[MRG+1] EHN/DOC Make error msg about Memory more explicit (scikit-learn#8865)

Author: glemaitre

sklearn/cluster/hierarchical.py

@@ -609,7 +609,8 @@ class AgglomerativeClustering(BaseEstimator, ClusterMixin):
         "manhattan", "cosine", or 'precomputed'.
         If linkage is "ward", only "euclidean" is accepted.
 
-    memory : Instance of joblib.Memory or string (optional)
+    memory : Instance of sklearn.externals.joblib.Memory or string, optional \
+            (default=None)
         Used to cache the output of the computation of the tree.
         By default, no caching is done. If a string is given, it is the
         path to the caching directory.
@@ -690,8 +691,10 @@ def fit(self, X, y=None):
         elif isinstance(memory, six.string_types):
             memory = Memory(cachedir=memory, verbose=0)
         elif not isinstance(memory, Memory):
-            raise ValueError('`memory` has to be a `str` or a `joblib.Memory`'
-                             ' instance')
+            raise ValueError("'memory' should either be a string or"
+                             " a sklearn.externals.joblib.Memory"
+                             " instance, got 'memory={!r}' instead.".format(
+                                 type(memory)))
 
         if self.n_clusters <= 0:
             raise ValueError("n_clusters should be an integer greater than 0."
@@ -776,7 +779,8 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
         "manhattan", "cosine", or 'precomputed'.
         If linkage is "ward", only "euclidean" is accepted.
 
-    memory : Instance of joblib.Memory or string, optional
+    memory : Instance of sklearn.externals.joblib.Memory or string, optional \
+            (default=None)
         Used to cache the output of the computation of the tree.
         By default, no caching is done. If a string is given, it is the
         path to the caching directory.

sklearn/linear_model/randomized_l1.py

@@ -99,8 +99,15 @@ def fit(self, X, y):
 
         estimator_func, params = self._make_estimator_and_params(X, y)
         memory = self.memory
-        if isinstance(memory, six.string_types):
-            memory = Memory(cachedir=memory)
+        if memory is None:
+            memory = Memory(cachedir=None, verbose=0)
+        elif isinstance(memory, six.string_types):
+            memory = Memory(cachedir=memory, verbose=0)
+        elif not isinstance(memory, Memory):
+            raise ValueError("'memory' should either be a string or"
+                             " a sklearn.externals.joblib.Memory"
+                             " instance, got 'memory={!r}' instead.".format(
+                                 type(memory)))
 
         scores_ = memory.cache(
             _resample_model, ignore=['verbose', 'n_jobs', 'pre_dispatch']
@@ -265,7 +272,8 @@ class RandomizedLasso(BaseRandomizedLinearModel):
             - A string, giving an expression as a function of n_jobs,
               as in '2*n_jobs'
 
-    memory : Instance of joblib.Memory or string
+    memory : Instance of sklearn.externals.joblib.Memory or string, optional \
+            (default=None)
         Used for internal caching. By default, no caching is done.
         If a string is given, it is the path to the caching directory.
 
@@ -307,7 +315,7 @@ def __init__(self, alpha='aic', scaling=.5, sample_fraction=.75,
                  max_iter=500,
                  eps=np.finfo(np.float).eps, random_state=None,
                  n_jobs=1, pre_dispatch='3*n_jobs',
-                 memory=Memory(cachedir=None, verbose=0)):
+                 memory=None):
         self.alpha = alpha
         self.scaling = scaling
         self.sample_fraction = sample_fraction
@@ -456,7 +464,8 @@ class RandomizedLogisticRegression(BaseRandomizedLinearModel):
             - A string, giving an expression as a function of n_jobs,
               as in '2*n_jobs'
 
-    memory : Instance of joblib.Memory or string
+    memory : Instance of sklearn.externals.joblib.Memory or string, optional \
+            (default=None)
         Used for internal caching. By default, no caching is done.
         If a string is given, it is the path to the caching directory.
 
