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run_models.py
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run_models.py
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# -*- coding: utf-8 -*-
"""
Created on Mar 18th 10:58:37 2016
run models, including training and validating
@author: hongyuan
"""
import pickle
import time
import numpy
import theano
from theano import sandbox
import theano.tensor as tensor
import os
#import scipy.io
from collections import defaultdict
from theano.tensor.shared_randomstreams import RandomStreams
import modules.utils as utils
import modules.models as models
import modules.optimizers as optimizers
import modules.controllers as controllers
import modules.data_processers as data_processers
import modules.organizers as organizers
dtype=theano.config.floatX
#
# Hawkes process
def train_hawkes_ctsm(input_train):
'''
this function is called to train hawkes ctsm model
'''
#TODO: pre-settings like random states
numpy.random.seed(
input_train['seed_random']
)
#
#command_mkdir = 'mkdir -p ' + os.path.abspath(
# input_train['save_file_path']
#)
#os.system(command_mkdir)
#
log_dict = {
'log_file': input_train['log_file'],
'save_file_path': input_train['save_file_path'],
'mode': 'create', 'compile_time': None,
'max_dev_log_likelihood': -1e6,
'min_dev_error_rate': 1e6,
'min_dev_rmse': 1e6,
#
'what_to_track': input_train['what_to_track'],
#
'args': input_train['args'],
#
'iteration': 0,
'track_period': input_train['track_period'],
'max_epoch': input_train['max_epoch'],
'size_batch': input_train['size_batch'],
#
'tracked_best': {},
#
'tracked': {
'track_cnt': None,
'train_log_likelihood': None,
'dev_log_likelihood': None,
'train_log_likelihood_time': None,
'dev_log_likelihood_time': None,
'train_log_likelihood_type': None,
'dev_log_likelihood_type': None,
#
'train_time': None, 'dev_time': None
}
}
#TODO: get the data and process the data
print "reading and processing data ... "
data_process = data_processers.DataProcesser(
{
'path_rawdata': input_train['path_rawdata'],
'size_batch': input_train['size_batch'],
'ratio_train': input_train['ratio_train'],
'to_read': [
'train', 'dev'
],
'partial_predict': input_train['partial_predict']
}
)
#
prune_stream = numpy.int32(
input_train['prune_stream']
)
assert(prune_stream >= 0)
if prune_stream > 0:
data_process.prune_stream(prune_stream)
#
#TODO: build the model
print "building model ... "
compile_start = time.time()
model_settings = {
'model': input_train['model'],
'dim_process': data_process.dim_process,
'coef_l2': input_train['coef_l2'],
'size_batch': input_train['size_batch'],
'optimizer': input_train['optimizer'],
'path_pre_train': input_train['path_pre_train'],
'learn_rate': input_train['learn_rate'],
'predict_lambda': input_train['predict_lambda']
}
control = controllers.ControlHawkesCTSM(
model_settings
)
compile_end = time.time()
compile_time = compile_end - compile_start
#'''
print "model finished, comilation time is ", round(compile_time, 0)
#TODO: start training, define the training functions
print "building training log ... "
log_dict['compile_time'] = round(compile_time, 0)
data_process.track_log(log_dict)
log_dict['mode'] = 'continue'
for epi in range(log_dict['max_epoch']):
#
print "training epoch ", epi
#
total_log_likelihood, total_log_likelihood_time, total_log_likelihood_type = 0.0, 0.0, 0.0
total_num_of_events = 0.0
#TODO: shuffle the training data and train this epoch
data_process.shuffle_train_data()
#
for step_train in range(data_process.max_nums['train'] ):
#
train_start = time.time()
#print "the step is ", step
#
data_process.process_data(
'train', step_train,
tag_model = 'hawkes',
predict_first = input_train['predict_first']
)
#
#print "training ... "
