Everything for space

app.py

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+from __future__ import annotations
+
+import numpy as np
+import gradio as gr
+from sklearn.svm import SVC
+import plotly.graph_objects as go
+
+def plot_decision(
+        clf: SVC,
+        X: np.ndarray,
+        x_range: np.array,
+        y_range: np.array,
+        weights: np.array,
+        colors: list[str],
+        title: str
+    ):
+    # plot the decision function
+    xx, yy = np.meshgrid(x_range, y_range)
+
+    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+
+    fig = go.Figure()
+
+    fig.add_trace(
+        go.Contour(
+            x=x_range,
+            y=y_range,
+            z=Z,
+            colorscale="gray",
+            opacity=0.75,        
+            showscale=False,
+        )
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=X[:, 0],
+            y=X[:, 1],
+            mode="markers",
+            marker=dict(
+                color=colors,
+                size=(weights + 5) * 2
+            ),
+        )
+    )
+
+    # Remove x and y ticks
+    fig.update_xaxes(showticklabels=False)
+    fig.update_yaxes(showticklabels=False)
+    # Add title
+    fig.update_layout(title=title)
+
+    return fig
+
+def app_fn(seed: int, weight_1: int, weight_2: int):
+    # we create 20 points
+    np.random.seed(seed)
+    X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
+    y = [1] * 10 + [-1] * 10
+
+    sample_weight_last_ten = abs(np.random.randn(len(X)))
+    sample_weight_constant = np.ones(len(X))
+
+    sample_weight_last_ten[15:] *= weight_1
+    sample_weight_last_ten[9] *= weight_2
+
+    # This model does not take into account sample weights.
+    clf_no_weights = SVC(gamma=1)
+    clf_no_weights.fit(X, y)
+
+    # This other model takes into account some dedicated sample weights.
+    clf_weights = SVC(gamma=1)
+    clf_weights.fit(X, y, sample_weight=sample_weight_last_ten)
+
+    # Plotting
+    x_range = np.arange(-4, 5, 0.1)
+    colors = np.where(np.array(y)==1, "white", "black")
+
+    fig_no_weights = plot_decision(
+        clf_no_weights, 
+        X,
+        x_range, 
+        x_range, 
+        sample_weight_constant, 
+        colors, 
+        "SVM without Weights"
+    )
+
+    fig_weights = plot_decision(
+        clf_weights,
+        X,
+        x_range,
+        x_range,
+        sample_weight_last_ten,
+        colors,
+        "SVM with Weights"
+    )
+
+    return fig_no_weights, fig_weights
+
+title = "SVM with Weighted Samples"
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(
+        """
+        ### This is a demo of how SVMs can be trained with weighted samples \
+        and the impact on the decision boundary. To represent that a synthetic \
+        dataset is generated with 20 points, 10 of which are assigned to the \
+        positive class and 10 to the negative class. A weight is assigned to \
+        each sample, which is the importance of that sample in the dataset. \
+        A model with and without weights is trained and the decision boundary \
+        is plotted. The size of the points is proportional to the weight of \
+        the sample.
+
+        Created by [@eduardopacheco](https://huggingface.co/EduardoPacheco) based on [scikit-learn-docs](https://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html#sphx-glr-auto-examples-svm-plot-weighted-samples-py)
+        """
+    )
+    with gr.Row():
+        seed = gr.inputs.Slider(0, 100, 1, default=0, label="Seed")
+        weight_1 = gr.inputs.Slider(0, 20, 1, default=5, label="Weight for last 5 Samples")
+        weight_2 = gr.inputs.Slider(0, 20, 1, default=15, label="Weight for Sample 10")
+    btn = gr.Button("Run")
+    with gr.Row():
+        fig_no_weights = gr.Plot(label="SVM without Weights")
+        fig_weights = gr.Plot(label="SVM with Weights")
+        
+    btn.click(fn=app_fn, outputs=[fig_no_weights, fig_weights], inputs=[seed, weight_1, weight_2])
+    demo.load(fn=app_fn, outputs=[fig_no_weights, fig_weights], inputs=[seed, weight_1, weight_2])
+
+demo.launch()
+            

requirements.txt

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+scikit-learn==1.2.2
+plotly==5.14.1