Multioutput support for PCovC/KPCovC

examples/pcovc/KPCovC_Comparison.py

@@ -88,7 +88,8 @@
 
 mixing = 0.5
 alpha_d = 0.5
-alpha_p = 0.4
+alpha_train = 0.2
+alpha_test = 0.8
 
 models = {
     PCA(n_components=n_components): "PCA",
@@ -107,8 +108,10 @@
     t_train = model.fit_transform(X_train_scaled, y_train)
     t_test = model.transform(X_test_scaled)
 
-    ax.scatter(t_test[:, 0], t_test[:, 1], alpha=alpha_p, cmap=cm_bright, c=y_test)
-    ax.scatter(t_train[:, 0], t_train[:, 1], cmap=cm_bright, c=y_train)
+    ax.scatter(
+        t_train[:, 0], t_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train
+    )
+    ax.scatter(t_test[:, 0], t_test[:, 1], alpha=alpha_test, cmap=cm_bright, c=y_test)
 
     ax.set_title(models[model])
     plt.tight_layout()
@@ -166,8 +169,10 @@
         eps=models[model]["eps"],
         grid_resolution=resolution,
     )
-    ax.scatter(t_test[:, 0], t_test[:, 1], alpha=alpha_p, cmap=cm_bright, c=y_test)
-    ax.scatter(t_train[:, 0], t_train[:, 1], cmap=cm_bright, c=y_train)
+    ax.scatter(
+        t_train[:, 0], t_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train
+    )
+    ax.scatter(t_test[:, 0], t_test[:, 1], alpha=alpha_test, cmap=cm_bright, c=y_test)
     ax.set_title(models[model]["title"])
 
     ax.text(
@@ -241,14 +246,22 @@
         grid_resolution=resolution,
     )
 
+    ax.scatter(
+        t_kpcovc_train[:, 0],
+        t_kpcovc_train[:, 1],
+        alpha=alpha_train,
+        cmap=cm_bright,
+        c=y_train,
+    )
+
     ax.scatter(
         t_kpcovc_test[:, 0],
         t_kpcovc_test[:, 1],
         cmap=cm_bright,
-        alpha=alpha_p,
+        alpha=alpha_test,
         c=y_test,
     )
-    ax.scatter(t_kpcovc_train[:, 0], t_kpcovc_train[:, 1], cmap=cm_bright, c=y_train)
+
     ax.text(
         0.70,
         0.03,

examples/pcovc/PCovC_multioutput.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Multioutput PCovC
+=================
+"""
+# %%
+#
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import load_digits
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.linear_model import LogisticRegressionCV
+from sklearn.multioutput import MultiOutputClassifier
+
+from skmatter.decomposition import PCovC
+
+plt.rcParams["image.cmap"] = "tab10"
+plt.rcParams["scatter.edgecolors"] = "k"
+# %%
+# For this, we will use the `sklearn.datasets.load_digits` dataset.
+# This dataset contains 8x8 images of handwritten digits (0-9).
+X, y = load_digits(return_X_y=True)
+x_scaler = StandardScaler()
+X_scaled = StandardScaler().fit_transform(X)
+
+np.unique(y)
+# %%
+# Let's begin by trying to make a PCovC map to separate the digits.
+# This is a one-label, ten-class classification problem.
+pca = PCA(n_components=2)
+T_pca = pca.fit_transform(X_scaled, y)
+
+pcovc = PCovC(n_components=2, mixing=0.5)
+T_pcovc = pcovc.fit_transform(X_scaled, y)
+
+fig, axs = plt.subplots(1, 2, figsize=(10, 6))
+
+scat_pca = axs[0].scatter(T_pca[:, 0], T_pca[:, 1], c=y)
+scat_pcovc = axs[1].scatter(T_pcovc[:, 0], T_pcovc[:, 1], c=y)
+fig.colorbar(scat_pca, ax=axs, orientation="horizontal")
+fig.suptitle("Multiclass PCovC with One Label")
+
+# %%
+# Next, let's try a two-label classification problem, with both labels
+# being binary classification tasks.
+
+is_even = (y % 2).reshape(-1, 1)
+is_less_than_five = (y < 5).reshape(-1, 1)
+
+y2 = np.hstack([is_even, is_less_than_five])
+y2.shape
+# %%
+# Here, we can build a map that considers both of these labels simultaneously.
+
+clf = MultiOutputClassifier(estimator=LogisticRegressionCV())
+pcovc = PCovC(n_components=2, mixing=0.5, classifier=clf)
+
+T_pcovc = pcovc.fit_transform(X_scaled, y2)
+
+fig, axs = plt.subplots(2, 3, figsize=(15, 10))
+cmap1 = "Set1"
+cmap2 = "Set2"
+cmap3 = "tab10"
+
+labels_list = [["Even", "Odd"], [">= 5", "< 5"]]
+
+for i, c, cmap in zip(range(3), [is_even, is_less_than_five, y], [cmap1, cmap2, cmap3]):
+    scat_pca = axs[0, i].scatter(T_pca[:, 0], T_pca[:, 1], c=c, cmap=cmap)
+    axs[1, i].scatter(T_pcovc[:, 0], T_pcovc[:, 1], c=c, cmap=cmap)
+
+    if i == 0 or i == 1:
+        handles, _ = scat_pca.legend_elements()
+        labels = labels_list[i]
+        axs[0, i].legend(handles, labels)
+
+axs[0, 0].set_title("Even/Odd")
+axs[0, 1].set_title("Greater/Less than 5")
+axs[0, 2].set_title("Digit")
+
+axs[0, 0].set_ylabel("PCA")
+axs[1, 0].set_ylabel("PCovC")
+fig.colorbar(scat_pca, ax=axs, orientation="horizontal")
+fig.suptitle("Multilabel PCovC with Binary Labels")
+# %%
+# Let's try a more complicated example:
+
+num_holes = np.array(
+    [0 if i in [1, 2, 3, 5, 7] else 1 if i in [0, 4, 6, 9] else 2 for i in y]
+).reshape(-1, 1)
+
+y3 = np.hstack([is_even, num_holes])
+# %%
+# Now, we have a two-label classification
+# problem, with one binary label and one label with three
+# possible classes.
+clf = MultiOutputClassifier(estimator=LogisticRegressionCV())
+pcovc = PCovC(n_components=2, mixing=0.5, classifier=clf)
+
+T_pcovc = pcovc.fit_transform(X_scaled, y3)
+
+fig, axs = plt.subplots(2, 3, figsize=(15, 10))
+cmap1 = "Set1"
+cmap2 = "Set3"
+cmap3 = "tab10"
+
+labels_list = [["Even", "Odd"], ["0", "1", "2"]]
+
+for i, c, cmap in zip(range(3), [is_even, num_holes, y], [cmap1, cmap2, cmap3]):
+    scat_pca = axs[0, i].scatter(T_pca[:, 0], T_pca[:, 1], c=c, cmap=cmap)
+    axs[1, i].scatter(T_pcovc[:, 0], T_pcovc[:, 1], c=c, cmap=cmap)
+
+    if i == 0 or i == 1:
+        handles, _ = scat_pca.legend_elements()
+        labels = labels_list[i]
+        axs[0, i].legend(handles, labels)
+
+axs[0, 0].set_title("Even/Odd")
+axs[0, 1].set_title("Number of Holes")
+axs[0, 2].set_title("Digit")
+
+axs[0, 0].set_ylabel("PCA")
+axs[1, 0].set_ylabel("PCovC")
+fig.colorbar(scat_pca, ax=axs, orientation="horizontal")
+fig.suptitle("Multiclass-Multilabel PCovC")
+
+# %%