def main():
train_file = os.path.join('../data', 'train.jsonl')
df = data.get_train(train_file)
#phase_1_evaluate_small_random_samples(df)
train_set, _ = train_test_split(
df, train_size=5,
random_state=seed) # need to look into NEI ratios and balance
phase_1(train_set)
def main():
np.random.seed(0) #
inp, labels = get_train(True)
N = len(labels)
train_inp, train_labels = inp[:N * 4 // 5], labels[:N * 4 // 5]
test_inp, test_labels = inp[N * 4 // 5:N], labels[N * 4 // 5:N]
weights = initialize([2500, 1])
# train_labels = np.zeros((2000))
# weights = [np.ones((3,3)), np.ones((3,1))]
# train_inp = np.ones((2,1,3))
losses = []
for i in range(10):
out, train_pred = feed_forward(weights, train_inp)
losses.append(get_error(train_labels, train_pred))
weights = train_logreg(weights, out, train_labels, train_pred)
print('***TRAIN***')
evaluate(train_labels, train_pred)
print('***TEST***')
_, test_pred = feed_forward(weights, test_inp)
evaluate(test_labels, test_pred)
plot(losses, '0 hidden layers')
def main():
np.random.seed(0) # 0 42 50
inp, labels = get_train(True)
N = len(labels)
train_inp, train_labels = inp[:N*4//5], labels[:N*4//5]
test_inp, test_labels = inp[N*4//5:N], labels[N*4//5:N]
for n_units in [30, 40, 50]:
weights = initialize([2500, n_units, 1])
# train_labels = np.zeros((2000))
# weights = [np.ones((3,3)), np.ones((3,1))]
# train_inp = np.ones((2,1,3))
losses = []
for i in range(150):
out, train_pred = feed_forward(weights, train_inp)
losses.append(get_error(train_labels, train_pred))
weights = backpropagate(weights, out, train_labels, train_pred)
print('***TRAIN***')
evaluate(train_labels, train_pred)
print('***TEST***')
_, test_pred = feed_forward(weights, test_inp)
evaluate(test_labels, test_pred)
plot(losses, f'Hidden Units = {n_units}')
metadata_path_all = glob.glob(sys.argv[1] + "*")
print("shape of metadata_path_all")
print(len(metadata_path_all))
if len(sys.argv) >= 3:
subset = sys.argv[2]
assert subset in ['train', 'valid', 'test', 'train_valid']
else:
subset = 'test'
if subset == "test":
X, mask, _, num_seq = data.get_test()
elif subset == "train":
X_train, _, _, _, mask_train, _, num_seq = data.get_train()
elif subset == "train_valid":
X_train, X_valid, _, _, mask_train, mask_valid, num_seq = data.get_train()
X = np.concatenate((X_train[:-30], X_valid))
mask = np.concatenate((mask_train[:-30], mask_valid))
else:
_, X, _, _, _, mask, num_seq = data.get_train()
for metadata_path in metadata_path_all:
print("Loading metadata file %s" % metadata_path)
metadata = np.load(metadata_path)
config_name = metadata['config_name']
def main():
sym_y = T.imatrix('target_output')
sym_mask = T.matrix('mask')
sym_x = T.tensor3()
TOL = 1e-5
num_epochs = config.epochs
batch_size = config.batch_size
#### DATA ####
# print "@@@@TESTING@@@@"
# l_in = nn.layers.InputLayer(shape=(None, 700, 42))
# l_dim_a = nn.layers.DimshuffleLayer(
# l_in, (0,2,1))
# l_conv_a = nn.layers.Conv1DLayer(
# incoming=l_dim_a, num_filters=42, border_mode='same',
# filter_size=3, stride=1, nonlinearity=nn.nonlinearities.rectify)
# l_dim_b = nn.layers.DimshuffleLayer(
# l_conv_a, (0,2,1))
# out = nn.layers.get_output(l_dim_b, sym_x)
# testvar = np.ones((128, 700, 42)).astype('float32')
# print "@@@@EVAL@@@@"
# john = out.eval({sym_x: testvar})
# print("Johns shape")
# print(john.shape)
print("Building network ...")
