Delete app.py with huggingface_hub

app.py

@@ -1,281 +0,0 @@
-import gradio as gr
-import math
-from functools import partial
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.cluster import (
-    AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth
-)
-from sklearn.datasets import make_blobs, make_circles, make_moons
-from sklearn.mixture import GaussianMixture
-from sklearn.neighbors import kneighbors_graph
-from sklearn.preprocessing import StandardScaler
-
-plt.style.use('seaborn')
-SEED = 0
-MAX_CLUSTERS = 10
-N_SAMPLES = 1000
-N_COLS = 3
-FIGSIZE = 7, 7  # does not affect size in webpage
-COLORS = [
-    'blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan'
-]
-assert len(COLORS) >= MAX_CLUSTERS, "Not enough different colors for all clusters"
-np.random.seed(SEED)
-
-
-def normalize(X):
-    return StandardScaler().fit_transform(X)
-
-def get_regular(n_clusters):
-    # spiral pattern
-    centers = [
-        [0, 0],
-        [1, 0],
-        [1, 1],
-        [0, 1],
-        [-1, 1],
-        [-1, 0],
-        [-1, -1],
-        [0, -1],
-        [1, -1],
-        [2, -1],
-    ][:n_clusters]
-    assert len(centers) == n_clusters
-    X, labels = make_blobs(n_samples=N_SAMPLES, centers=centers, cluster_std=0.25, random_state=SEED)
-    return normalize(X), labels
-
-
-def get_circles(n_clusters):
-    X, labels = make_circles(n_samples=N_SAMPLES, factor=0.5, noise=0.05, random_state=SEED)
-    return normalize(X), labels
-
-
-def get_moons(n_clusters):
-    X, labels = make_moons(n_samples=N_SAMPLES, noise=0.05, random_state=SEED)
-    return normalize(X), labels
-
-
-def get_noise(n_clusters):
-    np.random.seed(SEED)
-    X, labels = np.random.rand(N_SAMPLES, 2), np.random.randint(0, n_clusters, size=(N_SAMPLES,))
-    return normalize(X), labels
-
-
-def get_anisotropic(n_clusters):
-    X, labels = make_blobs(n_samples=N_SAMPLES, centers=n_clusters, random_state=170)
-    transformation = [[0.6, -0.6], [-0.4, 0.8]]
-    X = np.dot(X, transformation)
-    return X, labels
-
-
-def get_varied(n_clusters):
-    cluster_std = [1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0][:n_clusters]
-    assert len(cluster_std) == n_clusters
-    X, labels = make_blobs(
-        n_samples=N_SAMPLES, centers=n_clusters, cluster_std=cluster_std, random_state=SEED
-    )
-    return normalize(X), labels
-
-
-def get_spiral(n_clusters):
-    # from https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html
-    np.random.seed(SEED)
-    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 = np.ascontiguousarray(X.T)
-
-    labels = np.zeros(N_SAMPLES, dtype=int)
-    return normalize(X), labels
-
-
-DATA_MAPPING = {
-    'regular': get_regular,
-    'circles': get_circles,
-    'moons': get_moons,
-    'spiral': get_spiral,
-    'noise': get_noise,
-    'anisotropic': get_anisotropic,
-    'varied': get_varied,
-}
-
-
-def get_groundtruth_model(X, labels, n_clusters, **kwargs):
-    # dummy model to show true label distribution
-    class Dummy:
-        def __init__(self, y):
-            self.labels_ = labels
-
-    return Dummy(labels)
-
-
-def get_kmeans(X, labels, n_clusters, **kwargs):
-    model = KMeans(init="k-means++", n_clusters=n_clusters, n_init=10, random_state=SEED)
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_dbscan(X, labels, n_clusters, **kwargs):
-    model = DBSCAN(eps=0.3)
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_agglomerative(X, labels, n_clusters, **kwargs):
-    connectivity = kneighbors_graph(
-        X, n_neighbors=n_clusters, include_self=False
-    )
-    # make connectivity symmetric
-    connectivity = 0.5 * (connectivity + connectivity.T)
-    model = AgglomerativeClustering(
-        n_clusters=n_clusters, linkage="ward", connectivity=connectivity
-    )
