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

from __future__ import print_function, division

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', None),
    ('classify', LinearSVC())
])

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(cachedir=cachedir, verbose=10)
cached_pipe = Pipeline([('reduce_dim', PCA()),
                        ('classify', LinearSVC())],
                       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(copy=True, iterated_power=7, n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=2, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=2, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=2, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=2, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=2, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=4, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=4, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=4, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=4, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=4, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=8, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=8, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=8, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=8, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=8, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\7630a23b4a54defac925a56610d8d88a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\6ee7bf3ad1af81808530d98717aca42c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\b901760747e46067a8e02e3ce69c4182
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\e7263be6aaa4c6563c386df27e71f6ae
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\b60abd1fe1c3ac52278e8c67fd477907
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\df9f277cbd5d6dedfe65bdc89ffa446b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\542bc3c0692e7ff7e5a29a9b0b5dfb70
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\e328d07f7d27285b4efa85633dc0ae32
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\d4e36de6392376382e6f22ecb839f806
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\5bda2d847240472971feb73252c26651
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\d9620081177a0c30abf1627057c4b5ff
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\f30cc26efeb2b8374cfa2ea9642a8d5e
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\f0bfdf27e49baf36719029f6ec813b7c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\f0fc64669f4e0aefad379f9f83552ac1
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\6ab3fc400385f416313a9a36f9e91fcb
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\1e141fe01848dc860fd1d585ca4e8632
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from C:\Users\antares\AppData\Local\Temp\tmp3cs__ib2\joblib\sklearn\pipeline\_fit_transform_one\663b98eabfd03900b330a9fbfd73204e
___________________________________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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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 0x0000000008955C80>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
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[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
  n_components=8, random_state=None, shuffle=False, solver='cd',
  tol=0.0001, verbose=0),
array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 8]), 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: ( 2 minutes 5.611 seconds)

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