Rebuild dev docs at master=8aa9c19

Author: sklearn-ci

dev/_downloads/plot_ard.py

@@ -58,25 +58,27 @@
 # weights
 plt.figure(figsize=(6, 5))
 plt.title("Weights of the model")
-plt.plot(clf.coef_, 'b-', label="ARD estimate")
-plt.plot(ols.coef_, 'r--', label="OLS estimate")
-plt.plot(w, 'g-', label="Ground truth")
+plt.plot(clf.coef_, color='darkblue', linestyle='-', linewidth=2,
+         label="ARD estimate")
+plt.plot(ols.coef_, color='yellowgreen', linestyle=':', linewidth=2,
+         label="OLS estimate")
+plt.plot(w, color='orange', linestyle='-', linewidth=2, label="Ground truth")
 plt.xlabel("Features")
 plt.ylabel("Values of the weights")
 plt.legend(loc=1)
 
 plt.figure(figsize=(6, 5))
 plt.title("Histogram of the weights")
-plt.hist(clf.coef_, bins=n_features, log=True)
-plt.plot(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
-         'ro', label="Relevant features")
+plt.hist(clf.coef_, bins=n_features, color='navy', log=True)
+plt.scatter(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
+            color='gold', marker='o', label="Relevant features")
 plt.ylabel("Features")
 plt.xlabel("Values of the weights")
 plt.legend(loc=1)
 
 plt.figure(figsize=(6, 5))
 plt.title("Marginal log-likelihood")
-plt.plot(clf.scores_)
+plt.plot(clf.scores_, color='navy', linewidth=2)
 plt.ylabel("Score")
 plt.xlabel("Iterations")
 plt.show()

dev/_downloads/plot_bayesian_ridge.py

@@ -52,27 +52,29 @@
 
 ###############################################################################
 # Plot true weights, estimated weights and histogram of the weights
+lw = 2
 plt.figure(figsize=(6, 5))
 plt.title("Weights of the model")
-plt.plot(clf.coef_, 'b-', label="Bayesian Ridge estimate")
-plt.plot(w, 'g-', label="Ground truth")
-plt.plot(ols.coef_, 'r--', label="OLS estimate")
+plt.plot(clf.coef_, color='lightgreen', linewidth=lw,
+         label="Bayesian Ridge estimate")
+plt.plot(w, color='gold', linewidth=lw, label="Ground truth")
+plt.plot(ols.coef_, color='navy', linestyle='--', label="OLS estimate")
 plt.xlabel("Features")
 plt.ylabel("Values of the weights")
 plt.legend(loc="best", prop=dict(size=12))
 
 plt.figure(figsize=(6, 5))
 plt.title("Histogram of the weights")
-plt.hist(clf.coef_, bins=n_features, log=True)
-plt.plot(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
-         'ro', label="Relevant features")
+plt.hist(clf.coef_, bins=n_features, color='gold', log=True)
+plt.scatter(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
+            color='navy', label="Relevant features")
 plt.ylabel("Features")
 plt.xlabel("Values of the weights")
-plt.legend(loc="lower left")
+plt.legend(loc="upper left")
 
 plt.figure(figsize=(6, 5))
 plt.title("Marginal log-likelihood")
-plt.plot(clf.scores_)
+plt.plot(clf.scores_, color='navy', linewidth=lw)
 plt.ylabel("Score")
 plt.xlabel("Iterations")
 plt.show()

dev/_downloads/plot_lasso_and_elasticnet.py

@@ -58,9 +58,11 @@
 print(enet)
 print("r^2 on test data : %f" % r2_score_enet)
 
-plt.plot(enet.coef_, label='Elastic net coefficients')
-plt.plot(lasso.coef_, label='Lasso coefficients')
-plt.plot(coef, '--', label='original coefficients')
+plt.plot(enet.coef_, color='lightgreen', linewidth=2,
+         label='Elastic net coefficients')
+plt.plot(lasso.coef_, color='gold', linewidth=2,
+         label='Lasso coefficients')
+plt.plot(coef, '--', color='navy', label='original coefficients')
 plt.legend(loc='best')
 plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
           % (r2_score_lasso, r2_score_enet))

dev/_downloads/plot_lda.py

@@ -58,9 +58,9 @@ def generate_data(n_samples, n_features):
 features_samples_ratio = np.array(n_features_range) / n_train
 
 plt.plot(features_samples_ratio, acc_clf1, linewidth=2,
-         label="Linear Discriminant Analysis with shrinkage", color='r')
+         label="Linear Discriminant Analysis with shrinkage", color='navy')
 plt.plot(features_samples_ratio, acc_clf2, linewidth=2,
-         label="Linear Discriminant Analysis", color='g')
+         label="Linear Discriminant Analysis", color='gold')
 
 plt.xlabel('n_features / n_samples')
 plt.ylabel('Classification accuracy')

