Multioutput support for PCovC/KPCovC

.github/workflows/build.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python
       uses: actions/setup-python@v5

.github/workflows/docs.yml

@@ -11,7 +11,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: setup Python
         uses: actions/setup-python@v5

.github/workflows/lint.yml

@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python
       uses: actions/setup-python@v5

.github/workflows/release.yml

@@ -16,7 +16,7 @@ jobs:
       contents: write
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
       with:
         fetch-depth: 0
     - name: setup Python

.github/workflows/tests-dev.yml

@@ -10,10 +10,12 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.13"]
+        python-version: 
+          - "3.10"
+          - "3.13"
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5

.github/workflows/tests.yml

@@ -11,11 +11,16 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.10", "3.13"]
+        os: 
+          - ubuntu-latest
+          - macos-latest
+          - windows-latest
+        python-version: 
+          - "3.10"
+          - "3.13"
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5

examples/pcovc/KPCovC_Comparison.py

@@ -33,6 +33,7 @@
 
 random_state = 0
 n_components = 2
+scale_z = True
 
 # %%
 #
@@ -85,7 +86,7 @@
 # Both PCA and PCovC fail to produce linearly separable latent space
 # maps. We will need a kernel method to effectively separate the moon classes.
 
-mixing = 0.10
+mixing = 0.5
 alpha_d = 0.5
 alpha_p = 0.4
 
@@ -95,6 +96,7 @@
         n_components=n_components,
         random_state=random_state,
         mixing=mixing,
+        scale_z=scale_z,
         classifier=LinearSVC(),
     ): "PCovC",
 }
@@ -138,6 +140,7 @@
         random_state=random_state,
         mixing=mixing,
         center=center,
+        scale_z=scale_z,
         **kernel_params,
     ): {"title": "Kernel PCovC", "eps": 2},
 }
@@ -220,6 +223,7 @@
         mixing=mixing,
         classifier=model,
         center=center,
+        scale_z=scale_z,
         **models[model]["kernel_params"],
     )
     t_kpcovc_train = kpcovc.fit_transform(X_train_scaled, y_train)

examples/pcovc/KPCovC_Hyperparameters.py

@@ -65,7 +65,8 @@
 fig, axs = plt.subplots(2, len(kernels), figsize=(len(kernels) * 4, 8))
 
 center = True
-mixing = 0.10
+mixing = 0.5
+scale_z = True
 
 for i, kernel in enumerate(kernels):
     kpca = KernelPCA(
@@ -83,6 +84,7 @@
         random_state=random_state,
         **kernel_params.get(kernel, {}),
         center=center,
+        scale_z=scale_z,
     )
     t_kpcovc = kpcovc.fit_transform(X_scaled, y)
 
@@ -118,7 +120,7 @@
     kpcovc = KernelPCovC(
         n_components=n_components,
         random_state=random_state,
-        mixing=mixing,
+        mixing=0.1,
         center=center,
         kernel="rbf",
         gamma=gamma,

examples/pcovc/PCovC_Hyperparameters.py

@@ -77,6 +77,7 @@
         n_components=n_components,
         random_state=random_state,
         classifier=LogisticRegressionCV(),
+        scale_z=True,
     )
 
     pcovc.fit(X_scaled, y)
@@ -120,6 +121,7 @@
         n_components=n_components,
         random_state=random_state,
         classifier=model,
+        scale_z=True,
     )
 
     pcovc.fit(X_scaled, y)

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")
+
+# %%