scikit-learn · jnothman · Mar 9, 2017 · Mar 10, 2017 · Mar 10, 2017 · Mar 10, 2017
diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
@@ -57,7 +57,7 @@ is an estimator object::
     >>> pipe # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
     Pipeline(memory=None,
              steps=[('reduce_dim', PCA(copy=True,...)),
-                    ('clf', SVC(C=1.0,...))])
+                    ('clf', SVC(C=1.0,...))], verbose=False)
 
 The utility function :func:`make_pipeline` is a shorthand
 for constructing pipelines;
@@ -72,7 +72,8 @@ filling in the names automatically::
              steps=[('binarizer', Binarizer(copy=True, threshold=0.0)),
                     ('multinomialnb', MultinomialNB(alpha=1.0,
                                                     class_prior=None,
-                                                    fit_prior=True))])
+                                                    fit_prior=True))],
+             verbose=False)
 
 The estimators of a pipeline are stored as a list in the ``steps`` attribute::
 
@@ -92,7 +93,8 @@ Parameters of the estimators in the pipeline can be accessed using the
     >>> pipe.set_params(clf__C=10) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
     Pipeline(memory=None,
              steps=[('reduce_dim', PCA(copy=True, iterated_power='auto',...)),
-                    ('clf', SVC(C=10, cache_size=200, class_weight=None,...))])
+                    ('clf', SVC(C=10, cache_size=200, class_weight=None,...))],
+             verbose=False)
 
 Attributes of named_steps map to keys, enabling tab completion in interactive environments::
 
@@ -170,7 +172,7 @@ object::
     >>> pipe # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
     Pipeline(...,
              steps=[('reduce_dim', PCA(copy=True,...)),
-                    ('clf', SVC(C=1.0,...))])
+                    ('clf', SVC(C=1.0,...))], verbose=False)
     >>> # Clear the cache directory when you don't need it anymore
     >>> rmtree(cachedir)
 
@@ -187,7 +189,8 @@ object::
      >>> pipe.fit(digits.data, digits.target)
      ... # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
      Pipeline(memory=None,
-              steps=[('reduce_dim', PCA(...)), ('clf', SVC(...))])
+              steps=[('reduce_dim', PCA(...)), ('clf', SVC(...))],
+              verbose=False)
      >>> # The pca instance can be inspected directly
      >>> print(pca1.components_) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
          [[-1.77484909e-19  ... 4.07058917e-18]]
@@ -209,7 +212,8 @@ object::
      >>> cached_pipe.fit(digits.data, digits.target)
      ... # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
       Pipeline(memory=...,
-               steps=[('reduce_dim', PCA(...)), ('clf', SVC(...))])
+               steps=[('reduce_dim', PCA(...)), ('clf', SVC(...))],
+               verbose=False)
      >>> print(cached_pipe.named_steps['reduce_dim'].components_)
      ... # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
          [[-1.77484909e-19  ... 4.07058917e-18]]
@@ -337,7 +341,7 @@ and ``value`` is an estimator object::
     FeatureUnion(n_jobs=1,
                  transformer_list=[('linear_pca', PCA(copy=True,...)),
                                    ('kernel_pca', KernelPCA(alpha=1.0,...))],
-                 transformer_weights=None)
+                 transformer_weights=None, verbose=False)
 
 
 Like pipelines, feature unions have a shorthand constructor called
@@ -352,7 +356,7 @@ and ignored by setting to ``None``::
     FeatureUnion(n_jobs=1,
                  transformer_list=[('linear_pca', PCA(copy=True,...)),
                                    ('kernel_pca', None)],
-                 transformer_weights=None)
+                 transformer_weights=None, verbose=False)
 
 .. topic:: Examples:
 

diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -228,6 +228,12 @@ Model evaluation and meta-estimators
   group-based CV strategies. :issue:`9085` by :user:`Laurent Direr <ldirer>`
   and `Andreas Müller`_.
 
+- Added optional parameter ``verbose`` in :class:`pipeline.Pipeline` and
+  :class:`pipeline.FeatureUnion` and corresponding ``make_`` helpers for
+  showing progress and timing of each step. :issue:`9668` by :user:`Baze
+  Petrushev <petrushev>`, :user:`Karan Desai <karandesai-96>` and `Joel
+  Nothman`_.
+
 - The ``predict`` method of :class:`pipeline.Pipeline` now passes keyword
   arguments on to the pipeline's last estimator, enabling the use of parameters
   such as ``return_std`` in a pipeline with caution.

diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
@@ -21,11 +21,12 @@
 import scipy.sparse as sp
 
 from ..base import is_classifier, clone
-from ..utils import indexable, check_random_state, safe_indexing
+from ..utils import (indexable, check_random_state, safe_indexing,
+                     message_with_time)
 from ..utils.deprecation import DeprecationDict
 from ..utils.validation import _is_arraylike, _num_samples
 from ..utils.metaestimators import _safe_split
-from ..externals.joblib import Parallel, delayed, logger
+from ..externals.joblib import Parallel, delayed
 from ..externals.six.moves import zip
 from ..metrics.scorer import check_scoring, _check_multimetric_scoring
 from ..exceptions import FitFailedWarning
@@ -538,8 +539,7 @@ def _fit_and_score(estimator, X, y, scorer, train, test, verbose,
             msg += ", score=%s" % test_scores
     if verbose > 1:
         total_time = score_time + fit_time
-        end_msg = "%s, total=%s" % (msg, logger.short_format_time(total_time))
-        print("[CV] %s %s" % ((64 - len(end_msg)) * '.', end_msg))
+        print(message_with_time('CV', msg, total_time))
 
     ret = [train_scores, test_scores] if return_train_score else [test_scores]