Feature selection with pipelines – Orbifold Consulting

    from sklearn.datasets import samples_generator
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.feature_selection import SelectKBest, f_regression
    from sklearn.pipeline import Pipeline

    # generate sample data
    X, y = samples_generator.make_classification(
            n_informative=4, n_features=20, n_redundant=0, random_state=5)

    # Feature selector
    selector_k_best = SelectKBest(f_regression, k=10)

    # Random forest classifier
    classifier = RandomForestClassifier(n_estimators=50, max_depth=4)

    # Build the machine learning pipeline
    pipeline_classifier = Pipeline([('selector', selector_k_best), ('rf', classifier)])

    # We can set the parameters using the names we assigned
    # earlier. For example, if we want to set 'k' to 6 in the
    # feature selector and set 'n_estimators' in the Random
    # Forest Classifier to 25, we can do it as shown below
    pipeline_classifier.set_params(selector__k=6,
            rf__n_estimators=25)

    # Training the classifier
    pipeline_classifier.fit(X, y)

    # Predict the output
    prediction = pipeline_classifier.predict(X)
    print("\nPredictions:\n", prediction)

    # Print score
    print("\nScore:", pipeline_classifier.score(X, y))

    # Print the selected features chosen by the selector
    features_status = pipeline_classifier.named_steps['selector'].get_support()
    selected_features = []
    for count, item in enumerate(features_status):
        if item:
            selected_features.append(count)

    print("\nSelected features (0-indexed):", ', '.join([str(x) for x in selected_features]))

from sklearn.datasets import samples_generator

from sklearn.ensemble import RandomForestClassifier

from sklearn.feature_selection import SelectKBest, f_regression

from sklearn.pipeline import Pipeline

# generate sample data

X, y = samples_generator.make_classification(

n_informative=4, n_features=20, n_redundant=0, random_state=5)

# Feature selector

selector_k_best = SelectKBest(f_regression, k=10)

# Random forest classifier

classifier = RandomForestClassifier(n_estimators=50, max_depth=4)

# Build the machine learning pipeline

pipeline_classifier = Pipeline([('selector', selector_k_best), ('rf', classifier)])

# We can set the parameters using the names we assigned

# earlier. For example, if we want to set 'k' to 6 in the

# feature selector and set 'n_estimators' in the Random

# Forest Classifier to 25, we can do it as shown below

pipeline_classifier.set_params(selector__k=6,

rf__n_estimators=25)

# Training the classifier

pipeline_classifier.fit(X, y)

# Predict the output

prediction = pipeline_classifier.predict(X)

print("\nPredictions:\n", prediction)

# Print score

print("\nScore:", pipeline_classifier.score(X, y))

# Print the selected features chosen by the selector

features_status = pipeline_classifier.named_steps['selector'].get_support()

selected_features = []

for count, item in enumerate(features_status):

if item:

selected_features.append(count)

print("\nSelected features (0-indexed):", ', '.join([str(x) for x in selected_features]))

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