMQIOAxx1HhScCTQMO9/hxqKcER/u92HCoFj1DRagrHcDhHj8uaDiKaNyF/eFS7A+V4cNwGQ73+PVnjSWICU2/bR/A87tm4C8HayEAPHDh+1jccFR58j8a1/BC8wy09/ryZj+m0vJ4rKbSclljDpBzfoaQkTLRk1F2V5SplgnJQUZ9suyHP/whVq5ciWeeeQbz58/H5s2bcfPNNyMQCOBrX/saAODhhx/GY489hmeeeQazZs3Cvffei8svvxw7d+6Ez6ffM7ls2TK0tbVhzZo1iEQiuPnmm7F8+XL88pe/HLlRqrNoshOX00ZaXq5jZ4eQ1lVtWrUtl7EKTKjR0KaGX7x/KtwugTNrQkqzW8MlePzdOUi+0SsSd+GHb89Xlp1S1ocKT8TQpYaWUCkGY26UF0cwFHOjwhtBb6QID7y5AMFBj7KdrDDo6Rrw4onts6FBQEDDcx/MRGlxVHkQ09Hrw0+3ngaRPBjKi/04gvIqu2So8cQ05gD0M1nYwXGtbmsMNf75QAMeeHMhmma0YUpZPzRNYOexAD7qKcHxAQ/+sG8KjvQPPxciEtdwoLsUB5Jv6zZ1AJQWx1DsiqM/6kZJcRS9ET1kKymKQQPgL47itEndKCmK4Z2OSTjS70Nc6D7JyMstU9K3m43G4KAHwQEPoAEr1p6H6+YcxNyqMMo9UWgQKHIJDMVcWNdahxf3TkM07jJvW2m7WJKj+zHr8iq7ZKiRfmY0/Qwh48nJXgHGiTCSB2hCjO5I/vSnP426ujo88cQTqbTrrrsOfr8fzz77LIQQaGxsxB133IE777wTABAKhVBXV4enn34aS5cuxQcffIB58+bh7bffxnnnnQcAWL16NT71qU/h0KFDaGxszGhHOBxGIBAAfnwl4CtWO2QjdmfU5PxMgYrxbJldGblsNn2PxL6RtlMIGmE13DVzkZPSaOxD0W4m80akMYOefN2PhTBWc1FjfwS4/SWEQiFUVFQoCowP9DPguM5hjRoEit367fL6C4a0PNEoTNXoZ0CNqjL0M2PiZyZ6exBCSL4wkv/VUX8b5oUXXoi1a9di9+7dAIB3330Xr7/+Oq644goAQEtLC9rb29HU1JSqEwgEsGjRImzcuBEAsHHjRlRWVqYcCwA0NTXB5XJh06ZNyn4HBwcRDodNnxFhdO5QLGtSWWMdVVk5AJADB7k9uQ1hWLYKNqzmSaygRgwbLn1GVWOyTW0cNCb6MfZp7Chf92MhjNV80DhG0M8gd/c5NUJAw1DMnXjJjGau42iNGgS0xBXL9DNpZamRfiYX/AwhhJBRY9Rvw7z77rsRDodx+umnw+12IxaL4Qc/+AGWLVsGAGhvbwcA1NXVmerV1dWl8trb21FbW2s2tKgIVVVVqTIyDz74IL773e9mZ6Sdk1bl25U15ie/hSLNqo1sAgwrGzPlUyM1ylCjfRvUaG9jjkA/g9zY59RIjSqo0b4NarS3MUdwhJ8hhBAypoz6lWUvvPACnnvuOfzyl7/EO++8g2eeeQb/9m//hmeeeWa0uzJxzz33IBQKpT4HDx4czpSdudHxqxyzyqELxbfqjJkxmLAqo6ojp6kCEGNZK03yOjWmty9/UyM1ynXkNGrMfGA1jtDP2LTPcW1Oo0ZqNK5TY3r78ncuaZxActLPEEIIGVdG/cqyb37zm7j77ruxdOlSAMCCBQtw4MABPPjgg7jxxhtRX18PAOjo6EBDQ0OqXkdHB84++2wAQH19PTo7O03tRqNRdHV1perLeL1eeL3e9Ayjg5a/VcgOWw4sVGfLjHXkAEGDuk0N9oFNpuBIDnCMfVEjNRrTqTG9X7lNasxeYw5APwOO62R5aqRGYz41qnGaxhwg5/wMIYSQcWfUryzr6+uDy2Vu1u12Ix7XH3Y7a9Ys1NfXY+3atan8cDiMTZs2YfHixQCAxYsXIxgMYsuWLaky69atQzwex6JFi0ZmkNFxG527XXm7NAFzACAHJ3bLVkGDsayVvfK33DY1mqFGalT1bVymRnt77TROMPQzNssc1/b2UiM1ylBjbmqcYHLOzxBCCBl3Rv3Ksquuugo/+MEPMH36dMyfPx9bt27Fo48+ii984QsAAE3TcPvtt+P73/8+5syZk3rVcmNjI6655hoAwBlnnIElS5bg1ltvxeOPP45IJIIVK1Zg6dKlWb05Rol8xgtQO2UhLWswBw2aohxgHzBYOX9V23KAYmernE6N6XYZ61Cj2T5VW9RIjXa25gj0M4q2OK6p0c5WOZ0a0+0y1qFGs32qtuhnJsbPEEIIGTdGfbLspz/9Ke6991589atfRWdnJxobG/GlL30J9913X6rMt771LfT29mL58uUIBoO46KKLsHr1avh8vlSZ5557DitWrMCll14Kl8uF6667Do899tiJGyY7/EzBhrGOnK46g2cVCKjyrJatggtVYGIV7FAjNco2Z9Jhl0eN6X0XusYcgH4mizyO6/S+qZEa5fLUaC6fSYddHv3M+PgZQggh44YmhFC5R8cTDocRCASAH18J+ItPvCHZmau2VraOXa4rBwjJNGOwkClwGI3AghrNedm2papHjfZtUmP2duWixv4IcPtLCIVCqKioyNKI/IV+ZgRQozkv27ZU9ajRvk1qzN6uXNRIP2Mi6We4PQghZHQYyf/qqD+zLKcRFsvGNCHl252lU52xk9uR0aRvq76M/cn2CkW+nCcvq2yjxuE8akyHGtPboUaSiXzc54UwrqnR3jZqHM6jxnToZwghhOQh+T9ZJmDt4GUnbXToGsyOXq5vFawYgw6r4ETVnpUdVvYYv6nRum1qpEZqHH2NxEwh7HNqtG6bGqmRGulnCCGE5B2j/syynCPp3I0knbYx0FAFInKAAdgHGXK6sb6xT7kPVSCjKcpbtaMKLKjRXAaKdFkLpHLUSI3UaG6HqKGfoUZqpEa5b2pMr08/QwghxEHk/2SZjOzYZQefTV15ORNyHasgQdWXVVCjakeuS41qqNEaaqTGTBpJZvJtnxfCuKZGalRBjdbQzxBCCMlz8v82TMDseDXDt+yQNaQ7cTk/2Z4cEEDKk9s31pHzjO0KKQ02ecZ2qNGcR43p9srtU6O6b2q0zsv2IKoQyed9XgjjmhqpUQU1mvPoZwghhBQQhXFlmVUgYOeQR5qnCgSSgYMqSFHZpAoiMuXJ+dSotiHbPGqkRmq0ziPW5Os+L4RxTY3Wtp5oHjVSI/0MIYQQh5P/V5YJw0eT0lXLxnXjt/GjQhU0AObAQWVbMuhQ5avstdJCjdQot0GN1nZS48lpJGYKYZ9To7p/alRDjdRIP0MIIcTh5P+VZUZHb3TESUduXFZ9y/lJjHXltmWMbarsyqaeygZjW3Ido92yBmo0t2VsnxqpkRozayRm6GfSNVCjuS1j+9RIjdRIP0MIISTnyf/JMhkr52wXPKjSVYGEvKxq28qebNazCV6M+dRIjVb9USM1ZrOerUZiphD2OTVSIzVSo/FbZU826/QzhBBCcpTCvA1T5bSN35qhjhHZiRuDE2O+cV1OM7ZpF7gY6xttUQUt1KjunxrTbaRGdR8qO4x9G+tTI5EphH1Ojer+qTHdRmpU96Gyw9i3sT41EkIIIRNO/k+WaTA7drugQOXcjeXkZWM9Y76xP7ktuazctowcrMh2G/ujRmqU7aBGahwLjcRMIexzaqRGaqRG+hlCCCEFRP5PlgHpDtvo1FXBhFVwYeX4rc6ICcO6HHBYBQR25e3qUiM1quqo7KJGalTZOlKNxEwh7HNqpEZVHZVd1EiNKlvpZwghhDiIwpgsUzlfOUAwOn9VQGJ04sZ8VSBiFXiobLIKIOS+5KBCVUfVBzWm51EjNar6N9pJjZk1EjOFsM+pcbg8NVIjQI2q/o120s8QQghxMIUxWSY7bWPwAAw7cE1R1lhG5cBVgYbxWxXMyO3KAYSVnVb9Gu2lRmqkxuEy8jc1jp5GYqYQ9jk1UqOxDDVSoxX0M4QQQvKA/H8bZtLxyg44maYKLIz5kPLlduW2VQGAJtVRBQOyHar+VCTTqdGcRo3mtqkxvR41ppfPRiNJh36GGqmRGlX1qDG9PP0MIYQQh5D/V5bZBQjJdGMgoXLscrqxPVWgIrdtXM42mLBLl6FGaqRGajS2bVweC43ETCHsc2qkRmqkRmPbxmX6GUIIIXlI/k+WGdGkbyNCyjOuqwIOOTCQ0zUpza6OJtWRzxrKdhrbkbVQozmPGqmRGtV9nqhGYk8+7vNCGNfUmF7XmEeN1Eg/QwghpMAorMky2cEbnXK2Djrp5DXFurF9OYhQBROqgMFoi1xHDjxUUGN2UONw+9RIjSejkZgphH1OjdlBjcPtUyM10s8QQghxGPn/zDLA7JiFlJ5N3SRy0CCny33IgYHcnypdXrez0dgPNdpDjfZ9UCM1ZmMjD2Ssyfd9XgjjmhozQ432fVAj/QwhhJC8If8ny4wBgCqogCHdqq7qLJ0qL9mHnGZFpkBEZY8qSKHGzG3K+dRIjTLUqLZH1kjSyfd9XgjjmhrT7Va1KedTIzXK0M8QQgjJEwrjNkzZ8WqG76RTNyIUZVVtWrVrlaZJn2SaVXAi26QKbORvVT41UiM1pqdR44lpJGryeZ8XwrimRmq0s9tYV9WuVRo10s8QQghxLPl/ZVnSYScdsCpYkMvI+Uns2rFz8HZ9Z1tHDjbk4IIaqdEINWZvr6rtTHUKXSMxUwj7nBrVZazsoMbs7VW1nakONea/RkIIIWSCKYwry4xo0rddGSOqQMWqDQFz8GAMCpIf+ezaSGzJVJ8arfOoMb0sNVJjJltGUp8Uxj6nRus8akwvS43UmMkW+hlCCCE5Rv5fWZbEzgGrnH8S2aHLZ+A06RtSvqovq6BCVV7Vv509VlCjfZvUqO6LGjP3b2ePFU7WSKzJ131eCOOaGtX1qJEa6WcIIYQUKPl/ZZkqQEg6czkAkMuo2pLPwCW/5Ty5jLxuDCzktuX+VXXkvlX2UyM1yn1To3qdGrPXSNLJ931eCOOaGqmRGs316GcIIYQUOCOeLNuwYQOuuuoqNDY2QtM0rFq1ypQvhMB9992HhoYG+P1+NDU1Yc+ePaYyXV1dWLZsGSoqKlBZWYlbbrkFPT09pjLvvfceLr74Yvh8PkybNg0PP/zwyNUBZicNmAOMTE7ZGJBYlZXP3FkFHFZ2yWfj5LKqwMeqT2pUQ432/RnrUCM1ZtI4DtDPSEz0Pi+EcU2N1JjMp8aJ1zgOOM7PEEIIGXdGPFnW29uLs846Cz/72c+U+Q8//DAee+wxPP7449i0aRNKS0tx+eWXY2BgIFVm2bJl2LFjB9asWYM//vGP2LBhA5YvX57KD4fDuOyyyzBjxgxs2bIFjzzyCB544AH8x3/8xwlIhDqoMDru5LpAutNWBQjC8LELGmSE4qOy09i3MdiQbVX1T43UKNenRjXUeGIaxwH6GeTWPi+EcU2N5r6pMb0+Naqhnxk/P0MIIWRc0YQQJ+yWNE3D7373O1xzzTUAACEEGhsbcccdd+DOO+8EAIRCIdTV1eHpp5/G0qVL8cEHH2DevHl4++23cd555wEAVq9ejU996lM4dOgQGhsbsXLlSnz7299Ge3s7PB4PAODuu+/GqlWrsGvXrqxsC4fDCAQCwI+vBHzFaoevChqMeVZBglVAku2yVfvGPWGVp0qXg6Fs6lnZYJVHjZnryPaobKJGalTVs7LBKm+iNPZHgNtfQigUQkVFhYWxowv9jGKd49q+fWq0TqdGdT0rG6zyqDFzHdkelU30MxlJ+pnx3B6EEJLPjOR/dVSfWdbS0oL29nY0NTWl0gKBABYtWoSNGzcCADZu3IjKysqUYwGApqYmuFwubNq0KVXmkksuSTkWALj88svR3NyM48ePK/seHBxEOBw2fVIYnboRDdZBg1W6VZ5AepCQDGZUdVQ2aTDbpMqDlKdJ39SotiHbPGqkRtkeakzPm0DoZ8BxrWqDGq1tpUbreiobss2jRvqZ8fYzhBBCxpVRnSxrb28HANTV1ZnS6+rqUnnt7e2ora015RcVFaGqqspURtWGsQ+ZBx98EIFAIPWZNm2aniGgdvbCYtm4bvw2flRYBSdWwUwyLRl0qPJV9lppoUZqlNugRms7qfHkNE4g9DMW6ao+kmkc12qbqDG9jqp/alRDjfQz4+lnCCGEjDt58zbMe+65B6FQKPU5ePCgnmE8o2V02kZHbswzrstn7+QzY3ZBCaQ8Y3/Jsqp25Xoqm1Rn8qiRGq1socZ0W6nx5DQWKPQzoEZqpEbZHhXUSD9zglj6GUIIIeNO0Wg2Vl9fDwDo6OhAQ0NDKr2jowNnn312qkxnZ6epXjQaRVdXV6p+fX09Ojo6TGWS68kyMl6vF16vN7ORqoABMDt3OU+VbgwsrJZVbVvZk826qh8V1EiN1EiNxm+VPdmsZ6txHKGfQW7sc2qkRmqkRuO3yp5s1ulnTGTtZwghhIw5o3pl2axZs1BfX4+1a9em0sLhMDZt2oTFixcDABYvXoxgMIgtW7akyqxbtw7xeByLFi1KldmwYQMikUiqzJo1azB37lxMmjRpZEYJw8fKIQvpWzPUMSI7cWNwYsw3rstpxjbtAhdjfaMtqqCFGtX9U2O6jdSo7kNlh7FvY31qnFDoZxTtcFxTIzVSY75pnEBy0s8QQggZd0Y8WdbT04Nt27Zh27ZtAPSHYG7btg2tra3QNA233347vv/97+O//uu/8P777+OGG25AY2Nj6g0zZ5xxBpYsWYJbb70Vb731Ft544w2sWLECS5cuRWNjIwDg85//PDweD2655Rbs2LEDv/71r/GTn/wE3/jGN0auUIPZsdsFBSrnbiwnLxvrGfON/cltyWXltmXkYEW229gfNVKjbAc1UuNYaBxj6GeQe/ucGqmRGqmRfmbi/AwhhJBxZ8S3YW7evBmf/OQnU+vJP/wbb7wRTz/9NL71rW+ht7cXy5cvRzAYxEUXXYTVq1fD5/Ol6jz33HNYsWIFLr30UrhcLlx33XV47LHHUvmBQAB//vOfcdttt+Hcc89FTU0N7rvvPixfvvzEVMrO1xgIqIIJq+DCyonLZ8SMy6q6mdqS28lkr8pmaqRGaqTGsdQ4htDPZGif4zq7tuR2qFFtMzVSY65qHEMc6WcIIYSMK5oQQmQu5jzC4TACgQDw4ysBf7G6UNIxG7eAnZO3cuLGduRvILsAIlM5KPKyCSyoMd0OaqRGasxcDoq8vgjwP19CKBRCRUWFRaXCgX5GYZuxHys7qJEaqTFzOSjyCkEj/YyJpJ/h9iCEkNFhJP+refM2TFuE4lt25MmPXNZYRk5LltMMZeTvTEGNJpWTbVD1KfdrtJcaqZEah8vI39Q4ehqJmULY59RIjcYy1EiNVtDPEEIIyQNG9W2YOUnS8coOOJmmCiyM+ZDy5XbltlUBgCbVUQUDsh2q/lQk06nRnEaN5rapMb0eNaaXz0YjSYd+hhqpkRpV9agxvTz9DCGEEIeQ/1eW2QUIyXRjIKFy7HK6sT1VoCK3bVzONpiwS5ehRmqkRmo0tm1cHguNxEwh7HNqpEZqpEZj28Zl+hlCCCF5SP5PlhnRpG8jQsozrqsCDjkwkNM1Kc2ujibVkc8aynYa25G1UKM5jxqpkRrVfZ6oRmJPPu7zQhjX1Jhe15hHjdRIP0MIIaTAKKzJMtnBG51ytg466eQ1xbqxfTmIUAUTqoDBaItcRw48VFBjdlDjcPvUSI0no5GYKYR9To3ZQY3D7VMjNdLPEEIIcRj5/8wywOyYhZSeTd0kctAgp8t9yIGB3J8qXV63s9HYDzXaQ432fVAjNWZjIw9krMn3fV4I45oaM0ON9n1QI/0MIYSQvCH/J8uMAYAqqIAh3aqu6iydKi/Zh5xmRaZARGWPKkihxsxtyvnUSI0y1Ki2R9ZI0sn3fV4I45oa0+1WtSnnUyM1ytDPEEIIyRPydrJMiIR3HoikO+60wlAHBiNx2iNpP5t8K5vs6lMjNWYDNVLjiWociOhZyf/XAod+Jsv2qDGzndnkWeVTo7osNTpTI/2MieR2CIfDE2wJIYTkB8n/02z8TN5Olh07dkxfuOfPE2sIIYTkGd3d3QgEAhNtxoRDP0MIIWMD/YxOd3c3AGDatGkTbAkhhOQX2fiZvJ0sq6qqAgC0trY6wtmGw2FMmzYNBw8eREVFxUSbkxVOs9lp9gK0eTxwmr3AxNkshEB3dzcaGxvHrc9cxml+BnDeeHeavYDzbHaavYDzbHaavQD9TK7Q2NiInTt3Yt68eY4ZPxzvY4/T7AWcZ7PT7AWcZ7MT/EzeTpa5XPqLPgOBgCMGS5KKigpH2Qs4z2an2QvQ5vHAafYCE2OzUyaFxgOn+hnAeePdafYCzrPZafYCzrPZafYC9DMTjcvlwpQpUwA4b/w4zV7AeTY7zV7AeTY7zV7AeTbnsp9xjbEdhBBCCCGEEEIIIYQ4Bk6WEUIIIYQQQgghhBCSIG8ny7xeL+6//354vd6JNiUrnGYv4DybnWYvQJvHA6fZCzjT5nzEifvBaTY7zV7AeTY7zV7AeTY7zV7AmTbnK07bF06zF3CezU6zF3CezU6zF3CezU6wVxN8NzMhhBBCCCGEEEIIIQDy+MoyQgghhBBCCCGEEEJGCifLCCGEEEIIIYQQQghJwMkyQgghhBBCCCGEEEIScLKMEEIIIYQQQgghhJAEnCwjhBBCCCGEEEIIISRBXk6W/exnP8PMmTPh8/mwaNEivPXWWxNix4MPPoi/+7u/Q3l5OWpra3HNNdegubnZVObv//7voWma6fPlL3/ZVKa1tRVXXnklSkpKUFtbi29+85uIRqNjYvMDDzyQZs/pp5+eyh8YGMBtt92G6upqlJWV4brrrkNHR8eE2Ttz5sw0ezVNw2233QYgN7bvhg0bcNVVV6GxsRGapmHVqlWmfCEE7rvvPjQ0NMDv96OpqQl79uwxlenq6sKyZctQUVGByspK3HLLLejp6TGVee+993DxxRfD5/Nh2rRpePjhh8fE5kgkgrvuugsLFixAaWkpGhsbccMNN+Dw4cOmNlT75qGHHhoTmzNt45tuuinNliVLlpjK5NI2BqAc15qm4ZFHHkmVGc9tTNKhrzkxnOZngNz3NfQz9DMnYjP9TO5DP3PiOM3X5LqfAZzna5zmZzLZDOSer8l7PyPyjOeff154PB7x5JNPih07dohbb71VVFZWio6OjnG35fLLLxdPPfWU2L59u9i2bZv41Kc+JaZPny56enpSZT7xiU+IW2+9VbS1taU+oVAolR+NRsWZZ54pmpqaxNatW8XLL78sampqxD333DMmNt9///1i/vz5JnuOHDmSyv/yl78spk2bJtauXSs2b94sLrjgAnHhhRdOmL2dnZ0mW9esWSMAiNdee00IkRvb9+WXXxbf/va3xYsvvigAiN/97nem/IceekgEAgGxatUq8e6774rPfOYzYtasWaK/vz9VZsmSJeKss84Sf/vb38Rf//pXceqpp4rrr78+lR8KhURdXZ1YtmyZ2L59u/jVr34l/H6/+PnPfz7qNgeDQdHU1CR+/etfi127domNGzeK888/X5x77rmmNmbMmCG+973vmba9ceyPps2ZtvGNN94olixZYrKlq6vLVCaXtrEQwmRrW1ubePLJJ4WmaWLfvn2pMuO5jYkZ+poTx2l+Rojc9zX0M/QzJ2Iz/UxuQz9zcjjN1+S6nxHCeb7GaX4mk81C5J6vyXc/k3eTZeeff7647bbbUuuxWEw0NjaKBx98cAKt0uns7BQAxF/+8pdU2ic+8Qnx9a9/3bLOyy+/LFwul2hvb0+lrVy5UlRUVIjBwcFRt/H+++8XZ511ljIvGAyK4uJi8Zvf/CaV9sEHHwgAYuPGjRNir8zXv/51MXv2bBGPx4UQubd95T+ReDwu6uvrxSOPPJJKCwaDwuv1il/96ldCCCF27twpAIi33347VeaVV14RmqaJjz76SAghxL//+7+LSZMmmWy+6667xNy5c0fdZhVvvfWWACAOHDiQSpsxY4b40Y9+ZFlnrGy2cixXX321ZR0nbOOrr75a/MM//IMpbaK2MaGvORmc7meEyG1fQz8zDP2Mvc0y9DO5Bf3MyeF0X5PLfkYI5/kap/kZIZzna/LRz+TVbZhDQ0PYsmULmpqaUmkulwtNTU3YuHHjBFqmEwqFAABVVVWm9Oeeew41NTU488wzcc8996Cvry+Vt3HjRixYsAB1dXWptMsvvxzhcBg7duwYEzv37NmDxsZGnHLKKVi2bBlaW1sBAFu2bEEkEjFt39NPPx3Tp09Pbd+JsDfJ0NAQnn32WXzhC1+Apmmp9FzbvkZaWlrQ3t5u2qaBQACLFi0ybdPKykqcd955qTJNTU1wuVzYtGlTqswll1wCj8dj0tHc3Izjx4+PuY5QKARN01BZWWlKf+ihh1BdXY1zzjkHjzzyiOlS8PG2ef369aitrcXcuXPxla98BceOHTPZksvbuKOjAy+99BJuueWWtLxc2saFAn3NyeNUPwM4z9fQz9DPZAP9TG5BPzM6ONXXOM3PAPnha5zgZwDn+hon+pmiMW19nDl69ChisZjpTwIA6urqsGvXrgmySicej+P222/Hxz/+cZx55pmp9M9//vOYMWMGGhsb8d577+Guu+5Cc3MzXnzxRQBAe3u7Uk8yb7RZtGgRnn76acydOxdtbW347ne/i4svvhjbt29He3s7PB5P2h9IXV1dypbxttfIqlWrEAwGcdNNN6XScm37yiT7UNlg3Ka1tbWm/KKiIlRVVZnKzJo1K62NZN6kSZPGxH5Af+bDXXfdheuvvx4VFRWp9K997Wv42Mc+hqqqKrz55pu455570NbWhkcffXTcbV6yZAmuvfZazJo1C/v27cO//Mu/4IorrsDGjRvhdrtzfhs/88wzKC8vx7XXXmtKz6VtXEjQ15wcTvYzgPN8Df0M/Uw20M/kFvQzJ4+TfY3T/IyxD6f6Gif4GcDZvsaJfiavJstymdtuuw3bt2/H66+/bkpfvnx5annBggVoaGjApZdein379mH27NnjbSauuOKK1PLChQuxaNEizJgxAy+88AL8fv+42zMSnnjiCVxxxRVobGxMpeXa9s03IpEI/vEf/xFCCKxcudKU941vfCO1vHDhQng8HnzpS1/Cgw8+CK/XO652Ll26NLW8YMECLFy4ELNnz8b69etx6aWXjqstJ8KTTz6JZcuWwefzmdJzaRuT3MAJvsbJfgagrxlv6GfGB/oZki1O8DOAs30N/cz44hQ/Azjb1zjRz+TVbZg1NTVwu91pbzLp6OhAfX39BFkFrFixAn/84x/x2muvYerUqbZlFy1aBADYu3cvAKC+vl6pJ5k31lRWVuK0007D3r17UV9fj6GhIQSDwTR7krZMlL0HDhzAq6++ii9+8Yu25XJt+yb7sBuz9fX16OzsNOVHo1F0dXVN6HZPOpYDBw5gzZo1prMwKhYtWoRoNIr9+/dPmM1JTjnlFNTU1JjGQS5uYwD461//iubm5oxjG8itbZzP0NeMLk7xM4AzfQ39DP1MJuhncg/6mdHHKb7GiX7G2IfTfI2T/QzgHF/jVD+TV5NlHo8H5557LtauXZtKi8fjWLt2LRYvXjzu9gghsGLFCvzud7/DunXr0i4fVLFt2zYAQENDAwBg8eLFeP/9902DPvlDnjdv3pjYbaSnpwf79u1DQ0MDzj33XBQXF5u2b3NzM1pbW1Pbd6Lsfeqpp1BbW4srr7zStlyubd9Zs2ahvr7etE3D4TA2bdpk2qbBYBBbtmxJlVm3bh3i8XjKUS5evBgbNmxAJBIx6Zg7d+6YXJqadCx79uzBq6++iurq6ox1tm3bBpfLlbo0eLxtNnLo0CEcO3bMNA5ybRsneeKJJ3DuuefirLPOylg2l7ZxPkNfM7o4xc8AzvQ19DP0M5mgn8k96GdGH6f4Gif6GcCZvsbpfgZwjq9xrJ8Z81cIjDPPP/+88Hq94umnnxY7d+4Uy5cvF5WVlaY3g4wXX/nKV0QgEBDr1683vQq1r69PCCHE3r17xfe+9z2xefNm0dLSIn7/+9+LU045RVxyySWpNpKvAb7sssvEtm3bxOrVq8XkyZPH7LXFd9xxh1i/fr1oaWkRb7zxhmhqahI1NTWis7NTCKG/Znn69Oli3bp1YvPmzWLx4sVi8eLFE2avEPrbgaZPny7uuusuU3qubN/u7m6xdetWsXXrVgFAPProo2Lr1q2pN6089NBDorKyUvz+978X7733nrj66quVr1k+55xzxKZNm8Trr78u5syZY3oFcDAYFHV1deKf/umfxPbt28Xzzz8vSkpKTviVunY2Dw0Nic985jNi6tSpYtu2baaxnXxLyZtvvil+9KMfiW3btol9+/aJZ599VkyePFnccMMNY2Kznb3d3d3izjvvFBs3bhQtLS3i1VdfFR/72MfEnDlzxMDAQE5u4yShUEiUlJSIlStXptUf721MzNDXnDhO9DNC5LavoZ+hnxmpzUnoZ3IX+pmTw4m+Jpf9jBDO8zVO8zOZbM5FX5PvfibvJsuEEOKnP/2pmD59uvB4POL8888Xf/vb3ybEDgDKz1NPPSWEEKK1tVVccskloqqqSni9XnHqqaeKb37zmyIUCpna2b9/v7jiiiuE3+8XNTU14o477hCRSGRMbP7c5z4nGhoahMfjEVOmTBGf+9znxN69e1P5/f394qtf/aqYNGmSKCkpEZ/97GdFW1vbhNkrhBB/+tOfBADR3NxsSs+V7fvaa68px8GNN94ohNBftXzvvfeKuro64fV6xaWXXpqm5dixY+L6668XZWVloqKiQtx8882iu7vbVObdd98VF110kfB6vWLKlCnioYceGhObW1paLMf2a6+9JoQQYsuWLWLRokUiEAgIn88nzjjjDPGv//qvpj/y0bTZzt6+vj5x2WWXicmTJ4vi4mIxY8YMceutt6YFm7m0jZP8/Oc/F36/XwSDwbT6472NSTr0NSeGE/2MELnta+hn6GdGanMS+pnchn7mxHGir8llPyOE83yN0/xMJptz0dfku5/RhBBCccEZIYQQQgghhBBCCCEFR149s4wQQgghhBBCCCGEkJOBk2WEEEIIIYQQQgghhCTgZBkhhBBCCCGEEEIIIQk4WUYIIYQQQgghhBBCSAJOlhFCCCGEEEIIIYQQkoCTZYQQQgghhBBCCCGEJOBkGSGEEEIIIYQQQgghCThZRgghhBBCCCGEEEJIAk6WEUIIIYQQQgghhBCSgJNlhBBCCCGEEEIIIYQk4GQZIYQQQgghhBBCCCEJ/h8cNg3mcN6l2QAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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[00:02<00:00, 139541.06it/s]" - } - }, - "ddcc9e9cbe0c4ea4b5ebc2f28ab83085": { + "d52791b627f14da99bf4a1ad520ee9be": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4915,28 +5928,7 @@ "description_width": "" } }, - "e08c49dfadc040e1a31c85a1edb0999f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_bdc5d06e10d54b1a82da8050a1b86d32", - "placeholder": "​", - "style": "IPY_MODEL_7bc8a29ea1234c1e917f9f9441210d8e", - "value": " 300000/? [00:00<00:00, 3930612.32it/s]" - } - }, - "e1e8e9843f324af3b694fcb057809af5": { + "dfe5f701fec64b9fbc4f1c428587d056": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4988,7 +5980,7 @@ "width": null } }, - "eff5319bbd124c288123e6a103f4e01a": { + "e3a54f003870461da663fbd118bf8f5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5040,7 +6032,7 @@ "width": null } }, - "f89f10b5b639480c9ce8eb6ad81f9768": { + "e83cc4cddf724fd5816d01235b606067": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -5056,12 +6048,12 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_590c407b5d1e499a9903f93c6fa9fc5a", - "max": 244800.0, + "layout": "IPY_MODEL_024419d32420457aa48ecc1e63e1716d", + "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_c45fbbe6373a4208bdbf3c67ad4d5fa8", - "value": 244800.0 + "style": "IPY_MODEL_a26bb5ecf4c746029a338cb609f3df6e", + "value": 30.0 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb index 51f942eab..1d15e965f 100644 --- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:01.427173Z", - "iopub.status.busy": "2023-08-14T16:45:01.426490Z", - "iopub.status.idle": "2023-08-14T16:45:03.242185Z", - "shell.execute_reply": "2023-08-14T16:45:03.241372Z" + "iopub.execute_input": "2023-08-15T04:19:58.343444Z", + "iopub.status.busy": "2023-08-15T04:19:58.343087Z", + "iopub.status.idle": "2023-08-15T04:20:01.009076Z", + "shell.execute_reply": "2023-08-15T04:20:01.007930Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:03.246116Z", - "iopub.status.busy": "2023-08-14T16:45:03.245688Z", - "iopub.status.idle": "2023-08-14T16:45:03.283476Z", - "shell.execute_reply": "2023-08-14T16:45:03.282745Z" + "iopub.execute_input": "2023-08-15T04:20:01.014229Z", + "iopub.status.busy": "2023-08-15T04:20:01.013693Z", + "iopub.status.idle": "2023-08-15T04:20:01.077175Z", + "shell.execute_reply": "2023-08-15T04:20:01.076095Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:03.287201Z", - "iopub.status.busy": "2023-08-14T16:45:03.286800Z", - "iopub.status.idle": "2023-08-14T16:45:03.333392Z", - "shell.execute_reply": "2023-08-14T16:45:03.332589Z" + "iopub.execute_input": "2023-08-15T04:20:01.081958Z", + "iopub.status.busy": "2023-08-15T04:20:01.081521Z", + "iopub.status.idle": "2023-08-15T04:20:01.206790Z", + "shell.execute_reply": "2023-08-15T04:20:01.205727Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:03.337224Z", - "iopub.status.busy": "2023-08-14T16:45:03.336818Z", - "iopub.status.idle": "2023-08-14T16:45:03.341596Z", - "shell.execute_reply": "2023-08-14T16:45:03.340925Z" + "iopub.execute_input": "2023-08-15T04:20:01.211176Z", + "iopub.status.busy": "2023-08-15T04:20:01.210864Z", + "iopub.status.idle": "2023-08-15T04:20:01.221526Z", + "shell.execute_reply": "2023-08-15T04:20:01.220487Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:03.344689Z", - "iopub.status.busy": "2023-08-14T16:45:03.344251Z", - "iopub.status.idle": "2023-08-14T16:45:03.355263Z", - "shell.execute_reply": "2023-08-14T16:45:03.354667Z" + "iopub.execute_input": "2023-08-15T04:20:01.225567Z", + "iopub.status.busy": "2023-08-15T04:20:01.225212Z", + "iopub.status.idle": "2023-08-15T04:20:01.242636Z", + "shell.execute_reply": "2023-08-15T04:20:01.241647Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:03.358567Z", - "iopub.status.busy": "2023-08-14T16:45:03.357955Z", - "iopub.status.idle": "2023-08-14T16:45:03.361169Z", - "shell.execute_reply": "2023-08-14T16:45:03.360495Z" + "iopub.execute_input": "2023-08-15T04:20:01.247289Z", + "iopub.status.busy": "2023-08-15T04:20:01.246798Z", + "iopub.status.idle": "2023-08-15T04:20:01.251397Z", + "shell.execute_reply": "2023-08-15T04:20:01.250406Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:03.364052Z", - "iopub.status.busy": "2023-08-14T16:45:03.363686Z", - "iopub.status.idle": "2023-08-14T16:45:04.151769Z", - "shell.execute_reply": "2023-08-14T16:45:04.150983Z" + "iopub.execute_input": "2023-08-15T04:20:01.256335Z", + "iopub.status.busy": "2023-08-15T04:20:01.255793Z", + "iopub.status.idle": "2023-08-15T04:20:02.276037Z", + "shell.execute_reply": "2023-08-15T04:20:02.274904Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:04.155579Z", - "iopub.status.busy": "2023-08-14T16:45:04.155158Z", - "iopub.status.idle": "2023-08-14T16:45:06.766860Z", - "shell.execute_reply": "2023-08-14T16:45:06.765867Z" + "iopub.execute_input": "2023-08-15T04:20:02.287968Z", + "iopub.status.busy": "2023-08-15T04:20:02.287678Z", + "iopub.status.idle": "2023-08-15T04:20:06.181813Z", + "shell.execute_reply": "2023-08-15T04:20:06.180064Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.771445Z", - "iopub.status.busy": "2023-08-14T16:45:06.770176Z", - "iopub.status.idle": "2023-08-14T16:45:06.785989Z", - "shell.execute_reply": "2023-08-14T16:45:06.785332Z" + "iopub.execute_input": "2023-08-15T04:20:06.187597Z", + "iopub.status.busy": "2023-08-15T04:20:06.186229Z", + "iopub.status.idle": "2023-08-15T04:20:06.206961Z", + "shell.execute_reply": "2023-08-15T04:20:06.206048Z" } }, "outputs": [ @@ -517,56 +517,56 @@ " \n", " \n", " \n", - " 928\n", + " 456\n", + " 58.0\n", + " 92.0\n", + " 93.0\n", + " NaN\n", + " D\n", + " \n", + " \n", + " 827\n", + " 99.0\n", + " 86.0\n", " 74.0\n", - " 72.0\n", - " 97.0\n", - " missed class frequently -10\n", - " F\n", + " NaN\n", + " D\n", " \n", " \n", - " 911\n", - " 90.0\n", + " 637\n", " 0.0\n", - " 88.0\n", + " 79.0\n", + " 65.0\n", " cheated on exam, gets 0pts\n", " A\n", " \n", " \n", - " 864\n", - " 71.0\n", - " 78.0\n", - " 80.0\n", - " great final presentation +10\n", - " D\n", + " 120\n", + " 0.0\n", + " 81.0\n", + " 97.0\n", + " cheated on exam, gets 0pts\n", + " B\n", " \n", " \n", - " 83\n", + " 233\n", " 68.0\n", - " 99.0\n", - " 91.0\n", - " NaN\n", - " D\n", - " \n", - " \n", - " 498\n", - " 87.0\n", - " 64.0\n", - " 94.0\n", + " 83.0\n", + " 76.0\n", " NaN\n", - " D\n", + " F\n", " \n", " \n", "\n", "" ], "text/plain": [ - " exam_1 exam_2 exam_3 notes label\n", - "928 74.0 72.0 97.0 missed class frequently -10 F\n", - "911 90.0 0.0 88.0 cheated on exam, gets 0pts A\n", - "864 71.0 78.0 80.0 great final presentation +10 D\n", - "83 68.0 99.0 91.0 NaN D\n", - "498 87.0 64.0 94.0 NaN D" + " exam_1 exam_2 exam_3 notes label\n", + "456 58.0 92.0 93.0 NaN D\n", + "827 99.0 86.0 74.0 NaN D\n", + "637 0.0 79.0 65.0 cheated on exam, gets 0pts A\n", + "120 0.0 81.0 97.0 cheated on exam, gets 0pts B\n", + "233 68.0 83.0 76.0 NaN F" ] }, "execution_count": 9, @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.789319Z", - "iopub.status.busy": "2023-08-14T16:45:06.788762Z", - "iopub.status.idle": "2023-08-14T16:45:06.794264Z", - "shell.execute_reply": "2023-08-14T16:45:06.793584Z" + "iopub.execute_input": "2023-08-15T04:20:06.211561Z", + "iopub.status.busy": "2023-08-15T04:20:06.210509Z", + "iopub.status.idle": "2023-08-15T04:20:06.217973Z", + "shell.execute_reply": "2023-08-15T04:20:06.216954Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.797643Z", - "iopub.status.busy": "2023-08-14T16:45:06.797186Z", - "iopub.status.idle": "2023-08-14T16:45:06.807397Z", - "shell.execute_reply": "2023-08-14T16:45:06.806826Z" + "iopub.execute_input": "2023-08-15T04:20:06.222709Z", + "iopub.status.busy": "2023-08-15T04:20:06.222299Z", + "iopub.status.idle": "2023-08-15T04:20:06.238142Z", + "shell.execute_reply": "2023-08-15T04:20:06.236857Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.810314Z", - "iopub.status.busy": "2023-08-14T16:45:06.809852Z", - "iopub.status.idle": "2023-08-14T16:45:06.982629Z", - "shell.execute_reply": "2023-08-14T16:45:06.981151Z" + "iopub.execute_input": "2023-08-15T04:20:06.242192Z", + "iopub.status.busy": "2023-08-15T04:20:06.241643Z", + "iopub.status.idle": "2023-08-15T04:20:06.458238Z", + "shell.execute_reply": "2023-08-15T04:20:06.457110Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.986137Z", - "iopub.status.busy": "2023-08-14T16:45:06.985871Z", - "iopub.status.idle": "2023-08-14T16:45:06.990360Z", - "shell.execute_reply": "2023-08-14T16:45:06.989712Z" + "iopub.execute_input": "2023-08-15T04:20:06.462261Z", + "iopub.status.busy": "2023-08-15T04:20:06.461593Z", + "iopub.status.idle": "2023-08-15T04:20:06.466598Z", + "shell.execute_reply": "2023-08-15T04:20:06.465415Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.993331Z", - "iopub.status.busy": "2023-08-14T16:45:06.993093Z", - "iopub.status.idle": "2023-08-14T16:45:09.063219Z", - "shell.execute_reply": "2023-08-14T16:45:09.062038Z" + "iopub.execute_input": "2023-08-15T04:20:06.470199Z", + "iopub.status.busy": "2023-08-15T04:20:06.469687Z", + "iopub.status.idle": "2023-08-15T04:20:09.621754Z", + "shell.execute_reply": "2023-08-15T04:20:09.620225Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:09.068004Z", - "iopub.status.busy": "2023-08-14T16:45:09.067444Z", - "iopub.status.idle": "2023-08-14T16:45:09.084829Z", - "shell.execute_reply": "2023-08-14T16:45:09.084177Z" + "iopub.execute_input": "2023-08-15T04:20:09.626468Z", + "iopub.status.busy": "2023-08-15T04:20:09.626130Z", + "iopub.status.idle": "2023-08-15T04:20:09.662323Z", + "shell.execute_reply": "2023-08-15T04:20:09.661202Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:09.087955Z", - "iopub.status.busy": "2023-08-14T16:45:09.087485Z", - "iopub.status.idle": "2023-08-14T16:45:09.125418Z", - "shell.execute_reply": "2023-08-14T16:45:09.124680Z" + "iopub.execute_input": "2023-08-15T04:20:09.666467Z", + "iopub.status.busy": "2023-08-15T04:20:09.666145Z", + "iopub.status.idle": "2023-08-15T04:20:09.772343Z", + "shell.execute_reply": "2023-08-15T04:20:09.771225Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb index 488e3aa30..cf948214b 100644 --- a/master/.doctrees/nbsphinx/tutorials/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb @@ -116,10 +116,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:14.130347Z", - "iopub.status.busy": "2023-08-14T16:45:14.130050Z", - "iopub.status.idle": "2023-08-14T16:45:16.923847Z", - "shell.execute_reply": "2023-08-14T16:45:16.923063Z" + "iopub.execute_input": "2023-08-15T04:20:16.502415Z", + "iopub.status.busy": "2023-08-15T04:20:16.502140Z", + "iopub.status.idle": "2023-08-15T04:20:20.282952Z", + "shell.execute_reply": "2023-08-15T04:20:20.281809Z" }, "nbsphinx": "hidden" }, @@ -136,7 +136,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -161,10 +161,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.928387Z", - "iopub.status.busy": "2023-08-14T16:45:16.927680Z", - "iopub.status.idle": "2023-08-14T16:45:16.933287Z", - "shell.execute_reply": "2023-08-14T16:45:16.932648Z" + "iopub.execute_input": "2023-08-15T04:20:20.287773Z", + "iopub.status.busy": "2023-08-15T04:20:20.287241Z", + "iopub.status.idle": "2023-08-15T04:20:20.294554Z", + "shell.execute_reply": "2023-08-15T04:20:20.293358Z" } }, "outputs": [], @@ -186,10 +186,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.936391Z", - "iopub.status.busy": "2023-08-14T16:45:16.936019Z", - "iopub.status.idle": "2023-08-14T16:45:16.939783Z", - "shell.execute_reply": "2023-08-14T16:45:16.939094Z" + "iopub.execute_input": "2023-08-15T04:20:20.299346Z", + "iopub.status.busy": "2023-08-15T04:20:20.299028Z", + "iopub.status.idle": "2023-08-15T04:20:20.303850Z", + "shell.execute_reply": "2023-08-15T04:20:20.302817Z" }, "nbsphinx": "hidden" }, @@ -220,10 +220,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.942949Z", - "iopub.status.busy": "2023-08-14T16:45:16.942587Z", - "iopub.status.idle": "2023-08-14T16:45:16.986653Z", - "shell.execute_reply": "2023-08-14T16:45:16.985895Z" + "iopub.execute_input": "2023-08-15T04:20:20.308042Z", + "iopub.status.busy": "2023-08-15T04:20:20.307637Z", + "iopub.status.idle": "2023-08-15T04:20:20.457797Z", + "shell.execute_reply": "2023-08-15T04:20:20.456822Z" } }, "outputs": [ @@ -313,10 +313,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.990151Z", - "iopub.status.busy": "2023-08-14T16:45:16.989747Z", - "iopub.status.idle": "2023-08-14T16:45:16.994224Z", - "shell.execute_reply": "2023-08-14T16:45:16.993525Z" + "iopub.execute_input": "2023-08-15T04:20:20.461445Z", + "iopub.status.busy": "2023-08-15T04:20:20.461164Z", + "iopub.status.idle": "2023-08-15T04:20:20.467865Z", + "shell.execute_reply": "2023-08-15T04:20:20.466641Z" } }, "outputs": [], @@ -331,10 +331,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.997044Z", - "iopub.status.busy": "2023-08-14T16:45:16.996613Z", - "iopub.status.idle": "2023-08-14T16:45:17.000939Z", - "shell.execute_reply": "2023-08-14T16:45:17.000244Z" + "iopub.execute_input": "2023-08-15T04:20:20.471479Z", + "iopub.status.busy": "2023-08-15T04:20:20.471201Z", + "iopub.status.idle": "2023-08-15T04:20:20.476214Z", + "shell.execute_reply": "2023-08-15T04:20:20.475346Z" } }, "outputs": [ @@ -343,7 +343,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies'}\n" + "Classes: {'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'change_pin', 'getting_spare_card', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_about_to_expire', 'cancel_transfer'}\n" ] } ], @@ -366,10 +366,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:17.004757Z", - "iopub.status.busy": "2023-08-14T16:45:17.004236Z", - "iopub.status.idle": "2023-08-14T16:45:17.008239Z", - "shell.execute_reply": "2023-08-14T16:45:17.007518Z" + "iopub.execute_input": "2023-08-15T04:20:20.479748Z", + "iopub.status.busy": "2023-08-15T04:20:20.479395Z", + "iopub.status.idle": "2023-08-15T04:20:20.483695Z", + "shell.execute_reply": "2023-08-15T04:20:20.482989Z" } }, "outputs": [ @@ -410,10 +410,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:17.011923Z", - "iopub.status.busy": "2023-08-14T16:45:17.011562Z", - "iopub.status.idle": "2023-08-14T16:45:17.015793Z", - "shell.execute_reply": "2023-08-14T16:45:17.015102Z" + "iopub.execute_input": "2023-08-15T04:20:20.487861Z", + "iopub.status.busy": "2023-08-15T04:20:20.487397Z", + "iopub.status.idle": "2023-08-15T04:20:20.494526Z", + "shell.execute_reply": "2023-08-15T04:20:20.493207Z" } }, "outputs": [], @@ -454,10 +454,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:17.018942Z", - "iopub.status.busy": "2023-08-14T16:45:17.018346Z", - "iopub.status.idle": "2023-08-14T16:45:20.472247Z", - "shell.execute_reply": "2023-08-14T16:45:20.471082Z" + "iopub.execute_input": "2023-08-15T04:20:20.498757Z", + "iopub.status.busy": "2023-08-15T04:20:20.498143Z", + "iopub.status.idle": "2023-08-15T04:20:27.036822Z", + "shell.execute_reply": "2023-08-15T04:20:27.035688Z" } }, "outputs": [ @@ -472,7 +472,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\n", + "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight']\n", "- This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" ] @@ -513,10 +513,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:20.476238Z", - "iopub.status.busy": "2023-08-14T16:45:20.475721Z", - "iopub.status.idle": "2023-08-14T16:45:20.478964Z", - "shell.execute_reply": "2023-08-14T16:45:20.478418Z" + "iopub.execute_input": "2023-08-15T04:20:27.041706Z", + "iopub.status.busy": "2023-08-15T04:20:27.041032Z", + "iopub.status.idle": "2023-08-15T04:20:27.045018Z", + "shell.execute_reply": "2023-08-15T04:20:27.044246Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:20.481995Z", - "iopub.status.busy": "2023-08-14T16:45:20.481368Z", - "iopub.status.idle": "2023-08-14T16:45:20.484587Z", - "shell.execute_reply": "2023-08-14T16:45:20.484047Z" + "iopub.execute_input": "2023-08-15T04:20:27.049353Z", + "iopub.status.busy": "2023-08-15T04:20:27.048605Z", + "iopub.status.idle": "2023-08-15T04:20:27.052978Z", + "shell.execute_reply": "2023-08-15T04:20:27.052166Z" } }, "outputs": [], @@ -556,10 +556,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:20.487414Z", - "iopub.status.busy": "2023-08-14T16:45:20.486816Z", - "iopub.status.idle": "2023-08-14T16:45:23.297246Z", - "shell.execute_reply": "2023-08-14T16:45:23.296146Z" + "iopub.execute_input": "2023-08-15T04:20:27.056729Z", + "iopub.status.busy": "2023-08-15T04:20:27.056139Z", + "iopub.status.idle": "2023-08-15T04:20:30.786103Z", + "shell.execute_reply": "2023-08-15T04:20:30.784427Z" }, "scrolled": true }, @@ -582,10 +582,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.302727Z", - "iopub.status.busy": "2023-08-14T16:45:23.301582Z", - "iopub.status.idle": "2023-08-14T16:45:23.314614Z", - "shell.execute_reply": "2023-08-14T16:45:23.314001Z" + "iopub.execute_input": "2023-08-15T04:20:30.792248Z", + "iopub.status.busy": "2023-08-15T04:20:30.790996Z", + "iopub.status.idle": "2023-08-15T04:20:30.803827Z", + "shell.execute_reply": "2023-08-15T04:20:30.803056Z" } }, "outputs": [ @@ -620,35 +620,35 @@ " \n", " 0\n", " False\n", - " 0.858229\n", + " 0.858293\n", " 6\n", " 6\n", " \n", " \n", " 1\n", " False\n", - " 0.547047\n", + " 0.546974\n", " 3\n", " 3\n", " \n", " \n", " 2\n", " False\n", - " 0.823594\n", + " 0.823609\n", " 7\n", " 7\n", " \n", " \n", " 3\n", " False\n", - " 0.968831\n", + " 0.968828\n", " 8\n", " 8\n", " \n", " \n", " 4\n", " False\n", - " 0.792000\n", + " 0.791938\n", " 4\n", " 4\n", " \n", @@ -658,11 +658,11 @@ ], "text/plain": [ " is_label_issue label_quality given_label predicted_label\n", - "0 False 0.858229 6 6\n", - "1 False 0.547047 3 3\n", - "2 False 0.823594 7 7\n", - "3 False 0.968831 8 8\n", - "4 False 0.792000 4 4" + "0 False 0.858293 6 6\n", + "1 False 0.546974 3 3\n", + "2 False 0.823609 7 7\n", + "3 False 0.968828 8 8\n", + "4 False 0.791938 4 4" ] }, "execution_count": 13, @@ -686,10 +686,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.317709Z", - "iopub.status.busy": "2023-08-14T16:45:23.317265Z", - "iopub.status.idle": "2023-08-14T16:45:23.324209Z", - "shell.execute_reply": "2023-08-14T16:45:23.323453Z" + "iopub.execute_input": "2023-08-15T04:20:30.808010Z", + "iopub.status.busy": "2023-08-15T04:20:30.807479Z", + "iopub.status.idle": "2023-08-15T04:20:30.815103Z", + "shell.execute_reply": "2023-08-15T04:20:30.814069Z" } }, "outputs": [], @@ -703,10 +703,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.327086Z", - "iopub.status.busy": "2023-08-14T16:45:23.326640Z", - "iopub.status.idle": "2023-08-14T16:45:23.330640Z", - "shell.execute_reply": "2023-08-14T16:45:23.329922Z" + "iopub.execute_input": "2023-08-15T04:20:30.818893Z", + "iopub.status.busy": "2023-08-15T04:20:30.818588Z", + "iopub.status.idle": "2023-08-15T04:20:30.823490Z", + "shell.execute_reply": "2023-08-15T04:20:30.822692Z" } }, "outputs": [ @@ -741,10 +741,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.334338Z", - "iopub.status.busy": "2023-08-14T16:45:23.333977Z", - "iopub.status.idle": "2023-08-14T16:45:23.337578Z", - "shell.execute_reply": "2023-08-14T16:45:23.336888Z" + "iopub.execute_input": "2023-08-15T04:20:30.827685Z", + "iopub.status.busy": "2023-08-15T04:20:30.827171Z", + "iopub.status.idle": "2023-08-15T04:20:30.831887Z", + "shell.execute_reply": "2023-08-15T04:20:30.830936Z" } }, "outputs": [], @@ -764,10 +764,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.340411Z", - "iopub.status.busy": "2023-08-14T16:45:23.340053Z", - "iopub.status.idle": "2023-08-14T16:45:23.349128Z", - "shell.execute_reply": "2023-08-14T16:45:23.348437Z" + "iopub.execute_input": "2023-08-15T04:20:30.836710Z", + "iopub.status.busy": "2023-08-15T04:20:30.836174Z", + "iopub.status.idle": "2023-08-15T04:20:30.856004Z", + "shell.execute_reply": "2023-08-15T04:20:30.854909Z" } }, "outputs": [ @@ -892,10 +892,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.352459Z", - "iopub.status.busy": "2023-08-14T16:45:23.352026Z", - "iopub.status.idle": "2023-08-14T16:45:23.666709Z", - "shell.execute_reply": "2023-08-14T16:45:23.666054Z" + "iopub.execute_input": "2023-08-15T04:20:30.868020Z", + "iopub.status.busy": "2023-08-15T04:20:30.867172Z", + "iopub.status.idle": "2023-08-15T04:20:31.368103Z", + "shell.execute_reply": "2023-08-15T04:20:31.367240Z" }, "scrolled": true }, @@ -934,10 +934,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.670119Z", - "iopub.status.busy": "2023-08-14T16:45:23.669370Z", - "iopub.status.idle": "2023-08-14T16:45:23.957950Z", - "shell.execute_reply": "2023-08-14T16:45:23.957272Z" + "iopub.execute_input": "2023-08-15T04:20:31.372608Z", + "iopub.status.busy": "2023-08-15T04:20:31.371733Z", + "iopub.status.idle": "2023-08-15T04:20:31.808712Z", + "shell.execute_reply": "2023-08-15T04:20:31.807851Z" }, "scrolled": true }, @@ -970,10 +970,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.961266Z", - "iopub.status.busy": "2023-08-14T16:45:23.960766Z", - "iopub.status.idle": "2023-08-14T16:45:23.964809Z", - "shell.execute_reply": "2023-08-14T16:45:23.964244Z" + "iopub.execute_input": "2023-08-15T04:20:31.814265Z", + "iopub.status.busy": "2023-08-15T04:20:31.812698Z", + "iopub.status.idle": "2023-08-15T04:20:31.820582Z", + "shell.execute_reply": "2023-08-15T04:20:31.819766Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 92f76115c..2f66d0356 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:29.521268Z", - "iopub.status.busy": "2023-08-14T16:45:29.520802Z", - "iopub.status.idle": "2023-08-14T16:45:31.057021Z", - "shell.execute_reply": "2023-08-14T16:45:31.055892Z" + "iopub.execute_input": "2023-08-15T04:20:38.046727Z", + "iopub.status.busy": "2023-08-15T04:20:38.046085Z", + "iopub.status.idle": "2023-08-15T04:20:39.723865Z", + "shell.execute_reply": "2023-08-15T04:20:39.722473Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2023-08-14 16:45:29-- https://data.deepai.org/conll2003.zip\r\n", + "--2023-08-15 04:20:38-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.100, 2400:52e0:1a00::845:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.100|:443... connected.\r\n" + "185.93.1.250, 2400:52e0:1a00::845:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.\r\n" ] }, { @@ -122,10 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 88%[================> ] 845.70K 4.00MB/s \r", - "conll2003.zip 100%[===================>] 959.94K 4.19MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2023-08-14 16:45:30 (4.19 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2023-08-15 04:20:38 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +144,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2023-08-14 16:45:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.49.36, 52.216.108.11, 52.217.203.185, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.49.36|:443... connected.\r\n", + "--2023-08-15 04:20:38-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.164.193, 3.5.27.156, 16.182.68.65, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.164.193|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -168,7 +180,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 96%[==================> ] 15.71M 47.8MB/s " + "pred_probs.npz 7%[> ] 1.21M 6.05MB/s " ] }, { @@ -176,9 +188,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 42.9MB/s in 0.4s \r\n", + "pred_probs.npz 96%[==================> ] 15.66M 39.1MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 40.2MB/s in 0.4s \r\n", "\r\n", - "2023-08-14 16:45:30 (42.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2023-08-15 04:20:39 (40.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -195,10 +208,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:31.061227Z", - "iopub.status.busy": "2023-08-14T16:45:31.060760Z", - "iopub.status.idle": "2023-08-14T16:45:32.291949Z", - "shell.execute_reply": "2023-08-14T16:45:32.290838Z" + "iopub.execute_input": "2023-08-15T04:20:39.729138Z", + "iopub.status.busy": "2023-08-15T04:20:39.728250Z", + "iopub.status.idle": "2023-08-15T04:20:41.321946Z", + "shell.execute_reply": "2023-08-15T04:20:41.320777Z" }, "nbsphinx": "hidden" }, @@ -209,7 +222,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -235,10 +248,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:32.296595Z", - "iopub.status.busy": "2023-08-14T16:45:32.295910Z", - "iopub.status.idle": "2023-08-14T16:45:32.300250Z", - "shell.execute_reply": "2023-08-14T16:45:32.299554Z" + "iopub.execute_input": "2023-08-15T04:20:41.327860Z", + "iopub.status.busy": "2023-08-15T04:20:41.326164Z", + "iopub.status.idle": "2023-08-15T04:20:41.334821Z", + "shell.execute_reply": "2023-08-15T04:20:41.332813Z" } }, "outputs": [], @@ -288,10 +301,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:32.303535Z", - "iopub.status.busy": "2023-08-14T16:45:32.303152Z", - "iopub.status.idle": "2023-08-14T16:45:32.306871Z", - "shell.execute_reply": "2023-08-14T16:45:32.306208Z" + "iopub.execute_input": "2023-08-15T04:20:41.340149Z", + "iopub.status.busy": "2023-08-15T04:20:41.339817Z", + "iopub.status.idle": "2023-08-15T04:20:41.346867Z", + "shell.execute_reply": "2023-08-15T04:20:41.345236Z" }, "nbsphinx": "hidden" }, @@ -309,10 +322,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:32.309847Z", - "iopub.status.busy": "2023-08-14T16:45:32.309305Z", - "iopub.status.idle": "2023-08-14T16:45:44.200401Z", - "shell.execute_reply": "2023-08-14T16:45:44.199592Z" + "iopub.execute_input": "2023-08-15T04:20:41.351570Z", + "iopub.status.busy": "2023-08-15T04:20:41.351247Z", + "iopub.status.idle": "2023-08-15T04:20:54.553334Z", + "shell.execute_reply": "2023-08-15T04:20:54.552100Z" } }, "outputs": [], @@ -386,10 +399,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.204385Z", - "iopub.status.busy": "2023-08-14T16:45:44.203946Z", - "iopub.status.idle": "2023-08-14T16:45:44.211071Z", - "shell.execute_reply": "2023-08-14T16:45:44.210513Z" + "iopub.execute_input": "2023-08-15T04:20:54.558495Z", + "iopub.status.busy": "2023-08-15T04:20:54.558143Z", + "iopub.status.idle": "2023-08-15T04:20:54.567711Z", + "shell.execute_reply": "2023-08-15T04:20:54.566731Z" }, "nbsphinx": "hidden" }, @@ -429,10 +442,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.214319Z", - "iopub.status.busy": "2023-08-14T16:45:44.213710Z", - "iopub.status.idle": "2023-08-14T16:45:44.823151Z", - "shell.execute_reply": "2023-08-14T16:45:44.821614Z" + "iopub.execute_input": "2023-08-15T04:20:54.571239Z", + "iopub.status.busy": "2023-08-15T04:20:54.570928Z", + "iopub.status.idle": "2023-08-15T04:20:55.230372Z", + "shell.execute_reply": "2023-08-15T04:20:55.229244Z" } }, "outputs": [], @@ -469,10 +482,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.827168Z", - "iopub.status.busy": "2023-08-14T16:45:44.826656Z", - "iopub.status.idle": "2023-08-14T16:45:44.835279Z", - "shell.execute_reply": "2023-08-14T16:45:44.834655Z" + "iopub.execute_input": "2023-08-15T04:20:55.234193Z", + "iopub.status.busy": "2023-08-15T04:20:55.233932Z", + "iopub.status.idle": "2023-08-15T04:20:55.243155Z", + "shell.execute_reply": "2023-08-15T04:20:55.242041Z" } }, "outputs": [ @@ -544,10 +557,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.838250Z", - "iopub.status.busy": "2023-08-14T16:45:44.837809Z", - "iopub.status.idle": "2023-08-14T16:45:47.661496Z", - "shell.execute_reply": "2023-08-14T16:45:47.659859Z" + "iopub.execute_input": "2023-08-15T04:20:55.247751Z", + "iopub.status.busy": "2023-08-15T04:20:55.247315Z", + "iopub.status.idle": "2023-08-15T04:20:58.699672Z", + "shell.execute_reply": "2023-08-15T04:20:58.698130Z" } }, "outputs": [], @@ -569,10 +582,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.667172Z", - "iopub.status.busy": "2023-08-14T16:45:47.666002Z", - "iopub.status.idle": "2023-08-14T16:45:47.679744Z", - "shell.execute_reply": "2023-08-14T16:45:47.678178Z" + "iopub.execute_input": "2023-08-15T04:20:58.706443Z", + "iopub.status.busy": "2023-08-15T04:20:58.704834Z", + "iopub.status.idle": "2023-08-15T04:20:58.718617Z", + "shell.execute_reply": "2023-08-15T04:20:58.717298Z" } }, "outputs": [ @@ -608,10 +621,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.683111Z", - "iopub.status.busy": "2023-08-14T16:45:47.682631Z", - "iopub.status.idle": "2023-08-14T16:45:47.701333Z", - "shell.execute_reply": "2023-08-14T16:45:47.700691Z" + "iopub.execute_input": "2023-08-15T04:20:58.723002Z", + "iopub.status.busy": "2023-08-15T04:20:58.722634Z", + "iopub.status.idle": "2023-08-15T04:20:58.751238Z", + "shell.execute_reply": "2023-08-15T04:20:58.749416Z" } }, "outputs": [ @@ -769,10 +782,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.704745Z", - "iopub.status.busy": "2023-08-14T16:45:47.704227Z", - "iopub.status.idle": "2023-08-14T16:45:47.754004Z", - "shell.execute_reply": "2023-08-14T16:45:47.752818Z" + "iopub.execute_input": "2023-08-15T04:20:58.756048Z", + "iopub.status.busy": "2023-08-15T04:20:58.755656Z", + "iopub.status.idle": "2023-08-15T04:20:58.817288Z", + "shell.execute_reply": "2023-08-15T04:20:58.816292Z" } }, "outputs": [ @@ -801,11 +814,11 @@ "labeled as class `MISC` but predicted to actually be class `LOC` 1 times\n", "labeled as class `MISC` but predicted to actually be class `ORG` 1 times\n", "\n", - "Token 'Press' is potentially mislabeled 20 times throughout the dataset\n", + "Token 'Digest' is potentially mislabeled 20 times throughout the dataset\n", "---------------------------------------------------------------------------------------\n", "labeled as class `O` but predicted to actually be class `ORG` 20 times\n", "\n", - "Token 'Digest' is potentially mislabeled 20 times throughout the dataset\n", + "Token 'Press' is potentially mislabeled 20 times throughout the dataset\n", "---------------------------------------------------------------------------------------\n", "labeled as class `O` but predicted to actually be class `ORG` 20 times\n", "\n", @@ -813,8 +826,8 @@ "---------------------------------------------------------------------------------------\n", "labeled as class `ORG` but predicted to actually be class `LOC` 13 times\n", "labeled as class `LOC` but predicted to actually be class `ORG` 2 times\n", - "labeled as class `MISC` but predicted to actually be class `LOC` 1 times\n", "labeled as class `O` but predicted to actually be class `ORG` 1 times\n", + "labeled as class `MISC` but predicted to actually be class `LOC` 1 times\n", "\n", "Token 'and' is potentially mislabeled 16 times throughout the dataset\n", "---------------------------------------------------------------------------------------\n", @@ -874,10 +887,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.757599Z", - "iopub.status.busy": "2023-08-14T16:45:47.757074Z", - "iopub.status.idle": "2023-08-14T16:45:47.768429Z", - "shell.execute_reply": "2023-08-14T16:45:47.767794Z" + "iopub.execute_input": "2023-08-15T04:20:58.822509Z", + "iopub.status.busy": "2023-08-15T04:20:58.821634Z", + "iopub.status.idle": "2023-08-15T04:20:58.836039Z", + "shell.execute_reply": "2023-08-15T04:20:58.834998Z" } }, "outputs": [ @@ -945,10 +958,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.771551Z", - "iopub.status.busy": "2023-08-14T16:45:47.771099Z", - "iopub.status.idle": "2023-08-14T16:45:50.335528Z", - "shell.execute_reply": "2023-08-14T16:45:50.334340Z" + "iopub.execute_input": "2023-08-15T04:20:58.840562Z", + "iopub.status.busy": "2023-08-15T04:20:58.840030Z", + "iopub.status.idle": "2023-08-15T04:21:02.124593Z", + "shell.execute_reply": "2023-08-15T04:21:02.123588Z" } }, "outputs": [ @@ -1100,10 +1113,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:50.339346Z", - "iopub.status.busy": "2023-08-14T16:45:50.338845Z", - "iopub.status.idle": "2023-08-14T16:45:50.346061Z", - "shell.execute_reply": "2023-08-14T16:45:50.344415Z" + "iopub.execute_input": "2023-08-15T04:21:02.128404Z", + "iopub.status.busy": "2023-08-15T04:21:02.128086Z", + 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b/master/_images/tutorials_segmentation_32_2.png differ diff --git a/master/_images/tutorials_segmentation_32_3.png b/master/_images/tutorials_segmentation_32_3.png index ed6c54c40..5d40c7108 100644 Binary files a/master/_images/tutorials_segmentation_32_3.png and b/master/_images/tutorials_segmentation_32_3.png differ diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb index 9b87ade00..832869e35 100644 --- a/master/_sources/tutorials/audio.ipynb +++ b/master/_sources/tutorials/audio.ipynb @@ -91,7 +91,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb index ac6975672..908b6a760 100644 --- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb +++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb index 3f8008afe..70ec9eb67 100644 --- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index f171a1d7e..67d7221ba 100644 --- a/master/_sources/tutorials/datalab/tabular.ipynb +++ b/master/_sources/tutorials/datalab/tabular.ipynb @@ -81,7 +81,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb index a1507ba6e..593d7bfc1 100644 --- a/master/_sources/tutorials/datalab/text.ipynb +++ b/master/_sources/tutorials/datalab/text.ipynb @@ -90,7 +90,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index be494b4aa..660b7aa58 100644 --- a/master/_sources/tutorials/dataset_health.ipynb +++ b/master/_sources/tutorials/dataset_health.ipynb @@ -77,7 +77,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/image.ipynb b/master/_sources/tutorials/image.ipynb index 10a522e4b..3b4345b4f 100644 --- a/master/_sources/tutorials/image.ipynb +++ b/master/_sources/tutorials/image.ipynb @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"torch\", \"torchvision\", \"skorch\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb index e222cbf2d..6ea8bcd6c 100644 --- a/master/_sources/tutorials/indepth_overview.ipynb +++ b/master/_sources/tutorials/indepth_overview.ipynb @@ -62,7 +62,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb index 42a4a3f05..4ad297047 100644 --- a/master/_sources/tutorials/multiannotator.ipynb +++ b/master/_sources/tutorials/multiannotator.ipynb @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -707,12 +707,14 @@ "id": "e59f7d4f", "metadata": {}, "source": [ - "## Further model improvements \n", + "## Further improvements \n", "You can also repeatedly iterate this process of getting better consensus labels using the model's out-of-sample predicted probabilities and then retraining the model with the improved labels to get even better predicted probabilities!\n", "For details, see our [examples](https://github.com/cleanlab/examples) notebook on [Iterative use of Cleanlab to Improve Classification Models (and Consensus Labels) from Data Labeled by Multiple Annotators](https://github.com/cleanlab/examples/blob/master/multiannotator_cifar10/multiannotator_cifar10.ipynb).\n", "\n", "If possible, the best way to improve your model is to collect additional labels for both previously annotated data and extra not-yet-labeled examples (i.e. *active learning*). To decide which data is most informative to label next, use `cleanlab.multiannotator.get_active_learning_scores()` rather than the methods from this tutorial. This is demonstrated in our examples notebook on [Active Learning with Multiple Data Annotators via ActiveLab](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb).\n", "\n", + "While this notebook focused on analzying the labels of your data, Cleanlab can also check your data features for various issues. Learn how to do this by following our [Datalab tutorials](../tutorials/datalab/index.html), except you do not need to pass in `labels` now that you've already analyzed them with this notebook (or you can provide `labels` to Datalab as the consensus labels estimated here).\n", + "\n", "\n", "## How does cleanlab.multiannotator work?\n", "\n", diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb index a22c5adb0..b6cbf06cf 100644 --- a/master/_sources/tutorials/multilabel_classification.ipynb +++ b/master/_sources/tutorials/multilabel_classification.ipynb @@ -72,7 +72,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb index 3545f65e4..1ca9653ef 100644 --- a/master/_sources/tutorials/object_detection.ipynb +++ b/master/_sources/tutorials/object_detection.ipynb @@ -77,7 +77,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb index d5a4a76f4..e28689466 100644 --- a/master/_sources/tutorials/outliers.ipynb +++ b/master/_sources/tutorials/outliers.ipynb @@ -119,7 +119,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb index e4caf122a..903d64fe8 100644 --- a/master/_sources/tutorials/regression.ipynb +++ b/master/_sources/tutorials/regression.ipynb @@ -103,7 +103,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb index cb6a03f98..fe66aacc2 100644 --- a/master/_sources/tutorials/segmentation.ipynb +++ b/master/_sources/tutorials/segmentation.ipynb @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb index 7bd457fda..c1f498a36 100644 --- a/master/_sources/tutorials/tabular.ipynb +++ b/master/_sources/tutorials/tabular.ipynb @@ -119,7 +119,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb index fbc5e26f2..3483b3128 100644 --- a/master/_sources/tutorials/text.ipynb +++ b/master/_sources/tutorials/text.ipynb @@ -130,7 +130,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb index 75d525314..75faadd37 100644 --- a/master/_sources/tutorials/token_classification.ipynb +++ b/master/_sources/tutorials/token_classification.ipynb @@ -95,7 +95,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", 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3. Use pre-trained SpeechBrain model to featurize audio 
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+[[-14.196314     7.319463    12.478973   ...   2.289077     2.8170207
+  -10.892647  ]
+ [-24.898058     5.2561903   12.559641   ...  -3.5597146    9.620667
+  -10.28525   ]
+ [-21.709623     7.503369     7.913801   ...  -6.819831     3.1831489
+  -17.208763  ]
  ...
- [-16.08425      6.321053    12.005463   ...   1.216175     9.478231
-  -10.682177  ]
- [-15.053815     5.2424726    1.091422   ...  -0.78335106   9.039538
-  -23.569181  ]
- [-19.76109      1.1258249   16.75323    ...   3.3508852   11.598273
+ [-16.08426      6.321049    12.005462   ...   1.2161489    9.478238
+  -10.682178  ]
+ [-15.053809     5.2424674    1.0914197  ...  -0.78334606   9.039537
+  -23.569172  ]
+ [-19.761091     1.1258256   16.75323    ...   3.3508904   11.598279
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 Shape of array:  (2500, 512)
@@ -1180,11 +1180,11 @@