@@ -498,7 +507,7 @@ def __init__(self, C=1, scaling=.5, sample_fraction=.75,
                  normalize=True,
                  random_state=None,
                  n_jobs=1, pre_dispatch='3*n_jobs',
-                 memory=Memory(cachedir=None, verbose=0)):
+                 memory=None):
         self.C = C
         self.scaling = scaling
         self.sample_fraction = sample_fraction

sklearn/linear_model/tests/test_randomized_l1.py

@@ -1,12 +1,15 @@
 # Authors: Alexandre Gramfort <[email protected]>
 # License: BSD 3 clause
+from tempfile import mkdtemp
+import shutil
 
 import numpy as np
 from scipy import sparse
 
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_raises
+from sklearn.utils.testing import assert_raises_regex
 
 from sklearn.linear_model.randomized_l1 import (lasso_stability_path,
                                                 RandomizedLasso,
@@ -38,6 +41,19 @@ def test_lasso_stability_path():
                        np.argsort(np.sum(scores_path, axis=1))[-3:])
 
 
+def test_randomized_lasso_error_memory():
+    scaling = 0.3
+    selection_threshold = 0.5
+    tempdir = 5
+    clf = RandomizedLasso(verbose=False, alpha=[1, 0.8], random_state=42,
+                          scaling=scaling,
+                          selection_threshold=selection_threshold,
+                          memory=tempdir)
+    assert_raises_regex(ValueError, "'memory' should either be a string or"
+                        " a sklearn.externals.joblib.Memory instance",
+                        clf.fit, X, y)
+
+
 def test_randomized_lasso():
     # Check randomized lasso
     scaling = 0.3
@@ -57,6 +73,18 @@ def test_randomized_lasso():
     feature_scores = clf.fit(X, y).scores_
     assert_equal(clf.all_scores_.shape, (X.shape[1], 2))
     assert_array_equal(np.argsort(F)[-3:], np.argsort(feature_scores)[-3:])
+    # test caching
+    try:
+        tempdir = mkdtemp()
+        clf = RandomizedLasso(verbose=False, alpha=[1, 0.8], random_state=42,
+                              scaling=scaling,
+                              selection_threshold=selection_threshold,
+                              memory=tempdir)
+        feature_scores = clf.fit(X, y).scores_
+        assert_equal(clf.all_scores_.shape, (X.shape[1], 2))
+        assert_array_equal(np.argsort(F)[-3:], np.argsort(feature_scores)[-3:])
+    finally:
+        shutil.rmtree(tempdir)
 
     X_r = clf.transform(X)
     X_full = clf.inverse_transform(X_r)

sklearn/pipeline.py

@@ -52,7 +52,8 @@ class Pipeline(_BaseComposition):
         chained, in the order in which they are chained, with the last object
         an estimator.
 
-    memory : Instance of joblib.Memory or string, optional (default=None)
+    memory : Instance of sklearn.external.joblib.Memory or string, optional \
+            (default=None)
         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
@@ -193,8 +194,9 @@ def _fit(self, X, y=None, **fit_params):
             memory = Memory(cachedir=memory, verbose=0)
         elif not isinstance(memory, Memory):
             raise ValueError("'memory' should either be a string or"
-                             " a joblib.Memory instance, got"
-                             " 'memory={!r}' instead.".format(memory))
+                             " a sklearn.externals.joblib.Memory"
+                             " instance, got 'memory={!r}' instead.".format(
+                                 type(memory)))
 
         fit_transform_one_cached = memory.cache(_fit_transform_one)
 
@@ -536,7 +538,8 @@ def make_pipeline(*steps, **kwargs):
     ----------
     *steps : list of estimators,
 
-    memory : Instance of joblib.Memory or string, optional (default=None)
+    memory : Instance of sklearn.externals.joblib.Memory or string, optional \
+            (default=None)
         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

sklearn/tests/test_pipeline.py

@@ -853,7 +853,7 @@ def test_pipeline_wrong_memory():
     cached_pipe = Pipeline([('transf', DummyTransf()), ('svc', SVC())],
                            memory=memory)
     assert_raises_regex(ValueError, "'memory' should either be a string or a"
-                        " joblib.Memory instance, got 'memory=1' instead.",
+                        " sklearn.externals.joblib.Memory instance, got",
                         cached_pipe.fit, X, y)