import hw_utils
from datetime import datetime
X_train, Y_train, X_test, Y_test = hw_utils.loaddata("MiniBooNE_PID.txt")
X_train_norm, X_test_norm = hw_utils.normalize(X_train, X_test)
# # Part d linear activations
# print "Linear Activations"
# linear_arch1 = [[50, 2],[50, 50, 2],[50, 50, 50, 2],[50, 50, 50, 50, 2]]
# start = datetime.now()
# hw_utils.testmodels(X_train_norm, Y_train, X_test_norm, Y_test,
# linear_arch1, 'linear', 'softmax',[0.0], 30, 1000, 0.001, [0.0], [0.0], False, False, 1)
# end = datetime.now()
# print "Time taken in Linear activations part 1 : " + str((end - start).total_seconds())
#
# linear_arch2 = [[50, 50, 2], [50, 500, 2], [50, 500, 300, 2], [50, 800, 500, 300, 2], [50, 800, 800, 500, 300, 2]]
# start = datetime.now()
# hw_utils.testmodels(X_train_norm, Y_train, X_test_norm, Y_test,
# linear_arch2, 'linear', 'softmax',[0.0], 30, 1000, 0.001, [0.0], [0.0], False, False, 1)
# end = datetime.now()
# print "Time taken in Linear activations part 2 : " + str((end - start).total_seconds())
#
# arch = [[50, 50, 2], [50, 500, 2], [50, 500, 300, 2], [50, 800, 500, 300, 2], [50, 800, 800, 500, 300, 2]]
#
# # Part e sigmoid activation
# print "\n\nSigmoid activation"
# start = datetime.now()
# hw_utils.testmodels(X_train_norm, Y_train, X_test_norm, Y_test,
# arch, 'sigmoid', 'softmax',[0.0], 30, 1000, 0.001, [0.0], [0.0], False, False, 1)
# end = datetime.now()
# print "Time taken in Sigmoid activation : " + str((end - start).total_seconds())
import hw_utils as hw
from timeit import default_timer
xTrain, yTrain, xTest, yTest = hw.loaddata('MiniBooNE_PID.txt')
xTrainNorm, xTestNorm = hw.normalize(xTrain, xTest)
dIn = 50
dOut = 2
xTrain = xTrainNorm
yTrain = yTrain
xTest = xTestNorm
yTest = yTest
print "\nLinear Activations"
print "-------------------"
print "Architecture 1"
print "---------------"
architectures = [[dIn, dOut], [dIn, 50, dOut], [dIn, 50, 50, dOut],
[dIn, 50, 50, 50, dOut]]
startTime = default_timer()
hw.testmodels(xTrain, yTrain, xTest, yTest, architectures, 'linear', 'softmax',
[0.0], 30, 1000, 0.001, [0.0], [0.0], False, False, 0)
timeTaken = default_timer() - startTime
print "Training time = " + str(timeTaken) + " s"
print "Architecture 2"
print "---------------"
architectures = [[dIn, 50, dOut], [dIn, 500, dOut], [dIn, 500, 300, dOut],
[dIn, 800, 500, 300, dOut], [dIn, 800, 800, 500, 300, dOut]]
startTime = default_timer()
test.drop(test.columns[[0, 1, 2]], axis=1, inplace=True)
#remove extra col label
'''
validate_rows=test.shape[0]
test[test.shape[1]]=np.random.randint(2, size=validate_rows)
print test.shape,test.columns.tolist()
y_te = validate[[test.shape[1]-1]].copy()
test.drop(test.columns[test.shape[1]-1],1,inplace=True)
'''
#y_values=pd.DataFrame(y_values)
y_te = np.random.randint(2, size=test.shape[0])
print y_te.shape
X_te = test.copy()
test.to_csv("test.csv", sep=',')
X_tr, X_te = hw_utils.normalize(X_tr.values, X_te.values)
# to handle categorical represenatation
y_tr = to_categorical(y_tr.values)
y_te = to_categorical(y_te)
print X_tr.shape, X_te.shape, y_tr.shape, y_te.shape
din = X_tr.shape[1]
dout = 2
del train,test,questions,users,invited\
#,validate
print 'Try Neural Network'
print din, dout
time_e = hw_utils.start_time()
print test.shape, test.columns.tolist()
test.drop(test.columns[[0, 1, 2]], axis=1, inplace=True)
'''
validate_rows=test.shape[0]
test[test.shape[1]]=np.random.randint(2, size=validate_rows)
print test.shape,test.columns.tolist()
y_te = validate[[test.shape[1]-1]].copy()
test.drop(test.columns[test.shape[1]-1],1,inplace=True)
'''
#y_values=pd.DataFrame(y_values)
y_te = np.random.randint(2, size=test.shape[0])
print y_te.shape
X_te = test.copy()
test.to_csv("test.csv", sep=',')
X_tr, X_te = hw_utils.normalize(X_tr, X_te)
# to handle categorical represenatation
y_tr = to_categorical(y_tr.values)
y_te = to_categorical(y_te)
print X_tr.shape, X_te.shape, y_tr.shape, y_te.shape
din = X_tr.shape[1]
dout = 2
del train, test, questions, users, invited, validate
print 'Try Neural Network'
print din, dout
time_e = hw_utils.start_time()
arch_list_e = [[din, din, dout], [din, din * 10, dout],
sgd_Nesterov=True,
EStop=True,
verbose=0)
print " Time Taken = ", time.time() - start_time
displayJson(results)
if __name__ == "__main__":
#Part A) Load and normalize
start_time = time.time()
X_tr, y_tr, X_te, y_te = hw.loaddata(FILENAME)
print "Time taken to load data = ", time.time() - start_time
start_time = time.time()
X_tr, X_te = hw.normalize(X_tr, X_te)
print "Time taken to normalize data = ", time.time() - start_time
#Part D)
#partd_a(X_tr,y_tr,X_te,y_te)
#partd_b(X_tr,y_tr,X_te,y_te)
#parte(X_tr,y_tr,X_te,y_te)
#partf(X_tr,y_tr,X_te,y_te)
#partg(X_tr,y_tr,X_te,y_te)
#parth(X_tr,y_tr,X_te,y_te)
#parti(X_tr,y_tr,X_te,y_te)
