.. _sec_tabularinterpretability:

Interpretable rule-based modeling
=================================


*Note*: This addition was made through collaboration with `the Yu
Group <https://www.stat.berkeley.edu/~yugroup/>`__ of Statistics and
EECS from UC Berkeley.

**Tip**: Prior to reading this tutorial, it is recommended to have a
basic understanding of the TabularPredictor API covered in
:ref:`sec_tabularquick`.

In this tutorial, we will explain how to automatically use interpretable
models powered by integration with `🔍 the imodels
package <https://github.com/csinva/imodels>`__. This allows for
automatically learning models based on rules which are extremely concise
and can be useful for (1) understanding data or (2) building a
transparent predictive model.

*Note*: ``imodels`` must be installed for this tutorial. You can ensure
``imodels`` is installed via ``pip install autogluon.tabular[imodels]``.
``imodels`` is not installed by default.

Begin by loading in data to predict. Note: interpretable rule-based
modeling is currently only supported for binary classification.

.. code:: python

    from autogluon.tabular import TabularDataset, TabularPredictor
    train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
    subsample_size = 500  # subsample subset of data for faster demo, try setting this to much larger values
    train_data = train_data.sample(n=subsample_size, random_state=0)
    train_data.head()




.. raw:: html

    <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>workclass</th>
          <th>fnlwgt</th>
          <th>education</th>
          <th>education-num</th>
          <th>marital-status</th>
          <th>occupation</th>
          <th>relationship</th>
          <th>race</th>
          <th>sex</th>
          <th>capital-gain</th>
          <th>capital-loss</th>
          <th>hours-per-week</th>
          <th>native-country</th>
          <th>class</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>6118</th>
          <td>51</td>
          <td>Private</td>
          <td>39264</td>
          <td>Some-college</td>
          <td>10</td>
          <td>Married-civ-spouse</td>
          <td>Exec-managerial</td>
          <td>Wife</td>
          <td>White</td>
          <td>Female</td>
          <td>0</td>
          <td>0</td>
          <td>40</td>
          <td>United-States</td>
          <td>&gt;50K</td>
        </tr>
        <tr>
          <th>23204</th>
          <td>58</td>
          <td>Private</td>
          <td>51662</td>
          <td>10th</td>
          <td>6</td>
          <td>Married-civ-spouse</td>
          <td>Other-service</td>
          <td>Wife</td>
          <td>White</td>
          <td>Female</td>
          <td>0</td>
          <td>0</td>
          <td>8</td>
          <td>United-States</td>
          <td>&lt;=50K</td>
        </tr>
        <tr>
          <th>29590</th>
          <td>40</td>
          <td>Private</td>
          <td>326310</td>
          <td>Some-college</td>
          <td>10</td>
          <td>Married-civ-spouse</td>
          <td>Craft-repair</td>
          <td>Husband</td>
          <td>White</td>
          <td>Male</td>
          <td>0</td>
          <td>0</td>
          <td>44</td>
          <td>United-States</td>
          <td>&lt;=50K</td>
        </tr>
        <tr>
          <th>18116</th>
          <td>37</td>
          <td>Private</td>
          <td>222450</td>
          <td>HS-grad</td>
          <td>9</td>
          <td>Never-married</td>
          <td>Sales</td>
          <td>Not-in-family</td>
          <td>White</td>
          <td>Male</td>
          <td>0</td>
          <td>2339</td>
          <td>40</td>
          <td>El-Salvador</td>
          <td>&lt;=50K</td>
        </tr>
        <tr>
          <th>33964</th>
          <td>62</td>
          <td>Private</td>
          <td>109190</td>
          <td>Bachelors</td>
          <td>13</td>
          <td>Married-civ-spouse</td>
          <td>Exec-managerial</td>
          <td>Husband</td>
          <td>White</td>
          <td>Male</td>
          <td>15024</td>
          <td>0</td>
          <td>40</td>
          <td>United-States</td>
          <td>&gt;50K</td>
        </tr>
      </tbody>
    </table>
    </div>



Now, we create a predictor and fit it to the data. By specifying
``presets='interpretable'``, we tell the predictor to fit only
interpretable models.

