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import time
import warnings
from functools import partial
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.preprocessing import StandardScaler
from itertools import cycle, islice
def train_models(selected_data, n_samples, n_clusters, n_neighbors, cls_name):
np.random.seed(0)
default_base = {"n_neighbors": 10, "n_clusters": 3}
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = np.random.rand(n_samples, 2), None
# Anisotropicly distributed data
random_state = 170
X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
transformation = [[0.6, -0.6], [-0.4, 0.8]]
X_aniso = np.dot(X, transformation)
aniso = (X_aniso, y)
# blobs with varied variances
varied = datasets.make_blobs(
n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state
)
dataset_list = {
"Noisy Circles": [noisy_circles, {"n_clusters": n_clusters}],
"Noisy Moons": [noisy_moons, {"n_clusters": n_clusters}],
"Varied": [varied, {"n_neighbors": n_neighbors}],
"Aniso": [aniso, {"n_neighbors": n_neighbors}],
"Blobs": [blobs, {}],
"No Structure": [no_structure, {}],
}
params = default_base.copy()
params.update(dataset_list[selected_data][1])
X, y = dataset_list[selected_data][0]
X = StandardScaler().fit_transform(X)
ward = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="ward"
)
complete = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="complete"
)
average = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="average"
)
single = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="single"
)
clustering_algorithms = {
"Single Linkage": single,
"Average Linkage": average,
"Complete Linkage": complete,
"Ward Linkage": ward,
}
t0 = time.time()
algorithm = clustering_algorithms[cls_name]
# catch warnings related to kneighbors_graph
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="the number of connected components of the "
+ "connectivity matrix is [0-9]{1,2}"
+ " > 1. Completing it to avoid stopping the tree early.",
category=UserWarning,
)
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, "labels_"):
y_pred = algorithm.labels_.astype(int)
else:
y_pred = algorithm.predict(X)
fig, ax = plt.subplots()
colors = np.array(
list(
islice(
cycle(
[
"#377eb8",
"#ff7f00",
"#4daf4a",
"#f781bf",
"#a65628",
"#984ea3",
"#999999",
"#e41a1c",
"#dede00",
]
),
int(max(y_pred) + 1),
)
)
)
ax.scatter(X[:, 0], X[:, 1], color=colors[y_pred])
ax.set_xlim(-2.5, 2.5)
ax.set_ylim(-2.5, 2.5)
ax.set_xticks(())
ax.set_yticks(())
return fig
def iter_grid(n_rows, n_cols):
# create a grid using gradio Block
for _ in range(n_rows):
with gr.Row():
for _ in range(n_cols):
with gr.Column():
yield
title = "🧑🏻🔬 Compare linkages in hierarchical clustering 🧑🏻🔬"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"## {title}")
gr.Markdown("This app demonstrates different linkage methods in"
" hierarchical clustering 🔗")
input_models = ["Single Linkage", "Average Linkage", "Complete Linkage",
"Ward Linkage"]
input_data = gr.Radio(
choices=["Noisy Circles", "Noisy Moons",
"Varied", "Aniso", "Blobs", "No Structure"],
value="Noisy Moons"
)
n_samples = gr.Slider(minimum=500, maximum=2000, step=50,
label = "Number of Samples")
n_neighbors = gr.Slider(minimum=2, maximum=5, step=1,
label = "Number of neighbors")
n_clusters = gr.Slider(minimum=2, maximum=5, step=1,
label = "Number of Clusters")
counter = 0
for _ in iter_grid(2, 2):
if counter >= len(input_models):
break
input_model = input_models[counter]
plot = gr.Plot(label=input_model)
fn = partial(train_models, cls_name=input_model)
input_data.change(fn=fn, inputs=[input_data, n_samples, n_clusters, n_neighbors], outputs=plot)
n_samples.change(fn=fn, inputs=[input_data, n_samples, n_clusters, n_neighbors], outputs=plot)
n_neighbors.change(fn=fn, inputs=[input_data, n_samples, n_clusters, n_neighbors], outputs=plot)
n_clusters.change(fn=fn, inputs=[input_data, n_samples, n_clusters, n_neighbors], outputs=plot)
counter += 1
demo.launch() |