diff --git a/benchmarks/bench_plot_nmf.py b/benchmarks/bench_plot_nmf.py
index a1e0358e392a0b1513f4181a6ac937f87674920c..c48977a49a72582df379ab0fc1f9f00a9405c8b6 100644
--- a/benchmarks/bench_plot_nmf.py
+++ b/benchmarks/bench_plot_nmf.py
@@ -203,15 +203,11 @@ class _PGNMF(NMF):
def __init__(self, n_components=None, solver='pg', init=None,
tol=1e-4, max_iter=200, random_state=None,
alpha=0., l1_ratio=0., nls_max_iter=10):
+ super(_PGNMF, self).__init__(
+ n_components=n_components, init=init, solver=solver, tol=tol,
+ max_iter=max_iter, random_state=random_state, alpha=alpha,
+ l1_ratio=l1_ratio)
self.nls_max_iter = nls_max_iter
- self.n_components = n_components
- self.init = init
- self.solver = solver
- self.tol = tol
- self.max_iter = max_iter
- self.random_state = random_state
- self.alpha = alpha
- self.l1_ratio = l1_ratio
def fit(self, X, y=None, **params):
self.fit_transform(X, **params)
diff --git a/benchmarks/bench_plot_randomized_svd.py b/benchmarks/bench_plot_randomized_svd.py
index e4c2f63e056329c8276481e54837ad89aee39ec8..96a0e91fa4400f942f0f346fa74d909732944f6d 100644
--- a/benchmarks/bench_plot_randomized_svd.py
+++ b/benchmarks/bench_plot_randomized_svd.py
@@ -182,7 +182,7 @@ def plot_time_vs_s(time, norm, point_labels, title):
plt.figure()
colors = ['g', 'b', 'y']
for i, l in enumerate(sorted(norm.keys())):
- if l is not "fbpca":
+ if l != "fbpca":
plt.plot(time[l], norm[l], label=l, marker='o', c=colors.pop())
else:
plt.plot(time[l], norm[l], label=l, marker='^', c='red')
@@ -200,7 +200,7 @@ def scatter_time_vs_s(time, norm, point_labels, title):
plt.figure()
size = 100
for i, l in enumerate(sorted(norm.keys())):
- if l is not "fbpca":
+ if l != "fbpca":
plt.scatter(time[l], norm[l], label=l, marker='o', c='b', s=size)
for label, x, y in zip(point_labels, list(time[l]), list(norm[l])):
plt.annotate(label, xy=(x, y), xytext=(0, -80),
diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
index aa5be9cb5253f9c96117fe794f100e2d0dedc161..9292e9341208fe32eb9183a824f467531784ef1a 100644
--- a/sklearn/covariance/graph_lasso_.py
+++ b/sklearn/covariance/graph_lasso_.py
@@ -324,15 +324,13 @@ class GraphLasso(EmpiricalCovariance):
def __init__(self, alpha=.01, mode='cd', tol=1e-4, enet_tol=1e-4,
max_iter=100, verbose=False, assume_centered=False):
+ super(GraphLasso, self).__init__(assume_centered=assume_centered)
self.alpha = alpha
self.mode = mode
self.tol = tol
self.enet_tol = enet_tol
self.max_iter = max_iter
self.verbose = verbose
- self.assume_centered = assume_centered
- # The base class needs this for the score method
- self.store_precision = True
def fit(self, X, y=None):
@@ -551,18 +549,13 @@ class GraphLassoCV(GraphLasso):
def __init__(self, alphas=4, n_refinements=4, cv=None, tol=1e-4,
enet_tol=1e-4, max_iter=100, mode='cd', n_jobs=1,
verbose=False, assume_centered=False):
+ super(GraphLassoCV, self).__init__(
+ mode=mode, tol=tol, verbose=verbose, enet_tol=enet_tol,
+ max_iter=max_iter, assume_centered=assume_centered)
self.alphas = alphas
self.n_refinements = n_refinements
- self.mode = mode
- self.tol = tol
- self.enet_tol = enet_tol
- self.max_iter = max_iter
- self.verbose = verbose
self.cv = cv
self.n_jobs = n_jobs
- self.assume_centered = assume_centered
- # The base class needs this for the score method
- self.store_precision = True
@property
@deprecated("Attribute grid_scores was deprecated in version 0.19 and "
diff --git a/sklearn/decomposition/sparse_pca.py b/sklearn/decomposition/sparse_pca.py
index 23d1163fdc881032ef5b93e7da9706c12dca6cac..f5250cac8ace5b5625c15147b034e9634481c58e 100644
--- a/sklearn/decomposition/sparse_pca.py
+++ b/sklearn/decomposition/sparse_pca.py
@@ -257,18 +257,14 @@ class MiniBatchSparsePCA(SparsePCA):
def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01,
n_iter=100, callback=None, batch_size=3, verbose=False,
shuffle=True, n_jobs=1, method='lars', random_state=None):
-
- self.n_components = n_components
- self.alpha = alpha
- self.ridge_alpha = ridge_alpha
+ super(MiniBatchSparsePCA, self).__init__(
+ n_components=n_components, alpha=alpha, verbose=verbose,
+ ridge_alpha=ridge_alpha, n_jobs=n_jobs, method=method,
+ random_state=random_state)
self.n_iter = n_iter
self.callback = callback
self.batch_size = batch_size
- self.verbose = verbose
self.shuffle = shuffle
- self.n_jobs = n_jobs
- self.method = method
- self.random_state = random_state
def fit(self, X, y=None):
"""Fit the model from data in X.
