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.. "auto_examples/miscellaneous/plot_estimator_representation.py"
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.. only:: html
.. note::
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.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_miscellaneous_plot_estimator_representation.py:
===========================================
Displaying estimators and complex pipelines
===========================================
This example illustrates different ways estimators and pipelines can be
displayed.
.. GENERATED FROM PYTHON SOURCE LINES 9-16
.. code-block:: default
from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
.. GENERATED FROM PYTHON SOURCE LINES 17-23
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.
.. GENERATED FROM PYTHON SOURCE LINES 23-27
.. code-block:: default
lr = LogisticRegression(penalty="l1")
print(lr)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
LogisticRegression(penalty='l1')
.. GENERATED FROM PYTHON SOURCE LINES 28-36
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.
.. GENERATED FROM PYTHON SOURCE LINES 36-50
.. code-block:: default
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
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-44 {color: black;}#sk-container-id-44 pre{padding: 0;}#sk-container-id-44 div.sk-toggleable {background-color: white;}#sk-container-id-44 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-44 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-44 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-44 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-44 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-44 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-44 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-44 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-44 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-44 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-44 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-44 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-44 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-44 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-44 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-44 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-44 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-44 div.sk-item {position: relative;z-index: 1;}#sk-container-id-44 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-44 div.sk-item::before, #sk-container-id-44 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-44 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-44 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-44 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-44 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-44 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-44 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-44 div.sk-label-container {text-align: center;}#sk-container-id-44 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-44 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-44" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('pipeline-1',
Pipeline(steps=[('simpleimputer',
SimpleImputer(strategy='median')),
('standardscaler',
StandardScaler())]),
('feat1', 'feat3')),
('pipeline-2',
Pipeline(steps=[('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'))]),
('feat0', 'feat2'))])),
('logisticregression', LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-201" type="checkbox" ><label for="sk-estimator-id-201" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('pipeline-1',
Pipeline(steps=[('simpleimputer',
SimpleImputer(strategy='median')),
('standardscaler',
StandardScaler())]),
('feat1', 'feat3')),
('pipeline-2',
Pipeline(steps=[('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'))]),
('feat0', 'feat2'))])),
('logisticregression', LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-202" type="checkbox" ><label for="sk-estimator-id-202" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('pipeline-1',
Pipeline(steps=[('simpleimputer',
SimpleImputer(strategy='median')),
('standardscaler',
StandardScaler())]),
('feat1', 'feat3')),
('pipeline-2',
Pipeline(steps=[('simpleimputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'))]),
('feat0', 'feat2'))])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-203" type="checkbox" ><label for="sk-estimator-id-203" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline-1</label><div class="sk-toggleable__content"><pre>('feat1', 'feat3')</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-204" type="checkbox" ><label for="sk-estimator-id-204" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-205" type="checkbox" ><label for="sk-estimator-id-205" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-206" type="checkbox" ><label for="sk-estimator-id-206" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline-2</label><div class="sk-toggleable__content"><pre>('feat0', 'feat2')</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-207" type="checkbox" ><label for="sk-estimator-id-207" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-208" type="checkbox" ><label for="sk-estimator-id-208" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-209" type="checkbox" ><label for="sk-estimator-id-209" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>
</div>
<br />
<br />
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