mirror of
https://github.com/prise6/smart-iss-posts
synced 2024-05-05 15:13:09 +02:00
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
# -*- 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.metrics import silhouette_samples
|
|
from iss.tools import Tools
|
|
from sklearn.externals import joblib
|
|
from sklearn.manifold import TSNE
|
|
|
|
class ClassicalClustering(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.pkl"
|
|
|
|
self.kmeans_fit = None
|
|
self.kmeans_args = self.config['kmeans']
|
|
self.kmeans_labels = None
|
|
self.kmeans_centers = []
|
|
self.kmeans_save_name = "kmeans_model.pkl"
|
|
|
|
|
|
self.cah_fit = None
|
|
self.cah_args = self.config['CAH']
|
|
self.cah_labels = None
|
|
self.cah_save_name = "cah_model.pkl"
|
|
|
|
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):
|
|
|
|
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 compute_kmeans(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.pca_reduction[np.where(self.kmeans_labels == cl)]
|
|
self.kmeans_centers.append(np.mean(tmp, axis = 0))
|
|
return self
|
|
|
|
def compute_cah(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_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 save(self):
|
|
Tools.create_dir_if_not_exists(self.save_directory)
|
|
|
|
joblib.dump(self.pca_fit, os.path.join(self.save_directory, self.pca_save_name))
|
|
joblib.dump(self.kmeans_fit, os.path.join(self.save_directory, self.kmeans_save_name))
|
|
joblib.dump(self.cah_fit, os.path.join(self.save_directory, self.cah_save_name))
|
|
|
|
def load(self):
|
|
self.pca_fit = joblib.load(os.path.join(self.save_directory, self.pca_save_name))
|
|
self.kmeans_fit = joblib.load(os.path.join(self.save_directory, self.kmeans_save_name))
|
|
self.cah_fit = joblib.load(os.path.join(self.save_directory, self.cah_save_name))
|
|
|
|
|