Pushing the docs to dev/ for branch: main, commit 35af6dc808c8d317eb7017d1b16e271c9c8bba77

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -22,7 +22,8 @@
 height of one meter can be considered much more important than the
 change in weight of one kilogram, this is clearly incorrect.
 
-To illustrate this, PCA is performed comparing the use of data with
+To illustrate this, :class:`PCA <sklearn.decomposition.PCA>`
+is performed comparing the use of data with
 :class:`StandardScaler <sklearn.preprocessing.StandardScaler>` applied,
 to unscaled data. The results are visualized and a clear difference noted.
 The 1st principal component in the unscaled set can be seen. It can be seen
@@ -33,20 +34,20 @@
 
 The dataset used is the Wine Dataset available at UCI. This dataset
 has continuous features that are heterogeneous in scale due to differing
-properties that they measure (i.e alcohol content, and malic acid).
+properties that they measure (i.e. alcohol content and malic acid).
 
 The transformed data is then used to train a naive Bayes classifier, and a
 clear difference in prediction accuracies is observed wherein the dataset
 which is scaled before PCA vastly outperforms the unscaled version.
 
 """
+import matplotlib.pyplot as plt
 
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
 from sklearn.naive_bayes import GaussianNB
-from sklearn import metrics
-import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score
 from sklearn.datasets import load_wine
 from sklearn.pipeline import make_pipeline
 
@@ -66,58 +67,62 @@
     features, target, test_size=0.30, random_state=RANDOM_STATE
 )
 
-# Fit to data and predict using pipelined GNB and PCA.
+# Fit to data and predict using pipelined GNB and PCA
 unscaled_clf = make_pipeline(PCA(n_components=2), GaussianNB())
 unscaled_clf.fit(X_train, y_train)
 pred_test = unscaled_clf.predict(X_test)
 
-# Fit to data and predict using pipelined scaling, GNB and PCA.
+# Fit to data and predict using pipelined scaling, GNB and PCA
 std_clf = make_pipeline(StandardScaler(), PCA(n_components=2), GaussianNB())
 std_clf.fit(X_train, y_train)
 pred_test_std = std_clf.predict(X_test)
 
 # Show prediction accuracies in scaled and unscaled data.
 print("\nPrediction accuracy for the normal test dataset with PCA")
-print("{:.2%}\n".format(metrics.accuracy_score(y_test, pred_test)))
+print(f"{accuracy_score(y_test, pred_test):.2%}\n")
 
 print("\nPrediction accuracy for the standardized test dataset with PCA")
-print("{:.2%}\n".format(metrics.accuracy_score(y_test, pred_test_std)))
+print(f"{accuracy_score(y_test, pred_test_std):.2%}\n")
 
 # Extract PCA from pipeline
 pca = unscaled_clf.named_steps["pca"]
 pca_std = std_clf.named_steps["pca"]
 
 # Show first principal components
-print("\nPC 1 without scaling:\n", pca.components_[0])
-print("\nPC 1 with scaling:\n", pca_std.components_[0])
+print(f"\nPC 1 without scaling:\n{pca.components_[0]}")
+print(f"\nPC 1 with scaling:\n{pca_std.components_[0]}")
 
 # 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_transformed = pca_std.transform(scaler.transform(X_train))
+scaled_X_train = scaler.transform(X_train)
+X_train_std_transformed = pca_std.transform(scaled_X_train)
 
 # visualize standardized vs. untouched dataset with PCA performed
 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=FIG_SIZE)
 
+target_classes = range(0, 3)
+colors = ("blue", "red", "green")
+markers = ("^", "s", "o")
 
-for l, c, m in zip(range(0, 3), ("blue", "red", "green"), ("^", "s", "o")):
+for target_class, color, marker in zip(target_classes, colors, markers):
     ax1.scatter(
-        X_train_transformed[y_train == l, 0],
-        X_train_transformed[y_train == l, 1],
-        color=c,
-        label="class %s" % l,
+        x=X_train_transformed[y_train == target_class, 0],
+        y=X_train_transformed[y_train == target_class, 1],
+        color=color,
+        label=f"class {target_class}",
         alpha=0.5,
-        marker=m,
+        marker=marker,
     )
 
-for l, c, m in zip(range(0, 3), ("blue", "red", "green"), ("^", "s", "o")):
     ax2.scatter(
-        X_train_std_transformed[y_train == l, 0],
-        X_train_std_transformed[y_train == l, 1],
-        color=c,
-        label="class %s" % l,
+        x=X_train_std_transformed[y_train == target_class, 0],
+        y=X_train_std_transformed[y_train == target_class, 1],
+        color=color,
+        label=f"class {target_class}",
         alpha=0.5,
-        marker=m,
+        marker=marker,
     )
 
 ax1.set_title("Training dataset after PCA")