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import gradio as gr |
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import numpy as np |
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from functools import partial |
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from matplotlib import pyplot as plt |
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from scipy.cluster.hierarchy import dendrogram |
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from sklearn.datasets import load_iris |
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from sklearn.cluster import AgglomerativeClustering |
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theme = gr.themes.Monochrome( |
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primary_hue="indigo", |
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secondary_hue="blue", |
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neutral_hue="slate", |
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) |
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model_card = f""" |
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## Description |
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This demo shows the plot of the corresponding **Dendrogram of Hierarchical Clustering** using **AgglomerativeClustering** and the dendrogram method on the Iris dataset. |
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There are several metrics that use to compute the distance like `euclidean`, `l1`, `l2`, `manhattan` |
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You can play around with different ``linkage criterion``. The linkage criterion determines which distance to use between sets of observations. |
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Note: If `linkage criterion` is **ward**, only **euclidean** can use |
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## Dataset |
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Iris dataset |
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""" |
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iris = load_iris() |
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X = iris.data |
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def iter_grid(n_rows, n_cols): |
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for _ in range(n_rows): |
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with gr.Row(): |
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for _ in range(n_cols): |
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with gr.Column(): |
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yield |
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def plot_dendrogram(linkage_name, metric_name): |
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if linkage_name == "ward" and metric_name != "euclidean": |
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return None |
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model = AgglomerativeClustering(distance_threshold=0, n_clusters=None, metric=metric_name, linkage=linkage_name) |
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model = model.fit(X) |
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counts = np.zeros(model.children_.shape[0]) |
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n_samples = len(model.labels_) |
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for i, merge in enumerate(model.children_): |
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current_count = 0 |
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for child_idx in merge: |
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if child_idx < n_samples: |
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current_count += 1 |
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else: |
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current_count += counts[child_idx - n_samples] |
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counts[i] = current_count |
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linkage_matrix = np.column_stack( |
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[model.children_, model.distances_, counts] |
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).astype(float) |
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fig, axes = plt.subplots() |
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dn1 = dendrogram(linkage_matrix, ax=axes, truncate_mode="level", p=3) |
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axes.set_title(f"Hierarchical Clustering Dendrogram. Linkage criterion: {metric_name}") |
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axes.set_xlabel("Number of points in node (or index of point if no parenthesis).") |
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return fig |
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with gr.Blocks(theme=theme) as demo: |
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gr.Markdown(''' |
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<div> |
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<h1 style='text-align: center'>Hierarchical Clustering Dendrogram</h1> |
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</div> |
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''') |
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gr.Markdown(model_card) |
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gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py\">scikit-learn</a>") |
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input_linkage = gr.Radio(choices=["ward", "complete", "average", "single"], value="average", label="Linkage criterion to use") |
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metrics = ["euclidean", "l1", "l2", "manhattan"] |
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counter = 0 |
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for _ in iter_grid(2, 2): |
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if counter >= len(metrics): |
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break |
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input_metric = metrics[counter] |
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plot = gr.Plot(label=input_metric) |
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fn = partial(plot_dendrogram, metric_name=input_metric) |
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input_linkage.change(fn=fn, inputs=[input_linkage], outputs=plot) |
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counter += 1 |
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demo.launch() |
