Displaying Pipelines

The default configuration for displaying a pipeline in a Jupyter Notebook is 'diagram' where set_config(display='diagram'). To deactivate HTML representation, use set_config(display='text').

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 visualize the diagram, the default is display='diagram'.

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

To view the text pipeline, change to display='text'.

set_config(display="text")
pipe

Put back the default display

set_config(display="diagram")

Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier

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

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

steps = [
    ("standard_scaler", StandardScaler()),
    ("polynomial", PolynomialFeatures(degree=3)),
    ("classifier", LogisticRegression(C=2.0)),
]
pipe = Pipeline(steps)
pipe  # click on the diagram below to see the details of each step

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

steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))]
pipe = Pipeline(steps)
pipe  # click on the diagram below to see the details of each step

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

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))
pipe  # click on the diagram below to see the details of each step

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

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)
grid_search  # click on the diagram below to see the details of each step