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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([
('reduce_dim', PCA()),
('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()
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 sklearn.utils 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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, ..., 9]), None)
________________________________________________fit_transform_one - 0.2s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.1s, 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 - 0.2s, 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 - 0.2s, 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 - 0.3s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/1bf27be7972a60c9d2d8c9a16106dec2
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/118acd4d367dec57f2179a23f47c5cc1
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/16f8f016b2ae2c2c937ec68432160272
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/9847e1eeffb9fd5847cdaa065c13124b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/eb0de9c35b4820de6156126095b61515
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/81129a44af6685b5116436d813d312e6
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/eae05ab59792a48c4b673ef14f04c334
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/a138284acd57796087a9815d19f14450
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/0d155537afbf97708c84bb7fb6b72f5d
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/3971c76218de052c4541ed6d4b3a9218
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/0b059d708761697b71ff9674f8ef00ce
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/56c396594208af87a823c9eadb4c0ccc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/581707498b7d140bddb13175b9c2affc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/2b336e46229519ac952da37ad064a2b7
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________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), 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 0x7f5f3718f488>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 9]), None)
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[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/de5a7bedfde15e0c1845070a250917eb
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[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/d53cf5eb7f0d0aa0e4c99b2e25e25e64
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[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/7c9022da75316582f8db2a2c2b090579
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[Memory]0.0s, 0.0min : Loading _fit_transform_one from /tmp/tmps_a1zmb0/joblib/sklearn/pipeline/_fit_transform_one/f5d392ce8612bd0c49f7e4e3acecff2a
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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: ( 3 minutes 28.066 seconds)
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Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html