def main():
# args from Simple Queries paper
DIM = 30
WORDGRAMS = 2
MINCOUNT = 8
MINN = 3
MAXN = 3
BUCKET = 1000000
# adjust these
EPOCH = 5
LR = 0.15 # 0.15 good for ~5000
KERN = 'lin' # lin or rbf or poly
NUM_RUNS = 1 # number of test runs
SUBSET_VAL = 300 # number of subset instances for self reported dataset
LIN_C = 0.90 # hyperparameter for linear kernel
run = 0
print("starting dictionary creation.............................")
dictionary = Dictionary(WORDGRAMS, MINCOUNT, BUCKET, SUBSET_VAL, run)
X_train, X_test, y_train, y_test = dictionary.get_train_and_test()
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
n_train = dictionary.get_n_train_instances()
n_test = dictionary.get_n_manual_instances()
X_train = dictionary.get_trainset()
X_test = dictionary.get_manual_testset()
print()
print("starting optimization")
#coef = kernel_mean_matching(X_train, X_test, n_train, n_test, kern='lin', B=10)
coef = kernel_mean_matching(X_test, X_train[0], LIN_C, kern='lin', B=10)
print(coef)
def main():
# args from Simple Queries paper
DIM = 30
WORDGRAMS = 2
MINCOUNT = 8
MINN = 3
MAXN = 3
BUCKET = 1000000
# adjust these
EPOCH = 20
LR = 0.10 # 0.15 good for ~5000
KERN = 'lin' # lin or rbf or poly
NUM_RUNS = 1 # number of test runs
SUBSET_VAL = 1000 # number of subset instances for self reported dataset
LIN_C = 0.90 # hyperparameter for linear kernel
BATCHSIZE = 1 # number of instances in each batch
##### instantiations #######################################
print("starting dictionary creation")
# dictionary must be recreated each run to get different subsample each time
# initialize training
start = time.time()
dictionary = Dictionary(WORDGRAMS,
MINCOUNT,
BUCKET,
KERN,
SUBSET_VAL,
LIN_C,
model='original')
end = time.time()
print("dictionary took ", (end - start) / 60.0, " time to create.")
nwords = dictionary.get_nwords()
nclasses = dictionary.get_nclasses()
#initialize testing
X_train, X_test, y_train, y_test = dictionary.get_train_and_test()
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
N_train = dictionary.get_n_train_instances()
N_test = dictionary.get_n_test_instances()
print("Number of Train instances: ", N_train,
" Number of Test instances: ", N_test)
ntrain_eachclass = dictionary.get_nlabels_eachclass_train()
ntest_eachclass = dictionary.get_nlabels_eachclass_test()
print("N each class TRAIN: ", ntrain_eachclass, " N each class TEST: ",
ntest_eachclass)
# manual labeled set (Kaggle dataset)
X_manual = dictionary.get_manual_testset()
y_manual = dictionary.get_manual_set_labels()
N_manual = dictionary.get_n_manual_instances()
print()
print("Number of Manual testing instances: ", N_manual, " shape: ",
X_manual.shape)
nmanual_eachclass = dictionary.get_nlabels_eachclass_manual()
print("N each class Manual testing instances: ", nmanual_eachclass)
print("#####################################")
p = X_train.shape[1]
# A
#A_n = nwords + BUCKET # cols
A_n = p
A_m = DIM # rows
uniform_val = 1.0 / DIM
np.random.seed(0)
A = np.random.uniform(-uniform_val, uniform_val, (A_m, A_n))
# B
B_n = DIM # cols
B_m = nclasses # rows
B = np.zeros((B_m, B_n))
#### train ################################################
print("A: ", A.shape)
print("B: ", B.shape)
print("X_trian: ", X_train.shape)
print("labels: ", y_train.shape)
losses_train = []
losses_test = []
losses_manual = []
print()
print()
X_train = normalize(X_train, axis=1, norm='l1')
