mirror of
https://github.com/prise6/smart-iss-posts
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118 lines
4.6 KiB
Python
118 lines
4.6 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 sklearn.cluster import DBSCAN
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from iss.tools import Tools
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from sklearn.externals import joblib
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import pandas as pd
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class AdvancedClustering(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['strong_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.dbscan_fit = None
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self.dbscan_args = self.config['dbscan']
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self.dbscan_labels = None
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self.dbscan_save_name = "dbscan_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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np.random.seed(self.pca_args['random_state'])
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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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print(self.pca_reduction)
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return self
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def compute_kmeans(self):
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tmp_labels = pd.DataFrame()
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tmp_iter = self.kmeans_args['iter']
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tmp_range = range(0, tmp_iter)
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tmp_low = self.kmeans_args['low']
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tmp_high = self.kmeans_args['high']
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tmp_treshold = self.kmeans_args['threshold']
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tmp_cols = ['run_%s' % i for i in tmp_range]
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np.random.seed(self.kmeans_args['seed']*2)
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tmp_n_clusters = np.random.randint(low = tmp_low, high = tmp_high, size = tmp_iter)
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print(tmp_n_clusters)
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for i in tmp_range:
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km_model = KMeans(n_clusters = tmp_n_clusters[i], random_state = self.kmeans_args['seed']+i)
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km_res = km_model.fit(self.pca_reduction)
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tmp_labels[tmp_cols[i]] = km_res.labels_
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tmp_labels['dummy'] = 1
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tmp_labels['group_id'] = tmp_labels.groupby(by = tmp_cols, as_index = False).grouper.group_info[0]
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tmp_labels['count'] = tmp_labels.groupby(by = 'group_id', as_index = False)['dummy'].transform(np.size)
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tmp_labels = tmp_labels.drop(labels = 'dummy', axis = 1)
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tmp_group_id = tmp_labels[tmp_labels['count'] >= tmp_treshold]['group_id'].unique()
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print(tmp_group_id)
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pca_init = np.zeros((len(tmp_group_id), self.pca_reduction.shape[1]))
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for i in range(0, len(tmp_group_id)):
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gp_id = tmp_group_id[i]
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index_sel = tmp_labels[tmp_labels['group_id'] == gp_id].index
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pca_init[i, :] = np.mean(self.pca_reduction[index_sel, :], axis = 0)
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self.kmeans_fit = KMeans(n_clusters = pca_init.shape[0], init = pca_init, n_init = 1, random_state = self.kmeans_args['seed']+tmp_iter)
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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 list(np.unique(self.kmeans_fit.labels_)):
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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_dbscan(self):
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self.dbscan_fit = DBSCAN(**self.dbscan_args)
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self.dbscan_fit.fit_predict(self.kmeans_centers)
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self.dbscan_labels = self.dbscan_fit.labels_
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return self
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def compute_dbscan_labels(self):
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self.final_labels = [self.dbscan_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.dbscan_fit, os.path.join(self.config['save_directory'], self.dbscan_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.dbscan_fit = joblib.load(os.path.join(self.config['save_directory'], self.dbscan_save_name))
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