5. Use cleanlab to find label issues 
 Here are indices of the most likely errors:
- [1946  516  469 1871 1955 2132]
+ [1946  469  516 1871 1955 2132]

These examples flagged by cleanlab are those worth inspecting more closely.

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["IPY_MODEL_ce473a9f542945c7935aa4b5cede0cc1", "IPY_MODEL_7947cae42c084a94bc58249b7f99b5e6", "IPY_MODEL_588d854ffd8c4094b32a8fb10b3a1e26"], "layout": "IPY_MODEL_752a434742f84dd091b9f1a0a27d298b"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/audio.ipynb b/master/tutorials/audio.ipynb index 4565d11d1..3abe77e8c 100644 --- a/master/tutorials/audio.ipynb +++ b/master/tutorials/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:08.540462Z", - "iopub.status.busy": "2023-08-14T16:34:08.540014Z", - "iopub.status.idle": "2023-08-14T16:34:12.763425Z", - "shell.execute_reply": "2023-08-14T16:34:12.762632Z" + "iopub.execute_input": "2023-08-15T04:05:12.762164Z", + "iopub.status.busy": "2023-08-15T04:05:12.761899Z", + "iopub.status.idle": "2023-08-15T04:05:18.219217Z", + "shell.execute_reply": "2023-08-15T04:05:18.218250Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:12.768105Z", - "iopub.status.busy": "2023-08-14T16:34:12.767316Z", - "iopub.status.idle": "2023-08-14T16:34:12.771691Z", - "shell.execute_reply": "2023-08-14T16:34:12.770985Z" + "iopub.execute_input": "2023-08-15T04:05:18.224030Z", + "iopub.status.busy": "2023-08-15T04:05:18.223494Z", + "iopub.status.idle": "2023-08-15T04:05:18.230130Z", + "shell.execute_reply": "2023-08-15T04:05:18.229261Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:12.774937Z", - "iopub.status.busy": "2023-08-14T16:34:12.774410Z", - "iopub.status.idle": "2023-08-14T16:34:12.780609Z", - "shell.execute_reply": "2023-08-14T16:34:12.779966Z" + "iopub.execute_input": "2023-08-15T04:05:18.233550Z", + "iopub.status.busy": "2023-08-15T04:05:18.233291Z", + "iopub.status.idle": "2023-08-15T04:05:18.239821Z", + "shell.execute_reply": "2023-08-15T04:05:18.238808Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:12.783672Z", - "iopub.status.busy": "2023-08-14T16:34:12.783238Z", - "iopub.status.idle": "2023-08-14T16:34:14.748685Z", - "shell.execute_reply": "2023-08-14T16:34:14.747362Z" + "iopub.execute_input": "2023-08-15T04:05:18.243646Z", + "iopub.status.busy": "2023-08-15T04:05:18.243402Z", + "iopub.status.idle": "2023-08-15T04:05:20.262686Z", + "shell.execute_reply": "2023-08-15T04:05:20.261312Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:14.753317Z", - "iopub.status.busy": "2023-08-14T16:34:14.752707Z", - "iopub.status.idle": "2023-08-14T16:34:14.773571Z", - "shell.execute_reply": "2023-08-14T16:34:14.772852Z" + "iopub.execute_input": "2023-08-15T04:05:20.267169Z", + "iopub.status.busy": "2023-08-15T04:05:20.266837Z", + "iopub.status.idle": "2023-08-15T04:05:20.287552Z", + "shell.execute_reply": "2023-08-15T04:05:20.286755Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:14.814263Z", - "iopub.status.busy": "2023-08-14T16:34:14.813674Z", - "iopub.status.idle": "2023-08-14T16:34:14.820711Z", - "shell.execute_reply": "2023-08-14T16:34:14.820012Z" + "iopub.execute_input": "2023-08-15T04:05:20.345676Z", + "iopub.status.busy": "2023-08-15T04:05:20.344523Z", + "iopub.status.idle": "2023-08-15T04:05:20.353687Z", + "shell.execute_reply": "2023-08-15T04:05:20.352476Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:14.823992Z", - "iopub.status.busy": "2023-08-14T16:34:14.823548Z", - "iopub.status.idle": "2023-08-14T16:34:15.597206Z", - "shell.execute_reply": "2023-08-14T16:34:15.596453Z" + "iopub.execute_input": "2023-08-15T04:05:20.358060Z", + "iopub.status.busy": "2023-08-15T04:05:20.357310Z", + "iopub.status.idle": "2023-08-15T04:05:21.279259Z", + "shell.execute_reply": "2023-08-15T04:05:21.278116Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:15.601063Z", - "iopub.status.busy": "2023-08-14T16:34:15.600476Z", - "iopub.status.idle": "2023-08-14T16:34:17.769494Z", - "shell.execute_reply": "2023-08-14T16:34:17.768653Z" + "iopub.execute_input": "2023-08-15T04:05:21.284553Z", + "iopub.status.busy": "2023-08-15T04:05:21.283752Z", + "iopub.status.idle": "2023-08-15T04:05:23.543792Z", + "shell.execute_reply": "2023-08-15T04:05:23.542760Z" }, "id": "vL9lkiKsHvKr" }, @@ -472,10 +472,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:17.773501Z", - "iopub.status.busy": "2023-08-14T16:34:17.773223Z", - "iopub.status.idle": "2023-08-14T16:34:17.805124Z", - "shell.execute_reply": "2023-08-14T16:34:17.804409Z" + "iopub.execute_input": "2023-08-15T04:05:23.548837Z", + "iopub.status.busy": "2023-08-15T04:05:23.548044Z", + "iopub.status.idle": "2023-08-15T04:05:23.589300Z", + "shell.execute_reply": "2023-08-15T04:05:23.588291Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -555,10 +555,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:17.808280Z", - "iopub.status.busy": "2023-08-14T16:34:17.807902Z", - "iopub.status.idle": "2023-08-14T16:34:17.811889Z", - "shell.execute_reply": "2023-08-14T16:34:17.811225Z" + "iopub.execute_input": "2023-08-15T04:05:23.593624Z", + "iopub.status.busy": "2023-08-15T04:05:23.593335Z", + "iopub.status.idle": "2023-08-15T04:05:23.598636Z", + "shell.execute_reply": "2023-08-15T04:05:23.597424Z" }, "id": "I8JqhOZgi94g" }, @@ -580,10 +580,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:17.814740Z", - "iopub.status.busy": "2023-08-14T16:34:17.814382Z", - "iopub.status.idle": "2023-08-14T16:34:30.445435Z", - "shell.execute_reply": "2023-08-14T16:34:30.444681Z" + "iopub.execute_input": "2023-08-15T04:05:23.602808Z", + "iopub.status.busy": "2023-08-15T04:05:23.602522Z", + "iopub.status.idle": "2023-08-15T04:05:43.241588Z", + "shell.execute_reply": "2023-08-15T04:05:43.240516Z" }, "id": "2FSQ2GR9R_YA" }, @@ -615,10 +615,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:30.449248Z", - "iopub.status.busy": "2023-08-14T16:34:30.448815Z", - "iopub.status.idle": "2023-08-14T16:34:30.453940Z", - "shell.execute_reply": "2023-08-14T16:34:30.453354Z" + "iopub.execute_input": "2023-08-15T04:05:43.246179Z", + "iopub.status.busy": "2023-08-15T04:05:43.245794Z", + "iopub.status.idle": "2023-08-15T04:05:43.252164Z", + "shell.execute_reply": "2023-08-15T04:05:43.251414Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -628,18 +628,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-14.196308 7.319454 12.47899 ... 2.289091 2.817013\n", - " -10.892642 ]\n", - " [-24.898056 5.2561927 12.559636 ... -3.5597174 9.6206665\n", - " -10.285249 ]\n", - " [-21.709625 7.5033684 7.913807 ... -6.819826 3.1831462\n", - " -17.208761 ]\n", + "[[-14.196314 7.319463 12.478973 ... 2.289077 2.8170207\n", + " -10.892647 ]\n", + " [-24.898058 5.2561903 12.559641 ... -3.5597146 9.620667\n", + " -10.28525 ]\n", + " [-21.709623 7.503369 7.913801 ... -6.819831 3.1831489\n", + " -17.208763 ]\n", " ...\n", - " [-16.08425 6.321053 12.005463 ... 1.216175 9.478231\n", - " -10.682177 ]\n", - " [-15.053815 5.2424726 1.091422 ... -0.78335106 9.039538\n", - " -23.569181 ]\n", - " [-19.76109 1.1258249 16.75323 ... 3.3508852 11.598273\n", + " [-16.08426 6.321049 12.005462 ... 1.2161489 9.478238\n", + " -10.682178 ]\n", + " [-15.053809 5.2424674 1.0914197 ... -0.78334606 9.039537\n", + " -23.569172 ]\n", + " [-19.761091 1.1258256 16.75323 ... 3.3508904 11.598279\n", " -16.237118 ]]\n", "Shape of array: (2500, 512)\n" ] @@ -677,10 +677,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:30.457081Z", - "iopub.status.busy": "2023-08-14T16:34:30.456426Z", - "iopub.status.idle": "2023-08-14T16:34:36.144081Z", - "shell.execute_reply": "2023-08-14T16:34:36.143333Z" + "iopub.execute_input": "2023-08-15T04:05:43.257071Z", + "iopub.status.busy": "2023-08-15T04:05:43.256227Z", + "iopub.status.idle": "2023-08-15T04:05:53.661669Z", + "shell.execute_reply": "2023-08-15T04:05:53.660748Z" }, "id": "i_drkY9YOcw4" }, @@ -714,10 +714,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.148528Z", - "iopub.status.busy": "2023-08-14T16:34:36.147985Z", - "iopub.status.idle": "2023-08-14T16:34:36.155802Z", - "shell.execute_reply": "2023-08-14T16:34:36.155235Z" + "iopub.execute_input": "2023-08-15T04:05:53.666463Z", + "iopub.status.busy": "2023-08-15T04:05:53.665480Z", + "iopub.status.idle": "2023-08-15T04:05:53.677928Z", + "shell.execute_reply": "2023-08-15T04:05:53.676868Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -764,10 +764,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.160246Z", - "iopub.status.busy": "2023-08-14T16:34:36.158896Z", - "iopub.status.idle": "2023-08-14T16:34:36.268757Z", - "shell.execute_reply": "2023-08-14T16:34:36.267788Z" + "iopub.execute_input": "2023-08-15T04:05:53.685046Z", + "iopub.status.busy": "2023-08-15T04:05:53.683288Z", + "iopub.status.idle": "2023-08-15T04:05:53.842559Z", + "shell.execute_reply": "2023-08-15T04:05:53.841393Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.272961Z", - "iopub.status.busy": "2023-08-14T16:34:36.272352Z", - "iopub.status.idle": "2023-08-14T16:34:36.288028Z", - "shell.execute_reply": "2023-08-14T16:34:36.287351Z" + "iopub.execute_input": "2023-08-15T04:05:53.847371Z", + "iopub.status.busy": "2023-08-15T04:05:53.846891Z", + "iopub.status.idle": "2023-08-15T04:05:53.866752Z", + "shell.execute_reply": "2023-08-15T04:05:53.865809Z" }, "scrolled": true }, @@ -836,11 +836,11 @@ "\n", "Examples representing most severe instances of this issue:\n", " is_label_issue label_score given_label predicted_label\n", - "176 False 0.006548 7 8\n", - "2318 False 0.008296 3 6\n", - "986 False 0.010476 6 3\n", - "1946 True 0.012922 6 8\n", - "516 True 0.013263 6 8\n" + "176 False 0.006510 7 8\n", + "2318 False 0.008255 3 6\n", + "986 False 0.010404 6 3\n", + "1946 True 0.012856 6 8\n", + "469 True 0.013254 6 3\n" ] } ], @@ -862,10 +862,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.291140Z", - "iopub.status.busy": "2023-08-14T16:34:36.290665Z", - "iopub.status.idle": "2023-08-14T16:34:36.300713Z", - "shell.execute_reply": "2023-08-14T16:34:36.300006Z" + "iopub.execute_input": "2023-08-15T04:05:53.871163Z", + "iopub.status.busy": "2023-08-15T04:05:53.870826Z", + "iopub.status.idle": "2023-08-15T04:05:53.885810Z", + "shell.execute_reply": "2023-08-15T04:05:53.884701Z" } }, "outputs": [ @@ -900,35 +900,35 @@ " \n", " 0\n", " False\n", - " 0.100049\n", + " 0.100326\n", " 7\n", " 6\n", " \n", " \n", " 1\n", " False\n", - " 0.998728\n", + " 0.998723\n", " 0\n", " 0\n", " \n", " \n", " 2\n", " False\n", - " 0.998785\n", + " 0.998768\n", " 0\n", " 0\n", " \n", " \n", " 3\n", " False\n", - " 0.980984\n", + " 0.981028\n", " 8\n", " 8\n", " \n", " \n", " 4\n", " False\n", - " 0.998247\n", + " 0.998219\n", " 5\n", " 5\n", " \n", @@ -938,11 +938,11 @@ ], "text/plain": [ " is_label_issue label_score given_label predicted_label\n", - "0 False 0.100049 7 6\n", - "1 False 0.998728 0 0\n", - "2 False 0.998785 0 0\n", - "3 False 0.980984 8 8\n", - "4 False 0.998247 5 5" + "0 False 0.100326 7 6\n", + "1 False 0.998723 0 0\n", + "2 False 0.998768 0 0\n", + "3 False 0.981028 8 8\n", + "4 False 0.998219 5 5" ] }, "execution_count": 17, @@ -969,10 +969,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.304775Z", - "iopub.status.busy": "2023-08-14T16:34:36.304169Z", - "iopub.status.idle": "2023-08-14T16:34:36.309563Z", - "shell.execute_reply": "2023-08-14T16:34:36.308867Z" + "iopub.execute_input": "2023-08-15T04:05:53.891349Z", + "iopub.status.busy": "2023-08-15T04:05:53.891043Z", + "iopub.status.idle": "2023-08-15T04:05:53.898700Z", + "shell.execute_reply": "2023-08-15T04:05:53.897431Z" } }, "outputs": [ @@ -981,7 +981,7 @@ "output_type": "stream", "text": [ "Here are indices of the most likely errors: \n", - " [1946 516 469 1871 1955 2132]\n" + " [1946 469 516 1871 1955 2132]\n" ] } ], @@ -1010,10 +1010,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.312924Z", - "iopub.status.busy": "2023-08-14T16:34:36.312492Z", - "iopub.status.idle": "2023-08-14T16:34:36.321251Z", - "shell.execute_reply": "2023-08-14T16:34:36.320630Z" + "iopub.execute_input": "2023-08-15T04:05:53.904356Z", + "iopub.status.busy": "2023-08-15T04:05:53.904052Z", + "iopub.status.idle": "2023-08-15T04:05:53.915022Z", + "shell.execute_reply": "2023-08-15T04:05:53.913965Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1051,13 +1051,13 @@ " 6\n", " \n", " \n", - " 516\n", - " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav\n", + " 469\n", + " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_35.wav\n", " 6\n", " \n", " \n", - " 469\n", - " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_35.wav\n", + " 516\n", + " spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav\n", " 6\n", " \n", " \n", @@ -1082,16 +1082,16 @@ "text/plain": [ " wav_audio_file_path \\\n", "1946 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_14.wav \n", - "516 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav \n", "469 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_35.wav \n", + "516 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_yweweler_36.wav \n", "1871 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_theo_27.wav \n", "1955 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/4_george_31.wav \n", "2132 spoken_digits/free-spoken-digit-dataset-1.0.9/recordings/6_nicolas_8.wav \n", "\n", " label \n", "1946 6 \n", - "516 6 \n", "469 6 \n", + "516 6 \n", "1871 6 \n", "1955 4 \n", "2132 6 " @@ -1133,10 +1133,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.324459Z", - "iopub.status.busy": "2023-08-14T16:34:36.324114Z", - "iopub.status.idle": "2023-08-14T16:34:36.478689Z", - "shell.execute_reply": "2023-08-14T16:34:36.477918Z" + "iopub.execute_input": "2023-08-15T04:05:53.920241Z", + "iopub.status.busy": "2023-08-15T04:05:53.919956Z", + "iopub.status.idle": "2023-08-15T04:05:54.149960Z", + "shell.execute_reply": "2023-08-15T04:05:54.148803Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1190,10 +1190,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.482869Z", - "iopub.status.busy": "2023-08-14T16:34:36.482468Z", - "iopub.status.idle": "2023-08-14T16:34:36.628249Z", - "shell.execute_reply": "2023-08-14T16:34:36.627420Z" + "iopub.execute_input": "2023-08-15T04:05:54.154904Z", + "iopub.status.busy": "2023-08-15T04:05:54.154422Z", + "iopub.status.idle": "2023-08-15T04:05:54.369861Z", + "shell.execute_reply": "2023-08-15T04:05:54.368691Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1238,10 +1238,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.631458Z", - "iopub.status.busy": "2023-08-14T16:34:36.630952Z", - "iopub.status.idle": "2023-08-14T16:34:36.776318Z", - "shell.execute_reply": "2023-08-14T16:34:36.775525Z" + "iopub.execute_input": "2023-08-15T04:05:54.374625Z", + "iopub.status.busy": "2023-08-15T04:05:54.373926Z", + "iopub.status.idle": "2023-08-15T04:05:54.581120Z", + "shell.execute_reply": "2023-08-15T04:05:54.580253Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1282,10 +1282,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.779753Z", - "iopub.status.busy": "2023-08-14T16:34:36.779222Z", - "iopub.status.idle": "2023-08-14T16:34:36.924689Z", - "shell.execute_reply": "2023-08-14T16:34:36.923999Z" + "iopub.execute_input": "2023-08-15T04:05:54.585149Z", + "iopub.status.busy": "2023-08-15T04:05:54.584835Z", + "iopub.status.idle": "2023-08-15T04:05:54.784054Z", + "shell.execute_reply": "2023-08-15T04:05:54.783023Z" } }, "outputs": [ @@ -1333,10 +1333,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:36.927897Z", - "iopub.status.busy": "2023-08-14T16:34:36.927490Z", - "iopub.status.idle": "2023-08-14T16:34:36.932273Z", - "shell.execute_reply": "2023-08-14T16:34:36.931540Z" + "iopub.execute_input": "2023-08-15T04:05:54.787903Z", + "iopub.status.busy": 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Functionality 1: Incremental issue search @@ -1287,7 +1287,7 @@

Functionality 2: Specifying nondefault arguments 
-/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:219: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.
+/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:219: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.
   warnings.warn(
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   warnings.warn(
@@ -1387,8 +1387,8 @@ 
Functionality 2: Specifying nondefault arguments
-
+
@@ -1509,8 +1509,8 @@

Functionality 3: Save and load Datalab objects

diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index e9f6aff95..f316857e1 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:42.915488Z", - "iopub.status.busy": "2023-08-14T16:34:42.915086Z", - "iopub.status.idle": "2023-08-14T16:34:44.217392Z", - "shell.execute_reply": "2023-08-14T16:34:44.216629Z" + "iopub.execute_input": "2023-08-15T04:06:00.459386Z", + "iopub.status.busy": "2023-08-15T04:06:00.459070Z", + "iopub.status.idle": "2023-08-15T04:06:02.160285Z", + "shell.execute_reply": "2023-08-15T04:06:02.159205Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.221436Z", - "iopub.status.busy": "2023-08-14T16:34:44.220914Z", - "iopub.status.idle": "2023-08-14T16:34:44.225581Z", - "shell.execute_reply": "2023-08-14T16:34:44.224968Z" + "iopub.execute_input": "2023-08-15T04:06:02.165128Z", + "iopub.status.busy": "2023-08-15T04:06:02.164618Z", + "iopub.status.idle": "2023-08-15T04:06:02.171560Z", + "shell.execute_reply": "2023-08-15T04:06:02.170528Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.229924Z", - "iopub.status.busy": "2023-08-14T16:34:44.228919Z", - "iopub.status.idle": "2023-08-14T16:34:44.241570Z", - "shell.execute_reply": "2023-08-14T16:34:44.240961Z" + "iopub.execute_input": "2023-08-15T04:06:02.175888Z", + "iopub.status.busy": "2023-08-15T04:06:02.175604Z", + "iopub.status.idle": "2023-08-15T04:06:02.190133Z", + "shell.execute_reply": "2023-08-15T04:06:02.188944Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.244401Z", - "iopub.status.busy": "2023-08-14T16:34:44.244038Z", - "iopub.status.idle": "2023-08-14T16:34:44.251620Z", - "shell.execute_reply": "2023-08-14T16:34:44.250955Z" + "iopub.execute_input": "2023-08-15T04:06:02.194092Z", + "iopub.status.busy": "2023-08-15T04:06:02.193693Z", + "iopub.status.idle": "2023-08-15T04:06:02.203440Z", + "shell.execute_reply": "2023-08-15T04:06:02.202456Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:44.254956Z", - "iopub.status.busy": "2023-08-14T16:34:44.254553Z", - "iopub.status.idle": "2023-08-14T16:34:45.279737Z", - "shell.execute_reply": "2023-08-14T16:34:45.278867Z" + "iopub.execute_input": "2023-08-15T04:06:02.207966Z", + "iopub.status.busy": "2023-08-15T04:06:02.207677Z", + "iopub.status.idle": "2023-08-15T04:06:03.386462Z", + "shell.execute_reply": "2023-08-15T04:06:03.384766Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.283496Z", - "iopub.status.busy": "2023-08-14T16:34:45.282988Z", - "iopub.status.idle": "2023-08-14T16:34:45.681053Z", - "shell.execute_reply": "2023-08-14T16:34:45.680429Z" + "iopub.execute_input": "2023-08-15T04:06:03.390865Z", + "iopub.status.busy": "2023-08-15T04:06:03.390461Z", + "iopub.status.idle": "2023-08-15T04:06:04.027635Z", + "shell.execute_reply": "2023-08-15T04:06:04.026575Z" } }, "outputs": [ @@ -568,10 +568,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.684965Z", - "iopub.status.busy": "2023-08-14T16:34:45.684453Z", - "iopub.status.idle": "2023-08-14T16:34:45.711962Z", - "shell.execute_reply": "2023-08-14T16:34:45.711414Z" + "iopub.execute_input": "2023-08-15T04:06:04.031977Z", + "iopub.status.busy": "2023-08-15T04:06:04.031651Z", + "iopub.status.idle": "2023-08-15T04:06:04.082638Z", + "shell.execute_reply": "2023-08-15T04:06:04.081292Z" } }, "outputs": [], @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.715263Z", - "iopub.status.busy": "2023-08-14T16:34:45.714798Z", - "iopub.status.idle": "2023-08-14T16:34:45.731186Z", - "shell.execute_reply": "2023-08-14T16:34:45.730418Z" + "iopub.execute_input": "2023-08-15T04:06:04.087418Z", + "iopub.status.busy": "2023-08-15T04:06:04.086643Z", + "iopub.status.idle": "2023-08-15T04:06:04.108629Z", + "shell.execute_reply": "2023-08-15T04:06:04.107643Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:45.734368Z", - "iopub.status.busy": "2023-08-14T16:34:45.733830Z", - "iopub.status.idle": "2023-08-14T16:34:47.408298Z", - "shell.execute_reply": "2023-08-14T16:34:47.407504Z" + "iopub.execute_input": "2023-08-15T04:06:04.113569Z", + "iopub.status.busy": "2023-08-15T04:06:04.112999Z", + "iopub.status.idle": "2023-08-15T04:06:06.354211Z", + "shell.execute_reply": "2023-08-15T04:06:06.352263Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.412393Z", - "iopub.status.busy": "2023-08-14T16:34:47.411424Z", - "iopub.status.idle": "2023-08-14T16:34:47.444902Z", - "shell.execute_reply": "2023-08-14T16:34:47.444133Z" + "iopub.execute_input": "2023-08-15T04:06:06.359511Z", + "iopub.status.busy": "2023-08-15T04:06:06.358878Z", + "iopub.status.idle": "2023-08-15T04:06:06.410428Z", + "shell.execute_reply": "2023-08-15T04:06:06.409305Z" } }, "outputs": [ @@ -789,8 +789,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] } @@ -820,10 +820,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.448218Z", - "iopub.status.busy": "2023-08-14T16:34:47.447809Z", - "iopub.status.idle": "2023-08-14T16:34:47.474076Z", - "shell.execute_reply": "2023-08-14T16:34:47.473299Z" + "iopub.execute_input": "2023-08-15T04:06:06.414451Z", + "iopub.status.busy": "2023-08-15T04:06:06.414051Z", + "iopub.status.idle": "2023-08-15T04:06:06.456082Z", + "shell.execute_reply": "2023-08-15T04:06:06.453835Z" } }, "outputs": [ @@ -900,8 +900,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] }, @@ -909,7 +909,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:219: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n", + "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:219: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.\n", " warnings.warn(\n", "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:249: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.\n", " warnings.warn(\n", @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.477441Z", - "iopub.status.busy": "2023-08-14T16:34:47.476865Z", - "iopub.status.idle": "2023-08-14T16:34:47.495517Z", - "shell.execute_reply": "2023-08-14T16:34:47.494834Z" + "iopub.execute_input": "2023-08-15T04:06:06.461887Z", + "iopub.status.busy": "2023-08-15T04:06:06.461204Z", + "iopub.status.idle": "2023-08-15T04:06:06.487468Z", + "shell.execute_reply": "2023-08-15T04:06:06.486486Z" } }, "outputs": [ @@ -1024,8 +1024,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n", "52 False 3.857172e-02 [] 3.859087e-02\n", "5 False 4.085049e-02 [] 4.087324e-02\n", @@ -1068,17 +1068,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.498979Z", - "iopub.status.busy": "2023-08-14T16:34:47.498455Z", - "iopub.status.idle": "2023-08-14T16:34:47.528844Z", - "shell.execute_reply": "2023-08-14T16:34:47.528298Z" + "iopub.execute_input": "2023-08-15T04:06:06.491363Z", + "iopub.status.busy": "2023-08-15T04:06:06.490997Z", + "iopub.status.idle": "2023-08-15T04:06:06.543482Z", + "shell.execute_reply": "2023-08-15T04:06:06.542489Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b47b66d984614e9a892970ceaf1af373", + "model_id": "ad27969aed8544379888325be1f8523a", "version_major": 2, "version_minor": 0 }, @@ -1093,7 +1093,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved Datalab to folder: datalab-files\n" + "Saved Datalab to folder: datalab-files" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -1114,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.531903Z", - "iopub.status.busy": "2023-08-14T16:34:47.531204Z", - "iopub.status.idle": "2023-08-14T16:34:47.549746Z", - "shell.execute_reply": "2023-08-14T16:34:47.549202Z" + "iopub.execute_input": "2023-08-15T04:06:06.548878Z", + "iopub.status.busy": "2023-08-15T04:06:06.548341Z", + "iopub.status.idle": "2023-08-15T04:06:06.576112Z", + "shell.execute_reply": "2023-08-15T04:06:06.575247Z" } }, "outputs": [ @@ -1125,7 +1132,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Datalab loaded from folder: datalab-files\n", + "Datalab loaded from folder: datalab-files\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Here is a summary of the different kinds of issues found in the data:\n", "\n", " issue_type num_issues\n", @@ -1191,8 +1204,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] } @@ -1235,10 +1248,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.552574Z", - "iopub.status.busy": "2023-08-14T16:34:47.552133Z", - "iopub.status.idle": "2023-08-14T16:34:47.559455Z", - "shell.execute_reply": "2023-08-14T16:34:47.558906Z" + "iopub.execute_input": "2023-08-15T04:06:06.580499Z", + "iopub.status.busy": "2023-08-15T04:06:06.579988Z", + "iopub.status.idle": "2023-08-15T04:06:06.589379Z", + "shell.execute_reply": "2023-08-15T04:06:06.588523Z" } }, "outputs": [], @@ -1295,10 +1308,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:34:47.562288Z", - "iopub.status.busy": "2023-08-14T16:34:47.561852Z", - "iopub.status.idle": "2023-08-14T16:34:47.584937Z", - "shell.execute_reply": "2023-08-14T16:34:47.584392Z" + "iopub.execute_input": "2023-08-15T04:06:06.593272Z", + "iopub.status.busy": "2023-08-15T04:06:06.592675Z", + "iopub.status.idle": "2023-08-15T04:06:06.626746Z", + "shell.execute_reply": "2023-08-15T04:06:06.625887Z" } }, "outputs": [ @@ -1308,7 +1321,13 @@ "text": [ "Finding superstition issues ...\n", "\n", - "Audit complete. 32 issues found in the dataset.\n", + "Audit complete. 32 issues found in the dataset.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Here is a summary of the different kinds of issues found in the data:\n", "\n", " issue_type num_issues\n", @@ -1392,8 +1411,8 @@ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", "131 True 0.000000e+00 [123] 0.000000e+00\n", "123 True 0.000000e+00 [131] 0.000000e+00\n", - "130 True 4.463180e-07 [129] 4.463180e-07\n", "129 True 4.463180e-07 [130] 4.463180e-07\n", + "130 True 4.463180e-07 [129] 4.463180e-07\n", "51 False 3.857172e-02 [] 3.859087e-02\n" ] } @@ -1430,7 +1449,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1704a258aa2a41abb4ebb25e2cd9cb32": { + "494bccd68e0743bfa37eb2b9493479f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1482,38 +1501,46 @@ "width": null } }, - 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"description_tooltip": null, - "layout": "IPY_MODEL_1704a258aa2a41abb4ebb25e2cd9cb32", - "placeholder": "​", - "style": "IPY_MODEL_2465a04ab4f743fab176ee76ef39432c", - "value": "Saving the dataset (1/1 shards): 100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e4aefd00e11f43678813f3104864bee7", + "IPY_MODEL_837cff768e4640399025b2ed80394839", + "IPY_MODEL_9bda83e0c83245e0856e074b0a1c7d20" + ], + "layout": "IPY_MODEL_aaa7ec5cac3c4be18aa6ed8d119542cc" } }, - "b2d0e8a6aea940fdb0a42bb39f10d1f7": { + "c09fab7849b74c2cb34a734ea442fb24": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1698,29 +1687,7 @@ "width": null } }, - "b47b66d984614e9a892970ceaf1af373": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_933507d3a9eb4b1e8f2c2503bbbd3fcf", - "IPY_MODEL_87b29f22e4924236b12c26d71d96eba1", - "IPY_MODEL_2ce18b2ba28d4929bfc62773f2ead3c1" - ], - "layout": "IPY_MODEL_f3325770367e4d159dd977e63845a611" - } - }, - "f3325770367e4d159dd977e63845a611": { + "d34287291a56450c9e230d712a3e37a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1768,9 +1735,61 @@ "padding": null, "right": null, "top": null, - "visibility": "hidden", + "visibility": null, "width": null } + }, + "e4aefd00e11f43678813f3104864bee7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_494bccd68e0743bfa37eb2b9493479f2", + "placeholder": "​", + "style": "IPY_MODEL_f18d2992faa84c939e0a1f8c4d2da47e", + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "f18d2992faa84c939e0a1f8c4d2da47e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fef2ac9b21624c6fb5353408f5bc17dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.html b/master/tutorials/datalab/datalab_quickstart.html index 25608e1a2..05985357a 100644 --- a/master/tutorials/datalab/datalab_quickstart.html +++ b/master/tutorials/datalab/datalab_quickstart.html @@ -1150,8 +1150,8 @@

