def model(train_x, train_y, dev_x, dev_y, test_x, test_y, overal_maxlen, qwks):
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, GlobalAveragePooling1D
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM
from keras.initializers import Constant
from keras import optimizers
import keras.backend as K
from deepats.my_layers import MeanOverTime
from deepats.rwa import RWA
import pickle as pk
import numpy as np
import string
import random
import os
from deepats.optimizers import get_optimizer
from deepats.ets_evaluator import Evaluator
import deepats.ets_reader as dataset
from deepats.ets_config import get_args
import GPUtil
def random_id(size=6, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
def kappa_metric(t, x):
u = 0.5 * K.sum(K.square(x - t))
v = K.dot(K.transpose(x), t - K.mean(t))
return v / (v + u)
def kappa_loss(t, x):
u = K.sum(K.square(x - t))
v = K.dot(K.squeeze(x, 1), K.squeeze(t - K.mean(t), 1))
return u / (2 * v + u)
import time
ms = int(round(time.time() * 1000))
rand_seed = ms % (2**32 - 1)
random.seed(rand_seed)
args = get_args()
model_id = random_id()
abs_vocab_file = os.path.join(args.abs_out, 'vocab.pkl')
with open(abs_vocab_file, 'rb') as vocab_file:
vocab = pk.load(vocab_file)
vocab_size = len(vocab)
acts = ['tanh', 'relu', 'hard_sigmoid']
emb_dim = {{choice([50, 100, 200, 300])}}
rnn_dim = {{uniform(50, 500)}}
rnn_dim = int(rnn_dim)
rec_act = {{choice([0, 1, 2])}}
rec_act = acts[rec_act]
dropout = {{uniform(0.2, 0.95)}}
epochs = args.epochs
n_emb = vocab_size * emb_dim
n_rwa = (903 + 2 * rnn_dim) * rnn_dim
n_tot = n_emb + n_rwa + rnn_dim + 1
lr = {{lognormal(-3 * 2.3, .8)}}
lr = 1.5 * lr
rho = {{normal(.875, .04)}}
clipnorm = {{uniform(1, 15)}}
eps = {{loguniform(-8 * 2.3, -5 * 2.3)}}
opt = optimizers.RMSprop(lr=lr, rho=rho, clipnorm=clipnorm, epsilon=eps)
loss = kappa_loss
metric = kappa_metric
evl = Evaluator(dataset,
args.prompt_id,
args.abs_out,
dev_x,
test_x,
dev_df,
test_df,
model_id=model_id)
train_y_mean = train_y.mean(axis=0)
if train_y_mean.ndim == 0:
train_y_mean = np.expand_dims(train_y_mean, axis=1)
num_outputs = len(train_y_mean)
mask_zero = False
model = Sequential()
model.add(Embedding(vocab_size, emb_dim, mask_zero=mask_zero))
model.add(RWA(rnn_dim, recurrent_activation=rec_act))
model.add(Dropout(dropout))
bias_value = (np.log(train_y_mean) - np.log(1 - train_y_mean)).astype(
K.floatx())
model.add(Dense(num_outputs, bias_initializer=Constant(value=bias_value)))
model.add(Activation('tanh'))
model.emb_index = 0
from deepats.w2vEmbReader import W2VEmbReader as EmbReader
emb_reader = EmbReader(args.emb_path, emb_dim)
emb_reader.load_embeddings(vocab)
emb_wts = emb_reader.get_emb_matrix_given_vocab(
vocab, model.layers[model.emb_index].get_weights()[0])
wts = model.layers[model.emb_index].get_weights()
wts[0] = emb_wts
model.layers[model.emb_index].set_weights(wts)
model.compile(loss=loss, optimizer=opt, metrics=[metric])
model_yaml = model.to_yaml()
import GPUtil
if GPUtil.avail_mem() < 0.1:
return {'loss': 1, 'status': STATUS_OK, 'model': '', 'weights': None}
print('model_id: %s' % (model_id))
print(model_yaml)
print('PARAMS\t\
%s\t\
lr= %.4f\t\
rho= %.4f\t\
clip= %.4f\t\
eps= %.4f\t\
embDim= %.4f\t\
rnnDim= %.4f\t\
drop= %.4f\t\
recAct= %s' % (model_id, lr, rho, clipnorm, np.log(eps) / 2.3, emb_dim,
rnn_dim, dropout, rec_act))
for i in range(epochs):
train_history = model.fit(train_x,
train_y,
batch_size=args.batch_size,
epochs=1,
verbose=0)
evl.evaluate(model, i)
evl.output_info()
p = evl.stats[3] / qwks[0]
if i > 10 and p < 0.9:
break
i = evl.comp_idx
j = i + 2
best_dev_kappa = evl.best_dev[i]
best_test_kappa = evl.best_dev[j]
print('Test kappa:', best_dev_kappa)
return {
'loss': 1 - best_dev_kappa,
'status': STATUS_OK,
'model': model.to_yaml(),
'weights': pk.dumps(model.get_weights())
}
def run(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("-o",
"--out-dir",
dest="out_dir_path",
type=str,
metavar='<str>',
required=True,
help="The path to the output directory")
parser.add_argument(
"-p",
"--prompt",
dest="prompt_id",
type=int,
metavar='<int>',
required=False,
help="Promp ID for ASAP dataset. '0' means all prompts.")
