diff --git a/doc/datasets/mldata_fixture.py b/doc/datasets/mldata_fixture.py
index 17dae0e57192800e1e46d4609eccf824a02092b6..37d9f9af05dc3a8bd7fe49bb81e385f54af3caa4 100644
--- a/doc/datasets/mldata_fixture.py
+++ b/doc/datasets/mldata_fixture.py
@@ -42,4 +42,4 @@ def setup_module():
def teardown_module():
uninstall_mldata_mock()
- shutil.rmtree(custom_data_home)
\ No newline at end of file
+ shutil.rmtree(custom_data_home)
diff --git a/examples/covariance/plot_sparse_cov.py b/examples/covariance/plot_sparse_cov.py
index bd2013956b5e2954b0607a94259d3d870122aa59..aac87db41a8034fe87135860b1f6b4174ce65de3 100644
--- a/examples/covariance/plot_sparse_cov.py
+++ b/examples/covariance/plot_sparse_cov.py
@@ -126,7 +126,7 @@ for i, (name, this_prec) in enumerate(precs):
# plot the model selection metric
pl.figure(figsize=(4, 3))
pl.axes([.2, .15, .75, .7])
-pl.plot(model.cv_alphas_, np.mean(model.cv_scores, axis=1), 'o-')
+pl.plot(model.cv_alphas_, np.mean(model.grid_scores, axis=1), 'o-')
pl.axvline(model.alpha_, color='.5')
pl.title('Model selection')
pl.ylabel('Cross-validation score')
diff --git a/examples/grid_search_digits.py b/examples/grid_search_digits.py
index fc7d441a5020b9a1d1aa9861c4ec5191ea24fd40..f917d2124376a5568cc6ee191b666ef6910cfc96 100644
--- a/examples/grid_search_digits.py
+++ b/examples/grid_search_digits.py
@@ -60,7 +60,7 @@ for score in scores:
print()
print("Grid scores on development set:")
print()
- for params, mean_score, scores in clf.cv_scores_:
+ for params, mean_score, scores in clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() / 2, params))
print()
diff --git a/examples/plot_rfe_with_cross_validation.py b/examples/plot_rfe_with_cross_validation.py
index af64c9c29695a004c3cdcebb95193ea83f795c9d..24bc1ee82e531ce097ad25c48c04a2c4fa9bb3ff 100644
--- a/examples/plot_rfe_with_cross_validation.py
+++ b/examples/plot_rfe_with_cross_validation.py
@@ -32,5 +32,5 @@ import pylab as pl
pl.figure()
pl.xlabel("Number of features selected")
pl.ylabel("Cross validation score (nb of misclassifications)")
-pl.plot(range(1, len(rfecv.cv_scores_) + 1), rfecv.cv_scores_)
+pl.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
pl.show()
diff --git a/examples/randomized_search.py b/examples/randomized_search.py
index 540062698931b7ef135b3bcec66e0d3ec2db4e9a..3c3c54cd41a412ff031fb795533af7534177d7ce 100644
--- a/examples/randomized_search.py
+++ b/examples/randomized_search.py
@@ -39,8 +39,8 @@ clf = RandomForestClassifier(n_estimators=20)
# Utility function to report best scores
-def report(cv_scores, n_top=3):
- top_scores = sorted(cv_scores, key=itemgetter(1), reverse=True)[:n_top]
+def report(grid_scores, n_top=3):
+ top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]
for i, score in enumerate(top_scores):
print("Model with rank: {0}".format(i + 1))
print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
@@ -67,7 +67,7 @@ start = time()
random_search.fit(X, y)
print("RandomizedSearchCV took %.2f seconds for %d candidates"
" parameter settings." % ((time() - start), n_iter_search))
-report(random_search.cv_scores_)
+report(random_search.grid_scores_)
# use a full grid over all parameters
param_grid = {"max_depth": [3, None],
@@ -82,5 +82,5 @@ grid_search = GridSearchCV(clf, param_grid=param_grid)
grid_search.fit(X, y)
print("GridSearchCV took %.2f seconds for %d candidate parameter settings."
