print 'Accuracy : train: ', train_accuracy
#Prediction on test
test_pred_labels = projectFunctions.prediction_Winnow(test_matrix, test_labels,
w_train)
test_accuracy = projectFunctions.accuracyMETRIC(test_labels, test_pred_labels)
test_f1_score = projectFunctions.f1METRIC(test_labels, test_pred_labels)
print 'Accuracy : test: ', test_accuracy
#Prediction on eval.anon
eval_pred_labels = projectFunctions.prediction_Winnow(eval_matrix, eval_labels,
w_train)
eval_accuracy = projectFunctions.accuracyMETRIC(eval_labels, eval_pred_labels)
eval_f1_score = projectFunctions.f1METRIC(eval_labels, eval_pred_labels)
print 'Accuracy : eval.anon: ', eval_accuracy
#Write leaderboard file
#projectFunctions.write_solutions(eval_pred_labels,"simpleWinnow.csv")
'''
print '\n------------------ Simple Perceptron w Epochs: ------------------'
learningRate = 0.4
margin = 0
maxEpochs = 100
simple_10e_info = percepFunctions.algoFORepochs(maxEpochs, train_filepath, test_filepath, eval_anon_filepath,'winnow', learningRate, margin)
#
solution_filename = raw_input('Enter x__x in ./solutions_log/x__x_solutions.csv: ')
#Write leaderboard file
my_list = []
for list_ in simple_10e_info[1:]:
my_list.append(list_[5])
max_testacc_index, value = max(enumerate(my_list), key=operator.itemgetter(1))
w_val = simple_10e_info[int(max_testacc_index+1)][9]
#Simple-Weighted Perceptron w Epochs
learningRate = 0.675
margin = 0
maxEpochs = 1
bestLIST = percepFunctions.algoFORepochs(maxEpochs, trainMatrix, train_actual_labels, testMatrix, test_actual_labels, evalMatrix, eval_actual_labels, 'simple', learningRate, margin)
for i in range(1,7):
print bestLIST[i]
for i in range(8,14):
print bestLIST[i]
#Write leaderboard file
solution_filename = raw_input('Enter x__x in ./solutions_log/x__x_solutions.csv: ')
w_val = bestLIST[7]
eval_pred_labels = projectFunctions.prediction_Perceptron(eval_matrix, eval_actual_labels, w_val)
projectFunctions.write_solutions('perceptron',eval_pred_labels,'./solutions_log/solutions/'+solution_filename+'.solutions.csv')
'''
filepath = './solutions_log/simple_50e_infolist.txt'
thefile = open('./solutions_log/infoARRAY/'+solution_filename+'.infoARRAY.csv', 'w')
for item in simple_weighted_info:
thefile.write("%s\n" % item)
thefile.close()
'''
#end_time = timeit.default_timer()
#Simple or Weighted Perceptron
'''
#print '\n------------------ Simple Perceptron: ------------------'
import bewNeighborFunctions as kNN
import featureTRANSFORM
import projectFunctions as pF
train_matrix, train_labels, test_matrix, test_labels, eval_matrix, eval_labels = featureTRANSFORM.eliminateZEROfetures(
'01', -1)
k = 3
p = 2
'''
pred_train_labels = kNN.kneighbors(train_matrix,train_labels,train_matrix,k,p)
train_accuracy = pF.accuracyMETRIC(train_labels, pred_train_labels)
dum,pum,train_f1 = pF.f1METRIC(train_labels,pred_train_labels)
print 'train: Accuracy:',train_accuracy,'% F1:',train_f1
'''
pred_test_labels = kNN.kneighbors(train_matrix, train_labels, test_matrix, k,
p)
test_accuracy = pF.accuracyMETRIC(test_labels, pred_test_labels)
dum, pum, test_f1 = pF.f1METRIC(test_labels, pred_test_labels)
print 'test: Accuracy:', test_accuracy, '% F1:', test_f1
eval_pred_labels = kNN.kneighbors(train_matrix, train_labels, eval_matrix, k,
p)
solution_filename = raw_input(
'Enter x__x in ./solutions_log/x__x_solutions.csv: ')
pF.write_solutions('perceptron', eval_pred_labels,
'./' + solution_filename + '.solutions.csv')
predicted_train_list)
train_f1_score = projectFunctions.f1METRIC(train_actual_labels,
predicted_train_list)
test_accuracy = projectFunctions.accuracyMETRIC(test_actual_labels,
predicted_test_list)
test_f1_score = projectFunctions.f1METRIC(test_actual_labels,
predicted_test_list)
eval_accuracy = projectFunctions.accuracyMETRIC(eval_actual_labels,
predicted_eval_list)
eval_f1_score = projectFunctions.f1METRIC(eval_actual_labels,
predicted_eval_list)
solution_filename = raw_input(
'Enter x__x in ./solutions_log/x__x_solutions.csv: ')
projectFunctions.write_solutions(
'decision tree', predicted_eval_list,
'./solutions_log/solutions/' + solution_filename + '.solutions.csv')
#np.save('trees.npy', tree)
#read_tree = np.load('trees.npy').item()
#
print 'Train Accuracy: '
print train_accuracy
print 'Train f1: '
print train_f1_score
print 'Test Accuracy: '
print test_accuracy
print 'Test f1: '
print test_f1_score
print 'Eval Accuracy: '
print eval_accuracy
print 'Eval f1: '