243 lines
6.2 KiB
Python
243 lines
6.2 KiB
Python
from typing import Any, Dict, List
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import matplotlib.cm as cm
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import matplotlib.pyplot as plt
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import numpy as np
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from pandas import DataFrame
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from scipy.cluster import hierarchy
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from sklearn.cluster import KMeans
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def draw_data_2d(
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df: DataFrame,
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col1: int,
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col2: int,
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y: List | None = None,
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classes: List | None = None,
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subplot: Any | None = None,
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):
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ax = None
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if subplot is None:
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_, ax = plt.subplots()
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else:
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ax = subplot
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scatter = ax.scatter(df[df.columns[col1]], df[df.columns[col2]], c=y)
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ax.set(xlabel=df.columns[col1], ylabel=df.columns[col2])
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if classes is not None:
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ax.legend(
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scatter.legend_elements()[0], classes, loc="lower right", title="Classes"
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)
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def draw_dendrogram(linkage_matrix: np.ndarray):
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hierarchy.dendrogram(linkage_matrix, truncate_mode="level", p=3)
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def draw_cluster_results(
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df: DataFrame,
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col1: int,
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col2: int,
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labels: np.ndarray,
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cluster_centers: np.ndarray,
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subplot: Any | None = None,
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):
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ax = None
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if subplot is None:
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ax = plt
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else:
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ax = subplot
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centroids = cluster_centers
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u_labels = np.unique(labels)
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for i in u_labels:
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ax.scatter(
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df[labels == i][df.columns[col1]],
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df[labels == i][df.columns[col2]],
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label=i,
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)
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ax.scatter(centroids[:, col1], centroids[:, col2], s=80, color="k")
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def draw_clusters(reduced_data: np.ndarray, kmeans: KMeans):
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h = 0.02
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x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
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y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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plt.figure(1)
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plt.clf()
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plt.imshow(
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Z,
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interpolation="nearest",
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extent=(xx.min(), xx.max(), yy.min(), yy.max()),
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cmap=plt.cm.Paired, # type: ignore
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aspect="auto",
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origin="lower",
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)
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plt.plot(reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2)
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centroids = kmeans.cluster_centers_
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plt.scatter(
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centroids[:, 0],
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centroids[:, 1],
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marker="x",
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s=169,
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linewidths=3,
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color="w",
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zorder=10,
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)
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plt.title(
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"K-means clustering (PCA-reduced data)\n"
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"Centroids are marked with white cross"
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)
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plt.xlim(x_min, x_max)
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plt.ylim(y_min, y_max)
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plt.xticks(())
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plt.yticks(())
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def _draw_cluster_scores(
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data: List,
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clusters_range: range,
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score_name: str,
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title: str,
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):
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plt.figure(figsize=(8, 5))
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plt.plot(clusters_range, data, "bo-")
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plt.xlabel("$k$", fontsize=8)
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plt.ylabel(score_name, fontsize=8)
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plt.title(title)
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def draw_elbow_diagram(inertias: List, clusters_range: range):
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_draw_cluster_scores(inertias, clusters_range, "Inertia", "The Elbow Diagram")
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def draw_silhouettes_diagram(silhouette: List, clusters_range: range):
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_draw_cluster_scores(
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silhouette, clusters_range, "Silhouette score", "The Silhouette score"
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)
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def _draw_silhouette(
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ax: Any,
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reduced_data: np.ndarray,
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n_clusters: int,
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silhouette_avg: float,
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sample_silhouette_values: List,
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cluster_labels: List,
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):
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ax.set_xlim([-0.1, 1])
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ax.set_ylim([0, len(reduced_data) + (n_clusters + 1) * 10])
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y_lower = 10
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for i in range(n_clusters):
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ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]
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ith_cluster_silhouette_values.sort()
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size_cluster_i = ith_cluster_silhouette_values.shape[0]
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y_upper = y_lower + size_cluster_i
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color = cm.nipy_spectral(float(i) / n_clusters) # type: ignore
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ax.fill_betweenx(
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np.arange(y_lower, y_upper),
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0,
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ith_cluster_silhouette_values,
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facecolor=color,
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edgecolor=color,
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alpha=0.7,
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)
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ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
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y_lower = y_upper + 10 # 10 for the 0 samples
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ax.set_title("The silhouette plot for the various clusters.")
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ax.set_xlabel("The silhouette coefficient values")
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ax.set_ylabel("Cluster label")
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ax.axvline(x=silhouette_avg, color="red", linestyle="--")
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ax.set_yticks([])
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ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
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def _draw_cluster_data(
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ax: Any,
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reduced_data: np.ndarray,
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n_clusters: int,
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cluster_labels: np.ndarray,
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cluster_centers: np.ndarray,
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):
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colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters) # type: ignore
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ax.scatter(
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reduced_data[:, 0],
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reduced_data[:, 1],
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marker=".",
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s=30,
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lw=0,
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alpha=0.7,
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c=colors,
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edgecolor="k",
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)
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ax.scatter(
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cluster_centers[:, 0],
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cluster_centers[:, 1],
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marker="o",
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c="white",
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alpha=1,
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s=200,
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edgecolor="k",
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)
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for i, c in enumerate(cluster_centers):
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ax.scatter(c[0], c[1], marker="$%d$" % i, alpha=1, s=50, edgecolor="k")
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ax.set_title("The visualization of the clustered data.")
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ax.set_xlabel("Feature space for the 1st feature")
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ax.set_ylabel("Feature space for the 2nd feature")
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def draw_silhouettes(reduced_data: np.ndarray, silhouettes: Dict):
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for key, value in silhouettes.items():
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fig, (ax1, ax2) = plt.subplots(1, 2)
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fig.set_size_inches(18, 7)
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n_clusters = key
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silhouette_avg = value[0]
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sample_silhouette_values = value[1]
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cluster_labels = value[2].labels_
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cluster_centers = value[2].cluster_centers_
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_draw_silhouette(
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ax1,
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reduced_data,
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n_clusters,
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silhouette_avg,
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sample_silhouette_values,
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cluster_labels,
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)
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_draw_cluster_data(
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ax2,
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reduced_data,
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n_clusters,
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cluster_labels,
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cluster_centers,
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)
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plt.suptitle(
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"Silhouette analysis for KMeans clustering on sample data with n_clusters = %d"
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% n_clusters,
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fontsize=14,
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fontweight="bold",
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)
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