parser.add_argument("-t",
"--type",
dest="model_type",
type=str,
metavar='<str>',
default='regp',
help="Model type (reg|regp|breg|bregp) (default=regp)")
parser.add_argument(
"-u",
"--rec-unit",
dest="recurrent_unit",
type=str,
metavar='<str>',
default='lstm',
help="Recurrent unit type (lstm|gru|simple) (default=lstm)")
parser.add_argument(
"-a",
"--algorithm",
dest="algorithm",
type=str,
metavar='<str>',
default='rmsprop',
help=
"Optimization algorithm (rmsprop|sgd|adagrad|adadelta|adam|adamax) (default=rmsprop)"
)
parser.add_argument("-l",
"--loss",
dest="loss",
type=str,
metavar='<str>',
default='mse',
help="Loss function (mse|mae) (default=mse)")
parser.add_argument("-e",
"--embdim",
dest="emb_dim",
type=int,
metavar='<int>',
default=50,
help="Embeddings dimension (default=50)")
parser.add_argument(
"-c",
"--cnndim",
dest="cnn_dim",
type=int,
metavar='<int>',
default=0,
help="CNN output dimension. '0' means no CNN layer (default=0)")
parser.add_argument("-w",
"--cnnwin",
dest="cnn_window_size",
type=int,
metavar='<int>',
default=3,
help="CNN window size. (default=3)")
parser.add_argument(
"-r",
"--rnndim",
dest="rnn_dim",
type=int,
metavar='<int>',
default=300,
help="RNN dimension. '0' means no RNN layer (default=300)")
parser.add_argument("-b",
"--batch-size",
dest="batch_size",
type=int,
metavar='<int>',
default=32,
help="Batch size (default=32)")
parser.add_argument("-v",
"--vocab-size",
dest="vocab_size",
type=int,
metavar='<int>',
default=4000,
help="Vocab size (default=4000)")
parser.add_argument(
"--aggregation",
dest="aggregation",
type=str,
metavar='<str>',
default='mot',
help=
"The aggregation method for regp and bregp types (mot|attsum|attmean) (default=mot)"
)
parser.add_argument(
"--dropout",
dest="dropout_prob",
type=float,
metavar='<float>',
default=0.5,
help=
"The dropout probability. To disable, give a negative number (default=0.5)"
)
parser.add_argument(
"--vocab-path",
dest="vocab_path",
type=str,
metavar='<str>',
help="(Optional) The path to the existing vocab file (*.pkl)")
parser.add_argument("--skip-init-bias",
dest="skip_init_bias",
action='store_true',
help="Skip initialization of the last layer bias")
parser.add_argument(
"--emb",
dest="emb_path",
type=str,
metavar='<str>',
help="The path to the word embeddings file (Word2Vec format)")
parser.add_argument("--epochs",
dest="epochs",
type=int,
metavar='<int>',
default=100,
help="Number of epochs (default=50)")
parser.add_argument(
"--maxlen",
dest="maxlen",
type=int,
metavar='<int>',
default=0,
help=
"Maximum allowed number of words during training. '0' means no limit (default=0)"
)
parser.add_argument("--seed",
dest="seed",
type=int,
metavar='<int>',
default=1234,
help="Random seed (default=1234)")
## dsv
parser.add_argument("--min-word-freq",
dest="min_word_freq",
type=int,
metavar='<int>',
default=2,
help="Min word frequency")
parser.add_argument("--stack",
dest="stack",
type=int,
metavar='<int>',
default=1,
help="how deep to stack core RNN")
parser.add_argument("--skip-emb-preload",
dest="skip_emb_preload",
action='store_true',
help="Skip preloading embeddings")
parser.add_argument("--tokenize-old",
dest="tokenize_old",
action='store_true',
help="use old tokenizer")
parser.add_argument("-ar",
"--abs-root",
dest="abs_root",
type=str,
metavar='<str>',
required=False,
help="Abs path to root directory")
parser.add_argument("-ad",
"--abs-data",
dest="abs_data",
type=str,
metavar='<str>',
required=False,