log_likelihood_numpy, log_likelihood_time_numpy, log_likelihood_type_numpy, num_of_events_numpy = control.model_learn(
data_process.seq_time_to_end_numpy,
data_process.seq_time_to_current_numpy,
data_process.seq_type_event_numpy,
data_process.time_since_start_to_end_numpy,
data_process.seq_mask_numpy,
data_process.seq_mask_to_current_numpy
)
#
#
log_dict['iteration'] += 1
#
total_log_likelihood += log_likelihood_numpy
total_log_likelihood_time += log_likelihood_time_numpy
total_log_likelihood_type += log_likelihood_type_numpy
total_num_of_events += num_of_events_numpy
#
#
log_dict['tracked']['train_log_likelihood'] = round(
total_log_likelihood / total_num_of_events, 4
)
log_dict['tracked']['train_log_likelihood_time'] = round(
total_log_likelihood_time / total_num_of_events, 4
)
log_dict['tracked']['train_log_likelihood_type'] = round(
total_log_likelihood_type / total_num_of_events, 4
)
train_end = time.time()
log_dict['tracked']['train_time'] = round(
(train_end - train_start)*log_dict['track_period'], 0
)
#
if step_train % 10 == 9:
print "in training, the step is out of ", step_train, data_process.max_nums['train']
#
#print "in training, the step is out of ", step_train, data_process.max_nums['train']
########
# Now we track the performance and save the model for every # batches, so that we do not miss the convergence within the epoch -- one epoch is too large sometimes
########
if log_dict['iteration'] % log_dict['track_period'] == 0:
#TODO: go through the dev data and calculate the dev metrics
print "Now we start validating after batches ", log_dict['track_period']
dev_start = time.time()
#
#TODO: get the dev loss values
total_log_likelihood_dev = 0.0
total_log_likelihood_time_dev = 0.0
total_log_likelihood_type_dev = 0.0
total_num_of_events_dev = 0.0
for step_dev in range(data_process.max_nums['dev']):
#
data_process.process_data(
'dev', step_dev,
tag_model = 'hawkes',
predict_first = input_train['predict_first']
)
#
#print "validating ... "
log_likelihood_numpy, log_likelihood_time_numpy, log_likelihood_type_numpy, num_of_events_numpy = control.model_dev(
data_process.seq_time_to_end_numpy,
data_process.seq_time_to_current_numpy,
data_process.seq_type_event_numpy,
data_process.time_since_start_to_end_numpy,
data_process.seq_mask_numpy,
data_process.seq_mask_to_current_numpy
)
#
total_log_likelihood_dev += log_likelihood_numpy
total_log_likelihood_time_dev += log_likelihood_time_numpy
total_log_likelihood_type_dev += log_likelihood_type_numpy
total_num_of_events_dev += num_of_events_numpy
#
if step_dev % 10 == 9:
print "in dev, the step is out of ", step_dev, data_process.max_nums['dev']
#
#print "in dev, the step is out of ", step_dev, data_process.max_nums['dev']
#
#
log_dict['tracked']['dev_log_likelihood'] = round(
total_log_likelihood_dev / total_num_of_events_dev, 4
)
log_dict['tracked']['dev_log_likelihood_time'] = round(
total_log_likelihood_time_dev / total_num_of_events_dev, 4
)
log_dict['tracked']['dev_log_likelihood_type'] = round(
total_log_likelihood_type_dev / total_num_of_events_dev, 4
)
#
dev_end = time.time()
log_dict['tracked']['dev_time'] = round( dev_end - dev_start, 0 )
#
log_dict['tracked']['track_cnt'] = int(
log_dict['iteration']/log_dict['track_period']
)
#
#
if log_dict['tracked']['dev_log_likelihood'] > log_dict['max_dev_log_likelihood']:
#
#name_file = 'model_'+str(log_dict['tracked']['track_cnt'])+'.pkl'
name_file = 'model.pkl'
save_file = os.path.abspath(
log_dict['save_file_path']
) + '/'+name_file
#
control.save_model(save_file)
#
data_process.track_log(log_dict)
########
data_process.finish_log(log_dict)
print "finish training"
#
#
#
def test_hawkes_ctsm_and_save(input_test):
'''
this function is called to test hawkes ctsm model
and save
'''
#TODO: pre-settings like random states
numpy.random.seed(
input_test['seed_random']
)
