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@@ -0,0 +1 @@ +/model_inspector-0.27.4.tar.gz diff --git a/python-model-inspector.spec b/python-model-inspector.spec new file mode 100644 index 0000000..64063ff --- /dev/null +++ b/python-model-inspector.spec @@ -0,0 +1,2158 @@ +%global _empty_manifest_terminate_build 0 +Name: python-model-inspector +Version: 0.27.4 +Release: 1 +Summary: Inspect machine learning models +License: Apache Software License 2.0 +URL: https://github.com/gsganden/model_inspector/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b4/0a/b5d157541b43b5ddffa2802dab05149d42e76fb1db8c8e9235cd97805aa7/model_inspector-0.27.4.tar.gz +BuildArch: noarch + +Requires: python3-catboost +Requires: python3-fastcore +Requires: python3-IPython +Requires: python3-matplotlib +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scikit-learn +Requires: python3-scipy +Requires: python3-seaborn +Requires: python3-tqdm +Requires: python3-waterfallcharts +Requires: python3-black +Requires: python3-flake8 +Requires: python3-ipdb +Requires: python3-jinja2 +Requires: python3-jupyterlab +Requires: python3-nbdev +Requires: python3-nbqa +Requires: python3-twine + +%description +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> +`model_inspector` aims to help you train better +`scikit-learn`-compatible models by providing insights into their +behavior. +## Use +To use `model_inspector`, you create an `Inspector` object from a +`scikit-learn` model, a feature DataFrame `X`, and a target Series `y`. +Typically you will want to create it on held-out data, as shown below. +``` python +import sklearn.datasets +from sklearn.ensemble import RandomForestRegressor +from sklearn.model_selection import train_test_split +from model_inspector import get_inspector +``` +``` python +X, y = sklearn.datasets.load_diabetes(return_X_y=True, as_frame=True) +``` +``` python +X +``` +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + .dataframe tbody tr th { + vertical-align: top; + } + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>age</th> + <th>sex</th> + <th>bmi</th> + <th>bp</th> + <th>s1</th> + <th>s2</th> + <th>s3</th> + <th>s4</th> + <th>s5</th> + <th>s6</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>0.038076</td> + <td>0.050680</td> + <td>0.061696</td> + <td>0.021872</td> + <td>-0.044223</td> + <td>-0.034821</td> + <td>-0.043401</td> + <td>-0.002592</td> + <td>0.019907</td> + <td>-0.017646</td> + </tr> + <tr> + <th>1</th> + <td>-0.001882</td> + <td>-0.044642</td> + <td>-0.051474</td> + <td>-0.026328</td> + <td>-0.008449</td> + <td>-0.019163</td> + <td>0.074412</td> + <td>-0.039493</td> + <td>-0.068332</td> + <td>-0.092204</td> + </tr> + <tr> + <th>2</th> + <td>0.085299</td> + <td>0.050680</td> + <td>0.044451</td> + <td>-0.005670</td> + <td>-0.045599</td> + <td>-0.034194</td> + <td>-0.032356</td> + <td>-0.002592</td> + <td>0.002861</td> + <td>-0.025930</td> + </tr> + <tr> + <th>3</th> + <td>-0.089063</td> + <td>-0.044642</td> + <td>-0.011595</td> + <td>-0.036656</td> + <td>0.012191</td> + <td>0.024991</td> + <td>-0.036038</td> + <td>0.034309</td> + <td>0.022688</td> + <td>-0.009362</td> + </tr> + <tr> + <th>4</th> + <td>0.005383</td> + <td>-0.044642</td> + <td>-0.036385</td> + <td>0.021872</td> + <td>0.003935</td> + <td>0.015596</td> + <td>0.008142</td> + <td>-0.002592</td> + <td>-0.031988</td> + <td>-0.046641</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>437</th> + <td>0.041708</td> + <td>0.050680</td> + <td>0.019662</td> + <td>0.059744</td> + <td>-0.005697</td> + <td>-0.002566</td> + <td>-0.028674</td> + <td>-0.002592</td> + <td>0.031193</td> + <td>0.007207</td> + </tr> + <tr> + <th>438</th> + <td>-0.005515</td> + <td>0.050680</td> + <td>-0.015906</td> + <td>-0.067642</td> + <td>0.049341</td> + <td>0.079165</td> + <td>-0.028674</td> + <td>0.034309</td> + <td>-0.018114</td> + <td>0.044485</td> + </tr> + <tr> + <th>439</th> + <td>0.041708</td> + <td>0.050680</td> + <td>-0.015906</td> + <td>0.017293</td> + <td>-0.037344</td> + <td>-0.013840</td> + <td>-0.024993</td> + <td>-0.011080</td> + <td>-0.046883</td> + <td>0.015491</td> + </tr> + <tr> + <th>440</th> + <td>-0.045472</td> + <td>-0.044642</td> + <td>0.039062</td> + <td>0.001215</td> + <td>0.016318</td> + <td>0.015283</td> + <td>-0.028674</td> + <td>0.026560</td> + <td>0.044529</td> + <td>-0.025930</td> + </tr> + <tr> + <th>441</th> + <td>-0.045472</td> + <td>-0.044642</td> + <td>-0.073030</td> + <td>-0.081413</td> + <td>0.083740</td> + <td>0.027809</td> + <td>0.173816</td> + <td>-0.039493</td> + <td>-0.004222</td> + <td>0.003064</td> + </tr> + </tbody> +</table> +<p>442 rows × 10 columns</p> +</div> +``` python +y +``` + 0 151.0 + 1 75.0 + 2 141.0 + 3 206.0 + 4 135.0 + 437 178.0 + 438 104.0 + 439 132.0 + 440 220.0 + 441 57.0 + Name: target, Length: 442, dtype: float64 +``` python +X_train, X_test, y_train, y_test = train_test_split(X, y) +``` +``` python +rfr = RandomForestRegressor().fit(X_train, y_train) +``` +``` python +rfr.score(X_test, y_test) +``` + 0.4145806969881506 +``` python +inspector = get_inspector(rfr, X_test, y_test) +``` +You can then use various methods of `inspector` to learn about how your +model behaves on that data. +The methods that are available for a given inspector depends on the +types of its estimator and its target `y`. An attribute called `methods` +tells you what they are: +``` python +inspector.methods +``` + ['plot_feature_clusters', + 'plot_partial_dependence', + 'permutation_importance', + 'plot_permutation_importance', + 'plot_pred_vs_act', + 'plot_residuals', + 'show_correlation'] +``` python +ax = inspector.plot_feature_clusters() +``` + +``` python +most_important_features = inspector.permutation_importance().index[:2] +axes = inspector.plot_partial_dependence( + features=[*most_important_features, most_important_features] +) +axes[0, 0].get_figure().set_size_inches(12, 3) +``` + +``` python +inspector.permutation_importance() +``` + bmi 0.241886 + s5 0.153085 + sex 0.003250 + s3 0.000734 + bp 0.000461 + s4 -0.002687 + s2 -0.004366 + s1 -0.008953 + s6 -0.018925 + age -0.022768 + dtype: float64 +``` python +ax = inspector.plot_permutation_importance() +``` + +``` python +ax = inspector.plot_pred_vs_act() +``` + +``` python +axes = inspector.plot_residuals() +``` + +``` python +inspector.show_correlation() +``` +<style type="text/css"> +#T_c8180_row0_col0, #T_c8180_row1_col1, #T_c8180_row2_col2, #T_c8180_row3_col3, #T_c8180_row4_col4, #T_c8180_row5_col5, #T_c8180_row6_col6, #T_c8180_row7_col7, #T_c8180_row8_col8, #T_c8180_row9_col9, #T_c8180_row10_col10 { + background-color: #ff0000; + color: #f1f1f1; +} +#T_c8180_row0_col1, #T_c8180_row1_col0 { + background-color: #ffc6c6; + color: #000000; +} +#T_c8180_row0_col2, #T_c8180_row0_col5, #T_c8180_row2_col0, #T_c8180_row5_col0, #T_c8180_row5_col9, #T_c8180_row9_col5 { + background-color: #ffd2d2; + color: #000000; +} +#T_c8180_row0_col3, #T_c8180_row2_col5, #T_c8180_row3_col0, #T_c8180_row5_col2 { + background-color: #ffd0d0; + color: #000000; +} +#T_c8180_row0_col4, #T_c8180_row4_col0 { + background-color: #ffc4c4; + color: #000000; +} +#T_c8180_row0_col6, #T_c8180_row6_col0 { + background-color: #f4f4ff; + color: #000000; +} +#T_c8180_row0_col7, #T_c8180_row3_col7, #T_c8180_row7_col0, #T_c8180_row7_col3 { + background-color: #ffcece; + color: #000000; +} +#T_c8180_row0_col8, #T_c8180_row8_col0 { + background-color: #ffb6b6; + color: #000000; +} +#T_c8180_row0_col9, #T_c8180_row9_col0 { + background-color: #ffaeae; + color: #000000; +} +#T_c8180_row0_col10, #T_c8180_row1_col8, #T_c8180_row8_col1, #T_c8180_row10_col0 { + background-color: #ffdede; + color: #000000; +} +#T_c8180_row1_col2, #T_c8180_row1_col7, #T_c8180_row2_col1, #T_c8180_row7_col1 { + background-color: #ffb4b4; + color: #000000; +} +#T_c8180_row1_col3, #T_c8180_row3_col1 { + background-color: #ffb0b0; + color: #000000; +} +#T_c8180_row1_col4, #T_c8180_row4_col1 { + background-color: #f2f2ff; + color: #000000; +} +#T_c8180_row1_col5, #T_c8180_row5_col1 { + background-color: #ffeaea; + color: #000000; +} +#T_c8180_row1_col6, #T_c8180_row6_col1 { + background-color: #9898ff; + color: #f1f1f1; +} +#T_c8180_row1_col9, #T_c8180_row9_col1 { + background-color: #ffb8b8; + color: #000000; +} +#T_c8180_row1_col10, #T_c8180_row10_col1 { + background-color: #ffbaba; + color: #000000; +} +#T_c8180_row2_col3, #T_c8180_row3_col2 { + background-color: #ff7272; + color: #f1f1f1; +} +#T_c8180_row2_col4, #T_c8180_row4_col2 { + background-color: #ffd8d8; + color: #000000; +} +#T_c8180_row2_col6, #T_c8180_row6_col2 { + background-color: #9292ff; + color: #f1f1f1; +} +#T_c8180_row2_col7, #T_c8180_row7_col2 { + background-color: #ff8c8c; + color: #000000; +} +#T_c8180_row2_col8, #T_c8180_row8_col2 { + background-color: #ff9292; + color: #000000; +} +#T_c8180_row2_col9, #T_c8180_row9_col2 { + background-color: #ff8282; + color: #f1f1f1; +} +#T_c8180_row2_col10, #T_c8180_row5_col7, #T_c8180_row7_col5, #T_c8180_row10_col2 { + background-color: #ff5656; + color: #f1f1f1; +} +#T_c8180_row3_col4, #T_c8180_row4_col3, #T_c8180_row4_col10, #T_c8180_row5_col10, #T_c8180_row10_col4, #T_c8180_row10_col5 { + background-color: #ffe8e8; + color: #000000; +} +#T_c8180_row3_col5, #T_c8180_row5_col3 { + background-color: #fff6f6; + color: #000000; +} +#T_c8180_row3_col6, #T_c8180_row6_col3 { + background-color: #ccccff; + color: #000000; +} +#T_c8180_row3_col8, #T_c8180_row8_col3 { + background-color: #ffa2a2; + color: #000000; +} +#T_c8180_row3_col9, #T_c8180_row9_col3 { + background-color: #ff8e8e; + color: #000000; +} +#T_c8180_row3_col10, #T_c8180_row10_col3 { + background-color: #ff7c7c; + color: #f1f1f1; +} +#T_c8180_row4_col5, #T_c8180_row5_col4 { + background-color: #ff1e1e; + color: #f1f1f1; +} +#T_c8180_row4_col6, #T_c8180_row6_col4 { + background-color: #ffeeee; + color: #000000; +} +#T_c8180_row4_col7, #T_c8180_row7_col4 { + background-color: #ff6c6c; + color: #f1f1f1; +} +#T_c8180_row4_col8, #T_c8180_row8_col4 { + background-color: #ff7e7e; + color: #f1f1f1; +} +#T_c8180_row4_col9, #T_c8180_row9_col4 { + background-color: #ffbebe; + color: #000000; +} +#T_c8180_row5_col6, #T_c8180_row6_col5 { + background-color: #d6d6ff; + color: #000000; +} +#T_c8180_row5_col8, #T_c8180_row8_col5 { + background-color: #ffc2c2; + color: #000000; +} +#T_c8180_row6_col7, #T_c8180_row7_col6 { + background-color: #4646ff; + color: #f1f1f1; +} +#T_c8180_row6_col8, #T_c8180_row8_col6 { + background-color: #a0a0ff; + color: #f1f1f1; +} +#T_c8180_row6_col9, #T_c8180_row9_col6 { + background-color: #b4b4ff; + color: #000000; +} +#T_c8180_row6_col10, #T_c8180_row10_col6 { + background-color: #8a8aff; + color: #f1f1f1; +} +#T_c8180_row7_col8, #T_c8180_row8_col7 { + background-color: #ff6464; + color: #f1f1f1; +} +#T_c8180_row7_col9, #T_c8180_row7_col10, #T_c8180_row9_col7, #T_c8180_row10_col7 { + background-color: #ff9696; + color: #000000; +} +#T_c8180_row8_col9, #T_c8180_row9_col8 { + background-color: #ff7a7a; + color: #f1f1f1; +} +#T_c8180_row8_col10, #T_c8180_row10_col8 { + background-color: #ff8888; + color: #f1f1f1; +} +#T_c8180_row9_col10, #T_c8180_row10_col9 { + background-color: #ffa6a6; + color: #000000; +} +</style> +<table id="T_c8180"> + <thead> + <tr> + <th class="blank level0" > </th> + <th id="T_c8180_level0_col0" class="col_heading level0 col0" >age</th> + <th id="T_c8180_level0_col1" class="col_heading level0 col1" >sex</th> + <th id="T_c8180_level0_col2" class="col_heading level0 col2" >bmi</th> + <th id="T_c8180_level0_col3" class="col_heading level0 col3" >bp</th> + <th id="T_c8180_level0_col4" class="col_heading level0 col4" >s1</th> + <th id="T_c8180_level0_col5" class="col_heading level0 col5" >s2</th> + <th id="T_c8180_level0_col6" class="col_heading level0 col6" >s3</th> + <th id="T_c8180_level0_col7" class="col_heading level0 col7" >s4</th> + <th id="T_c8180_level0_col8" class="col_heading level0 col8" >s5</th> + <th id="T_c8180_level0_col9" class="col_heading level0 col9" >s6</th> + <th id="T_c8180_level0_col10" class="col_heading level0 col10" >target</th> + </tr> + </thead> + <tbody> + <tr> + <th id="T_c8180_level0_row0" class="row_heading level0 row0" >age</th> + <td id="T_c8180_row0_col0" class="data row0 col0" >1.00</td> + <td id="T_c8180_row0_col1" class="data row0 col1" >0.22</td> + <td id="T_c8180_row0_col2" class="data row0 col2" >0.18</td> + <td id="T_c8180_row0_col3" class="data row0 col3" >0.19</td> + <td id="T_c8180_row0_col4" class="data row0 col4" >0.23</td> + <td id="T_c8180_row0_col5" class="data row0 col5" >0.18</td> + <td id="T_c8180_row0_col6" class="data row0 col6" >-0.04</td> + <td id="T_c8180_row0_col7" class="data row0 col7" >0.19</td> + <td id="T_c8180_row0_col8" class="data row0 col8" >0.28</td> + <td id="T_c8180_row0_col9" class="data row0 col9" >0.32</td> + <td id="T_c8180_row0_col10" class="data row0 col10" >0.13</td> + </tr> + <tr> + <th id="T_c8180_level0_row1" class="row_heading level0 row1" >sex</th> + <td id="T_c8180_row1_col0" class="data row1 col0" >0.22</td> + <td id="T_c8180_row1_col1" class="data row1 col1" >1.00</td> + <td id="T_c8180_row1_col2" class="data row1 col2" >0.29</td> + <td id="T_c8180_row1_col3" class="data row1 col3" >0.31</td> + <td id="T_c8180_row1_col4" class="data row1 col4" >-0.05</td> + <td id="T_c8180_row1_col5" class="data row1 col5" >0.08</td> + <td id="T_c8180_row1_col6" class="data row1 col6" >-0.41</td> + <td id="T_c8180_row1_col7" class="data row1 col7" >0.30</td> + <td id="T_c8180_row1_col8" class="data row1 col8" >0.13</td> + <td id="T_c8180_row1_col9" class="data row1 col9" >0.27</td> + <td id="T_c8180_row1_col10" class="data row1 col10" >0.27</td> + </tr> + <tr> + <th id="T_c8180_level0_row2" class="row_heading level0 row2" >bmi</th> + <td id="T_c8180_row2_col0" class="data row2 col0" >0.18</td> + <td id="T_c8180_row2_col1" class="data row2 col1" >0.29</td> + <td id="T_c8180_row2_col2" class="data row2 col2" >1.00</td> + <td id="T_c8180_row2_col3" class="data row2 col3" >0.55</td> + <td id="T_c8180_row2_col4" class="data row2 col4" >0.16</td> + <td id="T_c8180_row2_col5" class="data row2 col5" >0.18</td> + <td id="T_c8180_row2_col6" class="data row2 col6" >-0.43</td> + <td id="T_c8180_row2_col7" class="data row2 col7" >0.45</td> + <td id="T_c8180_row2_col8" class="data row2 col8" >0.43</td> + <td id="T_c8180_row2_col9" class="data row2 col9" >0.49</td> + <td id="T_c8180_row2_col10" class="data row2 col10" >0.66</td> + </tr> + <tr> + <th id="T_c8180_level0_row3" class="row_heading level0 row3" >bp</th> + <td id="T_c8180_row3_col0" class="data row3 col0" >0.19</td> + <td id="T_c8180_row3_col1" class="data row3 col1" >0.31</td> + <td id="T_c8180_row3_col2" class="data row3 col2" >0.55</td> + <td id="T_c8180_row3_col3" class="data row3 col3" >1.00</td> + <td id="T_c8180_row3_col4" class="data row3 col4" >0.09</td> + <td id="T_c8180_row3_col5" class="data row3 col5" >0.04</td> + <td id="T_c8180_row3_col6" class="data row3 col6" >-0.20</td> + <td id="T_c8180_row3_col7" class="data row3 col7" >0.19</td> + <td id="T_c8180_row3_col8" class="data row3 col8" >0.36</td> + <td id="T_c8180_row3_col9" class="data row3 col9" >0.44</td> + <td id="T_c8180_row3_col10" class="data row3 col10" >0.51</td> + </tr> + <tr> + <th id="T_c8180_level0_row4" class="row_heading level0 row4" >s1</th> + <td id="T_c8180_row4_col0" class="data row4 col0" >0.23</td> + <td id="T_c8180_row4_col1" class="data row4 col1" >-0.05</td> + <td id="T_c8180_row4_col2" class="data row4 col2" >0.16</td> + <td id="T_c8180_row4_col3" class="data row4 col3" >0.09</td> + <td id="T_c8180_row4_col4" class="data row4 col4" >1.00</td> + <td id="T_c8180_row4_col5" class="data row4 col5" >0.88</td> + <td id="T_c8180_row4_col6" class="data row4 col6" >0.07</td> + <td id="T_c8180_row4_col7" class="data row4 col7" >0.57</td> + <td id="T_c8180_row4_col8" class="data row4 col8" >0.50</td> + <td id="T_c8180_row4_col9" class="data row4 col9" >0.26</td> + <td id="T_c8180_row4_col10" class="data row4 col10" >0.09</td> + </tr> + <tr> + <th id="T_c8180_level0_row5" class="row_heading level0 row5" >s2</th> + <td id="T_c8180_row5_col0" class="data row5 col0" >0.18</td> + <td id="T_c8180_row5_col1" class="data row5 col1" >0.08</td> + <td id="T_c8180_row5_col2" class="data row5 col2" >0.18</td> + <td