def run():
if len(sys.argv) < 3:
print("** Usage: python3 " + sys.argv[0] +
" <<Model Directory>> <<Test Set>>")
sys.exit(1)
np.random.seed(42)
model_dir = sys.argv[1]
config = Config.load(
['./default.conf',
os.path.join(model_dir, 'model.conf')])
model = create_model(config)
test_data = load_data(sys.argv[2], config.dictionary, config.grammar,
config.max_length)
print("unknown", unknown_tokens)
with tf.Graph().as_default():
tf.set_random_seed(1234)
with tf.device('/cpu:0'):
model.build()
test_eval = Seq2SeqEvaluator(model,
config.grammar,
test_data,
'test',
config.reverse_dictionary,
beam_size=config.beam_size,
batch_size=config.batch_size)
loader = tf.train.Saver()
with tf.Session() as sess:
loader.restore(sess, os.path.join(model_dir, 'best'))
test_eval.eval(sess, save_to_file=True)
def predict_whole_sequences(model, X, config_step, until=40):
n = X.shape[0]
true_steps = X.shape[1]
d = X.shape[2]
final_step = until + 1 if config_step else until
XX = np.zeros((n, final_step, d))
XX[:, :true_steps, :] = X
for j in range(true_steps, final_step):
pred = model.predict(XX[:, :j, :])
XX[:, j, -1] = pred[:, -1, 0]
"""if repeat_config:
XX[:, j, :-1] = XX[:, j-1, :-1]"""
return pred[:, (true_steps - 1):, 0]
configs, learning_curves = load_data(source_dir='./data')
until = 40
for n_steps in [-1, 5, 10, 20]:
randomize_length = n_steps == -1
config_step = True if randomize_length or n_steps == 10 else False
n_folds = 3
k_fold = KFold(n_splits=n_folds, shuffle=True, random_state=42)
fold = 0
fold_test_errors = []
y_e40 = []
y_hat_e40 = {5: [], 10: [], 20: [], 30: []}
def run():
if len(sys.argv) < 5:
print("** Usage: python " + sys.argv[0] +
" <<Input Vocab>> <<Word Embeddings>> <<Train Set> <<Test Set>>")
sys.exit(1)
np.random.seed(42)
words, reverse = load_dictionary(sys.argv[1], 'tt')
print("%d words in dictionary" % (len(words), ))
embeddings_matrix = load_embeddings(sys.argv[2], words, embed_size=300)
max_length = 60
grammar = ThingtalkGrammar()
train_data = load_data(sys.argv[3], words, grammar.dictionary, reverse,
grammar.tokens, max_length)
test_data = load_data(sys.argv[4], words, grammar.dictionary, reverse,
grammar.tokens, max_length)
print("unknown", unknown_tokens)
# Tell TensorFlow that the model will be built into the default Graph.
# (not required but good practice)
with tf.Graph().as_default():
# Create a session for running Ops in the Graph
with tf.Session() as sess:
input_embed_matrix = tf.constant(embeddings_matrix)
train_inputs = tf.nn.embedding_lookup([input_embed_matrix],
np.array(train_data[0]))
train_encoded = tf.reduce_sum(train_inputs, axis=1)
#print train_encoded.eval()
train_norm = tf.sqrt(
tf.reduce_sum(train_encoded * train_encoded, axis=1))
test_inputs = tf.nn.embedding_lookup([input_embed_matrix],
np.array(test_data[0]))
test_encoded = tf.reduce_sum(test_inputs, axis=1)
test_norm = tf.sqrt(
tf.reduce_sum(test_encoded * test_encoded, axis=1))
#print test_encoded.eval()
#print (train_encoded - test_encoded).eval()
distances = tf.matmul(test_encoded, tf.transpose(train_encoded))
distances /= tf.reshape(train_norm, (1, -1))
distances /= tf.reshape(test_norm, (-1, 1))
#print distances.eval()
indices = tf.argmax(distances, axis=1)
#print indices.eval()
ok_0 = 0
ok_ch = 0
ok_fn = 0
ok_full = 0
correct_programs = set()
gold_programs = set()
for gold in test_data[2]:
try:
gold = gold[:list(gold).index(grammar.end)]
except ValueError:
pass
gold_programs.add(tuple(gold))
indices = indices.eval(session=sess)
print(indices.shape)
for test_i, train_i in enumerate(indices):
gold = list(test_data[2][test_i])
decoded = list(train_data[2][train_i])
try:
decoded = decoded[:decoded.index(grammar.end)]
except ValueError:
pass
decoded_tuple = tuple(decoded)
try:
gold = gold[:gold.index(grammar.end)]
except ValueError:
pass
#print "GOLD:", ' '.join(grammar.tokens[l] for l in gold)
#print "DECODED:", ' '.join(grammar.tokens[l] for l in decoded)
if len(decoded) > 0 and len(
gold) > 0 and decoded[0] == gold[0]:
ok_0 += 1
def get_functions(seq):
return set([
x for x in [grammar.tokens[x] for x in seq] if
x.startswith('tt:') and not x.startswith('tt:param.')