.. code:: python

    predictor = TabularPredictor(label='class')
    predictor.fit(train_data, presets='interpretable')
    predictor.leaderboard()


.. parsed-literal::
    :class: output

    No path specified. Models will be saved in: "AutogluonModels/ag-20230304_204556/"
    Presets specified: ['interpretable']
    Beginning AutoGluon training ...
    AutoGluon will save models to "AutogluonModels/ag-20230304_204556/"
    AutoGluon Version:  0.7.0b20230304
    Python Version:     3.8.13
    Operating System:   Linux
    Platform Machine:   x86_64
    Platform Version:   #1 SMP Tue Nov 30 00:17:50 UTC 2021
    Train Data Rows:    500
    Train Data Columns: 14
    Label Column: class
    Preprocessing data ...
    AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
    	2 unique label values:  [' >50K', ' <=50K']
    	If 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
    Selected class <--> label mapping:  class 1 =  >50K, class 0 =  <=50K
    	Note: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive ( >50K) vs negative ( <=50K) class.
    	To explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.
    Using Feature Generators to preprocess the data ...
    Fitting AutoMLPipelineFeatureGenerator...
    	Available Memory:                    31500.45 MB
    	Train Data (Original)  Memory Usage: 0.29 MB (0.0% of available memory)
    	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
    	Stage 1 Generators:
    		Fitting AsTypeFeatureGenerator...
    			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
    	Stage 2 Generators:
    		Fitting FillNaFeatureGenerator...
    	Stage 3 Generators:
    		Fitting IdentityFeatureGenerator...
    		Fitting CategoryFeatureGenerator...
    			Fitting CategoryMemoryMinimizeFeatureGenerator...
    	Stage 4 Generators:
    		Fitting DropUniqueFeatureGenerator...
    	Types of features in original data (raw dtype, special dtypes):
    		('int', [])    : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
    		('object', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
    	Types of features in processed data (raw dtype, special dtypes):
    		('category', [])  : 7 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
    		('int', [])       : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
    		('int', ['bool']) : 1 | ['sex']
    	0.1s = Fit runtime
    	14 features in original data used to generate 14 features in processed data.
    	Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
    Data preprocessing and feature engineering runtime = 0.09s ...
    AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
    	To change this, specify the eval_metric parameter of Predictor()
    Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
    Fitting 12 L1 models ...
    Fitting model: RuleFit ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.73	 = Validation score   (accuracy)
    	2.3s	 = Training   runtime
    	0.05s	 = Validation runtime
    Fitting model: RuleFit_2 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.73	 = Validation score   (accuracy)
    	2.08s	 = Training   runtime
    	0.08s	 = Validation runtime
    Fitting model: RuleFit_3 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.77	 = Validation score   (accuracy)
    	2.1s	 = Training   runtime
    	0.08s	 = Validation runtime
    Fitting model: GreedyTree ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but GreedyTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.81	 = Validation score   (accuracy)
    	0.63s	 = Training   runtime
    	0.08s	 = Validation runtime
    Fitting model: BoostedRules ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but BoostedRulesClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.83	 = Validation score   (accuracy)
    	0.63s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: BoostedRules_2 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but BoostedRulesClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.81	 = Validation score   (accuracy)
    	0.64s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: Figs ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.27	 = Validation score   (accuracy)
    	0.65s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: Figs_2 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.27	 = Validation score   (accuracy)
    	0.66s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: Figs_3 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.27	 = Validation score   (accuracy)
    	0.69s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: HierarchicalShrinkageTree ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.82	 = Validation score   (accuracy)
    	1.03s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: HierarchicalShrinkageTree_2 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.82	 = Validation score   (accuracy)
    	1.03s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: HierarchicalShrinkageTree_3 ...
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names
      warnings.warn(
    /home/ci/opt/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:758: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.
      warnings.warn(
    	0.82	 = Validation score   (accuracy)
    	1.02s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: WeightedEnsemble_L2 ...
    	0.83	 = Validation score   (accuracy)
    	0.26s	 = Training   runtime
    	0.0s	 = Validation runtime
    AutoGluon training complete, total runtime = 14.91s ... Best model: "WeightedEnsemble_L2"
    TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230304_204556/")


