execution clustering
This commit is contained in:
Francois Vieille
parent
e7f6206a40
commit
8592ee01ab
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from .AbstractClustering import AbstractClustering
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from .ClassicalClustering import ClassicalClustering
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from .ClassicalClustering import ClassicalClustering
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from .AdvancedClustering import AdvancedClustering
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from .N2DClustering import N2DClustering
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import os
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import pandas as pd
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from bokeh.plotting import figure, output_file, show
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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.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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## 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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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 _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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## 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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silhouettes = clustering.compute_silhouette_score()
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clustering_res = clustering.get_results()
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if _DEBUG:
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print(clustering_res[:2])
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print(silhouettes)
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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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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 ['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 ['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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print(clustering.umap_embedding)
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print(clustering.umap_embedding.shape)
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x = clustering.umap_embedding[:, 0]
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y = clustering.umap_embedding[:, 1]
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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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output_file(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'umap_bokeh.html'))
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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.add_tools(HoverTool(tooltips="""
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<div>
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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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"""))
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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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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)
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plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'dendograms.png'))
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## Silhouette
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fig, ax = plt.subplots(figsize=(12, 7))
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ax.bar(silhouettes.keys(), silhouettes.values(), align='center')
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ax.set_xticks(list(silhouettes.keys()))
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ax.set_xticklabels(list(silhouettes.keys()))
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plt.savefig(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], 'silhouettes_score.png'))
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## Mosaic of each cluster
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clusters_id = np.unique(clustering.final_labels)
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for cluster_id in clusters_id:
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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]
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images_array = [Tools.read_np_picture(img_filename, target_size = (_OUTPUT_IMAGE_HEIGHT, _OUTPUT_IMAGE_WIDTH)) for img_filename in cluster_image_filenames]
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img = Tools.display_mosaic(images_array, nrow = _MOSAIC_NROW, ncol_max = _MOSAIC_NCOL_MAX)
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img.save(os.path.join(CONFIG.get('clustering')[_CLUSTERING_TYPE]['save_directory'], "cluster_%s.png" % str(cluster_id).zfill(2)), "PNG")
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@ -3,7 +3,11 @@
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import PIL
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import os
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import numpy as np
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from io import BytesIO
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import base64
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from scipy.cluster.hierarchy import dendrogram
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from keras_preprocessing.image.utils import load_img
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import matplotlib as plt
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class Tools:
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@ -26,6 +30,26 @@ class Tools:
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def display_index_picture(array, index = 0):
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return Tools.display_one_picture(array[index])
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@staticmethod
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def display_mosaic(array, nrow = 5, ncol_max = 10):
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tmp = []
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i = 0
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image_col = []
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while i < len(array):
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tmp.append(array[i])
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if len(tmp) % nrow == 0 and i > 0:
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image_col.append(np.concatenate(tuple(tmp)))
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tmp = []
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if len(image_col) == ncol_max:
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break
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i += 1
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if not image_col:
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image_col.append(np.concatenate(tuple(tmp)))
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image = np.concatenate(tuple(image_col), axis = 1)
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return Tools.display_one_picture(image)
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@staticmethod
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def create_dir_if_not_exists(path):
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if not os.path.exists(path):
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@ -35,10 +59,16 @@ class Tools:
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@staticmethod
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def encoded_pictures_from_generator(generator, model):
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predictions_tuple = tuple([model.get_encoded_prediction(imgs) for imgs in generator])
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predictions = np.concatenate(predictions_tuple, axis = 0)
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predictions_list = []
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predictions_id = []
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for imgs in generator:
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predictions_id.append(imgs[0])
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predictions_list.append(model.get_encoded_prediction(imgs[1]))
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predictions = np.concatenate(tuple(predictions_list), axis = 0)
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predictions_id = [os.path.splitext(os.path.basename(id))[0] for sub_id in predictions_id for id in sub_id]
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return predictions
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return predictions_id, predictions
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@staticmethod
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def read_np_picture(path, target_size = None, scale = 1):
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@ -51,7 +81,9 @@ class Tools:
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@staticmethod
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def list_directory_filenames(path):
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filenames = os.listdir(path)
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filenames = [path + f for f in filenames]
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np.random.seed(33213)
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np.random.shuffle(filenames)
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filenames = [os.path.join(path,f) for f in filenames]
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return filenames
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@ -65,15 +97,45 @@ class Tools:
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nb_batch = div[0] + 1 * (div[1] != 0)
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for i in range(nb_batch):
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# for i in [75, 76]:
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i_debut = i*batch
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i_fin = min(i_debut + batch, max_n)
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print("i_debut:" + str(i_debut))
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print("i_fin:" + str(i_fin))
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yield np.array([Tools.read_np_picture(f, target_size, scale) for f in filenames[i_debut:i_fin]])
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yield (filenames[i_debut:i_fin], np.array([Tools.read_np_picture(f, target_size, scale) for f in filenames[i_debut:i_fin]]))
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@staticmethod
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def bytes_image(array):
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image = Tools.display_one_picture(array)
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buffer = BytesIO()
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image.save(buffer, format='png')
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im_bytes = buffer.getvalue()
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return im_bytes
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@staticmethod
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def base64_image(array):
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for_encoding = Tools.bytes_image(array)
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return 'data:image/png;base64,' + base64.b64encode(for_encoding).decode()
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@staticmethod
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def get_color_from_label(label, n_labels = 50, palette = 'viridis'):
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cmap = plt.cm.get_cmap(palette, n_labels)
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return plt.colors.to_hex(cmap(int(label)))
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@staticmethod
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def plot_dendrogram(model, **kwargs):
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# Children of hierarchical clustering
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children = model.children_
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# Distances between each pair of children
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# Since we don't have this information, we can use a uniform one for plotting
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distance = np.arange(children.shape[0])
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# The number of observations contained in each cluster level
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no_of_observations = np.arange(2, children.shape[0]+2)
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# Create linkage matrix and then plot the dendrogram
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linkage_matrix = np.column_stack([children, distance, no_of_observations]).astype(float)
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# Plot the corresponding dendrogram
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dendrogram(linkage_matrix, **kwargs)
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