mirror of
https://github.com/prise6/smart-iss-posts
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84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
# -*- coding: utf-8 -*-
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import os
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import numpy as np
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from iss.clustering import AbstractClustering
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from sklearn.decomposition import PCA
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from sklearn.cluster import KMeans
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from sklearn.cluster import AgglomerativeClustering
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from iss.tools import Tools
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from sklearn.externals import joblib
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class ClassicalClustering(AbstractClustering):
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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.pca_fit = None
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self.pca_args = self.config['PCA']
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self.pca_reduction = None
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self.pca_save_name = "PCA_model_v%s.pkl" % (self.config['version'])
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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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self.cah_fit = None
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self.cah_args = self.config['CAH']
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self.cah_labels = None
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self.cah_save_name = "cah_model_v%s.pkl" % (self.config['version'])
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self.final_labels = None
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def compute_pca(self):
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self.pca_fit = PCA(**self.pca_args)
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self.pca_fit.fit(self.pictures_np)
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self.pca_reduction = self.pca_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.pca_reduction)
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self.kmeans_labels = self.kmeans_fit.labels_
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return self
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def compute_kmeans_centers(self):
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for cl in range(self.kmeans_args['n_clusters']):
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tmp = self.pca_reduction[np.where(self.kmeans_labels == cl)]
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self.kmeans_centers.append(np.mean(tmp, axis = 0))
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return self
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def compute_cah(self):
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self.cah_fit = AgglomerativeClustering(**self.cah_args)
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self.cah_fit.fit_predict(self.kmeans_centers)
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self.cah_labels = self.cah_fit.labels_
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return self
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def compute_cah_labels(self):
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self.final_labels = [self.cah_labels[old_cl] for old_cl in self.kmeans_labels]
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def get_zip_results(self):
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return zip(self.pictures_id, self.final_labels, self.kmeans_labels, self.pictures_np)
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def save(self):
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Tools.create_dir_if_not_exists(self.config['save_directory'])
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joblib.dump(self.pca_fit, os.path.join(self.config['save_directory'], self.pca_save_name))
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joblib.dump(self.kmeans_fit, os.path.join(self.config['save_directory'], self.kmeans_save_name))
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joblib.dump(self.cah_fit, os.path.join(self.config['save_directory'], self.cah_save_name))
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def load(self):
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self.pca_fit = joblib.load(os.path.join(self.config['save_directory'], self.pca_save_name))
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self.kmeans_fit = joblib.load(os.path.join(self.config['save_directory'], self.kmeans_save_name))
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self.cah_fit = joblib.load(os.path.join(self.config['save_directory'], self.cah_save_name))
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