help="Abs path to data directory")
parser.add_argument("-ao",
"--abs-out",
dest="abs_out",
type=str,
metavar='<str>',
required=False,
help="Abs path to output directory")
parser.add_argument("-dp",
"--data-path",
dest="data_path",
type=str,
metavar='<str>',
required=False,
help="Abs path to output directory")
##
if argv is None:
args = parser.parse_args()
else:
args = parser.parse_args(argv)
out_dir = args.abs_out
U.mkdir_p(os.path.join(out_dir, 'preds'))
U.set_logger(out_dir)
U.print_args(args)
assert args.model_type in {'reg', 'regp', 'breg', 'bregp', 'rwa'}
assert args.algorithm in {
'rmsprop', 'sgd', 'adagrad', 'adadelta', 'adam', 'adamax'
}
assert args.loss in {'mse', 'mae', 'kappa', 'soft_kappa'}
assert args.recurrent_unit in {'lstm', 'gru', 'simple', 'rwa'}
assert args.aggregation in {'mot', 'attsum', 'attmean'}
if args.seed > 0:
RANDSEED = args.seed
else:
RANDSEED = np.random.randint(10000)
np.random.seed(RANDSEED)
#######################
#from deepats.util import GPUtils as GPU
import GPUtil as GPU
mem = GPU.avail_mem()
logger.info('AVAIL GPU MEM == %.4f' % mem)
# if mem < 0.05:
# return None
###############################################################################################################################
## Prepare data
#
emb_words = None
if not args.skip_emb_preload: #if args.emb_path:
from deepats.w2vEmbReader import W2VEmbReader as EmbReader
logger.info('Loading embedding vocabulary...')
emb_reader = EmbReader(args.emb_path, emb_dim=args.emb_dim)
emb_words = emb_reader.load_words()
train_df, dev_df, test_df, vocab, overal_maxlen, qwks = dataset.get_data(
args.data_path, emb_words=emb_words, seed=RANDSEED)
vocab_size = len(vocab)
train_x = train_df['text'].values
train_y = train_df['y'].values
dev_x = dev_df['text'].values
dev_y = dev_df['y'].values
test_x = test_df['text'].values
test_y = test_df['y'].values
# Dump vocab
abs_vocab_file = os.path.join(out_dir, 'vocab.pkl')
with open(os.path.join(out_dir, 'vocab.pkl'), 'wb') as vocab_file:
pk.dump(vocab, vocab_file)
if args.recurrent_unit == 'rwa':
setattr(args, 'model_type', 'rwa')
# Pad sequences for mini-batch processing
from keras.preprocessing import sequence
if args.model_type in {'breg', 'bregp', 'rwa'}:
assert args.rnn_dim > 0
train_x = sequence.pad_sequences(train_x, maxlen=overal_maxlen)
dev_x = sequence.pad_sequences(dev_x, maxlen=overal_maxlen)
test_x = sequence.pad_sequences(test_x, maxlen=overal_maxlen)
else:
train_x = sequence.pad_sequences(train_x)
dev_x = sequence.pad_sequences(dev_x)
test_x = sequence.pad_sequences(test_x)
###############################################################################################################################
## Some statistics
# train_y = np.array(train_y, dtype=K.floatx())
# dev_y = np.array(dev_y, dtype=K.floatx())
# test_y = np.array(test_y, dtype=K.floatx())
bincounts, mfs_list = U.bincounts(train_y)
with open(os.path.join(out_dir, 'bincounts.txt'), 'w') as output_file:
for bincount in bincounts:
output_file.write(str(bincount) + '\n')
train_mean = train_y.mean(axis=0)
train_std = train_y.std(axis=0)
dev_mean = dev_y.mean(axis=0)
dev_std = dev_y.std(axis=0)
test_mean = test_y.mean(axis=0)
test_std = test_y.std(axis=0)
logger.info('Statistics:')
logger.info(' TEST KAPPAS (float, int)= \033[92m%.4f (%.4f)\033[0m ' %
(qwks[1], qwks[0]))
logger.info(' RANDSEED = ' + str(RANDSEED))
logger.info(' train_x shape: ' + str(np.array(train_x).shape))
logger.info(' dev_x shape: ' + str(np.array(dev_x).shape))