4. Use Datalab to find issues in the dataset5. Use cleanlab to find label issues @@ -1430,38 +1430,38 @@

Near-duplicate issues - 915 + 690 True 0.0 - [168] + [246] 0.0 - 294 + 185 True 0.0 - [691] + [582] 0.0 - 12 + 691 True 0.0 - [2, 1, 6, 9] + [294] 0.0 - 9 + 168 True 0.0 - [2, 12, 1, 6] + [915] 0.0 - 185 + 187 True 0.0 - [582] + [27] 0.0 diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 86f119404..387b9504c 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:02.227177Z", - "iopub.status.busy": "2023-08-14T16:35:02.226903Z", - "iopub.status.idle": "2023-08-14T16:35:03.428497Z", - "shell.execute_reply": "2023-08-14T16:35:03.427456Z" + "iopub.execute_input": "2023-08-15T04:06:24.339018Z", + "iopub.status.busy": "2023-08-15T04:06:24.338541Z", + "iopub.status.idle": "2023-08-15T04:06:25.871822Z", + "shell.execute_reply": "2023-08-15T04:06:25.870795Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.432932Z", - "iopub.status.busy": "2023-08-14T16:35:03.432110Z", - "iopub.status.idle": "2023-08-14T16:35:03.490575Z", - "shell.execute_reply": "2023-08-14T16:35:03.489827Z" + "iopub.execute_input": "2023-08-15T04:06:25.876083Z", + "iopub.status.busy": "2023-08-15T04:06:25.875613Z", + "iopub.status.idle": "2023-08-15T04:06:25.957591Z", + "shell.execute_reply": "2023-08-15T04:06:25.956593Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.494677Z", - "iopub.status.busy": "2023-08-14T16:35:03.494257Z", - "iopub.status.idle": "2023-08-14T16:35:03.783524Z", - "shell.execute_reply": "2023-08-14T16:35:03.782784Z" + "iopub.execute_input": "2023-08-15T04:06:25.962347Z", + "iopub.status.busy": "2023-08-15T04:06:25.961851Z", + "iopub.status.idle": "2023-08-15T04:06:26.378085Z", + "shell.execute_reply": "2023-08-15T04:06:26.375960Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.786897Z", - "iopub.status.busy": "2023-08-14T16:35:03.786499Z", - "iopub.status.idle": "2023-08-14T16:35:03.792961Z", - "shell.execute_reply": "2023-08-14T16:35:03.791865Z" + "iopub.execute_input": "2023-08-15T04:06:26.382009Z", + "iopub.status.busy": "2023-08-15T04:06:26.381651Z", + "iopub.status.idle": "2023-08-15T04:06:26.387860Z", + "shell.execute_reply": "2023-08-15T04:06:26.386877Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.796289Z", - "iopub.status.busy": "2023-08-14T16:35:03.795763Z", - "iopub.status.idle": "2023-08-14T16:35:03.806352Z", - "shell.execute_reply": "2023-08-14T16:35:03.805577Z" + "iopub.execute_input": "2023-08-15T04:06:26.391990Z", + "iopub.status.busy": "2023-08-15T04:06:26.391639Z", + "iopub.status.idle": "2023-08-15T04:06:26.408482Z", + "shell.execute_reply": "2023-08-15T04:06:26.407395Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.809681Z", - "iopub.status.busy": "2023-08-14T16:35:03.809137Z", - "iopub.status.idle": "2023-08-14T16:35:03.812266Z", - "shell.execute_reply": "2023-08-14T16:35:03.811600Z" + "iopub.execute_input": "2023-08-15T04:06:26.412913Z", + "iopub.status.busy": "2023-08-15T04:06:26.412614Z", + "iopub.status.idle": "2023-08-15T04:06:26.416768Z", + "shell.execute_reply": "2023-08-15T04:06:26.415747Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:03.815427Z", - "iopub.status.busy": "2023-08-14T16:35:03.814817Z", - "iopub.status.idle": "2023-08-14T16:35:09.165751Z", - "shell.execute_reply": "2023-08-14T16:35:09.165025Z" + "iopub.execute_input": "2023-08-15T04:06:26.420826Z", + "iopub.status.busy": "2023-08-15T04:06:26.420503Z", + "iopub.status.idle": "2023-08-15T04:06:34.308983Z", + "shell.execute_reply": "2023-08-15T04:06:34.307885Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:09.170070Z", - "iopub.status.busy": "2023-08-14T16:35:09.169399Z", - "iopub.status.idle": "2023-08-14T16:35:09.181712Z", - "shell.execute_reply": "2023-08-14T16:35:09.181076Z" + "iopub.execute_input": "2023-08-15T04:06:34.313717Z", + "iopub.status.busy": "2023-08-15T04:06:34.312990Z", + "iopub.status.idle": "2023-08-15T04:06:34.328344Z", + "shell.execute_reply": "2023-08-15T04:06:34.327513Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:09.184943Z", - "iopub.status.busy": "2023-08-14T16:35:09.184566Z", - "iopub.status.idle": "2023-08-14T16:35:10.830327Z", - "shell.execute_reply": "2023-08-14T16:35:10.828663Z" + "iopub.execute_input": "2023-08-15T04:06:34.332988Z", + "iopub.status.busy": "2023-08-15T04:06:34.332018Z", + "iopub.status.idle": "2023-08-15T04:06:36.543615Z", + "shell.execute_reply": "2023-08-15T04:06:36.542578Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.834565Z", - "iopub.status.busy": "2023-08-14T16:35:10.833819Z", - "iopub.status.idle": "2023-08-14T16:35:10.856910Z", - "shell.execute_reply": "2023-08-14T16:35:10.856116Z" + "iopub.execute_input": "2023-08-15T04:06:36.548564Z", + "iopub.status.busy": "2023-08-15T04:06:36.547711Z", + "iopub.status.idle": "2023-08-15T04:06:36.577076Z", + "shell.execute_reply": "2023-08-15T04:06:36.576297Z" }, "scrolled": true }, @@ -551,11 +551,11 @@ "\n", "Examples representing most severe instances of this issue:\n", " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", - "915 True 0.0 [168] 0.0\n", - "294 True 0.0 [691] 0.0\n", - "12 True 0.0 [2, 1, 6, 9] 0.0\n", - "9 True 0.0 [2, 12, 1, 6] 0.0\n", - "185 True 0.0 [582] 0.0\n" + "690 True 0.0 [246] 0.0\n", + "185 True 0.0 [582] 0.0\n", + "691 True 0.0 [294] 0.0\n", + "168 True 0.0 [915] 0.0\n", + "187 True 0.0 [27] 0.0\n" ] } ], @@ -577,10 +577,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.860336Z", - "iopub.status.busy": "2023-08-14T16:35:10.859870Z", - "iopub.status.idle": "2023-08-14T16:35:10.872563Z", - "shell.execute_reply": "2023-08-14T16:35:10.871903Z" + "iopub.execute_input": "2023-08-15T04:06:36.581022Z", + "iopub.status.busy": "2023-08-15T04:06:36.580768Z", + "iopub.status.idle": "2023-08-15T04:06:36.594191Z", + "shell.execute_reply": "2023-08-15T04:06:36.593380Z" } }, "outputs": [ @@ -684,10 +684,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.875499Z", - "iopub.status.busy": "2023-08-14T16:35:10.875049Z", - "iopub.status.idle": "2023-08-14T16:35:10.886836Z", - "shell.execute_reply": "2023-08-14T16:35:10.886133Z" + "iopub.execute_input": "2023-08-15T04:06:36.597642Z", + "iopub.status.busy": "2023-08-15T04:06:36.597392Z", + "iopub.status.idle": "2023-08-15T04:06:36.614765Z", + "shell.execute_reply": "2023-08-15T04:06:36.613773Z" } }, "outputs": [ @@ -816,10 +816,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.890115Z", - "iopub.status.busy": "2023-08-14T16:35:10.889558Z", - "iopub.status.idle": "2023-08-14T16:35:10.899616Z", - "shell.execute_reply": "2023-08-14T16:35:10.898918Z" + "iopub.execute_input": "2023-08-15T04:06:36.619010Z", + "iopub.status.busy": "2023-08-15T04:06:36.618732Z", + "iopub.status.idle": "2023-08-15T04:06:36.631223Z", + "shell.execute_reply": "2023-08-15T04:06:36.630047Z" } }, "outputs": [ @@ -933,10 +933,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.902603Z", - "iopub.status.busy": "2023-08-14T16:35:10.902236Z", - "iopub.status.idle": "2023-08-14T16:35:10.913787Z", - "shell.execute_reply": "2023-08-14T16:35:10.913056Z" + "iopub.execute_input": "2023-08-15T04:06:36.635132Z", + "iopub.status.busy": "2023-08-15T04:06:36.634834Z", + "iopub.status.idle": "2023-08-15T04:06:36.647954Z", + "shell.execute_reply": "2023-08-15T04:06:36.647001Z" } }, "outputs": [ @@ -969,38 +969,38 @@ " \n", " \n", " \n", - " 915\n", + " 690\n", " True\n", " 0.0\n", - " [168]\n", + " [246]\n", " 0.0\n", " \n", " \n", - " 294\n", + " 185\n", " True\n", " 0.0\n", - " [691]\n", + " [582]\n", " 0.0\n", " \n", " \n", - " 12\n", + " 691\n", " True\n", " 0.0\n", - " [2, 1, 6, 9]\n", + " [294]\n", " 0.0\n", " \n", " \n", - " 9\n", + " 168\n", " True\n", " 0.0\n", - " [2, 12, 1, 6]\n", + " [915]\n", " 0.0\n", " \n", " \n", - " 185\n", + " 187\n", " True\n", " 0.0\n", - " [582]\n", + " [27]\n", " 0.0\n", " \n", " \n", @@ -1009,18 +1009,18 @@ ], "text/plain": [ " is_near_duplicate_issue near_duplicate_score near_duplicate_sets \\\n", - "915 True 0.0 [168] \n", - "294 True 0.0 [691] \n", - "12 True 0.0 [2, 1, 6, 9] \n", - "9 True 0.0 [2, 12, 1, 6] \n", + "690 True 0.0 [246] \n", "185 True 0.0 [582] \n", + "691 True 0.0 [294] \n", + "168 True 0.0 [915] \n", + "187 True 0.0 [27] \n", "\n", " distance_to_nearest_neighbor \n", - "915 0.0 \n", - "294 0.0 \n", - "12 0.0 \n", - "9 0.0 \n", - "185 0.0 " + "690 0.0 \n", + "185 0.0 \n", + "691 0.0 \n", + "168 0.0 \n", + "187 0.0 " ] }, "execution_count": 14, @@ -1047,10 +1047,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.916620Z", - "iopub.status.busy": "2023-08-14T16:35:10.916256Z", - "iopub.status.idle": "2023-08-14T16:35:10.924387Z", - "shell.execute_reply": "2023-08-14T16:35:10.923680Z" + "iopub.execute_input": "2023-08-15T04:06:36.651799Z", + "iopub.status.busy": "2023-08-15T04:06:36.651522Z", + "iopub.status.idle": "2023-08-15T04:06:36.662712Z", + "shell.execute_reply": "2023-08-15T04:06:36.661634Z" } }, "outputs": [ @@ -1134,10 +1134,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.928211Z", - "iopub.status.busy": "2023-08-14T16:35:10.927783Z", - "iopub.status.idle": "2023-08-14T16:35:10.935693Z", - "shell.execute_reply": "2023-08-14T16:35:10.935004Z" + "iopub.execute_input": "2023-08-15T04:06:36.666802Z", + "iopub.status.busy": "2023-08-15T04:06:36.666383Z", + "iopub.status.idle": "2023-08-15T04:06:36.677129Z", + "shell.execute_reply": "2023-08-15T04:06:36.676261Z" } }, "outputs": [ @@ -1221,10 +1221,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:10.939524Z", - "iopub.status.busy": "2023-08-14T16:35:10.939096Z", - "iopub.status.idle": "2023-08-14T16:35:10.947356Z", - "shell.execute_reply": "2023-08-14T16:35:10.946744Z" + "iopub.execute_input": "2023-08-15T04:06:36.682148Z", + "iopub.status.busy": "2023-08-15T04:06:36.681858Z", + "iopub.status.idle": "2023-08-15T04:06:36.692440Z", + "shell.execute_reply": "2023-08-15T04:06:36.691471Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index f1a8d5e9f..d75ca9be3 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -924,7 +924,7 @@

2. Load and format the text dataset 
 This dataset has 10 classes.
-Classes: {'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'cancel_transfer'}
+Classes: {'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin'}

Let’s view the i-th example in the dataset:

@@ -971,43 +971,43 @@

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1015,7 +1015,7 @@

2. Load and format the text dataset 
 No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.
-Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense.weight']
+Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight']
 - This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
 - This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

@@ -1114,8 +1114,8 @@

4. Use cleanlab to find issues in your dataset - 148 + 160 True 0.006237 - [160] + [148] 0.006237 - 160 + 148 True 0.006237 - [148] + [160] 0.006237 - 514 + 546 True 0.006485 - [546] + [514] 0.006485 - 546 + 514 True 0.006485 - [514] + [546] 0.006485 481 False - 0.008165 + 0.008164 [] 0.008165 @@ -1694,7 +1694,7 @@

Near-duplicate issuesWe see that these two sets of request are indeed very similar to one another! Including near duplicates in a dataset may have unintended effects on models, and be wary about splitting them across training/test sets.

As demonstrated above, cleanlab can automatically shortlist the most likely issues in your dataset to help you better curate your dataset for subsequent modeling. With this shortlist, you can decide whether to fix these label issues or remove nonsensical or duplicated examples from your dataset to obtain a higher-quality dataset for training your next ML model. cleanlab’s issue detection can be run with outputs from any type of model you initially trained.

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 504833002..fdaa7dbee 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:16.394911Z", - "iopub.status.busy": "2023-08-14T16:35:16.394459Z", - "iopub.status.idle": "2023-08-14T16:35:19.711881Z", - "shell.execute_reply": "2023-08-14T16:35:19.711099Z" + "iopub.execute_input": "2023-08-15T04:06:42.702366Z", + "iopub.status.busy": "2023-08-15T04:06:42.701728Z", + "iopub.status.idle": "2023-08-15T04:06:46.542642Z", + "shell.execute_reply": "2023-08-15T04:06:46.541476Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef7ea2d184234585b196aaa1e6dfa9c6", + "model_id": "056557d1d4234df2b477a1bed7b33975", "version_major": 2, "version_minor": 0 }, @@ -118,7 +118,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.716204Z", - "iopub.status.busy": "2023-08-14T16:35:19.715631Z", - "iopub.status.idle": "2023-08-14T16:35:19.721608Z", - "shell.execute_reply": "2023-08-14T16:35:19.720566Z" + "iopub.execute_input": "2023-08-15T04:06:46.547123Z", + "iopub.status.busy": "2023-08-15T04:06:46.546604Z", + "iopub.status.idle": "2023-08-15T04:06:46.553528Z", + "shell.execute_reply": "2023-08-15T04:06:46.552604Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.724516Z", - "iopub.status.busy": "2023-08-14T16:35:19.724158Z", - "iopub.status.idle": "2023-08-14T16:35:19.727985Z", - "shell.execute_reply": "2023-08-14T16:35:19.727287Z" + "iopub.execute_input": "2023-08-15T04:06:46.557507Z", + "iopub.status.busy": "2023-08-15T04:06:46.557071Z", + "iopub.status.idle": "2023-08-15T04:06:46.563080Z", + "shell.execute_reply": "2023-08-15T04:06:46.562193Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.731033Z", - "iopub.status.busy": "2023-08-14T16:35:19.730672Z", - "iopub.status.idle": "2023-08-14T16:35:19.786572Z", - "shell.execute_reply": "2023-08-14T16:35:19.785812Z" + "iopub.execute_input": "2023-08-15T04:06:46.567085Z", + "iopub.status.busy": "2023-08-15T04:06:46.566592Z", + "iopub.status.idle": "2023-08-15T04:06:46.702263Z", + "shell.execute_reply": "2023-08-15T04:06:46.701336Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.790288Z", - "iopub.status.busy": "2023-08-14T16:35:19.789903Z", - "iopub.status.idle": "2023-08-14T16:35:19.794671Z", - "shell.execute_reply": "2023-08-14T16:35:19.793954Z" + "iopub.execute_input": "2023-08-15T04:06:46.706673Z", + "iopub.status.busy": "2023-08-15T04:06:46.706360Z", + "iopub.status.idle": "2023-08-15T04:06:46.714530Z", + "shell.execute_reply": "2023-08-15T04:06:46.713394Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_about_to_expire', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'cancel_transfer'}\n" + "Classes: {'cancel_transfer', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.798638Z", - "iopub.status.busy": "2023-08-14T16:35:19.798206Z", - "iopub.status.idle": "2023-08-14T16:35:19.802243Z", - "shell.execute_reply": "2023-08-14T16:35:19.801507Z" + "iopub.execute_input": "2023-08-15T04:06:46.717770Z", + "iopub.status.busy": "2023-08-15T04:06:46.717487Z", + "iopub.status.idle": "2023-08-15T04:06:46.722112Z", + "shell.execute_reply": "2023-08-15T04:06:46.721230Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:19.806022Z", - "iopub.status.busy": "2023-08-14T16:35:19.805568Z", - "iopub.status.idle": "2023-08-14T16:35:24.146211Z", - "shell.execute_reply": "2023-08-14T16:35:24.145149Z" + "iopub.execute_input": "2023-08-15T04:06:46.726330Z", + "iopub.status.busy": "2023-08-15T04:06:46.726030Z", + "iopub.status.idle": "2023-08-15T04:06:55.095272Z", + "shell.execute_reply": "2023-08-15T04:06:55.094045Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "854eceb183194d189d0644b94667a047", + "model_id": "78850cee016a4f549acb251e6907ec97", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38452b07808248799b994855f08c50f5", + "model_id": "27bea9547d554a1ebcce66dbf2f2123e", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "233135365c17440296d98899c896e852", + "model_id": "cb1e9c0f8ed84a158eb60a50605a5624", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f4215ba6842043d29bbd0b4489ad485c", + "model_id": "e670054d6e664342b8be1415cc2a08fd", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29692c2c1db748a893cfc1fb8abce2a3", + "model_id": "7ee46b182acc43ecb3825e2ce4fe7ee1", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a4cc6539e8c477da71b89a59c875743", + "model_id": "f8a45b46d98b455aa559106b59bfbb8f", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe05db40755d4e3da22eb5a28e07998a", + "model_id": "b785bc01e99945f493b4762b8ca2f014", "version_major": 2, "version_minor": 0 }, @@ -503,7 +503,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense.weight']\n", + "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight']\n", "- This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" ] @@ -544,10 +544,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:24.150734Z", - "iopub.status.busy": "2023-08-14T16:35:24.150339Z", - "iopub.status.idle": "2023-08-14T16:35:25.416439Z", - "shell.execute_reply": "2023-08-14T16:35:25.415713Z" + "iopub.execute_input": "2023-08-15T04:06:55.100814Z", + "iopub.status.busy": "2023-08-15T04:06:55.100494Z", + "iopub.status.idle": "2023-08-15T04:06:57.178018Z", + "shell.execute_reply": "2023-08-15T04:06:57.177086Z" }, "scrolled": true }, @@ -579,10 +579,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:25.421098Z", - "iopub.status.busy": "2023-08-14T16:35:25.420569Z", - "iopub.status.idle": "2023-08-14T16:35:25.425218Z", - "shell.execute_reply": "2023-08-14T16:35:25.424673Z" + "iopub.execute_input": "2023-08-15T04:06:57.182975Z", + "iopub.status.busy": "2023-08-15T04:06:57.181979Z", + "iopub.status.idle": "2023-08-15T04:06:57.186272Z", + "shell.execute_reply": "2023-08-15T04:06:57.185484Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:25.428714Z", - "iopub.status.busy": "2023-08-14T16:35:25.428300Z", - "iopub.status.idle": "2023-08-14T16:35:27.143901Z", - "shell.execute_reply": "2023-08-14T16:35:27.142970Z" + "iopub.execute_input": "2023-08-15T04:06:57.190330Z", + "iopub.status.busy": "2023-08-15T04:06:57.189507Z", + "iopub.status.idle": "2023-08-15T04:06:59.455915Z", + "shell.execute_reply": "2023-08-15T04:06:59.454479Z" }, "scrolled": true }, @@ -647,10 +647,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.148606Z", - "iopub.status.busy": "2023-08-14T16:35:27.148067Z", - "iopub.status.idle": "2023-08-14T16:35:27.174259Z", - "shell.execute_reply": "2023-08-14T16:35:27.173541Z" + "iopub.execute_input": "2023-08-15T04:06:59.460606Z", + "iopub.status.busy": "2023-08-15T04:06:59.459998Z", + "iopub.status.idle": "2023-08-15T04:06:59.496947Z", + "shell.execute_reply": "2023-08-15T04:06:59.495685Z" }, "scrolled": true }, @@ -685,8 +685,8 @@ "981 True 0.000005 card_about_to_expire card_payment_fee_charged\n", "974 True 0.000150 beneficiary_not_allowed change_pin\n", "982 True 0.000219 apple_pay_or_google_pay card_about_to_expire\n", - "990 True 0.000326 apple_pay_or_google_pay beneficiary_not_allowed\n", - "971 True 0.000512 beneficiary_not_allowed change_pin\n", + "990 True 0.000329 apple_pay_or_google_pay beneficiary_not_allowed\n", + "971 True 0.000511 beneficiary_not_allowed change_pin\n", "\n", "\n", "---------------------- outlier issues ----------------------\n", @@ -723,11 +723,11 @@ "\n", "Examples representing most severe instances of this issue:\n", " is_near_duplicate_issue near_duplicate_score near_duplicate_sets distance_to_nearest_neighbor\n", - "148 True 0.006237 [160] 0.006237\n", "160 True 0.006237 [148] 0.006237\n", - "514 True 0.006485 [546] 0.006485\n", + "148 True 0.006237 [160] 0.006237\n", "546 True 0.006485 [514] 0.006485\n", - "481 False 0.008165 [] 0.008165\n", + "514 True 0.006485 [546] 0.006485\n", + "481 False 0.008164 [] 0.008165\n", "\n", "\n", "---------------------- non_iid issues ----------------------\n", @@ -775,10 +775,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.177966Z", - "iopub.status.busy": "2023-08-14T16:35:27.177684Z", - "iopub.status.idle": "2023-08-14T16:35:27.190717Z", - "shell.execute_reply": "2023-08-14T16:35:27.190081Z" + "iopub.execute_input": "2023-08-15T04:06:59.500605Z", + "iopub.status.busy": "2023-08-15T04:06:59.500287Z", + "iopub.status.idle": "2023-08-15T04:06:59.515550Z", + "shell.execute_reply": "2023-08-15T04:06:59.514589Z" }, "scrolled": true }, @@ -814,35 +814,35 @@ " \n", " 0\n", " False\n", - " 0.806101\n", + " 0.806028\n", " cancel_transfer\n", " cancel_transfer\n", " \n", " \n", " 1\n", " False\n", - " 0.270940\n", + " 0.270972\n", " cancel_transfer\n", " cancel_transfer\n", " \n", " \n", " 2\n", " False\n", - " 0.695413\n", + " 0.695399\n", " cancel_transfer\n", " cancel_transfer\n", " \n", " \n", " 3\n", " False\n", - " 0.179787\n", + " 0.179756\n", " cancel_transfer\n", " apple_pay_or_google_pay\n", " \n", " \n", " 4\n", " False\n", - " 0.822664\n", + " 0.822703\n", " cancel_transfer\n", " cancel_transfer\n", " \n", @@ -852,11 +852,11 @@ ], "text/plain": [ " is_label_issue label_score given_label predicted_label\n", - "0 False 0.806101 cancel_transfer cancel_transfer\n", - "1 False 0.270940 cancel_transfer cancel_transfer\n", - "2 False 0.695413 cancel_transfer cancel_transfer\n", - "3 False 0.179787 cancel_transfer apple_pay_or_google_pay\n", - "4 False 0.822664 cancel_transfer cancel_transfer" + "0 False 0.806028 cancel_transfer cancel_transfer\n", + "1 False 0.270972 cancel_transfer cancel_transfer\n", + "2 False 0.695399 cancel_transfer cancel_transfer\n", + "3 False 0.179756 cancel_transfer apple_pay_or_google_pay\n", + "4 False 0.822703 cancel_transfer cancel_transfer" ] }, "execution_count": 12, @@ -888,10 +888,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.195259Z", - "iopub.status.busy": "2023-08-14T16:35:27.194094Z", - "iopub.status.idle": "2023-08-14T16:35:27.201705Z", - "shell.execute_reply": "2023-08-14T16:35:27.201141Z" + "iopub.execute_input": "2023-08-15T04:06:59.519240Z", + "iopub.status.busy": "2023-08-15T04:06:59.518954Z", + "iopub.status.idle": "2023-08-15T04:06:59.526800Z", + "shell.execute_reply": "2023-08-15T04:06:59.525013Z" } }, "outputs": [ @@ -929,10 +929,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.205957Z", - "iopub.status.busy": "2023-08-14T16:35:27.204832Z", - "iopub.status.idle": "2023-08-14T16:35:27.214086Z", - "shell.execute_reply": "2023-08-14T16:35:27.213360Z" + "iopub.execute_input": "2023-08-15T04:06:59.530250Z", + "iopub.status.busy": "2023-08-15T04:06:59.529964Z", + "iopub.status.idle": "2023-08-15T04:06:59.540547Z", + "shell.execute_reply": "2023-08-15T04:06:59.539409Z" } }, "outputs": [ @@ -1049,10 +1049,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.217224Z", - "iopub.status.busy": "2023-08-14T16:35:27.216792Z", - "iopub.status.idle": "2023-08-14T16:35:27.224870Z", - "shell.execute_reply": "2023-08-14T16:35:27.224176Z" + "iopub.execute_input": "2023-08-15T04:06:59.545252Z", + "iopub.status.busy": "2023-08-15T04:06:59.544750Z", + "iopub.status.idle": "2023-08-15T04:06:59.555510Z", + "shell.execute_reply": "2023-08-15T04:06:59.554341Z" } }, "outputs": [ @@ -1135,10 +1135,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.228701Z", - "iopub.status.busy": "2023-08-14T16:35:27.228337Z", - "iopub.status.idle": "2023-08-14T16:35:27.235709Z", - "shell.execute_reply": "2023-08-14T16:35:27.235029Z" + "iopub.execute_input": "2023-08-15T04:06:59.559273Z", + "iopub.status.busy": "2023-08-15T04:06:59.558912Z", + "iopub.status.idle": "2023-08-15T04:06:59.568322Z", + "shell.execute_reply": "2023-08-15T04:06:59.567442Z" } }, "outputs": [ @@ -1246,10 +1246,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:27.239722Z", - "iopub.status.busy": "2023-08-14T16:35:27.239173Z", - "iopub.status.idle": "2023-08-14T16:35:27.250441Z", - "shell.execute_reply": "2023-08-14T16:35:27.249734Z" + "iopub.execute_input": "2023-08-15T04:06:59.571892Z", + "iopub.status.busy": "2023-08-15T04:06:59.571626Z", + "iopub.status.idle": "2023-08-15T04:06:59.586680Z", + "shell.execute_reply": "2023-08-15T04:06:59.585542Z" } }, "outputs": [ @@ -1282,37 +1282,37 @@ " \n", " \n", " \n", - 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Start of tutorial: Evaluate the health of 8 popular dataset sunglass 836 40 - 28 + 27 0.80 - 0.736842 + 0.729730 0.20 1 - tiger cat - 282 + Toy Poodle + 265 38 - 30 + 8 0.76 - 0.714286 + 0.400000 0.24 2 - Toy Poodle - 265 + tiger cat + 282 38 - 8 + 29 0.76 - 0.400000 + 0.707317 0.24 @@ -1017,12 +1017,12 @@

Start of tutorial: Evaluate the health of 8 popular dataset 4 - laptop computer - 620 + Saharan horned viper + 66 36 - 14 + 4 0.72 - 0.500000 + 0.222222 0.28 @@ -1037,8 +1037,8 @@

Start of tutorial: Evaluate the health of 8 popular dataset 995 - croquet ball - 522 + ringlet + 322 0 2 0.00 @@ -1047,42 +1047,42 @@

Start of tutorial: Evaluate the health of 8 popular dataset 996 - automated teller machine - 480 + monarch butterfly + 323 + 0 0 - 4 0.00 - 0.074074 + 0.000000 1.00 997 - picket fence - 716 - 0 + magpie + 18 0 + 2 0.00 - 0.000000 + 0.038462 1.00 998 - ping-pong ball - 722 + go-kart + 573 0 - 2 + 5 0.00 - 0.038462 + 0.090909 1.00 999 - high-speed train - 466 + fox squirrel + 335 0 - 4 + 5 0.00 - 0.074074 + 0.090909 1.00 @@ -1189,45 +1189,45 @@

Start of tutorial: Evaluate the health of 8 popular dataset 499495 Kerry Blue Terrier - pickup truck + pill bottle 183 - 717 + 720 0 0.00000 499496 Kerry Blue Terrier - picket fence + piggy bank 183 - 716 + 719 0 0.00000 499497 Kerry Blue Terrier - Pickelhaube + pier 183 - 715 + 718 0 0.00000 499498 Kerry Blue Terrier - plectrum + pickup truck 183 - 714 + 717 0 0.00000 499499 - Kerry Blue Terrier - plate rack - 183 - 729 + ear + toilet paper + 998 + 999 0 0.00000 @@ -1311,9 +1311,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset skateboard 184 37 - 26 + 23 0.359223 - 0.282609 + 0.258427 0.640777 @@ -1321,9 +1321,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset chopsticks 38 29 - 19 + 20 0.341176 - 0.253333 + 0.263158 0.658824 @@ -1358,8 +1358,8 @@

Start of tutorial: Evaluate the health of 8 popular dataset 251 - hummingbird - 112 + raccoon + 167 0 0 0.000000 @@ -1368,8 +1368,8 @@

Start of tutorial: Evaluate the health of 8 popular dataset 252 - starfish - 200 + hummingbird + 112 0 0 0.000000 @@ -1378,18 +1378,18 @@

Start of tutorial: Evaluate the health of 8 popular dataset 253 - saturn - 176 + hourglass + 109 0 - 6 + 2 0.000000 - 0.058824 + 0.022989 1.000000 254 - raccoon - 167 + starfish + 200 0 0 0.000000 @@ -1398,12 +1398,12 @@

Start of tutorial: Evaluate the health of 8 popular dataset 255 - leopards - 128 - 0 + saturn + 176 0 + 5 0.000000 - 0.000000 + 0.049505 1.000000 @@ -1482,19 +1482,19 @@

Start of tutorial: Evaluate the health of 8 popular dataset 3 - frog - toad - 79 - 255 + beer-mug + coffee-mug + 9 + 40 22 0.000739 4 - beer-mug - coffee-mug - 9 - 40 + frog + toad + 79 + 255 22 0.000739 @@ -1509,46 +1509,46 @@

Start of tutorial: Evaluate the health of 8 popular dataset 32635 - conch - tennis-shoes - 47 - 254 + cormorant + covered-wagon + 48 + 49 0 0.000000 32636 conch - greyhound + toad 47 - 253 + 255 0 0.000000 32637 conch - faces-easy + tennis-shoes 47 - 252 + 254 0 0.000000 32638 conch - car-side + greyhound 47 - 251 + 253 0 0.000000 32639 - cormorant - duck - 48 - 59 + tennis-shoes + toad + 254 + 255 0 0.000000 @@ -1801,37 +1801,37 @@

Start of tutorial: Evaluate the health of 8 popular dataset 4 - 0 2 - 0 + 8 2 + 8 1 0.0001 5 - 3 - 7 - 3 - 7 + 4 + 6 + 4 + 6 1 0.0001 6 - 2 - 8 - 2 - 8 + 3 + 7 + 3 + 7 1 0.0001 7 - 4 - 6 - 4 - 6 + 0 + 2 + 0 + 2 1 0.0001 @@ -1855,316 +1855,316 @@

Start of tutorial: Evaluate the health of 8 popular dataset 10 - 0 1 - 0 + 2 1 + 2 0 0.0000 11 - 0 - 4 - 0 - 4 + 3 + 8 + 3 + 8 0 0.0000 12 - 0 - 3 - 0 - 3 + 4 + 5 + 4 + 5 0 0.0000 13 - 1 + 0 5 - 1 + 0 5 0 0.0000 14 - 1 4 - 1 + 7 4 + 7 0 0.0000 15 - 1 - 3 - 1 - 3 + 4 + 8 + 4 + 8 0 0.0000 16 - 1 - 2 - 1 - 2 + 0 + 4 + 0 + 4 0 0.0000 17 0 - 9 + 3 0 - 9 + 3 0 0.0000 18 - 0 - 8 - 0 - 8 + 5 + 7 + 5 + 7 0 0.0000 19 - 0 - 6 - 0 - 6 + 5 + 8 + 5 + 8 0 0.0000 20 - 0 5 - 0 + 9 5 + 9 0 0.0000 21 - 2 6 - 2 + 7 6 + 7 0 0.0000 22 - 2 - 5 - 2 - 5 + 6 + 8 + 6 + 8 0 0.0000 23 - 2 - 4 - 2 - 4 + 6 + 9 + 6 + 9 0 0.0000 24 - 2 - 3 - 2 - 3 + 7 + 8 + 7 + 8 0 0.0000 25 - 1 - 6 - 1 - 6 + 7 + 9 + 7 + 9 0 0.0000 26 - 1 - 7 - 1 - 7 + 3 + 9 + 3 + 9 0 0.0000 27 - 1 - 8 - 1 - 8 + 0 + 6 + 0 + 6 0 0.0000 28 1 - 9 + 3 1 - 9 + 3 0 0.0000 29 + 2 3 - 8 + 2 3 - 8 0 0.0000 30 - 3 - 6 - 3 - 6 + 1 + 4 + 1 + 4 0 0.0000 31 - 2 - 9 - 2 - 9 + 1 + 5 + 1 + 5 0 0.0000 32 - 3 - 4 - 3 - 4 + 1 + 6 + 1 + 6 0 0.0000 33 - 4 + 1 7 - 4 + 1 7 0 0.0000 34 - 4 - 5 - 4 - 5 + 1 + 8 + 1 + 8 0 0.0000 35 - 4 - 8 - 4 - 8 + 1 + 9 + 1 + 9 0 0.0000 36 - 3 - 9 - 3 - 9 + 2 + 4 + 2 + 4 0 0.0000 37 - 5 - 7 - 5 - 7 + 3 + 6 + 3 + 6 0 0.0000 38 + 2 5 - 8 + 2 5 - 8 0 0.0000 39 + 2 6 - 7 + 2 6 - 7 0 0.0000 40 - 5 + 0 9 - 5 + 0 9 0 0.0000 41 - 6 + 0 8 - 6 + 0 8 0 0.0000 42 - 6 + 2 9 - 6 + 2 9 0 0.0000 43 - 7 - 8 - 7 - 8 + 3 + 4 + 3 + 4 0 0.0000 44 - 7 - 9 - 7 - 9 + 0 + 1 + 0 + 1 0 0.0000 @@ -2247,9 +2247,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset dog 5 46 - 58 + 59 0.046 - 0.057312 + 0.058243 0.954 @@ -2307,9 +2307,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset airplane 0 16 - 32 + 31 0.016 - 0.031496 + 0.030542 0.984 @@ -2488,39 +2488,39 @@

Start of tutorial: Evaluate the health of 8 popular dataset 12 - airplane bird - 0 + dog 2 - 6 - 0.0006 + 5 + 7 + 0.0007 13 - cat + dog horse - 3 + 5 7 6 0.0006 14 - dog + cat horse - 5 + 3 7 6 0.0006 15 + airplane bird - dog + 0 2 5 - 6 - 0.0006 + 0.0005 16 @@ -2551,33 +2551,24 @@

Start of tutorial: Evaluate the health of 8 popular dataset 19 - bird horse - 2 + truck 7 + 9 3 0.0003 20 - bird - ship - 2 - 8 - 3 - 0.0003 - - - 21 + deer horse - truck + 4 7 - 9 3 0.0003 - 22 + 21 deer frog 4 @@ -2585,11 +2576,20 @@

Start of tutorial: Evaluate the health of 8 popular dataset 3 0.0003 + + 22 + bird + ship + 2 + 8 + 3 + 0.0003 + 23 - deer + bird horse - 4 + 2 7 3 0.0003 @@ -2605,9 +2605,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset 25 - airplane + automobile frog - 0 + 1 6 1 0.0001 @@ -2624,162 +2624,162 @@

Start of tutorial: Evaluate the health of 8 popular dataset 27 airplane - dog + frog 0 - 5 + 6 1 0.0001 28 - automobile - dog - 1 - 5 + cat + truck + 3 + 9 1 0.0001 29 - frog - ship - 6 - 8 + airplane + dog + 0 + 5 1 0.0001 30 + automobile dog - frog + 1 5 - 6 1 0.0001 31 - automobile - frog - 1 - 6 + bird + truck + 2 + 9 1 0.0001 32 - cat + deer truck - 3 + 4 9 1 0.0001 33 - deer - truck - 4 - 9 + dog + frog + 5 + 6 1 0.0001 34 - bird - truck - 2 - 9 + frog + ship + 6 + 8 1 0.0001 35 - automobile - bird - 1 - 2 + horse + ship + 7 + 8 0 0.0000 36 - airplane - deer - 0 - 4 + frog + truck + 6 + 9 0 0.0000 37 - automobile - deer - 1 - 4 + frog + horse + 6 + 7 0 0.0000 38 automobile - cat + deer 1 - 3 + 4 0 0.0000 39 - automobile - horse - 1 - 7 + dog + ship + 5 + 8 0 0.0000 40 + airplane deer - ship + 0 4 - 8 0 0.0000 41 - frog + automobile horse - 6 + 1 7 0 0.0000 42 - dog - ship - 5 - 8 + automobile + bird + 1 + 2 0 0.0000 43 - horse - ship - 7 - 8 + automobile + cat + 1 + 3 0 0.0000 44 - frog - truck - 6 - 9 + deer + ship + 4 + 8 0 0.0000 @@ -2872,9 +2872,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset seal 72 49 - 55 + 56 0.49 - 0.518868 + 0.523364 0.51 @@ -2929,22 +2929,22 @@

Start of tutorial: Evaluate the health of 8 popular dataset 97 - motorcycle - 48 + orange + 53 3 - 5 + 12 0.03 - 0.049020 + 0.110092 0.97 98 - orange - 53 + motorcycle + 48 3 - 12 + 5 0.03 - 0.110092 + 0.049020 0.97 @@ -3033,19 +3033,19 @@

Start of tutorial: Evaluate the health of 8 popular dataset 3 - baby - boy - 2 - 11 + maple_tree + oak_tree + 47 + 52 25 0.0025 4 - maple_tree - oak_tree - 47 - 52 + otter + seal + 55 + 72 25 0.0025 @@ -3061,45 +3061,45 @@

Start of tutorial: Evaluate the health of 8 popular dataset 4945 cattle - telephone + whale 19 - 86 + 95 0 0.0000 4946 cattle - tank + willow_tree 19 - 85 + 96 0 0.0000 4947 cattle - table + woman 19 - 84 + 98 0 0.0000 4948 cattle - sweet_pepper + worm 19 - 83 + 99 0 0.0000 4949 - cattle - trout - 19 - 91 + woman + worm + 98 + 99 0 0.0000 @@ -3479,18 +3479,18 @@

Start of tutorial: Evaluate the health of 8 popular dataset 17 - rec.sport.baseball - 9 + soc.religion.christian + 15 + 0 0 - 2 0.000000 - 0.005013 + 0.000000 1.000000 18 - soc.religion.christian - 15 + talk.politics.mideast + 17 0 0 0.000000 @@ -3499,12 +3499,12 @@

Start of tutorial: Evaluate the health of 8 popular dataset 19 - talk.politics.mideast - 17 - 0 + rec.sport.baseball + 9 0 + 2 0.000000 - 0.000000 + 0.005013 1.000000 @@ -3574,26 +3574,26 @@

Start of tutorial: Evaluate the health of 8 popular dataset 2 misc.forsale - rec.autos + sci.electronics 6 - 7 + 12 7 0.000929 3 misc.forsale - sci.electronics + rec.autos 6 - 12 + 7 7 0.000929 4 - comp.graphics + comp.os.ms-windows.misc comp.windows.x - 1 + 2 5 5 0.000664 @@ -3610,45 +3610,45 @@

Start of tutorial: Evaluate the health of 8 popular dataset 185 comp.sys.mac.hardware - rec.sport.baseball + rec.motorcycles 4 - 9 + 8 0 0.000000 186 comp.sys.mac.hardware - rec.sport.hockey + rec.sport.baseball 4 - 10 + 9 0 0.000000 187 comp.sys.mac.hardware - sci.crypt + rec.sport.hockey 4 - 11 + 10 0 0.000000 188 comp.sys.mac.hardware - sci.electronics + sci.crypt 4 - 12 + 11 0 0.000000 189 - comp.sys.ibm.pc.hardware - talk.politics.guns - 3 - 16 + talk.politics.misc + talk.religion.misc + 18 + 19 0 0.000000 diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 193713732..380bad948 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:32.902764Z", - "iopub.status.busy": "2023-08-14T16:35:32.902307Z", - "iopub.status.idle": "2023-08-14T16:35:34.113852Z", - "shell.execute_reply": "2023-08-14T16:35:34.112804Z" + "iopub.execute_input": "2023-08-15T04:07:05.139899Z", + "iopub.status.busy": "2023-08-15T04:07:05.139526Z", + "iopub.status.idle": "2023-08-15T04:07:06.732406Z", + "shell.execute_reply": "2023-08-15T04:07:06.730972Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:34.118331Z", - "iopub.status.busy": "2023-08-14T16:35:34.117836Z", - "iopub.status.idle": "2023-08-14T16:35:34.122367Z", - "shell.execute_reply": "2023-08-14T16:35:34.121609Z" + "iopub.execute_input": "2023-08-15T04:07:06.737551Z", + "iopub.status.busy": "2023-08-15T04:07:06.736932Z", + "iopub.status.idle": "2023-08-15T04:07:06.743683Z", + "shell.execute_reply": "2023-08-15T04:07:06.742320Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:34.126097Z", - "iopub.status.busy": "2023-08-14T16:35:34.125861Z", - "iopub.status.idle": "2023-08-14T16:35:34.174859Z", - "shell.execute_reply": "2023-08-14T16:35:34.174043Z" + "iopub.execute_input": "2023-08-15T04:07:06.748471Z", + "iopub.status.busy": "2023-08-15T04:07:06.748129Z", + "iopub.status.idle": "2023-08-15T04:07:06.807511Z", + "shell.execute_reply": "2023-08-15T04:07:06.806269Z" }, "nbsphinx": "hidden" }, @@ -301,10 +301,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:34.178210Z", - "iopub.status.busy": "2023-08-14T16:35:34.177706Z", - "iopub.status.idle": "2023-08-14T16:35:54.034304Z", - "shell.execute_reply": "2023-08-14T16:35:54.033593Z" + "iopub.execute_input": "2023-08-15T04:07:06.812076Z", + "iopub.status.busy": "2023-08-15T04:07:06.811768Z", + "iopub.status.idle": "2023-08-15T04:07:36.275789Z", + "shell.execute_reply": "2023-08-15T04:07:36.274906Z" }, "id": "dhTHOg8Pyv5G" }, @@ -378,29 +378,29 @@ " sunglass\n", " 836\n", " 40\n", - " 28\n", + " 27\n", " 0.80\n", - " 0.736842\n", + " 0.729730\n", " 0.20\n", " \n", " \n", " 1\n", - " tiger cat\n", - " 282\n", + " Toy Poodle\n", + " 265\n", " 38\n", - " 30\n", + " 8\n", " 0.76\n", - " 0.714286\n", + " 0.400000\n", " 0.24\n", " \n", " \n", " 2\n", - " Toy Poodle\n", - " 265\n", + " tiger cat\n", + " 282\n", " 38\n", - " 8\n", + " 29\n", " 0.76\n", - " 0.400000\n", + " 0.707317\n", " 0.24\n", " \n", " \n", @@ -415,12 +415,12 @@ " \n", " \n", " 4\n", - " laptop computer\n", - " 620\n", + " Saharan horned viper\n", + " 66\n", " 36\n", - " 14\n", + " 4\n", " 0.72\n", - " 0.500000\n", + " 0.222222\n", " 0.28\n", " \n", " \n", @@ -435,8 +435,8 @@ " \n", " \n", " 995\n", - " croquet ball\n", - " 522\n", + " ringlet\n", + " 322\n", " 0\n", " 2\n", " 0.00\n", @@ -445,42 +445,42 @@ " \n", " \n", " 996\n", - " automated teller machine\n", - " 480\n", + " monarch butterfly\n", + " 323\n", + " 0\n", " 0\n", - " 4\n", " 0.00\n", - " 0.074074\n", + " 0.000000\n", " 1.00\n", " \n", " \n", " 997\n", - " picket fence\n", - " 716\n", - " 0\n", + " magpie\n", + " 18\n", " 0\n", + " 2\n", " 0.00\n", - " 0.000000\n", + " 0.038462\n", " 1.00\n", " \n", " \n", " 998\n", - " ping-pong ball\n", - " 722\n", + " go-kart\n", + " 573\n", " 0\n", - " 2\n", + " 5\n", " 0.00\n", - " 0.038462\n", + " 0.090909\n", " 1.00\n", " \n", " \n", " 999\n", - " high-speed train\n", - " 466\n", + " fox squirrel\n", + " 335\n", " 0\n", - " 4\n", + " 5\n", " 0.00\n", - " 0.074074\n", + " 0.090909\n", " 1.00\n", " \n", " \n", @@ -489,44 +489,31 @@ "

