classes clustering

iss/clustering/AbstractClustering.py

@@ -1,12 +1,35 @@
 # -*- coding: utf-8 -*-
+from iss.tools import Tools
 
 class AbstractClustering:
 
-	def __init__(self, config, pictures_id, pictures_np):
+    def __init__(self, config, pictures_id, pictures_np):
 
-		self.config = config
-		self.pictures_id = pictures_id
-		self.pictures_np = pictures_np
+        self.config = config
+        self.pictures_id = pictures_id
+        self.pictures_np = pictures_np
+        self.final_labels = None
+        self.colors = None
 
+        if self.config['save_directory']:
+            Tools.create_dir_if_not_exists(self.config['save_directory'])
 
-	
+    def compute_final_labels(self):
+        raise NotImplementedError
+
+    def get_results(self):
+        return list(zip(self.pictures_id, self.final_labels, self.pictures_np))
+        
+    def compute_silhouette_score(self):
+        raise NotImplementedError
+
+    def compute_colors(self):
+        n_classes = len(list(set(self.final_labels)))
+        self.colors = [Tools.get_color_from_label(label, n_classes) for label in self.final_labels]
+        return self
+
+    def save(self):
+        raise NotImplementedError
+
+    def load(self):
+        raise NotImplementedError   

iss/clustering/AdvancedClustering.py

@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+
+import os
+import numpy as np
+from iss.clustering import AbstractClustering
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.cluster import DBSCAN
+from iss.tools import Tools
+from sklearn.externals import joblib
+import pandas as pd
+
+class AdvancedClustering(AbstractClustering):
+
+    def __init__(self, config, pictures_id = None, pictures_np = None):
+
+        super().__init__(config, pictures_id, pictures_np)
+
+        self.pca_fit = None
+        self.pca_args = self.config['PCA']
+        self.pca_reduction = None
+        self.pca_save_name = "PCA_model_v%s.pkl" % (self.config['version'])
+
+        self.kmeans_fit = None
+        self.kmeans_args = self.config['strong_kmeans']
+        self.kmeans_labels = None
+        self.kmeans_centers = []
+        self.kmeans_save_name = "kmeans_model_v%s.pkl" % (self.config['version'])
+
+        self.dbscan_fit = None
+        self.dbscan_args = self.config['dbscan']
+        self.dbscan_labels = None
+        self.dbscan_save_name = "dbscan_model_v%s.pkl" % (self.config['version'])
+
+        self.final_labels = None
+
+
+    def compute_pca(self):
+
+        np.random.seed(self.pca_args['random_state'])
+        self.pca_fit = PCA(**self.pca_args)
+        self.pca_fit.fit(self.pictures_np)
+        self.pca_reduction = self.pca_fit.transform(self.pictures_np)
+        print(self.pca_reduction)
+        return self
+
+    def compute_kmeans(self):
+
+        tmp_labels = pd.DataFrame()
+        tmp_iter = self.kmeans_args['iter']
+        tmp_range = range(0, tmp_iter)
+        tmp_low = self.kmeans_args['low']
+        tmp_high = self.kmeans_args['high']
+        tmp_treshold = self.kmeans_args['threshold']
+        tmp_cols = ['run_%s' % i for i in tmp_range]
+        np.random.seed(self.kmeans_args['seed']*2)
+        tmp_n_clusters = np.random.randint(low = tmp_low, high = tmp_high, size = tmp_iter)
+        print(tmp_n_clusters)
+
+        for i in tmp_range:
+            km_model = KMeans(n_clusters = tmp_n_clusters[i], random_state = self.kmeans_args['seed']+i)
+            km_res = km_model.fit(self.pca_reduction)
+            tmp_labels[tmp_cols[i]] = km_res.labels_
+
+        tmp_labels['dummy'] = 1
+        tmp_labels['group_id'] = tmp_labels.groupby(by = tmp_cols, as_index = False).grouper.group_info[0]
+        tmp_labels['count'] = tmp_labels.groupby(by = 'group_id', as_index = False)['dummy'].transform(np.size)
+        tmp_labels = tmp_labels.drop(labels = 'dummy', axis = 1)
+
+        tmp_group_id = tmp_labels[tmp_labels['count'] >= tmp_treshold]['group_id'].unique()
+
+        print(tmp_group_id)
+
+        pca_init = np.zeros((len(tmp_group_id), self.pca_reduction.shape[1]))
+
+        for i in range(0, len(tmp_group_id)):
+            gp_id = tmp_group_id[i]
+            index_sel = tmp_labels[tmp_labels['group_id'] == gp_id].index
+            pca_init[i, :] = np.mean(self.pca_reduction[index_sel, :], axis = 0)
+
+
+        self.kmeans_fit = KMeans(n_clusters = pca_init.shape[0], init = pca_init, n_init = 1, random_state = self.kmeans_args['seed']+tmp_iter)
+        self.kmeans_fit.fit(self.pca_reduction)
+        self.kmeans_labels = self.kmeans_fit.labels_
+        return self
+
+    def compute_kmeans_centers(self):
+        for cl in list(np.unique(self.kmeans_fit.labels_)):
+            tmp = self.pca_reduction[np.where(self.kmeans_labels == cl)]
+            self.kmeans_centers.append(np.mean(tmp, axis = 0))
+        return self
+    
+    def compute_dbscan(self):
+        self.dbscan_fit = DBSCAN(**self.dbscan_args)
+        self.dbscan_fit.fit_predict(self.kmeans_centers)
+        self.dbscan_labels = self.dbscan_fit.labels_
+        return self
+
+    def compute_dbscan_labels(self):
+        self.final_labels = [self.dbscan_labels[old_cl] for old_cl in self.kmeans_labels]
+
+    def get_zip_results(self):
+        return zip(self.pictures_id, self.final_labels, self.kmeans_labels, self.pictures_np)
+
+    def save(self):
+        Tools.create_dir_if_not_exists(self.config['save_directory'])
+
+        joblib.dump(self.pca_fit, os.path.join(self.config['save_directory'], self.pca_save_name))
+        joblib.dump(self.kmeans_fit, os.path.join(self.config['save_directory'], self.kmeans_save_name))
+        joblib.dump(self.dbscan_fit, os.path.join(self.config['save_directory'], self.dbscan_save_name))
+
+    def load(self):
+        self.pca_fit = joblib.load(os.path.join(self.config['save_directory'], self.pca_save_name))
+        self.kmeans_fit = joblib.load(os.path.join(self.config['save_directory'], self.kmeans_save_name))
+        self.dbscan_fit = joblib.load(os.path.join(self.config['save_directory'], self.dbscan_save_name))
+