])
gold_functions = get_functions(gold)
decoded_functions = get_functions(decoded)
gold_channels = set(
[x[x.index('.') + 1:] for x in gold_functions])
decoded_channels = set(
[x[x.index('.') + 1:] for x in decoded_functions])
if len(decoded) > 0 and len(gold) > 0 and decoded[0] == gold[
0] and gold_functions == decoded_functions:
ok_fn += 1
if gold_channels == decoded_channels:
ok_ch += 1
if grammar.compare(gold, decoded):
correct_programs.add(decoded_tuple)
ok_full += 1
print("ok 0:", float(ok_0) / len(test_data[0]))
print("ok channel:", float(ok_ch) / len(test_data[0]))
print("ok function:", float(ok_fn) / len(test_data[0]))
print("ok full:", float(ok_full) / len(test_data[0]))
print("recall:", float(len(correct_programs)) / len(gold_programs))
def run():
if len(sys.argv) < 6:
print(
"** Usage: python " + sys.argv[0] +
" <<Benchmark: tt/geo>> <<Model: bagofwords/seq2seq>> <<Input Vocab>> <<Word Embeddings>> <<Model Directory>> <<Train Set>> <<PCA Set>>"
)
sys.exit(1)
np.random.seed(42)
benchmark = sys.argv[1]
config, words, reverse, model = initialize(benchmark=benchmark,
model_type=sys.argv[2],
input_words=sys.argv[3],
embedding_file=sys.argv[4])
model_dir = sys.argv[5]
train_data = load_data(sys.argv[6], words, config.grammar.dictionary,
reverse, config.grammar.tokens, config.max_length)
pca_data = load_data(sys.argv[7], words, config.grammar.dictionary,
reverse, config.grammar.tokens, config.max_length)
config.apply_cmdline(sys.argv[8:])
print("unknown", unknown_tokens)
# Tell TensorFlow that the model will be built into the default Graph.
# (not required but good practice)
with tf.Graph().as_default():
# Build the model and add the variable initializer Op
model.capture_final_encoder_state = True
model.build()
loader = tf.train.Saver()
# Create a session for running Ops in the Graph
with tf.Session() as sess:
loader.restore(sess, os.path.join(model_dir, 'best'))
inputs, input_lengths, _, _ = train_data
final_encoder_state = None
final_encoder_size = None
if config.rnn_cell_type == 'lstm':
final_encoder_state = tf.concat([
model.final_encoder_state[-1].c,
model.final_encoder_state[-1].h
], 1)
final_encoder_size = 2 * config.hidden_size
else:
final_encoder_state = model.final_encoder_state[-1]
final_encoder_size = config.hidden_size
final_states_arrays = []
# capture all the final encoder states
for input_batch, input_length_batch in get_minibatches(
[inputs, input_lengths], config.batch_size):
feed_dict = model.create_feed_dict(input_batch,
input_length_batch)
state_array = sess.run(final_encoder_state,
feed_dict=feed_dict)
#print state_array.shape
final_states_arrays.append(state_array)
X = np.concatenate(final_states_arrays, axis=0)
assert X.shape == (len(inputs), final_encoder_size)
X = tf.constant(X)
mean = tf.reduce_mean(X, axis=0)
centered_X = X - mean
S, U, V = tf.svd(centered_X)
# take only the top 2 components
V = V[:2]
V_array, mean_array = sess.run([V, mean])
inputs, input_lengths, _, _ = pca_data
X = final_encoder_state
centered_X = X - tf.constant(mean_array)
transformed_X = tf.matmul(centered_X, tf.constant(V_array.T))
feed_dict = model.create_feed_dict(inputs, input_lengths)
X_pca = sess.run(transformed_X, feed_dict=feed_dict)
sentences = reconstruct_sentences(inputs, words['<<EOS>>'],
reverse)
show_pca(X_pca, sentences)
layer = tf.matmul(layer, w1)
layer = tf.nn.relu(layer)
layer = tf.matmul(layer, w2)
layer = tf.nn.softmax(layer)
return layer
image_width = 28
class_num = 10
fashion_mnist = keras.datasets.fashion_mnist
class_names = [
'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt',
'Sneaker', 'Bag', 'Ankle boot'
]
(train_images, train_labels), (test_images, test_labels) = loader.load_data()
train_images = (train_images / 255.0 - 0.5) * 2
test_images = (test_images / 255.0 - 0.5) * 2