" ], "text/plain": [ - " Class Name Class Index Label Issues \\\n", - "0 sunglass 836 40 \n", - "1 tiger cat 282 38 \n", - "2 Toy Poodle 265 38 \n", - "3 CRT screen 782 37 \n", - "4 laptop computer 620 36 \n", - ".. ... ... ... \n", - "995 croquet ball 522 0 \n", - "996 automated teller machine 480 0 \n", - "997 picket fence 716 0 \n", - "998 ping-pong ball 722 0 \n", - "999 high-speed train 466 0 \n", - "\n", - " Inverse Label Issues Label Noise Inverse Label Noise \\\n", - "0 28 0.80 0.736842 \n", - "1 30 0.76 0.714286 \n", - "2 8 0.76 0.400000 \n", - "3 22 0.74 0.628571 \n", - "4 14 0.72 0.500000 \n", - ".. ... ... ... \n", - "995 2 0.00 0.038462 \n", - "996 4 0.00 0.074074 \n", - "997 0 0.00 0.000000 \n", - "998 2 0.00 0.038462 \n", - "999 4 0.00 0.074074 \n", + " Class Name Class Index Label Issues Inverse Label Issues \\\n", + "0 sunglass 836 40 27 \n", + "1 Toy Poodle 265 38 8 \n", + "2 tiger cat 282 38 29 \n", + "3 CRT screen 782 37 22 \n", + "4 Saharan horned viper 66 36 4 \n", + ".. ... ... ... ... \n", + "995 ringlet 322 0 2 \n", + "996 monarch butterfly 323 0 0 \n", + "997 magpie 18 0 2 \n", + "998 go-kart 573 0 5 \n", + "999 fox squirrel 335 0 5 \n", "\n", - " Label Quality Score \n", - "0 0.20 \n", - "1 0.24 \n", - "2 0.24 \n", - "3 0.26 \n", - "4 0.28 \n", - ".. ... \n", - "995 1.00 \n", - "996 1.00 \n", - "997 1.00 \n", - "998 1.00 \n", - "999 1.00 \n", + " Label Noise Inverse Label Noise Label Quality Score \n", + "0 0.80 0.729730 0.20 \n", + "1 0.76 0.400000 0.24 \n", + "2 0.76 0.707317 0.24 \n", + "3 0.74 0.628571 0.26 \n", + "4 0.72 0.222222 0.28 \n", + ".. ... ... ... \n", + "995 0.00 0.038462 1.00 \n", + "996 0.00 0.000000 1.00 \n", + "997 0.00 0.038462 1.00 \n", + "998 0.00 0.090909 1.00 \n", + "999 0.00 0.090909 1.00 \n", "\n", "[1000 rows x 7 columns]" ] @@ -631,45 +618,45 @@ " \n", " 499495\n", " Kerry Blue Terrier\n", - " pickup truck\n", + " pill bottle\n", " 183\n", - " 717\n", + " 720\n", " 0\n", " 0.00000\n", " \n", " \n", " 499496\n", " Kerry Blue Terrier\n", - " picket fence\n", + " piggy bank\n", " 183\n", - " 716\n", + " 719\n", " 0\n", " 0.00000\n", " \n", " \n", " 499497\n", " Kerry Blue Terrier\n", - " Pickelhaube\n", + " pier\n", " 183\n", - " 715\n", + " 718\n", " 0\n", " 0.00000\n", " \n", " \n", " 499498\n", " Kerry Blue Terrier\n", - " plectrum\n", + " pickup truck\n", " 183\n", - " 714\n", + " 717\n", " 0\n", " 0.00000\n", " \n", " \n", " 499499\n", - " Kerry Blue Terrier\n", - " plate rack\n", - " 183\n", - " 729\n", + " ear\n", + " toilet paper\n", + " 998\n", + " 999\n", " 0\n", " 0.00000\n", " \n", @@ -686,11 +673,11 @@ "3 tights tank suit 638 \n", "4 laptop computer notebook computer 620 \n", "... ... ... ... \n", - "499495 Kerry Blue Terrier pickup truck 183 \n", - "499496 Kerry Blue Terrier picket fence 183 \n", - "499497 Kerry Blue Terrier Pickelhaube 183 \n", - "499498 Kerry Blue Terrier plectrum 183 \n", - "499499 Kerry Blue Terrier plate rack 183 \n", + "499495 Kerry Blue Terrier pill bottle 183 \n", + "499496 Kerry Blue Terrier piggy bank 183 \n", + "499497 Kerry Blue Terrier pier 183 \n", + "499498 Kerry Blue Terrier pickup truck 183 \n", + "499499 ear toilet paper 998 \n", "\n", " Class Index B Num Overlapping Examples Joint Probability \n", "0 998 44 0.00088 \n", @@ -699,11 +686,11 @@ "3 639 36 0.00072 \n", "4 681 35 0.00070 \n", "... ... ... ... \n", - "499495 717 0 0.00000 \n", - "499496 716 0 0.00000 \n", - "499497 715 0 0.00000 \n", - "499498 714 0 0.00000 \n", - "499499 729 0 0.00000 \n", + "499495 720 0 0.00000 \n", + "499496 719 0 0.00000 \n", + "499497 718 0 0.00000 \n", + "499498 717 0 0.00000 \n", + "499499 999 0 0.00000 \n", "\n", "[499500 rows x 6 columns]" ] @@ -790,9 +777,9 @@ " skateboard\n", " 184\n", " 37\n", - " 26\n", + " 23\n", " 0.359223\n", - " 0.282609\n", + " 0.258427\n", " 0.640777\n", " \n", " \n", @@ -800,9 +787,9 @@ " chopsticks\n", " 38\n", " 29\n", - " 19\n", + " 20\n", " 0.341176\n", - " 0.253333\n", + " 0.263158\n", " 0.658824\n", " \n", " \n", @@ -837,8 +824,8 @@ " \n", " \n", " 251\n", - " hummingbird\n", - " 112\n", + " raccoon\n", + " 167\n", " 0\n", " 0\n", " 0.000000\n", @@ -847,8 +834,8 @@ " \n", " \n", " 252\n", - " starfish\n", - " 200\n", + " hummingbird\n", + " 112\n", " 0\n", " 0\n", " 0.000000\n", @@ -857,18 +844,18 @@ " \n", " \n", " 253\n", - " saturn\n", - " 176\n", + " hourglass\n", + " 109\n", " 0\n", - " 6\n", + " 2\n", " 0.000000\n", - " 0.058824\n", + " 0.022989\n", " 1.000000\n", " \n", " \n", " 254\n", - " raccoon\n", - " 167\n", + " starfish\n", + " 200\n", " 0\n", " 0\n", " 0.000000\n", @@ -877,12 +864,12 @@ " \n", " \n", " 255\n", - " leopards\n", - " 128\n", - " 0\n", + " saturn\n", + " 176\n", " 0\n", + " 5\n", " 0.000000\n", - " 0.000000\n", + " 0.049505\n", " 1.000000\n", " \n", " \n", @@ -893,29 +880,29 @@ "text/plain": [ " Class Name Class Index Label Issues Inverse Label Issues \\\n", "0 tennis-shoes 254 37 33 \n", - "1 skateboard 184 37 26 \n", - "2 chopsticks 38 29 19 \n", + "1 skateboard 184 37 23 \n", + "2 chopsticks 38 29 20 \n", "3 drinking-straw 58 28 18 \n", "4 yo-yo 248 33 37 \n", ".. ... ... ... ... \n", - "251 hummingbird 112 0 0 \n", - "252 starfish 200 0 0 \n", - "253 saturn 176 0 6 \n", - "254 raccoon 167 0 0 \n", - "255 leopards 128 0 0 \n", + "251 raccoon 167 0 0 \n", + "252 hummingbird 112 0 0 \n", + "253 hourglass 109 0 2 \n", + "254 starfish 200 0 0 \n", + "255 saturn 176 0 5 \n", "\n", " Label Noise Inverse Label Noise Label Quality Score \n", "0 0.359223 0.333333 0.640777 \n", - "1 0.359223 0.282609 0.640777 \n", - "2 0.341176 0.253333 0.658824 \n", + "1 0.359223 0.258427 0.640777 \n", + "2 0.341176 0.263158 0.658824 \n", "3 0.337349 0.246575 0.662651 \n", "4 0.330000 0.355769 0.670000 \n", ".. ... ... ... \n", "251 0.000000 0.000000 1.000000 \n", "252 0.000000 0.000000 1.000000 \n", - "253 0.000000 0.058824 1.000000 \n", + "253 0.000000 0.022989 1.000000 \n", "254 0.000000 0.000000 1.000000 \n", - "255 0.000000 0.000000 1.000000 \n", + "255 0.000000 0.049505 1.000000 \n", "\n", "[256 rows x 7 columns]" ] @@ -992,19 +979,19 @@ " \n", " \n", " 3\n", - " frog\n", - " toad\n", - " 79\n", - " 255\n", + " beer-mug\n", + " coffee-mug\n", + " 9\n", + " 40\n", " 22\n", " 0.000739\n", " \n", " \n", " 4\n", - " beer-mug\n", - " coffee-mug\n", - " 9\n", - " 40\n", + " frog\n", + " toad\n", + " 79\n", + " 255\n", " 22\n", " 0.000739\n", " \n", @@ -1019,46 +1006,46 @@ " \n", " \n", " 32635\n", - " conch\n", - " tennis-shoes\n", - " 47\n", - " 254\n", + " cormorant\n", + " covered-wagon\n", + " 48\n", + " 49\n", " 0\n", " 0.000000\n", " \n", " \n", " 32636\n", " conch\n", - " greyhound\n", + " toad\n", " 47\n", - " 253\n", + " 255\n", " 0\n", " 0.000000\n", " \n", " \n", " 32637\n", " conch\n", - " faces-easy\n", + " tennis-shoes\n", " 47\n", - " 252\n", + " 254\n", " 0\n", " 0.000000\n", " \n", " \n", " 32638\n", " conch\n", - " car-side\n", + " greyhound\n", " 47\n", - " 251\n", + " 253\n", " 0\n", " 0.000000\n", " \n", " \n", " 32639\n", - " cormorant\n", - " duck\n", - " 48\n", - " 59\n", + " tennis-shoes\n", + " toad\n", + " 254\n", + " 255\n", " 0\n", " 0.000000\n", " \n", @@ -1068,18 +1055,18 @@ "" ], "text/plain": [ - " Class Name A Class Name B Class Index A Class Index B \\\n", - "0 sneaker tennis-shoes 190 254 \n", - "1 frisbee yo-yo 78 248 \n", - "2 duck goose 59 88 \n", - "3 frog toad 79 255 \n", - "4 beer-mug coffee-mug 9 40 \n", - "... ... ... ... ... \n", - "32635 conch tennis-shoes 47 254 \n", - "32636 conch greyhound 47 253 \n", - "32637 conch faces-easy 47 252 \n", - "32638 conch car-side 47 251 \n", - "32639 cormorant duck 48 59 \n", + " Class Name A Class Name B Class Index A Class Index B \\\n", + "0 sneaker tennis-shoes 190 254 \n", + "1 frisbee yo-yo 78 248 \n", + "2 duck goose 59 88 \n", + "3 beer-mug coffee-mug 9 40 \n", + "4 frog toad 79 255 \n", + "... ... ... ... ... \n", + "32635 cormorant covered-wagon 48 49 \n", + "32636 conch toad 47 255 \n", + "32637 conch tennis-shoes 47 254 \n", + "32638 conch greyhound 47 253 \n", + "32639 tennis-shoes toad 254 255 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 66 0.002216 \n", @@ -1375,37 +1362,37 @@ " \n", " \n", " 4\n", - " 0\n", " 2\n", - " 0\n", + " 8\n", " 2\n", + " 8\n", " 1\n", " 0.0001\n", " \n", " \n", " 5\n", - " 3\n", - " 7\n", - " 3\n", - " 7\n", + " 4\n", + " 6\n", + " 4\n", + " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 6\n", - " 2\n", - " 8\n", - " 2\n", - " 8\n", + " 3\n", + " 7\n", + " 3\n", + " 7\n", " 1\n", " 0.0001\n", " \n", " \n", " 7\n", - " 4\n", - " 6\n", - " 4\n", - " 6\n", + " 0\n", + " 2\n", + " 0\n", + " 2\n", " 1\n", " 0.0001\n", " \n", @@ -1429,316 +1416,316 @@ " \n", " \n", " 10\n", - " 0\n", " 1\n", - " 0\n", + " 2\n", " 1\n", + " 2\n", " 0\n", " 0.0000\n", " \n", " \n", " 11\n", - " 0\n", - " 4\n", - " 0\n", - " 4\n", + " 3\n", + " 8\n", + " 3\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 12\n", - " 0\n", - " 3\n", - " 0\n", - " 3\n", + " 4\n", + " 5\n", + " 4\n", + " 5\n", " 0\n", " 0.0000\n", " \n", " \n", " 13\n", - " 1\n", + " 0\n", " 5\n", - " 1\n", + " 0\n", " 5\n", " 0\n", " 0.0000\n", " \n", " \n", " 14\n", - " 1\n", " 4\n", - " 1\n", + " 7\n", " 4\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 15\n", - " 1\n", - " 3\n", - " 1\n", - " 3\n", + " 4\n", + " 8\n", + " 4\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 16\n", - " 1\n", - " 2\n", - " 1\n", - " 2\n", + " 0\n", + " 4\n", + " 0\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 17\n", " 0\n", - " 9\n", + " 3\n", " 0\n", - " 9\n", + " 3\n", " 0\n", " 0.0000\n", " \n", " \n", " 18\n", - " 0\n", - " 8\n", - " 0\n", - " 8\n", + " 5\n", + " 7\n", + " 5\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 19\n", - " 0\n", - " 6\n", - " 0\n", - " 6\n", + " 5\n", + " 8\n", + " 5\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 20\n", - " 0\n", " 5\n", - " 0\n", + " 9\n", " 5\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 21\n", - " 2\n", " 6\n", - " 2\n", + " 7\n", " 6\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 22\n", - " 2\n", - " 5\n", - " 2\n", - " 5\n", + " 6\n", + " 8\n", + " 6\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 23\n", - " 2\n", - " 4\n", - " 2\n", - " 4\n", + " 6\n", + " 9\n", + " 6\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 24\n", - " 2\n", - " 3\n", - " 2\n", - " 3\n", + " 7\n", + " 8\n", + " 7\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 25\n", - " 1\n", - " 6\n", - " 1\n", - " 6\n", + " 7\n", + " 9\n", + " 7\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 26\n", - " 1\n", - " 7\n", - " 1\n", - " 7\n", + " 3\n", + " 9\n", + " 3\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 27\n", - " 1\n", - " 8\n", - " 1\n", - " 8\n", + " 0\n", + " 6\n", + " 0\n", + " 6\n", " 0\n", " 0.0000\n", " \n", " \n", " 28\n", " 1\n", - " 9\n", + " 3\n", " 1\n", - " 9\n", + " 3\n", " 0\n", " 0.0000\n", " \n", " \n", " 29\n", + " 2\n", " 3\n", - " 8\n", + " 2\n", " 3\n", - " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 30\n", - " 3\n", - " 6\n", - " 3\n", - " 6\n", + " 1\n", + " 4\n", + " 1\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 31\n", - " 2\n", - " 9\n", - " 2\n", - " 9\n", + " 1\n", + " 5\n", + " 1\n", + " 5\n", " 0\n", " 0.0000\n", " \n", " \n", " 32\n", - " 3\n", - " 4\n", - " 3\n", - " 4\n", + " 1\n", + " 6\n", + " 1\n", + " 6\n", " 0\n", " 0.0000\n", " \n", " \n", " 33\n", - " 4\n", + " 1\n", " 7\n", - " 4\n", + " 1\n", " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 34\n", - " 4\n", - " 5\n", - " 4\n", - " 5\n", + " 1\n", + " 8\n", + " 1\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 35\n", - " 4\n", - " 8\n", - " 4\n", - " 8\n", + " 1\n", + " 9\n", + " 1\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 36\n", - " 3\n", - " 9\n", - " 3\n", - " 9\n", + " 2\n", + " 4\n", + " 2\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 37\n", - " 5\n", - " 7\n", - " 5\n", - " 7\n", + " 3\n", + " 6\n", + " 3\n", + " 6\n", " 0\n", " 0.0000\n", " \n", " \n", " 38\n", + " 2\n", " 5\n", - " 8\n", + " 2\n", " 5\n", - " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 39\n", + " 2\n", " 6\n", - " 7\n", + " 2\n", " 6\n", - " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 40\n", - " 5\n", + " 0\n", " 9\n", - " 5\n", + " 0\n", " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 41\n", - " 6\n", + " 0\n", " 8\n", - " 6\n", + " 0\n", " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 42\n", - " 6\n", + " 2\n", " 9\n", - " 6\n", + " 2\n", " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 43\n", - " 7\n", - " 8\n", - " 7\n", - " 8\n", + " 3\n", + " 4\n", + " 3\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 44\n", - " 7\n", - " 9\n", - " 7\n", - " 9\n", + " 0\n", + " 1\n", + " 0\n", + " 1\n", " 0\n", " 0.0000\n", " \n", @@ -1752,47 +1739,47 @@ "1 5 6 5 6 \n", "2 4 9 4 9 \n", "3 3 5 3 5 \n", - "4 0 2 0 2 \n", - "5 3 7 3 7 \n", - "6 2 8 2 8 \n", - "7 4 6 4 6 \n", + "4 2 8 2 8 \n", + "5 4 6 4 6 \n", + "6 3 7 3 7 \n", + "7 0 2 0 2 \n", "8 8 9 8 9 \n", "9 0 7 0 7 \n", - "10 0 1 0 1 \n", - "11 0 4 0 4 \n", - "12 0 3 0 3 \n", - "13 1 5 1 5 \n", - "14 1 4 1 4 \n", - "15 1 3 1 3 \n", - "16 1 2 1 2 \n", - "17 0 9 0 9 \n", - "18 0 8 0 8 \n", - "19 0 6 0 6 \n", - "20 0 5 0 5 \n", - "21 2 6 2 6 \n", - "22 2 5 2 5 \n", - "23 2 4 2 4 \n", - "24 2 3 2 3 \n", - "25 1 6 1 6 \n", - "26 1 7 1 7 \n", - "27 1 8 1 8 \n", - "28 1 9 1 9 \n", - "29 3 8 3 8 \n", - "30 3 6 3 6 \n", - "31 2 9 2 9 \n", - "32 3 4 3 4 \n", - "33 4 7 4 7 \n", - "34 4 5 4 5 \n", - "35 4 8 4 8 \n", - "36 3 9 3 9 \n", - "37 5 7 5 7 \n", - "38 5 8 5 8 \n", - "39 6 7 6 7 \n", - "40 5 9 5 9 \n", - "41 6 8 6 8 \n", - "42 6 9 6 9 \n", - "43 7 8 7 8 \n", - "44 7 9 7 9 \n", + "10 1 2 1 2 \n", + "11 3 8 3 8 \n", + "12 4 5 4 5 \n", + "13 0 5 0 5 \n", + "14 4 7 4 7 \n", + "15 4 8 4 8 \n", + "16 0 4 0 4 \n", + "17 0 3 0 3 \n", + "18 5 7 5 7 \n", + "19 5 8 5 8 \n", + "20 5 9 5 9 \n", + "21 6 7 6 7 \n", + "22 6 8 6 8 \n", + "23 6 9 6 9 \n", + "24 7 8 7 8 \n", + "25 7 9 7 9 \n", + "26 3 9 3 9 \n", + "27 0 6 0 6 \n", + "28 1 3 1 3 \n", + "29 2 3 2 3 \n", + "30 1 4 1 4 \n", + "31 1 5 1 5 \n", + "32 1 6 1 6 \n", + "33 1 7 1 7 \n", + "34 1 8 1 8 \n", + "35 1 9 1 9 \n", + "36 2 4 2 4 \n", + "37 3 6 3 6 \n", + "38 2 5 2 5 \n", + "39 2 6 2 6 \n", + "40 0 9 0 9 \n", + "41 0 8 0 8 \n", + "42 2 9 2 9 \n", + "43 3 4 3 4 \n", + "44 0 1 0 1 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 3 0.0003 \n", @@ -1924,9 +1911,9 @@ " dog\n", " 5\n", " 46\n", - " 58\n", + " 59\n", " 0.046\n", - " 0.057312\n", + " 0.058243\n", " 0.954\n", " \n", " \n", @@ -1984,9 +1971,9 @@ " airplane\n", " 0\n", " 16\n", - " 32\n", + " 31\n", " 0.016\n", - " 0.031496\n", + " 0.030542\n", " 0.984\n", " \n", " \n", @@ -2016,25 +2003,25 @@ "text/plain": [ " Class Name Class Index Label Issues Inverse Label Issues Label Noise \\\n", "0 cat 3 71 67 0.071 \n", - "1 dog 5 46 58 0.046 \n", + "1 dog 5 46 59 0.046 \n", "2 bird 2 35 32 0.035 \n", "3 truck 9 31 12 0.031 \n", "4 deer 4 22 26 0.022 \n", "5 frog 6 20 13 0.020 \n", "6 automobile 1 18 13 0.018 \n", - "7 airplane 0 16 32 0.016 \n", + "7 airplane 0 16 31 0.016 \n", "8 ship 8 13 21 0.013 \n", "9 horse 7 12 10 0.012 \n", "\n", " Inverse Label Noise Label Quality Score \n", "0 0.067269 0.929 \n", - "1 0.057312 0.954 \n", + "1 0.058243 0.954 \n", "2 0.032096 0.965 \n", "3 0.012232 0.969 \n", "4 0.025896 0.978 \n", "5 0.013092 0.980 \n", "6 0.013065 0.982 \n", - "7 0.031496 0.984 \n", + "7 0.030542 0.984 \n", "8 0.020833 0.987 \n", "9 0.010020 0.988 " ] @@ -2192,39 +2179,39 @@ " \n", " \n", " 12\n", - " airplane\n", " bird\n", - " 0\n", + " dog\n", " 2\n", - " 6\n", - " 0.0006\n", + " 5\n", + " 7\n", + " 0.0007\n", " \n", " \n", " 13\n", - " cat\n", + " dog\n", " horse\n", - " 3\n", + " 5\n", " 7\n", " 6\n", " 0.0006\n", " \n", " \n", " 14\n", - " dog\n", + " cat\n", " horse\n", - " 5\n", + " 3\n", " 7\n", " 6\n", " 0.0006\n", " \n", " \n", " 15\n", + " airplane\n", " bird\n", - " dog\n", + " 0\n", " 2\n", " 5\n", - " 6\n", - " 0.0006\n", + " 0.0005\n", " \n", " \n", " 16\n", @@ -2255,33 +2242,24 @@ " \n", " \n", " 19\n", - " bird\n", " horse\n", - " 2\n", + " truck\n", " 7\n", + " 9\n", " 3\n", " 0.0003\n", " \n", " \n", " 20\n", - " bird\n", - " ship\n", - " 2\n", - " 8\n", - " 3\n", - " 0.0003\n", - " \n", - " \n", - " 21\n", + " deer\n", " horse\n", - " truck\n", + " 4\n", " 7\n", - " 9\n", " 3\n", " 0.0003\n", " \n", " \n", - " 22\n", + " 21\n", " deer\n", " frog\n", " 4\n", @@ -2290,10 +2268,19 @@ " 0.0003\n", " \n", " \n", + " 22\n", + " bird\n", + " ship\n", + " 2\n", + " 8\n", + " 3\n", + " 0.0003\n", + " \n", + " \n", " 23\n", - " deer\n", + " bird\n", " horse\n", - " 4\n", + " 2\n", " 7\n", " 3\n", " 0.0003\n", @@ -2309,9 +2296,9 @@ " \n", " \n", " 25\n", - " airplane\n", + " automobile\n", " frog\n", - " 0\n", + " 1\n", " 6\n", " 1\n", " 0.0001\n", @@ -2328,162 +2315,162 @@ " \n", " 27\n", " airplane\n", - " dog\n", + " frog\n", " 0\n", - " 5\n", + " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 28\n", - " automobile\n", - " dog\n", - " 1\n", - " 5\n", + " cat\n", + " truck\n", + " 3\n", + " 9\n", " 1\n", " 0.0001\n", " \n", " \n", " 29\n", - " frog\n", - " ship\n", - " 6\n", - " 8\n", + " airplane\n", + " dog\n", + " 0\n", + " 5\n", " 1\n", " 0.0001\n", " \n", " \n", " 30\n", + " automobile\n", " dog\n", - " frog\n", + " 1\n", " 5\n", - " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 31\n", - " automobile\n", - " frog\n", - " 1\n", - " 6\n", + " bird\n", + " truck\n", + " 2\n", + " 9\n", " 1\n", " 0.0001\n", " \n", " \n", " 32\n", - " cat\n", + " deer\n", " truck\n", - " 3\n", + " 4\n", " 9\n", " 1\n", " 0.0001\n", " \n", " \n", " 33\n", - " deer\n", - " truck\n", - " 4\n", - " 9\n", + " dog\n", + " frog\n", + " 5\n", + " 6\n", " 1\n", " 0.0001\n", " \n", " \n", " 34\n", - " bird\n", - " truck\n", - " 2\n", - " 9\n", + " frog\n", + " ship\n", + " 6\n", + " 8\n", " 1\n", " 0.0001\n", " \n", " \n", " 35\n", - " automobile\n", - " bird\n", - " 1\n", - " 2\n", + " horse\n", + " ship\n", + " 7\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 36\n", - " airplane\n", - " deer\n", - " 0\n", - " 4\n", + " frog\n", + " truck\n", + " 6\n", + " 9\n", " 0\n", " 0.0000\n", " \n", " \n", " 37\n", - " automobile\n", - " deer\n", - " 1\n", - " 4\n", + " frog\n", + " horse\n", + " 6\n", + " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 38\n", " automobile\n", - " cat\n", + " deer\n", " 1\n", - " 3\n", + " 4\n", " 0\n", " 0.0000\n", " \n", " \n", " 39\n", - " automobile\n", - " horse\n", - " 1\n", - " 7\n", + " dog\n", + " ship\n", + " 5\n", + " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 40\n", + " airplane\n", " deer\n", - " ship\n", + " 0\n", " 4\n", - " 8\n", " 0\n", " 0.0000\n", " \n", " \n", " 41\n", - " frog\n", + " automobile\n", " horse\n", - " 6\n", + " 1\n", " 7\n", " 0\n", " 0.0000\n", " \n", " \n", " 42\n", - " dog\n", - " ship\n", - " 5\n", - " 8\n", + " automobile\n", + " bird\n", + " 1\n", + " 2\n", " 0\n", " 0.0000\n", " \n", " \n", " 43\n", - " horse\n", - " ship\n", - " 7\n", - " 8\n", + " automobile\n", + " cat\n", + " 1\n", + " 3\n", " 0\n", " 0.0000\n", " \n", " \n", " 44\n", - " frog\n", - " truck\n", - " 6\n", - " 9\n", + " deer\n", + " ship\n", + " 4\n", + " 8\n", " 0\n", " 0.0000\n", " \n", @@ -2505,39 +2492,39 @@ "9 airplane cat 0 3 \n", "10 airplane truck 0 9 \n", "11 ship truck 8 9 \n", - "12 airplane bird 0 2 \n", - "13 cat horse 3 7 \n", - "14 dog horse 5 7 \n", - "15 bird dog 2 5 \n", + "12 bird dog 2 5 \n", + "13 dog horse 5 7 \n", + "14 cat horse 3 7 \n", + "15 airplane bird 0 2 \n", "16 airplane automobile 0 1 \n", "17 automobile ship 1 8 \n", "18 cat ship 3 8 \n", - "19 bird horse 2 7 \n", - "20 bird ship 2 8 \n", - "21 horse truck 7 9 \n", - "22 deer frog 4 6 \n", - "23 deer horse 4 7 \n", + "19 horse truck 7 9 \n", + "20 deer horse 4 7 \n", + "21 deer frog 4 6 \n", + "22 bird ship 2 8 \n", + "23 bird horse 2 7 \n", "24 dog truck 5 9 \n", - "25 airplane frog 0 6 \n", + "25 automobile frog 1 6 \n", "26 airplane horse 0 7 \n", - "27 airplane dog 0 5 \n", - "28 automobile dog 1 5 \n", - "29 frog ship 6 8 \n", - "30 dog frog 5 6 \n", - "31 automobile frog 1 6 \n", - "32 cat truck 3 9 \n", - "33 deer truck 4 9 \n", - "34 bird truck 2 9 \n", - "35 automobile bird 1 2 \n", - "36 airplane deer 0 4 \n", - "37 automobile deer 1 4 \n", - "38 automobile cat 1 3 \n", - "39 automobile horse 1 7 \n", - "40 deer ship 4 8 \n", - "41 frog horse 6 7 \n", - "42 dog ship 5 8 \n", - "43 horse ship 7 8 \n", - "44 frog truck 6 9 \n", + "27 airplane frog 0 6 \n", + "28 cat truck 3 9 \n", + "29 airplane dog 0 5 \n", + "30 automobile dog 1 5 \n", + "31 bird truck 2 9 \n", + "32 deer truck 4 9 \n", + "33 dog frog 5 6 \n", + "34 frog ship 6 8 \n", + "35 horse ship 7 8 \n", + "36 frog truck 6 9 \n", + "37 frog horse 6 7 \n", + "38 automobile deer 1 4 \n", + "39 dog ship 5 8 \n", + "40 airplane deer 0 4 \n", + "41 automobile horse 1 7 \n", + "42 automobile bird 1 2 \n", + "43 automobile cat 1 3 \n", + "44 deer ship 4 8 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 73 0.0073 \n", @@ -2552,10 +2539,10 @@ "9 10 0.0010 \n", "10 8 0.0008 \n", "11 7 0.0007 \n", - "12 6 0.0006 \n", + "12 7 0.0007 \n", "13 6 0.0006 \n", "14 6 0.0006 \n", - "15 6 0.0006 \n", + "15 5 0.0005 \n", "16 5 0.0005 \n", "17 4 0.0004 \n", "18 3 0.0003 \n", @@ -2679,9 +2666,9 @@ " seal\n", " 72\n", " 49\n", - " 55\n", + " 56\n", " 0.49\n", - " 0.518868\n", + " 0.523364\n", " 0.51\n", " \n", " \n", @@ -2736,22 +2723,22 @@ " \n", " \n", " 97\n", - " motorcycle\n", - " 48\n", + " orange\n", + " 53\n", " 3\n", - " 5\n", + " 12\n", " 0.03\n", - " 0.049020\n", + " 0.110092\n", " 0.97\n", " \n", " \n", " 98\n", - " orange\n", - " 53\n", + " motorcycle\n", + " 48\n", " 3\n", - " 12\n", + " 5\n", " 0.03\n", - " 0.110092\n", + " 0.049020\n", " 0.97\n", " \n", " \n", @@ -2773,27 +2760,27 @@ " Class Name Class Index Label Issues Inverse Label Issues Label Noise \\\n", "0 boy 11 54 38 0.54 \n", "1 girl 35 53 40 0.53 \n", - "2 seal 72 49 55 0.49 \n", + "2 seal 72 49 56 0.49 \n", "3 man 46 45 47 0.45 \n", "4 shark 73 43 46 0.43 \n", ".. ... ... ... ... ... \n", "95 road 68 5 11 0.05 \n", "96 skunk 75 5 3 0.05 \n", - "97 motorcycle 48 3 5 0.03 \n", - "98 orange 53 3 12 0.03 \n", + "97 orange 53 3 12 0.03 \n", + "98 motorcycle 48 3 5 0.03 \n", "99 wardrobe 94 3 5 0.03 \n", "\n", " Inverse Label Noise Label Quality Score \n", "0 0.452381 0.46 \n", "1 0.459770 0.47 \n", - "2 0.518868 0.51 \n", + "2 0.523364 0.51 \n", "3 0.460784 0.55 \n", "4 0.446602 0.57 \n", ".. ... ... \n", "95 0.103774 0.95 \n", "96 0.030612 0.95 \n", - "97 0.049020 0.97 \n", - "98 0.110092 0.97 \n", + "97 0.110092 0.97 \n", + "98 0.049020 0.97 \n", "99 0.049020 0.97 \n", "\n", "[100 rows x 7 columns]" @@ -2871,19 +2858,19 @@ " \n", " \n", " 3\n", - " baby\n", - " boy\n", - " 2\n", - " 11\n", + " maple_tree\n", + " oak_tree\n", + " 47\n", + " 52\n", " 25\n", " 0.0025\n", " \n", " \n", " 4\n", - " maple_tree\n", - " oak_tree\n", - " 47\n", - " 52\n", + " otter\n", + " seal\n", + " 55\n", + " 72\n", " 25\n", " 0.0025\n", " \n", @@ -2899,45 +2886,45 @@ " \n", " 4945\n", " cattle\n", - " telephone\n", + " whale\n", " 19\n", - " 86\n", + " 95\n", " 0\n", " 0.0000\n", " \n", " \n", " 4946\n", " cattle\n", - " tank\n", + " willow_tree\n", " 19\n", - " 85\n", + " 96\n", " 0\n", " 0.0000\n", " \n", " \n", " 4947\n", " cattle\n", - " table\n", + " woman\n", " 19\n", - " 84\n", + " 98\n", " 0\n", " 0.0000\n", " \n", " \n", " 4948\n", " cattle\n", - " sweet_pepper\n", + " worm\n", " 19\n", - " 83\n", + " 99\n", " 0\n", " 0.0000\n", " \n", " \n", " 4949\n", - " cattle\n", - " trout\n", - " 19\n", - " 91\n", + " woman\n", + " worm\n", + " 98\n", + " 99\n", " 0\n", " 0.0000\n", " \n", @@ -2947,18 +2934,18 @@ "" ], "text/plain": [ - " Class Name A Class Name B Class Index A Class Index B \\\n", - "0 girl woman 35 98 \n", - "1 boy man 11 46 \n", - "2 maple_tree willow_tree 47 96 \n", - "3 baby boy 2 11 \n", - "4 maple_tree oak_tree 47 52 \n", - "... ... ... ... ... \n", - "4945 cattle telephone 19 86 \n", - "4946 cattle tank 19 85 \n", - "4947 cattle table 19 84 \n", - "4948 cattle sweet_pepper 19 83 \n", - "4949 cattle trout 19 91 \n", + " Class Name A Class Name B Class Index A Class Index B \\\n", + "0 girl woman 35 98 \n", + "1 boy man 11 46 \n", + "2 maple_tree willow_tree 47 96 \n", + "3 maple_tree oak_tree 47 52 \n", + "4 otter seal 55 72 \n", + "... ... ... ... ... \n", + "4945 cattle whale 19 95 \n", + "4946 cattle willow_tree 19 96 \n", + "4947 cattle woman 19 98 \n", + "4948 cattle worm 19 99 \n", + "4949 woman worm 98 99 \n", "\n", " Num Overlapping Examples Joint Probability \n", "0 34 0.0034 \n", @@ -3380,18 +3367,18 @@ " \n", " \n", " 17\n", - " rec.sport.baseball\n", - " 9\n", + " soc.religion.christian\n", + " 15\n", + " 0\n", " 0\n", - " 2\n", " 0.000000\n", - " 0.005013\n", + " 0.000000\n", " 1.000000\n", " \n", " \n", " 18\n", - " soc.religion.christian\n", - " 15\n", + " talk.politics.mideast\n", + " 17\n", " 0\n", " 0\n", " 0.000000\n", @@ -3400,12 +3387,12 @@ " \n", " \n", " 19\n", - " talk.politics.mideast\n", - " 17\n", - " 0\n", + " rec.sport.baseball\n", + " 9\n", " 0\n", + " 2\n", " 0.000000\n", - " 0.000000\n", + " 0.005013\n", " 1.000000\n", " \n", " \n", @@ -3431,9 +3418,9 @@ "14 sci.med 13 2 2 \n", "15 rec.motorcycles 8 2 0 \n", "16 rec.sport.hockey 10 2 0 \n", - "17 rec.sport.baseball 9 0 2 \n", - "18 soc.religion.christian 15 0 0 \n", - "19 talk.politics.mideast 17 0 0 \n", + "17 soc.religion.christian 15 0 0 \n", + "18 talk.politics.mideast 17 0 0 \n", + "19 rec.sport.baseball 9 0 2 \n", "\n", " Label Noise Inverse Label Noise Label Quality Score \n", "0 0.034483 0.009646 0.965517 \n", @@ -3453,9 +3440,9 @@ "14 0.005051 0.005051 0.994949 \n", "15 0.005025 0.000000 0.994975 \n", "16 0.005013 0.000000 0.994987 \n", - "17 0.000000 0.005013 1.000000 \n", + "17 0.000000 0.000000 1.000000 \n", "18 0.000000 0.000000 1.000000 \n", - "19 0.000000 0.000000 1.000000 " + "19 0.000000 0.005013 1.000000 " ] }, "metadata": {}, @@ -3522,26 +3509,26 @@ " \n", " 2\n", " misc.forsale\n", - " rec.autos\n", + " sci.electronics\n", " 6\n", - " 7\n", + " 12\n", " 7\n", " 0.000929\n", " \n", " \n", " 3\n", " misc.forsale\n", - " sci.electronics\n", + " rec.autos\n", " 6\n", - " 12\n", + " 7\n", " 7\n", " 0.000929\n", " \n", " \n", " 4\n", - " comp.graphics\n", + " comp.os.ms-windows.misc\n", " comp.windows.x\n", - " 1\n", + " 2\n", " 5\n", " 5\n", " 0.000664\n", @@ -3558,45 +3545,45 @@ " \n", " 185\n", " comp.sys.mac.hardware\n", - " rec.sport.baseball\n", + " rec.motorcycles\n", " 4\n", - " 9\n", + " 8\n", " 0\n", " 0.000000\n", " \n", " \n", " 186\n", " comp.sys.mac.hardware\n", - " rec.sport.hockey\n", + " rec.sport.baseball\n", " 4\n", - " 10\n", + " 9\n", " 0\n", " 0.000000\n", " \n", " \n", " 187\n", " comp.sys.mac.hardware\n", - " sci.crypt\n", + " rec.sport.hockey\n", " 4\n", - " 11\n", + " 10\n", " 0\n", " 0.000000\n", " \n", " \n", " 188\n", " comp.sys.mac.hardware\n", - " sci.electronics\n", + " sci.crypt\n", " 4\n", - " 12\n", + " 11\n", " 0\n", " 0.000000\n", " \n", " \n", " 189\n", - " comp.sys.ibm.pc.hardware\n", - " talk.politics.guns\n", - " 3\n", - " 16\n", + " talk.politics.misc\n", + " talk.religion.misc\n", + " 18\n", + " 19\n", " 0\n", " 0.000000\n", " \n", @@ -3606,31 +3593,31 @@ "" ], "text/plain": [ - " Class Name A Class Name B Class Index A \\\n", - "0 alt.atheism talk.religion.misc 0 \n", - "1 comp.os.ms-windows.misc comp.sys.ibm.pc.hardware 2 \n", - "2 misc.forsale rec.autos 6 \n", - "3 misc.forsale sci.electronics 6 \n", - "4 comp.graphics comp.windows.x 1 \n", - ".. ... ... ... \n", - "185 comp.sys.mac.hardware rec.sport.baseball 4 \n", - "186 comp.sys.mac.hardware rec.sport.hockey 4 \n", - "187 comp.sys.mac.hardware sci.crypt 4 \n", - "188 comp.sys.mac.hardware sci.electronics 4 \n", - "189 comp.sys.ibm.pc.hardware talk.politics.guns 3 \n", + " Class Name A Class Name B Class Index A \\\n", + "0 alt.atheism talk.religion.misc 0 \n", + "1 comp.os.ms-windows.misc comp.sys.ibm.pc.hardware 2 \n", + "2 misc.forsale sci.electronics 6 \n", + "3 misc.forsale rec.autos 6 \n", + "4 comp.os.ms-windows.misc comp.windows.x 2 \n", + ".. ... ... ... \n", + "185 comp.sys.mac.hardware rec.motorcycles 4 \n", + "186 comp.sys.mac.hardware rec.sport.baseball 4 \n", + "187 comp.sys.mac.hardware rec.sport.hockey 4 \n", + "188 comp.sys.mac.hardware sci.crypt 4 \n", + "189 talk.politics.misc talk.religion.misc 18 \n", "\n", " Class Index B Num Overlapping Examples Joint Probability \n", "0 19 14 0.001859 \n", "1 3 10 0.001328 \n", - "2 7 7 0.000929 \n", - "3 12 7 0.000929 \n", + "2 12 7 0.000929 \n", + "3 7 7 0.000929 \n", "4 5 5 0.000664 \n", ".. ... ... ... \n", - "185 9 0 0.000000 \n", - "186 10 0 0.000000 \n", - "187 11 0 0.000000 \n", - "188 12 0 0.000000 \n", - "189 16 0 0.000000 \n", + "185 8 0 0.000000 \n", + "186 9 0 0.000000 \n", + "187 10 0 0.000000 \n", + "188 11 0 0.000000 \n", + "189 19 0 0.000000 \n", "\n", "[190 rows x 6 columns]" ] diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index c8e5ee76b..1a57adb8b 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -918,13 +918,13 @@

How can I find label issues in big datasets with limited memory?
-
+
-
+
@@ -1131,7 +1131,7 @@

Can’t find an answer to your question?Cleanlab Github issues, Cleanlab Code Examples or our Slack Community.

If your question is not addressed anywhere, please open a new Github issue. Our developers may also provide personalized assistance in our Slack Community.

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index cb42f04fd..de2d5a8f0 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:56.431503Z", - "iopub.status.busy": "2023-08-14T16:35:56.430984Z", - "iopub.status.idle": "2023-08-14T16:35:57.647255Z", - "shell.execute_reply": "2023-08-14T16:35:57.645721Z" + "iopub.execute_input": "2023-08-15T04:07:38.998037Z", + "iopub.status.busy": "2023-08-15T04:07:38.997583Z", + "iopub.status.idle": "2023-08-15T04:07:40.503084Z", + "shell.execute_reply": "2023-08-15T04:07:40.501920Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:57.651515Z", - "iopub.status.busy": "2023-08-14T16:35:57.651120Z", - "iopub.status.idle": "2023-08-14T16:35:57.657122Z", - "shell.execute_reply": "2023-08-14T16:35:57.655981Z" + "iopub.execute_input": "2023-08-15T04:07:40.508143Z", + "iopub.status.busy": "2023-08-15T04:07:40.507750Z", + "iopub.status.idle": "2023-08-15T04:07:40.514509Z", + "shell.execute_reply": "2023-08-15T04:07:40.513502Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:35:57.660137Z", - "iopub.status.busy": "2023-08-14T16:35:57.659721Z", - "iopub.status.idle": "2023-08-14T16:36:00.302636Z", - "shell.execute_reply": "2023-08-14T16:36:00.301614Z" + "iopub.execute_input": "2023-08-15T04:07:40.518392Z", + "iopub.status.busy": "2023-08-15T04:07:40.518152Z", + "iopub.status.idle": "2023-08-15T04:07:43.942942Z", + "shell.execute_reply": "2023-08-15T04:07:43.941718Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.307500Z", - "iopub.status.busy": "2023-08-14T16:36:00.306351Z", - "iopub.status.idle": "2023-08-14T16:36:00.348507Z", - "shell.execute_reply": "2023-08-14T16:36:00.347621Z" + "iopub.execute_input": "2023-08-15T04:07:43.948436Z", + "iopub.status.busy": "2023-08-15T04:07:43.947659Z", + "iopub.status.idle": "2023-08-15T04:07:43.998509Z", + "shell.execute_reply": "2023-08-15T04:07:43.997139Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.352674Z", - "iopub.status.busy": "2023-08-14T16:36:00.352158Z", - "iopub.status.idle": "2023-08-14T16:36:00.387042Z", - "shell.execute_reply": "2023-08-14T16:36:00.386119Z" + "iopub.execute_input": "2023-08-15T04:07:44.004072Z", + "iopub.status.busy": "2023-08-15T04:07:44.003167Z", + "iopub.status.idle": "2023-08-15T04:07:44.055136Z", + "shell.execute_reply": "2023-08-15T04:07:44.053936Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.391275Z", - "iopub.status.busy": "2023-08-14T16:36:00.390770Z", - "iopub.status.idle": "2023-08-14T16:36:00.395273Z", - "shell.execute_reply": "2023-08-14T16:36:00.394579Z" + "iopub.execute_input": "2023-08-15T04:07:44.060637Z", + "iopub.status.busy": "2023-08-15T04:07:44.059764Z", + "iopub.status.idle": "2023-08-15T04:07:44.064694Z", + "shell.execute_reply": "2023-08-15T04:07:44.063643Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.398600Z", - "iopub.status.busy": "2023-08-14T16:36:00.398140Z", - "iopub.status.idle": "2023-08-14T16:36:00.401414Z", - "shell.execute_reply": "2023-08-14T16:36:00.400716Z" + "iopub.execute_input": "2023-08-15T04:07:44.068392Z", + "iopub.status.busy": "2023-08-15T04:07:44.068117Z", + "iopub.status.idle": "2023-08-15T04:07:44.073046Z", + "shell.execute_reply": "2023-08-15T04:07:44.072247Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.404714Z", - "iopub.status.busy": "2023-08-14T16:36:00.404166Z", - "iopub.status.idle": "2023-08-14T16:36:00.438673Z", - "shell.execute_reply": "2023-08-14T16:36:00.438099Z" + "iopub.execute_input": "2023-08-15T04:07:44.076654Z", + "iopub.status.busy": "2023-08-15T04:07:44.076382Z", + "iopub.status.idle": "2023-08-15T04:07:44.130173Z", + "shell.execute_reply": "2023-08-15T04:07:44.129123Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57d827bca52f42fca241fa1231dd06f3", + "model_id": "003673da9ffd448fa0f9d2438f7e792a", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02c368f9be2a42309ccb9edeff1a3016", + "model_id": "ccdbf65d6f614a8591dfa30a8ae31dcb", "version_major": 2, "version_minor": 0 }, @@ -375,10 +375,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.442504Z", - "iopub.status.busy": "2023-08-14T16:36:00.442049Z", - "iopub.status.idle": "2023-08-14T16:36:00.449748Z", - "shell.execute_reply": "2023-08-14T16:36:00.449183Z" + "iopub.execute_input": "2023-08-15T04:07:44.134538Z", + "iopub.status.busy": "2023-08-15T04:07:44.134218Z", + "iopub.status.idle": "2023-08-15T04:07:44.149376Z", + "shell.execute_reply": "2023-08-15T04:07:44.148204Z" }, "nbsphinx": "hidden" }, @@ -409,10 +409,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.452569Z", - "iopub.status.busy": "2023-08-14T16:36:00.452131Z", - "iopub.status.idle": "2023-08-14T16:36:00.456258Z", - "shell.execute_reply": "2023-08-14T16:36:00.455711Z" + "iopub.execute_input": "2023-08-15T04:07:44.154061Z", + "iopub.status.busy": "2023-08-15T04:07:44.153670Z", + "iopub.status.idle": "2023-08-15T04:07:44.161872Z", + "shell.execute_reply": "2023-08-15T04:07:44.160822Z" }, "nbsphinx": "hidden" }, @@ -435,10 +435,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:00.459245Z", - "iopub.status.busy": "2023-08-14T16:36:00.458710Z", - "iopub.status.idle": "2023-08-14T16:36:00.466924Z", - "shell.execute_reply": "2023-08-14T16:36:00.466371Z" + "iopub.execute_input": "2023-08-15T04:07:44.165977Z", + "iopub.status.busy": "2023-08-15T04:07:44.165471Z", + "iopub.status.idle": "2023-08-15T04:07:44.176406Z", + "shell.execute_reply": "2023-08-15T04:07:44.175289Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "b0a01109", "metadata": { "execution": { - 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5. Compute out-of-sample predicted probabilities
   epoch    train_loss    valid_acc    valid_loss     dur
 -------  ------------  -----------  ------------  ------
-      1        0.6908       0.9139        0.3099  3.3471
-      2        0.2112       0.9412        0.2002  3.3083
-      3        0.1521       0.9516        0.1574  3.4646
-      4        0.1240       0.9594        0.1332  3.2663
-      5        0.1066       0.9633        0.1178  3.2562
-      6        0.0948       0.9660        0.1072  3.2716
-      7        0.0860       0.9682        0.0994  3.2971
-      8        0.0792       0.9708        0.0934  3.2870
-      9        0.0737       0.9725        0.0886  3.4553
-     10        0.0690       0.9736        0.0847  3.4006
+      1        0.6908       0.9139        0.3099  5.8484
+      2        0.2112       0.9412        0.2000  4.9170
+      3        0.1521       0.9516        0.1574  4.8178
+      4        0.1239       0.9595        0.1332  4.9101
+      5        0.1066       0.9633        0.1179  4.8797
+      6        0.0948       0.9663        0.1071  5.0642
+      7        0.0860       0.9683        0.0995  4.9326
+      8        0.0792       0.9703        0.0934  4.8050
+      9        0.0736       0.9728        0.0886  4.8635
+     10        0.0690       0.9738        0.0847  4.7248
   epoch    train_loss    valid_acc    valid_loss     dur
 -------  ------------  -----------  ------------  ------
-      1        0.7043       0.9247        0.2786  3.3475
-      2        0.1907       0.9465        0.1817  3.4382
-      3        0.1355       0.9556        0.1477  3.3651
-      4        0.1100       0.9616        0.1289  3.3878
-      5        0.0943       0.9648        0.1166  3.4701
-      6        0.0834       0.9684        0.1079  3.4018
-      7        0.0751       0.9702        0.1014  3.3094
-      8        0.0687       0.9713        0.0963  3.3262
-      9        0.0634       0.9724        0.0921  3.3722
-     10        0.0589       0.9732        0.0887  3.3797
+      1        0.7043       0.9247        0.2785  4.8613
+      2        0.1907       0.9464        0.1817  4.8761
+      3        0.1355       0.9561        0.1477  4.8335
+      4        0.1100       0.9613        0.1290  4.8704
+      5        0.0943       0.9644        0.1168  4.8599
+      6        0.0834       0.9686        0.1078  4.8590
+      7        0.0751       0.9702        0.1015  4.8019
+      8        0.0687       0.9711        0.0965  4.8567
+      9        0.0634       0.9724        0.0921  4.8372
+     10        0.0589       0.9734        0.0887  4.6923
   epoch    train_loss    valid_acc    valid_loss     dur
 -------  ------------  -----------  ------------  ------
-      1        0.7931       0.9112        0.3372  3.4436
-      2        0.2282       0.9486        0.1948  3.3737
-      3        0.1533       0.9592        0.1501  3.3492
-      4        0.1217       0.9641        0.1277  3.4138
-      5        0.1032       0.9678        0.1135  3.3927
-      6        0.0903       0.9701        0.1037  3.3229
-      7        0.0809       0.9729        0.0964  3.4252
-      8        0.0736       0.9747        0.0903  3.3964
-      9        0.0677       0.9761        0.0861  3.3905
-     10        0.0630       0.9766        0.0825  3.3361
+      1        0.7932       0.9116        0.3376  5.0162
+      2        0.2283       0.9483        0.1951  4.8961
+      3        0.1534       0.9590        0.1502  4.9592
+      4        0.1218       0.9643        0.1278  4.9167
+      5        0.1032       0.9684        0.1134  4.8419
+      6        0.0904       0.9706        0.1038  4.9709
+      7        0.0809       0.9731        0.0962  4.9817
+      8        0.0736       0.9744        0.0904  4.9848
+      9        0.0678       0.9761        0.0860  4.9951
+     10        0.0630       0.9774        0.0825  4.9483

An additional benefit of cross-validation is that it facilitates more reliable evaluation of our model than a single training/validation split.