X_test = normalize(X_test, axis=1, norm='l1')
X_manual = normalize(X_manual, axis=1, norm='l1')
#X_train_batches = np.vsplit(X_train, BATCHES )
#X_train = np.toarray(X_train)
#X_train_batches = np.array_split(X_train.todense(), BATCHES)
#print("****** ", X_train_batches[0].shape)
#y_train_batches = np.array_split(y_train, BATCHES)
traintime_start = time.time()
for i in range(EPOCH):
print()
print("EPOCH: ", i)
alpha = LR * (1 - i / EPOCH) # linearly decaying lr alpha
train_loss = 0
start = 0
batchnum = 0
while start <= N_train:
batch = X_train.tocsr()[start:start + BATCHSIZE, :]
y_train_batch = y_train[start:start + BATCHSIZE, :]
# Forward Propogation
hidden = sparse.csr_matrix.dot(A, batch.T)
hidden = normalize(hidden, axis=1, norm='l1')
z2 = np.dot(B, hidden)
# softmax
Y_hat = softmax(z2, theta=1.0, axis=0)
#loglike = np.log(Y_hat)
#train_loss = -np.multiply(y_train_batches[batch_num], loglike.T) # need to multiply element wise here
#### Back prop #########################################################
# B update
gradient = alpha * np.dot(
np.subtract(Y_hat.T, y_train_batch).T, hidden.T)
B = np.subtract(B, gradient)
# A update
first = np.dot(np.subtract(Y_hat.T, y_train_batch), B)
gradient = alpha * sparse.csr_matrix.dot(first.T, batch)
A = np.subtract(A, gradient)
batchnum += 1
if start + BATCHSIZE >= N_train:
batch = X_train.tocsr()[start:-1, :] # rest of train set
y_train_batch = y_train[start:-1, :]
# Forward Propogation
hidden = sparse.csr_matrix.dot(A, batch.T)
hidden = normalize(hidden, axis=1, norm='l1')
z2 = np.dot(B, hidden)
# softmax
Y_hat = softmax(z2, theta=1.0, axis=0)
#loglike = np.log(Y_hat)
#train_loss = -np.multiply(y_train_batches[batch_num], loglike.T) # need to multiply element wise here
#### Back prop #########################################################
# B update
gradient = alpha * np.dot(
np.subtract(Y_hat.T, y_train_batch).T, hidden.T)
B = np.subtract(B, gradient)
# A update
first = np.dot(np.subtract(Y_hat.T, y_train_batch), B)
gradient = alpha * sparse.csr_matrix.dot(first.T, batch)
A = np.subtract(A, gradient)
break
else:
start = start + BATCHSIZE
# TRAINING LOSS
#train_loss = np.sum(train_loss)/N_train
hidden_train = sparse.csr_matrix.dot(A, X_train.T)
hidden_train = normalize(hidden_train, axis=1, norm='l1')
z2_train = np.dot(B, hidden_train)
Y_hat_train = softmax(z2_train, theta=1.0, axis=0)
loglike_train = np.log(Y_hat_train)
train_loss = -np.multiply(
y_train, loglike_train.T) # need to multiply element wise here
train_loss = np.sum(train_loss) / N_train
print("Train: ", train_loss)
## TESTING LOSS
hidden_test = sparse.csr_matrix.dot(A, X_test.T)
hidden_test = normalize(hidden_test, axis=1, norm='l1')
z2_test = np.dot(B, hidden_test)
Y_hat_test = softmax(z2_test, theta=1.0, axis=0)
loglike_test = np.log(Y_hat_test)
test_loss = -np.multiply(
y_test, loglike_test.T) # need to multiply element wise here
test_loss = np.sum(test_loss) / N_test
print("Test: ", test_loss)
## MANUAL SET TESTING LOSS
hidden_man = sparse.csr_matrix.dot(A, X_manual.T)
hidden_man = normalize(hidden_man, axis=1, norm='l1')
z2_man = np.dot(B, hidden_man)
Y_hat_man = softmax(z2_man, theta=1.0, axis=0)
loglike_manual = np.log(Y_hat_man)
manual_loss = -np.multiply(
y_manual, loglike_manual.T) # need to multiply element wise here
manual_loss = np.sum(manual_loss) / N_manual
print("Manual Set: ", manual_loss)
