[MRG+1] Fix alerts found with lgtm (scikit-learn#9278)

* compare values rather than references

* add __ne__ to class

* add missing self parameter

* properly initialise child class using super()

* remove useless loop variables and assigments

* remove useless return statements

* respect PEP8 style

* fix another flake8 check

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)

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),

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 "

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.

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)
 
 

sklearn/random_projection.py

@@ -309,7 +309,7 @@ def __init__(self, n_components='auto', eps=0.1, dense_output=False,
         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

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()
 

sklearn/utils/mocking.py

@@ -36,6 +36,9 @@ def __array__(self, dtype=None):
     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.

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)