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task.py
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task.py
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from model_param_space import ModelParamSpace
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval
from optparse import OptionParser
from utils import logging_utils
import numpy as np
import pandas as pd
from utils.data_utils import load_dict_from_txt
import os
import config
import datetime
import tensorflow as tf
from refe import *
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
class Task:
def __init__(self, model_name, data_name, cv_runs, params_dict, logger):
if data_name == "wn18":
self.train_triples = pd.read_csv(config.WN18_TRAIN, names=["e1", "r", "e2"]).as_matrix()
self.valid_triples = pd.read_csv(config.WN18_VALID, names=["e1", "r", "e2"]).as_matrix()
self.test_triples = pd.read_csv(config.WN18_TEST, names=["e1", "r", "e2"]).as_matrix()
self.e2id = load_dict_from_txt(config.WN18_E2ID)
self.r2id = load_dict_from_txt(config.WN18_R2ID)
elif data_name == "fb15k":
self.train_triples = pd.read_csv(config.FB15K_TRAIN, names=["e1", "r", "e2"]).as_matrix()
self.valid_triples = pd.read_csv(config.FB15K_VALID, names=["e1", "r", "e2"]).as_matrix()
self.test_triples = pd.read_csv(config.FB15K_TEST, names=["e1", "r", "e2"]).as_matrix()
self.e2id = load_dict_from_txt(config.FB15K_E2ID)
self.r2id = load_dict_from_txt(config.FB15K_R2ID)
elif data_name == "bp":
self.train_triples = pd.read_csv(config.BP_TRAIN, names=["e1", "r", "e2"]).as_matrix()
self.valid_triples = pd.read_csv(config.BP_VALID, names=["e1", "r", "e2"]).as_matrix()
self.test_triples = pd.read_csv(config.BP_TEST, names=["e1", "r", "e2"]).as_matrix()
self.e2id = load_dict_from_txt(config.BP_E2ID)
self.r2id = load_dict_from_txt(config.BP_R2ID)
elif data_name == "fb1m":
self.train_triples = pd.read_csv(config.FB1M_TRAIN, names=["e1", "r", "e2"]).as_matrix()
self.valid_triples = pd.read_csv(config.FB1M_VALID, names=["e1", "r", "e2"]).as_matrix()
self.test_triples = pd.read_csv(config.FB1M_TEST, names=["e1", "r", "e2"]).as_matrix()
self.e2id = load_dict_from_txt(config.FB1M_E2ID)
self.r2id = load_dict_from_txt(config.FB1M_R2ID)
else:
raise AttributeError("Invalid data name! (Valid data name: wn18, fb15k, bp)")
self.model_name = model_name
self.data_name = data_name
self.cv_runs = cv_runs
self.params_dict = params_dict
self.hparams = AttrDict(params_dict)
self.logger = logger
self.n_entities = len(self.e2id)
self.n_relations = len(self.r2id)
self.model = self._get_model()
self.saver = tf.train.Saver(tf.global_variables())
checkpoint_path = os.path.abspath(config.CHECKPOINT_PATH)
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
self.checkpoint_prefix = os.path.join(checkpoint_path, self.__str__())
def __str__(self):
return self.model_name
def _get_model(self):
args = [self.n_entities, self.n_relations, self.hparams]
if "DistMult" in self.model_name:
if "tanh" in self.model_name:
return DistMult_tanh(*args)
else:
return DistMult(*args)
else:
raise AttributeError("Invalid model name! (Check model_param_space.py)")
def _save(self, sess):
path = self.saver.save(sess, self.checkpoint_prefix)
print("Saved model to {}".format(path))
def _print_param_dict(self, d, prefix=" ", incr_prefix=" "):
for k, v in sorted(d.items()):
if isinstance(v, dict):
self.logger.info("%s%s:" % (prefix, k))
self.print_param_dict(v, prefix+incr_prefix, incr_prefix)
else:
self.logger.info("%s%s: %s" % (prefix, k, v))
def create_session(self):
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=8,
allow_soft_placement=True,
log_device_placement=False)
return tf.Session(config=session_conf)
def cv(self):
self.logger.info("=" * 50)
self.logger.info("Params")
self._print_param_dict(self.params_dict)
self.logger.info("Results")
self.logger.info("\t\tRun\t\tEpoch\t\tLoss\t\tAcc")
cv_loss = []
cv_acc = []
for i in range(self.cv_runs):
sess = self.create_session()
sess.run(tf.global_variables_initializer())
epoch, loss, acc = self.model.fit(sess, self.train_triples, self.valid_triples)
self.logger.info("\t\t%d\t\t%d\t\t%f\t\t%f" % (i+1, epoch, loss, acc))
cv_loss.append(loss)
cv_acc.append(acc)
sess.close()
self.loss = np.mean(cv_loss)
self.acc = np.mean(cv_acc)
self.logger.info("CV Loss: %.3f" % self.loss)
self.logger.info("CV Accuracy: %.3f" % self.acc)
self.logger.info("-" * 50)
def refit(self):
sess = self.create_session()
sess.run(tf.global_variables_initializer())
self.model.fit(sess, np.concatenate((self.train_triples, self.valid_triples)))
print("Evaluation:")
self.model.validation(sess, self.test_triples)
sess.close()
return res
class TaskOptimizer:
def __init__(self, model_name, data_name, max_evals, cv_runs, logger):
self.model_name = model_name
self.data_name = data_name
self.max_evals = max_evals
self.cv_runs = cv_runs
self.logger = logger
self.model_param_space = ModelParamSpace(self.model_name)
def _obj(self, param_dict):
param_dict = self.model_param_space._convert_into_param(param_dict)
self.task = Task(self.model_name, self.data_name, self.cv_runs, param_dict, self.logger)
self.task.cv()
tf.reset_default_graph()
ret = {
"loss": -self.task.acc,
"attachments": {
"loss": self.task.loss,
},
"status": STATUS_OK
}
return ret
def run(self):
trials = Trials()
best = fmin(self._obj, self.model_param_space._build_space(), tpe.suggest, self.max_evals, trials)
best_params = space_eval(self.model_param_space._build_space(), best)
best_params = self.model_param_space._convert_into_param(best_params)
trial_loss = np.asarray(trials.losses(), dtype=float)
best_ind = np.argmin(trial_loss)
acc = -trial_loss[best_ind]
loss = trials.trial_attachments(trials.trials[best_ind])["loss"]
self.logger.info("-"*50)
self.logger.info("Best CV Results:")
self.logger.info("Loss: %.3f" % loss)
self.logger.info("Accuracy: %.3f" % acc)
self.logger.info("Best Param:")
self.task._print_param_dict(best_params)
self.logger.info("-"*50)
def parse_args(parser):
parser.add_option("-m", "--model", type="string", dest="model_name", default="TransE_L2")
parser.add_option("-d", "--data", type="string", dest="data_name", default="wn18")
parser.add_option("-e", "--eval", type="int", dest="max_evals", default=100)
parser.add_option("-c", "--cv", type="int", dest="cv_runs", default=3)
options, args = parser.parse_args()
return options, args
def main(options):
time_str = datetime.datetime.now().isoformat()
logname = "[Model@%s]_[Data@%s]_%s.log" % (
options.model_name, options.data_name, time_str)
logger = logging_utils._get_logger(config.LOG_PATH, logname)
optimizer = TaskOptimizer(options.model_name, options.data_name, options.max_evals, options.cv_runs, logger)
optimizer.run()
if __name__ == "__main__":
parser = OptionParser()
options, args = parse_args(parser)
main(options)