#### Back prop #########################################################
# B update
#gradient = alpha * np.dot(np.subtract(Y_hat.T, y_train).T, hidden.T)
#B = np.subtract(B, gradient)
# A update
#first = np.dot(np.subtract(Y_hat.T, y_train), B)
#gradient = alpha * sparse.csr_matrix.dot(first.T, X_train)
#A = np.subtract(A, gradient)
#train_class_error, train_precision, train_recall, train_F1, train_AUC, train_FPR, train_TPR = metrics(Y_hat, y_train)
#test_class_error, test_precision, test_recall, test_F1, test_AUC, test_FPR, test_TPR = metrics(X_test, y_test, A, B, N_test)
#manual_class_error, manual_precision, manual_recall, manual_F1, manual_AUC, manual_FPR, manual_TPR = metrics(X_manual, y_manual, A, B, N_manual)
#print()
#print("TRAIN:")
#print(" Classification Err: ", train_class_error)
#print(" Precision: ", train_precision)
#print(" Recall: ", train_recall)
#print(" F1: ", train_F1)
#print("TEST:")
#print(" Classification Err: ", test_class_error)
#print(" Precision: ", test_precision)
#print(" Recall: ", test_recall)
#print(" F1: ", test_F1)
#print()
#print("MANUAL:")
#print(" Classification Err: ", manual_class_error)
#print(" Precision: ", manual_precision)
#print(" Recall: ", manual_recall)
#print(" F1: ", manual_F1)
losses_train.append(train_loss)
losses_test.append(test_loss)
losses_manual.append(manual_loss)
i += 1
traintime_end = time.time()
print("model took ", (traintime_end - traintime_start) / 60.0,
" time to train")
epochs = [l for l in range(EPOCH)]
plt.plot(epochs, losses_train, 'm', label="train")
plt.plot(epochs, losses_test, 'c', label="test")
plt.plot(epochs, losses_manual, 'g', label="manual")
title = "Main_temp: n_train: ", N_train, " n_test: ", N_test, " n_manual ", N_manual
plt.title(title)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(loc='upper left')
plt.show()
def main():
# args from Simple Queries paper
# DONT adjust these
DIM = 30
WORDGRAMS = 3
MINCOUNT = 2
MINN = 3
MAXN = 3
BUCKET = 1000000
# adjust these
EPOCH = 20
LR = 0.20 #0.15 good for ~5000
KERN = 'lin' # lin or rbf or poly
NUM_RUNS = 5 # number of test runs
SUBSET_VAL = 1000 # number of subset instances for self reported dataset
LIN_C = 0.90 # hyperparameter for linear kernel
model = 'kmm'
file_names = [
'KMMoutput/loss_train.txt', 'KMMoutput/loss_test.txt',
'KMMoutput/loss_manual.txt', 'KMMoutput/error_train.txt',
'KMMoutput/error_test.txt', 'KMMoutput/error_manual.txt',
'KMMoutput/precision_train.txt', 'KMMoutput/precision_test.txt',
'KMMoutput/precision_manual.txt', 'KMMoutput/recall_train.txt',
'KMMoutput/recall_test.txt', 'KMMoutput/recall_manual.txt',
'KMMoutput/F1_train.txt', 'KMMoutput/F1_test.txt',
'KMMoutput/F1_manual.txt', 'KMMoutput/AUC_train.txt',
'KMMoutput/AUC_test.txt', 'KMMoutput/AUC_manual.txt'
]
create_readme(DIM, WORDGRAMS, MINCOUNT, MINN, MAXN, BUCKET, EPOCH, LR,
KERN, NUM_RUNS, SUBSET_VAL, LIN_C)
##### instantiations #######################################
for run in range(NUM_RUNS):
print(
"*******************************************************RUN NUMBER: ",
run)
print()
print("starting dictionary creation")
# dictionary must be recreated each run to get different subsample each time
# initialize training
start = time.time()
dictionary = Dictionary(WORDGRAMS, MINCOUNT, BUCKET, KERN, SUBSET_VAL,
LIN_C, model)
end = time.time()
print("Dictionary took ", (end - start) / 60.0, " minutes to create.")