@@ -989,7 +989,7 @@

5. Compute out-of-sample predicted probabilities 
-Cross-validated estimate of accuracy on held-out data: 0.9752428571428572
+Cross-validated estimate of accuracy on held-out data: 0.9752571428571428
@@ -1017,7 +1017,7 @@

6. Use cleanlab to find label issues
 Finding label issues ...
 
-Audit complete. 143 issues found in the dataset.
+Audit complete. 144 issues found in the dataset.

After the audit is complete, we can view the results and information regarding the labels using Datalab’s get_issues method.

@@ -1063,35 +1063,35 @@

6. Use cleanlab to find label issues 
 Top 15 most likely label errors:
- [59915 24798 19124 53216  2720 59701  8200 50340 21601 32776 56014  2676
- 35480 46726  7010]
+ [59915 24798 19124 53216  2720 59701  8200 50340 21601 32776  2676 35480
+ 56014 46726  7010]

ranked_label_issues is a list of indices ranked by the label score of each example, the top indices in the list corresponding to examples that are worth inspecting more closely. To help visualize specific examples, we define a plot_examples function (can skip these details).

diff --git a/master/tutorials/image.ipynb b/master/tutorials/image.ipynb index d1448e934..7c4dde612 100644 --- a/master/tutorials/image.ipynb +++ b/master/tutorials/image.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:09.469287Z", - "iopub.status.busy": "2023-08-14T16:36:09.468775Z", - "iopub.status.idle": "2023-08-14T16:36:11.804719Z", - "shell.execute_reply": "2023-08-14T16:36:11.803211Z" + "iopub.execute_input": "2023-08-15T04:07:55.391429Z", + "iopub.status.busy": "2023-08-15T04:07:55.390926Z", + "iopub.status.idle": "2023-08-15T04:07:58.338831Z", + "shell.execute_reply": "2023-08-15T04:07:58.337802Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"torch\", \"torchvision\", \"skorch\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -120,10 +120,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:11.808909Z", - "iopub.status.busy": "2023-08-14T16:36:11.808498Z", - "iopub.status.idle": "2023-08-14T16:36:11.842679Z", - "shell.execute_reply": "2023-08-14T16:36:11.842008Z" + "iopub.execute_input": "2023-08-15T04:07:58.343300Z", + "iopub.status.busy": "2023-08-15T04:07:58.342795Z", + "iopub.status.idle": "2023-08-15T04:07:58.388556Z", + "shell.execute_reply": "2023-08-15T04:07:58.387569Z" } }, "outputs": [], @@ -141,10 +141,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:11.846047Z", - "iopub.status.busy": "2023-08-14T16:36:11.845806Z", - "iopub.status.idle": "2023-08-14T16:36:11.850649Z", - "shell.execute_reply": "2023-08-14T16:36:11.849965Z" + "iopub.execute_input": "2023-08-15T04:07:58.392775Z", + "iopub.status.busy": "2023-08-15T04:07:58.392129Z", + "iopub.status.idle": "2023-08-15T04:07:58.398736Z", + "shell.execute_reply": "2023-08-15T04:07:58.397802Z" }, "nbsphinx": "hidden" }, @@ -174,10 +174,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:11.853493Z", - "iopub.status.busy": "2023-08-14T16:36:11.853262Z", - "iopub.status.idle": "2023-08-14T16:36:57.542730Z", - "shell.execute_reply": "2023-08-14T16:36:57.541907Z" + "iopub.execute_input": "2023-08-15T04:07:58.402547Z", + "iopub.status.busy": "2023-08-15T04:07:58.402244Z", + "iopub.status.idle": "2023-08-15T04:08:58.354501Z", + "shell.execute_reply": "2023-08-15T04:08:58.353131Z" } }, "outputs": [ @@ -231,10 +231,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:57.547583Z", - "iopub.status.busy": "2023-08-14T16:36:57.546812Z", - "iopub.status.idle": "2023-08-14T16:36:57.552869Z", - "shell.execute_reply": "2023-08-14T16:36:57.552186Z" + "iopub.execute_input": "2023-08-15T04:08:58.359263Z", + "iopub.status.busy": "2023-08-15T04:08:58.358986Z", + "iopub.status.idle": "2023-08-15T04:08:58.366908Z", + "shell.execute_reply": "2023-08-15T04:08:58.365951Z" } }, "outputs": [], @@ -286,10 +286,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:57.555822Z", - "iopub.status.busy": "2023-08-14T16:36:57.555451Z", - "iopub.status.idle": "2023-08-14T16:36:57.558652Z", - "shell.execute_reply": "2023-08-14T16:36:57.557956Z" + "iopub.execute_input": "2023-08-15T04:08:58.379572Z", + "iopub.status.busy": "2023-08-15T04:08:58.371139Z", + "iopub.status.idle": "2023-08-15T04:08:58.384265Z", + "shell.execute_reply": "2023-08-15T04:08:58.383481Z" } }, "outputs": [], @@ -316,10 +316,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:36:57.561796Z", - "iopub.status.busy": "2023-08-14T16:36:57.561427Z", - "iopub.status.idle": "2023-08-14T16:38:41.836916Z", - "shell.execute_reply": "2023-08-14T16:38:41.836160Z" + "iopub.execute_input": "2023-08-15T04:08:58.387676Z", + "iopub.status.busy": "2023-08-15T04:08:58.387214Z", + "iopub.status.idle": "2023-08-15T04:11:30.677342Z", + "shell.execute_reply": "2023-08-15T04:11:30.676453Z" } }, "outputs": [ @@ -329,70 +329,70 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6908\u001b[0m \u001b[32m0.9139\u001b[0m \u001b[35m0.3099\u001b[0m 3.3471\n" + " 1 \u001b[36m0.6908\u001b[0m \u001b[32m0.9139\u001b[0m \u001b[35m0.3099\u001b[0m 5.8484\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2 \u001b[36m0.2112\u001b[0m \u001b[32m0.9412\u001b[0m \u001b[35m0.2002\u001b[0m 3.3083\n" + " 2 \u001b[36m0.2112\u001b[0m \u001b[32m0.9412\u001b[0m \u001b[35m0.2000\u001b[0m 4.9170\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3 \u001b[36m0.1521\u001b[0m \u001b[32m0.9516\u001b[0m \u001b[35m0.1574\u001b[0m 3.4646\n" + " 3 \u001b[36m0.1521\u001b[0m \u001b[32m0.9516\u001b[0m \u001b[35m0.1574\u001b[0m 4.8178\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 4 \u001b[36m0.1240\u001b[0m \u001b[32m0.9594\u001b[0m \u001b[35m0.1332\u001b[0m 3.2663\n" + " 4 \u001b[36m0.1239\u001b[0m \u001b[32m0.9595\u001b[0m \u001b[35m0.1332\u001b[0m 4.9101\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 5 \u001b[36m0.1066\u001b[0m \u001b[32m0.9633\u001b[0m \u001b[35m0.1178\u001b[0m 3.2562\n" + " 5 \u001b[36m0.1066\u001b[0m \u001b[32m0.9633\u001b[0m \u001b[35m0.1179\u001b[0m 4.8797\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6 \u001b[36m0.0948\u001b[0m \u001b[32m0.9660\u001b[0m \u001b[35m0.1072\u001b[0m 3.2716\n" + " 6 \u001b[36m0.0948\u001b[0m \u001b[32m0.9663\u001b[0m \u001b[35m0.1071\u001b[0m 5.0642\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 7 \u001b[36m0.0860\u001b[0m \u001b[32m0.9682\u001b[0m \u001b[35m0.0994\u001b[0m 3.2971\n" + " 7 \u001b[36m0.0860\u001b[0m \u001b[32m0.9683\u001b[0m \u001b[35m0.0995\u001b[0m 4.9326\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 8 \u001b[36m0.0792\u001b[0m \u001b[32m0.9708\u001b[0m \u001b[35m0.0934\u001b[0m 3.2870\n" + " 8 \u001b[36m0.0792\u001b[0m \u001b[32m0.9703\u001b[0m \u001b[35m0.0934\u001b[0m 4.8050\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 9 \u001b[36m0.0737\u001b[0m \u001b[32m0.9725\u001b[0m \u001b[35m0.0886\u001b[0m 3.4553\n" + " 9 \u001b[36m0.0736\u001b[0m \u001b[32m0.9728\u001b[0m \u001b[35m0.0886\u001b[0m 4.8635\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 10 \u001b[36m0.0690\u001b[0m \u001b[32m0.9736\u001b[0m \u001b[35m0.0847\u001b[0m 3.4006\n" + " 10 \u001b[36m0.0690\u001b[0m \u001b[32m0.9738\u001b[0m \u001b[35m0.0847\u001b[0m 4.7248\n" ] }, { @@ -401,70 +401,70 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.7043\u001b[0m \u001b[32m0.9247\u001b[0m \u001b[35m0.2786\u001b[0m 3.3475\n" + " 1 \u001b[36m0.7043\u001b[0m \u001b[32m0.9247\u001b[0m \u001b[35m0.2785\u001b[0m 4.8613\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2 \u001b[36m0.1907\u001b[0m \u001b[32m0.9465\u001b[0m \u001b[35m0.1817\u001b[0m 3.4382\n" + " 2 \u001b[36m0.1907\u001b[0m \u001b[32m0.9464\u001b[0m \u001b[35m0.1817\u001b[0m 4.8761\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3 \u001b[36m0.1355\u001b[0m \u001b[32m0.9556\u001b[0m \u001b[35m0.1477\u001b[0m 3.3651\n" + " 3 \u001b[36m0.1355\u001b[0m \u001b[32m0.9561\u001b[0m \u001b[35m0.1477\u001b[0m 4.8335\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 4 \u001b[36m0.1100\u001b[0m \u001b[32m0.9616\u001b[0m \u001b[35m0.1289\u001b[0m 3.3878\n" + " 4 \u001b[36m0.1100\u001b[0m \u001b[32m0.9613\u001b[0m \u001b[35m0.1290\u001b[0m 4.8704\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 5 \u001b[36m0.0943\u001b[0m \u001b[32m0.9648\u001b[0m \u001b[35m0.1166\u001b[0m 3.4701\n" + " 5 \u001b[36m0.0943\u001b[0m \u001b[32m0.9644\u001b[0m \u001b[35m0.1168\u001b[0m 4.8599\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6 \u001b[36m0.0834\u001b[0m \u001b[32m0.9684\u001b[0m \u001b[35m0.1079\u001b[0m 3.4018\n" + " 6 \u001b[36m0.0834\u001b[0m \u001b[32m0.9686\u001b[0m \u001b[35m0.1078\u001b[0m 4.8590\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 7 \u001b[36m0.0751\u001b[0m \u001b[32m0.9702\u001b[0m \u001b[35m0.1014\u001b[0m 3.3094\n" + " 7 \u001b[36m0.0751\u001b[0m \u001b[32m0.9702\u001b[0m \u001b[35m0.1015\u001b[0m 4.8019\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 8 \u001b[36m0.0687\u001b[0m \u001b[32m0.9713\u001b[0m \u001b[35m0.0963\u001b[0m 3.3262\n" + " 8 \u001b[36m0.0687\u001b[0m \u001b[32m0.9711\u001b[0m \u001b[35m0.0965\u001b[0m 4.8567\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 9 \u001b[36m0.0634\u001b[0m \u001b[32m0.9724\u001b[0m \u001b[35m0.0921\u001b[0m 3.3722\n" + " 9 \u001b[36m0.0634\u001b[0m \u001b[32m0.9724\u001b[0m \u001b[35m0.0921\u001b[0m 4.8372\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 10 \u001b[36m0.0589\u001b[0m \u001b[32m0.9732\u001b[0m \u001b[35m0.0887\u001b[0m 3.3797\n" + " 10 \u001b[36m0.0589\u001b[0m \u001b[32m0.9734\u001b[0m \u001b[35m0.0887\u001b[0m 4.6923\n" ] }, { @@ -473,70 +473,70 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.7931\u001b[0m \u001b[32m0.9112\u001b[0m \u001b[35m0.3372\u001b[0m 3.4436\n" + " 1 \u001b[36m0.7932\u001b[0m \u001b[32m0.9116\u001b[0m \u001b[35m0.3376\u001b[0m 5.0162\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2 \u001b[36m0.2282\u001b[0m \u001b[32m0.9486\u001b[0m \u001b[35m0.1948\u001b[0m 3.3737\n" + " 2 \u001b[36m0.2283\u001b[0m \u001b[32m0.9483\u001b[0m \u001b[35m0.1951\u001b[0m 4.8961\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3 \u001b[36m0.1533\u001b[0m \u001b[32m0.9592\u001b[0m \u001b[35m0.1501\u001b[0m 3.3492\n" + " 3 \u001b[36m0.1534\u001b[0m \u001b[32m0.9590\u001b[0m \u001b[35m0.1502\u001b[0m 4.9592\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 4 \u001b[36m0.1217\u001b[0m \u001b[32m0.9641\u001b[0m \u001b[35m0.1277\u001b[0m 3.4138\n" + " 4 \u001b[36m0.1218\u001b[0m \u001b[32m0.9643\u001b[0m \u001b[35m0.1278\u001b[0m 4.9167\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 5 \u001b[36m0.1032\u001b[0m \u001b[32m0.9678\u001b[0m \u001b[35m0.1135\u001b[0m 3.3927\n" + " 5 \u001b[36m0.1032\u001b[0m \u001b[32m0.9684\u001b[0m \u001b[35m0.1134\u001b[0m 4.8419\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6 \u001b[36m0.0903\u001b[0m \u001b[32m0.9701\u001b[0m \u001b[35m0.1037\u001b[0m 3.3229\n" + " 6 \u001b[36m0.0904\u001b[0m \u001b[32m0.9706\u001b[0m \u001b[35m0.1038\u001b[0m 4.9709\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 7 \u001b[36m0.0809\u001b[0m \u001b[32m0.9729\u001b[0m \u001b[35m0.0964\u001b[0m 3.4252\n" + " 7 \u001b[36m0.0809\u001b[0m \u001b[32m0.9731\u001b[0m \u001b[35m0.0962\u001b[0m 4.9817\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 8 \u001b[36m0.0736\u001b[0m \u001b[32m0.9747\u001b[0m \u001b[35m0.0903\u001b[0m 3.3964\n" + " 8 \u001b[36m0.0736\u001b[0m \u001b[32m0.9744\u001b[0m \u001b[35m0.0904\u001b[0m 4.9848\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 9 \u001b[36m0.0677\u001b[0m \u001b[32m0.9761\u001b[0m \u001b[35m0.0861\u001b[0m 3.3905\n" + " 9 \u001b[36m0.0678\u001b[0m \u001b[32m0.9761\u001b[0m \u001b[35m0.0860\u001b[0m 4.9951\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 10 \u001b[36m0.0630\u001b[0m \u001b[32m0.9766\u001b[0m \u001b[35m0.0825\u001b[0m 3.3361\n" + " 10 \u001b[36m0.0630\u001b[0m \u001b[32m0.9774\u001b[0m \u001b[35m0.0825\u001b[0m 4.9483\n" ] } ], @@ -563,10 +563,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:41.840983Z", - "iopub.status.busy": "2023-08-14T16:38:41.840439Z", - "iopub.status.idle": "2023-08-14T16:38:41.849447Z", - "shell.execute_reply": "2023-08-14T16:38:41.848724Z" + "iopub.execute_input": "2023-08-15T04:11:30.681804Z", + "iopub.status.busy": "2023-08-15T04:11:30.681266Z", + "iopub.status.idle": "2023-08-15T04:11:30.694199Z", + "shell.execute_reply": "2023-08-15T04:11:30.693378Z" } }, "outputs": [ @@ -574,7 +574,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cross-validated estimate of accuracy on held-out data: 0.9752428571428572\n" + "Cross-validated estimate of accuracy on held-out data: 0.9752571428571428\n" ] } ], @@ -605,10 +605,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:41.853072Z", - "iopub.status.busy": "2023-08-14T16:38:41.852507Z", - "iopub.status.idle": "2023-08-14T16:38:44.479863Z", - "shell.execute_reply": "2023-08-14T16:38:44.478936Z" + "iopub.execute_input": "2023-08-15T04:11:30.698029Z", + "iopub.status.busy": "2023-08-15T04:11:30.697509Z", + "iopub.status.idle": "2023-08-15T04:11:34.274191Z", + "shell.execute_reply": "2023-08-15T04:11:34.272479Z" } }, "outputs": [ @@ -624,7 +624,7 @@ "output_type": "stream", "text": [ "\n", - "Audit complete. 143 issues found in the dataset.\n" + "Audit complete. 144 issues found in the dataset.\n" ] } ], @@ -649,10 +649,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.483936Z", - "iopub.status.busy": "2023-08-14T16:38:44.483157Z", - "iopub.status.idle": "2023-08-14T16:38:44.521002Z", - "shell.execute_reply": "2023-08-14T16:38:44.520297Z" + "iopub.execute_input": "2023-08-15T04:11:34.278629Z", + "iopub.status.busy": "2023-08-15T04:11:34.278144Z", + "iopub.status.idle": "2023-08-15T04:11:34.323853Z", + "shell.execute_reply": "2023-08-15T04:11:34.322860Z" }, "scrolled": true }, @@ -688,35 +688,35 @@ " \n", " 0\n", " False\n", - " 0.828540\n", + " 0.822983\n", " 5\n", " 5\n", " \n", " \n", " 1\n", " False\n", - " 0.999814\n", + " 0.999811\n", " 0\n", " 0\n", " \n", " \n", " 2\n", " False\n", - " 0.992302\n", + " 0.992340\n", " 4\n", " 4\n", " \n", " \n", " 3\n", " False\n", - " 0.997823\n", + " 0.997847\n", " 1\n", " 1\n", " \n", " \n", " 4\n", " False\n", - " 0.990165\n", + " 0.989921\n", " 9\n", " 9\n", " \n", @@ -726,11 +726,11 @@ ], "text/plain": [ " is_label_issue label_score given_label predicted_label\n", - "0 False 0.828540 5 5\n", - "1 False 0.999814 0 0\n", - "2 False 0.992302 4 4\n", - "3 False 0.997823 1 1\n", - "4 False 0.990165 9 9" + "0 False 0.822983 5 5\n", + "1 False 0.999811 0 0\n", + "2 False 0.992340 4 4\n", + "3 False 0.997847 1 1\n", + "4 False 0.989921 9 9" ] }, "execution_count": 10, @@ -757,10 +757,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.524046Z", - "iopub.status.busy": "2023-08-14T16:38:44.523597Z", - "iopub.status.idle": "2023-08-14T16:38:44.533569Z", - "shell.execute_reply": "2023-08-14T16:38:44.532876Z" + "iopub.execute_input": "2023-08-15T04:11:34.328031Z", + "iopub.status.busy": "2023-08-15T04:11:34.327694Z", + "iopub.status.idle": "2023-08-15T04:11:34.347479Z", + "shell.execute_reply": "2023-08-15T04:11:34.345922Z" } }, "outputs": [ @@ -769,8 +769,8 @@ "output_type": "stream", "text": [ "Top 15 most likely label errors: \n", - " [59915 24798 19124 53216 2720 59701 8200 50340 21601 32776 56014 2676\n", - " 35480 46726 7010]\n" + " [59915 24798 19124 53216 2720 59701 8200 50340 21601 32776 2676 35480\n", + " 56014 46726 7010]\n" ] } ], @@ -815,10 +815,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.536542Z", - "iopub.status.busy": "2023-08-14T16:38:44.536126Z", - "iopub.status.idle": "2023-08-14T16:38:44.806282Z", - "shell.execute_reply": "2023-08-14T16:38:44.805072Z" + "iopub.execute_input": "2023-08-15T04:11:34.352449Z", + "iopub.status.busy": "2023-08-15T04:11:34.351333Z", + "iopub.status.idle": "2023-08-15T04:11:34.688566Z", + "shell.execute_reply": "2023-08-15T04:11:34.686739Z" }, "nbsphinx": "hidden" }, @@ -848,16 +848,16 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:44.810109Z", - "iopub.status.busy": "2023-08-14T16:38:44.809682Z", - "iopub.status.idle": "2023-08-14T16:38:45.578747Z", - "shell.execute_reply": "2023-08-14T16:38:45.577854Z" + "iopub.execute_input": "2023-08-15T04:11:34.693348Z", + "iopub.status.busy": "2023-08-15T04:11:34.693012Z", + "iopub.status.idle": "2023-08-15T04:11:35.922369Z", + "shell.execute_reply": "2023-08-15T04:11:35.921230Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -889,10 +889,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.583760Z", - "iopub.status.busy": "2023-08-14T16:38:45.582270Z", - "iopub.status.idle": "2023-08-14T16:38:45.681117Z", - "shell.execute_reply": "2023-08-14T16:38:45.680369Z" + "iopub.execute_input": "2023-08-15T04:11:35.927075Z", + "iopub.status.busy": "2023-08-15T04:11:35.926329Z", + "iopub.status.idle": "2023-08-15T04:11:36.070520Z", + "shell.execute_reply": "2023-08-15T04:11:36.069635Z" } }, "outputs": [ @@ -923,10 +923,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.685065Z", - "iopub.status.busy": "2023-08-14T16:38:45.684803Z", - "iopub.status.idle": "2023-08-14T16:38:45.781304Z", - "shell.execute_reply": "2023-08-14T16:38:45.780675Z" + "iopub.execute_input": "2023-08-15T04:11:36.076103Z", + "iopub.status.busy": "2023-08-15T04:11:36.074626Z", + "iopub.status.idle": "2023-08-15T04:11:36.211656Z", + "shell.execute_reply": "2023-08-15T04:11:36.210847Z" } }, "outputs": [ @@ -957,10 +957,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.786102Z", - "iopub.status.busy": "2023-08-14T16:38:45.784826Z", - "iopub.status.idle": "2023-08-14T16:38:45.880563Z", - "shell.execute_reply": "2023-08-14T16:38:45.879942Z" + "iopub.execute_input": "2023-08-15T04:11:36.217234Z", + "iopub.status.busy": "2023-08-15T04:11:36.215798Z", + "iopub.status.idle": "2023-08-15T04:11:36.353472Z", + "shell.execute_reply": "2023-08-15T04:11:36.352639Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.885148Z", - "iopub.status.busy": "2023-08-14T16:38:45.883999Z", - "iopub.status.idle": "2023-08-14T16:38:45.977970Z", - "shell.execute_reply": "2023-08-14T16:38:45.977315Z" + "iopub.execute_input": "2023-08-15T04:11:36.359304Z", + "iopub.status.busy": "2023-08-15T04:11:36.357629Z", + "iopub.status.idle": "2023-08-15T04:11:36.499605Z", + "shell.execute_reply": "2023-08-15T04:11:36.498645Z" } }, "outputs": [ @@ -1027,10 +1027,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:45.982623Z", - "iopub.status.busy": "2023-08-14T16:38:45.981459Z", - "iopub.status.idle": "2023-08-14T16:38:45.987703Z", - "shell.execute_reply": "2023-08-14T16:38:45.987147Z" + "iopub.execute_input": "2023-08-15T04:11:36.505724Z", + "iopub.status.busy": "2023-08-15T04:11:36.503866Z", + "iopub.status.idle": "2023-08-15T04:11:36.516157Z", + "shell.execute_reply": "2023-08-15T04:11:36.515355Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/indepth_overview.html b/master/tutorials/indepth_overview.html index fcee0c227..3ad506611 100644 --- a/master/tutorials/indepth_overview.html +++ b/master/tutorials/indepth_overview.html @@ -1344,13 +1344,13 @@

Visualize the twenty examples with lowest label quality to see if Cleanlab w 
CleanLearning(clf=LogisticRegression(random_state=0),
               find_label_issues_kwargs={'confident_joint': array([[68,  0,  8,  8],
        [ 5, 46,  3,  0],
-       [15,  3, 32, 13],
+       [15,  3, 31, 14],
        [ 2,  1, 12, 34]]),
                                         'min_examples_per_class': 10},
               seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -1415,8 +1415,8 @@

Workflow 4: Use cleanlab to find dataset-level and class-le yellow 2 3 - 25 - 0.100 + 26 + 0.104 1 @@ -1567,7 +1567,7 @@

Workflow 5: Clean your test set too if you’re doing ML wi 
  Noisy Test Accuracy (on given test labels) using yourFavoriteModel: 69%
- Noisy Test Accuracy (on given test labels) using yourFavoriteModel (+ CleanLearning): 70%
+ Noisy Test Accuracy (on given test labels) using yourFavoriteModel (+ CleanLearning): 71%
 Actual Test Accuracy (on corrected test labels) using yourFavoriteModel: 83%
 Actual Test Accuracy (on corrected test labels) using yourFavoriteModel (+ CleanLearning): 86%
@@ -1593,7 +1593,7 @@

Workflow 6: One score to rule them all – use cleanlab’s 
- * Overall, about 28% (70 of the 250) labels in your dataset have potential issues.
+ * Overall, about 28% (71 of the 250) labels in your dataset have potential issues.
  ** The overall label health score for this dataset is: 0.72.
@@ -1736,7 +1736,7 @@

Use cleanlab to estimate and visualize the joint distribution of label noise --- --- --- --- s=0 | 0.27 0.0 0.03 0.03 s=1 | 0.02 0.18 0.01 0.0 -s=2 | 0.06 0.01 0.13 0.05 +s=2 | 0.06 0.01 0.12 0.06 s=3 | 0.01 0.0 0.05 0.14 Trace(matrix) = 0.72 @@ -1744,11 +1744,11 @@

Use cleanlab to estimate and visualize the joint distribution of label noise Noise Matrix (aka Noisy Channel) P(given_label|true_label) of shape (4, 4) p(s|y) y=0 y=1 y=2 y=3 --- --- --- --- -s=0 | 0.76 0.0 0.15 0.15 -s=1 | 0.06 0.92 0.05 0.0 -s=2 | 0.17 0.06 0.58 0.24 -s=3 | 0.02 0.02 0.22 0.62 - Trace(matrix) = 2.88 +s=0 | 0.76 0.0 0.15 0.14 +s=1 | 0.06 0.92 0.06 0.0 +s=2 | 0.17 0.06 0.57 0.25 +s=3 | 0.02 0.02 0.22 0.61 + Trace(matrix) = 2.86 diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 7b343e9df..69b1f690e 100644 --- a/master/tutorials/indepth_overview.ipynb +++ b/master/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:50.776115Z", - "iopub.status.busy": "2023-08-14T16:38:50.775572Z", - "iopub.status.idle": "2023-08-14T16:38:52.091496Z", - "shell.execute_reply": "2023-08-14T16:38:52.090378Z" + "iopub.execute_input": "2023-08-15T04:11:42.217241Z", + "iopub.status.busy": "2023-08-15T04:11:42.216782Z", + "iopub.status.idle": "2023-08-15T04:11:43.845952Z", + "shell.execute_reply": "2023-08-15T04:11:43.844795Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.095900Z", - "iopub.status.busy": "2023-08-14T16:38:52.095380Z", - "iopub.status.idle": "2023-08-14T16:38:52.358817Z", - "shell.execute_reply": "2023-08-14T16:38:52.358028Z" + "iopub.execute_input": "2023-08-15T04:11:43.851897Z", + "iopub.status.busy": "2023-08-15T04:11:43.850757Z", + "iopub.status.idle": "2023-08-15T04:11:44.197410Z", + "shell.execute_reply": "2023-08-15T04:11:44.196275Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.363975Z", - "iopub.status.busy": "2023-08-14T16:38:52.362676Z", - "iopub.status.idle": "2023-08-14T16:38:52.482210Z", - "shell.execute_reply": "2023-08-14T16:38:52.481015Z" + "iopub.execute_input": "2023-08-15T04:11:44.202209Z", + "iopub.status.busy": "2023-08-15T04:11:44.201800Z", + "iopub.status.idle": "2023-08-15T04:11:44.224652Z", + "shell.execute_reply": "2023-08-15T04:11:44.223481Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.487306Z", - "iopub.status.busy": "2023-08-14T16:38:52.485958Z", - "iopub.status.idle": "2023-08-14T16:38:52.747309Z", - "shell.execute_reply": "2023-08-14T16:38:52.746514Z" + "iopub.execute_input": "2023-08-15T04:11:44.229037Z", + "iopub.status.busy": "2023-08-15T04:11:44.228210Z", + "iopub.status.idle": "2023-08-15T04:11:44.709696Z", + "shell.execute_reply": "2023-08-15T04:11:44.708776Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.750901Z", - "iopub.status.busy": "2023-08-14T16:38:52.750445Z", - "iopub.status.idle": "2023-08-14T16:38:52.780081Z", - "shell.execute_reply": "2023-08-14T16:38:52.779432Z" + "iopub.execute_input": "2023-08-15T04:11:44.714055Z", + "iopub.status.busy": "2023-08-15T04:11:44.713709Z", + "iopub.status.idle": "2023-08-15T04:11:44.762896Z", + "shell.execute_reply": "2023-08-15T04:11:44.761726Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:52.783344Z", - "iopub.status.busy": "2023-08-14T16:38:52.782875Z", - "iopub.status.idle": "2023-08-14T16:38:54.527350Z", - "shell.execute_reply": "2023-08-14T16:38:54.526237Z" + "iopub.execute_input": "2023-08-15T04:11:44.767437Z", + "iopub.status.busy": "2023-08-15T04:11:44.767034Z", + "iopub.status.idle": "2023-08-15T04:11:46.952056Z", + "shell.execute_reply": "2023-08-15T04:11:46.950612Z" } }, "outputs": [ @@ -471,10 +471,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:54.531701Z", - "iopub.status.busy": "2023-08-14T16:38:54.530933Z", - "iopub.status.idle": "2023-08-14T16:38:54.554957Z", - "shell.execute_reply": "2023-08-14T16:38:54.554308Z" + "iopub.execute_input": "2023-08-15T04:11:46.956615Z", + "iopub.status.busy": "2023-08-15T04:11:46.955724Z", + "iopub.status.idle": "2023-08-15T04:11:46.987375Z", + "shell.execute_reply": "2023-08-15T04:11:46.986222Z" }, "scrolled": true }, @@ -599,10 +599,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:54.558483Z", - "iopub.status.busy": "2023-08-14T16:38:54.557866Z", - "iopub.status.idle": "2023-08-14T16:38:55.782238Z", - "shell.execute_reply": "2023-08-14T16:38:55.781335Z" + "iopub.execute_input": "2023-08-15T04:11:46.992305Z", + "iopub.status.busy": "2023-08-15T04:11:46.991994Z", + "iopub.status.idle": "2023-08-15T04:11:48.499802Z", + "shell.execute_reply": "2023-08-15T04:11:48.498502Z" }, "id": "AaHC5MRKjruT" }, @@ -721,10 +721,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:55.786400Z", - "iopub.status.busy": "2023-08-14T16:38:55.785872Z", - "iopub.status.idle": "2023-08-14T16:38:55.803350Z", - "shell.execute_reply": "2023-08-14T16:38:55.802153Z" + "iopub.execute_input": "2023-08-15T04:11:48.504507Z", + "iopub.status.busy": "2023-08-15T04:11:48.504174Z", + "iopub.status.idle": "2023-08-15T04:11:48.534371Z", + "shell.execute_reply": "2023-08-15T04:11:48.533120Z" }, "id": "Wy27rvyhjruU" }, @@ -773,10 +773,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:55.806809Z", - "iopub.status.busy": "2023-08-14T16:38:55.806293Z", - "iopub.status.idle": "2023-08-14T16:38:55.901561Z", - "shell.execute_reply": "2023-08-14T16:38:55.900194Z" + "iopub.execute_input": "2023-08-15T04:11:48.538402Z", + "iopub.status.busy": "2023-08-15T04:11:48.537800Z", + "iopub.status.idle": "2023-08-15T04:11:48.683964Z", + "shell.execute_reply": "2023-08-15T04:11:48.682786Z" }, "id": "Db8YHnyVjruU" }, @@ -883,10 +883,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:55.905422Z", - "iopub.status.busy": "2023-08-14T16:38:55.904974Z", - "iopub.status.idle": "2023-08-14T16:38:56.127150Z", - "shell.execute_reply": "2023-08-14T16:38:56.126419Z" + "iopub.execute_input": "2023-08-15T04:11:48.689235Z", + "iopub.status.busy": "2023-08-15T04:11:48.688547Z", + "iopub.status.idle": "2023-08-15T04:11:49.008958Z", + "shell.execute_reply": "2023-08-15T04:11:49.008016Z" }, "id": "iJqAHuS2jruV" }, @@ -923,10 +923,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.130936Z", - "iopub.status.busy": "2023-08-14T16:38:56.130291Z", - "iopub.status.idle": "2023-08-14T16:38:56.154267Z", - "shell.execute_reply": "2023-08-14T16:38:56.153530Z" + "iopub.execute_input": "2023-08-15T04:11:49.013905Z", + "iopub.status.busy": "2023-08-15T04:11:49.013108Z", + "iopub.status.idle": "2023-08-15T04:11:49.044766Z", + "shell.execute_reply": "2023-08-15T04:11:49.043583Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -937,13 +937,13 @@ "
CleanLearning(clf=LogisticRegression(random_state=0),\n",
        "              find_label_issues_kwargs={'confident_joint': array([[68,  0,  8,  8],\n",
        "       [ 5, 46,  3,  0],\n",
-       "       [15,  3, 32, 13],\n",
+       "       [15,  3, 31, 14],\n",
        "       [ 2,  1, 12, 34]]),\n",
        "                                        'min_examples_per_class': 10},\n",
        "              seed=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" @@ -952,7 +952,7 @@ "CleanLearning(clf=LogisticRegression(random_state=0),\n", " find_label_issues_kwargs={'confident_joint': array([[68, 0, 8, 8],\n", " [ 5, 46, 3, 0],\n", - " [15, 3, 32, 13],\n", + " [15, 3, 31, 14],\n", " [ 2, 1, 12, 34]]),\n", " 'min_examples_per_class': 10},\n", " seed=0)" @@ -988,10 +988,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.157383Z", - "iopub.status.busy": "2023-08-14T16:38:56.157139Z", - "iopub.status.idle": "2023-08-14T16:38:56.171139Z", - "shell.execute_reply": "2023-08-14T16:38:56.170490Z" + "iopub.execute_input": "2023-08-15T04:11:49.049055Z", + "iopub.status.busy": "2023-08-15T04:11:49.048700Z", + "iopub.status.idle": "2023-08-15T04:11:49.079401Z", + "shell.execute_reply": "2023-08-15T04:11:49.077983Z" }, "id": "0lonvOYvjruV" }, @@ -1032,8 +1032,8 @@ " yellow\n", " 2\n", " 3\n", - " 25\n", - " 0.100\n", + " 26\n", + " 0.104\n", " \n", " \n", " 1\n", @@ -1094,7 +1094,7 @@ "5 blue yellow 1 3 \n", "\n", " Num Overlapping Examples Joint Probability \n", - "0 25 0.100 \n", + "0 26 0.104 \n", "1 23 0.092 \n", "2 10 0.040 \n", "3 6 0.024 \n", @@ -1138,10 +1138,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.174321Z", - "iopub.status.busy": "2023-08-14T16:38:56.173928Z", - "iopub.status.idle": "2023-08-14T16:38:56.281499Z", - "shell.execute_reply": "2023-08-14T16:38:56.280431Z" + "iopub.execute_input": "2023-08-15T04:11:49.083644Z", + "iopub.status.busy": "2023-08-15T04:11:49.083283Z", + "iopub.status.idle": "2023-08-15T04:11:49.257347Z", + "shell.execute_reply": "2023-08-15T04:11:49.255590Z" }, "id": "MfqTCa3kjruV" }, @@ -1222,10 +1222,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.285502Z", - "iopub.status.busy": "2023-08-14T16:38:56.285141Z", - "iopub.status.idle": "2023-08-14T16:38:56.437890Z", - "shell.execute_reply": "2023-08-14T16:38:56.437102Z" + "iopub.execute_input": "2023-08-15T04:11:49.262416Z", + "iopub.status.busy": "2023-08-15T04:11:49.262054Z", + "iopub.status.idle": "2023-08-15T04:11:49.521075Z", + "shell.execute_reply": "2023-08-15T04:11:49.519953Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1235,7 +1235,7 @@ "output_type": "stream", "text": [ " Noisy Test Accuracy (on given test labels) using yourFavoriteModel: 69%\n", - " Noisy Test Accuracy (on given test labels) using yourFavoriteModel (+ CleanLearning): 70%\n", + " Noisy Test Accuracy (on given test labels) using yourFavoriteModel (+ CleanLearning): 71%\n", "Actual Test Accuracy (on corrected test labels) using yourFavoriteModel: 83%\n", "Actual Test Accuracy (on corrected test labels) using yourFavoriteModel (+ CleanLearning): 86%\n" ] @@ -1285,10 +1285,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.441660Z", - "iopub.status.busy": "2023-08-14T16:38:56.441138Z", - "iopub.status.idle": "2023-08-14T16:38:56.447352Z", - "shell.execute_reply": "2023-08-14T16:38:56.446706Z" + "iopub.execute_input": "2023-08-15T04:11:49.525640Z", + "iopub.status.busy": "2023-08-15T04:11:49.524992Z", + "iopub.status.idle": "2023-08-15T04:11:49.533471Z", + "shell.execute_reply": "2023-08-15T04:11:49.532560Z" }, "id": "0rXP3ZPWjruW" }, @@ -1297,7 +1297,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " * Overall, about 28% (70 of the 250) labels in your dataset have potential issues.\n", + " * Overall, about 28% (71 of the 250) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.72.\n" ] } @@ -1326,10 +1326,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.450663Z", - "iopub.status.busy": "2023-08-14T16:38:56.450237Z", - "iopub.status.idle": "2023-08-14T16:38:56.456650Z", - "shell.execute_reply": "2023-08-14T16:38:56.456029Z" + "iopub.execute_input": "2023-08-15T04:11:49.537658Z", + "iopub.status.busy": "2023-08-15T04:11:49.537362Z", + "iopub.status.idle": "2023-08-15T04:11:49.545727Z", + "shell.execute_reply": "2023-08-15T04:11:49.544747Z" }, "id": "-iRPe8KXjruW" }, @@ -1384,10 +1384,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.459472Z", - "iopub.status.busy": "2023-08-14T16:38:56.459242Z", - "iopub.status.idle": "2023-08-14T16:38:56.510899Z", - "shell.execute_reply": "2023-08-14T16:38:56.510176Z" + "iopub.execute_input": "2023-08-15T04:11:49.549795Z", + "iopub.status.busy": "2023-08-15T04:11:49.549438Z", + "iopub.status.idle": "2023-08-15T04:11:49.625516Z", + "shell.execute_reply": "2023-08-15T04:11:49.624411Z" }, "id": "ZpipUliyjruW" }, @@ -1438,10 +1438,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.515925Z", - "iopub.status.busy": "2023-08-14T16:38:56.514491Z", - "iopub.status.idle": "2023-08-14T16:38:56.568719Z", - "shell.execute_reply": "2023-08-14T16:38:56.567917Z" + "iopub.execute_input": "2023-08-15T04:11:49.629886Z", + "iopub.status.busy": "2023-08-15T04:11:49.629429Z", + "iopub.status.idle": "2023-08-15T04:11:49.714256Z", + "shell.execute_reply": "2023-08-15T04:11:49.713055Z" }, "id": "SLq-3q4xjruX" }, @@ -1456,7 +1456,7 @@ "\t---\t---\t---\t---\n", "s=0 |\t0.27\t0.0\t0.03\t0.03\n", "s=1 |\t0.02\t0.18\t0.01\t0.0\n", - "s=2 |\t0.06\t0.01\t0.13\t0.05\n", + "s=2 |\t0.06\t0.01\t0.12\t0.06\n", "s=3 |\t0.01\t0.0\t0.05\t0.14\n", "\tTrace(matrix) = 0.72\n", "\n", @@ -1464,11 +1464,11 @@ " Noise Matrix (aka Noisy Channel) P(given_label|true_label) of shape (4, 4)\n", " p(s|y)\ty=0\ty=1\ty=2\ty=3\n", "\t---\t---\t---\t---\n", - "s=0 |\t0.76\t0.0\t0.15\t0.15\n", - "s=1 |\t0.06\t0.92\t0.05\t0.0\n", - "s=2 |\t0.17\t0.06\t0.58\t0.24\n", - "s=3 |\t0.02\t0.02\t0.22\t0.62\n", - "\tTrace(matrix) = 2.88\n", + "s=0 |\t0.76\t0.0\t0.15\t0.14\n", + "s=1 |\t0.06\t0.92\t0.06\t0.0\n", + "s=2 |\t0.17\t0.06\t0.57\t0.25\n", + "s=3 |\t0.02\t0.02\t0.22\t0.61\n", + "\tTrace(matrix) = 2.86\n", "\n" ] } @@ -1510,10 +1510,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.572993Z", - "iopub.status.busy": "2023-08-14T16:38:56.571730Z", - "iopub.status.idle": "2023-08-14T16:38:56.675161Z", - "shell.execute_reply": "2023-08-14T16:38:56.674234Z" + "iopub.execute_input": "2023-08-15T04:11:49.718778Z", + "iopub.status.busy": "2023-08-15T04:11:49.718460Z", + "iopub.status.idle": "2023-08-15T04:11:49.885722Z", + "shell.execute_reply": "2023-08-15T04:11:49.884310Z" }, "id": "g5LHhhuqFbXK" }, @@ -1545,10 +1545,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.679666Z", - "iopub.status.busy": "2023-08-14T16:38:56.678969Z", - "iopub.status.idle": "2023-08-14T16:38:56.799687Z", - "shell.execute_reply": "2023-08-14T16:38:56.798561Z" + "iopub.execute_input": "2023-08-15T04:11:49.891585Z", + "iopub.status.busy": "2023-08-15T04:11:49.890879Z", + "iopub.status.idle": "2023-08-15T04:11:50.065254Z", + "shell.execute_reply": "2023-08-15T04:11:50.062746Z" }, "id": "p7w8F8ezBcet" }, @@ -1605,10 +1605,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:56.803862Z", - "iopub.status.busy": "2023-08-14T16:38:56.803408Z", - "iopub.status.idle": "2023-08-14T16:38:57.033837Z", - "shell.execute_reply": "2023-08-14T16:38:57.032393Z" + "iopub.execute_input": "2023-08-15T04:11:50.070114Z", + "iopub.status.busy": "2023-08-15T04:11:50.069403Z", + "iopub.status.idle": "2023-08-15T04:11:50.391629Z", + "shell.execute_reply": "2023-08-15T04:11:50.390582Z" }, "id": "WETRL74tE_sU" }, @@ -1643,10 +1643,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.037711Z", - "iopub.status.busy": "2023-08-14T16:38:57.037050Z", - "iopub.status.idle": "2023-08-14T16:38:57.275493Z", - "shell.execute_reply": "2023-08-14T16:38:57.274608Z" + "iopub.execute_input": "2023-08-15T04:11:50.396282Z", + "iopub.status.busy": "2023-08-15T04:11:50.395715Z", + "iopub.status.idle": "2023-08-15T04:11:50.739603Z", + "shell.execute_reply": "2023-08-15T04:11:50.738379Z" }, "id": "kCfdx2gOLmXS" }, @@ -1808,10 +1808,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.279191Z", - "iopub.status.busy": "2023-08-14T16:38:57.278660Z", - "iopub.status.idle": "2023-08-14T16:38:57.288439Z", - "shell.execute_reply": "2023-08-14T16:38:57.287435Z" + "iopub.execute_input": "2023-08-15T04:11:50.744940Z", + "iopub.status.busy": "2023-08-15T04:11:50.743946Z", + "iopub.status.idle": "2023-08-15T04:11:50.755184Z", + "shell.execute_reply": "2023-08-15T04:11:50.754133Z" }, "id": "-uogYRWFYnuu" }, @@ -1865,10 +1865,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.291592Z", - "iopub.status.busy": "2023-08-14T16:38:57.291241Z", - "iopub.status.idle": "2023-08-14T16:38:57.525140Z", - "shell.execute_reply": "2023-08-14T16:38:57.524474Z" + "iopub.execute_input": "2023-08-15T04:11:50.759456Z", + "iopub.status.busy": "2023-08-15T04:11:50.758753Z", + "iopub.status.idle": "2023-08-15T04:11:51.099946Z", + "shell.execute_reply": "2023-08-15T04:11:51.098810Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1915,10 +1915,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:38:57.528536Z", - "iopub.status.busy": "2023-08-14T16:38:57.528030Z", - "iopub.status.idle": "2023-08-14T16:38:58.998091Z", - "shell.execute_reply": "2023-08-14T16:38:58.997122Z" + "iopub.execute_input": "2023-08-15T04:11:51.104497Z", + "iopub.status.busy": "2023-08-15T04:11:51.104131Z", + "iopub.status.idle": "2023-08-15T04:11:53.261276Z", + "shell.execute_reply": "2023-08-15T04:11:53.259913Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.html b/master/tutorials/multiannotator.html index b1b0da041..6e66fc203 100644 --- a/master/tutorials/multiannotator.html +++ b/master/tutorials/multiannotator.html @@ -1657,12 +1657,13 @@

5. Retrain model using improved consensus labels -

Further model improvements#

+
+

Further improvements#

You can also repeatedly iterate this process of getting better consensus labels using the model’s out-of-sample predicted probabilities and then retraining the model with the improved labels to get even better predicted probabilities! For details, see our examples notebook on Iterative use of Cleanlab to Improve Classification Models (and Consensus Labels) from Data Labeled by Multiple Annotators.

If possible, the best way to improve your model is to collect additional labels for both previously annotated data and extra not-yet-labeled examples (i.e. active learning). To decide which data is most informative to label next, use cleanlab.multiannotator.get_active_learning_scores() rather than the methods from this tutorial. This is demonstrated in our examples notebook on Active Learning with Multiple Data Annotators via ActiveLab.

+

While this notebook focused on analzying the labels of your data, Cleanlab can also check your data features for various issues. Learn how to do this by following our Datalab tutorials, except you do not need to pass in labels now that you’ve already analyzed them with this notebook (or you can provide labels to Datalab as the consensus labels estimated here).

