Create app.py

app.py

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+# Scikit learn example https://scikit-learn.org/stable/auto_examples/cluster/plot_optics.html
+
+import gradio as gr
+
+from sklearn.cluster import OPTICS, cluster_optics_dbscan
+import matplotlib.gridspec as gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.switch_backend("agg")
+
+# Theme from - https://huggingface.co/spaces/trl-lib/stack-llama/blob/main/app.py
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+    radius_size=gr.themes.sizes.radius_sm,
+    font=[
+        gr.themes.GoogleFont("Open Sans"),
+        "ui-sans-serif",
+        "system-ui",
+        "sans-serif",
+    ],
+)
+
+
+def do_submit(n_points_per_cluster, min_samples, xi, min_cluster_size):
+    # # Generate sample data
+    np.random.seed(0)
+    n_points_per_cluster = int(n_points_per_cluster)
+
+    C1 = [-5, -2] + 0.8 * np.random.randn(n_points_per_cluster, 2)
+    C2 = [4, -1] + 0.1 * np.random.randn(n_points_per_cluster, 2)
+    C3 = [1, -2] + 0.2 * np.random.randn(n_points_per_cluster, 2)
+    C4 = [-2, 3] + 0.3 * np.random.randn(n_points_per_cluster, 2)
+    C5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)
+    C6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)
+    X = np.vstack((C1, C2, C3, C4, C5, C6))
+
+    clust = OPTICS(
+        min_samples=int(min_samples),
+        xi=float(xi),
+        min_cluster_size=float(min_cluster_size),
+    )
+
+    # Run the fit
+    clust.fit(X)
+
+    labels_050 = cluster_optics_dbscan(
+        reachability=clust.reachability_,
+        core_distances=clust.core_distances_,
+        ordering=clust.ordering_,
+        eps=0.5,
+    )
+    labels_200 = cluster_optics_dbscan(
+        reachability=clust.reachability_,
+        core_distances=clust.core_distances_,
+        ordering=clust.ordering_,
+        eps=2,
+    )
+
+    space = np.arange(len(X))
+    reachability = clust.reachability_[clust.ordering_]
+    labels = clust.labels_[clust.ordering_]
+
+    plt.figure(figsize=(10, 7))
+    G = gridspec.GridSpec(2, 3)
+    ax1 = plt.subplot(G[0, :])
+    ax2 = plt.subplot(G[1, 0])
+    ax3 = plt.subplot(G[1, 1])
+    ax4 = plt.subplot(G[1, 2])
+
+    # Reachability plot
+    colors = ["g.", "r.", "b.", "y.", "c."]
+    for klass, color in zip(range(0, 5), colors):
+        Xk = space[labels == klass]
+        Rk = reachability[labels == klass]
+        ax1.plot(Xk, Rk, color, alpha=0.3)
+    ax1.plot(space[labels == -1], reachability[labels == -1], "k.", alpha=0.3)
+    ax1.plot(space, np.full_like(space, 2.0, dtype=float), "k-", alpha=0.5)
+    ax1.plot(space, np.full_like(space, 0.5, dtype=float), "k-.", alpha=0.5)
+    ax1.set_ylabel("Reachability (epsilon distance)")
+    ax1.set_title("Reachability Plot")
+
+    # OPTICS
+    colors = ["g.", "r.", "b.", "y.", "c."]
+    for klass, color in zip(range(0, 5), colors):
+        Xk = X[clust.labels_ == klass]
+        ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
+    ax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], "k+", alpha=0.1)
+    ax2.set_title("Automatic Clustering\nOPTICS")
+
+    # DBSCAN at 0.5
+    colors = ["g.", "r.", "b.", "c."]
+    for klass, color in zip(range(0, 4), colors):
+        Xk = X[labels_050 == klass]
+        ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
+    ax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], "k+", alpha=0.1)
+    ax3.set_title("Clustering at 0.5 epsilon cut\nDBSCAN")
+
+    # DBSCAN at 2.
+    colors = ["g.", "m.", "y.", "c."]
+    for klass, color in zip(range(0, 4), colors):
+        Xk = X[labels_200 == klass]
+        ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
+    ax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], "k+", alpha=0.1)
+    ax4.set_title("Clustering at 2.0 epsilon cut\nDBSCAN")
+
+    plt.tight_layout()
+
+    return plt
+
+
+title = "Demo of OPTICS clustering algorithm"
+with gr.Blocks(title=title, theme=theme) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(
+        "[Scikit-learn Example](https://scikit-learn.org/stable/auto_examples/cluster/plot_optics.html)"
+    )
+
+    gr.Markdown(
+        "Finds core samples of high density and expands clusters from them. This example uses data that is \
+        generated so that the clusters have different densities. The [OPTICS](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS) is first used with its Xi cluster detection \
+        method, and then setting specific thresholds on the reachability, which corresponds to [DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN). We can see that \
+        the different clusters of OPTICS’s Xi method can be recovered with different choices of thresholds in DBSCAN."
+    )
+
+    n_points_per_cluster = gr.Slider(
+        minimum=200,
+        maximum=500,
+        label="Number of points per cluster",
+        step=50,
+        value=250,
+    )
+    min_samples = gr.Slider(
+        minimum=10,
+        maximum=100,
+        label="OPTICS - Minimum number of samples",
+        step=5,
+        value=50,
+        info="The number of samples in a neighborhood for a point to be considered as a core point.",
+    )
+    xi = gr.Slider(
+        minimum=0,
+        maximum=0.2,
+        label="OPTICS - Xi",
+        step=0.05,
+        value=0.05,
+        info="Determines the minimum steepness on the reachability plot that constitutes a cluster boundary. ",
+    )
+
+    min_cluster_size = gr.Slider(
+        minimum=0.01,
+        maximum=0.1,
+        label="OPTICS - Minimum cluster size",
+        step=0.01,
+        value=0.05,
+        info="Minimum number of samples in an OPTICS cluster, expressed as an absolute number or a fraction of the number of samples (rounded to be at least 2).",
+    )
+
+    plt_out = gr.Plot()
+
+    sub_btn = gr.Button("Submit")
+    sub_btn.click(
+        fn=do_submit,
+        inputs=[n_points_per_cluster, min_samples, xi, min_cluster_size],
+        outputs=[plt_out],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()
+