Any Model

Inspector functionality for any model

import sklearn.datasets
from sklearn.ensemble import RandomForestRegressor

from model_inspector import get_inspector

X_diabetes, y_diabetes = sklearn.datasets.load_diabetes(return_X_y=True, as_frame=True)

inspector = get_inspector(
    RandomForestRegressor().fit(X_diabetes, y_diabetes), X_diabetes, y_diabetes
)

/Users/greg.gandenberger/repos/model_inspector/model_inspector/inspect/any_model.py:56: UserWarning: `model` does not have the `feature_names_in_`
                attribute, so we cannot confirm that `model`'s feature
                names match `X`'s column names. Proceed at your own
                risk!
                
  warnings.warn(

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_Inspector.methods

 _Inspector.methods ()

Show available methods.

inspector.methods

['_check_cols',
 'permutation_importance',
 'plot_feature_clusters',
 'plot_partial_dependence',
 'plot_permutation_importance',
 'plot_pred_vs_act',
 'plot_residuals',
 'show_correlation']

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_Inspector.permutation_importance

 _Inspector.permutation_importance (sort:bool=True, scoring=None,
                                    n_repeats=5, n_jobs=None,
                                    random_state=None, sample_weight=None,
                                    max_samples=1.0)

Calculate permutation importance.

Parameters:

• sort: Sort features by decreasing importance.

Remaining parameters are passed to sklearn.inspection._permutation_importance.permutation_importance.

inspector.permutation_importance()

bmi    0.482974
s5     0.472974
bp     0.137960
s6     0.092853
s3     0.088472
age    0.077993
s2     0.067045
s1     0.051492
sex    0.023468
s4     0.022805
dtype: float64

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_Inspector.plot_partial_dependence

 _Inspector.plot_partial_dependence (categorical_features=None,
                                     feature_names=None, target=None,
                                     response_method='auto', n_cols=3,
                                     grid_resolution=100,
                                     percentiles=(0.05, 0.95),
                                     method='auto', n_jobs=None,
                                     verbose=0, line_kw=None,
                                     ice_lines_kw=None, pd_line_kw=None,
                                     contour_kw=None, ax=None,
                                     kind='average', centered=False,
                                     subsample=1000, random_state=None)

Plot partial dependence.

Returns NumPy array of Axes objects.

Remaining parameters are passed to sklearn.inspection._plot.partial_dependence.from_estimator.

axes = inspector.plot_partial_dependence(features=["bp", "bmi", ["bp", "bmi"]])

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_Inspector.plot_feature_clusters

 _Inspector.plot_feature_clusters (corr_method:str='spearman',
                                   ax:matplotlib.axes._axes.Axes=None,
                                   p=30, truncate_mode=None,
                                   color_threshold=None, get_leaves=True,
                                   orientation='top', labels=None,
                                   count_sort=False, distance_sort=False,
                                   show_leaf_counts=True, no_plot=False,
                                   no_labels=False, leaf_font_size=None,
                                   leaf_rotation=None,
                                   leaf_label_func=None,
                                   show_contracted=False,
                                   link_color_func=None,
                                   above_threshold_color='C0')

Plot a dendrogram based on feature correlations.

Parameters:

• corr_method: Method of correlation to pass to df.corr()
• ax: Matplotlib Axes object. Plot will be added to this object if provided; otherwise a new Axes object will be generated.

Remaining parameters are passed to model_inspector.explore.plot_column_clusters.

ax = inspector.plot_feature_clusters()

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_Inspector.plot_permutation_importance

 _Inspector.plot_permutation_importance
                                         (ax:Optional[matplotlib.axes._axe
                                         s.Axes]=None, importance_kwargs:O
                                         ptional[dict]=None,
                                         plot_kwargs:Optional[dict]=None)

Plot a correlation matrix for self.X and self.y.

Parameters:

• ax: Matplotlib Axes object. Plot will be added to this object if provided; otherwise a new Axes object will be generated.
• importance_kwargs: kwargs to pass to sklearn.inspection.permutation_importance
• plot_kwargs: kwargs to pass to pd.Series.plot.barh

ax = inspector.plot_permutation_importance()

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_Inspector.show_correlation

 _Inspector.show_correlation (method='pearson', cmap:str|Colormap='PuBu',
                              low:float=0, high:float=0, axis:Axis|None=0,
                              subset:Subset|None=None,
                              text_color_threshold:float=0.408,
                              vmin:float|None=None, vmax:float|None=None,
                              gmap:Sequence|None=None)

Show a correlation matrix for self.X and self.y.

If output is not rendering properly when you reopen a notebook, make sure the notebook is trusted.

Remaining parameters are passed to model_inspector.explore.show_correlation.

inspector.show_correlation()

	age	sex	bmi	bp	s1	s2	s3	s4	s5	s6	target
age	1.00	0.17	0.19	0.34	0.26	0.22	-0.08	0.20	0.27	0.30	0.19
sex	0.17	1.00	0.09	0.24	0.04	0.14	-0.38	0.33	0.15	0.21	0.04
bmi	0.19	0.09	1.00	0.40	0.25	0.26	-0.37	0.41	0.45	0.39	0.59
bp	0.34	0.24	0.40	1.00	0.24	0.19	-0.18	0.26	0.39	0.39	0.44
s1	0.26	0.04	0.25	0.24	1.00	0.90	0.05	0.54	0.52	0.33	0.21
s2	0.22	0.14	0.26	0.19	0.90	1.00	-0.20	0.66	0.32	0.29	0.17
s3	-0.08	-0.38	-0.37	-0.18	0.05	-0.20	1.00	-0.74	-0.40	-0.27	-0.39
s4	0.20	0.33	0.41	0.26	0.54	0.66	-0.74	1.00	0.62	0.42	0.43
s5	0.27	0.15	0.45	0.39	0.52	0.32	-0.40	0.62	1.00	0.46	0.57
s6	0.30	0.21	0.39	0.39	0.33	0.29	-0.27	0.42	0.46	1.00	0.38
target	0.19	0.04	0.59	0.44	0.21	0.17	-0.39	0.43	0.57	0.38	1.00