nwords = dictionary.get_nwords()
nclasses = dictionary.get_nclasses()
#initialize testing
X_train, X_test, y_train, y_test = dictionary.get_train_and_test()
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
N_train = dictionary.get_n_train_instances()
N_test = dictionary.get_n_test_instances()
print("Number of Train instances: ", N_train,
" Number of Test instances: ", N_test)
ntrain_eachclass = dictionary.get_nlabels_eachclass_train()
ntest_eachclass = dictionary.get_nlabels_eachclass_test()
print("N each class TRAIN: ", ntrain_eachclass, " N each class TEST: ",
ntest_eachclass)
# manual labeled set (Kaggle dataset)
X_manual = dictionary.get_manual_testset()
y_manual = dictionary.get_manual_set_labels()
N_manual = dictionary.get_n_manual_instances()
print()
print("Number of Manual testing instances: ", N_manual, " shape: ",
X_manual.shape)
nmanual_eachclass = dictionary.get_nlabels_eachclass_manual()
print("N each class Manual testing instances: ", nmanual_eachclass)
print("#####################################")
p = X_train.shape[1]
# A
#A_n = nwords + BUCKET # cols
A_n = p
A_m = DIM # rows
uniform_val = 1.0 / DIM
np.random.seed(0)
A = np.random.uniform(-uniform_val, uniform_val, (A_m, A_n))
# B
B_n = DIM # cols
B_m = nclasses # rows
B = np.zeros((B_m, B_n))
beta = dictionary.get_optbeta(
) # NOTE: optimal KMM reweighting coefficient
# NOTE: run with ones to check implementation. Should get values close to original (w/out reweithting coef)
#beta = np.ones((N_train))
#### train ################################################
print()
print()
for i in range(EPOCH):
print()
print("EPOCH: ", i)
# linearly decaying lr alpha
alpha = LR * (1 - i / EPOCH)
l = 0
train_loss = 0
# TRAINING
for x in X_train:
beta_n = beta[l]
label = y_train[l]
B_old = B
A_old = A
# Forward Propogation
hidden = sparse.csr_matrix.dot(A_old, x.T)
if np.sum(x) > 0:
a1 = hidden / np.sum(x)
else:
a1 = hidden
z2 = np.dot(B, a1)
exps = np.exp(z2 - np.max(z2))
Y_hat = exps / np.sum(exps)
# Back prop with alt optimization
B = gradient_B(B_old, A_old, x, label, nclasses, alpha, DIM,
a1, Y_hat, beta_n)
A = gradient_A(B_old, A_old, x, label, nclasses, alpha, DIM,
Y_hat, beta_n)
# verify gradients
#check_B_gradient(B_old, A_old, label, x, Y_hat, a1)
#check_A_gradient(B_old, A_old, label, x, Y_hat)
loglike = np.log(Y_hat)
train_loss += -np.dot(label, loglike)
l += 1
# TRAINING LOSS
#train_loss = total_loss_function(X_train, y_train, A, B, N_train)
train_loss = (1.0 / N_train) * train_loss
print("Train: ", train_loss)
# TESTING LOSS
test_loss = total_loss_function(X_test, y_test, A_old, B_old,
N_test)
print("Test: ", test_loss)
print("Difference = ", test_loss - train_loss)
# MANUAL SET TESTING LOSS
manual_loss = total_loss_function(X_manual, y_manual, A_old, B_old,
N_manual)
print("Manual Set: ", manual_loss)
train_class_error, train_precision, train_recall, train_F1, train_AUC, train_FPR, train_TPR = metrics(
X_train, y_train, A, B, N_train)
test_class_error, test_precision, test_recall, test_F1, test_AUC, test_FPR, test_TPR = metrics(
X_test, y_test, A, B, N_test)
manual_class_error, manual_precision, manual_recall, manual_F1, manual_AUC, manual_FPR, manual_TPR = metrics(
X_manual, y_manual, A, B, N_manual)
print()
print("TRAIN:")
print(" Classification Err: ", train_class_error)
print(" Precision: ", train_precision)
print(" Recall: ", train_recall)