id="T_c8180_row5_col3" class="data row5 col3" >0.04</td> + <td id="T_c8180_row5_col4" class="data row5 col4" >0.88</td> + <td id="T_c8180_row5_col5" class="data row5 col5" >1.00</td> + <td id="T_c8180_row5_col6" class="data row5 col6" >-0.16</td> + <td id="T_c8180_row5_col7" class="data row5 col7" >0.66</td> + <td id="T_c8180_row5_col8" class="data row5 col8" >0.23</td> + <td id="T_c8180_row5_col9" class="data row5 col9" >0.18</td> + <td id="T_c8180_row5_col10" class="data row5 col10" >0.09</td> + </tr> + <tr> + <th id="T_c8180_level0_row6" class="row_heading level0 row6" >s3</th> + <td id="T_c8180_row6_col0" class="data row6 col0" >-0.04</td> + <td id="T_c8180_row6_col1" class="data row6 col1" >-0.41</td> + <td id="T_c8180_row6_col2" class="data row6 col2" >-0.43</td> + <td id="T_c8180_row6_col3" class="data row6 col3" >-0.20</td> + <td id="T_c8180_row6_col4" class="data row6 col4" >0.07</td> + <td id="T_c8180_row6_col5" class="data row6 col5" >-0.16</td> + <td id="T_c8180_row6_col6" class="data row6 col6" >1.00</td> + <td id="T_c8180_row6_col7" class="data row6 col7" >-0.72</td> + <td id="T_c8180_row6_col8" class="data row6 col8" >-0.37</td> + <td id="T_c8180_row6_col9" class="data row6 col9" >-0.30</td> + <td id="T_c8180_row6_col10" class="data row6 col10" >-0.46</td> + </tr> + <tr> + <th id="T_c8180_level0_row7" class="row_heading level0 row7" >s4</th> + <td id="T_c8180_row7_col0" class="data row7 col0" >0.19</td> + <td id="T_c8180_row7_col1" class="data row7 col1" >0.30</td> + <td id="T_c8180_row7_col2" class="data row7 col2" >0.45</td> + <td id="T_c8180_row7_col3" class="data row7 col3" >0.19</td> + <td id="T_c8180_row7_col4" class="data row7 col4" >0.57</td> + <td id="T_c8180_row7_col5" class="data row7 col5" >0.66</td> + <td id="T_c8180_row7_col6" class="data row7 col6" >-0.72</td> + <td id="T_c8180_row7_col7" class="data row7 col7" >1.00</td> + <td id="T_c8180_row7_col8" class="data row7 col8" >0.60</td> + <td id="T_c8180_row7_col9" class="data row7 col9" >0.41</td> + <td id="T_c8180_row7_col10" class="data row7 col10" >0.41</td> + </tr> + <tr> + <th id="T_c8180_level0_row8" class="row_heading level0 row8" >s5</th> + <td id="T_c8180_row8_col0" class="data row8 col0" >0.28</td> + <td id="T_c8180_row8_col1" class="data row8 col1" >0.13</td> + <td id="T_c8180_row8_col2" class="data row8 col2" >0.43</td> + <td id="T_c8180_row8_col3" class="data row8 col3" >0.36</td> + <td id="T_c8180_row8_col4" class="data row8 col4" >0.50</td> + <td id="T_c8180_row8_col5" class="data row8 col5" >0.23</td> + <td id="T_c8180_row8_col6" class="data row8 col6" >-0.37</td> + <td id="T_c8180_row8_col7" class="data row8 col7" >0.60</td> + <td id="T_c8180_row8_col8" class="data row8 col8" >1.00</td> + <td id="T_c8180_row8_col9" class="data row8 col9" >0.52</td> + <td id="T_c8180_row8_col10" class="data row8 col10" >0.46</td> + </tr> + <tr> + <th id="T_c8180_level0_row9" class="row_heading level0 row9" >s6</th> + <td id="T_c8180_row9_col0" class="data row9 col0" >0.32</td> + <td id="T_c8180_row9_col1" class="data row9 col1" >0.27</td> + <td id="T_c8180_row9_col2" class="data row9 col2" >0.49</td> + <td id="T_c8180_row9_col3" class="data row9 col3" >0.44</td> + <td id="T_c8180_row9_col4" class="data row9 col4" >0.26</td> + <td id="T_c8180_row9_col5" class="data row9 col5" >0.18</td> + <td id="T_c8180_row9_col6" class="data row9 col6" >-0.30</td> + <td id="T_c8180_row9_col7" class="data row9 col7" >0.41</td> + <td id="T_c8180_row9_col8" class="data row9 col8" >0.52</td> + <td id="T_c8180_row9_col9" class="data row9 col9" >1.00</td> + <td id="T_c8180_row9_col10" class="data row9 col10" >0.35</td> + </tr> + <tr> + <th id="T_c8180_level0_row10" class="row_heading level0 row10" >target</th> + <td id="T_c8180_row10_col0" class="data row10 col0" >0.13</td> + <td id="T_c8180_row10_col1" class="data row10 col1" >0.27</td> + <td id="T_c8180_row10_col2" class="data row10 col2" >0.66</td> + <td id="T_c8180_row10_col3" class="data row10 col3" >0.51</td> + <td id="T_c8180_row10_col4" class="data row10 col4" >0.09</td> + <td id="T_c8180_row10_col5" class="data row10 col5" >0.09</td> + <td id="T_c8180_row10_col6" class="data row10 col6" >-0.46</td> + <td id="T_c8180_row10_col7" class="data row10 col7" >0.41</td> + <td id="T_c8180_row10_col8" class="data row10 col8" >0.46</td> + <td id="T_c8180_row10_col9" class="data row10 col9" >0.35</td> + <td id="T_c8180_row10_col10" class="data row10 col10" >1.00</td> + </tr> + </tbody> +</table> +## Scope +`model_inspector` makes some attempt to support estimators from popular +libraries other than `scikit-learn` that mimic the `scikit-learn` +interface. The following estimators are specifically supported: +- From `catboost`: + - `CatBoostClassifier` + - `CatBoostRegressor` +- From `lightgbm`: + - `LGBMClassifier` + - `LGBMRegressor` +- From `xgboost`: + - `XGBClassifier` + - `XGBRegressor` +## Install +`pip install model_inspector` +## Alternatives +### Yellowbrick +[Yellowbrick](https://www.scikit-yb.org/en/latest/) is similar to Model +Inspector in that it provides tools for visualizing the behavior of +`scikit-learn` models. +The two libraries have different designs. Yellowbrick uses `Visualizer` +objects, each class of which corresponds to a single type of +visualization. The `Visualizer` interface is similar to the +`scikit-learn` transformer and estimator interfaces. In constrast, +`model_inspector` uses `Inspector` objects that bundle together a +`scikit-learn` model, an `X` feature DataFrame, and a `y` target Series. +The `Inspector` object does the work of identifying appropriate +visualization types for the specific model and dataset in question and +exposing corresponding methods, making it easy to visualize a given +model for a given dataset in a variety of ways. +Another fundamental difference is that Yellowbrick is framed as a +machine learning *visualization* library, while Model Inspector treats +visualization as just one approach to inspecting the behavior of machine +learning models. +### SHAP +[SHAP](https://github.com/slundberg/shap) is another library that +provides a set of tools for understanding the behavior of machine +learning models. It has a somewhat similar design to Model Inspector in +that it uses `Explainer` objects to provide access to methods that are +appropriate for a given model. It has broader scope than Model Inspector +in that it supports models from frameworks such as PyTorch and +TensorFlow. It has narrower scope in that it only implements methods +based on Shapley values. +## Acknowledgments +Many aspects of this library were inspired by [FastAI +courses](https://course.fast.ai/), including bundling together a model +with data in a class and providing certain specific visualization +methods such as feature importance bar plots, feature clusters +dendrograms, tree diagrams, waterfall plots, and partial dependence +plots. Its primary contribution is to make all of these methods +available in a single convenient interface. + +%package -n python3-model-inspector +Summary: Inspect machine learning models +Provides: python-model-inspector +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-model-inspector +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> +`model_inspector` aims to help you train better +`scikit-learn`-compatible models by providing insights into their +behavior. +## Use +To use `model_inspector`, you create an `Inspector` object from a +`scikit-learn` model, a feature DataFrame `X`, and a target Series `y`. +Typically you will want to create it on held-out data, as shown below. +``` python +import sklearn.datasets +from sklearn.ensemble import RandomForestRegressor +from sklearn.model_selection import train_test_split +from model_inspector import get_inspector +``` +``` python +X, y = sklearn.datasets.load_diabetes(return_X_y=True, as_frame=True) +``` +``` python +X +``` +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + .dataframe tbody tr th { + vertical-align: top; + } + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>age</th> + <th>sex</th> + <th>bmi</th> + <th>bp</th> + <th>s1</th> + <th>s2</th> + <th>s3</th> + <th>s4</th> + <th>s5</th> + <th>s6</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>0.038076</td> + <td>0.050680</td> + <td>0.061696</td> + <td>0.021872</td> + <td>-0.044223</td> + <td>-0.034821</td> + <td>-0.043401</td> + <td>-0.002592</td> + <td>0.019907</td> + <td>-0.017646</td> + </tr> + <tr> + <th>1</th> + <td>-0.001882</td> + <td>-0.044642</td> + <td>-0.051474</td> + <td>-0.026328</td> + <td>-0.008449</td> + <td>-0.019163</td> + <td>0.074412</td> + <td>-0.039493</td> + <td>-0.068332</td> + <td>-0.092204</td> + </tr> + <tr> + <th>2</th> + <td>0.085299</td> + <td>0.050680</td> + <td>0.044451</td> + <td>-0.005670</td> + <td>-0.045599</td> + <td>-0.034194</td> + <td>-0.032356</td> + <td>-0.002592</td> + <td>0.002861</td> + <td>-0.025930</td> + </tr> + <tr> + <th>3</th> + <td>-0.089063</td> + <td>-0.044642</td> + <td>-0.011595</td> + <td>-0.036656</td> + <td>0.012191</td> + <td>0.024991</td> + <td>-0.036038</td> + <td>0.034309</td> + <td>0.022688</td> + <td>-0.009362</td> + </tr> + <tr> + <th>4</th> + <td>0.005383</td> + <td>-0.044642</td> + <td>-0.036385</td> + <td>0.021872</td> + <td>0.003935</td> + <td>0.015596</td> + <td>0.008142</td> + <td>-0.002592</td> + <td>-0.031988</td> + <td>-0.046641</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>437</th> + <td>0.041708</td> + <td>0.050680</td> + <td>0.019662</td> + <td>0.059744</td> + <td>-0.005697</td> + <td>-0.002566</td> + <td>-0.028674</td> + <td>-0.002592</td> + <td>0.031193</td> + <td>0.007207</td> + </tr> + <tr> + <th>438</th> + <td>-0.005515</td> + <td>0.050680</td> + <td>-0.015906</td> + <td>-0.067642</td> + <td>0.049341</td> + <td>0.079165</td> + <td>-0.028674</td> + <td>0.034309</td> + <td>-0.018114</td> + <td>0.044485</td> + </tr> + <tr> + <th>439</th> + <td>0.041708</td> + <td>0.050680</td> + <td>-0.015906</td> + <td>0.017293</td> + <td>-0.037344</td> + <td>-0.013840</td> + <td>-0.024993</td> + <td>-0.011080</td> + <td>-0.046883</td> + <td>0.015491</td> + </tr> + <tr> + <th>440</th> + <td>-0.045472</td> + <td>-0.044642</td> + <td>0.039062</td> + <td>0.001215</td> + <td>0.016318</td> + <td>0.015283</td> + <td>-0.028674</td> + <td>0.026560</td> + <td>0.044529</td> + <td>-0.025930</td> + </tr> + <tr> + <th>441</th> + <td>-0.045472</td> + <td>-0.044642</td> + <td>-0.073030</td> + <td>-0.081413</td> + <td>0.083740</td> + <td>0.027809</td> + <td>0.173816</td> + <td>-0.039493</td> + <td>-0.004222</td> + <td>0.003064</td> + </tr> + </tbody> +</table> +<p>442 rows × 10 columns</p> +</div> +``` python +y +``` + 0 151.0 + 1 75.0 + 2 141.0 + 3 206.0 + 4 135.0 + 437 178.0 + 438 104.0 + 439 132.0 + 440 220.0 + 441 57.0 + Name: target, Length: 442, dtype: float64 +``` python +X_train, X_test, y_train, y_test = train_test_split(X, y) +``` +``` python +rfr = RandomForestRegressor().fit(X_train, y_train) +``` +``` python +rfr.score(X_test, y_test) +``` + 0.4145806969881506 +``` python +inspector = get_inspector(rfr, X_test, y_test) +``` +You can then use various methods of `inspector` to learn about how your +model behaves on that data. +The methods that are available for a given inspector depends on the +types of its estimator and its target `y`. An attribute called `methods` +tells you what they are: +``` python +inspector.methods +``` + ['plot_feature_clusters', + 'plot_partial_dependence', + 'permutation_importance', + 'plot_permutation_importance', + 'plot_pred_vs_act', + 'plot_residuals', + 'show_correlation'] +``` python +ax = inspector.plot_feature_clusters() +``` + +``` python +most_important_features = inspector.permutation_importance().index[:2] +axes = inspector.plot_partial_dependence( + features=[*most_important_features, most_important_features] +) +axes[0, 0].get_figure().set_size_inches(12, 3) +``` + +``` python +inspector.permutation_importance() +``` + bmi 0.241886 + s5 0.153085 + sex 0.003250 + s3 0.000734 + bp 0.000461 + s4 -0.002687 + s2 -0.004366 + s1 -0.008953 + s6 -0.018925 + age -0.022768 + dtype: float64 +``` python +ax = inspector.plot_permutation_importance() +``` + +``` python +ax = inspector.plot_pred_vs_act() +``` + +``` python +axes = inspector.plot_residuals() +``` + +``` python +inspector.show_correlation() +``` +<style type="text/css"> +#T_c8180_row0_col0, #T_c8180_row1_col1, #T_c8180_row2_col2, #T_c8180_row3_col3, #T_c8180_row4_col4, #T_c8180_row5_col5, #T_c8180_row6_col6, #T_c8180_row7_col7, #T_c8180_row8_col8, #T_c8180_row9_col9, #T_c8180_row10_col10 { + background-color: #ff0000; + color: #f1f1f1; +} +#T_c8180_row0_col1, #T_c8180_row1_col0 { + background-color: #ffc6c6; + color: #000000; +} +#T_c8180_row0_col2, #T_c8180_row0_col5, #T_c8180_row2_col0, #T_c8180_row5_col0, #T_c8180_row5_col9, #T_c8180_row9_col5 { + background-color: #ffd2d2; + color: #000000; +} +#T_c8180_row0_col3, #T_c8180_row2_col5, #T_c8180_row3_col0, #T_c8180_row5_col2 { + background-color: #ffd0d0; + color: #000000; +} +#T_c8180_row0_col4, #T_c8180_row4_col0 { + background-color: #ffc4c4; + color: #000000; +} +#T_c8180_row0_col6, #T_c8180_row6_col0 { + background-color: #f4f4ff; + color: #000000; +} +#T_c8180_row0_col7, #T_c8180_row3_col7, #T_c8180_row7_col0, #T_c8180_row7_col3 { + background-color: #ffcece; + color: #000000; +} +#T_c8180_row0_col8, #T_c8180_row8_col0 { + background-color: #ffb6b6; + color: #000000; +} +#T_c8180_row0_col9, #T_c8180_row9_col0 { + background-color: #ffaeae; + color: #000000; +} +#T_c8180_row0_col10, #T_c8180_row1_col8, #T_c8180_row8_col1, #T_c8180_row10_col0 { + background-color: #ffdede; + color: #000000; +} +#T_c8180_row1_col2, #T_c8180_row1_col7, #T_c8180_row2_col1, #T_c8180_row7_col1 { + background-color: #ffb4b4; + color: #000000; +} +#T_c8180_row1_col3, #T_c8180_row3_col1 { + background-color: #ffb0b0; + color: #000000; +} +#T_c8180_row1_col4, #T_c8180_row4_col1 { + background-color: #f2f2ff; + color: #000000; +} +#T_c8180_row1_col5, #T_c8180_row5_col1 { + background-color: #ffeaea; + color: #000000; +} +#T_c8180_row1_col6, #T_c8180_row6_col1 { + background-color: #9898ff; + color: #f1f1f1; +} +#T_c8180_row1_col9, #T_c8180_row9_col1 { + background-color: #ffb8b8; + color: #000000; +} +#T_c8180_row1_col10, #T_c8180_row10_col1 { + background-color: #ffbaba; + color: #000000; +} +#T_c8180_row2_col3, #T_c8180_row3_col2 { + background-color: #ff7272; + color: #f1f1f1; +} +#T_c8180_row2_col4, #T_c8180_row4_col2 { + background-color: #ffd8d8; + color: #000000; +} +#T_c8180_row2_col6, #T_c8180_row6_col2 { + background-color: #9292ff; + color: #f1f1f1; +} +#T_c8180_row2_col7, #T_c8180_row7_col2 { + background-color: #ff8c8c; + color: #000000; +} +#T_c8180_row2_col8, #T_c8180_row8_col2 { + background-color: #ff9292; + color: #000000; +} +#T_c8180_row2_col9, #T_c8180_row9_col2 { + background-color: #ff8282; + color: #f1f1f1; +} +#T_c8180_row2_col10, #T_c8180_row5_col7, #T_c8180_row7_col5, #T_c8180_row10_col2 { + background-color: #ff5656; + color: #f1f1f1; +} +#T_c8180_row3_col4, #T_c8180_row4_col3, #T_c8180_row4_col10, #T_c8180_row5_col10, #T_c8180_row10_col4, #T_c8180_row10_col5 { + background-color: #ffe8e8; + color: #000000; +} +#T_c8180_row3_col5, #T_c8180_row5_col3 { + background-color: #fff6f6; + color: #000000; +} +#T_c8180_row3_col6, #T_c8180_row6_col3 { + background-color: #ccccff; + color: #000000; +} +#T_c8180_row3_col8, #T_c8180_row8_col3 { + background-color: #ffa2a2; + color: #000000; +} +#T_c8180_row3_col9, #T_c8180_row9_col3 { + background-color: #ff8e8e; + color: #000000; +} +#T_c8180_row3_col10, #T_c8180_row10_col3 { + background-color: #ff7c7c; + color: #f1f1f1; +} +#T_c8180_row4_col5, #T_c8180_row5_col4 { + background-color: #ff1e1e; + color: #f1f1f1; +} +#T_c8180_row4_col6, #T_c8180_row6_col4 { + background-color: #ffeeee; + color: #000000; +} +#T_c8180_row4_col7, #T_c8180_row7_col4 { + background-color: #ff6c6c; + color: #f1f1f1; +} +#T_c8180_row4_col8, #T_c8180_row8_col4 { + background-color: #ff7e7e; + color: #f1f1f1; +} +#T_c8180_row4_col9, #T_c8180_row9_col4 { + background-color: #ffbebe; + color: #000000; +} +#T_c8180_row5_col6, #T_c8180_row6_col5 { + background-color: #d6d6ff; + color: #000000; +} +#T_c8180_row5_col8, #T_c8180_row8_col5 { + background-color: #ffc2c2; + color: #000000; +} +#T_c8180_row6_col7, #T_c8180_row7_col6 { + background-color: #4646ff; + color: #f1f1f1; +} +#T_c8180_row6_col8, #T_c8180_row8_col6 { + background-color: #a0a0ff; + color: #f1f1f1; +} +#T_c8180_row6_col9, #T_c8180_row9_col6 { + background-color: #b4b4ff; + color: #000000; +} +#T_c8180_row6_col10, #T_c8180_row10_col6 { + background-color: #8a8aff; + color: #f1f1f1; +} +#T_c8180_row7_col8, #T_c8180_row8_col7 { + background-color: #ff6464; + color: #f1f1f1; +} +#T_c8180_row7_col9, #T_c8180_row7_col10, #T_c8180_row9_col7, #T_c8180_row10_col7 { + background-color: #ff9696; + color: #000000; +} +#T_c8180_row8_col9, #T_c8180_row9_col8 { + background-color: #ff7a7a; + color: #f1f1f1; +} +#T_c8180_row8_col10, #T_c8180_row10_col8 { + background-color: #ff8888; + color: #f1f1f1; +} +#T_c8180_row9_col10, #T_c8180_row10_col9 { + background-color: #ffa6a6; + color: #000000; +} +</style> +<table id="T_c8180"> + <thead> + <tr> + <th class="blank level0" > </th> + <th id="T_c8180_level0_col0" class="col_heading level0 col0" >age</th> + <th id="T_c8180_level0_col1" class="col_heading level0 col1" >sex</th> + <th id="T_c8180_level0_col2" class="col_heading level0 col2" >bmi</th> + <th id="T_c8180_level0_col3" class="col_heading level0 col3" >bp</th> + <th id="T_c8180_level0_col4" class="col_heading level0 col4" >s1</th> + <th id="T_c8180_level0_col5" class="col_heading level0 col5" >s2</th> + <th id="T_c8180_level0_col6" class="col_heading level0 col6" >s3</th> + <th id="T_c8180_level0_col7" class="col_heading level0 col7" >s4</th> + <th id="T_c8180_level0_col8" class="col_heading level0 col8" >s5</th> + <th id="T_c8180_level0_col9" class="col_heading level0 col9" >s6</th> + <th id="T_c8180_level0_col10" class="col_heading level0 col10" >target</th> + </tr> + </thead> + <tbody> + <tr> + <th id="T_c8180_level0_row0" class="row_heading level0 row0" >age</th> + <td id="T_c8180_row0_col0" class="data row0 col0" >1.00</td> + <td id="T_c8180_row0_col1" class="data row0 col1" >0.22</td> + <td id="T_c8180_row0_col2" class="data row0 col2" >0.18</td> + <td id="T_c8180_row0_col3" class="data row0 col3" >0.19</td> + <td id="T_c8180_row0_col4" class="data row0 col4" >0.23</td> + <td id="T_c8180_row0_col5" class="data row0 col5" >0.18</td> + <td id="T_c8180_row0_col6" class="data row0 col6" >-0.04</td> + <td id="T_c8180_row0_col7" class="data row0 col7" >0.19</td> + <td id="T_c8180_row0_col8" class="data row0 col8" >0.28</td> + <td id="T_c8180_row0_col9" class="data row0 col9" >0.32</td> + <td id="T_c8180_row0_col10" class="data row0 col10" >0.13</td> + </tr> + <tr> + <th id="T_c8180_level0_row1" class="row_heading level0 row1" >sex</th> + <td id="T_c8180_row1_col0" class="data row1 col0" >0.22</td> + <td id="T_c8180_row1_col1" class="data row1 col1" >1.00</td> + <td id="T_c8180_row1_col2" class="data row1 col2" >0.29</td> + <td id="T_c8180_row1_col3" class="data row1 col3" >0.31</td> + <td id="T_c8180_row1_col4" class="data row1 col4" >-0.05</td> + <td id="T_c8180_row1_col5" class="data row1 col5" >0.08</td> + <td id="T_c8180_row1_col6" class="data row1 col6" >-0.41</td> + <td id="T_c8180_row1_col7" class="data row1 col7" >0.30</td> + <td id="T_c8180_row1_col8" class="data row1 col8" >0.13</td> + <td id="T_c8180_row1_col9" class="data row1 col9" >0.27</td> + <td id="T_c8180_row1_col10" class="data row1 col10" >0.27</td> + </tr> + <tr> + <th id="T_c8180_level0_row2" class="row_heading level0 row2" >bmi</th> + <td id="T_c8180_row2_col0" class="data row2 col0" >0.18</td> + <td id="T_c8180_row2_col1" class="data row2 col1" >0.29</td> + <td id="T_c8180_row2_col2" class="data row2 col2" >1.00</td> + <td id="T_c8180_row2_col3" class="data row2 col3" >0.55</td> + <td id="T_c8180_row2_col4" class="data row2 col4" >0.16</td> + <td id="T_c8180_row2_col5" class="data row2 col5" >0.18</td> + <td id="T_c8180_row2_col6" class="data row2 col6" >-0.43</td> + <td id="T_c8180_row2_col7" class="data row2 col7" >0.45</td> + <td id="T_c8180_row2_col8" class="data row2 col8" >0.43</td> + <td id="T_c8180_row2_col9" class="data row2 col9" >0.49</td> + <td id="T_c8180_row2_col10" class="data row2 col10" >0.66</td> + </tr> + <tr> + <th id="T_c8180_level0_row3" class="row_heading level0 row3" >bp</th> + <td id="T_c8180_row3_col0" class="data row3 col0" >0.19</td> + <td id="T_c8180_row3_col1" class="data row3 col1" >0.31</td> + <td id="T_c8180_row3_col2" class="data row3 col2" >0.55</td> + <td id="T_c8180_row3_col3" class="data row3 col3" >1.00</td> + <td id="T_c8180_row3_col4" class="data row3 col4" >0.09</td> + <td id="T_c8180_row3_col5" class="data row3 col5" >0.04</td> + <td id="T_c8180_row3_col6" class="data row3 col6" >-0.20</td> + <td id="T_c8180_row3_col7" class="data row3 col7" >0.19</td> + <td id="T_c8180_row3_col8" class="data row3 col8" >0.36</td> + <td id="T_c8180_row3_col9" class="data row3 col9" >0.44</td> + <td id="T_c8180_row3_col10" class="data row3 col10" >0.51</td> + </tr> + <tr> + <th id="T_c8180_level0_row4" class="row_heading level0 row4" >s1</th> + <td id="T_c8180_row4_col0" class="data row4 col0" >0.23</td> + <td id="T_c8180_row4_col1" class="data row4 col1" >-0.05</td> + <td id="T_c8180_row4_col2" class="data row4 col2" >0.16</td> + <td id="T_c8180_row4_col3" class="data row4 col3" >0.09</td> + <td id="T_c8180_row4_col4" class="data row4 col4" >1.00</td> + <td id="T_c8180_row4_col5" class="data row4 col5" >0.88</td> + <td id="T_c8180_row4_col6" class="data row4 col6" >0.07</td> + <td id="T_c8180_row4_col7" class="data row4 col7" >0.57</td> + <td id="T_c8180_row4_col8" class="data row4 col8" >0.50</td> + <td id="T_c8180_row4_col9" class="data row4 col9" >0.26</td> + <td id="T_c8180_row4_col10" class="data row4 col10" >0.09</td> + </tr> + <tr> + <th id="T_c8180_level0_row5" class="row_heading level0 row5" >s2</th> + <td id="T_c8180_row5_col0" class="data row5 col0" >0.18</td> + <td id="T_c8180_row5_col1" class="data row5 col1" >0.08</td> + <td id="T_c8180_row5_col2" class="data row5 col2" >0.18</td> + <td id="T_c8180_row5_col3" class="data row5 col3" >0.04</td> + <td id="T_c8180_row5_col4" class="data row5 col4" >0.88</td> + <td id="T_c8180_row5_col5" class="data row5 col5" >1.00</td> + <td id="T_c8180_row5_col6" class="data row5 col6" >-0.16</td> + <td id="T_c8180_row5_col7" class="data row5 col7" >0.66</td> + <td id="T_c8180_row5_col8" class="data row5 col8" >0.23</td> + <td id="T_c8180_row5_col9" class="data row5 col9" >0.18</td> + <td id="T_c8180_row5_col10" class="data row5 col10" >0.09</td> + </tr> + <tr> + <th id="T_c8180_level0_row6" class="row_heading level0 row6" >s3</th> + <td id="T_c8180_row6_col0" class="data row6 col0" >-0.04</td> + <td id="T_c8180_row6_col1" class="data row6 col1" >-0.41</td> + <td id="T_c8180_row6_col2" class="data row6 col2" >-0.43</td> + <td id="T_c8180_row6_col3" class="data row6 col3" >-0.20</td> + <td id="T_c8180_row6_col4" class="data row6 col4" >0.07</td> + <td id="T_c8180_row6_col5" class="data row6 col5" >-0.16</td> + <td id="T_c8180_row6_col6" class="data row6 col6" >1.00</td> + <td id="T_c8180_row6_col7" class="data row6 col7" >-0.72</td> + <td id="T_c8180_row6_col8" class="data row6 col8" >-0.37</td> + <td id="T_c8180_row6_col9" class="data row6 col9" >-0.30</td> + <td id="T_c8180_row6_col10" class="data row6 col10" >-0.46</td> + </tr> + <tr> + <th id="T_c8180_level0_row7" class="row_heading level0 row7" >s4</th> + <td id="T_c8180_row7_col0" class="data row7 col0" >0.19</td> + <td id="T_c8180_row7_col1" class="data row7 col1" >0.30</td> + <td id="T_c8180_row7_col2" class="data row7 col2" >0.45</td> + <td id="T_c8180_row7_col3" class="data row7 col3" >0.19</td> + <td id="T_c8180_row7_col4" class="data row7 col4" >0.57</td> + <td id="T_c8180_row7_col5" class="data row7 col5" >0.66</td> + <td id="T_c8180_row7_col6" class="data row7 col6" >-0.72</td> + <td id="T_c8180_row7_col7" class="data row7 col7" >1.00</td> + <td id="T_c8180_row7_col8" class="data row7 col8" >0.60</td> + <td id="T_c8180_row7_col9" class="data row7 col9" >0.41</td> + <td id="T_c8180_row7_col10" class="data row7 col10" >0.41</td> + </tr> + <tr> + <th id="T_c8180_level0_row8" class="row_heading level0 