Added core functionality to gradio implementation.

app.py

@@ -0,0 +1,132 @@
+# Original Author: Gael Varoquaux
+# Gradio Implementation: Lenix Carter
+# License: BSD 3-Clause or CC-0
+
+import gradio as gr
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as PathEffects
+
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics import pairwise_distances
+
+np.random.seed(0)
+matplotlib.use('agg')
+labels = ("Waveform 1", "Waveform 2", "Waveform 3")
+colors = ["#f7bd01", "#377eb8", "#f781bf"]
+n_clusters = 3
+
+def sqr(x):
+    return np.sign(np.cos(x))
+
+def ground_truth_plot(n_features):
+    t = np.pi * np.linspace(0, 1, n_features)
+
+    X = list()
+    y = list()
+    for i, (phi, a) in enumerate([(0.5, 0.15), (0.5, 0.6), (0.3, 0.2)]):
+        for _ in range(30):
+            phase_noise = 0.01 * np.random.normal()
+            amplitude_noise = 0.04 * np.random.normal()
+            additional_noise = 1 - 2 * np.random.rand(n_features)
+            # Make the noise sparse
+            additional_noise[np.abs(additional_noise) < 0.997] = 0
+
+            X.append(
+                12
+                * (
+                    (a + amplitude_noise) * (sqr(6 * (t + phi + phase_noise)))
+                    + additional_noise
+                )
+            )
+            y.append(i)
+
+    X = np.array(X)
+    y = np.array(y)
+
+    # Plot the ground-truth labelling
+    gt_plot = plt.figure()
+    plt.axes([0, 0, 1, 1])
+    for l, color, n in zip(range(n_clusters), colors, labels):
+        lines = plt.plot(X[y == l].T, c=color, alpha=0.5)
+        lines[0].set_label(n)
+
+    plt.legend(loc="best")
+
+    plt.axis("tight")
+    plt.axis("off")
+    plt.suptitle("Ground truth", size=20, y=1)
+    return gt_plot, X, y
+
+def plot_cluster_waves(metric, X, y):
+    model = AgglomerativeClustering(
+        n_clusters=n_clusters, linkage="average", metric=metric
+    )
+    model.fit(X)
+    clust_plot = plt.figure()
+    plt.axes([0, 0, 1, 1])
+    for l, color in zip(np.arange(model.n_clusters), colors):
+        plt.plot(X[model.labels_ == l].T, c=color, alpha=0.5)
+    plt.axis("tight")
+    plt.axis("off")
+    plt.suptitle("AgglomerativeClustering(metric=%s)" % metric, size=20, y=1)
+    return clust_plot
+
+def plot_distances(metric, X, y):
+    avg_dist = np.zeros((n_clusters, n_clusters))
+    dist_plot = plt.figure()
+    for i in range(n_clusters):
+        for j in range(n_clusters):
+            avg_dist[i, j] = pairwise_distances(
+                X[y == i], X[y == j], metric=metric
+            ).mean()
+    avg_dist /= avg_dist.max()
+    for i in range(n_clusters):
+        for j in range(n_clusters):
+            t = plt.text(
+                i,
+                j,
+                "%5.3f" % avg_dist[i, j],
+                verticalalignment="center",
+                horizontalalignment="center",
+            )
+            t.set_path_effects(
+                [PathEffects.withStroke(linewidth=5, foreground="w", alpha=0.5)]
+            )
+
+    plt.imshow(avg_dist, interpolation="nearest", cmap="cividis", vmin=0)
+    plt.xticks(range(n_clusters), labels, rotation=45)
+    plt.yticks(range(n_clusters), labels)
+    plt.colorbar()
+    plt.suptitle("Interclass %s distances" % metric, size=18, y=1)
+    plt.tight_layout()
+    return dist_plot
+
+def agg_cluster(n_feats, measure):
+    plt.clf()
+    gt_plt, X, y = ground_truth_plot(n_feats)
+    cluster_waves_plot = plot_cluster_waves(measure, X, y)
+    dist_plot = plot_distances(measure, X, y)
+    return gt_plt, cluster_waves_plot, dist_plot
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            n_feats = gr.Slider(10, 4000, 2000, label="Number of Features")
+            measure = gr.Dropdown(["cosine", "euclidean", "cityblock"], value="cosine")
+            btn = gr.Button(label="Run")
+        gt_graph = gr.Plot(label="Ground Truth Graph")
+    with gr.Row():
+        dist_plot = gr.Plot(label="Interclass Distances")
+        clust_waves = gr.Plot(label="Agglomerative Clustering")
+
+    btn.click(
+            fn=agg_cluster,
+            inputs=[n_feats, measure],
+            outputs=[gt_graph, clust_waves, dist_plot]
+            )
+
+if __name__ == '__main__':
+    demo.launch()
+