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prepare clustering industrialisation
This commit is contained in:
Francois Vieille
parent
8592ee01ab
commit
45dbfd8db7
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@ -1,4 +1,5 @@
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# -*- coding: utf-8 -*-
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import os
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from iss.tools import Tools
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class AbstractClustering:
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@ -6,13 +7,13 @@ class AbstractClustering:
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def __init__(self, config, pictures_id, pictures_np):
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self.config = config
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self.save_directory = os.path.join(self.config['save_directory'], '%s_%s_%s' % (self.config['model']['type'], self.config['model']['name'], self.config['version']))
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self.pictures_id = pictures_id
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self.pictures_np = pictures_np
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self.final_labels = None
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self.colors = None
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if self.config['save_directory']:
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Tools.create_dir_if_not_exists(self.config['save_directory'])
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Tools.create_dir_if_not_exists(self.save_directory)
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def compute_final_labels(self):
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raise NotImplementedError
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@ -20,19 +20,19 @@ class ClassicalClustering(AbstractClustering):
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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.pca_save_name = "PCA_model.pkl"
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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.kmeans_save_name = "kmeans_model.pkl"
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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.cah_save_name = "cah_model.pkl"
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self.tsne_fit = None
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self.tsne_args = self.config['TSNE']
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@ -88,15 +88,15 @@ class ClassicalClustering(AbstractClustering):
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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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Tools.create_dir_if_not_exists(self.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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joblib.dump(self.pca_fit, os.path.join(self.save_directory, self.pca_save_name))
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joblib.dump(self.kmeans_fit, os.path.join(self.save_directory, self.kmeans_save_name))
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joblib.dump(self.cah_fit, os.path.join(self.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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self.pca_fit = joblib.load(os.path.join(self.save_directory, self.pca_save_name))
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self.kmeans_fit = joblib.load(os.path.join(self.save_directory, self.kmeans_save_name))
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self.cah_fit = joblib.load(os.path.join(self.save_directory, self.cah_save_name))
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@ -21,12 +21,13 @@ class N2DClustering(AbstractClustering):
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self.umap_args = self.config['umap']
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self.umap_fit = None
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self.umap_embedding = None
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self.umap_save_name = 'UMAP_model.pkl'
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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.kmeans_save_name = "kmeans_model.pkl"
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def compute_umap(self):
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@ -50,3 +51,12 @@ class N2DClustering(AbstractClustering):
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cluster in np.unique(self.final_labels)}
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return self.silhouette_score_labels
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def save(self):
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Tools.create_dir_if_not_exists(self.save_directory)
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joblib.dump(self.umap_fit, os.path.join(self.save_directory, self.umap_save_name))
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joblib.dump(self.kmeans_fit, os.path.join(self.save_directory, self.kmeans_save_name))
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def load(self):
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self.umap_fit = joblib.load(os.path.join(self.save_directory, self.pca_save_name))
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self.kmeans_fit = joblib.load(os.path.join(self.save_directory, self.kmeans_save_name))
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@ -10,177 +10,240 @@ from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper
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from iss.init_config import CONFIG
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from iss.tools import Tools
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from iss.models import SimpleConvAutoEncoder
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from iss.models import SimpleConvAutoEncoder, SimpleAutoEncoder
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from iss.clustering import ClassicalClustering, AdvancedClustering, N2DClustering
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## variable globales
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_MODEL_TYPE = 'simple_conv'
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_MODEL_NAME = 'model_colab'
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_BATCH_SIZE = 496
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_N_BATCH = 10
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_DEBUG = True
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_CLUSTERING_TYPE = 'n2d'
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_OUTPUT_IMAGE_WIDTH = 96
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_OUTPUT_IMAGE_HEIGHT = 54
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_MOSAIC_NROW = 10
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_MOSAIC_NCOL_MAX = 10
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## Charger le modèle
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CONFIG.get('models')[_MODEL_TYPE]['model_name'] = _MODEL_NAME
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model = SimpleConvAutoEncoder(CONFIG.get('models')[_MODEL_TYPE])
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model_config = CONFIG.get('models')[_MODEL_TYPE]
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def load_model(config, clustering_type):
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"""
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Load model according to config
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"""
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## Charger les images
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filenames = Tools.list_directory_filenames(os.path.join(CONFIG.get('directory')['autoencoder']['train']))