dataset_size = len(train_images)
batch_size = 10
check_interval = 1000
steps = dataset_size // batch_size
steps = steps if dataset_size % batch_size == 0 else steps + 1
# t
train_x = tf.placeholder(tf.float32, shape=(None, 10), name='x-input')
# x
train_y = tf.placeholder(tf.float32, shape=(None, 28, 28), name='y-input')
def task3(return_dict,
config,
randomize_length,
n_steps,
epochs,
log_dir="logs"):
from sklearn.model_selection import KFold
from keras.models import clone_model
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from util.loader import load_data
from util.time_series_data import get_time_series, reshape_X, reshape_y
from models.lstm import lstm
from util.common import loss
from util.tensorboard import tensorboard_log_values
from preprocessing.standard_scaler import StandardScaler
from preprocessing.augmentation import add_nontraining_time_series, add_perturbed_time_series
n_steps_valid = n_steps
n_steps_test = n_steps
use_configs = True
config_step = config["config_step"]
repeat_config = config["repeat_config"]
scale_configs = True
validation_split = 0.3
evaluate_each = 1
lr = config["lr"]
batchsize = config["batchsize"]
lr_decay = config["lr_decay"]
decay = 0 if not lr_decay else config["decay"]
regularize = config["weight_decay"]
alpha = 0 if not regularize else config["alpha"]
remove_nonlearning = False
augment = config["augment"]
add_perturbed = 0 if not augment else config["add_perturbed"]
add_nontraining = 0 if not augment else config["add_nontraining"]
# title of current run
run_name = current_time_str()
if not randomize_length:
run_name += "_%is" % n_steps
else:
run_name += "_rnd"
run_name += "_lr%f" % lr
run_name += "_bs%i" % batchsize
if lr_decay:
run_name += "_dc%f" % decay
if regularize:
run_name += "_a%f" % alpha
run_name += "_cstp" if config_step else ""
run_name += "_rptcnfg" if repeat_config else ""
if augment:
run_name += "_augm_%i_%i" % (add_perturbed, add_nontraining)
print(run_name)
# functions
def plot_predicted_curves(model, X_test, test_indices, filename=None):
plt.figure(figsize=(20, 10))
n_plots = 20
pred = predict_whole_sequences(model,
X_test[:n_plots, :n_steps_test, :])
for i in range(n_plots):
plt.subplot(4, 5, i + 1)
plt.plot(learning_curves[test_indices[i]], "g")
if config_step:
plt.plot(range(40), pred[i, :, :], "r")
else:
plt.plot(range(1, 40), pred[i, :, :], "r")
if filename != None:
plt.savefig(filename)
plt.close()
def predict_whole_sequences(model, X):
n = X.shape[0]
true_steps = X.shape[1]
d = X.shape[2]
final_step = 41 if config_step else 40
XX = np.zeros((n, final_step, d))
XX[:, :true_steps, :] = X
for j in range(true_steps, final_step):
pred = model.predict(XX[:, :j, :])
XX[:, j, -1] = pred[:, -1, 0]
if repeat_config:
XX[:, j, :-1] = XX[:, j - 1, :-1]
return pred
def evaluate_step40_loss(model, X_test, test_indices, n_steps_test):
if config_step:
n_steps_test += 1
final_y = [learning_curves[index][-1] for index in test_indices]
pred = predict_whole_sequences(model, X_test[:, :n_steps_test, :])
final_y_hat = pred[:, -1, 0]
return loss(np.array(final_y), final_y_hat)
# file name for plots
tmp_file_name = "tmp/model_%s" % run_name
if config_step:
n_steps_train = n_steps
n_steps_valid += 1
n_steps_test += 1
else:
n_steps_train = n_steps - 1
# read data
configs, learning_curves = load_data(source_dir='./data')
if remove_nonlearning:
keep_indices = [
i for i in range(len(learning_curves))
if learning_curves[i][-1] < 0.8
]
configs = [configs[i] for i in keep_indices]
learning_curves = [learning_curves[i] for i in keep_indices]
n_params = len(configs[0]) if use_configs else 0
d = n_params + 1
# 3 fold CV:
n_folds = 3
k_fold = KFold(n_splits=n_folds, shuffle=True, random_state=42)
fold = 0
fold_test_errors = []
for training_indices, test_indices in k_fold.split(learning_curves):
