[MRG+1] Fix warnings in lgtm.com (remove redundant code) (scikit-lear…

…n#9719)

examples/cluster/plot_color_quantization.py

@@ -84,21 +84,18 @@ def recreate_image(codebook, labels, w, h):
 # Display all results, alongside original image
 plt.figure(1)
 plt.clf()
-ax = plt.axes([0, 0, 1, 1])
 plt.axis('off')
 plt.title('Original image (96,615 colors)')
 plt.imshow(china)
 
 plt.figure(2)
 plt.clf()
-ax = plt.axes([0, 0, 1, 1])
 plt.axis('off')
 plt.title('Quantized image (64 colors, K-Means)')
 plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))
 
 plt.figure(3)
 plt.clf()
-ax = plt.axes([0, 0, 1, 1])
 plt.axis('off')
 plt.title('Quantized image (64 colors, Random)')
 plt.imshow(recreate_image(codebook_random, labels_random, w, h))

examples/cluster/plot_dict_face_patches.py

@@ -41,7 +41,6 @@
 patch_size = (20, 20)
 
 buffer = []
-index = 1
 t0 = time.time()
 
 # The online learning part: cycle over the whole dataset 6 times

examples/cluster/plot_kmeans_stability_low_dim_dense.py

@@ -69,7 +69,7 @@ def make_data(random_state, n_samples_per_center, grid_size, scale):
 
 # Part 1: Quantitative evaluation of various init methods
 
-fig = plt.figure()
+plt.figure()
 plots = []
 legends = []
 
@@ -105,7 +105,7 @@ def make_data(random_state, n_samples_per_center, grid_size, scale):
 km = MiniBatchKMeans(n_clusters=n_clusters, init='random', n_init=1,
                      random_state=random_state).fit(X)
 
-fig = plt.figure()
+plt.figure()
 for k in range(n_clusters):
     my_members = km.labels_ == k
     color = cm.spectral(float(k) / n_clusters, 1)

examples/decomposition/plot_pca_3d.py

@@ -73,8 +73,6 @@ def plot_figs(fig_num, elev, azim):
     pca_score = pca.explained_variance_ratio_
     V = pca.components_
 
-    x_pca_axis, y_pca_axis, z_pca_axis = V.T * pca_score / pca_score.min()
-
     x_pca_axis, y_pca_axis, z_pca_axis = 3 * V.T
     x_pca_plane = np.r_[x_pca_axis[:2], - x_pca_axis[1::-1]]
     y_pca_plane = np.r_[y_pca_axis[:2], - y_pca_axis[1::-1]]

examples/ensemble/plot_forest_iris.py

@@ -46,7 +46,6 @@
 import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
 
-from sklearn import clone
 from sklearn.datasets import load_iris
 from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
                               AdaBoostClassifier)
@@ -90,10 +89,9 @@
         X = (X - mean) / std
 
         # Train
-        clf = clone(model)
-        clf = model.fit(X, y)
+        model.fit(X, y)
 
-        scores = clf.score(X, y)
+        scores = model.score(X, y)
         # Create a title for each column and the console by using str() and
         # slicing away useless parts of the string
         model_title = str(type(model)).split(

examples/gaussian_process/plot_gpc_isoprobability.py

@@ -85,7 +85,7 @@ def g(x):
 
 plt.plot(X[y > 0, 0], X[y > 0, 1], 'b.', markersize=12)
 
-cs = plt.contour(x1, x2, y_true, [0.], colors='k', linestyles='dashdot')
+plt.contour(x1, x2, y_true, [0.], colors='k', linestyles='dashdot')
 
 cs = plt.contour(x1, x2, y_prob, [0.666], colors='b',
                  linestyles='solid')

examples/gaussian_process/plot_gpr_noisy_targets.py

@@ -61,7 +61,7 @@ def f(x):
 
 # Plot the function, the prediction and the 95% confidence interval based on
 # the MSE
-fig = plt.figure()
+plt.figure()
 plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
 plt.plot(X, y, 'r.', markersize=10, label=u'Observations')
 plt.plot(x, y_pred, 'b-', label=u'Prediction')
@@ -97,7 +97,7 @@ def f(x):
 
