Note

Click here to download the full example code

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.

Additionally, Pipeline can be instantiated with the memory argument to memoize the transformers within the pipeline, avoiding to fit again the same transformers over and over.

Note that the use of memory to enable caching becomes interesting when the fitting of a transformer is costly.

Illustration of Pipeline and GridSearchCV

This section illustrates the use of a Pipeline with GridSearchCV
# Authors: Robert McGibbon, Joel Nothman, Guillaume Lemaitre


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

print(__doc__)

pipe = Pipeline([
    # the reduce_dim stage is populated by the param_grid
    ('reduce_dim', 'passthrough'),
    ('classify', LinearSVC(dual=False, max_iter=10000))
])

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)']

grid = GridSearchCV(pipe, cv=5, n_jobs=1, param_grid=param_grid, iid=False)
digits = load_digits()
grid.fit(digits.data, digits.target)

mean_scores = np.array(grid.cv_results_['mean_test_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)

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])

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()
../../_images/sphx_glr_plot_compare_reduction_001.png

Caching transformers within a Pipeline

It is sometimes worthwhile storing the state of a specific transformer since it could be used again. Using a pipeline in GridSearchCV triggers such situations. Therefore, we use the argument memory to enable caching.

Warning

Note that this example is, however, only an illustration since for this specific case fitting PCA is not necessarily slower than loading the cache. Hence, use the memory constructor parameter when the fitting of a transformer is costly.

from tempfile import mkdtemp
from shutil import rmtree
from joblib import Memory

# Create a temporary folder to store the transformers of the pipeline
cachedir = mkdtemp()
memory = Memory(location=cachedir, verbose=10)
cached_pipe = Pipeline([('reduce_dim', PCA()),
                        ('classify', LinearSVC(dual=False, max_iter=10000))],
                       memory=memory)

# This time, a cached pipeline will be used within the grid search
grid = GridSearchCV(cached_pipe, cv=5, n_jobs=1, param_grid=param_grid,
                    iid=False)
digits = load_digits()
grid.fit(digits.data, digits.target)

# Delete the temporary cache before exiting
rmtree(cachedir)

Out:

________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=2), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=4), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.1s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/591acae8e60c9632aa990e4fa0962a67
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/323909961fcc2a4950defb581f08007f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/3654e1d61587aee4f9980c99541d55d3
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/835820e75be3172b4e2e257028147bb2
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/90604ce8bb756e3ff1acd4c1ce065b1a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/3c66df347f488dfe044ecf991ca2498b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/bf4bec5fe5245bf3c0d271e31bc2342f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/ce0f911df93a8e38932c48f983e7fde6
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/ab59173e00ab4a7bdf7740e5c6d50123
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/42894538fd0ab2b4235e55ef53b9cd59
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7efe30bb2268>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/0c252f8c2af87fe4a595ca853ea983cd
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/5feb279acc70b729ede7514b133d0172
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/6742bad9e26dd16b831fc9abd1883c9e
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpl5ribdvu/joblib/sklearn/pipeline/_fit_transform_one/096380a7ad90487199a4a6e8ec8a5744
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[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(iterated_power=7, n_components=8), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None, message_clsname='Pipeline', message=None)
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The PCA fitting is only computed at the evaluation of the first configuration of the C parameter of the LinearSVC classifier. The other configurations of C will trigger the loading of the cached PCA estimator data, leading to save processing time. Therefore, the use of caching the pipeline using memory is highly beneficial when fitting a transformer is costly.

Total running time of the script: ( 0 minutes 20.695 seconds)

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