Add kernel selection

app.py

@@ -10,11 +10,18 @@ matplotlib.use('agg')
 import matplotlib.pyplot as plt
 import gradio as gr
 
-def fit_plot(n_comp, gamma, alpha):
+def fit_plot(kernel, gamma, alpha, degree, coef0):
     X, y = make_circles(n_samples=1_000, factor=0.3, noise=0.05, random_state=0)
     X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
-    pca = PCA(n_components=n_comp)
-    kernel_pca = KernelPCA(n_components=None, kernel="rbf", gamma=gamma, fit_inverse_transform=True, alpha=alpha)
+    pca = PCA(n_components=2)
+    if kernel=="linear":
+        kernel_pca = KernelPCA(n_components=2, kernel=kernel, fit_inverse_transform=True, alpha=alpha)
+    elif kernel=="poly":
+        kernel_pca = KernelPCA(n_components=2, kernel=kernel, gamma=gamma, degree=degree, coef0=coef0, fit_inverse_transform=True, alpha=alpha)
+    elif kernel=="rbf":
+        kernel_pca = KernelPCA(n_components=2, kernel=kernel, gamma=gamma, fit_inverse_transform=True, alpha=alpha)
+    elif kernel=="cosine": 
+        kernel_pca = KernelPCA(n_components=2, kernel=kernel, fit_inverse_transform=True, alpha=alpha)
     X_test_pca = pca.fit(X_train).transform(X_test)
     X_test_kernel_pca = kernel_pca.fit(X_train).transform(X_test)
     fig, (orig_data_ax, pca_proj_ax, kernel_pca_proj_ax) = plt.subplots(ncols=3, figsize=(14, 4))
@@ -34,11 +41,17 @@ def fit_plot(n_comp, gamma, alpha):
 
 with gr.Blocks() as demo:
     gr.Markdown("## PCA vs Kernel PCA")
-    with gr.Row():
-        p1 = gr.Slider(0, 10, label="Number of PCs", value=2, step=1)
-        p2 = gr.Slider(0, 10, label="Kernel coefficient", value=10, step=1e-3)
-        p3 = gr.Slider(0, 1, label="Hyperparameter for ridge regression", value=0.1, step=1e-3)
+    #state = gr.State([])
+    with gr.Row(variant='panel').style(equal_height=True):
+        p1 = gr.Dropdown(choices=["linear", "poly", "rbf", "cosine"], label="Kernel", value="rbf", interactive=True)
+    with gr.Row(variant='panel').style(equal_height=True):
+        p2 = gr.Slider(0, 10, label="Kernel coefficient (for rbf, poly and sigmoid kernels)", value=None, step=1e-3, interactive=True)
+        p3 = gr.Slider(0, 1, label="Alpha of ridge regression (for non-precomputed kernels)", value=1, step=1e-3, interactive=True)
+    with gr.Row(variant='panel').style(equal_height=True):
+        p4 = gr.Slider(0, 10, label="Degree (for poly kernel)", value=3, step=1, interactive=True)
+        p5 = gr.Slider(0, 10, label="Independent term (for poly and sigmoid kernels)", value=1, step=1e-1, interactive=True)
     btn = gr.Button(value="Submit")
-    btn.click(fit_plot, inputs=[p1,p2,p3], outputs=gr.Plot(label="Projecting data with PCA and Kernel PCA   ")) 
+    out = gr.Plot(label="Projecting data with PCA and Kernel PCA")
+    btn.click(fit_plot, inputs=[p1,p2,p3,p4,p5], outputs=out)
 
 demo.launch()