row8" >s5</th> + <td id="T_c8180_row8_col0" class="data row8 col0" >0.28</td> + <td id="T_c8180_row8_col1" class="data row8 col1" >0.13</td> + <td id="T_c8180_row8_col2" class="data row8 col2" >0.43</td> + <td id="T_c8180_row8_col3" class="data row8 col3" >0.36</td> + <td id="T_c8180_row8_col4" class="data row8 col4" >0.50</td> + <td id="T_c8180_row8_col5" class="data row8 col5" >0.23</td> + <td id="T_c8180_row8_col6" class="data row8 col6" >-0.37</td> + <td id="T_c8180_row8_col7" class="data row8 col7" >0.60</td> + <td id="T_c8180_row8_col8" class="data row8 col8" >1.00</td> + <td id="T_c8180_row8_col9" class="data row8 col9" >0.52</td> + <td id="T_c8180_row8_col10" class="data row8 col10" >0.46</td> + </tr> + <tr> + <th id="T_c8180_level0_row9" class="row_heading level0 row9" >s6</th> + <td id="T_c8180_row9_col0" class="data row9 col0" >0.32</td> + <td id="T_c8180_row9_col1" class="data row9 col1" >0.27</td> + <td id="T_c8180_row9_col2" class="data row9 col2" >0.49</td> + <td id="T_c8180_row9_col3" class="data row9 col3" >0.44</td> + <td id="T_c8180_row9_col4" class="data row9 col4" >0.26</td> + <td id="T_c8180_row9_col5" class="data row9 col5" >0.18</td> + <td id="T_c8180_row9_col6" class="data row9 col6" >-0.30</td> + <td id="T_c8180_row9_col7" class="data row9 col7" >0.41</td> + <td id="T_c8180_row9_col8" class="data row9 col8" >0.52</td> + <td id="T_c8180_row9_col9" class="data row9 col9" >1.00</td> + <td id="T_c8180_row9_col10" class="data row9 col10" >0.35</td> + </tr> + <tr> + <th id="T_c8180_level0_row10" class="row_heading level0 row10" >target</th> + <td id="T_c8180_row10_col0" class="data row10 col0" >0.13</td> + <td id="T_c8180_row10_col1" class="data row10 col1" >0.27</td> + <td id="T_c8180_row10_col2" class="data row10 col2" >0.66</td> + <td id="T_c8180_row10_col3" class="data row10 col3" >0.51</td> + <td id="T_c8180_row10_col4" class="data row10 col4" >0.09</td> + <td id="T_c8180_row10_col5" class="data row10 col5" >0.09</td> + <td id="T_c8180_row10_col6" class="data row10 col6" >-0.46</td> + <td id="T_c8180_row10_col7" class="data row10 col7" >0.41</td> + <td id="T_c8180_row10_col8" class="data row10 col8" >0.46</td> + <td id="T_c8180_row10_col9" class="data row10 col9" >0.35</td> + <td id="T_c8180_row10_col10" class="data row10 col10" >1.00</td> + </tr> + </tbody> +</table> +## Scope +`model_inspector` makes some attempt to support estimators from popular +libraries other than `scikit-learn` that mimic the `scikit-learn` +interface. The following estimators are specifically supported: +- From `catboost`: + - `CatBoostClassifier` + - `CatBoostRegressor` +- From `lightgbm`: + - `LGBMClassifier` + - `LGBMRegressor` +- From `xgboost`: + - `XGBClassifier` + - `XGBRegressor` +## Install +`pip install model_inspector` +## Alternatives +### Yellowbrick +[Yellowbrick](https://www.scikit-yb.org/en/latest/) is similar to Model +Inspector in that it provides tools for visualizing the behavior of +`scikit-learn` models. +The two libraries have different designs. Yellowbrick uses `Visualizer` +objects, each class of which corresponds to a single type of +visualization. The `Visualizer` interface is similar to the +`scikit-learn` transformer and estimator interfaces. In constrast, +`model_inspector` uses `Inspector` objects that bundle together a +`scikit-learn` model, an `X` feature DataFrame, and a `y` target Series. +The `Inspector` object does the work of identifying appropriate +visualization types for the specific model and dataset in question and +exposing corresponding methods, making it easy to visualize a given +model for a given dataset in a variety of ways. +Another fundamental difference is that Yellowbrick is framed as a +machine learning *visualization* library, while Model Inspector treats +visualization as just one approach to inspecting the behavior of machine +learning models. +### SHAP +[SHAP](https://github.com/slundberg/shap) is another library that +provides a set of tools for understanding the behavior of machine +learning models. It has a somewhat similar design to Model Inspector in +that it uses `Explainer` objects to provide access to methods that are +appropriate for a given model. It has broader scope than Model Inspector +in that it supports models from frameworks such as PyTorch and +TensorFlow. It has narrower scope in that it only implements methods +based on Shapley values. +## Acknowledgments +Many aspects of this library were inspired by [FastAI +courses](https://course.fast.ai/), including bundling together a model +with data in a class and providing certain specific visualization +methods such as feature importance bar plots, feature clusters +dendrograms, tree diagrams, waterfall plots, and partial dependence +plots. Its primary contribution is to make all of these methods +available in a single convenient interface. + +%package help +Summary: Development documents and examples for model-inspector +Provides: python3-model-inspector-doc +%description help +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> +`model_inspector` aims to help you train better +`scikit-learn`-compatible models by providing insights into their +behavior. +## Use +To use `model_inspector`, you create an `Inspector` object from a +`scikit-learn` model, a feature DataFrame `X`, and a target Series `y`. +Typically you will want to create it on held-out data, as shown below. +``` python +import sklearn.datasets +from sklearn.ensemble import RandomForestRegressor +from sklearn.model_selection import train_test_split +from model_inspector import get_inspector +``` +``` python +X, y = sklearn.datasets.load_diabetes(return_X_y=True, as_frame=True) +``` +``` python +X +``` +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + .dataframe tbody tr th { + vertical-align: top; + } + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>age</th> + <th>sex</th> + <th>bmi</th> + <th>bp</th> + <th>s1</th> + <th>s2</th> + <th>s3</th> + <th>s4</th> + <th>s5</th> + <th>s6</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>0.038076</td> + <td>0.050680</td> + <td>0.061696</td> + <td>0.021872</td> + <td>-0.044223</td> + <td>-0.034821</td> + <td>-0.043401</td> + <td>-0.002592</td> + <td>0.019907</td> + <td>-0.017646</td> + </tr> + <tr> + <th>1</th> + <td>-0.001882</td> + <td>-0.044642</td> + <td>-0.051474</td> + <td>-0.026328</td> + <td>-0.008449</td> + <td>-0.019163</td> + <td>0.074412</td> + <td>-0.039493</td> + <td>-0.068332</td> + <td>-0.092204</td> + </tr> + <tr> + <th>2</th> + <td>0.085299</td> + <td>0.050680</td> + <td>0.044451</td> + <td>-0.005670</td> + <td>-0.045599</td> + <td>-0.034194</td> + <td>-0.032356</td> + <td>-0.002592</td> + <td>0.002861</td> + <td>-0.025930</td> + </tr> + <tr> + <th>3</th> + <td>-0.089063</td> + <td>-0.044642</td> + <td>-0.011595</td> + <td>-0.036656</td> + <td>0.012191</td> + <td>0.024991</td> + <td>-0.036038</td> + <td>0.034309</td> + <td>0.022688</td> + <td>-0.009362</td> + </tr> + <tr> + <th>4</th> + <td>0.005383</td> + <td>-0.044642</td> + <td>-0.036385</td> + <td>0.021872</td> + <td>0.003935</td> + <td>0.015596</td> + <td>0.008142</td> + <td>-0.002592</td> + <td>-0.031988</td> + <td>-0.046641</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>437</th> + <td>0.041708</td> + <td>0.050680</td> + <td>0.019662</td> + <td>0.059744</td> + <td>-0.005697</td> + <td>-0.002566</td> + <td>-0.028674</td> + <td>-0.002592</td> + <td>0.031193</td> + <td>0.007207</td> + </tr> + <tr> + <th>438</th> + <td>-0.005515</td> + <td>0.050680</td> + <td>-0.015906</td> + <td>-0.067642</td> + <td>0.049341</td> + <td>0.079165</td> + <td>-0.028674</td> + <td>0.034309</td> + <td>-0.018114</td> + <td>0.044485</td> + </tr> + <tr> + <th>439</th> + <td>0.041708</td> + <td>0.050680</td> + <td>-0.015906</td> + <td>0.017293</td> + <td>-0.037344</td> + <td>-0.013840</td> + <td>-0.024993</td> + <td>-0.011080</td> + <td>-0.046883</td> + <td>0.015491</td> + </tr> + <tr> + <th>440</th> + <td>-0.045472</td> + <td>-0.044642</td> + <td>0.039062</td> + <td>0.001215</td> + <td>0.016318</td> + <td>0.015283</td> + <td>-0.028674</td> + <td>0.026560</td> + <td>0.044529</td> + <td>-0.025930</td> + </tr> + <tr> + <th>441</th> + <td>-0.045472</td> + <td>-0.044642</td> + <td>-0.073030</td> + <td>-0.081413</td> + <td>0.083740</td> + <td>0.027809</td> + <td>0.173816</td> + <td>-0.039493</td> + <td>-0.004222</td> + <td>0.003064</td> + </tr> + </tbody> +</table> +<p>442 