fold = fold + 1
# split into training and validation
training_indices = np.random.permutation(training_indices)
valid_split_index = int(validation_split * len(training_indices))
validation_indices = training_indices[:valid_split_index]
training_indices = training_indices[valid_split_index:]
# prepare training data:
configs_train = [configs[index] for index in training_indices]
learning_curves_train = [
learning_curves[index] for index in training_indices
]
if scale_configs:
scaler = StandardScaler()
configs_train = scaler.fit_transform(configs_train)
if add_perturbed > 0:
configs_train, learning_curves_train = add_perturbed_time_series(
configs_train, learning_curves_train, add_perturbed)
if add_nontraining > 0:
configs_train, learning_curves_train = add_nontraining_time_series(
configs_train, learning_curves_train, add_nontraining)
n_train = len(configs_train)
X_train = get_time_series(configs_train,
learning_curves_train,
use_configs=use_configs,
repeat_config=repeat_config,
config_step=config_step)
X_train = reshape_X(X_train)
Y_train = learning_curves_train
# prepare validation data:
configs_valid = [configs[index] for index in validation_indices]
learning_curves_valid = [
learning_curves[index] for index in validation_indices
]
if scale_configs:
configs_valid = scaler.transform(configs_valid)
X_valid = get_time_series(configs_valid,
learning_curves_valid,
use_configs=use_configs,
repeat_config=repeat_config,
config_step=config_step)
X_valid = reshape_X(X_valid)
# prepare test data:
configs_test = [configs[index] for index in test_indices]
learning_curves_test = [
learning_curves[index] for index in test_indices
]
if scale_configs:
configs_test = scaler.transform(configs_test)
X_test = get_time_series(configs_test,
learning_curves_test,
use_configs=use_configs,
repeat_config=repeat_config,
config_step=config_step)
X_test = reshape_X(X_test)
n_valid = len(validation_indices)
n_test = len(test_indices)
Y_train = reshape_y(Y_train)
Y_valid = [
learning_curves_valid[i][1:(n_steps_valid + 1)]
for i in range(n_valid)
]
Y_test = [
learning_curves_test[i][1:(n_steps_test + 1)]
for i in range(n_test)
]
n_batches = int(np.ceil(n_train / batchsize))
model = lstm(d,
lr,
decay=decay,
many2many=True,
regularize=regularize,
alpha=alpha,
batchsize=None)
best_valid_e40 = {}
for k in [5, 10, 20, 30]:
best_valid_e40[k] = float("inf")
best_mean_valid_e40 = float("inf")
best_valid_e40_epoch = -1
for epoch in range(epochs):
print("epoch = %i" % epoch)
# random permutation of training data
permutation = np.random.permutation(range(n_train))
X_train_permuted = X_train[permutation, :, :]
Y_train_permuted = Y_train[permutation, :, :]
training_losses = []
for batch in range(n_batches):
if randomize_length:
n_steps_train = int(np.random.uniform(5, 21))
if config_step:
n_steps_train += 1
batch_begin = batch * batchsize
batch_end = batch_begin + batchsize
x = X_train_permuted[batch_begin:batch_end, :n_steps_train, :]
y = Y_train_permuted[batch_begin:batch_end,
1:(n_steps_train + 1)]
y_hat = model.predict(x)
model.train_on_batch(x, y)
training_losses.append(loss(y, y_hat))
training_loss = np.mean(training_losses)
print("training loss = %f" % training_loss)
# validation
if (epoch + 1) % 1 == 0:
y_hat = model.predict(X_valid[:, :n_steps_valid, :])[:, :, 0]
validation_loss = np.mean(loss(Y_valid, y_hat))
print("validation loss = %f" % validation_loss)
if (epoch + 1) % evaluate_each == 0:
print(lr, decay, batchsize)
print("best[:5] = %f @ %i" %
(best_valid_e40[5], best_valid_e40_epoch))
print("best[:10] = %f @ %i" %
(best_valid_e40[10], best_valid_e40_epoch))
print("best[:20] = %f @ %i" %
(best_valid_e40[20], best_valid_e40_epoch))
print("best[:30] = %f @ %i" %
(best_valid_e40[30], best_valid_e40_epoch))
valid_e40_5 = evaluate_step40_loss(model, X_valid,
