Rohit Garg
326 linhas
13 KiB
Python
326 linhas
13 KiB
Python
"""
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Files required:
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cross_validate.py
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DREAMER/data_extracted/featuresDict/
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DEAP/data_extracted/featuresDict/
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OASIS/data_extracted/featuresDict/
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"""
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import accuracy_score
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from sklearn.feature_selection import chi2
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from sklearn.feature_selection import SelectKBest, f_classif
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from sklearn.model_selection import train_test_split
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from sklearn import preprocessing
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from sklearn.feature_selection import *
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from sklearn.model_selection import RandomizedSearchCV
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from sklearn.model_selection import GridSearchCV
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import sys
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import csv
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import os
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import math
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import glob
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from scipy import io,signal
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import numpy as np
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import pandas as pd
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import pickle
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from sklearn.metrics import mean_squared_error
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from sklearn.impute import SimpleImputer
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import matplotlib.pyplot as plt
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import seaborn as sns
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import copy
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from sklearn import feature_selection
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import argparse
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#import machine learning algorithms
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from sklearn.svm import SVR
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from sklearn.svm import SVC
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from sklearn.linear_model import LinearRegression
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from sklearn import tree #DTR
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.neighbors import KNeighborsRegressor
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def loadFeaturesDict(dataset):
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featuresDict = {'shannonEntropy': None,
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'ShannonRes_delta':None,
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'ShannonRes_theta':None,
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'ShannonRes_alpha':None,
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'ShannonRes_beta':None,
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'ShannonRes_gamma':None,
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'HjorthComp':None,
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'HjorthMob':None,
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'falseNearestNeighbor':None,
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'medianFreq':None,
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'bandPwr_delta':None,
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'bandPwr_theta':None,
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'bandPwr_alpha':None,
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'bandPwr_beta':None,
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'bandPwr_gamma':None,
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'stdDev':None,
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'diffuseSlowing':None,
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'spikeNum':None,
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'deltaBurstAfterSpike':None,
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'shortSpikeNum':None,
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'numBursts':None,
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'burstLenMean':None,
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'burstLenStd':None,
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'numSuppressions':None,
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'suppLenMean':None,
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'suppLenStd':None,
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'dasm_delta': None,
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'dasm_theta': None,
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'dasm_alpha': None,
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'dasm_beta': None,
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'dasm_gamma': None,
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'rasm_delta': None,
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'rasm_theta': None,
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'rasm_alpha': None,
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'rasm_beta': None,
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'rasm_gamma': None,
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}
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featurepath = os.getcwd() + '/' + dataset + '/data_extracted/featuresDict/'
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featuresDict['shannonEntropy'] = np.load(featurepath + "shannonEntropy_1_1.npz", allow_pickle=True)['features']
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featuresDict['ShannonRes_delta'] = np.load(featurepath + "ShannonRes_sub_bands_delta_1_1.npz", allow_pickle=True)['features']
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featuresDict['ShannonRes_theta'] = np.load(featurepath + "ShannonRes_sub_bands_theta_1_1.npz", allow_pickle=True)['features']
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featuresDict['ShannonRes_alpha'] = np.load(featurepath + "ShannonRes_sub_bands_alpha_1_1.npz", allow_pickle=True)['features']
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featuresDict['ShannonRes_beta'] = np.load(featurepath + "ShannonRes_sub_bands_beta_1_1.npz", allow_pickle=True)['features']
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featuresDict['ShannonRes_gamma'] = np.load(featurepath + "ShannonRes_sub_bands_gamma_1_1.npz", allow_pickle=True)['features']
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#lyapunov
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# featuresDict['hFD'] = np.load(featurepath + d/DEAP_hFD.npy",allow_pickle=True)['features']
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featuresDict['HjorthComp'] = np.load(featurepath + "Hjorth_complexity_1_1.npz", allow_pickle=True)['features']
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featuresDict['HjorthMob'] = np.load(featurepath + "Hjorth_mobilty_1_1.npz",allow_pickle=True)['features']
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featuresDict['falseNearestNeighbor'] = np.load(featurepath + "falseNearestNeighbor_1_1.npz",allow_pickle=True)['features']
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featuresDict['medianFreq'] = np.load(featurepath + "medianFreq_1_1.npz",allow_pickle=True)['features']
