Rohit Garg
173 linhas
6.9 KiB
Python
173 linhas
6.9 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# Script to import all the required libraries.<br>
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# It also defines a function to make a dictionary and load the features.
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#
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# In[ ]:
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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 cuml
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from cuml.svm import SVR
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from cuml.ensemble import RandomForestRegressor
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from cuml.svm import SVC
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from cuml.ensemble import RandomForestClassifier
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from cuml.metrics import accuracy_score
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# In[ ]:
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def loadFeaturesDict(dataset):
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# input parameters :- The name of the dataset
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# return :- Feature dictionary
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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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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['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['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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