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image_clustering_withoutGridpy.py
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image_clustering_withoutGridpy.py
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#importing the header files
import os
import numpy as np
import pandas as pd
import IPython.display as ipd
from sklearn.manifold import TSNE
import time
import sklearn
from sklearn.decomposition import PCA
import librosa
from sklearn import mixture
from numpy import unique
from numpy import where
from matplotlib import pyplot as plt
import librosa.display
from sklearn import metrics
from sklearn.preprocessing import MinMaxScaler,StandardScaler,LabelEncoder
from sklearn.model_selection import GridSearchCV
from kneed import DataGenerator, KneeLocator
import os, shutil, glob, os.path
from PIL import Image as pil_image
from matplotlib import pyplot as plt
import hdbscan
from scipy.spatial import distance
from scipy.cluster import hierarchy
#import tensorflow as tf
import warnings
warnings.filterwarnings('ignore')
from numpy import load,save
def load_filepaths():
imdir_ideology = 'ideology_image_dataset/'
imdir_muslim='muslim_image_dataset/'
ideology_files=os.listdir('ideology_image_dataset/')
muslim_files=os.listdir('muslim_image_dataset/')
len(ideology_files),len(muslim_files)
ideology_files_path=[os.path.join(imdir_ideology,file) for file in ideology_files ]
muslim_files_path=[os.path.join(imdir_muslim,file) for file in muslim_files]
return ideology_files_path,muslim_files_path
def loadFeatures(filename):
print("Loading file : ",filename)
features= load(filename)
return features
def evaluation_Score(features,y_pred,output_df,model):
try:
num_labels=len(set(y_pred))
total_samples=len(y_pred)
if(num_labels==1 or num_labels==total_samples):
output_df.loc[model,'silhouette'] =-1
output_df.loc[model,'calinski'] =-1
output_df.loc[model,'davies'] =-1
else:
output_df.loc[model,'silhouette'] =metrics.silhouette_score(features,y_pred)
output_df.loc[model,'calinski'] =metrics.calinski_harabasz_score(features, y_pred)
output_df.loc[model,'davies'] =metrics.davies_bouldin_score(features,y_pred)
except Exception as e:
print(e)
pass
return output_df
def runModels(train_data,output_df,filename):
print("Agglomerative2-scipy clustering results")
sim=0.5
timestamps=None
alpha=0
method='average'
metric='euclidean'
extra_out=False
print_stats=True
min_csize=2
dfps = distance.pdist(np.array(list(train_data)), metric)
Z = hierarchy.linkage(dfps, method=method, metric=metric)
predicted_labels = hierarchy.fcluster(Z, t=dfps.max()*(1.0-sim), criterion='distance')
if(set(predicted_labels).issuperset({-1})):
n_clustersLen=len(set(predicted_labels))-1
else:
n_clustersLen=len(set(predicted_labels))
evaluation_Score(train_data,predicted_labels,output_df,'Agglomerative clustering-scipy')
output_df.loc['Agglomerative clustering-scipy','n_clusters']=n_clustersLen
saved_filename=os.path.join('image-results2',filename)
np.save(saved_filename+'_agg-scipy_labels.npy', np.array(predicted_labels))
print(output_df)
print("HDBSCAN")
clusterer = hdbscan.HDBSCAN(min_cluster_size=20,cluster_selection_epsilon= 0.01,min_samples= 1)
predicted_labels = clusterer.fit_predict(features)
if(set(predicted_labels).issuperset({-1})):
n_clustersLen=len(set(predicted_labels))-1
else:
n_clustersLen=len(set(predicted_labels))
evaluation_Score(train_data,predicted_labels,output_df,'HDBSCAN')
output_df.loc['HDBSCAN','n_clusters']=n_clustersLen
saved_filename=os.path.join('image-results2',filename)
np.save(saved_filename+'_hdbscan_labels.npy', np.array(predicted_labels))
print(output_df)
print("Agglomerative clustering results")
# params_dict={'linkage':['ward','complete','average','single'],'distance_threshold':[500,1000,2000],'n_clusters':[None]}
# predicted_labels=runGridSearch(sklearn.cluster.AgglomerativeClustering(),params_dict,train_data)
predicted_labels=sklearn.cluster.AgglomerativeClustering().fit(train_data).labels_
evaluation_Score(train_data,predicted_labels,output_df,'Agglomerative clustering')
if(set(predicted_labels).issuperset({-1})):
n_clustersLen=len(set(predicted_labels))-1
else:
n_clustersLen=len(set(predicted_labels))
output_df.loc['Agglomerative clustering','n_clusters']=n_clustersLen
saved_filename=os.path.join('image-results2',filename)
np.save(saved_filename+'_agg_labels.npy', np.array(predicted_labels))
#print(output_df)
print("DBSCAN")
#epsilons=getEpsilon(train_data)
#params_dict = {'eps':epsilons,'min_samples':[20,30,40],'metric':['euclidean','manhattan','mahalanobis', 'minkowski']}
predicted_labels=sklearn.cluster.DBSCAN().fit(train_data).labels_
evaluation_Score(train_data,predicted_labels,output_df,'DBSCAN')
if(set(predicted_labels).issuperset({-1})):
n_clustersLen=len(set(predicted_labels))-1
else:
n_clustersLen=len(set(predicted_labels))
output_df.loc['DBSCAN','n_clusters']=n_clustersLen
print(output_df)
saved_filename=os.path.join('image-results2',filename)
np.save(saved_filename+'_dbscan_labels.npy', np.array(predicted_labels))
print("Mean shift")
#quantiles=[0.2,0.5,0.8,1]
#params_dict={}
#params_dict['bandwidth']=[]
#for quantile in quantiles:
# params_dict['bandwidth'].append(sklearn.cluster.estimate_bandwidth(train_data, quantile=quantile, n_samples=500))
predicted_labels=sklearn.cluster.MeanShift().fit(train_data).labels_
evaluation_Score(train_data,predicted_labels,output_df,'Mean-shift')
if(set(predicted_labels).issuperset({-1})):
n_clustersLen=len(set(predicted_labels))-1
else:
n_clustersLen=len(set(predicted_labels))
output_df.loc['Mean-shift','n_clusters']=n_clustersLen
print(output_df)
saved_filename=os.path.join('image-results2',filename)
np.save(saved_filename+'_mean_shift_labels.npy', np.array(predicted_labels))
print("Optics")
# epsilons=getEpsilon(train_data)
# params_dict = {'eps':epsilons,'min_samples':[20,30,40],'metric':['euclidean','manhattan','mahalanobis', 'minkowski']}
predicted_labels=sklearn.cluster.OPTICS().fit(train_data).labels_
if(set(predicted_labels).issuperset({-1})):
n_clustersLen=len(set(predicted_labels))-1
else:
n_clustersLen=len(set(predicted_labels))
evaluation_Score(train_data,predicted_labels,output_df,'Optics')
output_df.loc['Optics','n_clusters']=n_clustersLen
print(output_df)
saved_filename=os.path.join('image-results2',filename)
np.save(saved_filename+'_optics_labels.npy', np.array(predicted_labels))
return output_df
def runClustering(train_data,filename,dimensionality=None):
output_df = pd.DataFrame(index=['Agglomerative clustering','DBSCAN','Mean-shift','Optics'],columns=['n_clusters','silhouette','calinski','davies'])
if(dimensionality==None):
output_df=runModels(train_data,output_df,filename)
elif(dimensionality=='pca'):
#train_data_transform=pca_transform(train_data)
output_df=runModels(train_data_transform,output_df,filename)
elif(dimensionality=='tsne'):
#train_data_transform=tsne_transform(train_data)
output_df=runModels(train_data_transform,output_df,filename)
return output_df
if __name__ == "__main__":
ideology_files_path,muslim_files_path=load_filepaths()
# ideology_features,muslim_features=load_features(ideology_files_path,muslim_files_path,True)
for file in os.listdir('Image_features/'):
if file.endswith('.npy'):
# if 'fcn.npy' in set(file.split('_')):
print(file)
features=loadFeatures(os.path.join('Image_features',file))
output_df=runClustering(features,file)
print("saving the model")
saved_csv_filename=os.path.join('image-results2',file)
output_df.to_csv(saved_csv_filename+'.csv')