fix docs

Author: PicoCentauri

examples/pcovr/PCovR-WHODataset.py

@@ -109,7 +109,7 @@
 r_pcovr = Ridge(alpha=1e-4, fit_intercept=False, random_state=0).fit(
     T_train_pcovr, y_train
 )
-yp_pcovr = r_pcovr.predict(T_test_pcovr)
+yp_pcovr = r_pcovr.predict(T_test_pcovr).reshape(-1,1)
 
 plt.scatter(y_scaler.inverse_transform(y_test), y_scaler.inverse_transform(yp_pcovr))
 r_pcovr.score(T_test_pcovr, y_test)
@@ -128,7 +128,7 @@
 T_pca = pca.transform(X)
 
 r_pca = Ridge(alpha=1e-4, fit_intercept=False, random_state=0).fit(T_train_pca, y_train)
-yp_pca = r_pca.predict(T_test_pca)
+yp_pca = r_pca.predict(T_test_pca).reshape(-1,1)
 
 plt.scatter(y_scaler.inverse_transform(y_test), y_scaler.inverse_transform(yp_pca))
 r_pca.score(T_test_pca, y_test)
@@ -312,3 +312,5 @@ def add_subplot(ax, axy, T, yp, let=""):
     "Linear and Kernel PCovR for Predicting Life Expectancy", y=0.925, fontsize=10
 )
 plt.show()
+
+# %%

examples/pcovr/PCovR_Regressors.py

@@ -24,13 +24,12 @@
 mixing = 0.5
 
 X, y = load_diabetes(return_X_y=True)
-y = y.reshape(X.shape[0], -1)
 
 X_scaler = StandardScaler()
 X_scaled = X_scaler.fit_transform(X)
 
 y_scaler = StandardScaler()
-y_scaled = y_scaler.fit_transform(y)
+y_scaled = y_scaler.fit_transform(y.reshape(-1, 1)).ravel()
 
 
 # %%

examples/pcovr/PCovR_Scaling.py

@@ -78,7 +78,7 @@
 ax1_Y.set_title("Regression\nWithout Scaling")
 
 ax2_Y.scatter(
-    Yp_scaled, y, c=np.abs(y.flatten() - Yp_scaled.flatten()), cmap="bone_r", ec="k"
+    Yp_scaled, y, c=np.abs(y.ravel() - Yp_scaled.ravel()), cmap="bone_r", ec="k"
 )
 ax2_Y.plot(ax2_Y.get_xlim(), ax2_Y.get_xlim(), "r--")
 ax2_Y.set_xlabel("True Y, unscaled")