.. parsed-literal::
    :class: output

                              model  score_val  pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
    0                  BoostedRules       0.83       0.018882  0.632653                0.018882           0.632653            1       True          5
    1           WeightedEnsemble_L2       0.83       0.019408  0.893740                0.000527           0.261086            2       True         13
    2   HierarchicalShrinkageTree_2       0.82       0.017058  1.031940                0.017058           1.031940            1       True         11
    3   HierarchicalShrinkageTree_3       0.82       0.017885  1.024649                0.017885           1.024649            1       True         12
    4     HierarchicalShrinkageTree       0.82       0.018094  1.025846                0.018094           1.025846            1       True         10
    5                BoostedRules_2       0.81       0.019698  0.638120                0.019698           0.638120            1       True          6
    6                    GreedyTree       0.81       0.082269  0.625344                0.082269           0.625344            1       True          4
    7                     RuleFit_3       0.77       0.084206  2.098389                0.084206           2.098389            1       True          3
    8                       RuleFit       0.73       0.046434  2.296021                0.046434           2.296021            1       True          1
    9                     RuleFit_2       0.73       0.075744  2.080495                0.075744           2.080495            1       True          2
    10                       Figs_3       0.27       0.016755  0.690001                0.016755           0.690001            1       True          9
    11                       Figs_2       0.27       0.016876  0.662801                0.016876           0.662801            1       True          8
    12                         Figs       0.27       0.017934  0.647226                0.017934           0.647226            1       True          7




.. raw:: html

    <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>model</th>
          <th>score_val</th>
          <th>pred_time_val</th>
          <th>fit_time</th>
          <th>pred_time_val_marginal</th>
          <th>fit_time_marginal</th>
          <th>stack_level</th>
          <th>can_infer</th>
          <th>fit_order</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>BoostedRules</td>
          <td>0.83</td>
          <td>0.018882</td>
          <td>0.632653</td>
          <td>0.018882</td>
          <td>0.632653</td>
          <td>1</td>
          <td>True</td>
          <td>5</td>
        </tr>
        <tr>
          <th>1</th>
          <td>WeightedEnsemble_L2</td>
          <td>0.83</td>
          <td>0.019408</td>
          <td>0.893740</td>
          <td>0.000527</td>
          <td>0.261086</td>
          <td>2</td>
          <td>True</td>
          <td>13</td>
        </tr>
        <tr>
          <th>2</th>
          <td>HierarchicalShrinkageTree_2</td>
          <td>0.82</td>
          <td>0.017058</td>
          <td>1.031940</td>
          <td>0.017058</td>
          <td>1.031940</td>
          <td>1</td>
          <td>True</td>
          <td>11</td>
        </tr>
        <tr>
          <th>3</th>
          <td>HierarchicalShrinkageTree_3</td>
          <td>0.82</td>
          <td>0.017885</td>
          <td>1.024649</td>
          <td>0.017885</td>
          <td>1.024649</td>
          <td>1</td>
          <td>True</td>
          <td>12</td>
        </tr>
        <tr>
          <th>4</th>
          <td>HierarchicalShrinkageTree</td>
          <td>0.82</td>
          <td>0.018094</td>
          <td>1.025846</td>
          <td>0.018094</td>
          <td>1.025846</td>
          <td>1</td>
          <td>True</td>
          <td>10</td>
        </tr>
        <tr>
          <th>5</th>
          <td>BoostedRules_2</td>
          <td>0.81</td>
          <td>0.019698</td>
          <td>0.638120</td>
          <td>0.019698</td>
          <td>0.638120</td>
          <td>1</td>
          <td>True</td>
          <td>6</td>
        </tr>
        <tr>
          <th>6</th>
          <td>GreedyTree</td>
          <td>0.81</td>
          <td>0.082269</td>
          <td>0.625344</td>
          <td>0.082269</td>
          <td>0.625344</td>
          <td>1</td>
          <td>True</td>
          <td>4</td>
        </tr>
        <tr>
          <th>7</th>
          <td>RuleFit_3</td>
          <td>0.77</td>
          <td>0.084206</td>
          <td>2.098389</td>
          <td>0.084206</td>
          <td>2.098389</td>
          <td>1</td>
          <td>True</td>
          <td>3</td>
        </tr>
        <tr>
          <th>8</th>
          <td>RuleFit</td>
          <td>0.73</td>
          <td>0.046434</td>
          <td>2.296021</td>
          <td>0.046434</td>
          <td>2.296021</td>
          <td>1</td>
          <td>True</td>
          <td>1</td>
        </tr>
        <tr>
          <th>9</th>
          <td>RuleFit_2</td>
          <td>0.73</td>
          <td>0.075744</td>
          <td>2.080495</td>
          <td>0.075744</td>
          <td>2.080495</td>
          <td>1</td>
          <td>True</td>
          <td>2</td>
        </tr>
        <tr>
          <th>10</th>
          <td>Figs_3</td>
          <td>0.27</td>
          <td>0.016755</td>
          <td>0.690001</td>
          <td>0.016755</td>
          <td>0.690001</td>
          <td>1</td>
          <td>True</td>
          <td>9</td>
        </tr>
        <tr>
          <th>11</th>
          <td>Figs_2</td>
          <td>0.27</td>
          <td>0.016876</td>
          <td>0.662801</td>
          <td>0.016876</td>
          <td>0.662801</td>
          <td>1</td>
          <td>True</td>
          <td>8</td>
        </tr>
        <tr>
          <th>12</th>
          <td>Figs</td>
          <td>0.27</td>
          <td>0.017934</td>
          <td>0.647226</td>
          <td>0.017934</td>
          <td>0.647226</td>
          <td>1</td>
          <td>True</td>
          <td>7</td>
        </tr>
      </tbody>
    </table>
    </div>



