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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/2a39fe1a21d95f0261baa2c9ae258db0
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/811a7cad8fbf5dabea9ce723966584b0
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/c9c0eddcffc5931297f2b1c6f50f6ce9
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/351cab1df9482242dbac66261144ec48
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/1352eafab0ccd5c520dc0605d7f0b41f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/121bb33252d25ef284f038a44b627f4a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/c9a6911c0615b3a765f6dc8a11dc945a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/d7e856cf4386fa07e73f53afd5cf511b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/99b9f719a3d6654f6b63c52ecce6be87
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/112782a766c64431edd8c9ecf93ff56f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/919ab40344d9b878b70989b1e28bc158
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/359d31f0b11e395087fc47b05b07b21c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/83feb26db1bc079ba3b072b45b3d64ba
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/0694d6aafefd1a317a7887f08993329a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/49aacc7397f05394aa49a952cef7e7ab
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/fcc8636904f75699c351bb20cf0ec982
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/f68d52f9e047da667ab4e25d7d4a8a01
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/47e6ffa71bd5950e95f1a42bc4f0eb62
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/ce117734ca592a922bede4204a4415df
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/9816888197e30c6f255961553ba40e6e
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/407e74957040b43c326a74564feb6d78
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/3254ee53e25527f0550c9cfb428429c4
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/5cedbcd950efae3c27f6547b18aa34cd
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/8864b412cf3ecfd42e86d207b9608d48
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/b8672dc092dad1ccd16ea7436633dcea
___________________________________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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), 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 0x7f3f2a47c8c8>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/2d4732a0c53f1f759e69e2690aecf400
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/cefb113ddf522281305415aebe23dc18
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmptsu3ys1m/joblib/sklearn/pipeline/_fit_transform_one/a643e617f08975834be5367bb5ffe864
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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 19.833 seconds)

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