validation_indices, 5)
print("validation MSE[:5]@40 = %f" % valid_e40_5)
valid_e40_10 = evaluate_step40_loss(model, X_valid,
validation_indices, 10)
print("validation MSE[:10]@40 = %f" % valid_e40_10)
valid_e40_20 = evaluate_step40_loss(model, X_valid,
validation_indices, 20)
print("validation MSE[:20]@40 = %f" % valid_e40_20)
valid_e40_30 = evaluate_step40_loss(model, X_valid,
validation_indices, 30)
print("validation MSE[:30]@40 = %f" % valid_e40_30)
mean_valid_e40 = np.mean(
[valid_e40_5, valid_e40_10, valid_e40_20, valid_e40_30])
prefix = "losses_f%i/" % fold
tensorboard_log_values(
log_dir, run_name, epoch, {
prefix + "training": training_loss,
prefix + "validation": validation_loss,
prefix + "validation_E40_5": valid_e40_5,
prefix + "validation_E40_10": valid_e40_10,
prefix + "validation_E40_20": valid_e40_20,
prefix + "validation_E40_30": valid_e40_30,
prefix + "validation_E40_mean": mean_valid_e40
})
if mean_valid_e40 < best_mean_valid_e40:
print("* new best model *")
best_valid_e40_epoch = epoch
best_valid_e40[5] = valid_e40_5
best_valid_e40[10] = valid_e40_10
best_valid_e40[20] = valid_e40_20
best_valid_e40[30] = valid_e40_30
best_mean_valid_e40 = mean_valid_e40
best_model = clone_model(model)
best_model.set_weights(model.get_weights())
"""if (epoch + 1) % 10 == 0:
filename = tmp_file_name + "_f%i_e%i.png" % (fold, epoch)
print(filename)
plot_predicted_curves(model, X_test, test_indices, filename = filename)"""
# evaluation on test data
test_e40 = {}
test_e40[5] = evaluate_step40_loss(best_model, X_test, test_indices, 5)
test_e40[10] = evaluate_step40_loss(best_model, X_test, test_indices,
10)
test_e40[20] = evaluate_step40_loss(best_model, X_test, test_indices,
20)
test_e40[30] = evaluate_step40_loss(best_model, X_test, test_indices,
30)
fold_test_errors.append(test_e40)
print(test_e40)
#filename = tmp_file_name + "_f%i_best.png" % fold
#print(filename)
#plot_predicted_curves(best_model, X_test, test_indices, filename = filename)
means_e40 = {}
for steps in [5, 10, 20, 30]:
print("MSE@40 for %i input steps:" % steps)
e40_folds = [fold_res[steps] for fold_res in fold_test_errors]
print(e40_folds)
mean_e40 = np.mean(e40_folds)
print("mean = %f" % mean_e40)
means_e40[steps] = mean_e40
return_dict["results"] = means_e40
def main(estimators, n_folds=3):
# read data and transform it to numpy arrays
configs, learning_curves = load_data(source_dir='../data')
configs = np.array(list(map(lambda x: list(x.values()), configs)))
learning_curves = np.array(learning_curves)
# initialise CV
k_fold = KFold(n_splits=n_folds, shuffle=True, random_state=1)
# store predicted and true y
y_y_hat = np.zeros((len(estimators), 2, learning_curves.shape[0]))
performances = np.zeros((len(estimators), 6))
for m_idx, model_desc in enumerate(estimators):
current_fold = 0
print(model_desc)
for preprocessing in [False, True]:
# CV folds
for train_indices, test_indices in k_fold.split(configs):
# split into training and test data
train_configs = configs[train_indices]
train_curves = learning_curves[train_indices]
test_configs = configs[test_indices]
test_curves = learning_curves[test_indices]
# preprocessing
if preprocessing:
scaler = StandardScaler()
train_configs = scaler.fit_transform(train_configs)
test_configs = scaler.transform(test_configs)
# train model
model = eval(model_desc)
model.fit(train_configs, train_curves[:, -1])
# evaluate model
y = test_curves[:, -1]
y_hat = model.predict(test_configs)
test_loss = loss(y_hat, y)
performances[m_idx, current_fold] = test_loss
print("fold test loss = %f" % test_loss)
# store prediction
if preprocessing:
y_y_hat[m_idx, 0, test_indices] = y
y_y_hat[m_idx, 1, test_indices] = y_hat
current_fold += 1
print("mean CV loss w/o prep = {0:.5f}, w prep = {1:.5f}".format(