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (model_config['input_height'], model_config['input_width']), batch = _BATCH_SIZE, nb_batch = _N_BATCH)
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model_type = config.get('clustering')[clustering_type]['model']['type']
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model_name = config.get('clustering')[clustering_type]['model']['name']
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config.get('models')[model_type]['model_name'] = model_name
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pictures_id, pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model)
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intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], model_config['latent_width']*model_config['latent_height']*model_config['latent_channel']))
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if model_type == 'simple_conv':
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model = SimpleConvAutoEncoder(config.get('models')[model_type])
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elif model_type == 'simple':
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model = SimpleAutoEncoder(config.get('models')[model_type])
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else:
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raise Exception
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model_config = config.get('models')[model_type]
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return model, model_config
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if _DEBUG:
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for i, p_id in enumerate(pictures_id[:2]):
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print("%s: %s" % (p_id, pictures_preds[i]))
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print(len(pictures_id))
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print(len(intermediate_output))
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def load_images(config, clustering_type, model, model_config, batch_size, n_batch):
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"""
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load images and predictions
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"""
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model_type = config.get('clustering')[clustering_type]['model']['type']
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filenames = Tools.list_directory_filenames(os.path.join(config.get('sampling')['autoencoder']['directory']['train']))
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (model_config['input_height'], model_config['input_width']), batch = batch_size, nb_batch = n_batch)
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pictures_id, pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model)
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if model_type in ['simple_conv']:
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intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], model_config['latent_width']*model_config['latent_height']*model_config['latent_channel']))
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else:
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intermediate_output = pictures_preds
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return pictures_id, intermediate_output
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## Clustering
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if _CLUSTERING_TYPE == 'classical':
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if _DEBUG:
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print("Classical Clustering")
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clustering = ClassicalClustering(CONFIG.get('clustering')['classical'], pictures_id, intermediate_output)
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clustering.compute_pca()
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clustering.compute_kmeans()
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clustering.compute_kmeans_centers()
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clustering.compute_cah()
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clustering.compute_final_labels()
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clustering.compute_tsne()
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clustering.compute_colors()
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elif _CLUSTERING_TYPE == 'advanced':
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if _DEBUG:
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print("Advanced Clustering")
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clustering = AdvancedClustering(CONFIG.get('clustering')['classical'], pictures_id, intermediate_output)
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elif _CLUSTERING_TYPE == 'n2d':
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if _DEBUG:
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print("Not2Deep Clustering")
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clustering = N2DClustering(CONFIG.get('clustering')['n2d'], pictures_id, intermediate_output)
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clustering.compute_umap()
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clustering.compute_kmeans()
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clustering.compute_final_labels()
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clustering.compute_colors()
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def run_clustering(config, clustering_type, pictures_id, intermediate_output):
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"""
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Apply clustering on images
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"""
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silhouettes = clustering.compute_silhouette_score()
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clustering_res = clustering.get_results()
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if clustering_type == 'classical':
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if _DEBUG:
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print("Classical Clustering")
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clustering = ClassicalClustering(config.get('clustering')['classical'], pictures_id, intermediate_output)
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clustering.compute_pca()
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clustering.compute_kmeans()
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clustering.compute_kmeans_centers()
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clustering.compute_cah()
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clustering.compute_final_labels()
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clustering.compute_tsne()
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clustering.compute_colors()
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elif clustering_type == 'advanced':
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if _DEBUG:
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print("Advanced Clustering")
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clustering = AdvancedClustering(config.get('clustering')['classical'], pictures_id, intermediate_output)
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elif clustering_type == 'n2d':
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if _DEBUG:
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print("Not2Deep Clustering")
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clustering = N2DClustering(config.get('clustering')['n2d'], pictures_id, intermediate_output)