##########################DEBUG##########################
l_in, l_out = config.build_model()
##########################DEBUG##########################
all_layers = nn.layers.get_all_layers(l_out)
num_params = nn.layers.count_params(l_out)
print(" number of parameters: %d" % num_params)
print(" layer output shapes:")
for layer in all_layers:
name = layer.__class__.__name__
print(" %s %s" % (name, nn.layers.get_output_shape(layer)))
print("Creating cost function")
# lasagne.layers.get_output produces a variable for the output of the net
out_train = nn.layers.get_output(
l_out, sym_x, deterministic=False)
# testvar = np.ones((128, 700, 42)).astype('float32')
# john = out_train.eval({sym_x: testvar})
# print("@@@@@JOHN@@@@@")
# print(john.shape)
# print(john.reshape((-1, num_classes)).shape)
print("Creating eval function")
out_eval = nn.layers.get_output(
l_out, sym_x, deterministic=True)
probs_flat = out_train.reshape((-1, num_classes))
lambda_reg = config.lambda_reg
all_params = nn.layers.get_all_params(l_out)
for i, p in enumerate(all_params):
if p.ndim == 3:
values = p.get_value()
if side == 'right':
values[..., int(values.shape[2] / 2.0 - 0.5):] = 0
p.set_value(values)
all_params[i] = p[..., : int(values.shape[2] / 2.0 - 0.5)]
else:
values[..., : int(values.shape[2] / 2.0 + 0.5)] = 0
p.set_value(values)
all_params[i] = p[..., int(values.shape[2] / 2.0 + 0.5):]
params = [el for el in all_params if el.name == "W" or el.name == "gamma"]
reg_term = sum(T.sum(p ** 2) for p in params)
cost = T.nnet.categorical_crossentropy(T.clip(probs_flat, TOL, 1 - TOL), sym_y.flatten())
cost = T.sum(cost * sym_mask.flatten()) / T.sum(sym_mask) + lambda_reg * reg_term
# Retrieve all parameters from the network
all_params = [el for el in all_params if el.name == "W" or el.name == "gamma" or el.name == "beta"]
# Setting the weights
if hasattr(config, 'set_weights'):
nn.layers.set_all_param_values(l_out, config.set_weights())
# Compute SGD updates for training
print("Computing updates ...")
if hasattr(config, 'learning_rate_schedule'):
learning_rate_schedule = config.learning_rate_schedule # Import learning rate schedule
else:
learning_rate_schedule = {0: config.learning_rate}
learning_rate = theano.shared(np.float32(learning_rate_schedule[0]))
all_grads = T.grad(cost, all_params)
cut_norm = config.cut_grad
updates, norm_calc = nn.updates.total_norm_constraint(all_grads, max_norm=cut_norm, return_norm=True)
if optimizer == "rmsprop":
updates = nn.updates.rmsprop(updates, all_params, learning_rate)
elif optimizer == "adadelta":
updates = nn.updates.adadelta(updates, all_params, learning_rate)
elif optimizer == "adagrad":
updates = nn.updates.adagrad(updates, all_params, learning_rate)
elif optimizer == "nag":
momentum_schedule = config.momentum_schedule
momentum = theano.shared(np.float32(momentum_schedule[0]))
updates = nn.updates.nesterov_momentum(updates, all_params, learning_rate, momentum)
else:
sys.exit("please choose either <rmsprop/adagrad/adadelta/nag> in configfile")
# Theano functions for training and computing cost
print ("config.batch_size %d" % batch_size)
print ("data.num_classes %d" % num_classes)
if hasattr(config, 'build_model'):
print("has build model")
print("Compiling train ...")
# Use this for training (see deterministic = False above)
train = theano.function(
[sym_x, sym_y, sym_mask], [cost, out_train, norm_calc], updates=updates)
print("Compiling eval ...")