print(" F1: ", train_F1)
print("TEST:")
print(" Classification Err: ", test_class_error)
print(" Precision: ", test_precision)
print(" Recall: ", test_recall)
print(" F1: ", test_F1)
print()
print("MANUAL:")
print(" Classification Err: ", manual_class_error)
print(" Precision: ", manual_precision)
print(" Recall: ", manual_recall)
print(" F1: ", manual_F1)
#### WRITING LOSSES
with open('KMMoutput/loss_train.txt', '+a') as f:
f.write("%s," % train_loss)
with open('KMMoutput/loss_test.txt', '+a') as f:
f.write("%s," % test_loss)
with open('KMMoutput/loss_manual.txt', '+a') as f:
f.write("%s," % manual_loss)
#### WRITING ERROR
with open('KMMoutput/error_train.txt', '+a') as f:
f.write("%s," % train_class_error)
with open('KMMoutput/error_test.txt', '+a') as f:
f.write("%s," % test_class_error)
with open('KMMoutput/error_manual.txt', '+a') as f:
f.write("%s," % manual_class_error)
#### WRITING PRECISION
with open('KMMoutput/precision_train.txt', '+a') as f:
f.write("%s," % train_precision)
with open('KMMoutput/precision_test.txt', '+a') as f:
f.write("%s," % test_precision)
with open('KMMoutput/precision_manual.txt', '+a') as f:
f.write("%s," % manual_precision)
#### WRITING RECALL
with open('KMMoutput/recall_train.txt', '+a') as f:
f.write("%s," % train_recall)
with open('KMMoutput/recall_test.txt', '+a') as f:
f.write("%s," % test_recall)
with open('KMMoutput/recall_manual.txt', '+a') as f:
f.write("%s," % manual_recall)
#### WRITING F1
with open('KMMoutput/F1_train.txt', '+a') as f:
f.write("%s," % train_F1)
with open('KMMoutput/F1_test.txt', '+a') as f:
f.write("%s," % test_F1)
with open('KMMoutput/F1_manual.txt', '+a') as f:
f.write("%s," % manual_F1)
#### WRITING AUC
with open('KMMoutput/AUC_train.txt', '+a') as f:
f.write("%s," % train_AUC)
with open('KMMoutput/AUC_test.txt', '+a') as f:
f.write("%s," % test_AUC)
with open('KMMoutput/AUC_manual.txt', '+a') as f:
f.write("%s," % manual_AUC)
i += 1
# writing newline to file after each trial
for name in file_names:
with open(name, '+a') as f:
f.write('\n')
run += 1
return n_tr / n_te * np.sum(_sum)
def get_optbeta():
return beta
###################################################################
WORDGRAMS = 3
MINCOUNT = 2
BUCKET = 1000000
print("starting dictionary creation.............................")
dictionary = Dictionary(WORDGRAMS, MINCOUNT, BUCKET)
X_train, X_test, y_train, y_test = dictionary.get_train_and_test()
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
n_train = dictionary.get_n_train_instances()
n_test = dictionary.get_n_manual_instances()
X_train = dictionary.get_trainset()
X_test = dictionary.get_manual_testset()
B = n_train
#sigma = np.std(X_train) # compute standard deviation ????
sigma = 0.25
b = (0.0, B)
bounds = (b, b, b, b, b)
beta0 = np.zeros((n_train))
def main():
# args from Simple Queries paper
DIM = 30
LR = 0.20 #0.15 good for ~5000
WORDGRAMS = 3
MINCOUNT = 2
MINN = 3
MAXN = 3
BUCKET = 1000000
EPOCH = 20
KERN = 'lin' # lin or rbf or poly
NUM_RUNS = 5 # number of test runs
SUBSET_VAL = 1000 # number of subset instances for self reported dataset
LIN_C = 0.90 # hyperparameter for linear kernel
print("starting dictionary creation")
# initialize training
start = time.time()
dictionary = Dictionary(WORDGRAMS, MINCOUNT, BUCKET, KERN, SUBSET_VAL,
LIN_C)
end = time.time()
print("Dictionary took ", (end - start) / 60.0, " minutes to create.")