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_meanshift(X, labels, n_clusters, **kwargs):
-    bandwidth = estimate_bandwidth(X, quantile=0.25)
-    model = MeanShift(bandwidth=bandwidth, bin_seeding=True)
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_spectral(X, labels, n_clusters, **kwargs):
-    model = SpectralClustering(
-        n_clusters=n_clusters,
-        eigen_solver="arpack",
-        affinity="nearest_neighbors",
-    )
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_optics(X, labels, n_clusters, **kwargs):
-    model = OPTICS(
-        min_samples=7,
-        xi=0.05,
-        min_cluster_size=0.1,
-    )
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_birch(X, labels, n_clusters, **kwargs):
-    model = Birch(n_clusters=n_clusters)
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-def get_gaussianmixture(X, labels, n_clusters, **kwargs):
-    model = GaussianMixture(
-        n_components=n_clusters, covariance_type="full", random_state=SEED,
-    )
-    model.set_params(**kwargs)
-    return model.fit(X)
-
-
-MODEL_MAPPING = {
-    'True labels': get_groundtruth_model,
-    'KMeans': get_kmeans,
-    'DBSCAN': get_dbscan,
-    'MeanShift': get_meanshift,
-    'SpectralClustering': get_spectral,
-    'OPTICS': get_optics,
-    'Birch': get_birch,
-    'GaussianMixture': get_gaussianmixture,
-    'AgglomerativeClustering': get_agglomerative,
-}
-
-
-def plot_clusters(ax, X, labels):
-    set_clusters = set(labels)
-    set_clusters.discard(-1)  # -1 signifiies outliers, which we plot separately
-    for label, color in zip(sorted(set_clusters), COLORS):
-        idx = labels == label
-        if not sum(idx):
-            continue
-        ax.scatter(X[idx, 0], X[idx, 1], color=color)
-
-    # show outliers (if any)
-    idx = labels == -1
-    if sum(idx):
-        ax.scatter(X[idx, 0], X[idx, 1], c='k', marker='x')
-
-    ax.grid(None)
-    ax.set_xticks([])
-    ax.set_yticks([])
-    return ax
-
-
-def cluster(dataset: str, n_clusters: int, clustering_algorithm: str):
-    if isinstance(n_clusters, dict):
-        n_clusters = n_clusters['value']
-    else:
-        n_clusters = int(n_clusters)
-
-    X, labels = DATA_MAPPING[dataset](n_clusters)
-    model = MODEL_MAPPING[clustering_algorithm](X, labels, n_clusters=n_clusters)
-    if hasattr(model, "labels_"):
-        y_pred = model.labels_.astype(int)
-    else:
-        y_pred = model.predict(X)
-
-    fig, ax = plt.subplots(figsize=FIGSIZE)
-
-    plot_clusters(ax, X, y_pred)
-    ax.set_title(clustering_algorithm, fontsize=16)
-
-    return fig
-
-
-title = "Clustering with Scikit-learn"
-description = (
-    "This example shows how different clustering algorithms work. Simply pick "
-    "the dataset and the number of clusters to see how the clustering algorithms work. "
-    "Colored cirles are (predicted) labels and black x are outliers."
-)
-
-
-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
-
-with gr.Blocks(title=title) as demo:
-    gr.HTML(f"<b>{title}</b>")
-    gr.Markdown(description)
-
-    input_models = list(MODEL_MAPPING)
-    input_data = gr.Radio(
-        list(DATA_MAPPING),
-        value="regular",
-        label="dataset"
-    )
-    input_n_clusters = gr.Slider(
-        minimum=1,
-        maximum=MAX_CLUSTERS,
-        value=4,
-        step=1,
-        label='Number of clusters'
-    )
-    n_rows = int(math.ceil(len(input_models) / N_COLS))
-    counter = 0
-    for _ in iter_grid(n_rows, N_COLS):
-        if counter >= len(input_models):
-            break
-
-        input_model = input_models[counter]
-        plot = gr.Plot(label=input_model)
-        fn = partial(cluster, clustering_algorithm=input_model)
-        input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
-        input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
-        counter += 1
-
-demo.launch()