# use this for eval (deterministic = True + no updates)
eval = theano.function([sym_x, sym_y, sym_mask], [cost, out_eval])
# Start timers
start_time = time.time()
prev_time = start_time
all_losses_train = []
all_accuracy_train = []
all_losses_eval_train = []
all_losses_eval_valid = []
all_losses_eval_test = []
all_accuracy_eval_train = []
all_accuracy_eval_valid = []
all_accuracy_eval_test = []
all_mean_norm = []
import data
X_train, X_valid, y_train, y_valid, mask_train, mask_valid, num_seq_train \
= data.get_train()
X_train, X_valid = X_train[..., 21:], X_valid[..., 21:] # Only train with pssm scores
print("y shape")
print(y_valid.shape)
print("X shape")
print(X_valid.shape)
# Start training
for i in range(y_train.shape[0]):
for j in range(y_train.shape[1]):
if y_train[i][j] == 5:
y_train[i][j] = 1
else:
y_train[i][j] = 0
for i in range(y_valid.shape[0]):
for j in range(y_valid.shape[1]):
if y_valid[i][j] == 5:
y_valid[i][j] = 1
else:
y_valid[i][j] = 0
for epoch in range(num_epochs):
if (epoch % 10) == 0:
print ("Epoch %d of %d" % (epoch + 1, num_epochs))
if epoch in learning_rate_schedule:
lr = np.float32(learning_rate_schedule[epoch])
print (" setting learning rate to %.7f" % lr)
learning_rate.set_value(lr)
if optimizer == "nag":
if epoch in momentum_schedule:
mu = np.float32(momentum_schedule[epoch])
print (" setting learning rate to %.7f" % mu)
momentum.set_value(mu)
# print "Shuffling data"
seq_names = np.arange(0, num_seq_train)
np.random.shuffle(seq_names)
X_train = X_train[seq_names]
y_train = y_train[seq_names]
mask_train = mask_train[seq_names]
num_batches = num_seq_train // batch_size
losses = []
preds = []
norms = []
for i in range(num_batches):
idx = range(i * batch_size, (i + 1) * batch_size)
x_batch = X_train[idx]
y_batch = y_train[idx]
mask_batch = mask_train[idx]
loss, out, batch_norm = train(x_batch, y_batch, mask_batch)
# print(batch_norm)
norms.append(batch_norm)
preds.append(out)
losses.append(loss)
# if ((i+1) % config.write_every_batch == 0) | (i == 0):
# if i == 0:
# start_place = 0
# else:
# start_place = i-config.write_every_batch
# print "Batch %d of %d" % (i + 1, num_batches)
# print " curbatch training loss: %.5f" % np.mean(losses[start_place:(i+1)])
# print " curbatch training acc: %.5f" % np.mean(accuracy[start_place:(i+1)])
predictions = np.concatenate(preds, axis=0)
loss_train = np.mean(losses)
all_losses_train.append(loss_train)
acc_train = utils.proteins_acc(predictions, y_train[0:num_batches * batch_size],
mask_train[0:num_batches * batch_size])
all_accuracy_train.append(acc_train)
mean_norm = np.mean(norms)
all_mean_norm.append(mean_norm)
if 1 == 1:
print (" average training loss: %.5f" % loss_train)
print (" average training accuracy: %.5f" % acc_train)
print (" average norm: %.5f" % mean_norm)
sets = [ # ('train', X_train, y_train, mask_train, all_losses_eval_train, all_accuracy_eval_train),
('valid', X_valid, y_valid, mask_valid, all_losses_eval_valid, all_accuracy_eval_valid)]
for subset, X, y, mask, all_losses, all_accuracy in sets:
print (" validating: %s loss" % subset)
preds = []
num_batches = np.size(X, axis=0) // config.batch_size
for i in range(num_batches): ## +1 to get the "rest"
# print(i)
idx = range(i * batch_size, (i + 1) * batch_size)
x_batch = X[idx]
y_batch = y[idx]
mask_batch = mask[idx]
loss, out = eval(x_batch, y_batch, mask_batch)
preds.append(out)
# acc = utils.proteins_acc(out, y_batch, mask_batch)
losses.append(loss)
# accuracy.append(acc)
predictions = np.concatenate(preds, axis=0)
# print " pred"
# print(predictions.shape)
# print(predictions.dtype)
loss_eval = np.mean(losses)
all_losses.append(loss_eval)
# acc_eval = np.mean(accuracy)
acc_eval = utils.proteins_acc(predictions, y, mask)
all_accuracy.append(acc_eval)