 # Plot the function, the prediction and the 95% confidence interval based on
 # the MSE
-fig = plt.figure()
+plt.figure()
 plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
 plt.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label=u'Observations')
 plt.plot(x, y_pred, 'b-', label=u'Prediction')

examples/linear_model/plot_lasso_coordinate_descent_path.py

@@ -47,8 +47,6 @@
 # Display results
 
 plt.figure(1)
-ax = plt.gca()
-
 colors = cycle(['b', 'r', 'g', 'c', 'k'])
 neg_log_alphas_lasso = -np.log10(alphas_lasso)
 neg_log_alphas_enet = -np.log10(alphas_enet)
@@ -64,7 +62,6 @@
 
 
 plt.figure(2)
-ax = plt.gca()
 neg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)
 for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):
     l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
@@ -78,7 +75,6 @@
 
 
 plt.figure(3)
-ax = plt.gca()
 neg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)
 for (coef_e, coef_pe, c) in zip(coefs_enet, coefs_positive_enet, colors):
     l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)

examples/neighbors/plot_digits_kde_sampling.py

@@ -20,7 +20,6 @@
 
 # load the data
 digits = load_digits()
-data = digits.data
 
 # project the 64-dimensional data to a lower dimension
 pca = PCA(n_components=15, whiten=False)

examples/tree/plot_tree_regression_multioutput.py

@@ -42,7 +42,6 @@
 
 # Plot the results
 plt.figure()
-s = 50
 s = 25
 plt.scatter(y[:, 0], y[:, 1], c="navy", s=s,
             edgecolor="black", label="data")

sklearn/decomposition/dict_learning.py

@@ -824,7 +824,6 @@ def transform(self, X):
         check_is_fitted(self, 'components_')
 
         X = check_array(X)
-        n_samples, n_features = X.shape
 
         code = sparse_encode(
             X, self.components_, algorithm=self.transform_algorithm,

sklearn/decomposition/factor_analysis.py

@@ -326,7 +326,6 @@ def score_samples(self, X):
         Xr = X - self.mean_
         precision = self.get_precision()
         n_features = X.shape[1]
-        log_like = np.zeros(X.shape[0])
         log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
         log_like -= .5 * (n_features * log(2. * np.pi)
                           - fast_logdet(precision))

sklearn/decomposition/pca.py

@@ -550,7 +550,6 @@ def score_samples(self, X):
         X = check_array(X)
         Xr = X - self.mean_
         n_features = X.shape[1]
-        log_like = np.zeros(X.shape[0])
         precision = self.get_precision()
         log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
         log_like -= .5 * (n_features * log(2. * np.pi) -

sklearn/gaussian_process/gaussian_process.py

@@ -444,11 +444,6 @@ def predict(self, X, eval_MSE=False, batch_size=None):
             # Normalize input
             X = (X - self.X_mean) / self.X_std
 
-            # Initialize output
-            y = np.zeros(n_eval)
-            if eval_MSE:
-                MSE = np.zeros(n_eval)
-
             # Get pairwise componentwise L1-distances to the input training set
             dx = manhattan_distances(X, Y=self.X, sum_over_features=False)
             # Get regression function and correlation

sklearn/linear_model/least_angle.py

@@ -414,8 +414,6 @@ def lars_path(X, y, Xy=None, Gram=None, max_iter=500,
                 alphas[-add_features:] = 0
             coef = coefs[n_iter]
             prev_coef = coefs[n_iter - 1]
-            alpha = alphas[n_iter, np.newaxis]
-            prev_alpha = alphas[n_iter - 1, np.newaxis]
         else:
             # mimic the effect of incrementing n_iter on the array references
             prev_coef = coef

sklearn/mixture/dpgmm.py

@@ -273,7 +273,6 @@ def score_samples(self, X):
         X = check_array(X)
         if X.ndim == 1:
             X = X[:, np.newaxis]
-        z = np.zeros((X.shape[0], self.n_components))
         sd = digamma(self.gamma_.T[1] + self.gamma_.T[2])
         dgamma1 = digamma(self.gamma_.T[1]) - sd
         dgamma2 = np.zeros(self.n_components)
@@ -844,7 +843,6 @@ def _bound_proportions(self, z):
         return logprior
 
     def _bound_concentration(self):
-        logprior = 0.
         logprior = gammaln(np.sum(self.gamma_)) - gammaln(self.n_components
                                                           * self.alpha_)
         logprior -= np.sum(gammaln(self.gamma_) - gammaln(self.alpha_))

sklearn/utils/extmath.py

@@ -421,7 +421,6 @@ def weighted_mode(a, w, axis=0):
     else:
         a = np.asarray(a)
         w = np.asarray(w)
-        axis = axis
 
     if a.shape != w.shape:
         w = np.zeros(a.shape, dtype=w.dtype) + w