How does cleanlab.multiannotator work?#

@@ -1756,7 +1757,7 @@

How does cleanlab.multiannotator work?5. Retrain model using improved consensus labels -
  • Further model improvements
    • +
  • Further improvements
  • How does cleanlab.multiannotator work?
    • diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index d2e1c73be..6bb81ae12 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -84,10 +84,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:04.566223Z", - "iopub.status.busy": "2023-08-14T16:39:04.565970Z", - "iopub.status.idle": "2023-08-14T16:39:05.857895Z", - "shell.execute_reply": "2023-08-14T16:39:05.857048Z" + "iopub.execute_input": "2023-08-15T04:12:00.220721Z", + "iopub.status.busy": "2023-08-15T04:12:00.220036Z", + "iopub.status.idle": "2023-08-15T04:12:01.751652Z", + "shell.execute_reply": "2023-08-15T04:12:01.750507Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.861837Z", - "iopub.status.busy": "2023-08-14T16:39:05.861404Z", - "iopub.status.idle": "2023-08-14T16:39:05.867112Z", - "shell.execute_reply": "2023-08-14T16:39:05.866037Z" + "iopub.execute_input": "2023-08-15T04:12:01.756794Z", + "iopub.status.busy": "2023-08-15T04:12:01.756250Z", + "iopub.status.idle": "2023-08-15T04:12:01.763030Z", + "shell.execute_reply": "2023-08-15T04:12:01.761780Z" } }, "outputs": [], @@ -259,10 +259,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.870301Z", - "iopub.status.busy": "2023-08-14T16:39:05.869753Z", - "iopub.status.idle": "2023-08-14T16:39:05.880018Z", - "shell.execute_reply": "2023-08-14T16:39:05.879342Z" + "iopub.execute_input": "2023-08-15T04:12:01.767430Z", + "iopub.status.busy": "2023-08-15T04:12:01.767137Z", + "iopub.status.idle": "2023-08-15T04:12:01.781597Z", + "shell.execute_reply": "2023-08-15T04:12:01.780069Z" }, "nbsphinx": "hidden" }, @@ -346,10 +346,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.883073Z", - "iopub.status.busy": "2023-08-14T16:39:05.882490Z", - "iopub.status.idle": "2023-08-14T16:39:05.956367Z", - "shell.execute_reply": "2023-08-14T16:39:05.955119Z" + "iopub.execute_input": "2023-08-15T04:12:01.785865Z", + "iopub.status.busy": "2023-08-15T04:12:01.785175Z", + "iopub.status.idle": "2023-08-15T04:12:01.899306Z", + "shell.execute_reply": "2023-08-15T04:12:01.898246Z" } }, "outputs": [], @@ -375,10 +375,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.959658Z", - "iopub.status.busy": "2023-08-14T16:39:05.959273Z", - "iopub.status.idle": "2023-08-14T16:39:05.985253Z", - "shell.execute_reply": "2023-08-14T16:39:05.984575Z" + "iopub.execute_input": "2023-08-15T04:12:01.904183Z", + "iopub.status.busy": "2023-08-15T04:12:01.903864Z", + "iopub.status.idle": "2023-08-15T04:12:01.940877Z", + "shell.execute_reply": "2023-08-15T04:12:01.939857Z" } }, "outputs": [ @@ -593,10 +593,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.988337Z", - "iopub.status.busy": "2023-08-14T16:39:05.987894Z", - "iopub.status.idle": "2023-08-14T16:39:05.992506Z", - "shell.execute_reply": "2023-08-14T16:39:05.991818Z" + "iopub.execute_input": "2023-08-15T04:12:01.945644Z", + "iopub.status.busy": "2023-08-15T04:12:01.945346Z", + "iopub.status.idle": "2023-08-15T04:12:01.951831Z", + "shell.execute_reply": "2023-08-15T04:12:01.950840Z" } }, "outputs": [ @@ -667,10 +667,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:05.996364Z", - "iopub.status.busy": "2023-08-14T16:39:05.995846Z", - "iopub.status.idle": "2023-08-14T16:39:06.928954Z", - "shell.execute_reply": "2023-08-14T16:39:06.928140Z" + "iopub.execute_input": "2023-08-15T04:12:01.957068Z", + "iopub.status.busy": "2023-08-15T04:12:01.956792Z", + "iopub.status.idle": "2023-08-15T04:12:03.300272Z", + "shell.execute_reply": "2023-08-15T04:12:03.299271Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:06.933004Z", - "iopub.status.busy": "2023-08-14T16:39:06.932570Z", - "iopub.status.idle": "2023-08-14T16:39:06.964673Z", - "shell.execute_reply": "2023-08-14T16:39:06.963364Z" + "iopub.execute_input": "2023-08-15T04:12:03.305298Z", + "iopub.status.busy": "2023-08-15T04:12:03.304980Z", + "iopub.status.idle": "2023-08-15T04:12:03.359639Z", + "shell.execute_reply": "2023-08-15T04:12:03.358392Z" } }, "outputs": [], @@ -730,10 +730,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:06.968053Z", - "iopub.status.busy": "2023-08-14T16:39:06.967652Z", - "iopub.status.idle": "2023-08-14T16:39:17.298908Z", - "shell.execute_reply": "2023-08-14T16:39:17.298066Z" + "iopub.execute_input": "2023-08-15T04:12:03.364885Z", + "iopub.status.busy": "2023-08-15T04:12:03.364581Z", + "iopub.status.idle": "2023-08-15T04:12:17.526402Z", + "shell.execute_reply": "2023-08-15T04:12:17.525125Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.303423Z", - "iopub.status.busy": "2023-08-14T16:39:17.302590Z", - "iopub.status.idle": "2023-08-14T16:39:17.313405Z", - "shell.execute_reply": "2023-08-14T16:39:17.312806Z" + "iopub.execute_input": "2023-08-15T04:12:17.531305Z", + "iopub.status.busy": "2023-08-15T04:12:17.530680Z", + "iopub.status.idle": "2023-08-15T04:12:17.544840Z", + "shell.execute_reply": "2023-08-15T04:12:17.544018Z" }, "scrolled": true }, @@ -877,10 +877,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.316536Z", - "iopub.status.busy": "2023-08-14T16:39:17.315983Z", - "iopub.status.idle": "2023-08-14T16:39:17.335724Z", - "shell.execute_reply": "2023-08-14T16:39:17.334561Z" + "iopub.execute_input": "2023-08-15T04:12:17.549081Z", + "iopub.status.busy": "2023-08-15T04:12:17.548785Z", + "iopub.status.idle": "2023-08-15T04:12:17.577758Z", + "shell.execute_reply": "2023-08-15T04:12:17.576632Z" } }, "outputs": [ @@ -1130,10 +1130,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.338775Z", - "iopub.status.busy": "2023-08-14T16:39:17.338230Z", - "iopub.status.idle": "2023-08-14T16:39:17.346421Z", - "shell.execute_reply": "2023-08-14T16:39:17.345706Z" + "iopub.execute_input": "2023-08-15T04:12:17.582009Z", + "iopub.status.busy": "2023-08-15T04:12:17.581660Z", + "iopub.status.idle": "2023-08-15T04:12:17.593134Z", + "shell.execute_reply": "2023-08-15T04:12:17.591991Z" }, "scrolled": true }, @@ -1307,10 +1307,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.350196Z", - "iopub.status.busy": "2023-08-14T16:39:17.349658Z", - "iopub.status.idle": "2023-08-14T16:39:17.352939Z", - "shell.execute_reply": "2023-08-14T16:39:17.352233Z" + "iopub.execute_input": "2023-08-15T04:12:17.598424Z", + "iopub.status.busy": "2023-08-15T04:12:17.598149Z", + "iopub.status.idle": "2023-08-15T04:12:17.602758Z", + "shell.execute_reply": "2023-08-15T04:12:17.601720Z" } }, "outputs": [], @@ -1332,10 +1332,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.355865Z", - "iopub.status.busy": "2023-08-14T16:39:17.355431Z", - "iopub.status.idle": "2023-08-14T16:39:17.362085Z", - "shell.execute_reply": "2023-08-14T16:39:17.360816Z" + "iopub.execute_input": "2023-08-15T04:12:17.606831Z", + "iopub.status.busy": "2023-08-15T04:12:17.606490Z", + "iopub.status.idle": "2023-08-15T04:12:17.614848Z", + "shell.execute_reply": "2023-08-15T04:12:17.613814Z" }, "scrolled": true }, @@ -1387,10 +1387,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.364970Z", - "iopub.status.busy": "2023-08-14T16:39:17.364525Z", - "iopub.status.idle": "2023-08-14T16:39:17.367730Z", - "shell.execute_reply": "2023-08-14T16:39:17.367023Z" + "iopub.execute_input": "2023-08-15T04:12:17.618814Z", + "iopub.status.busy": "2023-08-15T04:12:17.618549Z", + "iopub.status.idle": "2023-08-15T04:12:17.624010Z", + "shell.execute_reply": "2023-08-15T04:12:17.623099Z" } }, "outputs": [], @@ -1414,10 +1414,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.370694Z", - "iopub.status.busy": "2023-08-14T16:39:17.370263Z", - "iopub.status.idle": "2023-08-14T16:39:17.375525Z", - "shell.execute_reply": "2023-08-14T16:39:17.374820Z" + "iopub.execute_input": "2023-08-15T04:12:17.627524Z", + "iopub.status.busy": "2023-08-15T04:12:17.627268Z", + "iopub.status.idle": "2023-08-15T04:12:17.635141Z", + "shell.execute_reply": "2023-08-15T04:12:17.634326Z" } }, "outputs": [ @@ -1472,10 +1472,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.379505Z", - "iopub.status.busy": "2023-08-14T16:39:17.378891Z", - "iopub.status.idle": "2023-08-14T16:39:17.415484Z", - "shell.execute_reply": "2023-08-14T16:39:17.414820Z" + "iopub.execute_input": "2023-08-15T04:12:17.638739Z", + "iopub.status.busy": "2023-08-15T04:12:17.638403Z", + "iopub.status.idle": "2023-08-15T04:12:17.703889Z", + "shell.execute_reply": "2023-08-15T04:12:17.702915Z" } }, "outputs": [], @@ -1496,12 +1496,14 @@ "id": "e59f7d4f", "metadata": {}, "source": [ - "## Further model improvements \n", + "## Further improvements \n", "You can also repeatedly iterate this process of getting better consensus labels using the model's out-of-sample predicted probabilities and then retraining the model with the improved labels to get even better predicted probabilities!\n", "For details, see our [examples](https://github.com/cleanlab/examples) notebook on [Iterative use of Cleanlab to Improve Classification Models (and Consensus Labels) from Data Labeled by Multiple Annotators](https://github.com/cleanlab/examples/blob/master/multiannotator_cifar10/multiannotator_cifar10.ipynb).\n", "\n", "If possible, the best way to improve your model is to collect additional labels for both previously annotated data and extra not-yet-labeled examples (i.e. *active learning*). To decide which data is most informative to label next, use `cleanlab.multiannotator.get_active_learning_scores()` rather than the methods from this tutorial. This is demonstrated in our examples notebook on [Active Learning with Multiple Data Annotators via ActiveLab](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb).\n", "\n", + "While this notebook focused on analzying the labels of your data, Cleanlab can also check your data features for various issues. Learn how to do this by following our [Datalab tutorials](../tutorials/datalab/index.html), except you do not need to pass in `labels` now that you've already analyzed them with this notebook (or you can provide `labels` to Datalab as the consensus labels estimated here).\n", + "\n", "\n", "## How does cleanlab.multiannotator work?\n", "\n", @@ -1516,10 +1518,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:17.418568Z", - "iopub.status.busy": "2023-08-14T16:39:17.418189Z", - "iopub.status.idle": "2023-08-14T16:39:17.424432Z", - "shell.execute_reply": "2023-08-14T16:39:17.423722Z" + "iopub.execute_input": "2023-08-15T04:12:17.708894Z", + "iopub.status.busy": "2023-08-15T04:12:17.708385Z", + "iopub.status.idle": "2023-08-15T04:12:17.717233Z", + "shell.execute_reply": "2023-08-15T04:12:17.716111Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 5ec6fbb9a..405ad1669 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -63,10 +63,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:23.132932Z", - "iopub.status.busy": "2023-08-14T16:39:23.132674Z", - "iopub.status.idle": "2023-08-14T16:39:24.423664Z", - "shell.execute_reply": "2023-08-14T16:39:24.422887Z" + "iopub.execute_input": "2023-08-15T04:12:23.679270Z", + "iopub.status.busy": "2023-08-15T04:12:23.678968Z", + "iopub.status.idle": "2023-08-15T04:12:25.352861Z", + "shell.execute_reply": "2023-08-15T04:12:25.351774Z" }, "nbsphinx": "hidden" }, @@ -78,7 +78,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -104,10 +104,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:24.427701Z", - "iopub.status.busy": "2023-08-14T16:39:24.427322Z", - "iopub.status.idle": "2023-08-14T16:39:24.819484Z", - "shell.execute_reply": "2023-08-14T16:39:24.818714Z" + "iopub.execute_input": "2023-08-15T04:12:25.359018Z", + "iopub.status.busy": "2023-08-15T04:12:25.358546Z", + "iopub.status.idle": "2023-08-15T04:12:25.832738Z", + "shell.execute_reply": "2023-08-15T04:12:25.831285Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:24.823419Z", - "iopub.status.busy": "2023-08-14T16:39:24.823156Z", - "iopub.status.idle": "2023-08-14T16:39:24.840240Z", - "shell.execute_reply": "2023-08-14T16:39:24.839632Z" + "iopub.execute_input": "2023-08-15T04:12:25.837950Z", + "iopub.status.busy": "2023-08-15T04:12:25.837590Z", + "iopub.status.idle": "2023-08-15T04:12:25.860001Z", + "shell.execute_reply": "2023-08-15T04:12:25.859033Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:24.843424Z", - "iopub.status.busy": "2023-08-14T16:39:24.842880Z", - "iopub.status.idle": "2023-08-14T16:39:27.980216Z", - "shell.execute_reply": "2023-08-14T16:39:27.979547Z" + "iopub.execute_input": "2023-08-15T04:12:25.864444Z", + "iopub.status.busy": "2023-08-15T04:12:25.863854Z", + "iopub.status.idle": "2023-08-15T04:12:31.363434Z", + "shell.execute_reply": "2023-08-15T04:12:31.362388Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:27.983940Z", - "iopub.status.busy": "2023-08-14T16:39:27.983589Z", - "iopub.status.idle": "2023-08-14T16:39:30.002008Z", - "shell.execute_reply": "2023-08-14T16:39:30.000328Z" + "iopub.execute_input": "2023-08-15T04:12:31.368176Z", + "iopub.status.busy": "2023-08-15T04:12:31.367846Z", + "iopub.status.idle": "2023-08-15T04:12:34.271660Z", + "shell.execute_reply": "2023-08-15T04:12:34.270454Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:30.006531Z", - "iopub.status.busy": "2023-08-14T16:39:30.005848Z", - "iopub.status.idle": "2023-08-14T16:39:30.024465Z", - "shell.execute_reply": "2023-08-14T16:39:30.023764Z" + "iopub.execute_input": "2023-08-15T04:12:34.276855Z", + "iopub.status.busy": "2023-08-15T04:12:34.276145Z", + "iopub.status.idle": "2023-08-15T04:12:34.297136Z", + "shell.execute_reply": "2023-08-15T04:12:34.296082Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:30.027444Z", - "iopub.status.busy": "2023-08-14T16:39:30.027004Z", - "iopub.status.idle": "2023-08-14T16:39:31.770450Z", - "shell.execute_reply": "2023-08-14T16:39:31.769590Z" + "iopub.execute_input": "2023-08-15T04:12:34.300992Z", + "iopub.status.busy": "2023-08-15T04:12:34.300717Z", + "iopub.status.idle": "2023-08-15T04:12:36.427573Z", + "shell.execute_reply": "2023-08-15T04:12:36.426108Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:31.774922Z", - "iopub.status.busy": "2023-08-14T16:39:31.773768Z", - "iopub.status.idle": "2023-08-14T16:39:34.891228Z", - "shell.execute_reply": "2023-08-14T16:39:34.890413Z" + "iopub.execute_input": "2023-08-15T04:12:36.432974Z", + "iopub.status.busy": "2023-08-15T04:12:36.432037Z", + "iopub.status.idle": "2023-08-15T04:12:41.919080Z", + "shell.execute_reply": "2023-08-15T04:12:41.917945Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:34.894830Z", - "iopub.status.busy": "2023-08-14T16:39:34.894325Z", - "iopub.status.idle": "2023-08-14T16:39:34.900277Z", - "shell.execute_reply": "2023-08-14T16:39:34.899560Z" + "iopub.execute_input": "2023-08-15T04:12:41.923694Z", + "iopub.status.busy": "2023-08-15T04:12:41.922889Z", + "iopub.status.idle": "2023-08-15T04:12:41.932487Z", + "shell.execute_reply": "2023-08-15T04:12:41.931491Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:34.903919Z", - "iopub.status.busy": "2023-08-14T16:39:34.903467Z", - "iopub.status.idle": "2023-08-14T16:39:34.908336Z", - "shell.execute_reply": "2023-08-14T16:39:34.907636Z" + "iopub.execute_input": "2023-08-15T04:12:41.936773Z", + "iopub.status.busy": "2023-08-15T04:12:41.935850Z", + "iopub.status.idle": "2023-08-15T04:12:41.943208Z", + "shell.execute_reply": "2023-08-15T04:12:41.942263Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:34.911327Z", - "iopub.status.busy": "2023-08-14T16:39:34.910960Z", - "iopub.status.idle": "2023-08-14T16:39:34.914947Z", - "shell.execute_reply": "2023-08-14T16:39:34.914235Z" + "iopub.execute_input": "2023-08-15T04:12:41.947171Z", + "iopub.status.busy": "2023-08-15T04:12:41.946865Z", + "iopub.status.idle": "2023-08-15T04:12:41.954123Z", + "shell.execute_reply": "2023-08-15T04:12:41.952819Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.html b/master/tutorials/object_detection.html index d3f675ca8..5a36ceab7 100644 --- a/master/tutorials/object_detection.html +++ b/master/tutorials/object_detection.html @@ -1266,7 +1266,7 @@

    4. Use ObjectLab to visualize label issues 
    -./example_images/000000009483.jpg | idx 50 | label quality score: 6.955697261680538e-05 | is issue: True
+./example_images/000000009483.jpg | idx 50 | label quality score: 6.95569726168054e-05 | is issue: True
    @@ -1452,7 +1452,7 @@

    Other uses of visualize 
    -./example_images/000000143931.jpg | idx 2 | label quality score: 0.9876495074395957 | is issue: False
+./example_images/000000143931.jpg | idx 2 | label quality score: 0.9876495074395956 | is issue: False

    diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 7d463c144..8c89fd49e 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:39.962599Z", - "iopub.status.busy": "2023-08-14T16:39:39.962143Z", - "iopub.status.idle": "2023-08-14T16:39:41.263291Z", - "shell.execute_reply": "2023-08-14T16:39:41.262506Z" + "iopub.execute_input": "2023-08-15T04:12:46.730680Z", + "iopub.status.busy": "2023-08-15T04:12:46.729765Z", + "iopub.status.idle": "2023-08-15T04:12:48.395501Z", + "shell.execute_reply": "2023-08-15T04:12:48.394591Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:41.267254Z", - "iopub.status.busy": "2023-08-14T16:39:41.266709Z", - "iopub.status.idle": "2023-08-14T16:39:42.609703Z", - "shell.execute_reply": "2023-08-14T16:39:42.608506Z" + "iopub.execute_input": "2023-08-15T04:12:48.400543Z", + "iopub.status.busy": "2023-08-15T04:12:48.400083Z", + "iopub.status.idle": "2023-08-15T04:12:52.439980Z", + "shell.execute_reply": "2023-08-15T04:12:52.438701Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:42.614057Z", - "iopub.status.busy": "2023-08-14T16:39:42.613581Z", - "iopub.status.idle": "2023-08-14T16:39:42.618988Z", - "shell.execute_reply": "2023-08-14T16:39:42.618047Z" + "iopub.execute_input": "2023-08-15T04:12:52.445260Z", + "iopub.status.busy": "2023-08-15T04:12:52.443674Z", + "iopub.status.idle": "2023-08-15T04:12:52.450922Z", + "shell.execute_reply": "2023-08-15T04:12:52.449696Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:42.622112Z", - "iopub.status.busy": "2023-08-14T16:39:42.621666Z", - "iopub.status.idle": "2023-08-14T16:39:42.628947Z", - "shell.execute_reply": "2023-08-14T16:39:42.628358Z" + "iopub.execute_input": "2023-08-15T04:12:52.455377Z", + "iopub.status.busy": "2023-08-15T04:12:52.455003Z", + "iopub.status.idle": "2023-08-15T04:12:52.463773Z", + "shell.execute_reply": "2023-08-15T04:12:52.462779Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:42.631948Z", - "iopub.status.busy": "2023-08-14T16:39:42.631508Z", - "iopub.status.idle": "2023-08-14T16:39:43.415293Z", - "shell.execute_reply": "2023-08-14T16:39:43.414599Z" + "iopub.execute_input": "2023-08-15T04:12:52.467789Z", + "iopub.status.busy": "2023-08-15T04:12:52.467507Z", + "iopub.status.idle": "2023-08-15T04:12:53.399657Z", + "shell.execute_reply": "2023-08-15T04:12:53.398574Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.418896Z", - "iopub.status.busy": "2023-08-14T16:39:43.418166Z", - "iopub.status.idle": "2023-08-14T16:39:43.425597Z", - "shell.execute_reply": "2023-08-14T16:39:43.424891Z" + "iopub.execute_input": "2023-08-15T04:12:53.403958Z", + "iopub.status.busy": "2023-08-15T04:12:53.403647Z", + "iopub.status.idle": "2023-08-15T04:12:53.412688Z", + "shell.execute_reply": "2023-08-15T04:12:53.411821Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.428659Z", - "iopub.status.busy": "2023-08-14T16:39:43.428209Z", - "iopub.status.idle": "2023-08-14T16:39:43.432703Z", - "shell.execute_reply": "2023-08-14T16:39:43.432169Z" + "iopub.execute_input": "2023-08-15T04:12:53.416963Z", + "iopub.status.busy": "2023-08-15T04:12:53.416686Z", + "iopub.status.idle": "2023-08-15T04:12:53.422310Z", + "shell.execute_reply": "2023-08-15T04:12:53.421300Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.435443Z", - "iopub.status.busy": "2023-08-14T16:39:43.435002Z", - "iopub.status.idle": "2023-08-14T16:39:43.666450Z", - "shell.execute_reply": "2023-08-14T16:39:43.665710Z" + "iopub.execute_input": "2023-08-15T04:12:53.427813Z", + "iopub.status.busy": "2023-08-15T04:12:53.427310Z", + "iopub.status.idle": "2023-08-15T04:12:53.700425Z", + "shell.execute_reply": "2023-08-15T04:12:53.699169Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.669988Z", - "iopub.status.busy": "2023-08-14T16:39:43.669441Z", - "iopub.status.idle": "2023-08-14T16:39:43.783457Z", - "shell.execute_reply": "2023-08-14T16:39:43.782576Z" + "iopub.execute_input": "2023-08-15T04:12:53.705133Z", + "iopub.status.busy": "2023-08-15T04:12:53.704539Z", + "iopub.status.idle": "2023-08-15T04:12:53.863686Z", + "shell.execute_reply": "2023-08-15T04:12:53.862366Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.786829Z", - "iopub.status.busy": "2023-08-14T16:39:43.786566Z", - "iopub.status.idle": "2023-08-14T16:39:43.793693Z", - "shell.execute_reply": "2023-08-14T16:39:43.793069Z" + "iopub.execute_input": "2023-08-15T04:12:53.868642Z", + "iopub.status.busy": "2023-08-15T04:12:53.868291Z", + "iopub.status.idle": "2023-08-15T04:12:53.878050Z", + "shell.execute_reply": "2023-08-15T04:12:53.876990Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:43.797050Z", - "iopub.status.busy": "2023-08-14T16:39:43.796488Z", - "iopub.status.idle": "2023-08-14T16:39:44.243208Z", - "shell.execute_reply": "2023-08-14T16:39:44.242500Z" + "iopub.execute_input": "2023-08-15T04:12:53.882368Z", + "iopub.status.busy": "2023-08-15T04:12:53.882082Z", + "iopub.status.idle": "2023-08-15T04:12:54.467969Z", + "shell.execute_reply": "2023-08-15T04:12:54.467053Z" } }, "outputs": [ @@ -706,7 +706,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "./example_images/000000009483.jpg | idx 50 | label quality score: 6.955697261680538e-05 | is issue: True\n" + "./example_images/000000009483.jpg | idx 50 | label quality score: 6.95569726168054e-05 | is issue: True\n" ] }, { @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:44.246825Z", - "iopub.status.busy": "2023-08-14T16:39:44.246174Z", - "iopub.status.idle": "2023-08-14T16:39:44.651342Z", - "shell.execute_reply": "2023-08-14T16:39:44.650504Z" + "iopub.execute_input": "2023-08-15T04:12:54.474238Z", + "iopub.status.busy": "2023-08-15T04:12:54.473708Z", + "iopub.status.idle": "2023-08-15T04:12:54.983491Z", + "shell.execute_reply": "2023-08-15T04:12:54.982595Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:44.655236Z", - "iopub.status.busy": "2023-08-14T16:39:44.654618Z", - "iopub.status.idle": "2023-08-14T16:39:45.115093Z", - "shell.execute_reply": "2023-08-14T16:39:45.114419Z" + "iopub.execute_input": "2023-08-15T04:12:54.989327Z", + "iopub.status.busy": "2023-08-15T04:12:54.988500Z", + "iopub.status.idle": "2023-08-15T04:12:55.630738Z", + "shell.execute_reply": "2023-08-15T04:12:55.629855Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:45.120918Z", - "iopub.status.busy": "2023-08-14T16:39:45.120404Z", - "iopub.status.idle": "2023-08-14T16:39:45.685123Z", - "shell.execute_reply": "2023-08-14T16:39:45.684468Z" + "iopub.execute_input": "2023-08-15T04:12:55.640582Z", + "iopub.status.busy": "2023-08-15T04:12:55.640012Z", + "iopub.status.idle": "2023-08-15T04:12:56.404999Z", + "shell.execute_reply": "2023-08-15T04:12:56.403866Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:45.689614Z", - "iopub.status.busy": "2023-08-14T16:39:45.688954Z", - "iopub.status.idle": "2023-08-14T16:39:46.255297Z", - "shell.execute_reply": "2023-08-14T16:39:46.254504Z" + "iopub.execute_input": "2023-08-15T04:12:56.408878Z", + "iopub.status.busy": "2023-08-15T04:12:56.408501Z", + "iopub.status.idle": "2023-08-15T04:12:57.123474Z", + "shell.execute_reply": "2023-08-15T04:12:57.122757Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:46.261200Z", - "iopub.status.busy": "2023-08-14T16:39:46.260660Z", - "iopub.status.idle": "2023-08-14T16:39:46.519231Z", - "shell.execute_reply": "2023-08-14T16:39:46.518588Z" + "iopub.execute_input": "2023-08-15T04:12:57.129895Z", + "iopub.status.busy": "2023-08-15T04:12:57.128975Z", + "iopub.status.idle": "2023-08-15T04:12:57.495427Z", + "shell.execute_reply": "2023-08-15T04:12:57.494687Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:46.522549Z", - "iopub.status.busy": "2023-08-14T16:39:46.522132Z", - "iopub.status.idle": "2023-08-14T16:39:46.751608Z", - "shell.execute_reply": "2023-08-14T16:39:46.750965Z" + "iopub.execute_input": "2023-08-15T04:12:57.499195Z", + "iopub.status.busy": "2023-08-15T04:12:57.498740Z", + "iopub.status.idle": "2023-08-15T04:12:57.780087Z", + "shell.execute_reply": "2023-08-15T04:12:57.778580Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "./example_images/000000143931.jpg | idx 2 | label quality score: 0.9876495074395957 | is issue: False\n" + "./example_images/000000143931.jpg | idx 2 | label quality score: 0.9876495074395956 | is issue: False\n" ] }, { @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:46.757727Z", - "iopub.status.busy": "2023-08-14T16:39:46.757275Z", - "iopub.status.idle": "2023-08-14T16:39:46.762434Z", - "shell.execute_reply": "2023-08-14T16:39:46.761822Z" + "iopub.execute_input": "2023-08-15T04:12:57.785129Z", + "iopub.status.busy": "2023-08-15T04:12:57.784702Z", + "iopub.status.idle": "2023-08-15T04:12:57.791156Z", + "shell.execute_reply": "2023-08-15T04:12:57.789935Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 643c63080..f9a5c2b28 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -912,7 +912,7 @@

    2. Pre-process the Cifar10 dataset

    -
    +
    @@ -1257,7 +1257,7 @@

    4. Use cleanlab and here.

    diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 54a230023..4ac133341 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:49.701332Z", - "iopub.status.busy": "2023-08-14T16:39:49.700869Z", - "iopub.status.idle": "2023-08-14T16:39:52.238174Z", - "shell.execute_reply": "2023-08-14T16:39:52.237139Z" + "iopub.execute_input": "2023-08-15T04:13:01.276358Z", + "iopub.status.busy": "2023-08-15T04:13:01.276030Z", + "iopub.status.idle": "2023-08-15T04:13:04.634021Z", + "shell.execute_reply": "2023-08-15T04:13:04.633035Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:52.242802Z", - "iopub.status.busy": "2023-08-14T16:39:52.242181Z", - "iopub.status.idle": "2023-08-14T16:39:52.681185Z", - "shell.execute_reply": "2023-08-14T16:39:52.680422Z" + "iopub.execute_input": "2023-08-15T04:13:04.638878Z", + "iopub.status.busy": "2023-08-15T04:13:04.638316Z", + "iopub.status.idle": "2023-08-15T04:13:05.178752Z", + "shell.execute_reply": "2023-08-15T04:13:05.177526Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:52.685555Z", - "iopub.status.busy": "2023-08-14T16:39:52.685121Z", - "iopub.status.idle": "2023-08-14T16:39:52.691385Z", - "shell.execute_reply": "2023-08-14T16:39:52.690733Z" + "iopub.execute_input": "2023-08-15T04:13:05.183546Z", + "iopub.status.busy": "2023-08-15T04:13:05.183092Z", + "iopub.status.idle": "2023-08-15T04:13:05.190329Z", + "shell.execute_reply": "2023-08-15T04:13:05.189444Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:39:52.694787Z", - "iopub.status.busy": "2023-08-14T16:39:52.694359Z", - "iopub.status.idle": "2023-08-14T16:40:01.520932Z", - "shell.execute_reply": "2023-08-14T16:40:01.520157Z" + "iopub.execute_input": "2023-08-15T04:13:05.193696Z", + "iopub.status.busy": "2023-08-15T04:13:05.193209Z", + "iopub.status.idle": "2023-08-15T04:13:13.098686Z", + "shell.execute_reply": "2023-08-15T04:13:13.097701Z" } }, "outputs": [ @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31d9a8a019bb4735b52e2e20691a104b", + "model_id": "6fb54979a8f547e087b7c624f518e4ca", "version_major": 2, "version_minor": 0 }, @@ -361,10 +361,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:01.524893Z", - "iopub.status.busy": "2023-08-14T16:40:01.524269Z", - "iopub.status.idle": "2023-08-14T16:40:01.531453Z", - "shell.execute_reply": "2023-08-14T16:40:01.530560Z" + "iopub.execute_input": "2023-08-15T04:13:13.103196Z", + "iopub.status.busy": "2023-08-15T04:13:13.102368Z", + "iopub.status.idle": "2023-08-15T04:13:13.111667Z", + "shell.execute_reply": "2023-08-15T04:13:13.110700Z" }, "nbsphinx": "hidden" }, @@ -415,10 +415,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:01.534441Z", - "iopub.status.busy": "2023-08-14T16:40:01.534066Z", - "iopub.status.idle": "2023-08-14T16:40:02.157718Z", - "shell.execute_reply": "2023-08-14T16:40:02.156990Z" + "iopub.execute_input": "2023-08-15T04:13:13.116484Z", + "iopub.status.busy": "2023-08-15T04:13:13.115927Z", + "iopub.status.idle": "2023-08-15T04:13:14.057425Z", + "shell.execute_reply": "2023-08-15T04:13:14.055757Z" } }, "outputs": [ @@ -451,10 +451,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:02.161321Z", - "iopub.status.busy": "2023-08-14T16:40:02.160728Z", - "iopub.status.idle": "2023-08-14T16:40:02.751301Z", - "shell.execute_reply": "2023-08-14T16:40:02.750423Z" + "iopub.execute_input": "2023-08-15T04:13:14.062107Z", + "iopub.status.busy": "2023-08-15T04:13:14.061803Z", + "iopub.status.idle": "2023-08-15T04:13:15.011711Z", + "shell.execute_reply": "2023-08-15T04:13:15.010731Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:02.754970Z", - "iopub.status.busy": "2023-08-14T16:40:02.754525Z", - "iopub.status.idle": "2023-08-14T16:40:02.759013Z", - "shell.execute_reply": "2023-08-14T16:40:02.758309Z" + "iopub.execute_input": "2023-08-15T04:13:15.016499Z", + "iopub.status.busy": "2023-08-15T04:13:15.016048Z", + "iopub.status.idle": "2023-08-15T04:13:15.021466Z", + "shell.execute_reply": "2023-08-15T04:13:15.020513Z" } }, "outputs": [], @@ -518,10 +518,10 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:02.762364Z", - "iopub.status.busy": "2023-08-14T16:40:02.761718Z", - "iopub.status.idle": "2023-08-14T16:40:14.768823Z", - "shell.execute_reply": "2023-08-14T16:40:14.767462Z" + "iopub.execute_input": "2023-08-15T04:13:15.025852Z", + "iopub.status.busy": "2023-08-15T04:13:15.025509Z", + "iopub.status.idle": "2023-08-15T04:13:40.096129Z", + "shell.execute_reply": "2023-08-15T04:13:40.095066Z" } }, "outputs": [ @@ -580,10 +580,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:14.772170Z", - "iopub.status.busy": "2023-08-14T16:40:14.771746Z", - "iopub.status.idle": "2023-08-14T16:40:16.547615Z", - "shell.execute_reply": "2023-08-14T16:40:16.546926Z" + "iopub.execute_input": "2023-08-15T04:13:40.101450Z", + "iopub.status.busy": "2023-08-15T04:13:40.100901Z", + "iopub.status.idle": "2023-08-15T04:13:42.878160Z", + "shell.execute_reply": "2023-08-15T04:13:42.877149Z" } }, "outputs": [ @@ -627,10 +627,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:16.550942Z", - "iopub.status.busy": "2023-08-14T16:40:16.550603Z", - "iopub.status.idle": "2023-08-14T16:40:16.849171Z", - "shell.execute_reply": "2023-08-14T16:40:16.848413Z" + "iopub.execute_input": "2023-08-15T04:13:42.882148Z", + "iopub.status.busy": "2023-08-15T04:13:42.881778Z", + "iopub.status.idle": "2023-08-15T04:13:43.314573Z", + "shell.execute_reply": "2023-08-15T04:13:43.313595Z" } }, "outputs": [ @@ -666,10 +666,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:16.852465Z", - "iopub.status.busy": "2023-08-14T16:40:16.852055Z", - "iopub.status.idle": "2023-08-14T16:40:17.739634Z", - "shell.execute_reply": "2023-08-14T16:40:17.738919Z" + "iopub.execute_input": "2023-08-15T04:13:43.318603Z", + "iopub.status.busy": "2023-08-15T04:13:43.318274Z", + "iopub.status.idle": "2023-08-15T04:13:44.563996Z", + "shell.execute_reply": "2023-08-15T04:13:44.563100Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:17.744650Z", - "iopub.status.busy": "2023-08-14T16:40:17.744303Z", - "iopub.status.idle": "2023-08-14T16:40:18.100429Z", - "shell.execute_reply": "2023-08-14T16:40:18.099724Z" + "iopub.execute_input": "2023-08-15T04:13:44.567882Z", + "iopub.status.busy": "2023-08-15T04:13:44.567595Z", + "iopub.status.idle": "2023-08-15T04:13:45.113671Z", + "shell.execute_reply": "2023-08-15T04:13:45.112827Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:18.103903Z", - "iopub.status.busy": "2023-08-14T16:40:18.103360Z", - "iopub.status.idle": "2023-08-14T16:40:18.398570Z", - "shell.execute_reply": "2023-08-14T16:40:18.397824Z" + "iopub.execute_input": "2023-08-15T04:13:45.117588Z", + "iopub.status.busy": "2023-08-15T04:13:45.117283Z", + "iopub.status.idle": "2023-08-15T04:13:45.548896Z", + "shell.execute_reply": "2023-08-15T04:13:45.547850Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:18.402624Z", - "iopub.status.busy": "2023-08-14T16:40:18.402351Z", - "iopub.status.idle": "2023-08-14T16:40:18.560477Z", - "shell.execute_reply": "2023-08-14T16:40:18.559639Z" + "iopub.execute_input": "2023-08-15T04:13:45.553159Z", + "iopub.status.busy": "2023-08-15T04:13:45.552800Z", + "iopub.status.idle": "2023-08-15T04:13:45.795047Z", + "shell.execute_reply": "2023-08-15T04:13:45.793980Z" } }, "outputs": [], @@ -853,10 +853,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:40:18.564583Z", - "iopub.status.busy": "2023-08-14T16:40:18.564027Z", - "iopub.status.idle": "2023-08-14T16:41:19.462057Z", - "shell.execute_reply": "2023-08-14T16:41:19.460497Z" + "iopub.execute_input": "2023-08-15T04:13:45.800039Z", + "iopub.status.busy": "2023-08-15T04:13:45.799746Z", + "iopub.status.idle": "2023-08-15T04:15:27.779244Z", + "shell.execute_reply": "2023-08-15T04:15:27.778165Z" } }, "outputs": [ @@ -893,10 +893,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:19.466012Z", - "iopub.status.busy": "2023-08-14T16:41:19.465529Z", - "iopub.status.idle": "2023-08-14T16:41:21.095602Z", - "shell.execute_reply": "2023-08-14T16:41:21.094820Z" + "iopub.execute_input": "2023-08-15T04:15:27.784226Z", + "iopub.status.busy": "2023-08-15T04:15:27.783438Z", + "iopub.status.idle": "2023-08-15T04:15:29.818974Z", + "shell.execute_reply": "2023-08-15T04:15:29.817577Z" } }, "outputs": [ @@ -927,10 +927,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:21.100022Z", - "iopub.status.busy": "2023-08-14T16:41:21.098968Z", - "iopub.status.idle": "2023-08-14T16:41:21.386873Z", - "shell.execute_reply": "2023-08-14T16:41:21.385880Z" + "iopub.execute_input": "2023-08-15T04:15:29.823698Z", + "iopub.status.busy": "2023-08-15T04:15:29.822995Z", + "iopub.status.idle": "2023-08-15T04:15:30.075021Z", + "shell.execute_reply": "2023-08-15T04:15:30.073745Z" } }, "outputs": [], @@ -944,10 +944,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:21.391192Z", - "iopub.status.busy": "2023-08-14T16:41:21.390701Z", - "iopub.status.idle": "2023-08-14T16:41:21.395331Z", - "shell.execute_reply": "2023-08-14T16:41:21.393882Z" + "iopub.execute_input": "2023-08-15T04:15:30.080553Z", + "iopub.status.busy": "2023-08-15T04:15:30.080203Z", + "iopub.status.idle": "2023-08-15T04:15:30.086749Z", + "shell.execute_reply": "2023-08-15T04:15:30.085382Z" } }, "outputs": [], @@ -969,10 +969,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:21.398357Z", - "iopub.status.busy": "2023-08-14T16:41:21.397907Z", - "iopub.status.idle": "2023-08-14T16:41:21.408299Z", - "shell.execute_reply": "2023-08-14T16:41:21.407684Z" + "iopub.execute_input": "2023-08-15T04:15:30.091068Z", + "iopub.status.busy": "2023-08-15T04:15:30.090755Z", + "iopub.status.idle": "2023-08-15T04:15:30.108498Z", + "shell.execute_reply": "2023-08-15T04:15:30.106424Z" }, "nbsphinx": "hidden" }, @@ -1017,23 +1017,31 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01a6598b5036437a9937e367ba5945e0": { + "03f3e3bbbd3e4f069ce93c9f77605447": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a7952551ea73471da7d1961607d2083e", + "max": 170498071.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e49f0205b8604488943e7af8ed1685b8", + "value": 170498071.0 } }, - "057b1902f56244ae9732302f3abb5b41": { + "2158e363bdc34ca6a7e8dd7fa89930ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1048,13 +1056,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_1ba9c3edcaa243efa4e9ca63cdaa629a", + "layout": "IPY_MODEL_3c305ca54c754287b8b47129cdd318ee", "placeholder": "​", - "style": "IPY_MODEL_efedb267c81645c5a3541ab2d4f2aa47", - "value": " 170498071/170498071 [00:01<00:00, 116569130.25it/s]" + "style": "IPY_MODEL_81d32f2b0cab4c8dad9119bda5d8b6a6", + "value": " 170498071/170498071 [00:02<00:00, 66755727.40it/s]" } }, - "1ba9c3edcaa243efa4e9ca63cdaa629a": { + "3c305ca54c754287b8b47129cdd318ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1106,22 +1114,7 @@ "width": null } }, - "1e71569d7e2d4dab965bc20efd956d0f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "31d9a8a019bb4735b52e2e20691a104b": { + "6fb54979a8f547e087b7c624f518e4ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1136,35 +1129,44 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3ea582420f404b10b74d8dabc145da17", - "IPY_MODEL_fc1cb9e0f95d411ab45e896daa259b85", - "IPY_MODEL_057b1902f56244ae9732302f3abb5b41" + "IPY_MODEL_b716a96f859c42ffb9a19fc47529653d", + "IPY_MODEL_03f3e3bbbd3e4f069ce93c9f77605447", + "IPY_MODEL_2158e363bdc34ca6a7e8dd7fa89930ba" ], - "layout": "IPY_MODEL_63a124ccef8640adb49350fd4617ac47" + "layout": "IPY_MODEL_8a90f3d0d7744a5d92a1df111880bcad" } }, - "3ea582420f404b10b74d8dabc145da17": { + "81d32f2b0cab4c8dad9119bda5d8b6a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6d2934806cb04e6692ab1956bd814684", - "placeholder": "​", - "style": "IPY_MODEL_1e71569d7e2d4dab965bc20efd956d0f", - "value": "100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "63a124ccef8640adb49350fd4617ac47": { + "8a5215a042d0433d840ef0639d2c44a8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8a90f3d0d7744a5d92a1df111880bcad": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1216,7 +1218,7 @@ "width": null } }, - "6d2934806cb04e6692ab1956bd814684": { + "a2589ff80d2145158e258121b8bf0168": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1268,7 +1270,7 @@ "width": null } }, - "94051f55351942e9ac89e5ad3e39ab07": { + "a7952551ea73471da7d1961607d2083e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1320,43 +1322,41 @@ "width": null } }, - "efedb267c81645c5a3541ab2d4f2aa47": { + "b716a96f859c42ffb9a19fc47529653d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a2589ff80d2145158e258121b8bf0168", + "placeholder": "​", + "style": "IPY_MODEL_8a5215a042d0433d840ef0639d2c44a8", + "value": "100%" } }, - "fc1cb9e0f95d411ab45e896daa259b85": { + "e49f0205b8604488943e7af8ed1685b8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_94051f55351942e9ac89e5ad3e39ab07", - "max": 170498071.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_01a6598b5036437a9937e367ba5945e0", - "value": 170498071.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } } }, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 3adefafc8..b2f48e7a7 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -94,10 +94,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:26.900240Z", - "iopub.status.busy": "2023-08-14T16:41:26.899956Z", - "iopub.status.idle": "2023-08-14T16:41:28.260647Z", - "shell.execute_reply": "2023-08-14T16:41:28.259848Z" + "iopub.execute_input": "2023-08-15T04:15:35.657044Z", + "iopub.status.busy": "2023-08-15T04:15:35.656740Z", + "iopub.status.idle": "2023-08-15T04:15:37.321011Z", + "shell.execute_reply": "2023-08-15T04:15:37.319956Z" }, "nbsphinx": "hidden" }, @@ -109,7 +109,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.264674Z", - "iopub.status.busy": "2023-08-14T16:41:28.264076Z", - "iopub.status.idle": "2023-08-14T16:41:28.292674Z", - "shell.execute_reply": "2023-08-14T16:41:28.291926Z" + "iopub.execute_input": "2023-08-15T04:15:37.325382Z", + "iopub.status.busy": "2023-08-15T04:15:37.324999Z", + "iopub.status.idle": "2023-08-15T04:15:37.356577Z", + "shell.execute_reply": "2023-08-15T04:15:37.355608Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.296447Z", - "iopub.status.busy": "2023-08-14T16:41:28.296029Z", - "iopub.status.idle": "2023-08-14T16:41:28.299897Z", - "shell.execute_reply": "2023-08-14T16:41:28.299173Z" + "iopub.execute_input": "2023-08-15T04:15:37.360428Z", + "iopub.status.busy": "2023-08-15T04:15:37.359963Z", + "iopub.status.idle": "2023-08-15T04:15:37.364967Z", + "shell.execute_reply": "2023-08-15T04:15:37.364077Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.303141Z", - "iopub.status.busy": "2023-08-14T16:41:28.302712Z", - "iopub.status.idle": "2023-08-14T16:41:28.394485Z", - "shell.execute_reply": "2023-08-14T16:41:28.393600Z" + "iopub.execute_input": "2023-08-15T04:15:37.369213Z", + "iopub.status.busy": "2023-08-15T04:15:37.368895Z", + "iopub.status.idle": "2023-08-15T04:15:37.577674Z", + "shell.execute_reply": "2023-08-15T04:15:37.576613Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.398606Z", - "iopub.status.busy": "2023-08-14T16:41:28.398121Z", - "iopub.status.idle": "2023-08-14T16:41:28.784028Z", - "shell.execute_reply": "2023-08-14T16:41:28.783225Z" + "iopub.execute_input": "2023-08-15T04:15:37.582270Z", + "iopub.status.busy": "2023-08-15T04:15:37.581669Z", + "iopub.status.idle": "2023-08-15T04:15:38.001213Z", + "shell.execute_reply": "2023-08-15T04:15:38.000064Z" }, "nbsphinx": "hidden" }, @@ -410,16 +410,16 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:28.788461Z", - "iopub.status.busy": "2023-08-14T16:41:28.788011Z", - "iopub.status.idle": "2023-08-14T16:41:29.076080Z", - "shell.execute_reply": "2023-08-14T16:41:29.075399Z" + "iopub.execute_input": "2023-08-15T04:15:38.006301Z", + "iopub.status.busy": "2023-08-15T04:15:38.005926Z", + "iopub.status.idle": "2023-08-15T04:15:38.415839Z", + "shell.execute_reply": "2023-08-15T04:15:38.414880Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.080183Z", - "iopub.status.busy": "2023-08-14T16:41:29.079807Z", - "iopub.status.idle": "2023-08-14T16:41:29.087407Z", - "shell.execute_reply": "2023-08-14T16:41:29.086656Z" + "iopub.execute_input": "2023-08-15T04:15:38.420585Z", + "iopub.status.busy": "2023-08-15T04:15:38.420274Z", + "iopub.status.idle": "2023-08-15T04:15:38.430755Z", + "shell.execute_reply": "2023-08-15T04:15:38.429006Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.090572Z", - "iopub.status.busy": "2023-08-14T16:41:29.090320Z", - "iopub.status.idle": "2023-08-14T16:41:29.098472Z", - "shell.execute_reply": "2023-08-14T16:41:29.097846Z" + "iopub.execute_input": "2023-08-15T04:15:38.435510Z", + "iopub.status.busy": "2023-08-15T04:15:38.434699Z", + "iopub.status.idle": "2023-08-15T04:15:38.445025Z", + "shell.execute_reply": "2023-08-15T04:15:38.443909Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.101369Z", - "iopub.status.busy": "2023-08-14T16:41:29.101129Z", - "iopub.status.idle": "2023-08-14T16:41:29.104208Z", - "shell.execute_reply": "2023-08-14T16:41:29.103502Z" + "iopub.execute_input": "2023-08-15T04:15:38.449301Z", + "iopub.status.busy": "2023-08-15T04:15:38.448583Z", + "iopub.status.idle": "2023-08-15T04:15:38.452749Z", + "shell.execute_reply": "2023-08-15T04:15:38.451981Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:29.107094Z", - "iopub.status.busy": "2023-08-14T16:41:29.106862Z", - "iopub.status.idle": "2023-08-14T16:41:44.013721Z", - "shell.execute_reply": "2023-08-14T16:41:44.012387Z" + "iopub.execute_input": "2023-08-15T04:15:38.456395Z", + "iopub.status.busy": "2023-08-15T04:15:38.455727Z", + "iopub.status.idle": "2023-08-15T04:15:59.540378Z", + "shell.execute_reply": "2023-08-15T04:15:59.539307Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - 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    3. Use cleanlab to find label issues
    -
    +
    -
    +
    @@ -1033,7 +1033,7 @@

    Classes which are commonly mislabeled overall 
    -100%|██████████| 4997436/4997436 [00:37<00:00, 133357.66it/s]
+100%|██████████| 4997436/4997436 [00:50<00:00, 98745.72it/s]

    @@ -1291,7 +1291,7 @@

    Get label quality scores

    -
    +

    Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().