print (" average evaluation loss (%s): %.5f" % (subset, loss_eval))
print (" average evaluation accuracy (%s): %.5f" % (subset, acc_eval))
now = time.time()
time_since_start = now - start_time
time_since_prev = now - prev_time
prev_time = now
est_time_left = time_since_prev * (num_epochs - epoch)
eta = datetime.now() + timedelta(seconds=est_time_left)
eta_str = eta.strftime("%c")
print (" %s since start (%.2f s)" % (utils.hms(time_since_start), time_since_prev))
print (" estimated %s to go (ETA: %s)" % (utils.hms(est_time_left), eta_str))
print()
if (epoch >= config.start_saving_at) and ((epoch % config.save_every) == 0):
print (" saving parameters and metadata")
with open((metadata_path + side + "-%d" % (epoch) + ".pkl"), 'wb') as f:
pickle.dump({
'config_name': config_name,
'param_values': nn.layers.get_all_param_values(l_out),
'losses_train': all_losses_train,
'accuracy_train': all_accuracy_train,
'losses_eval_train': all_losses_eval_train,
'losses_eval_valid': all_losses_eval_valid,
'losses_eval_test': all_losses_eval_test,
'accuracy_eval_valid': all_accuracy_eval_valid,
'accuracy_eval_train': all_accuracy_eval_train,
'accuracy_eval_test': all_accuracy_eval_test,
'mean_norm': all_mean_norm,
'time_since_start': time_since_start,
'i': i,
}, f, pickle.HIGHEST_PROTOCOL)
print (" stored in %s" % metadata_path)
print()
deltrnstim = np.hstack([np.roll(trnstim, d, 0) for d in delays])
delvalstim = np.hstack([np.roll(valstim, d, 0) for d in delays])
#sdeltrnstim = scipy.sparse.csr_matrix(deltrnstim)
#sdelvalstim = scipy.sparse.csr_matrix(delvalstim)
zs = lambda m: (m - m.mean(0)) / m.std(0)
sdeltrnstim = deltrnstim = np.nan_to_num(zs(deltrnstim))
sdelvalstim = delvalstim = np.nan_to_num(zs(delvalstim))
# Select some voxels
ebamask = cortex.get_roi_mask("MLfs", "20121210ML_auto1", roi="EBA")["EBA"] > 0
# Load training, test fMRI data
trndata = data.get_train(masked=ebamask)[:numtime]
valdata = data.get_val(masked=ebamask)
from ridge import _RidgeGridCV
ridge = _RidgeGridCV(alpha_min=1., alpha_max=1000., n_grid_points=5,
n_grid_refinements=2, cv=2)
ridge_coefs = ridge.fit(deltrnstim, trndata).coef_.T
Uridge, sridge, VridgeT = np.linalg.svd(ridge_coefs, full_matrices=False)
ranks = [1, 2, 5, 10]
results = []
corr_scores = []
r2_scores = []
batch,
NUM_SENTS,
RETRIEVER,
SELECTOR,
oracle_doc_ret=isinstance(RETRIEVER, data.OracleDocRetriever),
)
if __name__ == "__main__":
device = "cuda" if torch.cuda.is_available() else "cpu"
###########################################################################
# Setup the datasets/loaders #
###########################################################################
train = data.get_train(TRAIN_PATH)
train, test = train_test_split(train)
torch.cuda.empty_cache()
train_dataset = data.FastDataset(train)
test_dataset = data.TestDataset(test)
train_loader = DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
collate_fn=prepare,
num_workers=0, # doesn't work with more than 1
)
test_loader = DataLoader(
def main():
sym_y = T.imatrix('target_output')
sym_mask = T.matrix('mask')
sym_x = T.tensor3()
TOL = 1e-5
num_epochs = config.epochs
batch_size = config.batch_size
#### DATA ####
# print "@@@@TESTING@@@@"
# l_in = nn.layers.InputLayer(shape=(None, 700, 42))
# l_dim_a = nn.layers.DimshuffleLayer(
# l_in, (0,2,1))
# l_conv_a = nn.layers.Conv1DLayer(
# incoming=l_dim_a, num_filters=42, border_mode='same',
# filter_size=3, stride=1, nonlinearity=nn.nonlinearities.rectify)
# l_dim_b = nn.layers.DimshuffleLayer(
# l_conv_a, (0,2,1))
# out = nn.layers.get_output(l_dim_b, sym_x)
# testvar = np.ones((128, 700, 42)).astype('float32')
# print "@@@@EVAL@@@@"
# john = out.eval({sym_x: testvar})
# print("Johns shape")
# print(john.shape)
print("Building network ...")