- % (time() - start, len(grid_search.cv_scores_)))
-report(grid_search.cv_scores_)
+ % (time() - start, len(grid_search.grid_scores_)))
+report(grid_search.grid_scores_)
diff --git a/examples/svm/plot_rbf_parameters.py b/examples/svm/plot_rbf_parameters.py
index f298ebf01205caa195c974def39448e249605584..722abc4beae4c130e7e8b854b74191e0d2e30e67 100644
--- a/examples/svm/plot_rbf_parameters.py
+++ b/examples/svm/plot_rbf_parameters.py
@@ -105,8 +105,8 @@ for (k, (C, gamma, clf)) in enumerate(classifiers):
pl.axis('tight')
# plot the scores of the grid
-# cv_scores_ contains parameter settings and scores
-score_dict = grid.cv_scores_
+# grid_scores_ contains parameter settings and scores
+score_dict = grid.grid_scores_
# We extract just the scores
scores = [x[1] for x in score_dict]
diff --git a/examples/svm/plot_svm_scale_c.py b/examples/svm/plot_svm_scale_c.py
index b21a2a52f341eed8d6be1d44d3c109096463b28a..133fd8be31898298a7c5c8b09dac5700c0a986b6 100644
--- a/examples/svm/plot_svm_scale_c.py
+++ b/examples/svm/plot_svm_scale_c.py
@@ -131,7 +131,7 @@ for fignum, (clf, cs, X, y) in enumerate(clf_sets):
cv=ShuffleSplit(n=n_samples, train_size=train_size,
n_iter=250, random_state=1))
grid.fit(X, y)
- scores = [x[1] for x in grid.cv_scores_]
+ scores = [x[1] for x in grid.grid_scores_]
scales = [(1, 'No scaling'),
((n_samples * train_size), '1/n_samples'),
diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
index b72a67a527ee943415c43646485598ecbfe4db92..73f13d71c363a94e6296f79bab5c3bedd6d3ddf7 100644
--- a/sklearn/covariance/graph_lasso_.py
+++ b/sklearn/covariance/graph_lasso_.py
@@ -422,7 +422,7 @@ class GraphLassoCV(GraphLasso):
`cv_alphas_`: list of float
All penalization parameters explored.
- `cv_scores`: 2D numpy.ndarray (n_alphas, n_folds)
+ `grid_scores`: 2D numpy.ndarray (n_alphas, n_folds)
Log-likelihood score on left-out data across folds.
See Also
@@ -551,14 +551,14 @@ class GraphLassoCV(GraphLasso):
% (i + 1, n_refinements, time.time() - t0))
path = list(zip(*path))
- cv_scores = list(path[1])
+ grid_scores = list(path[1])
alphas = list(path[0])
# Finally, compute the score with alpha = 0
alphas.append(0)
- cv_scores.append(cross_val_score(EmpiricalCovariance(), X,
+ grid_scores.append(cross_val_score(EmpiricalCovariance(), X,
cv=cv, n_jobs=self.n_jobs,
verbose=inner_verbose))
- self.cv_scores = np.array(cv_scores)
+ self.grid_scores = np.array(grid_scores)
best_alpha = alphas[best_index]
self.alpha_ = best_alpha
self.cv_alphas_ = alphas
diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
index 19aa27b7c14d64fdc57d224e4255619112664e08..799e0aaee986984cca6cb0a8cc77c18473a59bb1 100644
--- a/sklearn/feature_selection/rfe.py
+++ b/sklearn/feature_selection/rfe.py
@@ -261,9 +261,9 @@ class RFECV(RFE, MetaEstimatorMixin):
Selected (i.e., estimated best)
features are assigned rank 1.
- `cv_scores_` : array of shape [n_subsets_of_features]
+ `grid_scores_` : array of shape [n_subsets_of_features]
The cross-validation scores such that
- `cv_scores_[i]` corresponds to
+ `grid_scores_[i]` corresponds to
the CV score of the i-th subset of features.
`estimator_` : object
@@ -373,5 +373,5 @@ class RFECV(RFE, MetaEstimatorMixin):
self.estimator_.set_params(**self.estimator_params)
self.estimator_.fit(self.transform(X), y)
- self.cv_scores_ = scores / n
+ self.grid_scores_ = scores / n
return self
diff --git a/sklearn/feature_selection/tests/test_rfe.py b/sklearn/feature_selection/tests/test_rfe.py
index 01a3a3f6fa4037a6b1423c59720d133f4458b962..853634ca3c70031b87ef249032d16088fe8908ec 100644
--- a/sklearn/feature_selection/tests/test_rfe.py
+++ b/sklearn/feature_selection/tests/test_rfe.py
@@ -72,7 +72,7 @@ def test_rfecv():
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=3)
rfecv.fit(X, y)
# non-regression test for missing worst feature:
- assert_equal(len(rfecv.cv_scores_), X.shape[1])
+ assert_equal(len(rfecv.grid_scores_), X.shape[1])
assert_equal(len(rfecv.ranking_), X.shape[1])
X_r = rfecv.transform(X)
diff --git a/sklearn/grid_search.py b/sklearn/grid_search.py
index 0ca929d8c494c112406d58eaa331f4009fc1d878..babf7a6b2e77b260bd4677d8489191de20e18243 100644
--- a/sklearn/grid_search.py
+++ b/sklearn/grid_search.py
@@ -488,8 +488,8 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
n_jobs=self.n_jobs, verbose=self.verbose,
pre_dispatch=pre_dispatch)(
delayed(fit_grid_point)(
- X, y, base_estimator, parameters, train, test, self.scorer_,
- self.verbose, **self.fit_params)
+ X, y, base_estimator, parameters, train, test,
+ self.scorer_, self.verbose, **self.fit_params)
for parameters in parameter_iterable
for train, test in cv)
@@ -498,7 +498,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
n_folds = len(cv)
scores = list()
- cv_scores = list()
+ grid_scores = list()
for grid_start in range(0, n_fits, n_folds):
n_test_samples = 0
score = 0
@@ -516,16 +516,16 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
score /= float(n_folds)
scores.append((score, parameters))
