sklearn-docs/Hierarchical-clustering-dendrogram

1import gradio as gr2import numpy as np3from functools import partial4 5from matplotlib import pyplot as plt6from scipy.cluster.hierarchy import dendrogram7from sklearn.datasets import load_iris8from sklearn.cluster import AgglomerativeClustering9 10 11theme = gr.themes.Monochrome(12    primary_hue="indigo",13    secondary_hue="blue",14    neutral_hue="slate",15)16model_card = f"""17## Description18 19This demo shows the plot of the corresponding **Dendrogram of Hierarchical Clustering** using **AgglomerativeClustering** and the dendrogram method on the Iris dataset.20There are several metrics that use to compute the distance like `euclidean`, `l1`, `l2`, `manhattan`21You can play around with different ``linkage criterion``. The linkage criterion determines which distance to use between sets of observations.22Note: If `linkage criterion` is **ward**, only **euclidean** can use23 24 25## Dataset26 27Iris dataset28"""29iris = load_iris()30X = iris.data31 32def iter_grid(n_rows, n_cols):33    # create a grid using gradio Block34    for _ in range(n_rows):35        with gr.Row():36            for _ in range(n_cols):37                with gr.Column():38                    yield39 40def plot_dendrogram(linkage_name, metric_name):41    # Create linkage matrix and then plot the dendrogram42    if linkage_name == "ward" and metric_name != "euclidean":43        return None44    # setting distance_threshold=0 ensures we compute the full tree.45    model = AgglomerativeClustering(distance_threshold=0, n_clusters=None, metric=metric_name, linkage=linkage_name)46 47    model = model.fit(X)48 49    # plot the top three levels of the dendrogram50 51    counts = np.zeros(model.children_.shape[0])52    n_samples = len(model.labels_)53    for i, merge in enumerate(model.children_):54        current_count = 055        for child_idx in merge:56            if child_idx < n_samples:57                current_count += 1  # leaf node58            else:59                current_count += counts[child_idx - n_samples]60        counts[i] = current_count61 62    linkage_matrix = np.column_stack(63        [model.children_, model.distances_, counts]64    ).astype(float)65    fig, axes = plt.subplots()66 67    dn1 = dendrogram(linkage_matrix, ax=axes, truncate_mode="level", p=3)68    # Plot the corresponding dendrogram69    axes.set_title(f"Hierarchical Clustering Dendrogram. Linkage criterion: {metric_name}")70    axes.set_xlabel("Number of points in node (or index of point if no parenthesis).")71    return fig72 73 74 75with gr.Blocks(theme=theme) as demo:76    gr.Markdown('''77            <div>78            <h1 style='text-align: center'>Hierarchical Clustering Dendrogram</h1>79            </div>80        ''')81    gr.Markdown(model_card)82    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>")83    input_linkage = gr.Radio(choices=["ward", "complete", "average", "single"], value="average", label="Linkage criterion to use")84    metrics = ["euclidean", "l1", "l2", "manhattan"]85    counter = 086    for _ in iter_grid(2, 2):87        if counter >= len(metrics):88            break89 90        input_metric = metrics[counter]91        plot = gr.Plot(label=input_metric)92        fn = partial(plot_dendrogram, metric_name=input_metric)93        input_linkage.change(fn=fn, inputs=[input_linkage], outputs=plot)94        counter += 195 96demo.launch()