diff --git a/sklearn/mixture/gaussian_mixture.py b/sklearn/mixture/gaussian_mixture.py
index 11784f86febfa8a80156d5859445ab72a35b6c28..59e4942d508c1692928ed2102262f0005a49cfd1 100644
--- a/sklearn/mixture/gaussian_mixture.py
+++ b/sklearn/mixture/gaussian_mixture.py
@@ -91,7 +91,7 @@ def _check_precision_matrix(precision, covariance_type):
def _check_precisions_full(precisions, covariance_type):
"""Check the precision matrices are symmetric and positive-definite."""
- for k, prec in enumerate(precisions):
+ for prec in precisions:
_check_precision_matrix(prec, covariance_type)
diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
index eebb8da80da4af3653e9474b9b041977a7a9b808..f498873d6694040220bca2b329b686598da90e43 100644
--- a/sklearn/random_projection.py
+++ b/sklearn/random_projection.py
@@ -309,7 +309,7 @@ class BaseRandomProjection(six.with_metaclass(ABCMeta, BaseEstimator,
self.random_state = random_state
@abstractmethod
- def _make_random_matrix(n_components, n_features):
+ def _make_random_matrix(self, n_components, n_features):
""" Generate the random projection matrix
Parameters
diff --git a/sklearn/tree/export.py b/sklearn/tree/export.py
index f526c771af04702d3b92be7e3b0ae36a2bbd92f7..451c0f0b1e93cc96c660218b0bd3e5f43bbdd7f5 100644
--- a/sklearn/tree/export.py
+++ b/sklearn/tree/export.py
@@ -66,7 +66,7 @@ def _color_brew(n):
class Sentinel(object):
- def __repr__():
+ def __repr__(self):
return '"tree.dot"'
SENTINEL = Sentinel()
diff --git a/sklearn/utils/mocking.py b/sklearn/utils/mocking.py
index 013644a285115bb5106a47cb4a37337101cf83ff..06d5a7cbd3671b558e38e9a853fd3c63bff651ec 100644
--- a/sklearn/utils/mocking.py
+++ b/sklearn/utils/mocking.py
@@ -36,6 +36,9 @@ class MockDataFrame(object):
def __eq__(self, other):
return MockDataFrame(self.array == other.array)
+ def __ne__(self, other):
+ return not self == other
+
class CheckingClassifier(BaseEstimator, ClassifierMixin):
"""Dummy classifier to test pipelining and meta-estimators.
diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py
index 9b081ec45f4212a1b204223f5b03753b372e8b54..38b8b0a6eff16931784b854aa06f3fc7f6e13bbb 100644
--- a/sklearn/utils/sparsefuncs.py
+++ b/sklearn/utils/sparsefuncs.py
@@ -302,9 +302,9 @@ def inplace_swap_row(X, m, n):
Index of the row of X to be swapped.
"""
if isinstance(X, sp.csc_matrix):
- return inplace_swap_row_csc(X, m, n)
+ inplace_swap_row_csc(X, m, n)
elif isinstance(X, sp.csr_matrix):
- return inplace_swap_row_csr(X, m, n)
+ inplace_swap_row_csr(X, m, n)
else:
_raise_typeerror(X)
@@ -329,9 +329,9 @@ def inplace_swap_column(X, m, n):
if n < 0:
n += X.shape[1]
if isinstance(X, sp.csc_matrix):
- return inplace_swap_row_csr(X, m, n)
+ inplace_swap_row_csr(X, m, n)
elif isinstance(X, sp.csr_matrix):
- return inplace_swap_row_csc(X, m, n)
+ inplace_swap_row_csc(X, m, n)
else:
_raise_typeerror(X)