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featuresDict['bandPwr_delta']=np.load(featurepath+"bandPwr_delta_1_1.npz", allow_pickle = True)['features']
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featuresDict['bandPwr_theta']=np.load(featurepath + "bandPwr_theta_1_1.npz", allow_pickle = True)['features']
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featuresDict['bandPwr_alpha']=np.load(featurepath + "bandPwr_alpha_1_1.npz", allow_pickle = True)['features']
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featuresDict['bandPwr_beta']=np.load(featurepath + "bandPwr_beta_1_1.npz", allow_pickle = True)['features']
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featuresDict['bandPwr_gamma']=np.load(featurepath + "bandPwr_gamma_1_1.npz", allow_pickle = True)['features']
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featuresDict['stdDev'] = np.load(featurepath + "stdDev_1_1.npz",allow_pickle=True)['features']
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# featuresDict['regularity'] = np.load(featurepath + "",allow_pickle=True)['features']
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# featuresDict['volt05'] = np.load(featurepath + "",allow_pickle=True)['features']
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# featuresDict['volt10'] = np.load(featurepath + "",allow_pickle=True)['features']
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# featuresDict['volt20'] = np.load(featurepath + "",allow_pickle=True)['features']
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featuresDict['diffuseSlowing'] = np.load(featurepath + "diffuseSlowing_1_1.npz",allow_pickle=True)['features']
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featuresDict['spikeNum'] = np.load(featurepath + "spikeNum_1_1.npz",allow_pickle=True)['features']
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featuresDict['deltaBurstAfterSpike'] = np.load(featurepath + "deltaBurstAfterSpike_1_1.npz",allow_pickle=True)['features']
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featuresDict['shortSpikeNum'] = np.load(featurepath + "shortSpikeNum_1_1.npz", allow_pickle=True)['features']
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featuresDict['numBursts'] = np.load(featurepath + "numBursts_1_1.npz",allow_pickle=True)['features']
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featuresDict['burstLenMean'] = np.load(featurepath + "burstLen_u_and_sigma_mean_1_1.npz",allow_pickle=True)['features']
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featuresDict['burstLenStd'] = np.load(featurepath + "burstLen_u_and_sigma_std_1_1.npz",allow_pickle=True)['features']
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# featuresDict['burstBandPowers'] = np.load(featurepath + "",allow_pickle=True)['features']
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featuresDict['numSuppressions'] = np.load(featurepath + "numSuppressions_1_1.npz",allow_pickle=True)['features']
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featuresDict['suppLenMean'] = np.load(featurepath + "suppressionLen_u_and_sigma_mean_1_1.npz",allow_pickle=True)['features']
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featuresDict['suppLenStd'] = np.load(featurepath + "suppressionLen_u_and_sigma_std_1_1.npz",allow_pickle=True)['features']
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featuresDict['dasm_delta'] = np.load(featurepath + "dasm_delta_1_1.npz",allow_pickle=True)['features']
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featuresDict['dasm_theta'] = np.load(featurepath + "dasm_theta_1_1.npz",allow_pickle=True)['features']
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featuresDict['dasm_alpha'] = np.load(featurepath + "dasm_alpha_1_1.npz",allow_pickle=True)['features']
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featuresDict['dasm_beta'] = np.load(featurepath + "dasm_beta_1_1.npz",allow_pickle=True)['features']
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featuresDict['dasm_gamma'] = np.load(featurepath + "dasm_gamma_1_1.npz",allow_pickle=True)['features']
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featuresDict['rasm_delta'] = np.load(featurepath + "rasm_delta_1_1.npz",allow_pickle=True)['features']
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featuresDict['rasm_theta'] = np.load(featurepath + "rasm_theta_1_1.npz",allow_pickle=True)['features']
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featuresDict['rasm_alpha'] = np.load(featurepath + "rasm_alpha_1_1.npz",allow_pickle=True)['features']
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featuresDict['rasm_beta'] = np.load(featurepath + "rasm_beta_1_1.npz",allow_pickle=True)['features']
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featuresDict['rasm_gamma'] = np.load(featurepath + "rasm_gamma_1_1.npz",allow_pickle=True)['features']
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return featuresDict
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np.random.seed(42)
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def cross_validate(dataset, window, stride, sfreq, label, best_features_list):
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# Parameters :-
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# dataset :- Name of the Dataset
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# window :- Length of the sliding window in seconds
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# stride :- Stride of the sliding window in seconds
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# sfreq :- sampling frequency of the EEG dataset
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# best_features_list :- Featrue list after performing top electrode and feature analysis for various datasets
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pwd = os.getcwd()
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fs = sfreq
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featurepath = os.getcwd() + '/' + dataset + '/data_extracted/featuresDict/'
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ans = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['features']
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Y_epoch = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['Y']
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#load saved epoched features
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featuresDict = None
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featuresDict = loadFeaturesDict(dataset)
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# pop out not best features
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for k in list(featuresDict.keys()):
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if k not in best_features_list:
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featuresDict.pop(k)
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featuresList = list(featuresDict.keys())
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print(featuresList)
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#make feature matrix with select best features
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featureMatrix = np.empty((0,ans.shape[1])) #[14*32 + 1,80640]
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for key,value in featuresDict.items():
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featureMatrix = np.append(featureMatrix,value,axis=0)
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#remove NaN features
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if np.isnan(featureMatrix).any():
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featureMatrix = np.nan_to_num(featureMatrix,nan=0)
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#set datatype of feature matrix
