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[MRG] ENH enable setting pipeline components as parameters (#1769)
Pipeline and FeatureUnion steps may now be set with set_params, and transformers may be replaced with None to effectively remove them. Also test and improve ducktyping of Pipeline methods
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#!/usr/bin/python | ||
# -*- coding: utf-8 -*- | ||
""" | ||
================================================================= | ||
Selecting dimensionality reduction with Pipeline and GridSearchCV | ||
================================================================= | ||
This example constructs a pipeline that does dimensionality | ||
reduction followed by prediction with a support vector | ||
classifier. It demonstrates the use of GridSearchCV and | ||
Pipeline to optimize over different classes of estimators in a | ||
single CV run -- unsupervised PCA and NMF dimensionality | ||
reductions are compared to univariate feature selection during | ||
the grid search. | ||
""" | ||
# Authors: Robert McGibbon, Joel Nothman | ||
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from __future__ import print_function, division | ||
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import numpy as np | ||
import matplotlib.pyplot as plt | ||
from sklearn.datasets import load_digits | ||
from sklearn.model_selection import GridSearchCV | ||
from sklearn.pipeline import Pipeline | ||
from sklearn.svm import LinearSVC | ||
from sklearn.decomposition import PCA, NMF | ||
from sklearn.feature_selection import SelectKBest, chi2 | ||
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print(__doc__) | ||
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pipe = Pipeline([ | ||
('reduce_dim', PCA()), | ||
('classify', LinearSVC()) | ||
]) | ||
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N_FEATURES_OPTIONS = [2, 4, 8] | ||
C_OPTIONS = [1, 10, 100, 1000] | ||
param_grid = [ | ||
{ | ||
'reduce_dim': [PCA(iterated_power=7), NMF()], | ||
'reduce_dim__n_components': N_FEATURES_OPTIONS, | ||
'classify__C': C_OPTIONS | ||
}, | ||
{ | ||
'reduce_dim': [SelectKBest(chi2)], | ||
'reduce_dim__k': N_FEATURES_OPTIONS, | ||
'classify__C': C_OPTIONS | ||
}, | ||
] | ||
reducer_labels = ['PCA', 'NMF', 'KBest(chi2)'] | ||
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grid = GridSearchCV(pipe, cv=3, n_jobs=2, param_grid=param_grid) | ||
digits = load_digits() | ||
grid.fit(digits.data, digits.target) | ||
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mean_scores = np.array(grid.results_['test_mean_score']) | ||
# scores are in the order of param_grid iteration, which is alphabetical | ||
mean_scores = mean_scores.reshape(len(C_OPTIONS), -1, len(N_FEATURES_OPTIONS)) | ||
# select score for best C | ||
mean_scores = mean_scores.max(axis=0) | ||
bar_offsets = (np.arange(len(N_FEATURES_OPTIONS)) * | ||
(len(reducer_labels) + 1) + .5) | ||
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plt.figure() | ||
COLORS = 'bgrcmyk' | ||
for i, (label, reducer_scores) in enumerate(zip(reducer_labels, mean_scores)): | ||
plt.bar(bar_offsets + i, reducer_scores, label=label, color=COLORS[i]) | ||
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plt.title("Comparing feature reduction techniques") | ||
plt.xlabel('Reduced number of features') | ||
plt.xticks(bar_offsets + len(reducer_labels) / 2, N_FEATURES_OPTIONS) | ||
plt.ylabel('Digit classification accuracy') | ||
plt.ylim((0, 1)) | ||
plt.legend(loc='upper left') | ||
plt.show() |
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