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 - 0.1s, 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=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 - 0.1s, 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 - 0.0s, 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 - 0.1s, 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, ..., 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/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/ce059dd4031139a6b2806c6eeb882177
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/fd2f9ad0f7503004c6c4e5daa84b3d81
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/f7699cba03794b00c4e7ad12918c3870
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/637c1c59f2a2bebcf577f17752da8e7b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/662bd46dd1274c93e6633e1814aa6d18
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/340ab152c19b30c5094b0cc3d48088ce
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/5f5b28b918cd25901092d13d441e4d26
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/cfb5ed9e762b82923af42c9cdd8a029f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/f43b55f88ba83096d0f246bc7cdeeacc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/4a4f9f5b5aef5a99552c40788a041c3a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/4d9657eb86031c9c3f4d22e2f3c9cb43
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/1251d528711edc2cb2ff337ef4d2b930
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/5899433ec17984cc1dc0c0b58c092f48
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/ac4979a637124e8de3021326e803952f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/62198984c58175f6c8de85a08adac475
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/6fff13cab5b44100846e3d5ebcf5b777
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/8d0ec968ae6c20d5b13136352e9b3221
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/9130f54ef4a686fa57506a07f278a220
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/0553e2fdefbc628d4fc448fe0485529b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/01ae613cb5d86163f07ea3ad33679ea1
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/7a8c73996adeba89c910d9043e4c8065
___________________________________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 /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/a2dfe8b09115d1094021450fa7dea05a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/e3695525c67b22b8572c03b14c0dc50b
___________________________________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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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=4, score_func=<function chi2 at 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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=8, score_func=<function chi2 at 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), 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 0x7ff1f89b1620>), array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([0, ..., 9]), None)
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/957fad013ca010289fccaf07eba68f62
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[Memory]0.0s, 0.0min    : Loading _fit_transform_one from /tmp/tmpd22i53nw/joblib/sklearn/pipeline/_fit_transform_one/caf47ab6c6c883d05da5da8684cefb47
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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 19.985 seconds)

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