##########################DEBUG##########################
l_in, l_out = config.build_model()
##########################DEBUG##########################
all_layers = nn.layers.get_all_layers(l_out)
num_params = nn.layers.count_params(l_out)
print(" number of parameters: %d" % num_params)
print(" layer output shapes:")
for layer in all_layers:
name = string.ljust(layer.__class__.__name__, 32)
print(" %s %s" % (name, nn.layers.get_output_shape(layer)))
print("Creating cost function")
# lasagne.layers.get_output produces a variable for the output of the net
out_train = nn.layers.get_output(
l_out, sym_x, deterministic=False)
# testvar = np.ones((128, 700, 42)).astype('float32')
# john = out_train.eval({sym_x: testvar})
# print("@@@@@JOHN@@@@@")
# print(john.shape)
# print(john.reshape((-1, num_classes)).shape)
print("Creating eval function")
out_eval = nn.layers.get_output(
l_out, sym_x, deterministic=True)
probs_flat = out_train.reshape((-1, num_classes))
lambda_reg = config.lambda_reg
params = nn.layers.get_all_params(l_out, regularizable=True)
reg_term = sum(T.sum(p**2) for p in params)
cost = T.nnet.categorical_crossentropy(T.clip(probs_flat, TOL, 1-TOL), sym_y.flatten())
cost = T.sum(cost*sym_mask.flatten()) / T.sum(sym_mask) + lambda_reg * reg_term
# Retrieve all parameters from the network
all_params = nn.layers.get_all_params(l_out, trainable=True)
# Setting the weights
if hasattr(config, 'set_weights'):
nn.layers.set_all_param_values(l_out, config.set_weights())
# Compute SGD updates for training
print("Computing updates ...")
if hasattr(config, 'learning_rate_schedule'):
learning_rate_schedule = config.learning_rate_schedule # Import learning rate schedule
else:
learning_rate_schedule = { 0: config.learning_rate }
learning_rate = theano.shared(np.float32(learning_rate_schedule[0]))
all_grads = T.grad(cost, all_params)
cut_norm = config.cut_grad
updates, norm_calc = nn.updates.total_norm_constraint(all_grads, max_norm=cut_norm, return_norm=True)
if optimizer == "rmsprop":
updates = nn.updates.rmsprop(updates, all_params, learning_rate)
elif optimizer == "adadelta":
updates = nn.updates.adadelta(updates, all_params, learning_rate)
elif optimizer == "adagrad":
updates = nn.updates.adagrad(updates, all_params, learning_rate)
elif optimizer == "nag":
momentum_schedule = config.momentum_schedule
momentum = theano.shared(np.float32(momentum_schedule[0]))
updates = nn.updates.nesterov_momentum(updates, all_params, learning_rate, momentum)
else:
sys.exit("please choose either <rmsprop/adagrad/adadelta/nag> in configfile")
# Theano functions for training and computing cost
print "config.batch_size %d" %batch_size
print "data.num_classes %d" %num_classes
if hasattr(config, 'build_model'):
print("has build model")
print("Compiling train ...")
# Use this for training (see deterministic = False above)
train = theano.function(
[sym_x, sym_y, sym_mask], [cost, out_train, norm_calc], updates=updates)
print("Compiling eval ...")
# use this for eval (deterministic = True + no updates)
eval = theano.function([sym_x, sym_y, sym_mask], [cost, out_eval])
# Start timers
start_time = time.time()
prev_time = start_time
all_losses_train = []
all_accuracy_train = []
all_losses_eval_train = []
all_losses_eval_valid = []
all_losses_eval_test = []
all_accuracy_eval_train = []
all_accuracy_eval_valid = []
all_accuracy_eval_test = []
all_mean_norm = []
import data
X_train, X_valid, y_train, y_valid, mask_train, mask_valid, num_seq_train \
= data.get_train()
print("y shape")
print(y_valid.shape)
print("X shape")
print(X_valid.shape)
# Start training
for epoch in range(num_epochs):
if (epoch % 10) == 0:
print "Epoch %d of %d" % (epoch + 1, num_epochs)
if epoch in learning_rate_schedule:
lr = np.float32(learning_rate_schedule[epoch])
print " setting learning rate to %.7f" % lr
learning_rate.set_value(lr)
if optimizer == "nag":
if epoch in momentum_schedule:
mu = np.float32(momentum_schedule[epoch])
print " setting learning rate to %.7f" % mu
momentum.set_value(mu)