nwords = dictionary.get_nwords()
nclasses = dictionary.get_nclasses()
#initialize testing
X_train, X_test, y_train, y_test = dictionary.get_train_and_test()
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
N_train = dictionary.get_n_train_instances()
N_test = dictionary.get_n_test_instances()
print("Number of Train instances: ", N_train,
" Number of Test instances: ", N_test)
ntrain_eachclass = dictionary.get_nlabels_eachclass_train()
ntest_eachclass = dictionary.get_nlabels_eachclass_test()
print("N each class TRAIN: ", ntrain_eachclass, " N each class TEST: ",
ntest_eachclass)
# manual labeled set (Kaggle dataset)
X_manual = dictionary.get_manual_testset()
y_manual = dictionary.get_manual_set_labels()
N_manual = dictionary.get_n_manual_instances()
print()
print("Number of Manual testing instances: ", N_manual, " shape: ",
X_manual.shape)
nmanual_eachclass = dictionary.get_nlabels_eachclass_manual()
print("N each class Manual testing instances: ", nmanual_eachclass)
print("################################################################")
beta = dictionary.get_optbeta(
) # NOTE: optimal KMM reweighting coefficient
# NOTE: run with ones to check implementation. Should get values close to original (w/out reweithting coef)
#beta = np.ones((N_train))
##### instantiations #######################################
p = X_train.shape[1]
# A
#A_n = nwords + BUCKET # cols
A_n = p
A_m = DIM # rows
uniform_val = 1.0 / DIM
np.random.seed(0)
A = np.random.uniform(-uniform_val, uniform_val, (A_m, A_n))
# B
B_n = DIM # cols
B_m = nclasses # rows
B = np.zeros((B_m, B_n))
#### train ################################################
losses_train = []
losses_test = []
losses_manual = []
class_error_train = []
class_error_test = []
class_error_manual = []
prec_train = []
prec_test = []
prec_manual = []
recall_train = []
recall_test = []
recall_manual = []
F1_train = []
F1_test = []
F1_manual = []
AUC_train = []
AUC_test = []
AUC_manual = []
print()
print()
for i in range(EPOCH):
print()
print("EPOCH: ", i)
# linearly decaying lr alpha
alpha = LR * (1 - i / EPOCH)
l = 0
train_loss = 0
# TRAINING
for x in X_train:
beta_n = beta[l]
label = y_train[l]
B_old = B
A_old = A
# Forward Propogation
hidden = sparse.csr_matrix.dot(A_old, x.T)
if np.sum(x) > 0:
a1 = hidden / np.sum(x)
else:
a1 = hidden
z2 = np.dot(B, a1)
exps = np.exp(z2 - np.max(z2))
Y_hat = exps / np.sum(exps)
# Back prop with alt optimization
B = gradient_B(B_old, A_old, x, label, nclasses, alpha, DIM, a1,
Y_hat, beta_n)
A = gradient_A(B_old, A_old, x, label, nclasses, alpha, DIM, Y_hat,
beta_n)
# verify gradients
#check_B_gradient(B_old, A_old, label, x, Y_hat, a1)
#check_A_gradient(B_old, A_old, label, x, Y_hat)
loglike = np.log(Y_hat)
#train_loss += -beta_n * np.dot(label, loglike)
train_loss += -np.dot(label, loglike)
l += 1
# TRAINING LOSS
#train_loss = total_loss_function(X_train, y_train, A, B, N_train)
train_loss = (1.0 / N_train) * train_loss
print("Train: ", train_loss)
# TESTING LOSS
test_loss = total_loss_function(X_test, y_test, A_old, B_old, N_test,
beta)
print("Test: ", test_loss)
print("Difference = ", test_loss - train_loss)
# MANUAL SET TESTING LOSS
manual_loss = total_loss_function(X_manual, y_manual, A_old, B_old,
N_manual, beta)
print("Manual Set: ", manual_loss)
train_class_error, train_precision, train_recall, train_F1, train_AUC, train_FPR, train_TPR = metrics(
X_train, y_train, A, B, N_train)
test_class_error, test_precision, test_recall, test_F1, test_AUC, test_FPR, test_TPR = metrics(
X_test, y_test, A, B, N_test)
manual_class_error, manual_precision, manual_recall, manual_F1, manual_AUC, manual_FPR, manual_TPR = metrics(
X_manual, y_manual, A, B, N_manual)
print()
print("TRAIN:")
print(" Classification Err: ", train_class_error)
print(" Precision: ", train_precision)
print(" Recall: ", train_recall)
print(" F1: ", train_F1)
print("TEST:")
print(" Classification Err: ", test_class_error)