    @@ -1347,7 +1347,7 @@

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"_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_958a791ce7634c24a0ac313284e327cd", "IPY_MODEL_e83cc4cddf724fd5816d01235b606067", "IPY_MODEL_52ef141e49264df5a717120caeb393ee"], "layout": "IPY_MODEL_e3a54f003870461da663fbd118bf8f5f"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index a95d983c2..49c3f8f36 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:50.802529Z", - "iopub.status.busy": "2023-08-14T16:41:50.801886Z", - "iopub.status.idle": "2023-08-14T16:41:52.374276Z", - "shell.execute_reply": "2023-08-14T16:41:52.372684Z" + "iopub.execute_input": "2023-08-15T04:16:06.887902Z", + "iopub.status.busy": "2023-08-15T04:16:06.887593Z", + "iopub.status.idle": "2023-08-15T04:16:10.746876Z", + "shell.execute_reply": "2023-08-15T04:16:10.745395Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:41:52.378948Z", - "iopub.status.busy": "2023-08-14T16:41:52.378308Z", - "iopub.status.idle": "2023-08-14T16:42:44.002414Z", - "shell.execute_reply": "2023-08-14T16:42:44.001156Z" + "iopub.execute_input": "2023-08-15T04:16:10.752135Z", + "iopub.status.busy": "2023-08-15T04:16:10.751782Z", + "iopub.status.idle": "2023-08-15T04:17:00.270162Z", + "shell.execute_reply": "2023-08-15T04:17:00.268781Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:44.007121Z", - "iopub.status.busy": "2023-08-14T16:42:44.006529Z", - "iopub.status.idle": "2023-08-14T16:42:45.243446Z", - "shell.execute_reply": "2023-08-14T16:42:45.242587Z" + "iopub.execute_input": "2023-08-15T04:17:00.275717Z", + "iopub.status.busy": "2023-08-15T04:17:00.274812Z", + "iopub.status.idle": "2023-08-15T04:17:01.948788Z", + "shell.execute_reply": "2023-08-15T04:17:01.947773Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.247465Z", - "iopub.status.busy": "2023-08-14T16:42:45.246844Z", - "iopub.status.idle": "2023-08-14T16:42:45.251006Z", - "shell.execute_reply": "2023-08-14T16:42:45.250305Z" + "iopub.execute_input": "2023-08-15T04:17:01.953074Z", + "iopub.status.busy": "2023-08-15T04:17:01.952608Z", + "iopub.status.idle": "2023-08-15T04:17:01.959803Z", + "shell.execute_reply": "2023-08-15T04:17:01.958708Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.254680Z", - "iopub.status.busy": "2023-08-14T16:42:45.254242Z", - "iopub.status.idle": "2023-08-14T16:42:45.258856Z", - "shell.execute_reply": "2023-08-14T16:42:45.258148Z" + "iopub.execute_input": "2023-08-15T04:17:01.963636Z", + "iopub.status.busy": "2023-08-15T04:17:01.963338Z", + "iopub.status.idle": "2023-08-15T04:17:01.969439Z", + "shell.execute_reply": "2023-08-15T04:17:01.968494Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.262614Z", - "iopub.status.busy": "2023-08-14T16:42:45.262183Z", - "iopub.status.idle": "2023-08-14T16:42:45.266363Z", - "shell.execute_reply": "2023-08-14T16:42:45.265636Z" + "iopub.execute_input": "2023-08-15T04:17:01.973873Z", + "iopub.status.busy": "2023-08-15T04:17:01.973393Z", + "iopub.status.idle": "2023-08-15T04:17:01.979134Z", + "shell.execute_reply": "2023-08-15T04:17:01.978143Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.270102Z", - "iopub.status.busy": "2023-08-14T16:42:45.269665Z", - "iopub.status.idle": "2023-08-14T16:42:45.273068Z", - "shell.execute_reply": "2023-08-14T16:42:45.272380Z" + "iopub.execute_input": "2023-08-15T04:17:01.983843Z", + "iopub.status.busy": "2023-08-15T04:17:01.983345Z", + "iopub.status.idle": "2023-08-15T04:17:01.988934Z", + "shell.execute_reply": "2023-08-15T04:17:01.987147Z" } }, "outputs": [], @@ -333,10 +333,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:42:45.276250Z", - "iopub.status.busy": "2023-08-14T16:42:45.275823Z", - "iopub.status.idle": "2023-08-14T16:43:49.330123Z", - "shell.execute_reply": "2023-08-14T16:43:49.328981Z" + "iopub.execute_input": "2023-08-15T04:17:01.993419Z", + "iopub.status.busy": "2023-08-15T04:17:01.992969Z", + "iopub.status.idle": "2023-08-15T04:18:23.345783Z", + "shell.execute_reply": "2023-08-15T04:18:23.344394Z" } }, "outputs": [ @@ -350,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79e22eb31bc045a5a7fc1711d7439f7c", + "model_id": "5969d9c86bfa4eb4a361b6db421fbf04", "version_major": 2, "version_minor": 0 }, @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4873d8069c004fb89cb850243aedf1bd", + "model_id": "6468ac6e511549ada6743c8ac8a6cbce", "version_major": 2, "version_minor": 0 }, @@ -407,10 +407,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:43:49.334755Z", - "iopub.status.busy": "2023-08-14T16:43:49.334182Z", - "iopub.status.idle": "2023-08-14T16:43:50.343458Z", - "shell.execute_reply": "2023-08-14T16:43:50.342661Z" + "iopub.execute_input": "2023-08-15T04:18:23.350917Z", + "iopub.status.busy": "2023-08-15T04:18:23.350569Z", + "iopub.status.idle": "2023-08-15T04:18:24.650680Z", + "shell.execute_reply": "2023-08-15T04:18:24.649513Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:43:50.347465Z", - "iopub.status.busy": "2023-08-14T16:43:50.346664Z", - "iopub.status.idle": "2023-08-14T16:43:53.283808Z", - "shell.execute_reply": "2023-08-14T16:43:53.283128Z" + "iopub.execute_input": "2023-08-15T04:18:24.654885Z", + "iopub.status.busy": "2023-08-15T04:18:24.654365Z", + "iopub.status.idle": "2023-08-15T04:18:28.499715Z", + "shell.execute_reply": "2023-08-15T04:18:28.498600Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:43:53.287368Z", - "iopub.status.busy": "2023-08-14T16:43:53.286940Z", - "iopub.status.idle": "2023-08-14T16:44:31.179688Z", - "shell.execute_reply": "2023-08-14T16:44:31.178604Z" + "iopub.execute_input": "2023-08-15T04:18:28.504223Z", + "iopub.status.busy": "2023-08-15T04:18:28.503602Z", + "iopub.status.idle": "2023-08-15T04:19:19.644095Z", + "shell.execute_reply": "2023-08-15T04:19:19.643208Z" } }, "outputs": [ @@ -546,7 +546,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 13131/4997436 [00:00<00:37, 131297.29it/s]" + " 0%| | 9043/4997436 [00:00<00:55, 90421.46it/s]" ] }, { @@ -554,7 +554,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 26448/4997436 [00:00<00:37, 132391.49it/s]" + " 0%| | 18630/4997436 [00:00<00:53, 93621.03it/s]" ] }, { @@ -562,7 +562,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 39725/4997436 [00:00<00:37, 132560.62it/s]" + " 1%| | 28336/4997436 [00:00<00:52, 95187.25it/s]" ] }, { @@ -570,7 +570,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 53044/4997436 [00:00<00:37, 132803.63it/s]" + " 1%| | 38205/4997436 [00:00<00:51, 96565.24it/s]" ] }, { @@ -578,7 +578,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 66339/4997436 [00:00<00:37, 132852.46it/s]" + " 1%| | 47862/4997436 [00:00<00:51, 96031.52it/s]" ] }, { @@ -586,7 +586,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 79652/4997436 [00:00<00:36, 132942.44it/s]" + " 1%| | 57596/4997436 [00:00<00:51, 96472.29it/s]" ] }, { @@ -594,7 +594,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 92947/4997436 [00:00<00:36, 132619.67it/s]" + " 1%|▏ | 68471/4997436 [00:00<00:49, 100472.41it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 106210/4997436 [00:00<00:36, 132276.11it/s]" + " 2%|▏ | 78520/4997436 [00:00<00:49, 100314.39it/s]" ] }, { @@ -610,7 +610,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 119458/4997436 [00:00<00:36, 132335.07it/s]" + " 2%|▏ | 88609/4997436 [00:00<00:48, 100488.27it/s]" ] }, { @@ -618,7 +618,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 132777/4997436 [00:01<00:36, 132594.50it/s]" + " 2%|▏ | 98713/4997436 [00:01<00:48, 100653.84it/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 146094/4997436 [00:01<00:36, 132768.87it/s]" + " 2%|▏ | 108779/4997436 [00:01<00:49, 99253.53it/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 159372/4997436 [00:01<00:36, 132162.88it/s]" + " 2%|▏ | 118904/4997436 [00:01<00:48, 99851.78it/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 172589/4997436 [00:01<00:36, 131996.99it/s]" + " 3%|▎ | 129279/4997436 [00:01<00:48, 101022.03it/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 185790/4997436 [00:01<00:36, 131354.76it/s]" + " 3%|▎ | 139385/4997436 [00:01<00:48, 100901.47it/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 199148/4997436 [00:01<00:36, 132018.73it/s]" + " 3%|▎ | 149478/4997436 [00:01<00:48, 99223.11it/s] " ] }, { @@ -666,7 +666,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 212584/4997436 [00:01<00:36, 132717.88it/s]" + " 3%|▎ | 159408/4997436 [00:01<00:49, 98042.56it/s]" ] }, { @@ -674,7 +674,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 225932/4997436 [00:01<00:35, 132943.28it/s]" + " 3%|▎ | 169907/4997436 [00:01<00:48, 100089.89it/s]" ] }, { @@ -682,7 +682,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 239228/4997436 [00:01<00:35, 132747.42it/s]" + " 4%|▎ | 180121/4997436 [00:01<00:47, 100683.58it/s]" ] }, { @@ -690,7 +690,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 252512/4997436 [00:01<00:35, 132770.64it/s]" + " 4%|▍ | 190197/4997436 [00:01<00:48, 98343.13it/s] " ] }, { @@ -698,7 +698,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 266008/4997436 [00:02<00:35, 133423.41it/s]" + " 4%|▍ | 200725/4997436 [00:02<00:47, 100375.50it/s]" ] }, { @@ -706,7 +706,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 279484/4997436 [00:02<00:35, 133822.03it/s]" + " 4%|▍ | 210778/4997436 [00:02<00:48, 99398.18it/s] " ] }, { @@ -714,7 +714,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 292988/4997436 [00:02<00:35, 134185.36it/s]" + " 4%|▍ | 220973/4997436 [00:02<00:47, 100008.67it/s]" ] }, { @@ -722,7 +722,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 306407/4997436 [00:02<00:35, 133753.57it/s]" + " 5%|▍ | 231084/4997436 [00:02<00:47, 100327.61it/s]" ] }, { @@ -730,7 +730,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 319783/4997436 [00:02<00:34, 133714.32it/s]" + " 5%|▍ | 241124/4997436 [00:02<00:48, 98167.25it/s] " ] }, { @@ -738,7 +738,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 333155/4997436 [00:02<00:35, 133011.63it/s]" + " 5%|▌ | 250954/4997436 [00:02<00:48, 97426.98it/s]" ] }, { @@ -746,7 +746,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 346458/4997436 [00:02<00:35, 132722.66it/s]" + " 5%|▌ | 260706/4997436 [00:02<00:48, 96895.09it/s]" ] }, { @@ -754,7 +754,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 359747/4997436 [00:02<00:34, 132741.94it/s]" + " 5%|▌ | 270402/4997436 [00:02<00:49, 94618.01it/s]" ] }, { @@ -762,7 +762,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 373054/4997436 [00:02<00:34, 132836.91it/s]" + " 6%|▌ | 280048/4997436 [00:02<00:49, 95152.63it/s]" ] }, { @@ -770,7 +770,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 386469/4997436 [00:02<00:34, 133227.46it/s]" + " 6%|▌ | 289574/4997436 [00:02<00:49, 94507.84it/s]" ] }, { @@ -778,7 +778,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 399933/4997436 [00:03<00:34, 133647.99it/s]" + " 6%|▌ | 299032/4997436 [00:03<00:49, 94221.06it/s]" ] }, { @@ -786,7 +786,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 413409/4997436 [00:03<00:34, 133979.09it/s]" + " 6%|▌ | 308547/4997436 [00:03<00:49, 94492.53it/s]" ] }, { @@ -794,7 +794,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 426849/4997436 [00:03<00:34, 134104.04it/s]" + " 6%|▋ | 318244/4997436 [00:03<00:49, 95224.35it/s]" ] }, { @@ -802,7 +802,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 440295/4997436 [00:03<00:33, 134207.24it/s]" + " 7%|▋ | 328094/4997436 [00:03<00:48, 96196.64it/s]" ] }, { @@ -810,7 +810,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 453780/4997436 [00:03<00:33, 134396.85it/s]" + " 7%|▋ | 337817/4997436 [00:03<00:48, 96501.76it/s]" ] }, { @@ -818,7 +818,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 467220/4997436 [00:03<00:33, 133967.84it/s]" + " 7%|▋ | 347598/4997436 [00:03<00:47, 96888.69it/s]" ] }, { @@ -826,7 +826,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 480618/4997436 [00:03<00:33, 133556.44it/s]" + " 7%|▋ | 357289/4997436 [00:03<00:47, 96730.76it/s]" ] }, { @@ -834,7 +834,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 493975/4997436 [00:03<00:33, 133420.22it/s]" + " 7%|▋ | 366964/4997436 [00:03<00:48, 96451.70it/s]" ] }, { @@ -842,7 +842,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 507318/4997436 [00:03<00:33, 132933.23it/s]" + " 8%|▊ | 376812/4997436 [00:03<00:47, 97038.61it/s]" ] }, { @@ -850,7 +850,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 520924/4997436 [00:03<00:33, 133841.74it/s]" + " 8%|▊ | 386686/4997436 [00:03<00:47, 97545.09it/s]" ] }, { @@ -858,7 +858,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 534309/4997436 [00:04<00:33, 133651.18it/s]" + " 8%|▊ | 396476/4997436 [00:04<00:47, 97649.68it/s]" ] }, { @@ -866,7 +866,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 547794/4997436 [00:04<00:33, 134008.35it/s]" + " 8%|▊ | 406242/4997436 [00:04<00:47, 97027.45it/s]" ] }, { @@ -874,7 +874,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 561196/4997436 [00:04<00:33, 133896.95it/s]" + " 8%|▊ | 415946/4997436 [00:04<00:47, 95960.47it/s]" ] }, { @@ -882,7 +882,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█▏ | 574587/4997436 [00:04<00:33, 133684.63it/s]" + " 9%|▊ | 425622/4997436 [00:04<00:47, 96176.35it/s]" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 587956/4997436 [00:04<00:33, 133211.39it/s]" + " 9%|▊ | 435242/4997436 [00:04<00:47, 95628.84it/s]" ] }, { @@ -898,7 +898,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 601278/4997436 [00:04<00:33, 132958.27it/s]" + " 9%|▉ | 444807/4997436 [00:04<00:48, 94227.00it/s]" ] }, { @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 614703/4997436 [00:04<00:32, 133342.11it/s]" + " 9%|▉ | 454450/4997436 [00:04<00:47, 94873.93it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 628038/4997436 [00:04<00:32, 133244.56it/s]" + " 9%|▉ | 464490/4997436 [00:04<00:46, 96509.17it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 641363/4997436 [00:04<00:32, 132797.23it/s]" + " 9%|▉ | 474466/4997436 [00:04<00:46, 97472.37it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 654644/4997436 [00:04<00:32, 132673.99it/s]" + " 10%|▉ | 485168/4997436 [00:04<00:44, 100314.21it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 668018/4997436 [00:05<00:32, 132991.05it/s]" + " 10%|▉ | 495570/4997436 [00:05<00:44, 101417.84it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 681318/4997436 [00:05<00:32, 132975.74it/s]" + " 10%|█ | 505998/4997436 [00:05<00:43, 102270.35it/s]" ] }, { @@ -954,7 +954,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 694750/4997436 [00:05<00:32, 133375.25it/s]" + " 10%|█ | 516288/4997436 [00:05<00:43, 102455.29it/s]" ] }, { @@ -962,7 +962,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 708088/4997436 [00:05<00:32, 133260.67it/s]" + " 11%|█ | 526917/4997436 [00:05<00:43, 103602.21it/s]" ] }, { @@ -970,7 +970,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 721415/4997436 [00:05<00:32, 133023.45it/s]" + " 11%|█ | 537279/4997436 [00:05<00:43, 102401.60it/s]" ] }, { @@ -978,7 +978,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 734718/4997436 [00:05<00:32, 132213.42it/s]" + " 11%|█ | 547524/4997436 [00:05<00:43, 102287.39it/s]" ] }, { @@ -986,7 +986,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 748019/4997436 [00:05<00:32, 132448.19it/s]" + " 11%|█ | 557756/4997436 [00:05<00:43, 101729.16it/s]" ] }, { @@ -994,7 +994,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 761360/4997436 [00:05<00:31, 132732.64it/s]" + " 11%|█▏ | 567932/4997436 [00:05<00:43, 101146.03it/s]" ] }, { @@ -1002,7 +1002,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 774908/4997436 [00:05<00:31, 133550.28it/s]" + " 12%|█▏ | 578049/4997436 [00:05<00:43, 100970.05it/s]" ] }, { @@ -1010,7 +1010,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 788264/4997436 [00:05<00:31, 133541.88it/s]" + " 12%|█▏ | 588148/4997436 [00:05<00:44, 99485.94it/s] " ] }, { @@ -1018,7 +1018,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 801858/4997436 [00:06<00:31, 134258.54it/s]" + " 12%|█▏ | 598102/4997436 [00:06<00:45, 96860.54it/s]" ] }, { @@ -1026,7 +1026,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 815425/4997436 [00:06<00:31, 134679.93it/s]" + " 12%|█▏ | 608118/4997436 [00:06<00:44, 97818.81it/s]" ] }, { @@ -1034,7 +1034,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 828894/4997436 [00:06<00:30, 134486.04it/s]" + " 12%|█▏ | 617914/4997436 [00:06<00:44, 97819.68it/s]" ] }, { @@ -1042,7 +1042,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 842376/4997436 [00:06<00:30, 134582.75it/s]" + " 13%|█▎ | 627994/4997436 [00:06<00:44, 98697.73it/s]" ] }, { @@ -1050,7 +1050,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 855835/4997436 [00:06<00:30, 134123.46it/s]" + " 13%|█▎ | 638085/4997436 [00:06<00:43, 99351.21it/s]" ] }, { @@ -1058,7 +1058,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 869248/4997436 [00:06<00:30, 133545.40it/s]" + " 13%|█▎ | 648983/4997436 [00:06<00:42, 102216.41it/s]" ] }, { @@ -1066,7 +1066,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 882623/4997436 [00:06<00:30, 133603.26it/s]" + " 13%|█▎ | 659212/4997436 [00:06<00:43, 100417.42it/s]" ] }, { @@ -1074,7 +1074,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 895984/4997436 [00:06<00:30, 133508.65it/s]" + " 13%|█▎ | 669265/4997436 [00:06<00:43, 99668.22it/s] " ] }, { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 909336/4997436 [00:06<00:30, 133508.85it/s]" + " 14%|█▎ | 679240/4997436 [00:06<00:43, 98703.30it/s]" ] }, { @@ -1090,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 922688/4997436 [00:06<00:30, 133480.31it/s]" + " 14%|█▍ | 689117/4997436 [00:07<00:44, 97781.21it/s]" ] }, { @@ -1098,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 936037/4997436 [00:07<00:30, 133395.34it/s]" + " 14%|█▍ | 698916/4997436 [00:07<00:43, 97840.92it/s]" ] }, { @@ -1106,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 949377/4997436 [00:07<00:30, 132766.88it/s]" + " 14%|█▍ | 708704/4997436 [00:07<00:44, 97089.19it/s]" ] }, { @@ -1114,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 962693/4997436 [00:07<00:30, 132882.06it/s]" + " 14%|█▍ | 718459/4997436 [00:07<00:44, 97222.81it/s]" ] }, { @@ -1122,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 976056/4997436 [00:07<00:30, 133103.40it/s]" + " 15%|█▍ | 728438/4997436 [00:07<00:43, 97982.69it/s]" ] }, { @@ -1130,7 +1130,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 989428/4997436 [00:07<00:30, 133285.63it/s]" + " 15%|█▍ | 738239/4997436 [00:07<00:43, 97123.22it/s]" ] }, { @@ -1138,7 +1138,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1002929/4997436 [00:07<00:29, 133800.74it/s]" + " 15%|█▍ | 747955/4997436 [00:07<00:43, 96839.67it/s]" ] }, { @@ -1146,7 +1146,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1016376/4997436 [00:07<00:29, 133999.23it/s]" + " 15%|█▌ | 757641/4997436 [00:07<00:44, 96107.36it/s]" ] }, { @@ -1154,7 +1154,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1029777/4997436 [00:07<00:29, 133778.29it/s]" + " 15%|█▌ | 767274/4997436 [00:07<00:43, 96169.61it/s]" ] }, { @@ -1162,7 +1162,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1043177/4997436 [00:07<00:29, 133841.00it/s]" + " 16%|█▌ | 776935/4997436 [00:07<00:43, 96297.32it/s]" ] }, { @@ -1170,7 +1170,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1056697/4997436 [00:07<00:29, 134245.17it/s]" + " 16%|█▌ | 786566/4997436 [00:08<00:43, 96011.42it/s]" ] }, { @@ -1178,7 +1178,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1070122/4997436 [00:08<00:29, 133811.84it/s]" + " 16%|█▌ | 796168/4997436 [00:08<00:44, 95442.98it/s]" ] }, { @@ -1186,7 +1186,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1083504/4997436 [00:08<00:29, 133337.80it/s]" + " 16%|█▌ | 805714/4997436 [00:08<00:43, 95429.66it/s]" ] }, { @@ -1194,7 +1194,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1096839/4997436 [00:08<00:29, 132545.23it/s]" + " 16%|█▋ | 815258/4997436 [00:08<00:43, 95276.06it/s]" ] }, { @@ -1202,7 +1202,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1110095/4997436 [00:08<00:29, 132537.43it/s]" + " 17%|█▋ | 824787/4997436 [00:08<00:43, 94916.41it/s]" ] }, { @@ -1210,7 +1210,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1123350/4997436 [00:08<00:29, 132496.06it/s]" + " 17%|█▋ | 834280/4997436 [00:08<00:43, 94869.72it/s]" ] }, { @@ -1218,7 +1218,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1136601/4997436 [00:08<00:29, 132242.39it/s]" + " 17%|█▋ | 843768/4997436 [00:08<00:43, 94776.90it/s]" ] }, { @@ -1226,7 +1226,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1149859/4997436 [00:08<00:29, 132338.02it/s]" + " 17%|█▋ | 853246/4997436 [00:08<00:43, 94626.68it/s]" ] }, { @@ -1234,7 +1234,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1163132/4997436 [00:08<00:28, 132452.10it/s]" + " 17%|█▋ | 862904/4997436 [00:08<00:43, 95208.23it/s]" ] }, { @@ -1242,7 +1242,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▎ | 1176381/4997436 [00:08<00:28, 132460.74it/s]" + " 17%|█▋ | 872676/4997436 [00:08<00:42, 95957.72it/s]" ] }, { @@ -1250,7 +1250,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1189628/4997436 [00:08<00:28, 132360.13it/s]" + " 18%|█▊ | 882498/4997436 [00:09<00:42, 96631.76it/s]" ] }, { @@ -1258,7 +1258,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1202865/4997436 [00:09<00:28, 132236.49it/s]" + " 18%|█▊ | 892489/4997436 [00:09<00:42, 97609.94it/s]" ] }, { @@ -1266,7 +1266,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1216089/4997436 [00:09<00:28, 131837.05it/s]" + " 18%|█▊ | 902282/4997436 [00:09<00:41, 97703.73it/s]" ] }, { @@ -1274,7 +1274,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1229273/4997436 [00:09<00:28, 131784.42it/s]" + " 18%|█▊ | 912678/4997436 [00:09<00:41, 99576.83it/s]" ] }, { @@ -1282,7 +1282,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1242452/4997436 [00:09<00:28, 131326.51it/s]" + " 18%|█▊ | 923280/4997436 [00:09<00:40, 101502.76it/s]" ] }, { @@ -1290,7 +1290,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1255586/4997436 [00:09<00:28, 130713.00it/s]" + " 19%|█▊ | 934275/4997436 [00:09<00:39, 104030.04it/s]" ] }, { @@ -1298,7 +1298,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1268658/4997436 [00:09<00:28, 130536.80it/s]" + " 19%|█▉ | 944679/4997436 [00:09<00:39, 102342.95it/s]" ] }, { @@ -1306,7 +1306,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1281789/4997436 [00:09<00:28, 130763.37it/s]" + " 19%|█▉ | 955278/4997436 [00:09<00:39, 103417.32it/s]" ] }, { @@ -1314,7 +1314,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1295435/4997436 [00:09<00:27, 132463.09it/s]" + " 19%|█▉ | 965626/4997436 [00:09<00:39, 101661.32it/s]" ] }, { @@ -1322,7 +1322,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1308825/4997436 [00:09<00:27, 132888.98it/s]" + " 20%|█▉ | 975802/4997436 [00:09<00:39, 101060.47it/s]" ] }, { @@ -1330,7 +1330,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1322115/4997436 [00:09<00:27, 132715.05it/s]" + " 20%|█▉ | 985915/4997436 [00:10<00:39, 100327.52it/s]" ] }, { @@ -1338,7 +1338,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1335569/4997436 [00:10<00:27, 133257.15it/s]" + " 20%|█▉ | 995953/4997436 [00:10<00:40, 99996.36it/s] " ] }, { @@ -1346,7 +1346,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1348896/4997436 [00:10<00:27, 132876.17it/s]" + " 20%|██ | 1005956/4997436 [00:10<00:40, 99137.28it/s]" ] }, { @@ -1354,7 +1354,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1362305/4997436 [00:10<00:27, 133237.41it/s]" + " 20%|██ | 1015873/4997436 [00:10<00:40, 98449.14it/s]" ] }, { @@ -1362,7 +1362,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1375706/4997436 [00:10<00:27, 133467.19it/s]" + " 21%|██ | 1025792/4997436 [00:10<00:40, 98664.61it/s]" ] }, { @@ -1370,7 +1370,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1389134/4997436 [00:10<00:26, 133706.58it/s]" + " 21%|██ | 1036109/4997436 [00:10<00:39, 99997.63it/s]" ] }, { @@ -1378,7 +1378,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1402505/4997436 [00:10<00:26, 133678.04it/s]" + " 21%|██ | 1046359/4997436 [00:10<00:39, 100733.72it/s]" ] }, { @@ -1386,7 +1386,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1416004/4997436 [00:10<00:26, 134069.67it/s]" + " 21%|██ | 1056435/4997436 [00:10<00:39, 100482.27it/s]" ] }, { @@ -1394,7 +1394,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▊ | 1429412/4997436 [00:10<00:26, 133896.03it/s]" + " 21%|██▏ | 1066485/4997436 [00:10<00:40, 98249.95it/s] " ] }, { @@ -1402,7 +1402,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1442802/4997436 [00:10<00:26, 133704.25it/s]" + " 22%|██▏ | 1076322/4997436 [00:10<00:40, 97548.19it/s]" ] }, { @@ -1410,7 +1410,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1456173/4997436 [00:10<00:26, 133545.57it/s]" + " 22%|██▏ | 1087632/4997436 [00:11<00:38, 102122.43it/s]" ] }, { @@ -1418,7 +1418,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1469528/4997436 [00:11<00:26, 133281.37it/s]" + " 22%|██▏ | 1097859/4997436 [00:11<00:38, 101757.72it/s]" ] }, { @@ -1426,7 +1426,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1482857/4997436 [00:11<00:26, 132945.42it/s]" + " 22%|██▏ | 1108045/4997436 [00:11<00:38, 100888.83it/s]" ] }, { @@ -1434,7 +1434,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1496152/4997436 [00:11<00:26, 132213.55it/s]" + " 22%|██▏ | 1118142/4997436 [00:11<00:39, 99247.20it/s] " ] }, { @@ -1442,7 +1442,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1509491/4997436 [00:11<00:26, 132562.23it/s]" + " 23%|██▎ | 1128076/4997436 [00:11<00:39, 96881.65it/s]" ] }, { @@ -1450,7 +1450,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1522749/4997436 [00:11<00:26, 131883.92it/s]" + " 23%|██▎ | 1137792/4997436 [00:11<00:39, 96956.31it/s]" ] }, { @@ -1458,7 +1458,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1536110/4997436 [00:11<00:26, 132396.17it/s]" + " 23%|██▎ | 1147498/4997436 [00:11<00:40, 95845.76it/s]" ] }, { @@ -1466,7 +1466,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1549499/4997436 [00:11<00:25, 132839.93it/s]" + " 23%|██▎ | 1157091/4997436 [00:11<00:40, 95382.92it/s]" ] }, { @@ -1474,7 +1474,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███▏ | 1562784/4997436 [00:11<00:25, 132628.46it/s]" + " 23%|██▎ | 1166997/4997436 [00:11<00:39, 96461.45it/s]" ] }, { @@ -1482,7 +1482,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1576160/4997436 [00:11<00:25, 132965.07it/s]" + " 24%|██▎ | 1176649/4997436 [00:11<00:40, 95301.33it/s]" ] }, { @@ -1490,7 +1490,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1589458/4997436 [00:11<00:25, 132888.79it/s]" + " 24%|██▎ | 1186259/4997436 [00:12<00:39, 95533.98it/s]" ] }, { @@ -1498,7 +1498,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1602750/4997436 [00:12<00:25, 132896.47it/s]" + " 24%|██▍ | 1195817/4997436 [00:12<00:41, 92690.97it/s]" ] }, { @@ -1506,7 +1506,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1616040/4997436 [00:12<00:25, 132876.31it/s]" + " 24%|██▍ | 1205373/4997436 [00:12<00:40, 93522.97it/s]" ] }, { @@ -1514,7 +1514,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1629328/4997436 [00:12<00:25, 132369.62it/s]" + " 24%|██▍ | 1215080/4997436 [00:12<00:39, 94560.20it/s]" ] }, { @@ -1522,7 +1522,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1642566/4997436 [00:12<00:25, 131799.72it/s]" + " 25%|██▍ | 1224700/4997436 [00:12<00:39, 95042.99it/s]" ] }, { @@ -1530,7 +1530,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1655828/4997436 [00:12<00:25, 132040.46it/s]" + " 25%|██▍ | 1234541/4997436 [00:12<00:39, 96022.79it/s]" ] }, { @@ -1538,7 +1538,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1669044/4997436 [00:12<00:25, 132072.08it/s]" + " 25%|██▍ | 1244531/4997436 [00:12<00:38, 97173.88it/s]" ] }, { @@ -1546,7 +1546,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1682303/4997436 [00:12<00:25, 132222.73it/s]" + " 25%|██▌ | 1254506/4997436 [00:12<00:38, 97940.02it/s]" ] }, { @@ -1554,7 +1554,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1695690/4997436 [00:12<00:24, 132712.58it/s]" + " 25%|██▌ | 1264305/4997436 [00:12<00:38, 97687.62it/s]" ] }, { @@ -1562,7 +1562,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1709097/4997436 [00:12<00:24, 133117.19it/s]" + " 25%|██▌ | 1274226/4997436 [00:12<00:37, 98137.95it/s]" ] }, { @@ -1570,7 +1570,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1722518/4997436 [00:12<00:24, 133442.18it/s]" + " 26%|██▌ | 1284043/4997436 [00:13<00:37, 97764.69it/s]" ] }, { @@ -1578,7 +1578,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1735903/4997436 [00:13<00:24, 133563.14it/s]" + " 26%|██▌ | 1293822/4997436 [00:13<00:38, 97041.52it/s]" ] }, { @@ -1586,7 +1586,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1749374/4997436 [00:13<00:24, 133903.53it/s]" + " 26%|██▌ | 1304078/4997436 [00:13<00:37, 98678.93it/s]" ] }, { @@ -1594,7 +1594,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1762765/4997436 [00:13<00:24, 133699.88it/s]" + " 26%|██▋ | 1314639/4997436 [00:13<00:36, 100742.11it/s]" ] }, { @@ -1602,7 +1602,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1776159/4997436 [00:13<00:24, 133767.56it/s]" + " 27%|██▋ | 1324717/4997436 [00:13<00:36, 100371.33it/s]" ] }, { @@ -1610,7 +1610,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1789580/4997436 [00:13<00:23, 133896.09it/s]" + " 27%|██▋ | 1335238/4997436 [00:13<00:35, 101797.97it/s]" ] }, { @@ -1618,7 +1618,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1802970/4997436 [00:13<00:23, 133433.50it/s]" + " 27%|██▋ | 1345448/4997436 [00:13<00:35, 101886.57it/s]" ] }, { @@ -1626,7 +1626,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 1816487/4997436 [00:13<00:23, 133948.96it/s]" + " 27%|██▋ | 1355639/4997436 [00:13<00:35, 101704.51it/s]" ] }, { @@ -1634,7 +1634,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1829899/4997436 [00:13<00:23, 133998.20it/s]" + " 27%|██▋ | 1365826/4997436 [00:13<00:35, 101752.35it/s]" ] }, { @@ -1642,7 +1642,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1843300/4997436 [00:13<00:23, 133980.91it/s]" + " 28%|██▊ | 1376047/4997436 [00:14<00:35, 101884.26it/s]" ] }, { @@ -1650,7 +1650,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1856699/4997436 [00:13<00:23, 133646.33it/s]" + " 28%|██▊ | 1386237/4997436 [00:14<00:35, 101345.60it/s]" ] }, { @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1870064/4997436 [00:14<00:23, 133355.10it/s]" + " 28%|██▊ | 1396462/4997436 [00:14<00:35, 101613.09it/s]" ] }, { @@ -1666,7 +1666,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1883400/4997436 [00:14<00:23, 133328.09it/s]" + " 28%|██▊ | 1406625/4997436 [00:14<00:35, 100502.93it/s]" ] }, { @@ -1674,7 +1674,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1896794/4997436 [00:14<00:23, 133507.44it/s]" + " 28%|██▊ | 1417196/4997436 [00:14<00:35, 102047.03it/s]" ] }, { @@ -1682,7 +1682,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1910395/4997436 [00:14<00:22, 134253.38it/s]" + " 29%|██▊ | 1428237/4997436 [00:14<00:34, 104535.71it/s]" ] }, { @@ -1690,7 +1690,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1923821/4997436 [00:14<00:22, 134242.55it/s]" + " 29%|██▉ | 1438695/4997436 [00:14<00:34, 104523.00it/s]" ] }, { @@ -1698,7 +1698,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1937246/4997436 [00:14<00:22, 134192.08it/s]" + " 29%|██▉ | 1449601/4997436 [00:14<00:33, 105872.25it/s]" ] }, { @@ -1706,7 +1706,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1950712/4997436 [00:14<00:22, 134330.35it/s]" + " 29%|██▉ | 1460191/4997436 [00:14<00:33, 105229.22it/s]" ] }, { @@ -1714,7 +1714,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1964146/4997436 [00:14<00:22, 134201.10it/s]" + " 29%|██▉ | 1470833/4997436 [00:14<00:33, 105532.22it/s]" ] }, { @@ -1722,7 +1722,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1977569/4997436 [00:14<00:22, 134206.15it/s]" + " 30%|██▉ | 1482038/4997436 [00:15<00:32, 107476.57it/s]" ] }, { @@ -1730,7 +1730,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1990990/4997436 [00:14<00:22, 133907.23it/s]" + " 30%|██▉ | 1492788/4997436 [00:15<00:33, 103580.94it/s]" ] }, { @@ -1738,7 +1738,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2004459/4997436 [00:15<00:22, 134140.16it/s]" + " 30%|███ | 1503894/4997436 [00:15<00:33, 105759.68it/s]" ] }, { @@ -1746,7 +1746,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2017874/4997436 [00:15<00:22, 133940.16it/s]" + " 30%|███ | 1514791/4997436 [00:15<00:32, 106701.46it/s]" ] }, { @@ -1754,7 +1754,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2031269/4997436 [00:15<00:22, 133504.44it/s]" + " 31%|███ | 1525483/4997436 [00:15<00:32, 105837.68it/s]" ] }, { @@ -1762,7 +1762,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2044622/4997436 [00:15<00:22, 133508.12it/s]" + " 31%|███ | 1536083/4997436 [00:15<00:33, 103665.61it/s]" ] }, { @@ -1770,7 +1770,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2057974/4997436 [00:15<00:22, 133083.27it/s]" + " 31%|███ | 1546468/4997436 [00:15<00:34, 101248.23it/s]" ] }, { @@ -1778,7 +1778,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 2071283/4997436 [00:15<00:21, 133047.13it/s]" + " 31%|███ | 1556613/4997436 [00:15<00:34, 100550.14it/s]" ] }, { @@ -1786,7 +1786,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2084588/4997436 [00:15<00:21, 132998.43it/s]" + " 31%|███▏ | 1566681/4997436 [00:15<00:34, 98941.86it/s] " ] }, { @@ -1794,7 +1794,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2097889/4997436 [00:15<00:21, 132894.30it/s]" + " 32%|███▏ | 1576586/4997436 [00:15<00:35, 97737.48it/s]" ] }, { @@ -1802,7 +1802,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2111221/4997436 [00:15<00:21, 133018.82it/s]" + " 32%|███▏ | 1586367/4997436 [00:16<00:35, 97130.25it/s]" ] }, { @@ -1810,7 +1810,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2124523/4997436 [00:15<00:21, 132567.27it/s]" + " 32%|███▏ | 1596084/4997436 [00:16<00:35, 95951.17it/s]" ] }, { @@ -1818,7 +1818,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2137785/4997436 [00:16<00:21, 132579.57it/s]" + " 32%|███▏ | 1605732/4997436 [00:16<00:35, 96101.15it/s]" ] }, { @@ -1826,7 +1826,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2151069/4997436 [00:16<00:21, 132655.75it/s]" + " 32%|███▏ | 1615575/4997436 [00:16<00:34, 96783.31it/s]" ] }, { @@ -1834,7 +1834,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2164552/4997436 [00:16<00:21, 133304.83it/s]" + " 33%|███▎ | 1625383/4997436 [00:16<00:34, 97164.84it/s]" ] }, { @@ -1842,7 +1842,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▎ | 2177948/4997436 [00:16<00:21, 133499.94it/s]" + " 33%|███▎ | 1635103/4997436 [00:16<00:35, 95987.84it/s]" ] }, { @@ -1850,7 +1850,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2191299/4997436 [00:16<00:21, 133252.55it/s]" + " 33%|███▎ | 1644706/4997436 [00:16<00:35, 95645.98it/s]" ] }, { @@ -1858,7 +1858,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2204741/4997436 [00:16<00:20, 133600.91it/s]" + " 33%|███▎ | 1654414/4997436 [00:16<00:34, 96033.68it/s]" ] }, { @@ -1866,7 +1866,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2218102/4997436 [00:16<00:20, 133432.59it/s]" + " 33%|███▎ | 1664178/4997436 [00:16<00:34, 96509.11it/s]" ] }, { @@ -1874,7 +1874,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2231498/4997436 [00:16<00:20, 133587.41it/s]" + " 33%|███▎ | 1674030/4997436 [00:16<00:34, 97104.45it/s]" ] }, { @@ -1882,7 +1882,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2244911/4997436 [00:16<00:20, 133745.91it/s]" + " 34%|███▎ | 1683761/4997436 [00:17<00:34, 97161.49it/s]" ] }, { @@ -1890,7 +1890,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2258286/4997436 [00:16<00:20, 133371.38it/s]" + " 34%|███▍ | 1693479/4997436 [00:17<00:34, 95512.19it/s]" ] }, { @@ -1898,7 +1898,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2271652/4997436 [00:17<00:20, 133454.07it/s]" + " 34%|███▍ | 1703055/4997436 [00:17<00:34, 95581.52it/s]" ] }, { @@ -1906,7 +1906,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2284998/4997436 [00:17<00:20, 133058.80it/s]" + " 34%|███▍ | 1712700/4997436 [00:17<00:34, 95836.34it/s]" ] }, { @@ -1914,7 +1914,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2298305/4997436 [00:17<00:20, 132499.45it/s]" + " 34%|███▍ | 1722288/4997436 [00:17<00:34, 95761.91it/s]" ] }, { @@ -1922,7 +1922,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2311638/4997436 [00:17<00:20, 132731.56it/s]" + " 35%|███▍ | 1731867/4997436 [00:17<00:34, 95636.18it/s]" ] }, { @@ -1930,7 +1930,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2325035/4997436 [00:17<00:20, 133097.06it/s]" + " 35%|███▍ | 1741821/4997436 [00:17<00:33, 96797.94it/s]" ] }, { @@ -1938,7 +1938,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2338461/4997436 [00:17<00:19, 133440.95it/s]" + " 35%|███▌ | 1752159/4997436 [00:17<00:32, 98757.97it/s]" ] }, { @@ -1946,7 +1946,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2351946/4997436 [00:17<00:19, 133858.30it/s]" + " 35%|███▌ | 1762189/4997436 [00:17<00:32, 99217.12it/s]" ] }, { @@ -1954,7 +1954,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2365462/4997436 [00:17<00:19, 134245.92it/s]" + " 35%|███▌ | 1772512/4997436 [00:17<00:32, 100417.10it/s]" ] }, { @@ -1962,7 +1962,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2378985/4997436 [00:17<00:19, 134537.92it/s]" + " 36%|███▌ | 1783009/4997436 [00:18<00:31, 101778.46it/s]" ] }, { @@ -1970,7 +1970,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2392440/4997436 [00:17<00:19, 134534.37it/s]" + " 36%|███▌ | 1793188/4997436 [00:18<00:31, 100781.01it/s]" ] }, { @@ -1978,7 +1978,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2405987/4997436 [00:18<00:19, 134813.79it/s]" + " 36%|███▌ | 1803269/4997436 [00:18<00:32, 99457.13it/s] " ] }, { @@ -1986,7 +1986,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2419567/4997436 [00:18<00:19, 135106.01it/s]" + " 36%|███▋ | 1813220/4997436 [00:18<00:32, 99339.05it/s]" ] }, { @@ -1994,7 +1994,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▊ | 2433078/4997436 [00:18<00:18, 135050.85it/s]" + " 36%|███▋ | 1823537/4997436 [00:18<00:31, 100468.65it/s]" ] }, { @@ -2002,7 +2002,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2446584/4997436 [00:18<00:18, 134866.58it/s]" + " 37%|███▋ | 1834006/4997436 [00:18<00:31, 101703.01it/s]" ] }, { @@ -2010,7 +2010,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2460071/4997436 [00:18<00:18, 134632.22it/s]" + " 37%|███▋ | 1844180/4997436 [00:18<00:31, 100432.47it/s]" ] }, { @@ -2018,7 +2018,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2473709/4997436 [00:18<00:18, 135151.90it/s]" + " 37%|███▋ | 1855214/4997436 [00:18<00:30, 103363.95it/s]" ] }, { @@ -2026,7 +2026,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2487311/4997436 [00:18<00:18, 135409.27it/s]" + " 37%|███▋ | 1866461/4997436 [00:18<00:29, 106062.57it/s]" ] }, { @@ -2034,7 +2034,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2500853/4997436 [00:18<00:18, 135080.90it/s]" + " 38%|███▊ | 1877075/4997436 [00:18<00:29, 106079.05it/s]" ] }, { @@ -2042,7 +2042,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2514362/4997436 [00:18<00:18, 134742.47it/s]" + " 38%|███▊ | 1888248/4997436 [00:19<00:28, 107763.01it/s]" ] }, { @@ -2050,7 +2050,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2527837/4997436 [00:18<00:18, 134260.26it/s]" + " 38%|███▊ | 1899029/4997436 [00:19<00:30, 103252.16it/s]" ] }, { @@ -2058,7 +2058,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2541316/4997436 [00:19<00:18, 134416.13it/s]" + " 38%|███▊ | 1909397/4997436 [00:19<00:30, 101240.68it/s]" ] }, { @@ -2066,7 +2066,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2554917/4997436 [00:19<00:18, 134891.12it/s]" + " 38%|███▊ | 1919554/4997436 [00:19<00:30, 100540.05it/s]" ] }, { @@ -2074,7 +2074,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 2568407/4997436 [00:19<00:18, 134659.58it/s]" + " 39%|███▊ | 1930051/4997436 [00:19<00:30, 101824.90it/s]" ] }, { @@ -2082,7 +2082,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2581897/4997436 [00:19<00:17, 134727.11it/s]" + " 39%|███▉ | 1940505/4997436 [00:19<00:29, 102619.55it/s]" ] }, { @@ -2090,7 +2090,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2595370/4997436 [00:19<00:17, 134642.48it/s]" + " 39%|███▉ | 1950783/4997436 [00:19<00:30, 101183.89it/s]" ] }, { @@ -2098,7 +2098,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2608835/4997436 [00:19<00:17, 134079.46it/s]" + " 39%|███▉ | 1961846/4997436 [00:19<00:29, 103955.15it/s]" ] }, { @@ -2106,7 +2106,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2622384/4997436 [00:19<00:17, 134497.98it/s]" + " 39%|███▉ | 1972963/4997436 [00:19<00:28, 106085.38it/s]" ] }, { @@ -2114,7 +2114,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2635835/4997436 [00:19<00:17, 133948.65it/s]" + " 40%|███▉ | 1983600/4997436 [00:20<00:28, 106157.35it/s]" ] }, { @@ -2122,7 +2122,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2649231/4997436 [00:19<00:17, 133602.93it/s]" + " 40%|███▉ | 1994226/4997436 [00:20<00:29, 103300.81it/s]" ] }, { @@ -2130,7 +2130,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2662592/4997436 [00:19<00:17, 132682.81it/s]" + " 40%|████ | 2004579/4997436 [00:20<00:29, 100599.06it/s]" ] }, { @@ -2138,7 +2138,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 2675862/4997436 [00:20<00:17, 131904.77it/s]" + " 40%|████ | 2014665/4997436 [00:20<00:30, 96250.17it/s] " ] }, { @@ -2146,7 +2146,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2689080/4997436 [00:20<00:17, 131983.83it/s]" + " 41%|████ | 2024336/4997436 [00:20<00:30, 96343.59it/s]" ] }, { @@ -2154,7 +2154,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2702491/4997436 [00:20<00:17, 132615.00it/s]" + " 41%|████ | 2034199/4997436 [00:20<00:30, 96999.90it/s]" ] }, { @@ -2162,7 +2162,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2715877/4997436 [00:20<00:17, 132982.41it/s]" + " 41%|████ | 2044041/4997436 [00:20<00:30, 97405.15it/s]" ] }, { @@ -2170,7 +2170,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2729349/4997436 [00:20<00:16, 133497.89it/s]" + " 41%|████ | 2053801/4997436 [00:20<00:30, 96578.36it/s]" ] }, { @@ -2178,7 +2178,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2742700/4997436 [00:20<00:16, 133496.60it/s]" + " 41%|████▏ | 2063528/4997436 [00:20<00:30, 96779.86it/s]" ] }, { @@ -2186,7 +2186,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2756255/4997436 [00:20<00:16, 134109.74it/s]" + " 41%|████▏ | 2073431/4997436 [00:20<00:30, 97440.90it/s]" ] }, { @@ -2194,7 +2194,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2769678/4997436 [00:20<00:16, 134143.61it/s]" + " 42%|████▏ | 2083184/4997436 [00:21<00:29, 97464.83it/s]" ] }, { @@ -2202,7 +2202,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2783093/4997436 [00:20<00:16, 134130.03it/s]" + " 42%|████▏ | 2093032/4997436 [00:21<00:29, 97763.13it/s]" ] }, { @@ -2210,7 +2210,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2796507/4997436 [00:20<00:16, 134129.03it/s]" + " 42%|████▏ | 2102813/4997436 [00:21<00:29, 97400.36it/s]" ] }, { @@ -2218,7 +2218,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2809976/4997436 [00:21<00:16, 134294.01it/s]" + " 42%|████▏ | 2112596/4997436 [00:21<00:29, 97522.37it/s]" ] }, { @@ -2226,7 +2226,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2823415/4997436 [00:21<00:16, 134319.19it/s]" + " 42%|████▏ | 2122351/4997436 [00:21<00:29, 97236.86it/s]" ] }, { @@ -2234,7 +2234,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2836848/4997436 [00:21<00:16, 134057.32it/s]" + " 43%|████▎ | 2132134/4997436 [00:21<00:29, 97410.92it/s]" ] }, { @@ -2242,7 +2242,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2850254/4997436 [00:21<00:16, 133690.98it/s]" + " 43%|████▎ | 2142029/4997436 [00:21<00:29, 97868.75it/s]" ] }, { @@ -2250,7 +2250,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2863624/4997436 [00:21<00:16, 132386.22it/s]" + " 43%|████▎ | 2151923/4997436 [00:21<00:28, 98180.70it/s]" ] }, { @@ -2258,7 +2258,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2876866/4997436 [00:21<00:16, 132177.81it/s]" + " 43%|████▎ | 2161786/4997436 [00:21<00:28, 98312.11it/s]" ] }, { @@ -2266,7 +2266,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2890167/4997436 [00:21<00:15, 132421.98it/s]" + " 43%|████▎ | 2171618/4997436 [00:21<00:28, 97497.87it/s]" ] }, { @@ -2274,7 +2274,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2903505/4997436 [00:21<00:15, 132704.89it/s]" + " 44%|████▎ | 2181549/4997436 [00:22<00:28, 98034.45it/s]" ] }, { @@ -2282,7 +2282,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2916777/4997436 [00:21<00:15, 132370.15it/s]" + " 44%|████▍ | 2191355/4997436 [00:22<00:29, 96535.66it/s]" ] }, { @@ -2290,7 +2290,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▊ | 2930027/4997436 [00:21<00:15, 132407.74it/s]" + " 44%|████▍ | 2201845/4997436 [00:22<00:28, 98999.63it/s]" ] }, { @@ -2298,7 +2298,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2943269/4997436 [00:22<00:15, 131845.77it/s]" + " 44%|████▍ | 2212070/4997436 [00:22<00:27, 99962.61it/s]" ] }, { @@ -2306,7 +2306,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2956489/4997436 [00:22<00:15, 131947.12it/s]" + " 44%|████▍ | 2222182/4997436 [00:22<00:27, 100304.21it/s]" ] }, { @@ -2314,7 +2314,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2969685/4997436 [00:22<00:15, 131859.57it/s]" + " 45%|████▍ | 2232596/4997436 [00:22<00:27, 101446.09it/s]" ] }, { @@ -2322,7 +2322,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2982900/4997436 [00:22<00:15, 131943.74it/s]" + " 45%|████▍ | 2242807/4997436 [00:22<00:27, 101642.64it/s]" ] }, { @@ -2330,7 +2330,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2996227/4997436 [00:22<00:15, 132335.71it/s]" + " 45%|████▌ | 2253202/4997436 [00:22<00:26, 102331.64it/s]" ] }, { @@ -2338,7 +2338,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3009513/4997436 [00:22<00:15, 132490.05it/s]" + " 45%|████▌ | 2263438/4997436 [00:22<00:27, 100462.44it/s]" ] }, { @@ -2346,7 +2346,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3022788/4997436 [00:22<00:14, 132565.91it/s]" + " 45%|████▌ | 2273494/4997436 [00:22<00:27, 100159.59it/s]" ] }, { @@ -2354,7 +2354,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3036183/4997436 [00:22<00:14, 132976.58it/s]" + " 46%|████▌ | 2283530/4997436 [00:23<00:27, 100217.23it/s]" ] }, { @@ -2362,7 +2362,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3049556/4997436 [00:22<00:14, 133198.33it/s]" + " 46%|████▌ | 2293556/4997436 [00:23<00:27, 98673.23it/s] " ] }, { @@ -2370,7 +2370,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████▏ | 3062876/4997436 [00:22<00:14, 133023.20it/s]" + " 46%|████▌ | 2303431/4997436 [00:23<00:27, 97093.16it/s]" ] }, { @@ -2378,7 +2378,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3076211/4997436 [00:23<00:14, 133117.83it/s]" + " 46%|████▋ | 2314120/4997436 [00:23<00:26, 99960.63it/s]" ] }, { @@ -2386,7 +2386,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3089587/4997436 [00:23<00:14, 133306.45it/s]" + " 47%|████▋ | 2325330/4997436 [00:23<00:25, 103533.89it/s]" ] }, { @@ -2394,7 +2394,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3102918/4997436 [00:23<00:14, 133284.69it/s]" + " 47%|████▋ | 2335784/4997436 [00:23<00:25, 103812.95it/s]" ] }, { @@ -2402,7 +2402,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3116247/4997436 [00:23<00:14, 133189.49it/s]" + " 47%|████▋ | 2346176/4997436 [00:23<00:25, 102037.53it/s]" ] }, { @@ -2410,7 +2410,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3129649/4997436 [00:23<00:13, 133435.98it/s]" + " 47%|████▋ | 2356416/4997436 [00:23<00:25, 102142.40it/s]" ] }, { @@ -2418,7 +2418,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3143022/4997436 [00:23<00:13, 133522.54it/s]" + " 47%|████▋ | 2366981/4997436 [00:23<00:25, 103177.28it/s]" ] }, { @@ -2426,7 +2426,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3156441/4997436 [00:23<00:13, 133719.76it/s]" + " 48%|████▊ | 2378336/4997436 [00:23<00:24, 106257.59it/s]" ] }, { @@ -2434,7 +2434,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3169814/4997436 [00:23<00:13, 133660.85it/s]" + " 48%|████▊ | 2388970/4997436 [00:24<00:25, 102909.93it/s]" ] }, { @@ -2442,7 +2442,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 3183262/4997436 [00:23<00:13, 133904.29it/s]" + " 48%|████▊ | 2399289/4997436 [00:24<00:26, 99912.33it/s] " ] }, { @@ -2450,7 +2450,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3196702/4997436 [00:23<00:13, 134050.97it/s]" + " 48%|████▊ | 2409312/4997436 [00:24<00:26, 98589.45it/s]" ] }, { @@ -2458,7 +2458,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3210108/4997436 [00:24<00:13, 134029.95it/s]" + " 48%|████▊ | 2419193/4997436 [00:24<00:26, 97717.22it/s]" ] }, { @@ -2466,7 +2466,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3223512/4997436 [00:24<00:13, 133841.70it/s]" + " 49%|████▊ | 2428979/4997436 [00:24<00:26, 97363.73it/s]" ] }, { @@ -2474,7 +2474,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3236981/4997436 [00:24<00:13, 134091.41it/s]" + " 49%|████▉ | 2438724/4997436 [00:24<00:26, 97205.79it/s]" ] }, { @@ -2482,7 +2482,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3250391/4997436 [00:24<00:13, 133748.12it/s]" + " 49%|████▉ | 2448451/4997436 [00:24<00:26, 96628.16it/s]" ] }, { @@ -2490,7 +2490,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3263907/4997436 [00:24<00:12, 134165.91it/s]" + " 49%|████▉ | 2458225/4997436 [00:24<00:26, 96951.88it/s]" ] }, { @@ -2498,7 +2498,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3277347/4997436 [00:24<00:12, 134232.89it/s]" + " 49%|████▉ | 2467924/4997436 [00:24<00:26, 95707.86it/s]" ] }, { @@ -2506,7 +2506,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3290914/4997436 [00:24<00:12, 134660.84it/s]" + " 50%|████▉ | 2477711/4997436 [00:25<00:26, 96340.51it/s]" ] }, { @@ -2514,7 +2514,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3304381/4997436 [00:24<00:12, 134044.23it/s]" + " 50%|████▉ | 2487350/4997436 [00:25<00:26, 96274.43it/s]" ] }, { @@ -2522,7 +2522,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▋ | 3317787/4997436 [00:24<00:12, 133970.14it/s]" + " 50%|████▉ | 2496981/4997436 [00:25<00:25, 96184.12it/s]" ] }, { @@ -2530,7 +2530,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3331185/4997436 [00:24<00:12, 133557.91it/s]" + " 50%|█████ | 2506602/4997436 [00:25<00:26, 95384.34it/s]" ] }, { @@ -2538,7 +2538,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3344542/4997436 [00:25<00:12, 133176.88it/s]" + " 50%|█████ | 2516498/4997436 [00:25<00:25, 96442.43it/s]" ] }, { @@ -2546,7 +2546,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3357861/4997436 [00:25<00:12, 132697.62it/s]" + " 51%|█████ | 2526146/4997436 [00:25<00:25, 96013.40it/s]" ] }, { @@ -2554,7 +2554,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3371132/4997436 [00:25<00:12, 132335.67it/s]" + " 51%|█████ | 2535934/4997436 [00:25<00:25, 96565.33it/s]" ] }, { @@ -2562,7 +2562,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3384525/4997436 [00:25<00:12, 132807.27it/s]" + " 51%|█████ | 2545644/4997436 [00:25<00:25, 96716.39it/s]" ] }, { @@ -2570,7 +2570,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3397957/4997436 [00:25<00:12, 133256.87it/s]" + " 51%|█████ | 2555318/4997436 [00:25<00:25, 96277.40it/s]" ] }, { @@ -2578,7 +2578,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3411449/4997436 [00:25<00:11, 133749.64it/s]" + " 51%|█████▏ | 2565176/4997436 [00:25<00:25, 96960.01it/s]" ] }, { @@ -2586,7 +2586,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 3424839/4997436 [00:25<00:11, 133792.78it/s]" + " 52%|█████▏ | 2574874/4997436 [00:26<00:25, 96690.60it/s]" ] }, { @@ -2594,7 +2594,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3438219/4997436 [00:25<00:11, 133714.58it/s]" + " 52%|█████▏ | 2584545/4997436 [00:26<00:25, 93995.06it/s]" ] }, { @@ -2602,7 +2602,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3451591/4997436 [00:25<00:11, 133406.11it/s]" + " 52%|█████▏ | 2594401/4997436 [00:26<00:25, 95330.54it/s]" ] }, { @@ -2610,7 +2610,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3464932/4997436 [00:25<00:11, 132435.85it/s]" + " 52%|█████▏ | 2604274/4997436 [00:26<00:24, 96330.49it/s]" ] }, { @@ -2618,7 +2618,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3478197/4997436 [00:26<00:11, 132497.90it/s]" + " 52%|█████▏ | 2613919/4997436 [00:26<00:24, 95952.49it/s]" ] }, { @@ -2626,7 +2626,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3491457/4997436 [00:26<00:11, 132526.72it/s]" + " 53%|█████▎ | 2623856/4997436 [00:26<00:24, 96963.29it/s]" ] }, { @@ -2634,7 +2634,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3504790/4997436 [00:26<00:11, 132765.02it/s]" + " 53%|█████▎ | 2634484/4997436 [00:26<00:23, 99734.04it/s]" ] }, { @@ -2642,7 +2642,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3518068/4997436 [00:26<00:11, 132642.24it/s]" + " 53%|█████▎ | 2646011/4997436 [00:26<00:22, 104366.29it/s]" ] }, { @@ -2650,7 +2650,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3531511/4997436 [00:26<00:11, 133173.63it/s]" + " 53%|█████▎ | 2656906/4997436 [00:26<00:22, 105734.13it/s]" ] }, { @@ -2658,7 +2658,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3544829/4997436 [00:26<00:10, 133154.28it/s]" + " 53%|█████▎ | 2667486/4997436 [00:26<00:22, 104688.49it/s]" ] }, { @@ -2666,7 +2666,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3558306/4997436 [00:26<00:10, 133636.50it/s]" + " 54%|█████▎ | 2677961/4997436 [00:27<00:22, 102063.08it/s]" ] }, { @@ -2674,7 +2674,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 3571864/4997436 [00:26<00:10, 134217.43it/s]" + " 54%|█████▍ | 2688425/4997436 [00:27<00:22, 102809.99it/s]" ] }, { @@ -2682,7 +2682,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3585387/4997436 [00:26<00:10, 134519.31it/s]" + " 54%|█████▍ | 2698811/4997436 [00:27<00:22, 103117.56it/s]" ] }, { @@ -2690,7 +2690,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3598908/4997436 [00:26<00:10, 134723.47it/s]" + " 54%|█████▍ | 2709133/4997436 [00:27<00:22, 101988.71it/s]" ] }, { @@ -2698,7 +2698,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3612381/4997436 [00:27<00:10, 134546.92it/s]" + " 54%|█████▍ | 2719341/4997436 [00:27<00:22, 100958.52it/s]" ] }, { @@ -2706,7 +2706,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3625836/4997436 [00:27<00:10, 133988.47it/s]" + " 55%|█████▍ | 2729444/4997436 [00:27<00:22, 100669.75it/s]" ] }, { @@ -2714,7 +2714,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3639471/4997436 [00:27<00:10, 134691.73it/s]" + " 55%|█████▍ | 2739516/4997436 [00:27<00:22, 100675.19it/s]" ] }, { @@ -2722,7 +2722,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3652941/4997436 [00:27<00:09, 134555.35it/s]" + " 55%|█████▌ | 2750369/4997436 [00:27<00:21, 103001.02it/s]" ] }, { @@ -2730,7 +2730,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3666549/4997436 [00:27<00:09, 135008.14it/s]" + " 55%|█████▌ | 2760674/4997436 [00:27<00:21, 102275.09it/s]" ] }, { @@ -2738,7 +2738,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3680051/4997436 [00:27<00:09, 134482.21it/s]" + " 55%|█████▌ | 2771747/4997436 [00:27<00:21, 104784.01it/s]" ] }, { @@ -2746,7 +2746,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3693500/4997436 [00:27<00:09, 134233.75it/s]" + " 56%|█████▌ | 2782231/4997436 [00:28<00:21, 102748.69it/s]" ] }, { @@ -2754,7 +2754,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3707000/4997436 [00:27<00:09, 134459.27it/s]" + " 56%|█████▌ | 2792518/4997436 [00:28<00:21, 101339.07it/s]" ] }, { @@ -2762,7 +2762,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3720525/4997436 [00:27<00:09, 134694.11it/s]" + " 56%|█████▌ | 2802662/4997436 [00:28<00:22, 98827.73it/s] " ] }, { @@ -2770,7 +2770,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3733995/4997436 [00:28<00:09, 134530.94it/s]" + " 56%|█████▋ | 2813189/4997436 [00:28<00:21, 100692.84it/s]" ] }, { @@ -2778,7 +2778,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3747449/4997436 [00:28<00:09, 134422.51it/s]" + " 56%|█████▋ | 2823402/4997436 [00:28<00:21, 101111.80it/s]" ] }, { @@ -2786,7 +2786,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3760892/4997436 [00:28<00:09, 133983.49it/s]" + " 57%|█████▋ | 2833556/4997436 [00:28<00:21, 101231.22it/s]" ] }, { @@ -2794,7 +2794,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3774330/4997436 [00:28<00:09, 134093.42it/s]" + " 57%|█████▋ | 2843689/4997436 [00:28<00:21, 99794.11it/s] " ] }, { @@ -2802,7 +2802,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3787740/4997436 [00:28<00:09, 134024.69it/s]" + " 57%|█████▋ | 2853679/4997436 [00:28<00:21, 99277.17it/s]" ] }, { @@ -2810,7 +2810,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3801274/4997436 [00:28<00:08, 134417.08it/s]" + " 57%|█████▋ | 2863614/4997436 [00:28<00:21, 98800.60it/s]" ] }, { @@ -2818,7 +2818,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 3814775/4997436 [00:28<00:08, 134591.82it/s]" + " 57%|█████▋ | 2873499/4997436 [00:29<00:21, 97409.33it/s]" ] }, { @@ -2826,7 +2826,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3828380/4997436 [00:28<00:08, 135026.70it/s]" + " 58%|█████▊ | 2883246/4997436 [00:29<00:22, 92791.55it/s]" ] }, { @@ -2834,7 +2834,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3841883/4997436 [00:28<00:08, 134729.81it/s]" + " 58%|█████▊ | 2892568/4997436 [00:29<00:23, 89686.43it/s]" ] }, { @@ -2842,7 +2842,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3855357/4997436 [00:28<00:08, 134252.92it/s]" + " 58%|█████▊ | 2901884/4997436 [00:29<00:23, 90665.76it/s]" ] }, { @@ -2850,7 +2850,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3868783/4997436 [00:29<00:08, 133248.17it/s]" + " 58%|█████▊ | 2911728/4997436 [00:29<00:22, 92897.25it/s]" ] }, { @@ -2858,7 +2858,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3882171/4997436 [00:29<00:08, 133433.68it/s]" + " 58%|█████▊ | 2921533/4997436 [00:29<00:21, 94394.63it/s]" ] }, { @@ -2866,7 +2866,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3895633/4997436 [00:29<00:08, 133785.70it/s]" + " 59%|█████▊ | 2931362/4997436 [00:29<00:21, 95535.13it/s]" ] }, { @@ -2874,7 +2874,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3909013/4997436 [00:29<00:08, 133656.32it/s]" + " 59%|█████▉ | 2940938/4997436 [00:29<00:22, 92446.28it/s]" ] }, { @@ -2882,7 +2882,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3922562/4997436 [00:29<00:08, 134201.11it/s]" + " 59%|█████▉ | 2950588/4997436 [00:29<00:21, 93620.30it/s]" ] }, { @@ -2890,7 +2890,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3935983/4997436 [00:29<00:07, 133737.01it/s]" + " 59%|█████▉ | 2960040/4997436 [00:29<00:21, 93862.40it/s]" ] }, { @@ -2898,7 +2898,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3949443/4997436 [00:29<00:07, 133992.67it/s]" + " 59%|█████▉ | 2969715/4997436 [00:30<00:21, 94710.29it/s]" ] }, { @@ -2906,7 +2906,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3962863/4997436 [00:29<00:07, 134052.14it/s]" + " 60%|█████▉ | 2979511/4997436 [00:30<00:21, 95666.87it/s]" ] }, { @@ -2914,7 +2914,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3976271/4997436 [00:29<00:07, 134057.46it/s]" + " 60%|█████▉ | 2989090/4997436 [00:30<00:21, 95487.15it/s]" ] }, { @@ -2922,7 +2922,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3989678/4997436 [00:29<00:07, 133875.47it/s]" + " 60%|██████ | 2998647/4997436 [00:30<00:21, 95129.74it/s]" ] }, { @@ -2930,7 +2930,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4003066/4997436 [00:30<00:07, 133538.47it/s]" + " 60%|██████ | 3008233/4997436 [00:30<00:20, 95344.92it/s]" ] }, { @@ -2938,7 +2938,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4016530/4997436 [00:30<00:07, 133865.63it/s]" + " 60%|██████ | 3017781/4997436 [00:30<00:20, 95382.66it/s]" ] }, { @@ -2946,7 +2946,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4029983/4997436 [00:30<00:07, 134060.90it/s]" + " 61%|██████ | 3027652/4997436 [00:30<00:20, 96374.99it/s]" ] }, { @@ -2954,7 +2954,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4043390/4997436 [00:30<00:07, 133832.47it/s]" + " 61%|██████ | 3037489/4997436 [00:30<00:20, 96965.41it/s]" ] }, { @@ -2962,7 +2962,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4056774/4997436 [00:30<00:07, 133718.73it/s]" + " 61%|██████ | 3047303/4997436 [00:30<00:20, 97314.19it/s]" ] }, { @@ -2970,7 +2970,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4070175/4997436 [00:30<00:06, 133803.28it/s]" + " 61%|██████ | 3057872/4997436 [00:30<00:19, 99821.72it/s]" ] }, { @@ -2978,7 +2978,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4083648/4997436 [00:30<00:06, 134079.08it/s]" + " 61%|██████▏ | 3067856/4997436 [00:31<00:19, 99331.54it/s]" ] }, { @@ -2986,7 +2986,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4097057/4997436 [00:30<00:06, 133799.81it/s]" + " 62%|██████▏ | 3077791/4997436 [00:31<00:19, 97863.94it/s]" ] }, { @@ -2994,7 +2994,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4110445/4997436 [00:30<00:06, 133819.46it/s]" + " 62%|██████▏ | 3087583/4997436 [00:31<00:19, 97755.42it/s]" ] }, { @@ -3002,7 +3002,7 @@ "output_type": "stream", "text": [ "\r", - " 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"text": [ + "\r", + "100%|█████████▉| 4980262/4997436 [00:50<00:00, 101902.52it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4990796/4997436 [00:50<00:00, 102924.23it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 4997436/4997436 [00:50<00:00, 98745.72it/s] " ] }, { @@ -3769,10 +4761,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:31.182966Z", - "iopub.status.busy": "2023-08-14T16:44:31.182564Z", - "iopub.status.idle": "2023-08-14T16:44:43.079410Z", - "shell.execute_reply": "2023-08-14T16:44:43.075978Z" + "iopub.execute_input": "2023-08-15T04:19:19.648053Z", + "iopub.status.busy": "2023-08-15T04:19:19.647752Z", + "iopub.status.idle": "2023-08-15T04:19:33.479380Z", + "shell.execute_reply": "2023-08-15T04:19:33.474545Z" } }, "outputs": [], @@ -3786,10 +4778,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:43.087336Z", - "iopub.status.busy": "2023-08-14T16:44:43.086338Z", - "iopub.status.idle": "2023-08-14T16:44:47.501499Z", - "shell.execute_reply": "2023-08-14T16:44:47.500617Z" + "iopub.execute_input": "2023-08-15T04:19:33.510002Z", + "iopub.status.busy": "2023-08-15T04:19:33.509461Z", + "iopub.status.idle": "2023-08-15T04:19:39.214389Z", + "shell.execute_reply": "2023-08-15T04:19:39.211982Z" } }, "outputs": [ @@ -3858,17 +4850,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:47.505607Z", - "iopub.status.busy": "2023-08-14T16:44:47.504971Z", - "iopub.status.idle": "2023-08-14T16:44:49.943168Z", - "shell.execute_reply": "2023-08-14T16:44:49.942102Z" + "iopub.execute_input": "2023-08-15T04:19:39.222168Z", + "iopub.status.busy": "2023-08-15T04:19:39.221577Z", + "iopub.status.idle": "2023-08-15T04:19:42.694909Z", + "shell.execute_reply": "2023-08-15T04:19:42.693802Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "517e39fd77f4484b90525fcded82b444", + "model_id": "622c4463e94d45d98eee2008d6ca938b", "version_major": 2, "version_minor": 0 }, @@ -3898,10 +4890,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:49.947215Z", - "iopub.status.busy": "2023-08-14T16:44:49.946784Z", - "iopub.status.idle": "2023-08-14T16:44:50.231957Z", - "shell.execute_reply": "2023-08-14T16:44:50.230385Z" + "iopub.execute_input": "2023-08-15T04:19:42.702534Z", + "iopub.status.busy": "2023-08-15T04:19:42.701769Z", + "iopub.status.idle": "2023-08-15T04:19:43.094484Z", + "shell.execute_reply": "2023-08-15T04:19:43.093317Z" } }, "outputs": [], @@ -3915,10 +4907,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:44:50.236508Z", - "iopub.status.busy": "2023-08-14T16:44:50.235999Z", - "iopub.status.idle": "2023-08-14T16:44:57.439232Z", - "shell.execute_reply": "2023-08-14T16:44:57.438425Z" + "iopub.execute_input": "2023-08-15T04:19:43.099368Z", + "iopub.status.busy": "2023-08-15T04:19:43.099052Z", + "iopub.status.idle": "2023-08-15T04:19:53.166865Z", + "shell.execute_reply": "2023-08-15T04:19:53.165809Z" } }, "outputs": [ @@ -3944,7 +4936,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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[00:02<00:00, 139541.06it/s]" - } - }, - "ddcc9e9cbe0c4ea4b5ebc2f28ab83085": { + "d52791b627f14da99bf4a1ad520ee9be": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4915,28 +5928,7 @@ "description_width": "" } }, - "e08c49dfadc040e1a31c85a1edb0999f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_bdc5d06e10d54b1a82da8050a1b86d32", - "placeholder": "​", - "style": "IPY_MODEL_7bc8a29ea1234c1e917f9f9441210d8e", - "value": " 300000/? [00:00<00:00, 3930612.32it/s]" - } - }, - "e1e8e9843f324af3b694fcb057809af5": { + "dfe5f701fec64b9fbc4f1c428587d056": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4988,7 +5980,7 @@ "width": null } }, - "eff5319bbd124c288123e6a103f4e01a": { + "e3a54f003870461da663fbd118bf8f5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5040,7 +6032,7 @@ "width": null } }, - "f89f10b5b639480c9ce8eb6ad81f9768": { + "e83cc4cddf724fd5816d01235b606067": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -5056,12 +6048,12 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_590c407b5d1e499a9903f93c6fa9fc5a", - "max": 244800.0, + "layout": "IPY_MODEL_024419d32420457aa48ecc1e63e1716d", + "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_c45fbbe6373a4208bdbf3c67ad4d5fa8", - "value": 244800.0 + "style": "IPY_MODEL_a26bb5ecf4c746029a338cb609f3df6e", + "value": 30.0 } } }, diff --git a/master/tutorials/tabular.html b/master/tutorials/tabular.html index 85b44110a..8ec8f187f 100644 --- a/master/tutorials/tabular.html +++ b/master/tutorials/tabular.html @@ -1082,44 +1082,44 @@