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clustering.compute_umap()
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clustering.compute_kmeans()
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clustering.compute_final_labels()
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clustering.compute_colors()
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if _DEBUG:
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print(clustering_res[:2])
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print(silhouettes)
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return clustering
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if _CLUSTERING_TYPE in ['classical']:
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## Graphs of PCA and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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scatter = ax.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'pca_clusters.png'))
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def run_plots(config, clustering_type, clustering):
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"""
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Plots specifics graphs
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"""
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if _CLUSTERING_TYPE in ['classical']:
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## Graphs of TSNE and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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classes = clustering.final_labels
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scatter = ax.scatter(clustering.tsne_embedding[:, 0], clustering.tsne_embedding[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'tsne_clusters.png'))
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if clustering_type in ['classical']:
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## Graphs of PCA and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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scatter = ax.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(clustering.save_directory, 'pca_clusters.png'))
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if _CLUSTERING_TYPE in ['n2d']:
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## Graphs of TSNE and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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classes = clustering.final_labels
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scatter = ax.scatter(clustering.umap_embedding[:, 0], clustering.umap_embedding[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'umap_clusters.png'))
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if clustering_type in ['classical']:
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## Graphs of TSNE and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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classes = clustering.final_labels
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scatter = ax.scatter(clustering.tsne_embedding[:, 0], clustering.tsne_embedding[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(clustering.save_directory, 'tsne_clusters.png'))
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if _CLUSTERING_TYPE in ['n2d']:
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filenames = [os.path.join(CONFIG.get('directory')['collections'], "%s.jpg" % one_res[0]) for one_res in clustering_res]
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images_array = [Tools.read_np_picture(img_filename, target_size = (54, 96)) for img_filename in filenames]
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base64_images = [Tools.base64_image(img) for img in images_array]
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if clustering_type in ['n2d']:
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## Graphs of TSNE and final clusters
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fig, ax = plt.subplots(figsize=(24, 14))
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classes = clustering.final_labels
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scatter = ax.scatter(clustering.umap_embedding[:, 0], clustering.umap_embedding[:, 1], c = clustering.colors)
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legend1 = ax.legend(*scatter.legend_elements(), loc="lower left", title="Classes")
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ax.add_artist(legend1)
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plt.savefig(os.path.join(clustering.save_directory, 'umap_clusters.png'))
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print(clustering.umap_embedding)
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print(clustering.umap_embedding.shape)
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if clustering_type in ['n2d', 'classical']:
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filenames = [os.path.join(config.get('directory')['collections'], "%s.jpg" % one_res[0]) for one_res in clustering.get_results()]
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images_array = [Tools.read_np_picture(img_filename, target_size = (54, 96)) for img_filename in filenames]
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base64_images = [Tools.base64_image(img) for img in images_array]
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x = clustering.umap_embedding[:, 0]
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y = clustering.umap_embedding[:, 1]
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if clustering_type == 'n2d':
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x = clustering.umap_embedding[:, 0]
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y = clustering.umap_embedding[:, 1]
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html_file = 'umap_bokeh.html'
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title = 'UMAP projection of iss clusters'
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elif clustering_type == 'classical':
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x = clustering.tsne_embedding[:, 0]
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y = clustering.tsne_embedding[:, 1]
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html_file = 'tsne_bokeh.html'
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title = 't-SNE projection of iss clusters'
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df = pd.DataFrame({'x': x, 'y': y})
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df['image'] = base64_images
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df['label'] = clustering.final_labels.astype(str)
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df['color'] = df['label'].apply(Tools.get_color_from_label)
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df = pd.DataFrame({'x': x, 'y': y})
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df['image'] = base64_images
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df['label'] = clustering.final_labels.astype(str)
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df['color'] = df['label'].apply(Tools.get_color_from_label)
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datasource = ColumnDataSource(df)
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datasource = ColumnDataSource(df)
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output_file(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'umap_bokeh.html'))
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output_file(os.path.join(clustering.save_directory, html_file))
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plot_figure = figure(
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title='UMAP projection of iss clusters',
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# plot_width=1200,
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# plot_height=1200,