print(" Precision: ", test_precision)
print(" Recall: ", test_recall)
print(" F1: ", test_F1)
print()
print("MANUAL:")
print(" Classification Err: ", manual_class_error)
print(" Precision: ", manual_precision)
print(" Recall: ", manual_recall)
print(" F1: ", manual_F1)
losses_train.append(train_loss)
losses_test.append(test_loss)
losses_manual.append(manual_loss)
class_error_train.append(train_class_error)
class_error_test.append(test_class_error)
class_error_manual.append(manual_class_error)
prec_train.append(train_precision)
prec_test.append(test_precision)
prec_manual.append(manual_precision)
recall_train.append(train_recall)
recall_test.append(test_recall)
recall_manual.append(manual_recall)
F1_train.append(train_F1)
F1_test.append(test_F1)
F1_manual.append(manual_F1)
AUC_train.append(train_AUC)
AUC_test.append(test_AUC)
AUC_manual.append(manual_AUC)
i += 1
epochs = [l for l in range(EPOCH)]
txt = "LR: ", LR, " Kern: ", KERN,
plt.plot(epochs, losses_train, 'm', label="train")
plt.plot(epochs, losses_test, 'c', label="test")
plt.plot(epochs, losses_manual, 'g', label="manual")
plt.ylabel('loss')
plt.xlabel('epoch')
title = "KMM LOSS, n_train: ", N_train, " n_test: ", N_test, " n_manual ", N_manual
plt.title(title)
plt.legend(loc='upper left')
plt.text(.5, .05, txt, ha='center')
plt.show()
plt.plot(epochs,
class_error_train,
'm',
label="train classification error")
plt.plot(epochs, class_error_test, 'c', label="test classification error")
plt.plot(epochs,
class_error_manual,
'g',
label="manual classification error")
plt.ylabel('loss')
plt.xlabel('epoch')
title = "KMM CLASS ERROR, n_train: ", N_train, " n_test: ", N_test, " n_manual ", N_manual, " kern: ", KERN
plt.title(title)
plt.legend(loc='upper left')
plt.text(.5, .05, txt, ha='center')
plt.show()
def main():
# args from Simple Queries paper
DIM = 30
WORDGRAMS = 2
MINCOUNT = 8
MINN = 3
MAXN = 3
BUCKET = 1000000
# adjust these
EPOCH = 20
LR = 0.15 # 0.15 good for ~5000
KERN = 'lin' # lin or rbf or poly
NUM_RUNS = 1 # number of test runs
SUBSET_VAL = 800 # number of subset instances for self reported dataset
LIN_C = 0.90 # hyperparameter for linear kernel
BATCHSIZE = 2 # number of instances in each batch
##### instantiations #######################################
print("starting dictionary creation")
# dictionary must be recreated each run to get different subsample each time
# initialize training
start = time.time()
dictionary = Dictionary(WORDGRAMS,
MINCOUNT,
BUCKET,
KERN,
SUBSET_VAL,
LIN_C,
model='original')
end = time.time()
print("dictionary took ", (end - start) / 60.0, " time to create.")
nwords = dictionary.get_nwords()
nclasses = dictionary.get_nclasses()
#initialize testing
X_train, X_test, y_train, y_test = dictionary.get_train_and_test()
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
N_train = dictionary.get_n_train_instances()
N_test = dictionary.get_n_test_instances()
print("Number of Train instances: ", N_train,
" Number of Test instances: ", N_test)
ntrain_eachclass = dictionary.get_nlabels_eachclass_train()
ntest_eachclass = dictionary.get_nlabels_eachclass_test()
print("N each class TRAIN: ", ntrain_eachclass, " N each class TEST: ",
ntest_eachclass)
# manual labeled set (Kaggle dataset)
X_manual = dictionary.get_manual_testset()
y_manual = dictionary.get_manual_set_labels()
N_manual = dictionary.get_n_manual_instances()
print()
print("Number of Manual testing instances: ", N_manual, " shape: ",
X_manual.shape)
nmanual_eachclass = dictionary.get_nlabels_eachclass_manual()
print("N each class Manual testing instances: ", nmanual_eachclass)
print("#####################################")
p = X_train.shape[1]
# A
#A_n = nwords + BUCKET # cols
A_n = p
A_m = DIM # rows
uniform_val = 1.0 / DIM
np.random.seed(0)
A = np.random.uniform(-uniform_val, uniform_val, (A_m, A_n))
# B
B_n = DIM # cols
B_m = nclasses # rows
B = np.zeros((B_m, B_n))