The rule-based models take slightly different forms (see below), but all
try to optimize predictive performance using as few rules as possible.
See the `imodels package <https://github.com/csinva/imodels>`__ for more
details.

|image0|

Specifically, the interpretable preset fits different hyperparameter
configurations of 5 models types: 1. Greedy CART decision tree - returns
a tree learned via greedy optimization
(`Description <https://scikit-learn.org/stable/modules/tree.html#tree>`__,
`Paper <https://www.taylorfrancis.com/books/mono/10.1201/9781315139470/classification-regression-trees-leo-breiman-jerome-friedman-richard-olshen-charles-stone>`__)
2. Hierarchical Shrinkage tree - returns regularized version of CART
decision tree
(`Description <https://csinva.io/imodels/shrinkage.html>`__,
`Paper <https://arxiv.org/abs/2202.00858>`__) 3. Fast interpretable
greedy-tree sum - returns a *sum* of trees, which are greedily grown
simultaneously (`Description <https://csinva.io/imodels/figs.html>`__,
`Paper <https://arxiv.org/abs/2202.00858>`__) 4. RuleFit - returns a set
of weighted rules, which are learned by a sparse linear model on rules
extracted from decision trees
(`Description <https://christophm.github.io/interpretable-ml-book/rulefit.html>`__,
`Paper <https://arxiv.org/abs/0811.1679>`__) 5. Boosted rule set -
returns a set of rules, which are learned sequentially via AdaBoost
(`Description <https://scikit-learn.org/stable/modules/ensemble.html#adaboost>`__,
`Paper <https://www.sciencedirect.com/science/article/pii/S002200009791504X>`__)

In addition to the usual functions in ``TabularPredictor``, this
predictor fitted with interpretable models has some additional
functionality. For example, we can now inspect the complexity of the
fitted models (i.e. how many rules they contain).