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featureMatrix = featureMatrix.astype('float64')
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#transpose feature matrix to prepare X
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X = pd.DataFrame(featureMatrix.T)
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#replace infinity with NaN value and fill it with zero
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X = X.replace([np.inf, -np.inf], np.nan)
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X = X.fillna(0)
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X = X.astype(np.float32)
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#convert ndarray to dataframe
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Y_epoch = pd.DataFrame(Y_epoch)
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print("Number of feature vectors in X = ", X.shape[1])
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print("X.shape = " ,X.shape)
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#***********************************************************
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#Leave-one-subject-out-CV
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#number of folds = numbParticipants
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numbParticipants = 0
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numbRecordings = 0
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if(dataset == 'DEAP'):
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numbParticipants = 32
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numbRecordings = 40
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elif(dataset == 'DREAMER'):
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# Dreamer dataset has 23 subjects, each subject was shown 18 videos
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numbParticipants = 23
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numbRecordings = 18
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elif(dataset == 'OASIS'):
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numbParticipants = 15
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numbRecordings = 40
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#numbEpochs
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numbEpochs = X.shape[0]//(numbParticipants*numbRecordings)
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print(X.shape[0])
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print("numbParticipants = ", numbParticipants)
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print("numbRecordings = " , numbRecordings)
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print("numbEpochs = ", numbEpochs)
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print(type(X))
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print(type(Y_epoch))
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cv_rmse = []
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for i in range(numbParticipants):
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s = i*numbRecordings*numbEpochs
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e = (i+1)*numbRecordings*numbEpochs
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X_test = copy.deepcopy(X.iloc[s:e, :])
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y_test = copy.deepcopy(Y_epoch.iloc[s:e, label])
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X_train = copy.deepcopy(X.iloc[:s, :])
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X_train = np.append(X_train, X.iloc[e:, :],axis=0)
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y_train = copy.deepcopy(Y_epoch.iloc[:s, label])
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y_train = np.append(y_train, Y_epoch.iloc[e:, label],axis=0)
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print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
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clf = RandomForestRegressor()
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clf.fit(X_train, y_train)
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y_predict = clf.predict(X_test)
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rmse = mean_squared_error(y_test, y_predict,squared=False)
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print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse))
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cv_rmse.append(rmse)
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print(cv_rmse)
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output_file = 'output.csv';
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with open(output_file,'a') as fd:
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fd.write(f'{dataset}_{label},')
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writer = csv.writer(fd)
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writer.writerow(cv_rmse)
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fd.write('\n')
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if __name__ == '__main__':
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"""##CV-Method B-Valence Label
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####DREAMER
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"""
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best_features_list = ['ShannonRes_gamma','ShannonRes_beta','HjorthMob','HjorthComp','bandPwr_alpha','stdDev','ShannonRes_alpha','bandPwr_beta','rasm_beta','dasm_beta','dasm_alpha']
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cross_validate(dataset = 'DREAMER', window = 1, stride = 1, sfreq=128, label = 0, best_features_list = best_features_list)
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"""####DEAP"""
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best_features_list = ['rasm_gamma','rasm_beta','stdDev','dasm_beta','ShannonRes_beta','dasm_gamma','bandPwr_beta','ShannonRes_gamma']
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cross_validate(dataset = 'DEAP', window = 1, stride = 1, sfreq=128, label = 0, best_features_list = best_features_list)
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"""####OASIS"""
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best_features_list = ['ShannonRes_gamma', 'HjorthMob']
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cross_validate(dataset = 'OASIS', window = 1, stride = 1, sfreq=128, label = 0, best_features_list = best_features_list)
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"""##CV-Method-B-Arousal Label
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####DREAMER
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"""
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best_features_list = ['ShannonRes_gamma','HjorthComp','ShannonRes_beta','dasm_alpha','rasm_alpha','HjorthMob','bandPwr_alpha','stdDev','bandPwr_beta']
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cross_validate(dataset = 'DREAMER', window = 1, stride = 1, sfreq=128, label = 1, best_features_list = best_features_list)
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"""####DEAP"""
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best_features_list = ['rasm_beta','rasm_gamma','bandPwr_gamma','dasm_beta','dasm_gamma','ShannonRes_beta','stdDev','bandPwr_beta','ShannonRes_gamma']
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cross_validate(dataset = 'DEAP', window = 1, stride = 1, sfreq=128, label = 1, best_features_list = best_features_list)
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"""####OASIS"""
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best_features_list = ['HjorthMob']
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cross_validate(dataset = 'OASIS', window = 1, stride = 1, sfreq=128, label = 1, best_features_list = best_features_list)
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