dev/_downloads/plot_multi_task_lasso_support.py

@@ -56,10 +56,13 @@
 
 feature_to_plot = 0
 plt.figure()
-plt.plot(coef[:, feature_to_plot], 'k', label='Ground truth')
-plt.plot(coef_lasso_[:, feature_to_plot], 'g', label='Lasso')
-plt.plot(coef_multi_task_lasso_[:, feature_to_plot],
-         'r', label='MultiTaskLasso')
+lw = 2
+plt.plot(coef[:, feature_to_plot], color='seagreen', linewidth=lw,
+         label='Ground truth')
+plt.plot(coef_lasso_[:, feature_to_plot], color='cornflowerblue', linewidth=lw,
+         label='Lasso')
+plt.plot(coef_multi_task_lasso_[:, feature_to_plot], color='gold', linewidth=lw,
+         label='MultiTaskLasso')
 plt.legend(loc='upper center')
 plt.axis('tight')
 plt.ylim([-1.1, 1.1])

dev/_downloads/plot_polynomial_interpolation.py

@@ -54,14 +54,18 @@ def f(x):
 X = x[:, np.newaxis]
 X_plot = x_plot[:, np.newaxis]
 
-plt.plot(x_plot, f(x_plot), label="ground truth")
-plt.scatter(x, y, label="training points")
+colors = ['teal', 'yellowgreen', 'gold']
+lw = 2
+plt.plot(x_plot, f(x_plot), color='cornflowerblue', linewidth=lw,
+         label="ground truth")
+plt.scatter(x, y, color='navy', s=30, marker='o', label="training points")
 
-for degree in [3, 4, 5]:
+for count, degree in enumerate([3, 4, 5]):
     model = make_pipeline(PolynomialFeatures(degree), Ridge())
     model.fit(X, y)
     y_plot = model.predict(X_plot)
-    plt.plot(x_plot, y_plot, label="degree %d" % degree)
+    plt.plot(x_plot, y_plot, color=colors[count], linewidth=lw,
+             label="degree %d" % degree)
 
 plt.legend(loc='lower left')
 

dev/_downloads/plot_ransac.py

@@ -45,9 +45,14 @@
 print("Estimated coefficients (true, normal, RANSAC):")
 print(coef, model.coef_, model_ransac.estimator_.coef_)
 
-plt.plot(X[inlier_mask], y[inlier_mask], '.g', label='Inliers')
-plt.plot(X[outlier_mask], y[outlier_mask], '.r', label='Outliers')
-plt.plot(line_X, line_y, '-k', label='Linear regressor')
-plt.plot(line_X, line_y_ransac, '-b', label='RANSAC regressor')
+lw = 2
+plt.scatter(X[inlier_mask], y[inlier_mask], color='yellowgreen', marker='.',
+            label='Inliers')
+plt.scatter(X[outlier_mask], y[outlier_mask], color='gold', marker='.',
+            label='Outliers')
+plt.plot(line_X, line_y, color='navy', linestyle='-', linewidth=lw,
+         label='Linear regressor')
+plt.plot(line_X, line_y_ransac, color='cornflowerblue', linestyle='-',
+         linewidth=lw, label='RANSAC regressor')
 plt.legend(loc='lower right')
 plt.show()

dev/_downloads/plot_robust_fit.py

@@ -59,28 +59,30 @@
 estimators = [('OLS', linear_model.LinearRegression()),
               ('Theil-Sen', linear_model.TheilSenRegressor(random_state=42)),
               ('RANSAC', linear_model.RANSACRegressor(random_state=42)), ]
-
+colors = {'OLS': 'turquoise', 'Theil-Sen': 'gold', 'RANSAC': 'lightgreen'}
+linestyle = {'OLS': '-', 'Theil-Sen': '-.', 'RANSAC': '--'}
+lw = 3
 x_plot = np.linspace(X.min(), X.max())
-
 for title, this_X, this_y in [
-        ('Modeling errors only', X, y),
-        ('Corrupt X, small deviants', X_errors, y),
-        ('Corrupt y, small deviants', X, y_errors),
-        ('Corrupt X, large deviants', X_errors_large, y),
-        ('Corrupt y, large deviants', X, y_errors_large)]:
+        ('Modeling Errors Only', X, y),
+        ('Corrupt X, Small Deviants', X_errors, y),
+        ('Corrupt y, Small Deviants', X, y_errors),
+        ('Corrupt X, Large Deviants', X_errors_large, y),
+        ('Corrupt y, Large Deviants', X, y_errors_large)]:
     plt.figure(figsize=(5, 4))
-    plt.plot(this_X[:, 0], this_y, 'k+')
+    plt.plot(this_X[:, 0], this_y, 'b+')
 
     for name, estimator in estimators:
         model = make_pipeline(PolynomialFeatures(3), estimator)
         model.fit(this_X, this_y)
         mse = metrics.mean_squared_error(model.predict(X_test), y_test)
         y_plot = model.predict(x_plot[:, np.newaxis])
-        plt.plot(x_plot, y_plot,
-                 label='%s: error = %.3f' % (name, mse))
+        plt.plot(x_plot, y_plot, color=colors[name], linestyle=linestyle[name],
+                 linewidth=lw, label='%s: error = %.3f' % (name, mse))
 