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tools=('pan, wheel_zoom, reset')
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)
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plot_figure = figure(
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title=title,
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# plot_width=1200,
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# plot_height=1200,
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tools=('pan, wheel_zoom, reset')
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)
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plot_figure.add_tools(HoverTool(tooltips="""
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<div>
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plot_figure.add_tools(HoverTool(tooltips="""
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<div>
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<img src='@image' style='float: left; margin: 5px 5px 5px 5px'/>
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<div>
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<img src='@image' style='float: left; margin: 5px 5px 5px 5px'/>
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</div>
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<div>
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<span style='font-size: 16px'>Cluster:</span>
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<span style='font-size: 18px'>@label</span>
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</div>
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</div>
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<div>
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<span style='font-size: 16px'>Cluster:</span>
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<span style='font-size: 18px'>@label</span>
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</div>
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</div>
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"""))
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"""))
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plot_figure.circle(
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'x',
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'y',
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source=datasource,
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color=dict(field='color'),
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line_alpha=0.6,
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fill_alpha=0.6,
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size=4
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)
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plot_figure.circle(
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'x',
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'y',
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source=datasource,
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color=dict(field='color'),
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line_alpha=0.6,
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fill_alpha=0.6,
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size=4
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)
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show(plot_figure)
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show(plot_figure)
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if _CLUSTERING_TYPE in ['classical']:
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## Dendogram
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fig, ax = plt.subplots(figsize=(24, 14))
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plt.title('Hierarchical Clustering Dendrogram')
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||||
Tools.plot_dendrogram(clustering.cah_fit, labels=clustering.cah_labels)
|
||||
plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'dendograms.png'))
|
||||
if clustering_type in ['classical']:
|
||||
## Dendogram
|
||||
fig, ax = plt.subplots(figsize=(24, 14))
|
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plt.title('Hierarchical Clustering Dendrogram')
|
||||
Tools.plot_dendrogram(clustering.cah_fit, labels=clustering.cah_labels)
|
||||
plt.savefig(os.path.join(clustering.save_directory, 'dendograms.png'))
|
||||
|
||||
return True
|
||||
|
||||
def plot_silhouette(config, clustering_type, clustering):
|
||||
|
||||
silhouettes = clustering.compute_silhouette_score()
|
||||
|
||||
fig, ax = plt.subplots(figsize=(12, 7))
|
||||
ax.bar(silhouettes.keys(), silhouettes.values(), align='center')
|
||||
ax.set_xticks(list(silhouettes.keys()))
|
||||
ax.set_xticklabels(list(silhouettes.keys()))
|
||||
plt.savefig(os.path.join(clustering.save_directory, 'silhouettes_score.png'))
|
||||
|
||||
return silhouettes
|
||||
|
||||
|
||||
## Silhouette
|
||||
fig, ax = plt.subplots(figsize=(12, 7))
|
||||
ax.bar(silhouettes.keys(), silhouettes.values(), align='center')
|
||||
ax.set_xticks(list(silhouettes.keys()))
|
||||
ax.set_xticklabels(list(silhouettes.keys()))
|
||||
plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'silhouettes_score.png'))
|
||||
def plot_mosaics(config, clustering_type, clustering, output_image_width, output_image_height, mosaic_nrow, mosaic_ncol_max):
|
||||
"""
|
||||
Mosaic of each cluster
|
||||
"""
|
||||
clusters_id = np.unique(clustering.final_labels)
|
||||
clustering_res = clustering.get_results()
|
||||
|
||||
for cluster_id in clusters_id:
|
||||
cluster_image_filenames = [os.path.join(config.get('directory')['collections'], "%s.jpg" % one_res[0]) for one_res in clustering_res if one_res[1] == cluster_id]
|
||||
|
||||
images_array = [Tools.read_np_picture(img_filename, target_size = (output_image_height, output_image_width)) for img_filename in cluster_image_filenames]
|
||||
|
||||
img = Tools.display_mosaic(images_array, nrow = mosaic_nrow, ncol_max = mosaic_ncol_max)
|
||||
img.save(os.path.join(clustering.save_directory, "cluster_%s.png" % str(cluster_id).zfill(2)), "PNG")
|
||||
|
||||
return clusters_id
|
||||
|
||||
|
||||
## Mosaic of each cluster
|
||||
clusters_id = np.unique(clustering.final_labels)
|
||||
for cluster_id in clusters_id:
|
||||
cluster_image_filenames = [os.path.join(CONFIG.get('directory')['collections'], "%s.jpg" % one_res[0]) for one_res in clustering_res if one_res[1] == cluster_id]
|
||||
def main():
|
||||
_CLUSTERING_TYPE = 'classical'
|
||||
_BATCH_SIZE = 496
|
||||
_N_BATCH = 1
|
||||
_PLOTS = True
|
||||
_MOSAICS = True
|
||||
_SILHOUETTE = True
|
||||
_OUTPUT_IMAGE_WIDTH = 96
|
||||
_OUTPUT_IMAGE_HEIGHT = 54
|
||||
_MOSAIC_NROW = 10
|
||||
_MOSAIC_NCOL_MAX = 10
|
||||
|
||||
images_array = [Tools.read_np_picture(img_filename, target_size = (_OUTPUT_IMAGE_HEIGHT, _OUTPUT_IMAGE_WIDTH)) for img_filename in cluster_image_filenames]
|
||||
model, model_config = load_model(CONFIG, _CLUSTERING_TYPE)
|
||||
pictures_id, intermediate_output = load_images(CONFIG, _CLUSTERING_TYPE, model, model_config, _BATCH_SIZE, _N_BATCH)
|
||||
|
||||
clustering = run_clustering(CONFIG, _CLUSTERING_TYPE, pictures_id, intermediate_output)
|
||||
|
||||
clustering.save()
|
||||
|
||||
img = Tools.display_mosaic(images_array, nrow = _MOSAIC_NROW, ncol_max = _MOSAIC_NCOL_MAX)
|
||||
img.save(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], "cluster_%s.png" % str(cluster_id).zfill(2)), "PNG")
|
||||
if _PLOTS:
|
||||
run_plots(CONFIG, _CLUSTERING_TYPE, clustering)
|
||||
|
||||
if _SILHOUETTE:
|
||||
plot_silhouette(CONFIG, _CLUSTERING_TYPE, clustering)
|
||||
|
||||
if _MOSAICS:
|
||||
plot_mosaics(CONFIG, _CLUSTERING_TYPE, clustering, _OUTPUT_IMAGE_WIDTH, _OUTPUT_IMAGE_HEIGHT, _MOSAIC_NROW, _MOSAIC_NCOL_MAX)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
|
|||
Loading…
Reference in a new issue