# print "Shuffling data"
seq_names = np.arange(0,num_seq_train)
np.random.shuffle(seq_names)
X_train = X_train[seq_names]
y_train = y_train[seq_names]
mask_train = mask_train[seq_names]
num_batches = num_seq_train // batch_size
losses = []
preds = []
norms = []
for i in range(num_batches):
idx = range(i*batch_size, (i+1)*batch_size)
x_batch = X_train[idx]
y_batch = y_train[idx]
mask_batch = mask_train[idx]
loss, out, batch_norm = train(x_batch, y_batch, mask_batch)
# print(batch_norm)
norms.append(batch_norm)
preds.append(out)
losses.append(loss)
# if ((i+1) % config.write_every_batch == 0) | (i == 0):
# if i == 0:
# start_place = 0
# else:
# start_place = i-config.write_every_batch
# print "Batch %d of %d" % (i + 1, num_batches)
# print " curbatch training loss: %.5f" % np.mean(losses[start_place:(i+1)])
# print " curbatch training acc: %.5f" % np.mean(accuracy[start_place:(i+1)])
predictions = np.concatenate(preds, axis = 0)
loss_train = np.mean(losses)
all_losses_train.append(loss_train)
acc_train = utils.proteins_acc(predictions, y_train[0:num_batches*batch_size], mask_train[0:num_batches*batch_size])
all_accuracy_train.append(acc_train)
mean_norm = np.mean(norms)
all_mean_norm.append(mean_norm)
if 1==1:
print " average training loss: %.5f" % loss_train
print " average training accuracy: %.5f" % acc_train
print " average norm: %.5f" % mean_norm
sets = [#('train', X_train, y_train, mask_train, all_losses_eval_train, all_accuracy_eval_train),
('valid', X_valid, y_valid, mask_valid, all_losses_eval_valid, all_accuracy_eval_valid)]
for subset, X, y, mask, all_losses, all_accuracy in sets:
print " validating: %s loss" % subset
preds = []
num_batches = np.size(X,axis=0) // config.batch_size
for i in range(num_batches): ## +1 to get the "rest"
# print(i)
idx = range(i*batch_size, (i+1)*batch_size)
x_batch = X[idx]
y_batch = y[idx]
mask_batch = mask[idx]
loss, out = eval(x_batch, y_batch, mask_batch)
preds.append(out)
# acc = utils.proteins_acc(out, y_batch, mask_batch)
losses.append(loss)
# accuracy.append(acc)
predictions = np.concatenate(preds, axis = 0)
# print " pred"
# print(predictions.shape)
# print(predictions.dtype)
loss_eval = np.mean(losses)
all_losses.append(loss_eval)
# acc_eval = np.mean(accuracy)
acc_eval = utils.proteins_acc(predictions, y, mask)
all_accuracy.append(acc_eval)
print " average evaluation loss (%s): %.5f" % (subset, loss_eval)
print " average evaluation accuracy (%s): %.5f" % (subset, acc_eval)
now = time.time()
time_since_start = now - start_time
time_since_prev = now - prev_time
prev_time = now
est_time_left = time_since_start * num_epochs
eta = datetime.now() + timedelta(seconds=est_time_left)
eta_str = eta.strftime("%c")
print " %s since start (%.2f s)" % (utils.hms(time_since_start), time_since_prev)
print " estimated %s to go (ETA: %s)" % (utils.hms(est_time_left), eta_str)
print
if (epoch >= config.start_saving_at) and ((epoch % config.save_every) == 0):
print " saving parameters and metadata"
with open((metadata_path + "-%d" % (epoch) + ".pkl"), 'w') as f:
pickle.dump({
'config_name': config_name,
'param_values': nn.layers.get_all_param_values(l_out),
'losses_train': all_losses_train,
'accuracy_train': all_accuracy_train,
'losses_eval_train': all_losses_eval_train,
'losses_eval_valid': all_losses_eval_valid,
'losses_eval_test': all_losses_eval_test,
'accuracy_eval_valid': all_accuracy_eval_valid,
'accuracy_eval_train': all_accuracy_eval_train,
'accuracy_eval_test': all_accuracy_eval_test,
'mean_norm' : all_mean_norm,
'time_since_start': time_since_start,
'i': i,
}, f, pickle.HIGHEST_PROTOCOL)
print " stored in %s" % metadata_path
print
# -*- coding: utf-8 -*-
import numpy as np
import data
#%% Simple Mean-based Modell
# define the logmean
def logmean(x):
return np.exp(np.mean(np.log(x+1)))-1
# load the training data
print('load training data')
df_train = data.get_train(nrows = 10000)
# compute the means for different configurations
print('compute means')
mean_tab = df_train.groupby('ProductId').agg({'AdjDemand': logmean})