iss/clustering/ClassicalClustering.py

@@ -6,8 +6,10 @@ from iss.clustering import AbstractClustering
 from sklearn.decomposition import PCA
 from sklearn.cluster import KMeans
 from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics import silhouette_samples
 from iss.tools import Tools
 from sklearn.externals import joblib
+from sklearn.manifold import TSNE
 
 class ClassicalClustering(AbstractClustering):
 
@@ -31,8 +33,13 @@ class ClassicalClustering(AbstractClustering):
 		self.cah_args = self.config['CAH']
 		self.cah_labels = None
 		self.cah_save_name = "cah_model_v%s.pkl" % (self.config['version'])
+		
+		self.tsne_fit = None
+		self.tsne_args = self.config['TSNE']
+		self.tsne_embedding = None
 
 		self.final_labels = None
+		self.silhouette_score_labels = {}
 
 
 	def compute_pca(self):
@@ -62,11 +69,23 @@ class ClassicalClustering(AbstractClustering):
 		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]
+	def compute_final_labels(self):
+		self.final_labels = np.array([self.cah_labels[old_cl] for old_cl in self.kmeans_labels])
+
+	def compute_tsne(self):
+		self.tsne_fit = TSNE(**self.tsne_args)
+		self.tsne_embedding = self.tsne_fit.fit_transform(self.pca_reduction)
+		return self
+		
+	def get_results(self):
+		return list(zip(self.pictures_id, self.final_labels, self.kmeans_labels, self.pictures_np))
+
+	def compute_silhouette_score(self):
+		self.silhouette_score = silhouette_samples(self.pictures_np, self.final_labels)
+		self.silhouette_score_labels = {cluster: np.mean(self.silhouette_score[self.final_labels == cluster]) for 
+		cluster in np.unique(self.final_labels)}
+		return self.silhouette_score_labels
 
-	def get_zip_results(self):
-		return zip(self.pictures_id, self.final_labels, self.kmeans_labels, self.pictures_np)
 
 	def save(self):
 		Tools.create_dir_if_not_exists(self.config['save_directory'])

iss/clustering/N2DClustering.py

@@ -0,0 +1,52 @@
+# -*- coding: utf-8 -*-
+
+import os
+import numpy as np
+import umap
+from iss.tools import Tools
+from iss.clustering import AbstractClustering
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_samples
+from sklearn.externals import joblib
+
+class N2DClustering(AbstractClustering):
+    """
+    Cf: https://github.com/rymc/n2d
+    """
+
+    def __init__(self, config, pictures_id = None, pictures_np = None):
+
+        super().__init__(config, pictures_id, pictures_np)
+        
+        self.umap_args = self.config['umap']
+        self.umap_fit = None
+        self.umap_embedding = None
+
+        self.kmeans_fit = None
+        self.kmeans_args = self.config['kmeans']
+        self.kmeans_labels = None
+        self.kmeans_centers = []
+        self.kmeans_save_name = "kmeans_model_v%s.pkl" % (self.config['version'])
+
+        
+    def compute_umap(self):
+        self.umap_fit = umap.UMAP(**self.umap_args)
+        self.umap_embedding = self.umap_fit.fit_transform(self.pictures_np)
+        return self
+
+    def compute_kmeans(self):
+        self.kmeans_fit = KMeans(**self.kmeans_args)
+        self.kmeans_fit.fit(self.umap_embedding)
+        self.kmeans_labels = self.kmeans_fit.labels_
+        return self
+
+    def compute_final_labels(self):
+        self.final_labels  = self.kmeans_labels
+        return self
+
+    def compute_silhouette_score(self):
+        self.silhouette_score = silhouette_samples(self.pictures_np, self.final_labels)
+        self.silhouette_score_labels = {cluster: np.mean(self.silhouette_score[self.final_labels == cluster]) for 
+        cluster in np.unique(self.final_labels)}
+        return self.silhouette_score_labels
+

iss/clustering_debug.py

@@ -0,0 +1,22 @@
+# -*- coding: utf-8 -*-
+
+from iss.tools import Config
+from iss.tools import Tools
+from iss.models import SimpleConvAutoEncoder
+from iss.clustering import ClassicalClustering
+from dotenv import find_dotenv, load_dotenv
+
+## Config
+load_dotenv(find_dotenv())
+cfg = Config(project_dir = os.getenv("PROJECT_DIR"), mode = os.getenv("MODE"))
+
+## charger le modèle
+model_type = 'simple_conv'
+cfg.get('models')[model_type]['model_name'] = 'model_colab'
+model = SimpleConvAutoEncoder(cfg.get('models')[model_type])
+
+## Générateur d'image
+filenames = Tools.list_directory_filenames('data/processed/models/autoencoder/train/k/')
+generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (27, 48), batch = 496, nb_batch = 2)
+
+## Générer des images