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)
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)
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 - 0.1s, 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 - 0.1s, 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, ..., 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 - 0.0s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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, ..., 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=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(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 - 0.1s, 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 - 0.2s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/333e7fe41c5d806e143dc9819ac702cd
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/afedf0156c05055d105640e0f6b3fc74
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/65b9b9207f1ae17551536e7b7e912566
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/46b3a2b0bf51f040de8b0f5563cf1547
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/2bc700e7da6c3f2bffda4097622b64a0
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/86565af7cb717224d7a72a1891e9251e
___________________________________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] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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=4, score_func=<function chi2 at 0x7fe8314b5950>), 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=4, score_func=<function chi2 at 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), 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 0x7fe8314b5950>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None, message_clsname='Pipeline', message=None)
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/f01cf5044f9ccef4e52ae3ec6f7c8040
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/8a701d0907223d8e92eee8008f620d39
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmp_yuawjl8/joblib/sklearn/pipeline/_fit_transform_one/e8f90adaba647f609ccca62ffa0adb3a
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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 15.088 seconds)

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