Pipelining¶

This plot is generated by pipelining a PCA and a logisitic regression.
Python source code: plot_digits_pipe.py
print __doc__


# Code source: Gael Varoqueux
# Modified for Documentation merge by Jaques Grobler
# License: BSD


import numpy as np
import pylab as pl

from sklearn import linear_model, decomposition, datasets, cross_validation

logistic = linear_model.LogisticRegression()

pca = decomposition.PCA()
from sklearn.pipeline import Pipeline
pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target

################################################################################
# Plot the PCA spectrum
pca.fit(X_digits)

pl.figure(1, figsize=(4, 3))
pl.clf()
pl.axes([.2, .2, .7, .7])
pl.plot(pca.explained_variance_, linewidth=2)
pl.axis('tight')
pl.xlabel('n_components')
pl.ylabel('explained_variance_')

################################################################################
# Prediction
scores = cross_validation.cross_val_score(pipe, X_digits, y_digits, n_jobs=-1)

from sklearn.grid_search import GridSearchCV

n_components = [10, 15, 20, 30, 40, 50, 64]
Cs = np.logspace(-4, 4, 16)

#Parameters of pipelines can be set using ‘__’ separated parameter names:

estimator = GridSearchCV(pipe,
                         dict(pca__n_components=n_components,
                              logistic__C=Cs),
                         n_jobs=-1)
estimator.fit(X_digits, y_digits)

# Plot the PCA spectrum
pca.fit(X_digits)

pl.show()
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Pipelining¶