-    plt.legend(loc='best', frameon=False,
-               title='Error: mean absolute deviation\n to non corrupt data')
+    legend_title = 'Error of Mean\nAbsolute Deviation\nto Non-corrupt Data'
+    legend = plt.legend(loc='upper right', frameon=False, title=legend_title,
+                        fontsize='x-small')
     plt.xlim(-4, 10.2)
     plt.ylim(-2, 10.2)
     plt.title(title)

dev/_downloads/plot_sgd_loss_functions.py

@@ -22,18 +22,19 @@ def modified_huber_loss(y_true, y_pred):
 
 xmin, xmax = -4, 4
 xx = np.linspace(xmin, xmax, 100)
-plt.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
+lw = 2
+plt.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], color='gold', lw=lw,
          label="Zero-one loss")
-plt.plot(xx, np.where(xx < 1, 1 - xx, 0), 'g-',
+plt.plot(xx, np.where(xx < 1, 1 - xx, 0), color='teal', lw=lw,
          label="Hinge loss")
-plt.plot(xx, -np.minimum(xx, 0), 'm-',
+plt.plot(xx, -np.minimum(xx, 0), color='yellowgreen', lw=lw,
          label="Perceptron loss")
-plt.plot(xx, np.log2(1 + np.exp(-xx)), 'r-',
+plt.plot(xx, np.log2(1 + np.exp(-xx)), color='cornflowerblue', lw=lw,
          label="Log loss")
-plt.plot(xx, np.where(xx < 1, 1 - xx, 0) ** 2, 'b-',
+plt.plot(xx, np.where(xx < 1, 1 - xx, 0) ** 2, color='orange', lw=lw,
          label="Squared hinge loss")
-plt.plot(xx, modified_huber_loss(xx, 1), 'y--',
-         label="Modified Huber loss")
+plt.plot(xx, modified_huber_loss(xx, 1), color='darkorchid', lw=lw,
+         linestyle='--', label="Modified Huber loss")
 plt.ylim((0, 8))
 plt.legend(loc="upper right")
 plt.xlabel(r"Decision function $f(x)$")

dev/_downloads/plot_sgd_penalties.py

@@ -44,20 +44,25 @@ def cross(ext):
 
 cross(1.2)
 
-plt.plot(xs, l1(xs), "r-", label="L1")
-plt.plot(xs, -1.0 * l1(xs), "r-")
-plt.plot(-1 * xs, l1(xs), "r-")
-plt.plot(-1 * xs, -1.0 * l1(xs), "r-")
-
-plt.plot(xs, l2(xs), "b-", label="L2")
-plt.plot(xs, -1.0 * l2(xs), "b-")
-plt.plot(-1 * xs, l2(xs), "b-")
-plt.plot(-1 * xs, -1.0 * l2(xs), "b-")
-
-plt.plot(xs, el(xs, alpha), "y-", label="Elastic Net")
-plt.plot(xs, -1.0 * el(xs, alpha), "y-")
-plt.plot(-1 * xs, el(xs, alpha), "y-")
-plt.plot(-1 * xs, -1.0 * el(xs, alpha), "y-")
+l1_color = "navy"
+l2_color = "c"
+elastic_net_color = "darkorange"
+lw = 2
+
+plt.plot(xs, l1(xs), color=l1_color, label="L1", lw=lw)
+plt.plot(xs, -1.0 * l1(xs), color=l1_color, lw=lw)
+plt.plot(-1 * xs, l1(xs), color=l1_color, lw=lw)
+plt.plot(-1 * xs, -1.0 * l1(xs), color=l1_color, lw=lw)
+
+plt.plot(xs, l2(xs), color=l2_color, label="L2", lw=lw)
+plt.plot(xs, -1.0 * l2(xs), color=l2_color, lw=lw)
+plt.plot(-1 * xs, l2(xs), color=l2_color, lw=lw)
+plt.plot(-1 * xs, -1.0 * l2(xs), color=l2_color, lw=lw)
+
+plt.plot(xs, el(xs, alpha), color=elastic_net_color, label="Elastic Net", lw=lw)
+plt.plot(xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)
+plt.plot(-1 * xs, el(xs, alpha), color=elastic_net_color, lw=lw)
+plt.plot(-1 * xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)
 
 plt.xlabel(r"$w_0$")
 plt.ylabel(r"$w_1$")