Add memory to make_pipeline function

doc/whats_new/v0.0.4.rst

@@ -74,7 +74,11 @@ Bug fixes
 
 - Fix bug which was not preserving the dtype of X and y when generating
   samples.
-  issue:`448` by :user:`Guillaume Lemaitre <glemaitre>`.
+  :issue:`448` by :user:`Guillaume Lemaitre <glemaitre>`.
+
+- Add the option to pass a ``Memory`` object to :func:`make_pipeline` like 
+  in :class:`pipeline.Pipeline` class.
+  :issue:`458` by :user:`Christos Aridas <chkoar>`.
 
 Maintenance
 ...........
@@ -117,4 +121,4 @@ Deprecation
 - Deprecate :class:`imblearn.ensemble.EasyEnsemble` in favor of meta-estimator
   :class:`imblearn.ensemble.EasyEnsembleClassifier` which follow the exact
   algorithm described in the literature.
-  :issue:`455` by :user:`Guillaume Lemaitre <glemaitre>`.
+  :issue:`455` by :user:`Guillaume Lemaitre <glemaitre>`.

imblearn/pipeline.py

@@ -600,15 +600,50 @@ def _fit_sample_one(sampler, X, y, **fit_params):
     return X_res, y_res, sampler
 
 
-def make_pipeline(*steps):
+def make_pipeline(*steps, **kwargs):
     """Construct a Pipeline from the given estimators.
 
     This is a shorthand for the Pipeline constructor; it does not require, and
     does not permit, naming the estimators. Instead, their names will be set
     to the lowercase of their types automatically.
 
+    Parameters
+    ----------
+    *steps : list of estimators.
+
+    memory : None, str or object with the joblib.Memory interface, optional
+        Used to cache the fitted transformers of the pipeline. By default,
+        no caching is performed. If a string is given, it is the path to
+        the caching directory. Enabling caching triggers a clone of
+        the transformers before fitting. Therefore, the transformer
+        instance given to the pipeline cannot be inspected
+        directly. Use the attribute ``named_steps`` or ``steps`` to
+        inspect estimators within the pipeline. Caching the
+        transformers is advantageous when fitting is time consuming.
+
     Returns
     -------
     p : Pipeline
+
+    See also
+    --------
+    imblearn.pipeline.Pipeline : Class for creating a pipeline of
+        transforms with a final estimator.
+
+    Examples
+    --------
+    >>> from sklearn.naive_bayes import GaussianNB
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
+    ...     # doctest: +NORMALIZE_WHITESPACE
+    Pipeline(memory=None,
+             steps=[('standardscaler',
+                     StandardScaler(copy=True, with_mean=True, with_std=True)),
+                    ('gaussiannb',
+                     GaussianNB(priors=None))])
     """
-    return Pipeline(pipeline._name_estimators(steps))
+    memory = kwargs.pop('memory', None)
+    if kwargs:
+        raise TypeError('Unknown keyword arguments: "{}"'
+                        .format(list(kwargs.keys())[0]))
+    return Pipeline(pipeline._name_estimators(steps), memory=memory)

imblearn/tests/test_pipeline.py

@@ -12,6 +12,7 @@
 import numpy as np
 from pytest import raises
 
+from sklearn.utils.testing import assert_true
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_allclose
@@ -31,6 +32,7 @@
 from imblearn.under_sampling import (RandomUnderSampler,
                                      EditedNearestNeighbours as ENN)
 
+
 JUNK_FOOD_DOCS = (
     "the pizza pizza beer copyright",
     "the pizza burger beer copyright",
@@ -1073,3 +1075,15 @@ def test_pipeline_fit_then_sample_3_samplers_with_sampler_last_estimator():
     X_fit_then_sample_res, y_fit_then_sample_res = pipeline.sample(X, y)
     assert_array_equal(X_fit_sample_resampled, X_fit_then_sample_res)
     assert_array_equal(y_fit_sample_resampled, y_fit_then_sample_res)
+
+
+def test_make_pipeline_memory():
+    cachedir = mkdtemp()
+    try:
+        memory = Memory(cachedir=cachedir, verbose=10)
+        pipeline = make_pipeline(DummyTransf(), SVC(), memory=memory)
+        assert pipeline.memory is memory
+        pipeline = make_pipeline(DummyTransf(), SVC())
+        assert pipeline.memory is None
+    finally:
+        shutil.rmtree(cachedir)