np.mean(performances[m_idx, :3]), np.mean(performances[m_idx,
3:6])))
data = {
'no prep': np.mean(performances[:, :3], axis=1),
'prep': np.mean(performances[:, 3:6], axis=1)
}
frame = pd.DataFrame(data, index=estimators)
print(frame)
return performances, y_y_hat
def run():
if len(sys.argv) < 3:
print("** Usage: python3 " + sys.argv[0] +
" <<Model Directory>> <<Test Set>>")
sys.exit(1)
np.random.seed(42)
model_dir = sys.argv[1]
config = Config.load(
['./default.conf',
os.path.join(model_dir, 'model.conf')])
model = create_model(config)
test_data = load_data(sys.argv[2], config.dictionary, config.grammar,
config.max_length)
with tf.Graph().as_default():
tf.set_random_seed(1234)
model.build()
loader = tf.train.Saver()
inputs, input_lengths, parses, labels, label_lengths = test_data
final_encoder_state = tf.concat(nest.flatten(
model.final_encoder_state),
axis=1)
final_encoder_size = final_encoder_state.get_shape()[1]
final_states = OrderedDict()
with tf.Session() as sess:
loader.restore(sess, os.path.join(model_dir, 'best'))
# capture all the final encoder states
for input_batch, input_length_batch, parse_batch, label_batch, label_length_batch in get_minibatches(
[inputs, input_lengths, parses, labels, label_lengths],
config.batch_size):
feed_dict = model.create_feed_dict(input_batch,
input_length_batch,
parse_batch)
state_array = sess.run(final_encoder_state,
feed_dict=feed_dict)
#print state_array.shape
for state, input, input_length, label, length in zip(
state_array, input_batch, input_length_batch,
label_batch, label_length_batch):
label = label[:length]
program = ' '.join(
config.grammar.tokens[x] for x in
label) # if is_function(config.grammar.tokens[x]))
if not program in final_states:
final_states[program] = [(state, input[:input_length])]
else:
final_states[program].append(
(state, input[:input_length]))
prog_array = [prog for prog in final_states
] #if len(final_states[prog]) > 1]
prog_index = dict()
num_programs = len(prog_array)
print('num programs', num_programs)
centers = np.zeros((num_programs, final_encoder_size),
dtype=np.float32)
for i, program in enumerate(prog_array):
prog_index[program] = i
centers[i] = np.mean([x[0] for x in final_states[program]], axis=0)
eval_data = []
with open(sys.argv[3]) as fp:
for line in fp:
sentence, gold, predicted, _ = line.strip().split('\t')
if gold == predicted:
continue
gold += ' <<EOS>>'
predicted += ' <<EOS>>'
if gold in prog_index and predicted in prog_index:
sentence_vector, sentence_length = vectorize(
sentence, config.dictionary, config.max_length)
gold_index = prog_index[gold]
gold_center = centers[gold_index]
predicted_index = prog_index[predicted]
predicted_center = centers[predicted_index]
eval_data.append(
(gold, predicted, gold_center, predicted_center,
sentence_vector, sentence_length))
#print(np.linalg.norm(gold_center-predicted_center), gold, predicted, sentence, sep='\t')
elif gold not in prog_index:
#print('no gold', gold, file=sys.stderr)
pass
elif predicted not in prog_index:
#print('no predicted', file=sys.stderr)
pass
with tf.Session() as sess:
loader.restore(sess, os.path.join(model_dir, 'best'))
def flip(list_of_tuples):
inner_length = len(list_of_tuples[0])
tuple_of_lists = [[x[i] for x in list_of_tuples]
for i in range(inner_length)]
return tuple_of_lists
with open('./eval.tsv', 'w') as out:
for gold_batch, predicted_batch, gold_center_batch, predicted_center_batch, input_batch, input_length_batch in get_minibatches(
flip(eval_data), config.batch_size):
parse_batch = np.zeros(
(len(input_batch), 2 * config.max_length - 1),
dtype=np.bool)
feed_dict = model.create_feed_dict(input_batch,
input_length_batch,
parse_batch)
state_array = sess.run(final_encoder_state,
feed_dict=feed_dict)
assert len(state_array) == len(gold_batch)
