colors: relabelling the classes using the first centroids values

relabels.py

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# colors: relabelling the classes using the first centroids values

# calculate all the class centers in data space
y = X.groupby(labels).mean().values
# position of the data feature used to sort lables
feature_index = find_nearest(700)
# here the sorting index
centroids_sorting_index = np.argsort(y[:, feature_index])
# here the sorting labels, not the index!!
centroids_sorted_labels = np.argsort(centroids_sorting_index) 
# # use pd.Series.map(dict) di directly change values in place 
labels = pd.Series(labels).map(dict(zip(np.arange(n_clusters),centroids_sorted_labels))).values
print('index for label sort :',feature_index)
# print(' features y[:,index] :',y[:, feature_index])
# print(centroids_sorting_index)
# print(centroids_sorted_labels)
print(f'ind:y_feat  > new_index')
for i,yf,ni in zip(range(len(y[:, feature_index])),y[:, feature_index],centroids_sorted_labels):
    print(f'{i:3}:{yf:.5f} > {ni:>4}')