logger.info(' test_x shape: ' + str(np.array(test_x).shape))
logger.info(' train_y shape: ' + str(train_y.shape))
logger.info(' dev_y shape: ' + str(dev_y.shape))
logger.info(' test_y shape: ' + str(test_y.shape))
logger.info(' train_y mean: %s, stdev: %s, MFC: %s' %
(str(train_mean), str(train_std), str(mfs_list)))
logger.info(' overal_maxlen: ' + str(overal_maxlen))
###############################################################################################################################
## Optimizaer algorithm
#
from deepats.optimizers import get_optimizer
#optimizer = get_optimizer(args)
from keras import optimizers
## RMS-PROP
#optimizer = optimizers.RMSprop(lr=0.00075, rho=0.9, clipnorm=1)
#optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, clipnorm=1)
optimizer = optimizers.RMSprop(lr=0.001,
rho=0.9,
epsilon=1e-6,
clipnorm=10)
#optimizer = optimizers.RMSprop(lr=0.0018, rho=0.88, epsilon=1e-6, clipnorm=10)
#optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-8, clipnorm=10)
#optimizer = optimizers.RMSprop(lr=0.004, rho=0.85, epsilon=1e-6, clipnorm=10)# best 2.1 (RWA)
#optimizer = optimizers.RMSprop(lr=0.0025, rho=0.8, epsilon=1e-8, clipnorm=10) # best 2.1 (RWA)
#optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-8, clipnorm=10) # best 2.3 (RWA)
#optimizer = optimizers.RMSprop(lr=0.0025, rho=0.88, epsilon=1e-8, clipnorm=10) # best 2.3 (RWA)
#optimizer = optimizers.RMSprop(lr=0.004, rho=0.85, epsilon=1e-8, clipnorm=10) # best 2.10 (RWA)
## OTHER METHODS
#optimizer = optimizers.Adam(lr=0.0018, clipnorm=5)
#optimizer = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=1)
#optimizer = optimizers.Adam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-06, clipnorm=10)
#optimizer = optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=10)
#optimizer = optimizers.SGD(lr=0.05, momentum=0, decay=0.0, nesterov=False, clipnorm=10)
#optimizer = optimizers.Adagrad(lr=0.03, epsilon=1e-08, clipnorm=10)
#optimizer = optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-06, clipnorm=10)
###############################################################################################################################
## Building model
#
if args.loss == 'mse':
loss = 'mean_squared_error'
metric = kappa_metric
metric_name = 'kappa_metric'
elif args.loss == 'mae':
loss = 'mean_absolute_error'
metric = kappa_metric
metric_name = 'kappa_metric'
elif args.loss == 'kappa':
loss = kappa_loss
metric = kappa_metric
metric_name = 'kappa_metric'
########################################################
from deepats.models import create_model
model = create_model(args, train_y.mean(axis=0), overal_maxlen, vocab)
############################################
'''
# test yaml serialization/de-serialization
yaml = model.to_yaml()
print yaml
from deepats.my_layers import MeanOverTime
from deepats.rwa import RWA
model = model_from_yaml(yaml, custom_objects={'MeanOverTime': MeanOverTime, 'RWA':RWA})
'''
############################################
model.compile(loss=loss, optimizer=optimizer, metrics=[metric])
print(model.summary())
###############################################################################################################################
## Plotting model
#
# from keras.utils.visualize_util import plot
# plot(model, to_file = os.path.join(out_dir,'model.png'))
###############################################################################################################################
