This is documentation for an old release of Scikit-learn (version 1.0). Try the latest stable release (version 1.6) or development (unstable) versions.

Displaying Pipelines

The default configuration for displaying a pipeline is 'text' where set_config(display='text'). To visualize the diagram in Jupyter Notebook, use set_config(display='diagram') and then output the pipeline object.

To see more detailed steps in the visualization of the pipeline, click on the steps in the pipeline.

Displaying a Pipeline with a Preprocessing Step and Classifier

This section constructs a Pipeline with a preprocessing step, StandardScaler, and classifier, LogisticRegression, and displays its visual representation.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn import set_config

steps = [
    ("preprocessing", StandardScaler()),
    ("classifier", LogisticRegression()),
]
pipe = Pipeline(steps)

To view the text pipeline, the default is display='text'.

set_config(display="text")
pipe

Out:

Pipeline(steps=[('preprocessing', StandardScaler()),
                ('classifier', LogisticRegression())])

To visualize the diagram, change display='diagram'.

set_config(display="diagram")
pipe  # click on the diagram below to see the details of each step

Pipeline(steps=[('preprocessing', StandardScaler()),
                ('classifier', LogisticRegression())])

Please rerun this cell to show the HTML repr or trust the notebook.

Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier

This section constructs a Pipeline with multiple preprocessing steps, PolynomialFeatures and StandardScaler, and a classifer step, LogisticRegression, and displays its visual representation.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.linear_model import LogisticRegression
from sklearn import set_config

steps = [
    ("standard_scaler", StandardScaler()),
    ("polynomial", PolynomialFeatures(degree=3)),
    ("classifier", LogisticRegression(C=2.0)),
]
pipe = Pipeline(steps)

To visualize the diagram, change to display=’diagram’

set_config(display="diagram")
pipe  # click on the diagram below to see the details of each step

Pipeline(steps=[('standard_scaler', StandardScaler()),
                ('polynomial', PolynomialFeatures(degree=3)),
                ('classifier', LogisticRegression(C=2.0))])

Please rerun this cell to show the HTML repr or trust the notebook.

Displaying a Pipeline and Dimensionality Reduction and Classifier

This section constructs a Pipeline with a dimensionality reduction step, PCA, a classifier, SVC, and displays its visual representation.

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn import set_config

steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))]
pipe = Pipeline(steps)

To visualize the diagram, change to display='diagram'.

set_config(display="diagram")
pipe  # click on the diagram below to see the details of each step

Pipeline(steps=[('reduce_dim', PCA(n_components=4)),
                ('classifier', SVC(kernel='linear'))])

Please rerun this cell to show the HTML repr or trust the notebook.

Displaying a Complex Pipeline Chaining a Column Transformer

This section constructs a complex Pipeline with a ColumnTransformer and a classifier, LogisticRegression, and displays its visual representation.

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn import set_config

numeric_preprocessor = Pipeline(
    steps=[
        ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
        ("scaler", StandardScaler()),
    ]
)

categorical_preprocessor = Pipeline(
    steps=[
        (
            "imputation_constant",
            SimpleImputer(fill_value="missing", strategy="constant"),
        ),
        ("onehot", OneHotEncoder(handle_unknown="ignore")),
    ]
)

preprocessor = ColumnTransformer(
    [
        ("categorical", categorical_preprocessor, ["state", "gender"]),
        ("numerical", numeric_preprocessor, ["age", "weight"]),
    ]
)

pipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500))

To visualize the diagram, change to display='diagram'

set_config(display="diagram")
pipe  # click on the diagram below to see the details of each step

Pipeline(steps=[('columntransformer',
                 ColumnTransformer(transformers=[('categorical',
                                                  Pipeline(steps=[('imputation_constant',
                                                                   SimpleImputer(fill_value='missing',
                                                                                 strategy='constant')),
                                                                  ('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['state', 'gender']),
                                                 ('numerical',
                                                  Pipeline(steps=[('imputation_mean',
                                                                   SimpleImputer()),
                                                                  ('scaler',
                                                                   StandardScaler())]),
                                                  ['age', 'weight'])])),
                ('logisticregression', LogisticRegression(max_iter=500))])

Please rerun this cell to show the HTML repr or trust the notebook.

Displaying a Grid Search over a Pipeline with a Classifier

This section constructs a GridSearchCV over a Pipeline with RandomForestClassifier and displays its visual representation.

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn import set_config

numeric_preprocessor = Pipeline(
    steps=[
        ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
        ("scaler", StandardScaler()),
    ]
)

categorical_preprocessor = Pipeline(
    steps=[
        (
            "imputation_constant",
            SimpleImputer(fill_value="missing", strategy="constant"),
        ),
        ("onehot", OneHotEncoder(handle_unknown="ignore")),
    ]
)

preprocessor = ColumnTransformer(
    [
        ("categorical", categorical_preprocessor, ["state", "gender"]),
        ("numerical", numeric_preprocessor, ["age", "weight"]),
    ]
)

pipe = Pipeline(
    steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier())]
)

param_grid = {
    "classifier__n_estimators": [200, 500],
    "classifier__max_features": ["auto", "sqrt", "log2"],
    "classifier__max_depth": [4, 5, 6, 7, 8],
    "classifier__criterion": ["gini", "entropy"],
}

grid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1)

To visualize the diagram, change to display='diagram'.

set_config(display="diagram")
grid_search  # click on the diagram below to see the details of each step

GridSearchCV(estimator=Pipeline(steps=[('preprocessor',
                                        ColumnTransformer(transformers=[('categorical',
                                                                         Pipeline(steps=[('imputation_constant',
                                                                                          SimpleImputer(fill_value='missing',
                                                                                                        strategy='constant')),
                                                                                         ('onehot',
                                                                                          OneHotEncoder(handle_unknown='ignore'))]),
                                                                         ['state',
                                                                          'gender']),
                                                                        ('numerical',
                                                                         Pipeline(steps=[('imputation_mean',
                                                                                          SimpleImputer()),
                                                                                         ('scaler',
                                                                                          StandardScaler())]),
                                                                         ['age',
                                                                          'weight'])])),
                                       ('classifier',
                                        RandomForestClassifier())]),
             n_jobs=1,
             param_grid={'classifier__criterion': ['gini', 'entropy'],
                         'classifier__max_depth': [4, 5, 6, 7, 8],
                         'classifier__max_features': ['auto', 'sqrt', 'log2'],
                         'classifier__n_estimators': [200, 500]})

Please rerun this cell to show the HTML repr or trust the notebook.