mean_tab2 = df_train.groupby(['ProductId', 'ClientId']).agg({'AdjDemand': logmean})
global_mean = logmean(df_train['AdjDemand'])
# generate estimation for each ProductID-ClientID-pair
def estimate(key):
key = tuple(key) # key needs to be a tuple
try:
est = mean_tab2.at[key,'AdjDemand']
except KeyError:
try :
def main():
#unzip raw_content file
os.system("unzip zipRawcontent; mkdir data; mv raw_content data/")
os.system("cp trainfile data/train.tsv")
os.system("cp testfile data/test.tsv")
os.system("mkdir ../generated ")
data = get_train() + get_test()
f = file('extracted_text', 'w')
for i, item in enumerate(data):
# status update
if (i % 500) == 0:
print i, datetime.datetime.now().time()
# parse file
data = {}
soup = boil_soup(item['urlid'])
# given boilerplate
data['boilerplate'] = [item['title'], item['body']]
# extract text
extractor = Extractor(extractor='ArticleExtractor', html=unicode(soup))
data['boilerpipe'] = [extractor.getText()]
# remove non-text tags
for tag in ['script', 'style']:
for el in soup.find_all(tag):
el.extract()
# extract text for each tag
for tag in TAGS:
items = []
for el in soup.find_all(tag):
el.extract()
if tag == 'img':
try:
items.append(el['alt'])
except KeyError:
pass
try:
items.append(el['title'])
except KeyError:
pass
else:
items.append(el.text)
data[tag] = items
# extract meta tags
meta = soup.find_all('meta')
for el in meta:
prop = el.get('property') if el.get('property') else el.get('name')
if not prop:
continue
prop = prop.lower()
try:
s = unicode(el['content'])
except:
continue
data['meta-'+prop] = s.split(u',') if prop == 'keywords' else [s]
# preprocess string
for item in data:
data[item] = map(clean_string, data[item])
data[item] = filter(None, data[item])
print >>f, json.dumps(data)
f.close()
from multi_task_ridge import _multi_target_ridge as multi_task_ridge
beta_old = betas_indep
# beta_old = np.load("/auto/k8/meickenberg/cache/thresh_-1.00_0.80_mt_ridge_with_corr_gamma_300.00.npz")['beta']
import os
cachedir = os.environ["DEFAULT_CACHE_DIR"]
from delayed import make_delayed
from ridge import _multi_corr_score
X_train_raw = data.get_wordnet(mode="train")
X_train = make_delayed(X_train_raw, [2, 3, 4])
X_val_raw = data.get_wordnet(mode="val")
X_val = make_delayed(X_val_raw, [2, 3, 4])
Y_train = data.get_train()
Y_val = data.get_val()
print "Starting loop"
import time
for gamma in [100, 500, 1000, 5000, 10000]:
t = time.time()
print "evaluating gamma=%f" % gamma
beta_new = multi_task_ridge(X_train, Y_train,
M=M_matrix, gamma=gamma,
A=A, alpha=1.,
warmstart=beta_old,
maxiter=61)
y_pred_new = X_val.dot(beta_new)
"""
import numpy as np
import data
#%% Simple Mean-based Modell
# define the logmean
def logmean(x):
return np.exp(np.mean(np.log(x + 1))) - 1
# load the training data
print('load training data')
df_train = data.get_train(nrows=10000)
# compute the means for different configurations
print('compute means')
mean_tab = df_train.groupby('ProductId').agg({'AdjDemand': logmean})
mean_tab2 = df_train.groupby(['ProductId',
'ClientId']).agg({'AdjDemand': logmean})
global_mean = logmean(df_train['AdjDemand'])
# generate estimation for each ProductID-ClientID-pair
def estimate(key):
key = tuple(key) # key needs to be a tuple
try:
est = mean_tab2.at[key, 'AdjDemand']
except KeyError:
numtime = 1000
# load stimuli
trnstim = data.get_wordnet("train")
valstim = data.get_wordnet("val")[90:]
delays = [2, 3, 4]
deltrnstim = np.hstack([np.roll(trnstim, d, 0) for d in delays])
delvalstim = np.hstack([np.roll(valstim, d, 0) for d in delays])
sdeltrnstim = scipy.sparse.csr_matrix(deltrnstim)
ebamask = cortex.get_roi_mask("MLfs", "20121210ML_auto1", roi="EBA")["EBA"] > 0
trndata = data.get_train(masked=ebamask)
# use first block for noise covariance estimation
valdata_repeats = data.get_val(masked=ebamask, repeats=True)[:90]
# use second and third block for evaluation
valdata = data.get_val(masked=ebamask)[90:]
# zscore it?
valdata_repeats = ((valdata_repeats -
valdata_repeats.mean(0)[np.newaxis, ...]) /
valdata_repeats.std(0)[np.newaxis, ...])