.. |image0| image:: https://raw.githubusercontent.com/csinva/imodels/master/docs/img/model_table_rules.png

.. code:: python

    predictor.interpretable_models_summary()




.. raw:: html

    <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>model_types</th>
          <th>model_performance</th>
          <th>complexity</th>
          <th>model_best</th>
          <th>model_paths</th>
          <th>model_fit_times</th>
          <th>model_pred_times</th>
          <th>num_bag_folds</th>
          <th>max_stack_level</th>
          <th>num_classes</th>
          <th>model_hyperparams</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>BoostedRules</th>
          <td>BoostedRulesModel</td>
          <td>0.83</td>
          <td>5.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Boos...</td>
          <td>0.632653</td>
          <td>0.018882</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'n_estimators': 5}</td>
        </tr>
        <tr>
          <th>HierarchicalShrinkageTree</th>
          <td>HSTreeModel</td>
          <td>0.82</td>
          <td>19.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Hier...</td>
          <td>1.025846</td>
          <td>0.018094</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 6}</td>
        </tr>
        <tr>
          <th>HierarchicalShrinkageTree_2</th>
          <td>HSTreeModel</td>
          <td>0.82</td>
          <td>19.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Hier...</td>
          <td>1.031940</td>
          <td>0.017058</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 12}</td>
        </tr>
        <tr>
          <th>HierarchicalShrinkageTree_3</th>
          <td>HSTreeModel</td>
          <td>0.82</td>
          <td>19.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Hier...</td>
          <td>1.024649</td>
          <td>0.017885</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 18}</td>
        </tr>
        <tr>
          <th>BoostedRules_2</th>
          <td>BoostedRulesModel</td>
          <td>0.81</td>
          <td>10.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Boos...</td>
          <td>0.638120</td>
          <td>0.019698</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'n_estimators': 10}</td>
        </tr>
        <tr>
          <th>GreedyTree</th>
          <td>GreedyTreeModel</td>
          <td>0.81</td>
          <td>17.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Gree...</td>
          <td>0.625344</td>
          <td>0.082269</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_leaf_nodes': 18}</td>
        </tr>
        <tr>
          <th>RuleFit_3</th>
          <td>RuleFitModel</td>
          <td>0.77</td>
          <td>35.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Rule...</td>
          <td>2.098389</td>
          <td>0.084206</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 18}</td>
        </tr>
        <tr>
          <th>RuleFit</th>
          <td>RuleFitModel</td>
          <td>0.73</td>
          <td>8.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Rule...</td>
          <td>2.296021</td>
          <td>0.046434</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 7}</td>
        </tr>
        <tr>
          <th>RuleFit_2</th>
          <td>RuleFitModel</td>
          <td>0.73</td>
          <td>29.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Rule...</td>
          <td>2.080495</td>
          <td>0.075744</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 12}</td>
        </tr>
        <tr>
          <th>Figs</th>
          <td>FigsModel</td>
          <td>0.27</td>
          <td>6.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Figs/</td>
          <td>0.647226</td>
          <td>0.017934</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 6}</td>
        </tr>
        <tr>
          <th>Figs_2</th>
          <td>FigsModel</td>
          <td>0.27</td>
          <td>10.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Figs_2/</td>
          <td>0.662801</td>
          <td>0.016876</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 10}</td>
        </tr>
        <tr>
          <th>Figs_3</th>
          <td>FigsModel</td>
          <td>0.27</td>
          <td>15.0</td>
          <td>WeightedEnsemble_L2</td>
          <td>AutogluonModels/ag-20230304_204556/models/Figs_3/</td>
          <td>0.690001</td>
          <td>0.016755</td>
          <td>0</td>
          <td>2</td>
          <td>2</td>
          <td>{'random_state': 0, 'max_rules': 15}</td>
        </tr>
      </tbody>
    </table>
    </div>



We can also explicitly inspect the rules of the best-performing model.

.. code:: python

    predictor.print_interpretable_rules() # can optionally specify a model name or complexity threshold


.. parsed-literal::
    :class: output

    BoostedRulesClassifier(n_estimators=5, random_state=0)


In some cases, these rules are sufficient to accurately make
predictions. In other cases, they may just be used to gain a better
understanding of the data before proceeding with more black-box models.