XXX_SomedayLater.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO \n",
    "use xxx model from [TensorFlow detection zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md)\n",
    "And analyse it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequirements\n",
    "Prerequirement for this is TensorFlow 2.X and object_detection API installed\n",
    "```bash\n",
    "git clone https://github.com/tensorflow/models.git tf_models\n",
    "cd tf_models/research\n",
    "protoc object_detection/protos/*.proto --python_out=.\n",
    "cp object_detection/packages/tf2/setup.py .\n",
    "sudo pip3 install .\n",
    "````\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from jetutils import SimpleTimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First download and convert a TensorFlow object detection pretrained model. This takes significant amount of time\n",
    "\n",
    "Like: `\n",
    "model converted in 823.534474207 seconds`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz'\n",
    "MODEL_NAME = MODEL_URL.split('/')[-1].split('.')[0]\n",
    "MODEL_PATH = './models/{}'.format(MODEL_NAME)\n",
    "TRT_MODEL_PATH ='./models/{}_trt'.format(MODEL_NAME)\n",
    "\n",
    "\n",
    "if not os.path.exists(MODEL_PATH):\n",
    "    import subprocess\n",
    "    subprocess.call(['wget', MODEL_URL, '-O', '{}.tgz'.format(MODEL_PATH)])\n",
    "    subprocess.call(['tar', '-xzf', '{}.tgz'.format(MODEL_PATH), '-C', './models'])\n",
    "\n",
    "if not os.path.exists(TRT_MODEL_PATH):\n",
    "    from tensorflow.python.compiler.tensorrt import trt_convert as trt\n",
    "    from jetutils import CAPTURE_WIDTH, CAPTURE_HEIGHT\n",
    "    BATCH_SIZE = 1\n",
    "    ITERATIONS = 10\n",
    "    for gpu in tf.config.experimental.list_physical_devices('GPU'):\n",
    "        print(gpu)\n",
    "        tf.config.experimental.set_memory_growth(gpu, True)\n",
    "\n",
    "    conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS\n",
    "    conversion_params = conversion_params._replace(precision_mode=trt.TrtPrecisionMode.FP16)\n",
    "    conversion_params = conversion_params._replace(minimum_segment_size=50)\n",
    "    conversion_params = conversion_params._replace(max_batch_size=BATCH_SIZE)\n",
    "  \n",
    "    def get_dataset(batch_size, input_h, input_w):\n",
    "        features = np.random.normal(loc=112, scale=70,\n",
    "              size=(batch_size, input_h, input_w, 3)).astype(np.float32)\n",
    "        features = np.clip(features, 0.0, 255.0).astype(np.uint8)\n",
    "        features = tf.convert_to_tensor(value=tf.compat.v1.get_variable(\n",
    "            \"features\", initializer=tf.constant(features)))\n",
    "        dataset = tf.data.Dataset.from_tensor_slices([features])\n",
    "        dataset = dataset.repeat()\n",
    "        return dataset\n",
    "\n",
    "    def input_fn():\n",
    "        dataset = get_dataset(BATCH_SIZE, CAPTURE_HEIGHT, CAPTURE_WIDTH)\n",
    "        for i, batch in enumerate(dataset):\n",
    "            if i > ITERATIONS:\n",
    "                break\n",
    "            yield (batch,)\n",
    "            print(' step {}'.format(i+1))\n",
    "\n",
    "    converter = trt.TrtGraphConverterV2(\n",
    "        input_saved_model_dir='{}/saved_model'.format(MODEL_PATH),\n",
    "        conversion_params=conversion_params)\n",
    "\n",
    "    timer = SimpleTimer()\n",
    "    print('converting...')\n",
    "    with timer:\n",
    "        converter.convert()\n",
    "    print('model converted in {} seconds'.format(timer.time))\n",
    "    print('building...')\n",
    "    with timer:\n",
    "        converter.build(input_fn=input_fn)\n",
    "    print('model engine build in {} seconds'.format(timer.time))\n",
    "    converter.save(TRT_MODEL_PATH)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Postponing this as shap installation turned to be problematic.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "create_target_machine() got an unexpected keyword argument 'jitdebug'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-efbb001a1501>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/shap/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"As of version 0.29.0 shap only supports Python 3 (not 2)!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_explanation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mExplanation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCohorts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# explainers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/shap/_explanation.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mslicer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSlicer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAlias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mObj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# from ._order import Order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_general\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOpChain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# slicer confuses pylint...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/shap/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_clustering\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhclust_ordering\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartition_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartition_tree_shuffle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelta_minimization_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhclust\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_general\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mapproximate_interactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpotential_interactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msafe_isinstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0massert_import\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecord_import_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_general\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshapley_coefficients\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mordinal_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOpChain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_show_progress\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_progress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_masked_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMaskedModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmake_masks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/shap/utils/_clustering.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;31m#switch = np.random.randn(M) < 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0m_pt_shuffle_rec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartition_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartition_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_pt_shuffle_rec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartition_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/decorators.py\u001b[0m in \u001b[0;36mjit\u001b[0;34m(signature_or_function, locals, target, cache, **options)\u001b[0m\n\u001b[1;32m    172\u001b[0m                    targetoptions=options)\n\u001b[1;32m    173\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpyfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpyfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(func)\u001b[0m\n\u001b[1;32m    188\u001b[0m         disp = dispatcher(py_func=func, locals=locals,\n\u001b[1;32m    189\u001b[0m                           \u001b[0mtargetoptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtargetoptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m                           **dispatcher_args)\n\u001b[0m\u001b[1;32m    191\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0mdisp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_caching\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, py_func, locals, targetoptions, impl_kind)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \"\"\"\n\u001b[1;32m    466\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypingctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargetdescr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyping_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargetctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargetdescr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m         \u001b[0mpysig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpysignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/targets/registry.py\u001b[0m in \u001b[0;36mtarget_context\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mnested\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_toplevel_target_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/utils.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, instance, type)\u001b[0m\n\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minstance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/targets/registry.py\u001b[0m in \u001b[0;36m_toplevel_target_context\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_toplevel_target_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;31m# Lazily-initialized top-level target context, for all threads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCPUContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyping_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcached_property\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/targets/base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, typing_context)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;31m# Initialize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/targets/cpu.py\u001b[0m in \u001b[0;36minit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis32bit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMACHINE_BITS\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_internal_codegen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcodegen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJITCPUCodegen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"numba.exec\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;31m# Map external C functions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/targets/codegen.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, module_name)\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_llvm_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"global_codegen_module\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rtlinker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRuntimeLinker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 537\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_llvm_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllvm_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/numba/targets/codegen.py\u001b[0m in \u001b[0;36m_init\u001b[0;34m(self, llvm_module)\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tm_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_customize_tm_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_customize_tm_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m         \u001b[0mtm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_target_machine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtm_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    547\u001b[0m         \u001b[0mengine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllvmts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_mcjit_compiler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mllvm_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: create_target_machine() got an unexpected keyword argument 'jitdebug'"
     ]
    }
   ],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}