valdata_noise = valdata_repeats - valdata_repeats.mean(-1)[..., np.newaxis]
# fit Independent Ridge Regression
def main():
data = get_train() + get_test()
f = file('generated/extracted_text', 'w')
for i, item in enumerate(data):
# status update
if (i % 500) == 0:
print i, datetime.datetime.now().time()
# parse file
data = {}
soup = boil_soup(item['urlid'])
# given boilerplate
data['boilerplate'] = [item['title'], item['body']]
# extract text
extractor = Extractor(extractor='ArticleExtractor', html=unicode(soup))
data['boilerpipe'] = [extractor.getText()]
# remove non-text tags
for tag in ['script', 'style']:
for el in soup.find_all(tag):
el.extract()
# extract text for each tag
for tag in TAGS:
items = []
for el in soup.find_all(tag):
el.extract()
if tag == 'img':
try:
items.append(el['alt'])
except KeyError:
pass
try:
items.append(el['title'])
except KeyError:
pass
else:
items.append(el.text)
data[tag] = items
# extract meta tags
meta = soup.find_all('meta')
for el in meta:
prop = el.get('property') if el.get('property') else el.get('name')
if not prop:
continue
prop = prop.lower()
try:
s = unicode(el['content'])
except:
continue
data['meta-' + prop] = s.split(u',') if prop == 'keywords' else [s]
# preprocess string
for item in data:
data[item] = map(clean_string, data[item])
data[item] = filter(None, data[item])
print >> f, json.dumps(data)
f.close()
outdir = "../data/clean/"
index = data.index_wiki('../data/wiki-pages')
for file in tqdm(index.keys()):
wiki = data.get_wiki(file)
lines = wiki["lines"].apply(
lambda l: "<SPLIT>".join(data.clean_article(l)))
wiki["text"] = lines
wiki = wiki.drop("lines", axis=1).reset_index()
new_file = outdir + file.split("/")[-1]
wiki.to_json(new_file, orient="records", lines=True)
###########################################################################
# Setup #
###########################################################################
# Load the data
train = data.get_train("../data/train.jsonl")
train = train.explode("evidence").reset_index()
train, test = train_test_split(train)
# Load the model
embedder = ret.SentEmbed("distilroberta-base-msmarco-v2")
# Build the dataset objects and loaders
train_dataset = data.SentenceDataset(train, embedder, "../data/wiki.db", 4)
test_dataset = data.SentenceDataset(test, embedder, "../data/wiki.db", 4)
train_loader = DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
collate_fn=train_dataset.collate,
sdeltrnstim = deltrnstim = np.nan_to_num(zs(deltrnstim))
sdelvalstim = delvalstim = np.nan_to_num(zs(delvalstim))
# Select some voxels
cort_mask = cortex.get_cortical_mask("MLfs", "20121210ML_auto1", "thick")
#rois = ["V1", "V2", "V3"]
rois = ["V1"]
masks = [cortex.get_roi_mask("MLfs",
"20121210ML_auto1",
roi=roi)[roi] > 0 for roi in rois]
roimask = reduce(lambda x, y: (x + y), masks)
wardmask = cort_mask - roimask
# Load training, test fMRI data
trndata_roi = np.nan_to_num(data.get_train(masked=roimask)[:numtime])
trndata_ward = np.nan_to_num(data.get_train(masked=wardmask)[:numtime])
connectivity = image.grid_to_graph(n_x=wardmask.shape[0],
n_y=wardmask.shape[1],
n_z=wardmask.shape[2],
mask=wardmask)
ward = WardAgglomeration(n_clusters=numclusters, connectivity=connectivity,
memory='nilearn_cache')
ward.fit(trndata_ward)
labels = ward.labels_
trndata_collapsed = np.array([trndata_ward[:, labels == i].mean(1)
for i in range(numclusters)])
trndata = np.hstack((trndata_roi, trndata_collapsed.T))
valdata = data.get_val(masked=roimask)