#### train ################################################
losses_train = []
losses_test = []
losses_manual = []
print()
print()
traintime_start = time.time()
for i in range(EPOCH):
print()
print("EPOCH: ", i)
# linearly decaying lr alpha
alpha = LR * (1 - i / EPOCH)
l = 0
train_loss = 0
start = 0
batchnum = 0
while start <= N_train:
batch = X_train.tocsr()[start:start + BATCHSIZE, :]
y_train_batch = y_train[start:start + BATCHSIZE, :]
B_old = B
A_old = A
# Forward Propogation
hidden = sparse.csr_matrix.dot(A, batch.T)
sum_ = np.sum(batch, axis=1)
sum_[sum_ == 0] = 1 # replace zeros with ones so divide will work
sum_ = np.array(sum_).flatten()
a1 = (hidden.T / sum_[:, None]).T
z2 = np.dot(B, a1)
Y_hat = stable_softmax(z2)
# Back prop with alt optimization
B = gradient_B(B_old, A_old, y_train_batch, alpha, a1, Y_hat)
A = gradient_A(B_old, A_old, batch, y_train_batch, alpha, sum_,
Y_hat)
#loglike = np.log(Y_hat)
#train_loss += -np.dot(y_train_batch, loglike)
batchnum += 1
# NOTE figure this out, Might be missing last sample
if start + BATCHSIZE >= N_train and start < N_train - 1:
batch = X_train.tocsr()[start:-1, :] # rest of train set
y_train_batch = y_train[start:-1, :]
B_old = B
A_old = A
# Forward Propogation
hidden = sparse.csr_matrix.dot(A, batch.T)
sum_ = np.sum(batch, axis=1)
sum_[sum_ ==
0] = 1 # replace zeros with ones so divide will work
sum_ = np.array(sum_).flatten()
a1 = (hidden.T / sum_[:, None]).T
z2 = np.dot(B, a1)
Y_hat = stable_softmax(z2)
# Back prop with alt optimization
B = gradient_B(B_old, A_old, y_train_batch, alpha, a1, Y_hat)
A = gradient_A(B_old, A_old, batch, y_train_batch, alpha, sum_,
Y_hat)
#loglike = np.log(Y_hat)
#train_loss += -np.dot(y_train_batch, loglike)
break
else:
start = start + BATCHSIZE
# TRAINING LOSS
#train_loss = train_loss * (1.0/N_train)
#print("Train: ", train_loss)
train_loss = get_total_loss(A, B, X_train, y_train, N_train)
print("Train: ", train_loss)
## TESTING LOSS
test_loss = get_total_loss(A, B, X_test, y_test, N_test)
print("Test: ", test_loss)
#print("Difference = ", test_loss - train_loss)
## MANUAL SET TESTING LOSS
manual_loss = get_total_loss(A, B, X_manual, y_manual, N_manual)
print("Manual Set: ", manual_loss)
#train_class_error, train_precision, train_recall, train_F1, train_AUC, train_FPR, train_TPR = metrics(X_train, y_train, A, B, N_train)
train_class_error = metrics(X_train, y_train, A, B, N_train)
#test_class_error, test_precision, test_recall, test_F1, test_AUC, test_FPR, test_TPR = metrics(X_test, y_test, A, B, N_test)
test_class_error = metrics(X_test, y_test, A, B, N_test)
#manual_class_error, manual_precision, manual_recall, manual_F1, manual_AUC, manual_FPR, manual_TPR = metrics(X_manual, y_manual, A, B, N_manual)
manual_class_error = metrics(X_manual, y_manual, A, B, N_manual)
print()
print("TRAIN:")
print(" Classification Err: ", train_class_error)
#print(" Precision: ", train_precision)
#print(" Recall: ", train_recall)
#print(" F1: ", train_F1)
print("TEST:")
print(" Classification Err: ", test_class_error)
#print(" Precision: ", test_precision)
#print(" Recall: ", test_recall)
#print(" F1: ", test_F1)
print()
print("MANUAL:")
print(" Classification Err: ", manual_class_error)
#print(" Precision: ", manual_precision)
#print(" Recall: ", manual_recall)
#print(" F1: ", manual_F1)
losses_train.append(train_loss)
losses_test.append(test_loss)
losses_manual.append(manual_loss)
i += 1
traintime_end = time.time()
print("model took ", (traintime_end - traintime_start) / 60.0,
" time to train")
epochs = [l for l in range(EPOCH)]
plt.plot(epochs, losses_train, 'm', label="train")
plt.plot(epochs, losses_test, 'c', label="test")
plt.plot(epochs, losses_manual, 'g', label="manual")
title = "Main_temp: n_train: ", N_train, " n_test: ", N_test, " n_manual ", N_manual
#title = "Main_temp: n_train: ", N_train, " n_test: ", N_test
plt.title(title)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(loc='upper left')
plt.show()