50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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"""
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===========================================
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Displaying estimators and complex pipelines
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===========================================
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This example illustrates different ways estimators and pipelines can be
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displayed.
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"""
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from sklearn.compose import make_column_transformer
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from sklearn.impute import SimpleImputer
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from sklearn.linear_model import LogisticRegression
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import OneHotEncoder, StandardScaler
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# %%
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# Compact text representation
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# ---------------------------
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#
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# Estimators will only show the parameters that have been set to non-default
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# values when displayed as a string. This reduces the visual noise and makes it
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# easier to spot what the differences are when comparing instances.
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lr = LogisticRegression(penalty="l1")
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print(lr)
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# %%
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# Rich HTML representation
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# ------------------------
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# In notebooks estimators and pipelines will use a rich HTML representation.
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# This is particularly useful to summarise the
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# structure of pipelines and other composite estimators, with interactivity to
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# provide detail. Click on the example image below to expand Pipeline
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# elements. See :ref:`visualizing_composite_estimators` for how you can use
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# this feature.
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num_proc = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())
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cat_proc = make_pipeline(
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SimpleImputer(strategy="constant", fill_value="missing"),
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OneHotEncoder(handle_unknown="ignore"),
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)
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preprocessor = make_column_transformer(
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(num_proc, ("feat1", "feat3")), (cat_proc, ("feat0", "feat2"))
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)
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clf = make_pipeline(preprocessor, LogisticRegression())
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clf
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