etapes du clustering simple : PCA + Kmeans + CAH

iss/clustering/AbstractClustering.py

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+# -*- coding: utf-8 -*-
+
+class AbstractClustering:
+
+	def __init__(config, pictures_id, pictures_np):
+
+		self.config = config
+		self.pictures_id = pictures_id
+		self.pictures_np = pictures_np
+
+
+	

iss/clustering/ClassicalClustering.py

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+from iss.clustering import AbstractClustering
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+from sklearn.cluster import AgglomerativeClustering
+
+class ClassicalClustering(AbstractClustering):
+
+	def __init__(config, pictures_id, pictures_np):
+
+		self.pca_fit = None
+		self.pca_args = self.config['PCA']
+		self.pca_reduction = None
+
+		self.kmeans_fit = None
+		self.kmeans_args = self.config['kmeans']
+		self.kmeans_labels = None
+		self.kmeans_centers = []
+
+		self.cah_fit = None
+		self.cah_args = self.config['CAH']
+		self.cah_labels = None
+
+		self.final_labels = None
+
+		super().__init__(config, pictures_id, pictures_np)
+
+
+	def pca_fit(self):
+
+		self.pca_fit = PCA(**self.pca_args**)
+		self.pca_fit.fit(self.pictures_np)
+		self.pca_reduction = self.pca_fit.transform(self.pictures_np)
+
+		return self
+
+	def kmeans_fit(self):
+		self.kmeans_fit = KMeans(self.kmeans_args**)
+		self.kmeans_fit.fit(self.pca_reduction)
+		self.kmeans_labels = self.kmeans_fit.labels_
+		return self
+
+	def compute_kmeans_centers(self):
+		for cl in range(self.kmeans_args['n_clusters']):
+			tmp = self.[np.where(self.kmeans_labels == cl)]
+			self.kmeans_centers.append(np.mean(tmp, axis = 0))
+		return self
+
+	def cah_fit(self):
+
+		self.cah_fit = AgglomerativeClustering(self.cah_args**)
+		self.cah_fit.fit_predict(self.kmeans_centers)
+		self.cah_labels = self.cah_fit.labels_
+		return self
+
+	def compute_cah_labels(self):
+		self.final_labels = [self.cah_labels[old_cl] for old_cl in self.kmeans_labels]
+
+

iss/clustering/__init__.py

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+from .AbstractClustering import AbstractClustering