mirror of
https://github.com/prise6/smart-iss-posts
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53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
# -*- coding: utf-8 -*-
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import os
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import numpy as np
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import umap
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from iss.tools import Tools
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from iss.clustering import AbstractClustering
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from sklearn.cluster import KMeans
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from sklearn.metrics import silhouette_samples
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from sklearn.externals import joblib
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class N2DClustering(AbstractClustering):
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"""
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Cf: https://github.com/rymc/n2d
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"""
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def __init__(self, config, pictures_id = None, pictures_np = None):
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super().__init__(config, pictures_id, pictures_np)
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self.umap_args = self.config['umap']
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self.umap_fit = None
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self.umap_embedding = None
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self.kmeans_fit = None
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self.kmeans_args = self.config['kmeans']
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self.kmeans_labels = None
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self.kmeans_centers = []
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self.kmeans_save_name = "kmeans_model_v%s.pkl" % (self.config['version'])
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def compute_umap(self):
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self.umap_fit = umap.UMAP(**self.umap_args)
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self.umap_embedding = self.umap_fit.fit_transform(self.pictures_np)
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return self
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def compute_kmeans(self):
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self.kmeans_fit = KMeans(**self.kmeans_args)
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self.kmeans_fit.fit(self.umap_embedding)
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self.kmeans_labels = self.kmeans_fit.labels_
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return self
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def compute_final_labels(self):
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self.final_labels = self.kmeans_labels
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return self
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def compute_silhouette_score(self):
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self.silhouette_score = silhouette_samples(self.pictures_np, self.final_labels)
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self.silhouette_score_labels = {cluster: np.mean(self.silhouette_score[self.final_labels == cluster]) for
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cluster in np.unique(self.final_labels)}
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return self.silhouette_score_labels
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