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February 23, 2021 14:36
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k-means silhouette analysis using sklearn and matplotlib on Iris data.
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| import numpy as np | |
| import matplotlib.cm as cm | |
| import matplotlib.pyplot as plt | |
| from matplotlib.colors import colorConverter | |
| __license__ = 'MIT' | |
| __author__ = 'clintval' | |
| def darken_rgb(rgb, p): | |
| """ | |
| Will darken an rgb value by p percent | |
| """ | |
| assert 0 <= p <= 1, "Proportion must be [0, 1]" | |
| return [int(x * (1 - p)) for x in rgb] | |
| def lighten_rgb(rgb, p): | |
| """ | |
| Will lighten an rgb value by p percent | |
| """ | |
| assert 0 <= p <= 1, "Proportion must be [0, 1]" | |
| return [int((255 - x) * p + x) for x in rgb] | |
| def is_luminous(rgb): | |
| new_color = [] | |
| for c in rgb: | |
| if c <= 0.03928: | |
| new_color.append(c / 12.92) | |
| else: | |
| new_color.append(((c + 0.055) / 1.055) ** 2.4) | |
| L = sum([x * y for x, y in zip([0.2126, 0.7152, 0.0722], new_color)]) | |
| return True if L < 0.179 else False | |
| def kmeans_plot(X, y, cluster_centers, ax=None): | |
| import matplotlib.patheffects as path_effects | |
| from sklearn.metrics.pairwise import pairwise_distances_argmin_min | |
| if ax is None: | |
| ax = plt.gca() | |
| colors = cm.spectral(y.astype(float) / len(cluster_centers)) | |
| ax.scatter(*list(zip(*X)), lw=0, c=colors, s=30) | |
| offset = max(list(zip(*cluster_centers))[0]) * 0.2 | |
| for i, cluster in enumerate(cluster_centers): | |
| index, _ = pairwise_distances_argmin_min(cluster.reshape(1, -1), Y=X) | |
| cluster_color = colorConverter.to_rgb(colors[index[0]]) | |
| if is_luminous(cluster_color) is False: | |
| cluster_color = darken_rgb(cluster_color, 0.35) | |
| label = ax.text(x=cluster[0] + offset, | |
| y=cluster[1], | |
| s='{:d}'.format(i + 1), | |
| color=cluster_color) | |
| label.set_path_effects([path_effects.Stroke(lw=2, foreground='white'), | |
| path_effects.Normal()]) | |
| limit = max(*ax.get_xlim(), *ax.get_xlim()) | |
| ax.set_xlim(0, limit) | |
| ax.set_ylim(0, limit) | |
| ax.set_xlabel("Feature space for the 1st feature") | |
| ax.set_ylabel("Feature space for the 2nd feature") | |
| return ax | |
| def silhouette_plot(X, y, n_clusters, ax=None): | |
| from sklearn.metrics import silhouette_samples, silhouette_score | |
| if ax is None: | |
| ax = plt.gca() | |
| # Compute the silhouette scores for each sample | |
| silhouette_avg = silhouette_score(X, y) | |
| sample_silhouette_values = silhouette_samples(X, y) | |
| y_lower = padding = 2 | |
| for i in range(n_clusters): | |
| # Aggregate the silhouette scores for samples belonging to | |
| ith_cluster_silhouette_values = sample_silhouette_values[y == i] | |
| ith_cluster_silhouette_values.sort() | |
| size_cluster_i = ith_cluster_silhouette_values.shape[0] | |
| y_upper = y_lower + size_cluster_i | |
| color = cm.spectral(float(i) / n_clusters) | |
| ax.fill_betweenx(np.arange(y_lower, y_upper), | |
| 0, | |
| ith_cluster_silhouette_values, | |
| facecolor=color, | |
| edgecolor=color, | |
| alpha=0.7) | |
| # Label the silhouette plots with their cluster numbers at the middle | |
| ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i + 1)) | |
| # Compute the new y_lower for next plot | |
| y_lower = y_upper + padding | |
| ax.set_xlabel("The silhouette coefficient values") | |
| ax.set_ylabel("Cluster label") | |
| # The vertical line for average silhoutte score of all the values | |
| ax.axvline(x=silhouette_avg, c='r', alpha=0.8, lw=0.8, ls='-') | |
| ax.annotate('Average', | |
| xytext=(silhouette_avg, y_lower * 1.025), | |
| xy=(0, 0), | |
| ha='center', | |
| alpha=0.8, | |
| c='r') | |
| ax.set_yticks([]) # Clear the yaxis labels / ticks | |
| ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1]) | |
| ax.set_ylim(0, y_upper + 1) | |
| ax.set_xlim(-0.075, 1.0) | |
| return ax |
same issue here.
Ha, I've not seen these comments until now. Have you tried removing the lw=0 call to ax.scatter? Right here: https://gist.github.com/clintval/e9afc246e77f6488cda79f86e4d37148#file-kmeansplots-py-L47
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Hi,
I continuously get error running this code: