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PCA_feature.py
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PCA_feature.py
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import os
import argparse
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import math
from utils_analysis.lib import feature_extraction as fe
plt.rcParams['figure.dpi'] = 300
plt.rcParams.update({'font.size': 15})
# plt.rc('axes', labelsize=15)
# plt.rc('legend', fontsize=15)
# plt.rc('figure', titlesize=15)
def ConcatAllClasses(datapath, nice_feature):
'''Concatenate features of all images (all classes) in a dataset.'''
list_class = os.listdir(datapath)
list.sort(list_class)
df_all_feat = pd.DataFrame()
for iclass in list_class:
class_datapath = datapath + iclass + '/'
df_class_feat = fe.ConcatAllFeatures(class_datapath, nice_feature)
df_class_feat['class'] = iclass
df_all_feat = pd.concat([df_all_feat, df_class_feat], ignore_index=True)
return df_all_feat
def Standardize(dataframe):
'''Standardize an input pandas dataframe.'''
data = dataframe.iloc[:, :-1].values
data = StandardScaler().fit_transform(data)
cols = dataframe.columns[:-1]
# cols = [i + '_standardized' for i in cols]
df_standardized = pd.DataFrame(data, columns=cols)
df_standardized['class'] = dataframe['class']
return df_standardized
def Normalize_minmax(dataframe):
'''Normalize an input pandas dataframe.'''
df = dataframe.drop(columns=['class'])
df_normalized=(df-df.min())/(df.max()-df.min())
df_normalized['class'] = dataframe['class']
return df_normalized
def Normalize_std(dataframe):
'''Normalize an input pandas dataframe.'''
df = dataframe.drop(columns=['class'])
df_normalized = (df-df.mean())/df.std()
df_normalized['class'] = dataframe['class']
return df_normalized
def Center(dataframe):
'''Normalize an input pandas dataframe.'''
df = dataframe.drop(columns=['class'])
df_centered = df-df.mean()
df_centered['class'] = dataframe['class']
return df_centered
def Rescale_log(dataframe):
df = dataframe.drop(columns=['class'])
# df_rescale = (math.e - 1) * np.divide((df - df.min()), (df.max() - df.min())) + 1
df_rescale = (10 - 1) * np.divide((df - df.min()), (df.max() - df.min())) + 1
# data_rescale_log = np.log(df_rescale)
data_rescale_log = np.emath.logn(10, df_rescale)
df_rescale_log = pd.DataFrame(data=data_rescale_log, columns=dataframe.columns[:-1])
# df_rescale_log_center = df_rescale_log - df_rescale_log.mean()
df_rescale_log['class'] = dataframe['class']
return df_rescale_log
def PrincipalComponentAnalysis(dataframe, n_components):
'''Principal component analysis on a dataframe.'''
pca = PCA(n_components=n_components)
pca.fit(dataframe.iloc[:, :-1].values)
return pca
def PCA_train_val_test(dataframe, pca):
'''Implement PCA on in-distribution datasets.'''
principal_components = pca.transform(dataframe.iloc[:, :-1].values)
df_pca_split = pd.DataFrame(data=principal_components, columns=['principal_component_{}'.format(i+1) for i in range(np.shape(principal_components)[1])])
df_pca_split['class'] = dataframe['class']
return df_pca_split
def PCA_OOD(dataframe_OOD, pca):
'''Implement PCA on out-of-distribution datasets.'''
principal_components = pca.transform(dataframe_OOD.iloc[:, :-1].values)
df_pca_OOD = pd.DataFrame(data=principal_components, columns=['principal_component_{}'.format(i+1) for i in range(np.shape(principal_components)[1])])
df_pca_OOD['class'] = dataframe_OOD['class']
return df_pca_OOD
parser = argparse.ArgumentParser(description='Principal component analysis on datasets')
parser.add_argument('-Zoolake2_datapath', help='path of the Zoolake2 dataset')
parser.add_argument('-in_distribution_datapaths', nargs='*', help='paths of the in-domain datasets, in an order of: train_val_test')
parser.add_argument('-OOD_datapaths', nargs='*', help='paths of the out-of-distribution datasets')
parser.add_argument('-outpath', help='path for saving output csv')
parser.add_argument('-n_components', type=float, help='number of principal components')
parser.add_argument('-nice_feature', choices=['yes', 'no'], help='only use nice features or not')
parser.add_argument('-global_x', choices=['yes', 'no'], default='no', help='PCA on data over all classes or not')
args = parser.parse_args()
if __name__ == '__main__':
if args.n_components >= 1:
args.n_components = int(args.n_components)
if args.global_x == 'no':
df = ConcatAllClasses(args.Zoolake2_datapath, args.nice_feature)
df_train = ConcatAllClasses(args.in_distribution_datapaths[0], args.nice_feature)
df_val = ConcatAllClasses(args.in_distribution_datapaths[1], args.nice_feature)
df_test = ConcatAllClasses(args.in_distribution_datapaths[2], args.nice_feature)
df_pca_train = pd.DataFrame()
df_pca_val = pd.DataFrame()
df_pca_test = pd.DataFrame()
df_OODs = []
df_pca_OODs = []
for i in range(len(args.OOD_datapaths)):
df_OOD = ConcatAllClasses(args.OOD_datapaths[i], args.nice_feature)
df_pca_OOD = pd.DataFrame()
df_OODs.append(df_OOD)
df_pca_OODs.append(df_pca_OOD)
for iclass in np.unique(df['class'].values):
df_class = df[df['class'] == iclass]
df_class_standardized = Standardize(df_class)
pca = PrincipalComponentAnalysis(df_class_standardized, n_components=args.n_components)
outpath_class = args.outpath + 'PCA_class/' + iclass + '/'
Path(outpath_class).mkdir(parents=True, exist_ok=True)
df_components = pd.DataFrame(data=pca.components_, index=['principal_component_{}'.format(i+1) for i in range(len(pca.explained_variance_))], columns=df_class.columns[:-1])
df_components.to_excel(outpath_class + 'PCA_components_feature.xlsx')
Path(outpath_class + 'components/').mkdir(parents=True, exist_ok=True)
for i, ipc in enumerate(df_components.index):
plt.figure(figsize=(15, 10))
plt.ylabel('Component loading')
# plt.bar(x=range(len(df_components.columns)), height=abs(df_components.loc[ipc]))
plt.bar(x=range(len(df_components.columns)), height=df_components.loc[ipc])
plt.xticks(range(len(df_components.columns)), df_components.columns, rotation=45, rotation_mode='anchor', ha='right')
plt.tight_layout()
plt.savefig(outpath_class + 'components/' + 'PCA_components_feature_PC_' + str(i+1) + '.png')
plt.close()
# df_explained_variance = pd.DataFrame(data=pca.explained_variance_, index=['principal_component_{}'.format(i+1) for i in range(len(pca.explained_variance_))])
# df_explained_variance.to_excel(outpath_class + 'PCA_explained_variance_feature.xlsx')
df_explained_variance_ratio = pd.DataFrame(data=pca.explained_variance_ratio_, index=['principal_component_{}'.format(i+1) for i in range(len(pca.explained_variance_))])
df_explained_variance_ratio.to_excel(outpath_class + 'PCA_explained_variance_ratio_feature.xlsx')
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Number of components')
plt.ylabel('Explained variance ratio')
plt.grid()
plt.tight_layout()
plt.savefig(outpath_class + 'PCA_explained_variance_ratio_feature.png')
plt.close()
df_train_class = df_train[df_train['class'] == iclass].reset_index(drop=True)
df_val_class = df_val[df_val['class'] == iclass].reset_index(drop=True)
df_test_class = df_test[df_test['class'] == iclass].reset_index(drop=True)
df_train_class_standardized = Standardize(df_train_class)
df_val_class_standardized = Standardize(df_val_class)
df_test_class_standardized = Standardize(df_test_class)
df_pca_train_class = PCA_train_val_test(df_train_class_standardized, pca)
df_pca_val_class = PCA_train_val_test(df_val_class_standardized, pca)
df_pca_test_class = PCA_train_val_test(df_test_class_standardized, pca)
df_pca_train_class.to_csv(outpath_class + 'PCA_train_feature.csv')
df_pca_val_class.to_csv(outpath_class + 'PCA_val_feature.csv')
df_pca_test_class.to_csv(outpath_class + 'PCA_test_feature.csv')
df_pca_train = pd.concat([df_pca_train, df_pca_train_class], ignore_index=True)
df_pca_val = pd.concat([df_pca_val, df_pca_val_class], ignore_index=True)
df_pca_test = pd.concat([df_pca_test, df_pca_test_class], ignore_index=True)
for i in range(len(args.OOD_datapaths)):
df_OOD = df_OODs[i]
df_OOD_class = df_OOD[df_OOD['class'] == iclass].reset_index(drop=True)
if len(df_OOD_class) == 0:
continue
df_OOD_class_standardized = Standardize(df_OOD_class)
df_pca_OOD_class = PCA_OOD(df_OOD_class_standardized, pca)
df_pca_OOD_class.to_csv(outpath_class + 'PCA_OOD{}_feature.csv'.format(i + 1))
df_pca_OODs[i] = pd.concat([df_pca_OODs[i], df_pca_OOD_class], ignore_index=True)
df_pca_train.to_csv(args.outpath + 'PCA_train_feature.csv')
df_pca_val.to_csv(args.outpath + 'PCA_val_feature.csv')
df_pca_test.to_csv(args.outpath + 'PCA_test_feature.csv')
for i in range(len(args.OOD_datapaths)):
df_pca_OODs[i].to_csv(args.outpath + 'PCA_OOD{}_feature.csv'.format(i + 1))
elif args.global_x == 'yes':
df = ConcatAllClasses(args.Zoolake2_datapath, args.nice_feature)
df_standardized = Standardize(df)
pca = PrincipalComponentAnalysis(df_standardized, n_components=args.n_components)
Path(args.outpath).mkdir(parents=True, exist_ok=True)
df_components = pd.DataFrame(data=pca.components_, index=['principal_component_{}'.format(i+1) for i in range(len(pca.explained_variance_))], columns=df.columns[:-1])
df_components.to_excel(args.outpath + 'PCA_components_feature.xlsx')
Path(args.outpath + 'components/').mkdir(parents=True, exist_ok=True)
for i, ipc in enumerate(df_components.index):
plt.figure(figsize=(15, 10))
plt.ylabel('Component loading')
plt.bar(x=range(len(df_components.columns)), height=df_components.loc[ipc])
plt.xticks(range(len(df_components.columns)), df_components.columns, rotation=45, rotation_mode='anchor', ha='right')
plt.tight_layout()
plt.savefig(args.outpath + 'components/' + 'PCA_components_feature_PC_' + str(i+1) + '.png')
plt.close()
# df_explained_variance = pd.DataFrame(data=pca.explained_variance_, index=['principal_component_{}'.format(i+1) for i in range(len(pca.explained_variance_))])
# df_explained_variance.to_excel(args.outpath + 'PCA_explained_variance_feature.xlsx')
df_explained_variance_ratio = pd.DataFrame(data=pca.explained_variance_ratio_, index=['principal_component_{}'.format(i+1) for i in range(len(pca.explained_variance_))])
df_explained_variance_ratio.to_excel(args.outpath + 'PCA_explained_variance_ratio_feature.xlsx')
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Number of components')
plt.ylabel('Explained variance ratio')
plt.grid()
plt.tight_layout()
plt.savefig(args.outpath + 'PCA_explained_variance_ratio_feature.png')
df_train = ConcatAllClasses(args.in_distribution_datapaths[0], args.nice_feature)
df_val = ConcatAllClasses(args.in_distribution_datapaths[1], args.nice_feature)
df_test = ConcatAllClasses(args.in_distribution_datapaths[2], args.nice_feature)
df_train_standardized = Standardize(df_train)
df_val_standardized = Standardize(df_val)
df_test_standardized = Standardize(df_test)
df_pca_train = PCA_train_val_test(df_train_standardized, pca)
df_pca_val = PCA_train_val_test(df_val_standardized, pca)
df_pca_test = PCA_train_val_test(df_test_standardized, pca)
df_pca_train.to_csv(args.outpath + 'PCA_train_feature.csv')
df_pca_val.to_csv(args.outpath + 'PCA_val_feature.csv')
df_pca_test.to_csv(args.outpath + 'PCA_test_feature.csv')
for i in range(len(args.OOD_datapaths)):
df_OOD = ConcatAllClasses(args.OOD_datapaths[i], args.nice_feature)
df_OOD_standardized = Standardize(df_OOD)
df_pca_OOD = PCA_OOD(df_OOD_standardized, pca)
df_pca_OOD.to_csv(args.outpath + 'PCA_OOD{}_feature.csv'.format(i + 1))
# df_standardized = Standardize(df)
# # df_normalized = Normalize_minmax(df)
# # df_normalized = Normalize_std(df)
# # df_normalized = Center(df)
# # df_normalized = Rescale_log(df)
# pca, df_pca = PrincipalComponentAnalysis(df_standardized, n_components=args.n_components)
# # pca, df_pca = PrincipalComponentAnalysis(df_normalized, n_components=args.n_components)
# # pca, df_pca = PrincipalComponentAnalysis(df, n_components=args.n_components)
# # loadings = pca.components_.T * np.sqrt(pca.explained_variance_)
# # df_loadings = pd.DataFrame(data=loadings.T, index=['principal_component_{}'.format(i+1) for i in range(args.n_components)], columns=df_normalized.columns[:-1])
# # df_loadings.to_excel(args.outpath + 'PCA_loadings_feature.xlsx')
# # pca, df_pca = PrincipalComponentAnalysis(df, n_components=args.n_components)
# Path(args.outpath).mkdir(parents=True, exist_ok=True)
# df_pca.to_csv(args.outpath + 'PCA_Zoolake2_feature.csv')
# # np.savetxt(args.outpath + 'PCA_explained_variance_ratio_feature.txt', pca.explained_variance_ratio_)
# # np.savetxt(args.outpath + 'PCA_components_feature.txt', pca.components_)
# df_components = pd.DataFrame(data=pca.components_, index=['principal_component_{}'.format(i+1) for i in range(args.n_components)], columns=df.columns[:-1])
# df_components.to_excel(args.outpath + 'PCA_components_feature.xlsx')
# Path(args.outpath + 'components/').mkdir(parents=True, exist_ok=True)
# for i, ipc in enumerate(df_components.index):
# plt.figure(figsize=(15, 10))
# plt.ylabel('Component loading')
# # plt.bar(x=range(len(df_components.columns)), height=abs(df_components.loc[ipc]))
# plt.bar(x=range(len(df_components.columns)), height=df_components.loc[ipc])
# plt.xticks(range(len(df_components.columns)), df_components.columns, rotation=45, rotation_mode='anchor', ha='right')
# plt.tight_layout()
# plt.savefig(args.outpath + 'components/' + 'PCA_components_feature_PC_' + str(i+1) + '.png')
# plt.close()
# df_explained_variance = pd.DataFrame(data=pca.explained_variance_, index=['principal_component_{}'.format(i+1) for i in range(args.n_components)])
# df_explained_variance.to_excel(args.outpath + 'PCA_explained_variance_feature.xlsx')
# df_explained_variance_ratio = pd.DataFrame(data=pca.explained_variance_ratio_, index=['principal_component_{}'.format(i+1) for i in range(args.n_components)])
# df_explained_variance_ratio.to_excel(args.outpath + 'PCA_explained_variance_ratio_feature.xlsx')
# plt.plot(np.cumsum(pca.explained_variance_ratio_))
# plt.xlabel('Number of components')
# plt.ylabel('Explained variance ratio')
# plt.grid()
# plt.tight_layout()
# plt.savefig(args.outpath + 'PCA_explained_variance_ratio_feature.png')
# df_train = ConcatAllClasses(args.in_distribution_datapaths[0], args.nice_feature)
# df_val = ConcatAllClasses(args.in_distribution_datapaths[1], args.nice_feature)
# df_test = ConcatAllClasses(args.in_distribution_datapaths[2], args.nice_feature)
# df_train_standardized = Standardize(df_train)
# df_val_standardized = Standardize(df_val)
# df_test_standardized = Standardize(df_test)
# # df_train_normalized = Normalize_minmax(df_train)
# # df_val_normalized = Normalize_minmax(df_val)
# # df_test_normalized = Normalize_minmax(df_test)
# # df_train_normalized = Normalize_std(df_train)
# # df_val_normalized = Normalize_std(df_val)
# # df_test_normalized = Normalize_std(df_test)
# # df_train_normalized = Center(df_train)
# # df_val_normalized = Center(df_val)
# # df_test_normalized = Center(df_test)
# # df_train_normalized = Rescale_log(df_train)
# # df_val_normalized = Rescale_log(df_val)
# # df_test_normalized = Rescale_log(df_test)
# df_pca_train = PCA_train_val_test(df_train_standardized, pca)
# df_pca_val = PCA_train_val_test(df_val_standardized, pca)
# df_pca_test = PCA_train_val_test(df_test_standardized, pca)
# # df_pca_train = PCA_train_val_test(df_train_normalized, pca)
# # df_pca_val = PCA_train_val_test(df_val_normalized, pca)
# # df_pca_test = PCA_train_val_test(df_test_normalized, pca)
# # df_pca_train = PCA_train_val_test(df_train, pca)
# # df_pca_val = PCA_train_val_test(df_val, pca)
# # df_pca_test = PCA_train_val_test(df_test, pca)
# df_pca_train.to_csv(args.outpath + 'PCA_train_feature.csv')
# df_pca_val.to_csv(args.outpath + 'PCA_val_feature.csv')
# df_pca_test.to_csv(args.outpath + 'PCA_test_feature.csv')
# for i in range(len(args.OOD_datapaths)):
# df_OOD = ConcatAllClasses(args.OOD_datapaths[i], args.nice_feature)
# df_OOD_standardized = Standardize(df_OOD)
# # df_OOD_normalized = Normalize_minmax(df_OOD)
# # df_OOD_normalized = Normalize_std(df_OOD)
# # df_OOD_normalized = Center(df_OOD)
# # df_OOD_normalized = Rescale_log(df_OOD)
# df_pca_OOD = PCA_OOD(df_OOD_standardized, pca)
# # df_pca_OOD = PCA_OOD(df_OOD_normalized, pca)
# # df_pca_OOD = PCA_OOD(df_OOD, pca)
# df_pca_OOD.to_csv(args.outpath + 'PCA_OOD{}_feature.csv'.format(i + 1))