1d62b0c431c821671ef8f251d14cc8ae79b93c76,examples/combine/plot_smote_enn.py,,,#,17

Before Change

print(__doc__)

sns.set()

// Define some color for the plotting
almost_black = "/�"
palette = sns.color_palette()


// Generate the dataset
X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                           n_informative=3, n_redundant=1, flip_y=0,
                           n_features=20, n_clusters_per_class=1,
                           n_samples=5000, random_state=10)

// Instanciate a PCA object for the sake of easy visualisation
pca = PCA(n_components=2)
// Fit and transform x to visualise inside a 2D feature space
X_vis = pca.fit_transform(X)

// Apply SMOTE + ENN
sm = SMOTEENN()
X_resampled, y_resampled = sm.fit_sample(X, y)
X_res_vis = pca.transform(X_resampled)

// Two subplots, unpack the axes array immediately
f, (ax1, ax2) = plt.subplots(1, 2)

ax1.scatter(X_vis[y == 0, 0], X_vis[y == 0, 1], label="Class /ǖ", alpha=0.5,
            edgecolor=almost_black, facecolor=palette[0], linewidth=0.15)
ax1.scatter(X_vis[y == 1, 0], X_vis[y == 1, 1], label="Class /Ǘ", alpha=0.5,
            edgecolor=almost_black, facecolor=palette[2], linewidth=0.15)
ax1.set_title("Original set")

ax2.scatter(X_res_vis[y_resampled == 0, 0], X_res_vis[y_resampled == 0, 1],
            label="Class /ǖ", alpha=.5, edgecolor=almost_black,
            facecolor=palette[0], linewidth=0.15)
ax2.scatter(X_res_vis[y_resampled == 1, 0], X_res_vis[y_resampled == 1, 1],
            label="Class /Ǘ", alpha=.5, edgecolor=almost_black,
            facecolor=palette[2], linewidth=0.15)
ax2.set_title("SMOTE + ENN")

plt.show()

After Change

f.legend((c0, c1), ("Class /ǖ", "Class /Ǘ"), loc="lower center",
         ncol=2, labelspacing=0.)
plt.tight_layout(pad=3)
plt.show()

Instances