83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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"""
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=========================================================
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Pipelining: chaining a PCA and a logistic regression
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=========================================================
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The PCA does an unsupervised dimensionality reduction, while the logistic
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regression does the prediction.
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We use a GridSearchCV to set the dimensionality of the PCA
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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from sklearn import datasets
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from sklearn.decomposition import PCA
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from sklearn.linear_model import LogisticRegression
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV
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from sklearn.preprocessing import StandardScaler
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# Define a pipeline to search for the best combination of PCA truncation
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# and classifier regularization.
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pca = PCA()
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# Define a Standard Scaler to normalize inputs
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scaler = StandardScaler()
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# set the tolerance to a large value to make the example faster
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logistic = LogisticRegression(max_iter=10000, tol=0.1)
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pipe = Pipeline(steps=[("scaler", scaler), ("pca", pca), ("logistic", logistic)])
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X_digits, y_digits = datasets.load_digits(return_X_y=True)
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# Parameters of pipelines can be set using ‘__’ separated parameter names:
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param_grid = {
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"pca__n_components": [5, 15, 30, 45, 60],
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"logistic__C": np.logspace(-4, 4, 4),
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}
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search = GridSearchCV(pipe, param_grid, n_jobs=2)
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search.fit(X_digits, y_digits)
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print("Best parameter (CV score=%0.3f):" % search.best_score_)
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print(search.best_params_)
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# Plot the PCA spectrum
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pca.fit(X_digits)
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fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
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ax0.plot(
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np.arange(1, pca.n_components_ + 1), pca.explained_variance_ratio_, "+", linewidth=2
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)
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ax0.set_ylabel("PCA explained variance ratio")
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ax0.axvline(
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search.best_estimator_.named_steps["pca"].n_components,
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linestyle=":",
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label="n_components chosen",
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)
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ax0.legend(prop=dict(size=12))
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# For each number of components, find the best classifier results
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results = pd.DataFrame(search.cv_results_)
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components_col = "param_pca__n_components"
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best_clfs = results.groupby(components_col).apply(
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lambda g: g.nlargest(1, "mean_test_score")
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)
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best_clfs.plot(
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x=components_col, y="mean_test_score", yerr="std_test_score", legend=False, ax=ax1
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)
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ax1.set_ylabel("Classification accuracy (val)")
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ax1.set_xlabel("n_components")
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plt.xlim(-1, 70)
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plt.tight_layout()
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plt.show()
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