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Sep 16, 2022
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DOC Update estimator representation example #24439

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033f535
Update estimator representation example
betatim Sep 14, 2022
2fa5ec2
Add redirect from old to new name
betatim Sep 16, 2022
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3 changes: 3 additions & 0 deletions doc/conf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -271,6 +271,9 @@
"examples/model_selection/grid_search_text_feature_extraction.py": (
"examples/model_selection/plot_grid_search_text_feature_extraction.py"
),
"examples/miscellaneous/plot_changed_only_pprint_parameter": (
"examples/miscellaneous/plot_estimator_representation"
),
}
html_context["redirects"] = redirects
for old_link in redirects:
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30 changes: 0 additions & 30 deletions examples/miscellaneous/plot_changed_only_pprint_parameter.py
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50 changes: 50 additions & 0 deletions examples/miscellaneous/plot_estimator_representation.py
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@@ -0,0 +1,50 @@
"""
===========================================
Displaying estimators and complex pipelines
===========================================

This example illustrates different ways estimators and pipelines can be
displayed.
"""

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.compose import make_column_transformer
from sklearn.linear_model import LogisticRegression


# %%
# Compact text representation
# ---------------------------
#
# Estimators will only show the parameters that have been set to non-default
# values when displayed as a string. This reduces the visual noise and makes it
# easier to spot what the differences are when comparing instances.

lr = LogisticRegression(penalty="l1")
print(lr)

# %%
# Rich HTML representation
# ------------------------
# In notebooks estimators and pipelines will use a rich HTML representation.
# This is particularly useful to summarise the
# structure of pipelines and other composite estimators, with interactivity to
# provide detail. Click on the example image below to expand Pipeline
# elements. See :ref:`visualizing_composite_estimators` for how you can use
# this feature.

num_proc = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())

cat_proc = make_pipeline(
SimpleImputer(strategy="constant", fill_value="missing"),
OneHotEncoder(handle_unknown="ignore"),
)

preprocessor = make_column_transformer(
(num_proc, ("feat1", "feat3")), (cat_proc, ("feat0", "feat2"))
)

clf = make_pipeline(preprocessor, LogisticRegression())
clf