8000 [MRG+1] Modify General examples for matplotlib v2 comp (#8413) · scikit-learn/scikit-learn@93bbe54 · GitHub
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[MRG+1] Modify General examples for matplotlib v2 comp (#8413)
* Modify plot_cv_predict.py for matplotlib v2 comp Add `edgecolors` in scatter plot for matplotlib v2. Issue: #8364 * Modify plot_multilabel.py for matplotlib v2 Add `edgecolors` in scatterr plot for matplotlib v2. Issue: #8364 * Modify plot_kernel_ridge_regression for matplotlib v2 Add `edgecolors` in scatter plot and `makeredgecolor` in plot. Issue: #8364 * Modify plot_kernel_approximation.py for matplotlib v2 Add `edgecolors` in scatter plot for matplotlib v2. Issue: #8364 * Change plot_kernel_ridge_regression for better plot Remove `markeredgecolor` for better plot. Issue:#8364
1 parent 2c20b0b commit 93bbe54

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-7
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‎examples/plot_cv_predict.py

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@@ -21,7 +21,7 @@
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predicted = cross_val_predict(lr, boston.data, y, cv=10)
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fig, ax = plt.subplots()
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ax.scatter(y, predicted)
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ax.scatter(y, predicted, edgecolors=(0, 0, 0))
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ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
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ax.set_xlabel('Measured')
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ax.set_ylabel('Predicted')

‎examples/plot_kernel_approximation.py

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@@ -205,7 +205,8 @@
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plt.axis('off')
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired)
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plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired,
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edgecolors=(0, 0, 0))
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plt.title(titles[i])
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plt.tight_layout()

‎examples/plot_kernel_ridge_regression.py

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@@ -101,8 +101,9 @@
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# look at the results
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sv_ind = svr.best_estimator_.support_
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plt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors',
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zorder=2)
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plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1)
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zorder=2, edgecolors=(0, 0, 0))
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plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1,
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edgecolors=(0, 0, 0))
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plt.hold('on')
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plt.plot(X_plot, y_svr, c='r',
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label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))

‎examples/plot_multilabel.py

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@@ -73,11 +73,11 @@ def plot_subfigure(X, Y, subplot, title, transform):
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zero_class = np.where(Y[:, 0])
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one_class = np.where(Y[:, 1])
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plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')
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plt.scatter(X[:, 0], X[:, 1], s=40, c='gray', edgecolors=(0, 0, 0))
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plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
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facecolors='none', linewidths=2, label='Class 1')
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facecolors='none', linewidths=2, label='Class 1')
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plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',
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facecolors='none', linewidths=2, label='Class 2')
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facecolors='none', linewidths=2, label='Class 2')
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plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
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'Boundary\nfor class 1')

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