    4. Use cleanlab to find label issues\n", " \n", " \n", - " 928\n", + " 456\n", + " 58.0\n", + " 92.0\n", + " 93.0\n", + " NaN\n", + " D\n", + " \n", + " \n", + " 827\n", + " 99.0\n", + " 86.0\n", " 74.0\n", - " 72.0\n", - " 97.0\n", - " missed class frequently -10\n", - " F\n", + " NaN\n", + " D\n", " \n", " \n", - " 911\n", - " 90.0\n", + " 637\n", " 0.0\n", - " 88.0\n", + " 79.0\n", + " 65.0\n", " cheated on exam, gets 0pts\n", " A\n", " \n", " \n", - " 864\n", - " 71.0\n", - " 78.0\n", - " 80.0\n", - " great final presentation +10\n", - " D\n", + " 120\n", + " 0.0\n", + " 81.0\n", + " 97.0\n", + " cheated on exam, gets 0pts\n", + " B\n", " \n", " \n", - " 83\n", + " 233\n", " 68.0\n", - " 99.0\n", - " 91.0\n", - " NaN\n", - " D\n", - " \n", - " \n", - " 498\n", - " 87.0\n", - " 64.0\n", - " 94.0\n", + " 83.0\n", + " 76.0\n", " NaN\n", - " D\n", + " F\n", " \n", " \n", "\n", "

    " ], "text/plain": [ - " exam_1 exam_2 exam_3 notes label\n", - "928 74.0 72.0 97.0 missed class frequently -10 F\n", - "911 90.0 0.0 88.0 cheated on exam, gets 0pts A\n", - "864 71.0 78.0 80.0 great final presentation +10 D\n", - "83 68.0 99.0 91.0 NaN D\n", - "498 87.0 64.0 94.0 NaN D" + " exam_1 exam_2 exam_3 notes label\n", + "456 58.0 92.0 93.0 NaN D\n", + "827 99.0 86.0 74.0 NaN D\n", + "637 0.0 79.0 65.0 cheated on exam, gets 0pts A\n", + "120 0.0 81.0 97.0 cheated on exam, gets 0pts B\n", + "233 68.0 83.0 76.0 NaN F" ] }, "execution_count": 9, @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.789319Z", - "iopub.status.busy": "2023-08-14T16:45:06.788762Z", - "iopub.status.idle": "2023-08-14T16:45:06.794264Z", - "shell.execute_reply": "2023-08-14T16:45:06.793584Z" + "iopub.execute_input": "2023-08-15T04:20:06.211561Z", + "iopub.status.busy": "2023-08-15T04:20:06.210509Z", + "iopub.status.idle": "2023-08-15T04:20:06.217973Z", + "shell.execute_reply": "2023-08-15T04:20:06.216954Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.797643Z", - "iopub.status.busy": "2023-08-14T16:45:06.797186Z", - "iopub.status.idle": "2023-08-14T16:45:06.807397Z", - "shell.execute_reply": "2023-08-14T16:45:06.806826Z" + "iopub.execute_input": "2023-08-15T04:20:06.222709Z", + "iopub.status.busy": "2023-08-15T04:20:06.222299Z", + "iopub.status.idle": "2023-08-15T04:20:06.238142Z", + "shell.execute_reply": "2023-08-15T04:20:06.236857Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.810314Z", - "iopub.status.busy": "2023-08-14T16:45:06.809852Z", - "iopub.status.idle": "2023-08-14T16:45:06.982629Z", - "shell.execute_reply": "2023-08-14T16:45:06.981151Z" + "iopub.execute_input": "2023-08-15T04:20:06.242192Z", + "iopub.status.busy": "2023-08-15T04:20:06.241643Z", + "iopub.status.idle": "2023-08-15T04:20:06.458238Z", + "shell.execute_reply": "2023-08-15T04:20:06.457110Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.986137Z", - "iopub.status.busy": "2023-08-14T16:45:06.985871Z", - "iopub.status.idle": "2023-08-14T16:45:06.990360Z", - "shell.execute_reply": "2023-08-14T16:45:06.989712Z" + "iopub.execute_input": "2023-08-15T04:20:06.462261Z", + "iopub.status.busy": "2023-08-15T04:20:06.461593Z", + "iopub.status.idle": "2023-08-15T04:20:06.466598Z", + "shell.execute_reply": "2023-08-15T04:20:06.465415Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:06.993331Z", - "iopub.status.busy": "2023-08-14T16:45:06.993093Z", - "iopub.status.idle": "2023-08-14T16:45:09.063219Z", - "shell.execute_reply": "2023-08-14T16:45:09.062038Z" + "iopub.execute_input": "2023-08-15T04:20:06.470199Z", + "iopub.status.busy": "2023-08-15T04:20:06.469687Z", + "iopub.status.idle": "2023-08-15T04:20:09.621754Z", + "shell.execute_reply": "2023-08-15T04:20:09.620225Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:09.068004Z", - "iopub.status.busy": "2023-08-14T16:45:09.067444Z", - "iopub.status.idle": "2023-08-14T16:45:09.084829Z", - "shell.execute_reply": "2023-08-14T16:45:09.084177Z" + "iopub.execute_input": "2023-08-15T04:20:09.626468Z", + "iopub.status.busy": "2023-08-15T04:20:09.626130Z", + "iopub.status.idle": "2023-08-15T04:20:09.662323Z", + "shell.execute_reply": "2023-08-15T04:20:09.661202Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:09.087955Z", - "iopub.status.busy": "2023-08-14T16:45:09.087485Z", - "iopub.status.idle": "2023-08-14T16:45:09.125418Z", - "shell.execute_reply": "2023-08-14T16:45:09.124680Z" + "iopub.execute_input": "2023-08-15T04:20:09.666467Z", + "iopub.status.busy": "2023-08-15T04:20:09.666145Z", + "iopub.status.idle": "2023-08-15T04:20:09.772343Z", + "shell.execute_reply": "2023-08-15T04:20:09.771225Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/text.html b/master/tutorials/text.html index c754078cb..8ce204264 100644 --- a/master/tutorials/text.html +++ b/master/tutorials/text.html @@ -951,7 +951,7 @@

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies'}
+Classes: {'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'change_pin', 'getting_spare_card', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_about_to_expire', 'cancel_transfer'}

    Let’s print the first example in the train set.

    @@ -1016,7 +1016,7 @@

    2. Load and format the text dataset 
     No sentence-transformers model found with name /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator. Creating a new one with MEAN pooling.
-Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']
+Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight']
 - This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
 - This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
    @@ -1098,35 +1098,35 @@

    3. Define a classification model and use cleanlab to find potential label er 0 False - 0.858229 + 0.858293 6 6 1 False - 0.547047 + 0.546974 3 3 2 False - 0.823594 + 0.823609 7 7 3 False - 0.968831 + 0.968828 8 8 4 False - 0.792000 + 0.791938 4 4 diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb index 488e3aa30..cf948214b 100644 --- a/master/tutorials/text.ipynb +++ b/master/tutorials/text.ipynb @@ -116,10 +116,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:14.130347Z", - "iopub.status.busy": "2023-08-14T16:45:14.130050Z", - "iopub.status.idle": "2023-08-14T16:45:16.923847Z", - "shell.execute_reply": "2023-08-14T16:45:16.923063Z" + "iopub.execute_input": "2023-08-15T04:20:16.502415Z", + "iopub.status.busy": "2023-08-15T04:20:16.502140Z", + "iopub.status.idle": "2023-08-15T04:20:20.282952Z", + "shell.execute_reply": "2023-08-15T04:20:20.281809Z" }, "nbsphinx": "hidden" }, @@ -136,7 +136,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -161,10 +161,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.928387Z", - "iopub.status.busy": "2023-08-14T16:45:16.927680Z", - "iopub.status.idle": "2023-08-14T16:45:16.933287Z", - "shell.execute_reply": "2023-08-14T16:45:16.932648Z" + "iopub.execute_input": "2023-08-15T04:20:20.287773Z", + "iopub.status.busy": "2023-08-15T04:20:20.287241Z", + "iopub.status.idle": "2023-08-15T04:20:20.294554Z", + "shell.execute_reply": "2023-08-15T04:20:20.293358Z" } }, "outputs": [], @@ -186,10 +186,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.936391Z", - "iopub.status.busy": "2023-08-14T16:45:16.936019Z", - "iopub.status.idle": "2023-08-14T16:45:16.939783Z", - "shell.execute_reply": "2023-08-14T16:45:16.939094Z" + "iopub.execute_input": "2023-08-15T04:20:20.299346Z", + "iopub.status.busy": "2023-08-15T04:20:20.299028Z", + "iopub.status.idle": "2023-08-15T04:20:20.303850Z", + "shell.execute_reply": "2023-08-15T04:20:20.302817Z" }, "nbsphinx": "hidden" }, @@ -220,10 +220,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.942949Z", - "iopub.status.busy": "2023-08-14T16:45:16.942587Z", - "iopub.status.idle": "2023-08-14T16:45:16.986653Z", - "shell.execute_reply": "2023-08-14T16:45:16.985895Z" + "iopub.execute_input": "2023-08-15T04:20:20.308042Z", + "iopub.status.busy": "2023-08-15T04:20:20.307637Z", + "iopub.status.idle": "2023-08-15T04:20:20.457797Z", + "shell.execute_reply": "2023-08-15T04:20:20.456822Z" } }, "outputs": [ @@ -313,10 +313,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.990151Z", - "iopub.status.busy": "2023-08-14T16:45:16.989747Z", - "iopub.status.idle": "2023-08-14T16:45:16.994224Z", - "shell.execute_reply": "2023-08-14T16:45:16.993525Z" + "iopub.execute_input": "2023-08-15T04:20:20.461445Z", + "iopub.status.busy": "2023-08-15T04:20:20.461164Z", + "iopub.status.idle": "2023-08-15T04:20:20.467865Z", + "shell.execute_reply": "2023-08-15T04:20:20.466641Z" } }, "outputs": [], @@ -331,10 +331,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:16.997044Z", - "iopub.status.busy": "2023-08-14T16:45:16.996613Z", - "iopub.status.idle": "2023-08-14T16:45:17.000939Z", - "shell.execute_reply": "2023-08-14T16:45:17.000244Z" + "iopub.execute_input": "2023-08-15T04:20:20.471479Z", + "iopub.status.busy": "2023-08-15T04:20:20.471201Z", + "iopub.status.idle": "2023-08-15T04:20:20.476214Z", + "shell.execute_reply": "2023-08-15T04:20:20.475346Z" } }, "outputs": [ @@ -343,7 +343,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'cancel_transfer', 'lost_or_stolen_phone', 'change_pin', 'card_about_to_expire', 'getting_spare_card', 'supported_cards_and_currencies'}\n" + "Classes: {'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'change_pin', 'getting_spare_card', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'card_about_to_expire', 'cancel_transfer'}\n" ] } ], @@ -366,10 +366,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:17.004757Z", - "iopub.status.busy": "2023-08-14T16:45:17.004236Z", - "iopub.status.idle": "2023-08-14T16:45:17.008239Z", - "shell.execute_reply": "2023-08-14T16:45:17.007518Z" + "iopub.execute_input": "2023-08-15T04:20:20.479748Z", + "iopub.status.busy": "2023-08-15T04:20:20.479395Z", + "iopub.status.idle": "2023-08-15T04:20:20.483695Z", + "shell.execute_reply": "2023-08-15T04:20:20.482989Z" } }, "outputs": [ @@ -410,10 +410,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:17.011923Z", - "iopub.status.busy": "2023-08-14T16:45:17.011562Z", - "iopub.status.idle": "2023-08-14T16:45:17.015793Z", - "shell.execute_reply": "2023-08-14T16:45:17.015102Z" + "iopub.execute_input": "2023-08-15T04:20:20.487861Z", + "iopub.status.busy": "2023-08-15T04:20:20.487397Z", + "iopub.status.idle": "2023-08-15T04:20:20.494526Z", + "shell.execute_reply": "2023-08-15T04:20:20.493207Z" } }, "outputs": [], @@ -454,10 +454,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:17.018942Z", - "iopub.status.busy": "2023-08-14T16:45:17.018346Z", - "iopub.status.idle": "2023-08-14T16:45:20.472247Z", - "shell.execute_reply": "2023-08-14T16:45:20.471082Z" + "iopub.execute_input": "2023-08-15T04:20:20.498757Z", + "iopub.status.busy": "2023-08-15T04:20:20.498143Z", + "iopub.status.idle": "2023-08-15T04:20:27.036822Z", + "shell.execute_reply": "2023-08-15T04:20:27.035688Z" } }, "outputs": [ @@ -472,7 +472,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\n", + "Some weights of the model checkpoint at /home/runner/.cache/torch/sentence_transformers/google_electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight']\n", "- This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" ] @@ -513,10 +513,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:20.476238Z", - "iopub.status.busy": "2023-08-14T16:45:20.475721Z", - "iopub.status.idle": "2023-08-14T16:45:20.478964Z", - "shell.execute_reply": "2023-08-14T16:45:20.478418Z" + "iopub.execute_input": "2023-08-15T04:20:27.041706Z", + "iopub.status.busy": "2023-08-15T04:20:27.041032Z", + "iopub.status.idle": "2023-08-15T04:20:27.045018Z", + "shell.execute_reply": "2023-08-15T04:20:27.044246Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:20.481995Z", - "iopub.status.busy": "2023-08-14T16:45:20.481368Z", - "iopub.status.idle": "2023-08-14T16:45:20.484587Z", - "shell.execute_reply": "2023-08-14T16:45:20.484047Z" + "iopub.execute_input": "2023-08-15T04:20:27.049353Z", + "iopub.status.busy": "2023-08-15T04:20:27.048605Z", + "iopub.status.idle": "2023-08-15T04:20:27.052978Z", + "shell.execute_reply": "2023-08-15T04:20:27.052166Z" } }, "outputs": [], @@ -556,10 +556,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:20.487414Z", - "iopub.status.busy": "2023-08-14T16:45:20.486816Z", - "iopub.status.idle": "2023-08-14T16:45:23.297246Z", - "shell.execute_reply": "2023-08-14T16:45:23.296146Z" + "iopub.execute_input": "2023-08-15T04:20:27.056729Z", + "iopub.status.busy": "2023-08-15T04:20:27.056139Z", + "iopub.status.idle": "2023-08-15T04:20:30.786103Z", + "shell.execute_reply": "2023-08-15T04:20:30.784427Z" }, "scrolled": true }, @@ -582,10 +582,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.302727Z", - "iopub.status.busy": "2023-08-14T16:45:23.301582Z", - "iopub.status.idle": "2023-08-14T16:45:23.314614Z", - "shell.execute_reply": "2023-08-14T16:45:23.314001Z" + "iopub.execute_input": "2023-08-15T04:20:30.792248Z", + "iopub.status.busy": "2023-08-15T04:20:30.790996Z", + "iopub.status.idle": "2023-08-15T04:20:30.803827Z", + "shell.execute_reply": "2023-08-15T04:20:30.803056Z" } }, "outputs": [ @@ -620,35 +620,35 @@ " \n", " 0\n", " False\n", - " 0.858229\n", + " 0.858293\n", " 6\n", " 6\n", " \n", " \n", " 1\n", " False\n", - " 0.547047\n", + " 0.546974\n", " 3\n", " 3\n", " \n", " \n", " 2\n", " False\n", - " 0.823594\n", + " 0.823609\n", " 7\n", " 7\n", " \n", " \n", " 3\n", " False\n", - " 0.968831\n", + " 0.968828\n", " 8\n", " 8\n", " \n", " \n", " 4\n", " False\n", - " 0.792000\n", + " 0.791938\n", " 4\n", " 4\n", " \n", @@ -658,11 +658,11 @@ ], "text/plain": [ " is_label_issue label_quality given_label predicted_label\n", - "0 False 0.858229 6 6\n", - "1 False 0.547047 3 3\n", - "2 False 0.823594 7 7\n", - "3 False 0.968831 8 8\n", - "4 False 0.792000 4 4" + "0 False 0.858293 6 6\n", + "1 False 0.546974 3 3\n", + "2 False 0.823609 7 7\n", + "3 False 0.968828 8 8\n", + "4 False 0.791938 4 4" ] }, "execution_count": 13, @@ -686,10 +686,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.317709Z", - "iopub.status.busy": "2023-08-14T16:45:23.317265Z", - "iopub.status.idle": "2023-08-14T16:45:23.324209Z", - "shell.execute_reply": "2023-08-14T16:45:23.323453Z" + "iopub.execute_input": "2023-08-15T04:20:30.808010Z", + "iopub.status.busy": "2023-08-15T04:20:30.807479Z", + "iopub.status.idle": "2023-08-15T04:20:30.815103Z", + "shell.execute_reply": "2023-08-15T04:20:30.814069Z" } }, "outputs": [], @@ -703,10 +703,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.327086Z", - "iopub.status.busy": "2023-08-14T16:45:23.326640Z", - "iopub.status.idle": "2023-08-14T16:45:23.330640Z", - "shell.execute_reply": "2023-08-14T16:45:23.329922Z" + "iopub.execute_input": "2023-08-15T04:20:30.818893Z", + "iopub.status.busy": "2023-08-15T04:20:30.818588Z", + "iopub.status.idle": "2023-08-15T04:20:30.823490Z", + "shell.execute_reply": "2023-08-15T04:20:30.822692Z" } }, "outputs": [ @@ -741,10 +741,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.334338Z", - "iopub.status.busy": "2023-08-14T16:45:23.333977Z", - "iopub.status.idle": "2023-08-14T16:45:23.337578Z", - "shell.execute_reply": "2023-08-14T16:45:23.336888Z" + "iopub.execute_input": "2023-08-15T04:20:30.827685Z", + "iopub.status.busy": "2023-08-15T04:20:30.827171Z", + "iopub.status.idle": "2023-08-15T04:20:30.831887Z", + "shell.execute_reply": "2023-08-15T04:20:30.830936Z" } }, "outputs": [], @@ -764,10 +764,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.340411Z", - "iopub.status.busy": "2023-08-14T16:45:23.340053Z", - "iopub.status.idle": "2023-08-14T16:45:23.349128Z", - "shell.execute_reply": "2023-08-14T16:45:23.348437Z" + "iopub.execute_input": "2023-08-15T04:20:30.836710Z", + "iopub.status.busy": "2023-08-15T04:20:30.836174Z", + "iopub.status.idle": "2023-08-15T04:20:30.856004Z", + "shell.execute_reply": "2023-08-15T04:20:30.854909Z" } }, "outputs": [ @@ -892,10 +892,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.352459Z", - "iopub.status.busy": "2023-08-14T16:45:23.352026Z", - "iopub.status.idle": "2023-08-14T16:45:23.666709Z", - "shell.execute_reply": "2023-08-14T16:45:23.666054Z" + "iopub.execute_input": "2023-08-15T04:20:30.868020Z", + "iopub.status.busy": "2023-08-15T04:20:30.867172Z", + "iopub.status.idle": "2023-08-15T04:20:31.368103Z", + "shell.execute_reply": "2023-08-15T04:20:31.367240Z" }, "scrolled": true }, @@ -934,10 +934,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.670119Z", - "iopub.status.busy": "2023-08-14T16:45:23.669370Z", - "iopub.status.idle": "2023-08-14T16:45:23.957950Z", - "shell.execute_reply": "2023-08-14T16:45:23.957272Z" + "iopub.execute_input": "2023-08-15T04:20:31.372608Z", + "iopub.status.busy": "2023-08-15T04:20:31.371733Z", + "iopub.status.idle": "2023-08-15T04:20:31.808712Z", + "shell.execute_reply": "2023-08-15T04:20:31.807851Z" }, "scrolled": true }, @@ -970,10 +970,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:23.961266Z", - "iopub.status.busy": "2023-08-14T16:45:23.960766Z", - "iopub.status.idle": "2023-08-14T16:45:23.964809Z", - "shell.execute_reply": "2023-08-14T16:45:23.964244Z" + "iopub.execute_input": "2023-08-15T04:20:31.814265Z", + "iopub.status.busy": "2023-08-15T04:20:31.812698Z", + "iopub.status.idle": "2023-08-15T04:20:31.820582Z", + "shell.execute_reply": "2023-08-15T04:20:31.819766Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index c3688e8bb..aeb1f69a8 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -843,16 +843,16 @@

    1. Install required dependencies and download data 
    ---2023-08-14 16:45:29--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 169.150.236.100, 2400:52e0:1a00::845:1
-Connecting to data.deepai.org (data.deepai.org)|169.150.236.100|:443... connected.
+--2023-08-15 04:20:38--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 185.93.1.250, 2400:52e0:1a00::845:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.250|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  4.19MB/s    in 0.2s
+conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s
 
-2023-08-14 16:45:30 (4.19 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2023-08-15 04:20:38 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
 mkdir: cannot create directory ‘data’: File exists
 Archive:  conll2003.zip
@@ -860,16 +860,16 @@ 
    1. Install required dependencies and download data
    @@ -1237,11 +1237,11 @@

    Most common word-level token mislabels ] 845.70K 4.00MB/s \r", - "conll2003.zip 100%[===================>] 959.94K 4.19MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2023-08-14 16:45:30 (4.19 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2023-08-15 04:20:38 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +144,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2023-08-14 16:45:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.49.36, 52.216.108.11, 52.217.203.185, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.49.36|:443... connected.\r\n", + "--2023-08-15 04:20:38-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.164.193, 3.5.27.156, 16.182.68.65, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.164.193|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -168,7 +180,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 96%[==================> ] 15.71M 47.8MB/s " + "pred_probs.npz 7%[> ] 1.21M 6.05MB/s " ] }, { @@ -176,9 +188,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 42.9MB/s in 0.4s \r\n", + "pred_probs.npz 96%[==================> ] 15.66M 39.1MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 40.2MB/s in 0.4s \r\n", "\r\n", - "2023-08-14 16:45:30 (42.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2023-08-15 04:20:39 (40.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -195,10 +208,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:31.061227Z", - "iopub.status.busy": "2023-08-14T16:45:31.060760Z", - "iopub.status.idle": "2023-08-14T16:45:32.291949Z", - "shell.execute_reply": "2023-08-14T16:45:32.290838Z" + "iopub.execute_input": "2023-08-15T04:20:39.729138Z", + "iopub.status.busy": "2023-08-15T04:20:39.728250Z", + "iopub.status.idle": "2023-08-15T04:20:41.321946Z", + "shell.execute_reply": "2023-08-15T04:20:41.320777Z" }, "nbsphinx": "hidden" }, @@ -209,7 +222,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@396bd68e661dcfb31da64c7b4e43e725a1cd6fab\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@e081acc0d15419fa41b2e9c9fe784d70044e1fae\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -235,10 +248,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:32.296595Z", - "iopub.status.busy": "2023-08-14T16:45:32.295910Z", - "iopub.status.idle": "2023-08-14T16:45:32.300250Z", - "shell.execute_reply": "2023-08-14T16:45:32.299554Z" + "iopub.execute_input": "2023-08-15T04:20:41.327860Z", + "iopub.status.busy": "2023-08-15T04:20:41.326164Z", + "iopub.status.idle": "2023-08-15T04:20:41.334821Z", + "shell.execute_reply": "2023-08-15T04:20:41.332813Z" } }, "outputs": [], @@ -288,10 +301,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:32.303535Z", - "iopub.status.busy": "2023-08-14T16:45:32.303152Z", - "iopub.status.idle": "2023-08-14T16:45:32.306871Z", - "shell.execute_reply": "2023-08-14T16:45:32.306208Z" + "iopub.execute_input": "2023-08-15T04:20:41.340149Z", + "iopub.status.busy": "2023-08-15T04:20:41.339817Z", + "iopub.status.idle": "2023-08-15T04:20:41.346867Z", + "shell.execute_reply": "2023-08-15T04:20:41.345236Z" }, "nbsphinx": "hidden" }, @@ -309,10 +322,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:32.309847Z", - "iopub.status.busy": "2023-08-14T16:45:32.309305Z", - "iopub.status.idle": "2023-08-14T16:45:44.200401Z", - "shell.execute_reply": "2023-08-14T16:45:44.199592Z" + "iopub.execute_input": "2023-08-15T04:20:41.351570Z", + "iopub.status.busy": "2023-08-15T04:20:41.351247Z", + "iopub.status.idle": "2023-08-15T04:20:54.553334Z", + "shell.execute_reply": "2023-08-15T04:20:54.552100Z" } }, "outputs": [], @@ -386,10 +399,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.204385Z", - "iopub.status.busy": "2023-08-14T16:45:44.203946Z", - "iopub.status.idle": "2023-08-14T16:45:44.211071Z", - "shell.execute_reply": "2023-08-14T16:45:44.210513Z" + "iopub.execute_input": "2023-08-15T04:20:54.558495Z", + "iopub.status.busy": "2023-08-15T04:20:54.558143Z", + "iopub.status.idle": "2023-08-15T04:20:54.567711Z", + "shell.execute_reply": "2023-08-15T04:20:54.566731Z" }, "nbsphinx": "hidden" }, @@ -429,10 +442,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.214319Z", - "iopub.status.busy": "2023-08-14T16:45:44.213710Z", - "iopub.status.idle": "2023-08-14T16:45:44.823151Z", - "shell.execute_reply": "2023-08-14T16:45:44.821614Z" + "iopub.execute_input": "2023-08-15T04:20:54.571239Z", + "iopub.status.busy": "2023-08-15T04:20:54.570928Z", + "iopub.status.idle": "2023-08-15T04:20:55.230372Z", + "shell.execute_reply": "2023-08-15T04:20:55.229244Z" } }, "outputs": [], @@ -469,10 +482,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.827168Z", - "iopub.status.busy": "2023-08-14T16:45:44.826656Z", - "iopub.status.idle": "2023-08-14T16:45:44.835279Z", - "shell.execute_reply": "2023-08-14T16:45:44.834655Z" + "iopub.execute_input": "2023-08-15T04:20:55.234193Z", + "iopub.status.busy": "2023-08-15T04:20:55.233932Z", + "iopub.status.idle": "2023-08-15T04:20:55.243155Z", + "shell.execute_reply": "2023-08-15T04:20:55.242041Z" } }, "outputs": [ @@ -544,10 +557,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:44.838250Z", - "iopub.status.busy": "2023-08-14T16:45:44.837809Z", - "iopub.status.idle": "2023-08-14T16:45:47.661496Z", - "shell.execute_reply": "2023-08-14T16:45:47.659859Z" + "iopub.execute_input": "2023-08-15T04:20:55.247751Z", + "iopub.status.busy": "2023-08-15T04:20:55.247315Z", + "iopub.status.idle": "2023-08-15T04:20:58.699672Z", + "shell.execute_reply": "2023-08-15T04:20:58.698130Z" } }, "outputs": [], @@ -569,10 +582,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.667172Z", - "iopub.status.busy": "2023-08-14T16:45:47.666002Z", - "iopub.status.idle": "2023-08-14T16:45:47.679744Z", - "shell.execute_reply": "2023-08-14T16:45:47.678178Z" + "iopub.execute_input": "2023-08-15T04:20:58.706443Z", + "iopub.status.busy": "2023-08-15T04:20:58.704834Z", + "iopub.status.idle": "2023-08-15T04:20:58.718617Z", + "shell.execute_reply": "2023-08-15T04:20:58.717298Z" } }, "outputs": [ @@ -608,10 +621,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.683111Z", - "iopub.status.busy": "2023-08-14T16:45:47.682631Z", - "iopub.status.idle": "2023-08-14T16:45:47.701333Z", - "shell.execute_reply": "2023-08-14T16:45:47.700691Z" + "iopub.execute_input": "2023-08-15T04:20:58.723002Z", + "iopub.status.busy": "2023-08-15T04:20:58.722634Z", + "iopub.status.idle": "2023-08-15T04:20:58.751238Z", + "shell.execute_reply": "2023-08-15T04:20:58.749416Z" } }, "outputs": [ @@ -769,10 +782,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.704745Z", - "iopub.status.busy": "2023-08-14T16:45:47.704227Z", - "iopub.status.idle": "2023-08-14T16:45:47.754004Z", - "shell.execute_reply": "2023-08-14T16:45:47.752818Z" + "iopub.execute_input": "2023-08-15T04:20:58.756048Z", + "iopub.status.busy": "2023-08-15T04:20:58.755656Z", + "iopub.status.idle": "2023-08-15T04:20:58.817288Z", + "shell.execute_reply": "2023-08-15T04:20:58.816292Z" } }, "outputs": [ @@ -801,11 +814,11 @@ "labeled as class `MISC` but predicted to actually be class `LOC` 1 times\n", "labeled as class `MISC` but predicted to actually be class `ORG` 1 times\n", "\n", - "Token 'Press' is potentially mislabeled 20 times throughout the dataset\n", + "Token 'Digest' is potentially mislabeled 20 times throughout the dataset\n", "---------------------------------------------------------------------------------------\n", "labeled as class `O` but predicted to actually be class `ORG` 20 times\n", "\n", - "Token 'Digest' is potentially mislabeled 20 times throughout the dataset\n", + "Token 'Press' is potentially mislabeled 20 times throughout the dataset\n", "---------------------------------------------------------------------------------------\n", "labeled as class `O` but predicted to actually be class `ORG` 20 times\n", "\n", @@ -813,8 +826,8 @@ "---------------------------------------------------------------------------------------\n", "labeled as class `ORG` but predicted to actually be class `LOC` 13 times\n", "labeled as class `LOC` but predicted to actually be class `ORG` 2 times\n", - "labeled as class `MISC` but predicted to actually be class `LOC` 1 times\n", "labeled as class `O` but predicted to actually be class `ORG` 1 times\n", + "labeled as class `MISC` but predicted to actually be class `LOC` 1 times\n", "\n", "Token 'and' is potentially mislabeled 16 times throughout the dataset\n", "---------------------------------------------------------------------------------------\n", @@ -874,10 +887,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.757599Z", - "iopub.status.busy": "2023-08-14T16:45:47.757074Z", - "iopub.status.idle": "2023-08-14T16:45:47.768429Z", - "shell.execute_reply": "2023-08-14T16:45:47.767794Z" + "iopub.execute_input": "2023-08-15T04:20:58.822509Z", + "iopub.status.busy": "2023-08-15T04:20:58.821634Z", + "iopub.status.idle": "2023-08-15T04:20:58.836039Z", + "shell.execute_reply": "2023-08-15T04:20:58.834998Z" } }, "outputs": [ @@ -945,10 +958,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:47.771551Z", - "iopub.status.busy": "2023-08-14T16:45:47.771099Z", - "iopub.status.idle": "2023-08-14T16:45:50.335528Z", - "shell.execute_reply": "2023-08-14T16:45:50.334340Z" + "iopub.execute_input": "2023-08-15T04:20:58.840562Z", + "iopub.status.busy": "2023-08-15T04:20:58.840030Z", + "iopub.status.idle": "2023-08-15T04:21:02.124593Z", + "shell.execute_reply": "2023-08-15T04:21:02.123588Z" } }, "outputs": [ @@ -1100,10 +1113,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-14T16:45:50.339346Z", - "iopub.status.busy": "2023-08-14T16:45:50.338845Z", - "iopub.status.idle": "2023-08-14T16:45:50.346061Z", - "shell.execute_reply": "2023-08-14T16:45:50.344415Z" + "iopub.execute_input": "2023-08-15T04:21:02.128404Z", + "iopub.status.busy": "2023-08-15T04:21:02.128086Z", + "iopub.status.idle": "2023-08-15T04:21:02.134516Z", + "shell.execute_reply": "2023-08-15T04:21:02.133577Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 8147c6873..7777d9bca 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.4.0", - commit_hash: "396bd68e661dcfb31da64c7b4e43e725a1cd6fab", + commit_hash: "e081acc0d15419fa41b2e9c9fe784d70044e1fae", }; \ No newline at end of file