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# 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)
gt_plot, ax = plt.subplots()
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.subplots_adjust(top=0.8, bottom=0, left=0, right=1.0)
ax.set_title("Ground Truth", size=20, pad=1)
plt.legend(loc="best")
plt.axis("off")
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, ax = plt.subplots()
for l, color in zip(np.arange(model.n_clusters), colors):
plt.plot(X[model.labels_ == l].T, c=color, alpha=0.5)
plt.subplots_adjust(top=0.75, bottom=0, left=0, right=1.0)
ax.set_title("Agglomerative Clustering\n(metric=%s)" % metric, size=20, pad=1.0)
plt.axis("tight")
plt.axis("off")
return clust_plot
def plot_distances(metric, X, y):
avg_dist = np.zeros((n_clusters, n_clusters))
dist_plot, ax = plt.subplots()
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.subplots_adjust(top=0.8)
ax.set_title("Interclass %s distances" % metric, size=20, pad=1.0)
plt.axis("off")
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
title = "Agglomerative clustering with different metrics"
with gr.Blocks() as demo:
gr.Markdown(f" # {title}")
gr.Markdown(
"""
This example demonstrates the effect of different metrics on hierarchical clustering.
This is based on the example [here](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py)
"""
)
with gr.Row():
with gr.Column():
n_feats = gr.Slider(10, 4000, 2000, label="Number of Features")
measure = gr.Radio(["cosine", "euclidean", "cityblock"], label="Metric", value="cosine")
gt_graph = gr.Plot(label="Ground Truth Graph")
gt_graph.style()
with gr.Row():
dist_plot = gr.Plot(label="Interclass Distances")
clust_waves = gr.Plot(label="Agglomerative Clustering")
n_feats.change(
fn=agg_cluster,
inputs=[n_feats, measure],
outputs=[gt_graph, clust_waves, dist_plot]
)
measure.change(
fn=agg_cluster,
inputs=[n_feats, measure],
outputs=[gt_graph, clust_waves, dist_plot]
)
if __name__ == '__main__':
demo.launch()
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