adding all the files

README.md

@@ -1,6 +1,6 @@
 ---
 title: Kernel Ridge And Gaussian Process Regression
-emoji: 🔥
+emoji: 📊
 colorFrom: indigo
 colorTo: green
 sdk: gradio

app.py

@@ -0,0 +1,121 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+from io import BytesIO
+
+from sklearn.linear_model import Ridge
+from sklearn.gaussian_process.kernels import ExpSineSquared
+from sklearn.kernel_ridge import KernelRidge
+
+import gradio as gr
+import random
+
+
+def generate_data(n_samples: int) -> tuple:
+    rng = np.random.RandomState(random.randint(0, 1000))
+    data = np.linspace(0, 30, num=n_samples).reshape(-1, 1)
+    target = np.sin(data).ravel()
+    training_sample_indices = rng.choice(
+        np.arange(0, int(0.4 * n_samples)), size=int(0.2 * n_samples), replace=False
+    )
+    training_data = data[training_sample_indices]
+    training_noisy_target = (
+        target[training_sample_indices] + 0.5 * rng.randn(len(training_sample_indices))
+    )
+
+    return data, target, training_data, training_noisy_target
+
+
+# def plot_ridge_and_kernel(n_samples: int) -> plt.figure:
+
+#     data, target, training_data, training_noisy_target = generate_data(n_samples)
+
+#     ridge = Ridge().fit(training_data, training_noisy_target)
+#     kernel_ridge = KernelRidge(kernel=ExpSineSquared())
+#     kernel_ridge.fit(training_data, training_noisy_target)
+
+#     fig, ax = plt.subplots(figsize=(8, 4))
+
+#     ax.plot(data, target, label="True signal", linewidth=2)
+#     ax.scatter(
+#         training_data,
+#         training_noisy_target,
+#         color="black",
+#         label="Noisy measurements",
+#     )
+#     ax.plot(data, ridge.predict(data), label="Ridge regression")
+#     ax.plot(
+#         data,
+#         kernel_ridge.predict(data),
+#         label="Kernel ridge",
+#         linewidth=2,
+#         linestyle="dashdot",
+#     )
+#     ax.legend()
+#     ax.set_xlabel("data")
+#     ax.set_ylabel("target")
+#     _ = ax.set_title("Ridge vs Kernel Ridge")
+
+#     return fig
+
+def plot_ridge_and_kernel(n_samples: int) -> plt.figure:
+    data, target, training_data, training_noisy_target = generate_data(n_samples)
+
+    ridge = Ridge().fit(training_data, training_noisy_target)
+    kernel_ridge = KernelRidge(kernel=ExpSineSquared())
+    kernel_ridge.fit(training_data, training_noisy_target)
+
+    fig, ax = plt.subplots(figsize=(8, 4))
+
+    ridge_predictions = ridge.predict(data)
+    kernel_ridge_predictions = kernel_ridge.predict(data)
+
+    ax.plot(data, target, label="True signal", linewidth=2)
+    ax.scatter(
+        training_data,
+        training_noisy_target,
+        color="black",
+        label="Noisy measurements",
+    )
+    ax.plot(data, ridge_predictions, label="Ridge regression")
+    ax.plot(
+        data,
+        kernel_ridge_predictions,
+        label="Kernel ridge",
+        linewidth=2,
+        linestyle="dashdot",
+    )
+    ax.fill_between(
+        data.ravel(),
+        ridge_predictions,
+        kernel_ridge_predictions,
+        color="lightgrey",
+        alpha=0.4,
+    )
+    ax.legend()
+    ax.set_xlabel("data")
+    ax.set_ylabel("target")
+    _ = ax.set_title("Ridge vs Kernel Ridge with the area between highlighted")
+
+    return fig
+
+
+def gradio_plot(n_samples: int) -> Image.Image:
+    fig = plot_ridge_and_kernel(n_samples)
+    buf = BytesIO()
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+    im = Image.open(buf)
+    return im
+
+
+inputs = [
+    gr.inputs.Slider(minimum=100, maximum=5000, step=100, label="n_samples", default=1000),
+]
+
+
+# Create the Gradio app
+title = "Comparison of kernel ridge and Gaussian process regression"
+description = "Kernel ridge regression and Gaussian process regression both use the kernel trick to fit data. While kernel ridge regression aims to find a single target function minimizing the loss (mean squared error), Gaussian process regression takes a probabilistic approach, defining a Gaussian posterior distribution over target functions using Bayes' theorem. Essentially, kernel ridge regression seeks one best function, while Gaussian process regression considers a range of probable functions based on prior probabilities and observed data. \n \n link to the official doc https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py"
+iface = gr.Interface(fn=gradio_plot, inputs=inputs, outputs="image", title = title , description = description)
+iface.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio==3.32.0
+matplotlib==3.7.1
+numpy==1.24.3
+Pillow==9.5.0
+Pillow==9.5.0
+scikit_learn==1.2.2