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feat: Add TimeBoundedPopScore for time-bounded popularity #493
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Original file line number | Diff line number | Diff line change |
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# This file is part of LensKit. | ||
# Copyright (C) 2018-2023 Boise State University | ||
# Copyright (C) 2023-2024 Drexel University | ||
# Licensed under the MIT license, see LICENSE.md for details. | ||
# SPDX-License-Identifier: MIT | ||
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from datetime import datetime, timedelta | ||
import pickle | ||
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import numpy as np | ||
import pandas as pd | ||
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from lenskit.lenskit.data.convert import from_interactions_df | ||
from lenskit.algorithms import basic | ||
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day = timedelta(days=1) | ||
now = int(datetime.now().timestamp()) | ||
one_day_ago = now - day.total_seconds() | ||
simple_df = pd.DataFrame( | ||
{ | ||
"item": [1, 2, 2, 3], | ||
"user": [10, 12, 10, 13], | ||
"rating": [4.0, 3.0, 5.0, 2.0], | ||
"timestamp": [now, one_day_ago, one_day_ago, one_day_ago], | ||
} | ||
) | ||
simple_ds = from_interactions_df(simple_df) | ||
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def test_time_bounded_pop_score_quantile_one_day_window(): | ||
algo = basic.TimeBoundedPopScore(day) | ||
algo.fit(simple_ds) | ||
assert algo.item_scores_.equals(pd.Series([1.0, 0.0, 0.0], index=[1, 2, 3])) | ||
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def test_time_bounded_pop_score_quantile_two_day_window(): | ||
algo = basic.TimeBoundedPopScore(2 * day) | ||
algo.fit(simple_ds) | ||
assert algo.item_scores_.equals(pd.Series([0.25, 1.0, 0.5], index=[1, 2, 3])) | ||
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def test_time_bounded_pop_score_rank(): | ||
algo = basic.TimeBoundedPopScore(2 * day, "rank") | ||
algo.fit(simple_ds) | ||
assert algo.item_scores_.equals(pd.Series([1.5, 3.0, 1.5], index=[1, 2, 3])) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. These tests here seems very low level but I think it is OK since the internals are important and are likely to be stable over time? |
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def test_time_bounded_pop_score_counts(rng): | ||
algo = basic.TimeBoundedPopScore(2 * day, "count") | ||
algo.fit(simple_ds) | ||
assert algo.item_scores_.equals(pd.Series([1, 2, 1], index=[1, 2, 3], dtype=np.int32)) | ||
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def test_time_bounded_pop_score_save_load(): | ||
original = basic.TimeBoundedPopScore(day) | ||
original.fit(simple_ds) | ||
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mod = pickle.dumps(original) | ||
algo = pickle.loads(mod) | ||
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assert all(algo.item_scores_ == original.item_scores_) |
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I was thinking about adding a function to time bound a
DataSet
, but realized that might be too much since we are just going to use the counts - I assume that would still be useful for some things (validation set?) but doing the calculation here seems cheaper. LMK if this seems right!