## Save model architecture
#
logger.info('Saving model architecture')
with open(os.path.join(out_dir, 'model_arch.json'), 'w') as arch:
arch.write(model.to_json(indent=2))
logger.info(' Done')
###############################################################################################################################
## Evaluator
#
evl = Evaluator(dataset, args.prompt_id, out_dir, dev_x, test_x, dev_df,
test_df)
###############################################################################################################################
## Training
#
logger.info(
'----------------------------------------------------------------')
logger.info('Initial Evaluation:')
evl.evaluate(model, -1, print_info=True)
total_train_time = 0
total_eval_time = 0
for ii in range(args.epochs):
# Training
t0 = time()
train_history = model.fit(train_x,
train_y,
batch_size=args.batch_size,
epochs=1,
verbose=0)
tr_time = time() - t0
total_train_time += tr_time
# Evaluate
t0 = time()
evl.evaluate(model, ii)
evl_time = time() - t0
total_eval_time += evl_time
# Print information
train_loss = train_history.history['loss'][0]
train_metric = train_history.history[metric_name][0]
logger.info('Epoch %d, train: %is, evaluation: %is' %
(ii, tr_time, evl_time))
logger.info('[Train] loss: %.4f, metric: %.4f' %
(train_loss, train_metric))
evl.print_info()
###############################################################################################################################
## Summary of the results
#
logger.info('Training: %i seconds in total' % total_train_time)
logger.info('Evaluation: %i seconds in total' % total_eval_time)
evl.print_final_info()
def run(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("-o",
"--out-dir",
dest="out_dir_path",
type=str,
metavar='<str>',
required=True,
help="The path to the output directory")
parser.add_argument(
"-p",
"--prompt",
dest="prompt_id",
type=int,
metavar='<int>',
required=False,
help="Promp ID for ASAP dataset. '0' means all prompts.")
parser.add_argument("-t",
"--type",
dest="model_type",
type=str,
metavar='<str>',
default='regp',
help="Model type (reg|regp|breg|bregp) (default=regp)")
parser.add_argument(
"-u",
"--rec-unit",
dest="recurrent_unit",
type=str,
metavar='<str>',
default='lstm',
help="Recurrent unit type (lstm|gru|simple) (default=lstm)")
parser.add_argument(
"-a",
"--algorithm",
dest="algorithm",
type=str,
metavar='<str>',
default='rmsprop',
help=
"Optimization algorithm (rmsprop|sgd|adagrad|adadelta|adam|adamax) (default=rmsprop)"
)
parser.add_argument("-l",
"--loss",
dest="loss",
type=str,
metavar='<str>',
default='mse',
help="Loss function (mse|mae) (default=mse)")
parser.add_argument("-e",
"--embdim",
dest="emb_dim",
type=int,
metavar='<int>',
default=50,
help="Embeddings dimension (default=50)")
parser.add_argument(
"-c",
"--cnndim",
dest="cnn_dim",
type=int,
metavar='<int>',
default=0,
help="CNN output dimension. '0' means no CNN layer (default=0)")
parser.add_argument("-w",
"--cnnwin",
dest="cnn_window_size",
type=int,
metavar='<int>',
default=3,
help="CNN window size. (default=3)")
parser.add_argument(
"-r",
"--rnndim",
dest="rnn_dim",
type=int,
metavar='<int>',
default=300,
help="RNN dimension. '0' means no RNN layer (default=300)")
parser.add_argument("-b",
"--batch-size",
dest="batch_size",
type=int,
metavar='<int>',
default=32,
help="Batch size (default=32)")
parser.add_argument("-v",
"--vocab-size",
