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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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, ..., 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=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 - 0.1s, 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, ..., 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 - 0.1s, 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, ..., 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/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/33bdd5888cdf55166db7898e3725b3ef
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/89e6c6511794b298aa59ba72cc5e05c6
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/317c7fee8f520472e9a9faf4061a974c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/891c8047d700c2b6120d72fee9ff6660
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/c0af6f3506caf1b8718e1500a19f9765
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/e5c3534f5eaab799952b8934e641b601
___________________________________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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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 0x7f931e9500d0>), 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/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/8ce89542c6e3cda54cf97cf437b309d7
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/4ef46b1a83f295944b9393d574646825
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpof_9umsu/joblib/sklearn/pipeline/_fit_transform_one/99f36d39cbb1e85294e583b4d10ae4b5
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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 15.150 seconds)

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