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SVM_Task3.py
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# coding: utf-8
# In[2]:
import pandas as pd
import numpy as np
import tarfile
import os
from sklearn import svm
from sklearn.model_selection import train_test_split
import argparse
import os.path as op
from os.path import join as pjoin
# In[7]:
def getAllFilesOfAUserInDirectory():
parser = argparse.ArgumentParser(description='Getting the User ID')
parser.add_argument("UserID", help='1st argument is the Users 1st session')
parser.add_argument("dir", help='2nd argument is the Directory in which the files are located')
args = parser.parse_args()
#Reading all files from the directory
#allFilesInDirectory = os.listdir("/home/abhishek/Downloads/GQP/Folder")
allFilesInDirectory = os.listdir(str(args.dir))
#Getting all files of User whose ID = 37
#allFiles = [s for s in allFilesInDirectory if args.first in s]
allFiles = [s for s in allFilesInDirectory if args.UserID in s]
return allFiles, str(args.dir)
def readFilesAndImplementSVM(allFiles, directory):
dataFile = []
#For all files, extracting the tar.gz into a pandas csv
for fileNames in range(len(allFiles)):
print("Reading and extracting file", allFiles[fileNames])
fileLoc = directory + str(allFiles[fileNames])
#Opening the tar.gz file
# tar = tarfile.open(fileLoc, "r:gz")
# #This loop iterates over tar files
# for member in tar.getmembers():
# #Extracts the tar.gz file
# f = tar.extractfile(member)
#Reads the tar.gz file into a pandas dataframe
data = pd.read_csv(fileLoc, sep = ' ', header=None)
firstThirthySeconds = data.iloc[0:61440]
lastThirtySeconds = data.iloc[len(data)-61441:len(data)-1]
firstThirthySeconds['Y_Variable'] = np.repeat(0,len(firstThirthySeconds))
lastThirtySeconds['Y_Variable'] = np.repeat(1,len(lastThirtySeconds))
df = firstThirthySeconds.append(lastThirtySeconds)
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,2:130], df.iloc[:,130], test_size=0.20, random_state=42)
trainAccuracy, testAccuracy = svmImplementation(X_train, X_test, y_train, y_test, allFiles[fileNames])
def svmImplementation(X_train, X_test, y_train, y_test, tar):
print("Implementing SVM")
clf = svm.SVC()
clf.fit(X_train,y_train)
print("Predicting the train data")
trainAccuracy = clf.score(X_train,y_train)
print("Train accuracy =",trainAccuracy)
print("Predicting the test data")
testAccuracy = clf.score(X_test,y_test)
print("Test accuracy =",testAccuracy)
#Fitting decision function for Train and Test data
print("Fitting the decision function for train and test data")
decisionFuncValsTrain = clf.decision_function(X_train)
decisionFuncValsTest = clf.decision_function(X_test)
#Writing the decision function values to csv
print("Writing the decision fuction values to csv")
decisionFunctionValuesTrainData = pd.DataFrame(decisionFuncValsTrain,columns=['Dec_Values'])
fileNameForDecisionFunctionTrain = 'Output/'+str(tar)+'_decisionFunctionValuesTrainData.csv'
decisionFunctionValuesTrainData.to_csv(fileNameForDecisionFunctionTrain)
decisionFunctionValuesTestData = pd.DataFrame(decisionFuncValsTest,columns=['Dec_Values'])
fileNameForDecisionFunctionTest = 'Output/'+str(tar)+'_decisionFunctionValuesTestData.csv'
decisionFunctionValuesTestData.to_csv(fileNameForDecisionFunctionTest)
decValuesTrain = np.array(decisionFuncValsTrain)
decValuesTest = np.array(decisionFuncValsTest)
#Finding the medians of train, test and whole data at a time for both classes
print("Calculating median")
medianOfClassATrainData = np.median(np.select(decValuesTrain > 0, decValuesTrain))
medianOfClassATestData = np.median(np.select(decValuesTest > 0, decValuesTest))
medianOfClassBTrainData = np.median(np.select(decValuesTrain < 0, decValuesTrain))
medianOfClassBTestData = np.median(np.select(decValuesTest < 0, decValuesTest))
print("Median of Class A for train Data =",medianOfClassATrainData)
print("Median of Class B for train Data =",medianOfClassBTrainData)
print("Median of Class A for test Data =",medianOfClassATestData)
print("Median of Class B for test Data =",medianOfClassBTestData)
mergedDfOfDecisionFnValues = decisionFunctionValuesTrainData.append(decisionFunctionValuesTestData)
medianOfClassA = np.median(np.select(np.array(mergedDfOfDecisionFnValues) > 0, np.array(mergedDfOfDecisionFnValues)))
medianOfClassB = np.median(np.select(np.array(mergedDfOfDecisionFnValues) < 0, np.array(mergedDfOfDecisionFnValues)))
print("Median of Class A for whole data =",medianOfClassA)
print("Median of Class B for whole Data =",medianOfClassB)
filename = str(tar) + '.txt'
path_to_file = pjoin("Output", filename)
FILE = open(path_to_file, "w")
trainAccWr = 'Train Accuracy =' + str(trainAccuracy)
testAccWr = 'Test Accuracy =' + str(testAccuracy)
medianOfClassATrainDataWr = 'Median of Class A for train Data =' + str(medianOfClassATrainData)
medianOfClassATestDataWr = 'Median of Class A for train Data =' + str(medianOfClassATestData)
medianOfClassBTrainDataWr = 'Median of Class A for train Data =' + str(medianOfClassBTrainData)
medianOfClassBTestDataWr = 'Median of Class A for train Data =' + str(medianOfClassBTestData)
medianOfClassAWr = 'Median of Class A for whole data =' + str(medianOfClassA) + '\n'
medianOfClassBWr = 'Median of Class B for whole data =' + str(medianOfClassB) + '\n'
#FILE.write(trainAccWr)
#FILE.write(testAccWr)
#FILE.write(medianOfClassATrainDataWr)
#FILE.write(medianOfClassATestDataWr)
#FILE.write(medianOfClassBTrainDataWr)
#FILE.write(medianOfClassBTestDataWr)
FILE.write(medianOfClassAWr)
FILE.write(medianOfClassBWr)
FILE.close()
# Cs = [0.001, 0.01, 0.1, 1, 10]
# gammas = [0.001, 0.01, 0.1, 1]
# #kernel = ['linear', 'poly', 'rbf', 'sigmoid']
# param_grid = {'C': Cs, 'gamma' : gammas}
# grid_search = gs.GridSearchCV(svm.SVC(kernel='rbf'), param_grid,verbose=1)
# grid_search.fit(X_train, y_train)
# print("Best parameters for this dataset are",grid_search.best_params_)
# print("Predicting the train data")
# y_pred = grid_search.predict(X_train)
# print("Calculating Train Accuracy")
# trainAccuracy = (np.sum(y_pred == y_train)/len(y_train))*100
# print("Train accuracy =",trainAccuracy)
# print("Predicting the train data")
# y_test_pred = grid_search.predict(X_test)
# print("Calculating Test Accuracy")
# testAccuracy = (np.sum(y_test_pred == y_test)/len(y_test))*100
# print("Test accuracy =",testAccuracy)
return trainAccuracy, testAccuracy
# In[8]:
def main():
allFiles, directory = getAllFilesOfAUserInDirectory()
readFilesAndImplementSVM(allFiles, directory)
if __name__ == "__main__":
main()