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=3, 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')
../_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 sklearn.externals.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=3, 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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________fit_transform_one - 0.1s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/418724be7e0f221c63842489d2d8dd8f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/3e8d95d340d603afdf4d48f022bf17e5
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/883d0a1cdb71638bbb069032713ce9dc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/958eff19e89776bd76df5aa2c245d3da
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/77797263aafc1cb48306ec67242c6ad5
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/f0515fdac3406fc25644d355469c0533
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/c5cbf3ce208ba301242b09931977b0ba
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/02ad81983f20f4c5cf0d47056cdacf9e
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/8ac7812389fd8128d90760e78d84e3af
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/c832e7ce545bf8ed19c9646b7df57b94
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/5a7c149c4c779fcfb3e3b5d4859f28e5
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________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 0x2b3aa35f6950>), None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 4]))
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/5fe54eb7ee530b63acb3dfa6861e7e31
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/4a03a48a7aad7233e42fab570e812d55
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/ece18c14ce6fdfec2f6f7eac72d33d65
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/df50d8982a4424bfd6b6eb0cc9345085
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/4a3e478397b79f5dd3eac4d1ed5f18ff
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/86fd5ede2d28c646cee97060abe98fa2
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/e2dec44788d6d43bbacc041a12a5b694
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/598e66f4fb276b36069528d316e749ad
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/03b4bcf60a35f2c1b7169e05514a8b9f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/5fe54eb7ee530b63acb3dfa6861e7e31
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/4a03a48a7aad7233e42fab570e812d55
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/ece18c14ce6fdfec2f6f7eac72d33d65
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/df50d8982a4424bfd6b6eb0cc9345085
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/4a3e478397b79f5dd3eac4d1ed5f18ff
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/86fd5ede2d28c646cee97060abe98fa2
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/e2dec44788d6d43bbacc041a12a5b694
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/598e66f4fb276b36069528d316e749ad
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/03b4bcf60a35f2c1b7169e05514a8b9f
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/5fe54eb7ee530b63acb3dfa6861e7e31
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/4a03a48a7aad7233e42fab570e812d55
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/ece18c14ce6fdfec2f6f7eac72d33d65
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/df50d8982a4424bfd6b6eb0cc9345085
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/4a3e478397b79f5dd3eac4d1ed5f18ff
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/86fd5ede2d28c646cee97060abe98fa2
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/e2dec44788d6d43bbacc041a12a5b694
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/598e66f4fb276b36069528d316e749ad
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpt93z6hvw/joblib/sklearn/pipeline/_fit_transform_one/03b4bcf60a35f2c1b7169e05514a8b9f
___________________________________fit_transform_one cache loaded - 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),
None, array([[ 0., ...,  0.],
       ...,
       [ 0., ...,  0.]]), array([0, ..., 8]))
________________________________________________fit_transform_one - 0.0s, 0.0min

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: ( 1 minutes 23.229 seconds)

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