rows × 10 columns</p> +</div> +``` python +y +``` + 0 151.0 + 1 75.0 + 2 141.0 + 3 206.0 + 4 135.0 + 437 178.0 + 438 104.0 + 439 132.0 + 440 220.0 + 441 57.0 + Name: target, Length: 442, dtype: float64 +``` python +X_train, X_test, y_train, y_test = train_test_split(X, y) +``` +``` python +rfr = RandomForestRegressor().fit(X_train, y_train) +``` +``` python +rfr.score(X_test, y_test) +``` + 0.4145806969881506 +``` python +inspector = get_inspector(rfr, X_test, y_test) +``` +You can then use various methods of `inspector` to learn about how your +model behaves on that data. +The methods that are available for a given inspector depends on the +types of its estimator and its target `y`. An attribute called `methods` +tells you what they are: +``` python +inspector.methods +``` + ['plot_feature_clusters', + 'plot_partial_dependence', + 'permutation_importance', + 'plot_permutation_importance', + 'plot_pred_vs_act', + 'plot_residuals', + 'show_correlation'] +``` python +ax = inspector.plot_feature_clusters() +``` + +``` python +most_important_features = inspector.permutation_importance().index[:2] +axes = inspector.plot_partial_dependence( + features=[*most_important_features, most_important_features] +) +axes[0, 0].get_figure().set_size_inches(12, 3) +``` + +``` python +inspector.permutation_importance() +``` + bmi 0.241886 + s5 0.153085 + sex 0.003250 + s3 0.000734 + bp 0.000461 + s4 -0.002687 + s2 -0.004366 + s1 -0.008953 + s6 -0.018925 + age -0.022768 + dtype: float64 +``` python +ax = inspector.plot_permutation_importance() +``` + +``` python +ax = inspector.plot_pred_vs_act() +``` + +``` python +axes = inspector.plot_residuals() +``` + +``` python +inspector.show_correlation() +``` +<style type="text/css"> +#T_c8180_row0_col0, #T_c8180_row1_col1, #T_c8180_row2_col2, #T_c8180_row3_col3, #T_c8180_row4_col4, #T_c8180_row5_col5, #T_c8180_row6_col6, #T_c8180_row7_col7, #T_c8180_row8_col8, #T_c8180_row9_col9, #T_c8180_row10_col10 { + background-color: #ff0000; + color: #f1f1f1; +} +#T_c8180_row0_col1, #T_c8180_row1_col0 { + background-color: #ffc6c6; + color: #000000; +} +#T_c8180_row0_col2, #T_c8180_row0_col5, #T_c8180_row2_col0, #T_c8180_row5_col0, #T_c8180_row5_col9, #T_c8180_row9_col5 { + background-color: #ffd2d2; + color: #000000; +} +#T_c8180_row0_col3, #T_c8180_row2_col5, #T_c8180_row3_col0, #T_c8180_row5_col2 { + background-color: #ffd0d0; + color: #000000; +} +#T_c8180_row0_col4, #T_c8180_row4_col0 { + background-color: #ffc4c4; + color: #000000; +} +#T_c8180_row0_col6, #T_c8180_row6_col0 { + background-color: #f4f4ff; + color: #000000; +} +#T_c8180_row0_col7, #T_c8180_row3_col7, #T_c8180_row7_col0, #T_c8180_row7_col3 { + 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#ffc2c2; + color: #000000; +} +#T_c8180_row6_col7, #T_c8180_row7_col6 { + background-color: #4646ff; + color: #f1f1f1; +} +#T_c8180_row6_col8, #T_c8180_row8_col6 { + background-color: #a0a0ff; + color: #f1f1f1; +} +#T_c8180_row6_col9, #T_c8180_row9_col6 { + background-color: #b4b4ff; + color: #000000; +} +#T_c8180_row6_col10, #T_c8180_row10_col6 { + background-color: #8a8aff; + color: #f1f1f1; +} +#T_c8180_row7_col8, #T_c8180_row8_col7 { + background-color: #ff6464; + color: #f1f1f1; +} +#T_c8180_row7_col9, #T_c8180_row7_col10, #T_c8180_row9_col7, #T_c8180_row10_col7 { + background-color: #ff9696; + color: #000000; +} +#T_c8180_row8_col9, #T_c8180_row9_col8 { + background-color: #ff7a7a; + color: #f1f1f1; +} +#T_c8180_row8_col10, #T_c8180_row10_col8 { + background-color: #ff8888; + color: #f1f1f1; +} +#T_c8180_row9_col10, #T_c8180_row10_col9 { + background-color: #ffa6a6; + color: #000000; +} +</style> +<table id="T_c8180"> + <thead> + <tr> + <th class="blank level0" > </th> + <th id="T_c8180_level0_col0" class="col_heading level0 col0" >age</th> + <th id="T_c8180_level0_col1" class="col_heading level0 col1" >sex</th> + <th id="T_c8180_level0_col2" class="col_heading level0 col2" >bmi</th> + <th id="T_c8180_level0_col3" class="col_heading level0 col3" >bp</th> + <th id="T_c8180_level0_col4" class="col_heading level0 col4" >s1</th> + <th id="T_c8180_level0_col5" class="col_heading level0 col5" >s2</th> + <th id="T_c8180_level0_col6" class="col_heading level0 col6" >s3</th> + <th id="T_c8180_level0_col7" class="col_heading level0 col7" >s4</th> + <th id="T_c8180_level0_col8" class="col_heading level0 col8" >s5</th> + <th id="T_c8180_level0_col9" class="col_heading level0 col9" >s6</th> + <th id="T_c8180_level0_col10" class="col_heading level0 col10" >target</th> + </tr> + </thead> + <tbody> + <tr> + <th id="T_c8180_level0_row0" class="row_heading level0 row0" >age</th> + <td id="T_c8180_row0_col0" class="data row0 col0" >1.00</td> + <td id="T_c8180_row0_col1" class="data row0 col1" >0.22</td> + <td id="T_c8180_row0_col2" class="data row0 col2" >0.18</td> + <td id="T_c8180_row0_col3" class="data row0 col3" >0.19</td> + <td id="T_c8180_row0_col4" class="data row0 col4" >0.23</td> + <td id="T_c8180_row0_col5" class="data row0 col5" >0.18</td> + <td id="T_c8180_row0_col6" class="data row0 col6" >-0.04</td> + <td id="T_c8180_row0_col7" class="data row0 col7" >0.19</td> + <td id="T_c8180_row0_col8" class="data row0 col8" >0.28</td> + <td id="T_c8180_row0_col9" class="data row0 col9" >0.32</td> + <td id="T_c8180_row0_col10" class="data row0 col10" >0.13</td> + </tr> + <tr> + <th id="T_c8180_level0_row1" class="row_heading level0 row1" >sex</th> + <td id="T_c8180_row1_col0" class="data row1 col0" >0.22</td> + <td id="T_c8180_row1_col1" class="data row1 col1" >1.00</td> + <td id="T_c8180_row1_col2" class="data row1 col2" >0.29</td> + <td id="T_c8180_row1_col3" class="data row1 col3" >0.31</td> + <td id="T_c8180_row1_col4" class="data row1 col4" >-0.05</td> + <td id="T_c8180_row1_col5" class="data row1 col5" >0.08</td> + <td id="T_c8180_row1_col6" class="data row1 col6" >-0.41</td> + <td id="T_c8180_row1_col7" class="data row1 col7" >0.30</td> + <td id="T_c8180_row1_col8" class="data row1 col8" >0.13</td> + <td id="T_c8180_row1_col9" class="data row1 col9" >0.27</td> + <td id="T_c8180_row1_col10" class="data row1 col10" >0.27</td> + </tr> + <tr> + <th id="T_c8180_level0_row2" class="row_heading level0 row2" >bmi</th> + <td id="T_c8180_row2_col0" class="data row2 col0" >0.18</td> + <td id="T_c8180_row2_col1" class="data row2 col1" >0.29</td> + <td id="T_c8180_row2_col2" class="data row2 col2" >1.00</td> + <td id="T_c8180_row2_col3" class="data row2 col3" >0.55</td> + <td id="T_c8180_row2_col4" class="data row2 col4" >0.16</td> + <td id="T_c8180_row2_col5" class="data row2 col5" >0.18</td> + <td id="T_c8180_row2_col6" class="data row2 col6" >-0.43</td> + <td id="T_c8180_row2_col7" class="data row2 col7" >0.45</td> + <td id="T_c8180_row2_col8" class="data row2 col8" >0.43</td> + <td id="T_c8180_row2_col9" class="data row2 col9" >0.49</td> + <td id="T_c8180_row2_col10" class="data row2 col10" >0.66</td> + </tr> + <tr> + <th id="T_c8180_level0_row3" class="row_heading level0 row3" >bp</th> + <td id="T_c8180_row3_col0" class="data row3 col0" >0.19</td> + <td id="T_c8180_row3_col1" class="data row3 col1" >0.31</td> + <td id="T_c8180_row3_col2" class="data row3 col2" >0.55</td> + <td id="T_c8180_row3_col3" class="data row3 col3" >1.00</td> + <td id="T_c8180_row3_col4" class="data row3 col4" >0.09</td> + <td id="T_c8180_row3_col5" class="data row3 col5" >0.04</td> + <td id="T_c8180_row3_col6" class="data row3 col6" >-0.20</td> + <td id="T_c8180_row3_col7" class="data row3 col7" >0.19</td> + <td id="T_c8180_row3_col8" class="data row3 col8" >0.36</td> + <td id="T_c8180_row3_col9" class="data row3 col9" >0.44</td> + <td id="T_c8180_row3_col10" class="data row3 col10" >0.51</td> + </tr> + <tr> + <th id="T_c8180_level0_row4" class="row_heading level0 row4" >s1</th> + <td id="T_c8180_row4_col0" class="data row4 col0" >0.23</td> + <td id="T_c8180_row4_col1" class="data row4 