# TODO: shall we also store the test_fold_sizes?
- cv_scores.append(_CVScoreTuple(
+ grid_scores.append(_CVScoreTuple(
parameters,
score,
np.array(all_scores)))
# Store the computed scores
- self.cv_scores_ = cv_scores
+ self.grid_scores_ = grid_scores
# Find the best parameters by comparing on the mean validation score:
# note that `sorted` is deterministic in the way it breaks ties
- best = sorted(cv_scores, key=lambda x: x.mean_validation_score,
+ best = sorted(grid_scores, key=lambda x: x.mean_validation_score,
reverse=True)[0]
self.best_params_ = best.parameters
self.best_score_ = best.mean_validation_score
@@ -630,7 +630,7 @@ class GridSearchCV(BaseSearchCV):
Attributes
----------
- `cv_scores_` : list of named tuples
+ `grid_scores_` : list of named tuples
Contains scores for all parameter combinations in param_grid.
Each entry corresponds to one parameter setting.
Each named tuple has the attributes:
@@ -685,12 +685,6 @@ class GridSearchCV(BaseSearchCV):
self.param_grid = param_grid
_check_param_grid(param_grid)
- @property
- def grid_scores_(self):
- warnings.warn("grid_scores_ is deprecated and will be removed in 0.15."
- " Use cv_scores_ instead.", DeprecationWarning)
- return self.cv_scores_
-
def fit(self, X, y=None, **params):
"""Run fit with all sets of parameters.
@@ -789,7 +783,7 @@ class RandomizedSearchCV(BaseSearchCV):
Attributes
----------
- `cv_scores_` : list of named tuples
+ `grid_scores_` : list of named tuples
Contains scores for all parameter combinations in param_grid.
Each entry corresponds to one parameter setting.
Each named tuple has the attributes:
diff --git a/sklearn/tests/test_grid_search.py b/sklearn/tests/test_grid_search.py
index b44eff0dad2a857e26956eee0c82f9b213c945bc..c7c3a70c7d035bcc0104f2ed14ebd759863ae98d 100644
--- a/sklearn/tests/test_grid_search.py
+++ b/sklearn/tests/test_grid_search.py
@@ -157,7 +157,7 @@ def test_grid_search():
assert_equal(grid_search.best_estimator_.foo_param, 2)
for i, foo_i in enumerate([1, 2, 3]):
- assert_true(grid_search.cv_scores_[i][0]
+ assert_true(grid_search.grid_scores_[i][0]
== {'foo_param': foo_i})
# Smoke test the score etc:
grid_search.score(X, y)
@@ -194,19 +194,19 @@ def test_grid_search_no_score():
GridSearchCV, clf_no_score, {'C': Cs})
-def test_trivial_cv_scores():
+def test_trivial_grid_scores():
"""Test search over a "grid" with only one point.
- Non-regression test: cv_scores_ wouldn't be set by GridSearchCV.
+ Non-regression test: grid_scores_ wouldn't be set by GridSearchCV.
"""
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1]})
grid_search.fit(X, y)
- assert_true(hasattr(grid_search, "cv_scores_"))
+ assert_true(hasattr(grid_search, "grid_scores_"))
random_search = RandomizedSearchCV(clf, {'foo_param': [0]})
random_search.fit(X, y)
- assert_true(hasattr(random_search, "cv_scores_"))
+ assert_true(hasattr(random_search, "grid_scores_"))
def test_no_refit():
@@ -245,7 +245,7 @@ def test_grid_search_iid():
# once with iid=True (default)
grid_search = GridSearchCV(svm, param_grid={'C': [1, 10]}, cv=cv)
grid_search.fit(X, y)
- first = grid_search.cv_scores_[0]
+ first = grid_search.grid_scores_[0]
assert_equal(first.parameters['C'], 1)
assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# for first split, 1/4 of dataset is in test, for second 3/4.
@@ -257,7 +257,7 @@ def test_grid_search_iid():
grid_search = GridSearchCV(svm, param_grid={'C': [1, 10]}, cv=cv,
iid=False)
grid_search.fit(X, y)
- first = grid_search.cv_scores_[0]
+ first = grid_search.grid_scores_[0]
assert_equal(first.parameters['C'], 1)
# scores are the same as above
assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
@@ -471,7 +471,7 @@ def test_X_as_list():
cv = KFold(n=len(X), n_folds=3)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)
grid_search.fit(X.tolist(), y).score(X, y)
- assert_true(hasattr(grid_search, "cv_scores_"))
+ assert_true(hasattr(grid_search, "grid_scores_"))
def test_unsupervised_grid_search():
@@ -511,7 +511,7 @@ def test_param_sampler():
assert_true(0 <= sample["C"] <= 1)
-def test_randomized_search_cv_scores():
+def test_randomized_search_grid_scores():
# Make a dataset with a lot of noise to get various kind of prediction
# errors across CV folds and parameter settings
X, y = make_classification(n_samples=200, n_features=100, n_informative=3,
@@ -527,10 +527,10 @@ def test_randomized_search_cv_scores():
search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_cv_iter,
param_distributions=params, iid=False)
search.fit(X, y)
- assert_equal(len(search.cv_scores_), n_search_iter)
+ assert_equal(len(search.grid_scores_), n_search_iter)
# Check consistency of the structure of each cv_score item
- for cv_score in search.cv_scores_:
+ for cv_score in search.grid_scores_:
assert_equal(len(cv_score.cv_validation_scores), n_cv_iter)
# Because we set iid to False, the mean_validation score is the
# mean of the fold mean scores instead of the aggregate sample-wise
@@ -541,12 +541,12 @@ def test_randomized_search_cv_scores():
list(sorted(params.keys())))
# Check the consistency with the best_score_ and best_params_ attributes
- sorted_cv_scores = list(sorted(search.cv_scores_,
+ sorted_grid_scores = list(sorted(search.grid_scores_,
key=lambda x: x.mean_validation_score))
- best_score = sorted_cv_scores[-1].mean_validation_score
+ best_score = sorted_grid_scores[-1].mean_validation_score
assert_equal(search.best_score_, best_score)
- tied_best_params = [s.parameters for s in sorted_cv_scores
+ tied_best_params = [s.parameters for s in sorted_grid_scores
if s.mean_validation_score == best_score]
assert_true(search.best_params_ in tied_best_params,
"best_params_={0} is not part of the"
@@ -563,7 +563,7 @@ def test_grid_search_score_consistency():
grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score)
grid_search.fit(X, y)
cv = StratifiedKFold(n_folds=3, y=y)
- for C, scores in zip(Cs, grid_search.cv_scores_):
+ for C, scores in zip(Cs, grid_search.grid_scores_):
clf.set_params(C=C)
scores = scores[2] # get the separate runs from grid scores
i = 0
@@ -607,7 +607,7 @@ def test_grid_search_with_multioutput_data():
for est in estimators:
grid_search = GridSearchCV(est, est_parameters, cv=cv)
grid_search.fit(X, y)
- for parameters, _, cv_validation_scores in grid_search.cv_scores_:
+ for parameters, _, cv_validation_scores in grid_search.grid_scores_:
est.set_params(**parameters)
for i, (train, test) in enumerate(cv):
@@ -620,7 +620,7 @@ def test_grid_search_with_multioutput_data():
for est in estimators:
random_search = RandomizedSearchCV(est, est_parameters, cv=cv)
random_search.fit(X, y)
- for parameters, _, cv_validation_scores in random_search.cv_scores_:
+ for parameters, _, cv_validation_scores in random_search.grid_scores_:
est.set_params(**parameters)
for i, (train, test) in enumerate(cv):
@@ -633,7 +633,7 @@ def test_grid_search_with_multioutput_data():
for est in estimators:
random_search = RandomizedSearchCV(est, est_parameters, cv=cv)
random_search.fit(X, y)
- for parameters, _, cv_validation_scores in random_search.cv_scores_:
+ for parameters, _, cv_validation_scores in random_search.grid_scores_:
est.set_params(**parameters)
for i, (train, test) in enumerate(cv):