dest="vocab_size",
type=int,
metavar='<int>',
default=4000,
help="Vocab size (default=4000)")
parser.add_argument(
"--aggregation",
dest="aggregation",
type=str,
metavar='<str>',
default='mot',
help=
"The aggregation method for regp and bregp types (mot|attsum|attmean) (default=mot)"
)
parser.add_argument(
"--dropout",
dest="dropout_prob",
type=float,
metavar='<float>',
default=0.5,
help=
"The dropout probability. To disable, give a negative number (default=0.5)"
)
parser.add_argument(
"--vocab-path",
dest="vocab_path",
type=str,
metavar='<str>',
help="(Optional) The path to the existing vocab file (*.pkl)")
parser.add_argument("--skip-init-bias",
dest="skip_init_bias",
action='store_true',
help="Skip initialization of the last layer bias")
parser.add_argument(
"--emb",
dest="emb_path",
type=str,
metavar='<str>',
help="The path to the word embeddings file (Word2Vec format)")
parser.add_argument("--epochs",
dest="epochs",
type=int,
metavar='<int>',
default=100,
help="Number of epochs (default=50)")
parser.add_argument(
"--maxlen",
dest="maxlen",
type=int,
metavar='<int>',
default=0,
help=
"Maximum allowed number of words during training. '0' means no limit (default=0)"
)
parser.add_argument("--seed",
dest="seed",
type=int,
metavar='<int>',
default=1234,
help="Random seed (default=1234)")
## dsv
parser.add_argument("--min-word-freq",
dest="min_word_freq",
type=int,
metavar='<int>',
default=2,
help="Min word frequency")
parser.add_argument("--stack",
dest="stack",
type=int,
metavar='<int>',
default=1,
help="how deep to stack core RNN")
parser.add_argument("--skip-emb-preload",
dest="skip_emb_preload",
action='store_true',
help="Skip preloading embeddings")
parser.add_argument("--tokenize-old",
dest="tokenize_old",
action='store_true',
help="use old tokenizer")
parser.add_argument("-ar",
"--abs-root",
dest="abs_root",
type=str,
metavar='<str>',
required=False,
help="Abs path to root directory")
parser.add_argument("-ad",
"--abs-data",
dest="abs_data",
type=str,
metavar='<str>',
required=False,
help="Abs path to data directory")
parser.add_argument("-ao",
"--abs-out",
dest="abs_out",
type=str,
metavar='<str>',
required=False,
help="Abs path to output directory")
parser.add_argument("-dp",
"--data-path",
dest="data_path",
type=str,
metavar='<str>',
required=False,
help="Abs path to output directory")
##
if argv is None:
args = parser.parse_args()
else:
args = parser.parse_args(argv)
out_dir = args.abs_out
U.mkdir_p(os.path.join(out_dir, 'preds'))
U.set_logger(out_dir)
U.print_args(args)
assert args.model_type in {'reg', 'regp', 'breg', 'bregp', 'rwa'}
assert args.algorithm in {
'rmsprop', 'sgd', 'adagrad', 'adadelta', 'adam', 'adamax'
}
assert args.loss in {'mse', 'mae', 'kappa', 'soft_kappa'}
assert args.recurrent_unit in {'lstm', 'gru', 'simple', 'rwa'}
assert args.aggregation in {'mot', 'attsum', 'attmean'}
if args.seed > 0:
RANDSEED = args.seed
else:
RANDSEED = np.random.randint(10000)
np.random.seed(RANDSEED)
#######################
#from deepats.util import GPUtils as GPU
import GPUtil as GPU
mem = GPU.avail_mem()
logger.info('AVAIL GPU MEM == %.4f' % mem)
# if mem < 0.05:
# return None
###############################################################################################################################
## Prepare data
#
emb_words = None
if not args.skip_emb_preload: #if args.emb_path:
from deepats.w2vEmbReader import W2VEmbReader as EmbReader
logger.info('Loading embedding vocabulary...')