col1" >-0.05</td> + <td id="T_c8180_row4_col2" class="data row4 col2" >0.16</td> + <td id="T_c8180_row4_col3" class="data row4 col3" >0.09</td> + <td id="T_c8180_row4_col4" class="data row4 col4" >1.00</td> + <td id="T_c8180_row4_col5" class="data row4 col5" >0.88</td> + <td id="T_c8180_row4_col6" class="data row4 col6" >0.07</td> + <td id="T_c8180_row4_col7" class="data row4 col7" >0.57</td> + <td id="T_c8180_row4_col8" class="data row4 col8" >0.50</td> + <td id="T_c8180_row4_col9" class="data row4 col9" >0.26</td> + <td id="T_c8180_row4_col10" class="data row4 col10" >0.09</td> + </tr> + <tr> + <th id="T_c8180_level0_row5" class="row_heading level0 row5" >s2</th> + <td id="T_c8180_row5_col0" class="data row5 col0" >0.18</td> + <td id="T_c8180_row5_col1" class="data row5 col1" >0.08</td> + <td id="T_c8180_row5_col2" class="data row5 col2" >0.18</td> + <td id="T_c8180_row5_col3" class="data row5 col3" >0.04</td> + <td id="T_c8180_row5_col4" class="data row5 col4" >0.88</td> + <td id="T_c8180_row5_col5" class="data row5 col5" >1.00</td> + <td id="T_c8180_row5_col6" class="data row5 col6" >-0.16</td> + <td id="T_c8180_row5_col7" class="data row5 col7" >0.66</td> + <td id="T_c8180_row5_col8" class="data row5 col8" >0.23</td> + <td id="T_c8180_row5_col9" class="data row5 col9" >0.18</td> + <td id="T_c8180_row5_col10" class="data row5 col10" >0.09</td> + </tr> + <tr> + <th id="T_c8180_level0_row6" class="row_heading level0 row6" >s3</th> + <td id="T_c8180_row6_col0" class="data row6 col0" >-0.04</td> + <td id="T_c8180_row6_col1" class="data row6 col1" >-0.41</td> + <td id="T_c8180_row6_col2" class="data row6 col2" >-0.43</td> + <td id="T_c8180_row6_col3" class="data row6 col3" >-0.20</td> + <td id="T_c8180_row6_col4" class="data row6 col4" >0.07</td> + <td id="T_c8180_row6_col5" class="data row6 col5" >-0.16</td> + <td id="T_c8180_row6_col6" class="data row6 col6" >1.00</td> + <td id="T_c8180_row6_col7" class="data row6 col7" >-0.72</td> + <td id="T_c8180_row6_col8" class="data row6 col8" >-0.37</td> + <td id="T_c8180_row6_col9" class="data row6 col9" >-0.30</td> + <td id="T_c8180_row6_col10" class="data row6 col10" >-0.46</td> + </tr> + <tr> + <th id="T_c8180_level0_row7" class="row_heading level0 row7" >s4</th> + <td id="T_c8180_row7_col0" class="data row7 col0" >0.19</td> + <td id="T_c8180_row7_col1" class="data row7 col1" >0.30</td> + <td id="T_c8180_row7_col2" class="data row7 col2" >0.45</td> + <td id="T_c8180_row7_col3" class="data row7 col3" >0.19</td> + <td id="T_c8180_row7_col4" class="data row7 col4" >0.57</td> + <td id="T_c8180_row7_col5" class="data row7 col5" >0.66</td> + <td id="T_c8180_row7_col6" class="data row7 col6" >-0.72</td> + <td id="T_c8180_row7_col7" class="data row7 col7" >1.00</td> + <td id="T_c8180_row7_col8" class="data row7 col8" >0.60</td> + <td id="T_c8180_row7_col9" class="data row7 col9" >0.41</td> + <td id="T_c8180_row7_col10" class="data row7 col10" >0.41</td> + </tr> + <tr> + <th id="T_c8180_level0_row8" class="row_heading level0 row8" >s5</th> + <td id="T_c8180_row8_col0" class="data row8 col0" >0.28</td> + <td id="T_c8180_row8_col1" class="data row8 col1" >0.13</td> + <td id="T_c8180_row8_col2" class="data row8 col2" >0.43</td> + <td id="T_c8180_row8_col3" class="data row8 col3" >0.36</td> + <td id="T_c8180_row8_col4" class="data row8 col4" >0.50</td> + <td id="T_c8180_row8_col5" class="data row8 col5" >0.23</td> + <td id="T_c8180_row8_col6" class="data row8 col6" >-0.37</td> + <td id="T_c8180_row8_col7" class="data row8 col7" >0.60</td> + <td id="T_c8180_row8_col8" class="data row8 col8" >1.00</td> + <td id="T_c8180_row8_col9" class="data row8 col9" >0.52</td> + <td id="T_c8180_row8_col10" class="data row8 col10" >0.46</td> + </tr> + <tr> + <th id="T_c8180_level0_row9" class="row_heading level0 row9" >s6</th> + <td id="T_c8180_row9_col0" class="data row9 col0" >0.32</td> + <td id="T_c8180_row9_col1" class="data row9 col1" >0.27</td> + <td id="T_c8180_row9_col2" class="data row9 col2" >0.49</td> + <td id="T_c8180_row9_col3" class="data row9 col3" >0.44</td> + <td id="T_c8180_row9_col4" class="data row9 col4" >0.26</td> + <td id="T_c8180_row9_col5" class="data row9 col5" >0.18</td> + <td id="T_c8180_row9_col6" class="data row9 col6" >-0.30</td> + <td id="T_c8180_row9_col7" class="data row9 col7" >0.41</td> + <td id="T_c8180_row9_col8" class="data row9 col8" >0.52</td> + <td id="T_c8180_row9_col9" class="data row9 col9" >1.00</td> + <td id="T_c8180_row9_col10" class="data row9 col10" >0.35</td> + </tr> + <tr> + <th id="T_c8180_level0_row10" class="row_heading level0 row10" >target</th> + <td id="T_c8180_row10_col0" class="data row10 col0" >0.13</td> + <td id="T_c8180_row10_col1" class="data row10 col1" >0.27</td> + <td id="T_c8180_row10_col2" class="data row10 col2" >0.66</td> + <td id="T_c8180_row10_col3" class="data row10 col3" >0.51</td> + <td id="T_c8180_row10_col4" class="data row10 col4" >0.09</td> + <td id="T_c8180_row10_col5" class="data row10 col5" >0.09</td> + <td id="T_c8180_row10_col6" class="data row10 col6" >-0.46</td> + <td id="T_c8180_row10_col7" class="data row10 col7" >0.41</td> + <td id="T_c8180_row10_col8" class="data row10 col8" >0.46</td> + <td id="T_c8180_row10_col9" class="data row10 col9" >0.35</td> + <td id="T_c8180_row10_col10" class="data row10 col10" >1.00</td> + </tr> + </tbody> +</table> +## Scope +`model_inspector` makes some attempt to support estimators from popular +libraries other than `scikit-learn` that mimic the `scikit-learn` +interface. The following estimators are specifically supported: +- From `catboost`: + - `CatBoostClassifier` + - `CatBoostRegressor` +- From `lightgbm`: + - `LGBMClassifier` + - `LGBMRegressor` +- From `xgboost`: + - `XGBClassifier` + - `XGBRegressor` +## Install +`pip install model_inspector` +## Alternatives +### Yellowbrick +[Yellowbrick](https://www.scikit-yb.org/en/latest/) is similar to Model +Inspector in that it provides tools for visualizing the behavior of +`scikit-learn` models. +The two libraries have different designs. Yellowbrick uses `Visualizer` +objects, each class of which corresponds to a single type of +visualization. The `Visualizer` interface is similar to the +`scikit-learn` transformer and estimator interfaces. In constrast, +`model_inspector` uses `Inspector` objects that bundle together a +`scikit-learn` model, an `X` feature DataFrame, and a `y` target Series. +The `Inspector` object does the work of identifying appropriate +visualization types for the specific model and dataset in question and +exposing corresponding methods, making it easy to visualize a given +model for a given dataset in a variety of ways. +Another fundamental difference is that Yellowbrick is framed as a +machine learning *visualization* library, while Model Inspector treats +visualization as just one approach to inspecting the behavior of machine +learning models. +### SHAP +[SHAP](https://github.com/slundberg/shap) is another library that +provides a set of tools for understanding the behavior of machine +learning models. It has a somewhat similar design to Model Inspector in +that it uses `Explainer` objects to provide access to methods that are +appropriate for a given model. It has broader scope than Model Inspector +in that it supports models from frameworks such as PyTorch and +TensorFlow. It has narrower scope in that it only implements methods +based on Shapley values. +## Acknowledgments +Many aspects of this library were inspired by [FastAI +courses](https://course.fast.ai/), including bundling together a model +with data in a class and providing certain specific visualization +methods such as feature importance bar plots, feature clusters +dendrograms, tree diagrams, waterfall plots, and partial dependence +plots. Its primary contribution is to make all of these methods +available in a single convenient interface. + +%prep +%autosetup -n model-inspector-0.27.4 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-model-inspector -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.27.4-1 +- Package Spec generated @@ -0,0 +1 @@ +eb4d27e8b63050aafa897d107e54dd7b model_inspector-0.27.4.tar.gz |