for state, input, input_length, gold, predicted, gold_center, predicted_center in zip(
state_array, input_batch, input_length_batch,
gold_batch, predicted_batch, gold_center_batch,
predicted_center_batch):
gold_predicted_dist = np.linalg.norm(gold_center -
predicted_center)
sentence_gold_dist = np.linalg.norm(state -
gold_center)
sentence_predicted_dist = np.linalg.norm(
state - predicted_center)
sentence = ' '.join(config.reverse_dictionary[x]
for x in input[:input_length])
print(gold_predicted_dist,
sentence_gold_dist,
sentence_predicted_dist,
gold,
predicted,
sentence,
sep='\t',
file=out)
print('written eval.tsv')
num_good_sentences = np.zeros((num_programs, ), dtype=np.int32)
sum_good_distance = np.zeros((num_programs, ), dtype=np.float32)
num_bad_sentences = np.zeros((num_programs, ), dtype=np.int32)
sum_bad_distance = np.zeros((num_programs, ), dtype=np.float32)
for i, program in enumerate(prog_array):
num_good_sentences[i] = len(final_states[program])
for encoding, sentence in final_states[program]:
dist = np.linalg.norm(encoding - centers[i])
sum_good_distance[i] += dist
# negative examples
for negative in np.random.choice(prog_array,
size=(10, ),
replace=False):
if negative == program:
continue
num_bad_sentences[i] += len(final_states[negative])
for negative_enc, negative_sentence in final_states[negative]:
dist = np.linalg.norm(negative_enc - centers[i])
sum_bad_distance[i] += dist
avg_good_distance = sum_good_distance / num_good_sentences
avg_bad_distance = sum_bad_distance / num_bad_sentences
with open('./encoded.csv', 'w') as fp:
writer = csv.writer(fp)
writer.writerows(
zip(num_good_sentences, num_bad_sentences, avg_good_distance,
avg_bad_distance, sum_good_distance, sum_bad_distance))
def run():
if len(sys.argv) < 4:
print("** Usage: python3 " + sys.argv[0] +
" <<Model Directory>> <<Train Set>> <<Test Set>>")
sys.exit(1)
np.random.seed(42)
model_dir = sys.argv[1]
config = Config.load(
['./default.conf',
os.path.join(model_dir, 'model.conf')])
model = create_model(config)
train_data = load_data(sys.argv[2], config.dictionary, config.grammar,
config.max_length)
pca_data = load_data(sys.argv[3], config.dictionary, config.grammar,
config.max_length)
print("unknown", unknown_tokens)
with tf.Graph().as_default():
model.build()
loader = tf.train.Saver()
with tf.Session() as sess:
loader.restore(sess, os.path.join(model_dir, 'best'))
inputs, input_lengths, parses, _, _ = train_data
final_encoder_state = tf.concat(nest.flatten(
model.final_encoder_state),
axis=1)
final_encoder_size = final_encoder_state.get_shape()[1]
final_states_arrays = []
# capture all the final encoder states
for input_batch, input_length_batch, parse_batch in get_minibatches(
[inputs, input_lengths, parses], config.batch_size):
feed_dict = model.create_feed_dict(input_batch,
input_length_batch,
parse_batch)
state_array = sess.run(final_encoder_state,
feed_dict=feed_dict)
#print state_array.shape
final_states_arrays.append(state_array)
X = np.concatenate(final_states_arrays, axis=0)
assert X.shape == (len(inputs), final_encoder_size)
X = tf.constant(X)
mean = tf.reduce_mean(X, axis=0)
centered_X = X - mean
S, U, V = tf.svd(centered_X)
# take only the top 2 components
V = V[:2]
V_array, mean_array = sess.run([V, mean])
inputs, input_lengths, parses, labels, label_lengths = pca_data
X = final_encoder_state
centered_X = X - tf.constant(mean_array)
transformed_X = tf.matmul(centered_X, tf.constant(V_array.T))
feed_dict = model.create_feed_dict(inputs, input_lengths, parses)
X_pca = sess.run(transformed_X, feed_dict=feed_dict)
if False:
sentences = reconstruct_sentences(inputs, input_lengths,
config.reverse_dictionary)
else:
sentences = reconstruct_sentences(labels, label_lengths,
config.grammar.tokens)
show_pca(X_pca, sentences)
def run():
if len(sys.argv) < 3:
print("** Usage: python3 " + sys.argv[0] +
" <<Model Directory>> <<Train Set>> [<<Dev Set>>]")
sys.exit(1)
np.random.seed(42)
model_dir = sys.argv[1]
model_conf = os.path.join(model_dir, 'model.conf')
config = Config.load(['./default.conf', model_conf])
model = create_model(config)
train_data = load_data(sys.argv[2], config.dictionary, config.grammar,
config.max_length)
if len(sys.argv) > 3:
dev_data = load_data(sys.argv[3], config.dictionary, config.grammar,
config.max_length)
else:
dev_data = None
print("unknown", unknown_tokens)
try:
os.mkdir(model_dir)
except OSError:
pass
if not os.path.exists(model_conf):
config.save(model_conf)
with tf.Graph().as_default():
tf.set_random_seed(1234)
model.build()
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=config.n_epochs)
train_eval = Seq2SeqEvaluator(model,
config.grammar,
train_data,
'train',
config.reverse_dictionary,
beam_size=config.beam_size,
batch_size=config.batch_size)
dev_eval = Seq2SeqEvaluator(model,
config.grammar,
dev_data,
'dev',
config.reverse_dictionary,
beam_size=config.beam_size,
batch_size=config.batch_size)
trainer = Trainer(model,
train_data,
train_eval,
dev_eval,
saver,
model_dir=model_dir,
max_length=config.max_length,
batch_size=config.batch_size,
n_epochs=config.n_epochs,
dropout=config.dropout)
with tf.Session() as sess:
# Run the Op to initialize the variables.
sess.run(init)
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
# Fit the model
best_dev, best_train = trainer.fit(sess)
print("best train", best_train)
print("best dev", best_dev)
def main(n_steps=10, epochs=200):
"""
Trains and evaluates the MSE of a LSTM model in a 3-fold CV.
n_steps: how many steps of the learning curve are used for training and predicting
epochs: number of training epochs
repeat_config: if True the configuration is fed into the network in each
time step, otherwise only in the first time step
"""
configs, learning_curves = load_data(source_dir='./data')
Y = [curve[-1] for curve in learning_curves]
n_params = len(configs[0])
d = n_params + 1
# 3 fold CV:
n_folds = 3
k_fold = KFold(n_splits=n_folds)
fold_mses = []
fold = 0
for training_indices, test_indices in k_fold.split(Y):
fold = fold + 1
print("***** FOLD %i *****" % fold)
# prepare training data:
configs_train = [configs[index] for index in training_indices]
learning_curves_train = [
learning_curves[index][:n_steps] for index in training_indices
]
if scale_configs:
scaler = StandardScaler()
configs_train = scaler.fit_transform(configs_train)
X_train = get_time_series(configs_train,
learning_curves_train,
repeat_config=repeat_config)
Y_train = [Y[index] for index in training_indices]
# prepare test data:
configs_test = [configs[index] for index in test_indices]
learning_curves_test = [
learning_curves[index][:n_steps] for index in test_indices
]
if scale_configs:
configs_test = scaler.transform(configs_test)
X_test = get_time_series(configs_test,
learning_curves_test,
repeat_config=repeat_config)
Y_test = [Y[index] for index in test_indices]
n_train = len(training_indices)
model = lstm(d, lr, regularize=regularize, alpha=alpha)
# training:
for epoch in range(epochs):
print("epoch = %i" % epoch)
for i in range(n_train):
x = X_train[i].reshape(1, -1, d)
y = Y_train[i]
model.train_on_batch(x, np.array([[y]]))
model.reset_states()
# validation output:
mse_train = evaluate(model, X_train, Y_train)
mse_test = evaluate(model, X_test, Y_test)
print("training mse = %f" % mse_train)
print("test mse = %f" % mse_test)
#if (epoch + 1) % 10 == 0:
# predictions = predict(model, X_test)
# print("y_hat", "y")
# for y_hat, y in zip(predictions, Y_test):
# print(y_hat, y)
# evaluation:
fold_mses.append(evaluate(model, X_test, Y_test))
print("\nmse per fold:")
print(fold_mses)
print("mean mse:")
print(np.mean(fold_mses))