emb_reader = EmbReader(args.emb_path, emb_dim=args.emb_dim)
emb_words = emb_reader.load_words()
train_df, dev_df, test_df, vocab, overal_maxlen, qwks = dataset.get_data(
args.data_path, emb_words=emb_words, seed=RANDSEED)
vocab_size = len(vocab)
train_x = train_df['text'].values
train_y = train_df['y'].values
dev_x = dev_df['text'].values
dev_y = dev_df['y'].values
test_x = test_df['text'].values
test_y = test_df['y'].values
# Dump vocab
abs_vocab_file = os.path.join(out_dir, 'vocab.pkl')
with open(os.path.join(out_dir, 'vocab.pkl'), 'wb') as vocab_file:
pk.dump(vocab, vocab_file)
if args.recurrent_unit == 'rwa':
setattr(args, 'model_type', 'rwa')
# Pad sequences for mini-batch processing
from keras.preprocessing import sequence
train_x = sequence.pad_sequences(train_x, maxlen=overal_maxlen)
dev_x = sequence.pad_sequences(dev_x, maxlen=overal_maxlen)
test_x = sequence.pad_sequences(test_x, maxlen=overal_maxlen)
###############################################################################################################################
## Some statistics
bincounts, mfs_list = U.bincounts(train_y)
with open(os.path.join(out_dir, 'bincounts.txt'), 'w') as output_file:
for bincount in bincounts:
output_file.write(str(bincount) + '\n')
train_mean = train_y.mean(axis=0)
train_std = train_y.std(axis=0)
dev_mean = dev_y.mean(axis=0)
dev_std = dev_y.std(axis=0)
test_mean = test_y.mean(axis=0)
test_std = test_y.std(axis=0)
logger.info('Statistics:')
logger.info(' PROMPT_ID\t= ' + U.b_green(args.prompt_id))
logger.info(
' TEST KAPPAS\t= {} (float, int)'.format(U.b_green('%.4f (%.4f)')) %
(qwks[1], qwks[0]))
logger.info(' RANDSEED\t= ' + U.b_green(str(RANDSEED)))
logger.info(' train_x shape: ' + str(np.array(train_x).shape))
logger.info(' dev_x shape: ' + str(np.array(dev_x).shape))
logger.info(' test_x shape: ' + str(np.array(test_x).shape))
logger.info(' train_y shape: ' + str(train_y.shape))
logger.info(' dev_y shape: ' + str(dev_y.shape))
logger.info(' test_y shape: ' + str(test_y.shape))
logger.info(' train_y mean: %s, stdev: %s, MFC: %s' %
(str(train_mean), str(train_std), str(mfs_list)))
logger.info(' overal_maxlen: ' + str(overal_maxlen))
###############################################################################################################################
## Optimizaer algorithm
from keras import optimizers
from deepats.optimizers import get_optimizer
#optimizer = get_optimizer(args)
## RMS-PROP
#optimizer = optimizers.RMSprop(lr=0.00075, rho=0.9, clipnorm=1)
#optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, clipnorm=1)
optimizer = optimizers.RMSprop(lr=0.003,
rho=0.88,
epsilon=1e-6,
clipnorm=10)
#optimizer = optimizers.RMSprop(lr=0.0018, rho=0.88, epsilon=1e-6, clipnorm=10)
#optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-8, clipnorm=10)
#optimizer = optimizers.RMSprop(lr=0.004, rho=0.85, epsilon=1e-6, clipnorm=10)# best 2.1 (RWA)
#optimizer = optimizers.RMSprop(lr=0.0025, rho=0.8, epsilon=1e-8, clipnorm=10) # best 2.1 (RWA)
#optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-8, clipnorm=10) # best 2.3 (RWA)
#optimizer = optimizers.RMSprop(lr=0.0025, rho=0.88, epsilon=1e-8, clipnorm=10) # best 2.3 (RWA)
#optimizer = optimizers.RMSprop(lr=0.004, rho=0.85, epsilon=1e-8, clipnorm=10) # best 2.10 (RWA)
## OTHER METHODS
#optimizer = optimizers.Adam(lr=0.0018, clipnorm=5)
#optimizer = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=1)
#optimizer = optimizers.Adam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-06, clipnorm=10)
#optimizer = optimizers.Nadam(lr=0.002)
#optimizer = optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clipnorm=10)
#optimizer = optimizers.SGD(lr=0.05, momentum=0, decay=0.0, nesterov=False, clipnorm=10)
#optimizer = optimizers.Adagrad(lr=0.03, epsilon=1e-08, clipnorm=10)
#optimizer = optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-06, clipnorm=10)
###############################################################################################################################
## Building model
#
loss = kappa_loss
metric_name = 'kappa'
########################################################
from deepats.models import create_model
model = create_model(args, train_y.mean(axis=0), overal_maxlen, vocab)
############################################
'''
# test yaml serialization/de-serialization
yaml = model.to_yaml()
print yaml
from deepats.my_layers import MeanOverTime
from deepats.rwa import RWA
model = model_from_yaml(yaml, custom_objects={'MeanOverTime': MeanOverTime, 'RWA':RWA})
'''
############################################
model.compile(loss=loss, optimizer=optimizer, metrics=[kappa])
print(model.summary())
###############################################################################################################################
## Callbacks
callbacks = []
##############################
''' Evaluate test_kappa '''
class Eval(Callback):
def __init__(self, x, y, funcs, prefix='test', batch_size=128):
super(Eval, self).__init__()
self.x = x
self.y = y
self.funcs = funcs
self.prefix = prefix
self.batch_size = batch_size
self.epoch = 0
def on_epoch_end(self, batch, logs={}):
self.epoch += 1
p = np.asarray(
self.model.predict(self.x,
batch_size=self.batch_size).squeeze())
for func in self.funcs:
f = func(self.y, p)
name = '{}_{}'.format(self.prefix, func.__name__)
logs[name] = f
print(' - {0}: {1:0.4f}'.format(name, f))
eval = Eval(test_x, test_df['y'].values, [nkappa], 'test')
callbacks.append(eval)
##############################
''' ModelCheckpoint '''
wt_path = os.path.join(out_dir, 'weights.{}.hdf5'.format('rwa'))
checkpt = ModelCheckpoint(wt_path,
monitor='val_kappa',
verbose=1,
save_best_only=True,
mode='max')
callbacks.append(checkpt)
##############################
''' LRplateau '''
class LRplateau(ReduceLROnPlateau):
def __init__(self,
monitor='val_loss',
factor=0.1,
patience=10,
verbose=0,
mode='auto',
epsilon=1e-4,
cooldown=0,
min_lr=0,
checkpoint=None):
super(LRplateau, self).__init__(monitor, factor, patience, verbose,
mode, epsilon, cooldown, min_lr)
self.checkpoint = checkpoint
def on_lr_reduce(self, epoch):
if self.checkpoint:
if self.verbose > 0:
print('Epoch {}: loading wts from {}.\n'.format(
epoch, self.checkpoint.filepath))
self.model.load_weights(self.checkpoint.filepath)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
logs['lr'] = K.get_value(self.model.optimizer.lr)
current = logs.get(self.monitor)
if current is None:
warnings.warn(
'Learning Rate Plateau Reducing requires %s available!' %
self.monitor, RuntimeWarning)
else:
if self.in_cooldown():
self.cooldown_counter -= 1
self.wait = 0
if self.monitor_op(current, self.best):
self.best = current
self.wait = 0
elif not self.in_cooldown():
if self.wait >= self.patience:
old_lr = float(K.get_value(self.model.optimizer.lr))
if old_lr > self.min_lr + self.lr_epsilon:
new_lr = old_lr * self.factor
new_lr = max(new_lr, self.min_lr)
K.set_value(self.model.optimizer.lr, new_lr)
if self.verbose > 0:
print(
'\nEpoch {0}: reducing learning rate to {1:0.4g}.'
.format(epoch, new_lr))
self.cooldown_counter = self.cooldown
self.wait = 0
self.on_lr_reduce(epoch)
self.wait += 1
reduce_lr = LRplateau(monitor='val_kappa',
mode='max',
patience=3,
factor=0.33,
min_lr=0.00001,
verbose=1,
checkpoint=checkpt)
callbacks.append(reduce_lr)
###############################################################################################################################
## Training
model.fit(train_x,
train_y,
validation_data=(dev_x, dev_df['y'].values),
batch_size=args.batch_size,
epochs=args.epochs,
callbacks=callbacks,
verbose=1)
