Partial dependence for multiclass #1554
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## main #1554 +/- ##
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+ Coverage 100.0% 100.0% +0.1%
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Files 236 236
Lines 16877 16933 +56
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+ Hits 16869 16925 +56
Misses 8 8
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freddyaboulton
requested review from
bchen1116,
angela97lin,
ParthivNaresh,
christopherbunn,
eccabay and
jeremyliweishih
evalml/model_understanding/graphs.py
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| fig.add_trace(_go.Scatter(x=part_dep.loc[part_dep.class_label == label, 'feature_values'], | ||
| y=part_dep.loc[part_dep.class_label == label, 'partial_dependence'], | ||
| line=dict(width=3)), | ||
| row=1, col=i + 1) |
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@freddyaboulton Great! Also if the dataset has a larger number of labels, it might be difficult for the user to see all the partial dependency plots in one row.
Maybe:
_subplots.make_subplots(rows=(len(class_labels)+1) // 2, cols=2 ...etc)
and then in line 523:
row=(i+2) // 2 and col=(i%2) + 1.
Not really a big deal either way
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Sounds good! I think the only thing missing was that we should only use two columns in the case where class_label=None or else there would be an empty second column in the plot.
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| data = pd.DataFrame({"feature_values": np.tile(values[0], avg_pred.shape[0]), | ||
| "partial_dependence": np.concatenate([pred for pred in avg_pred])}) |
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| data = pd.DataFrame({"feature_values": np.tile(values[0], avg_pred.shape[0]), | ||
| "partial_dependence": np.concatenate([pred for pred in avg_pred])}) | ||
| if classes is not None: | ||
| data['class_label'] = np.repeat(classes, len(values[0])) |
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Nit-pick: Since we're changing the output to return this new field in the DF, could be good to update this docstring too?
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Good suggestion! Done!
evalml/model_understanding/graphs.py
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| @@ -476,6 +486,10 @@ def graph_partial_dependence(pipeline, X, feature, grid_resolution=100): | |||
| feature (int, string): The target feature for which to create the partial dependence plot for. | |||
| If feature is an int, it must be the index of the feature to use. | |||
| If feature is a string, it must be a valid column name in X. | |||
| class_label (string, None): Name of class to plot for multiclass problems. If None, will plot | |||
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Alternatively:
class_label (string, optional): Name of class to plot for multiclass problems. If None, will plot...; Defaults to None.
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Looks great locally too; just one nit-picky suggestion, if it's not too difficult: is it possible to update the trace names 🤔 |
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@angela97lin good catch! I just pushed this up and updated the tests for multiclass/not multiclass.
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LGTM! One thing I think would be cool to have is to allow users to pass in a list of classes to create these plots for in multiclass scenarios. For instance, I, as a user, decided to create a multiclass pipeline with 20 possible target classes, I might want to plot partial dependencies for a subset of the classes only, rather than for 1 or all. Certainly not blocking, but wanted to bring that suggestion up for possible discussion.
| y=part_dep.loc[part_dep.class_label == label, 'partial_dependence'], | ||
| line=dict(width=3), | ||
| name=label), | ||
| row=(i + 2) // 2, col=(i % 2) + 1) |
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Great suggestion @bchen1116 ! I'm on board but let's continue the discussion to #1565 since this issue/PR tracks returning the partial dependence for all the classes as opposed to just the first one in multiclass problems. |
Pull Request Description
Fixes #1404
Example on wine dataset
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docs/source/release_notes.rstto include this pull request by adding :pr:123.