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[MRG] Fix bad data visualisation in "Importance of Feature Scaling" #12280

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Merged
merged 2 commits into from
Oct 5, 2018
Merged

[MRG] Fix bad data visualisation in "Importance of Feature Scaling" #12280

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b8b2588
DOC Fix bad data visualization
benjastudio Oct 4, 2018
826c34e
Fix flake8 (E501)
benjastudio Oct 4, 2018
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11 changes: 7 additions & 4 deletions examples/preprocessing/plot_scaling_importance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,24 +93,27 @@
print('\nPC 1 without scaling:\n', pca.components_[0])
print('\nPC 1 with scaling:\n', pca_std.components_[0])

# Scale and use PCA on X_train data for visualization.
# Use PCA without and with scale on X_train data for visualization.
X_train_transformed = pca.transform(X_train)
scaler = std_clf.named_steps['standardscaler']
X_train_std = pca_std.transform(scaler.transform(X_train))
X_train_std_transformed = pca_std.transform(scaler.transform(X_train))

# visualize standardized vs. untouched dataset with PCA performed
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=FIG_SIZE)


for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):
ax1.scatter(X_train[y_train == l, 0], X_train[y_train == l, 1],
ax1.scatter(X_train_transformed[y_train == l, 0],
X_train_transformed[y_train == l, 1],
color=c,
label='class %s' % l,
alpha=0.5,
marker=m
)

for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):
ax2.scatter(X_train_std[y_train == l, 0], X_train_std[y_train == l, 1],
ax2.scatter(X_train_std_transformed[y_train == l, 0],
X_train_std_transformed[y_train == l, 1],
color=c,
label='class %s' % l,
alpha=0.5,
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