| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.cluster import AgglomerativeClustering | |
| from sklearn.neighbors import kneighbors_graph | |
| import gradio as gr | |
| plt.switch_backend("agg") | |
| np.random.seed(42) | |
| def agglomorative_cluster(n_samples: int, n_neighbours: int, n_clusters: int, linkage: str, connectivity: bool) -> "plt.Figure": | |
| t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, n_samples)) | |
| x = t * np.cos(t) | |
| y = t * np.sin(t) | |
| X = np.concatenate((x, y)) | |
| X += 0.7 * np.random.randn(2, n_samples) | |
| X = X.T | |
| knn_graph = kneighbors_graph(X, n_neighbors=n_neighbours, include_self=False) | |
| connectivity = knn_graph if not connectivity else None | |
| fig, ax = plt.subplots(1, 1, figsize=(24, 15)) | |
| model = AgglomerativeClustering(linkage=linkage, connectivity=connectivity, n_clusters=int(n_clusters)) | |
| model.fit(X) | |
| ax.scatter(X[:, 0], X[:, 1], c=model.labels_, cmap=plt.cm.nipy_spectral) | |
| ax.axis("equal") | |
| ax.axis("off") | |
| return fig | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # Agglomorative Clustering with and without Structure | |
| This space is an implementation of the scikit-learn document [Agglomorative clustering with and without structure](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py) | |
| This space shows the effects of imposing **connectivity graph** to capture local structure in the data. | |
| You can uncheck the checkbox `connectivity` to see the effect on data clustering without **connectivity graph**. There are other parameters in this space | |
| which you can play with such as `n_samples` (the number of data samples), `n_neighbours` (the number of neighbours), `n_clusters` (the number of clusters) and | |
| what type of linkage to use for Agglomorative clustering `linkage`. | |
| Have fun playing with the tool 🤗 | |
| """) | |
| n_samples = gr.Slider(0, 20_000, label="n_samples", info="the number of samples in the data.", step=1) | |
| n_neighbours = gr.Slider(0, 30, label="n_neighbours", info="the number of neighbours in the data", step=1) | |
| n_clusters = gr.Slider(3, 30, label="n_clusters", info="the number of clusters in the data", step=2) | |
| linkage = gr.Dropdown(['average', 'complete', 'ward', 'single'], label="linkage", info="the different types of aggolomorative clustering techniques") | |
| connectivity = gr.Checkbox(True, label="connectivity", info="whether to impose a connectivity into the graph") | |
| output = gr.Plot(label="Plot") | |
| plot_btn = gr.Button("Plot") | |
| plot_btn.click(fn=agglomorative_cluster, inputs=[n_samples, n_neighbours, n_clusters, linkage, connectivity], | |
| outputs=output, api_name="plotcluster") | |
| demo.launch() | |