#
log_dict = {
'log_file': None,
'save_file_path': None,
'mode': 'create', 'compile_time': None,
'max_dev_log_likelihood': -1e6,
#
'args': input_test['args'],
#
'iteration': 0,
'track_period': None,
'max_epoch': input_test['max_epoch'],
'size_batch': input_test['size_batch'],
# this size batch has to be 1 !!!
'tracked': {
'track_cnt': None,
'train_log_likelihood': None,
'dev_log_likelihood': None,
'train_log_likelihood_time': None,
'dev_log_likelihood_time': None,
'train_log_likelihood_type': None,
'dev_log_likelihood_type': None,
#
'train_time': None, 'dev_time': None
}
}
#TODO: get the data and process the data
print "reading and processing data ... "
data_process = data_processers.DataProcesser(
{
'path_rawdata': input_test['path_rawdata'],
'size_batch': input_test['size_batch'],
'ratio_train': numpy.float32(0.0),
'to_read': [input_test['tag_split']],
'partial_predict': input_test['partial_predict']
}
)
#
prune_stream = numpy.int32(
input_test['prune_stream']
)
assert(prune_stream >= 0)
if prune_stream > 0:
data_process.prune_stream(prune_stream)
#
#TODO: build the model
print "building model ... "
compile_start = time.time()
# in testing, regularizer is not used,
# but we still pass it in, to make sure the model is built
model_settings = {
'model': input_test['model'],
'dim_process': data_process.dim_process,
'coef_l2': input_test['coef_l2'],
'size_batch': input_test['size_batch'],
#'size_batch': numpy.int32(1),
'optimizer': input_test['optimizer'],
'path_pre_train': input_test['path_pre_train'],
'predict_lambda': input_test['predict_lambda']
}
control = controllers.ControlHawkesCTSM(
model_settings
)
compile_end = time.time()
compile_time = compile_end - compile_start
#'''
print "model finished, comilation time is ", round(compile_time, 0)
#TODO: start training, define the training functions
tag_split = input_test['tag_split']
for epi in range(log_dict['max_epoch']):
#
list_log_likelihood_seq = []
list_log_likelihood_type = []
list_log_likelihood_time = []
list_num_of_events = []
#
dev_start = time.time()
print "validating for ", tag_split
#
#TODO: get the dev loss values
total_log_likelihood_dev = 0.0
total_log_likelihood_time_dev = 0.0
total_log_likelihood_type_dev = 0.0
total_num_of_events_dev = 0.0
for step_dev in range(data_process.max_nums[tag_split]):
#
data_process.process_data(
tag_split, step_dev,
tag_model = 'hawkes',
predict_first = input_test['predict_first']
)
#
#print "validating for ", tag_split
log_likelihood_numpy, log_likelihood_time_numpy, log_likelihood_type_numpy, num_of_events_numpy = control.model_dev(
data_process.seq_time_to_end_numpy,
data_process.seq_time_to_current_numpy,
data_process.seq_type_event_numpy,
data_process.time_since_start_to_end_numpy,
data_process.seq_mask_numpy,
data_process.seq_mask_to_current_numpy
)
#
list_log_likelihood_seq.append(
log_likelihood_numpy
)
list_log_likelihood_type.append(
log_likelihood_type_numpy
)
list_log_likelihood_time.append(
log_likelihood_time_numpy
)
list_num_of_events.append(
num_of_events_numpy
)
#
total_log_likelihood_dev += log_likelihood_numpy
total_log_likelihood_time_dev += log_likelihood_time_numpy
total_log_likelihood_type_dev += log_likelihood_type_numpy
total_num_of_events_dev += num_of_events_numpy
#
if step_dev % 100 == 99:
print "in validation, the step is out of ", step_dev, data_process.max_nums[tag_split]
#
log_likelihood_final = round(
total_log_likelihood_dev / total_num_of_events_dev, 4
)
log_likelihood_time_final = round(
total_log_likelihood_time_dev / total_num_of_events_dev, 4
)
log_likelihood_type_final = round(
total_log_likelihood_type_dev / total_num_of_events_dev, 4
)
#
dev_end = time.time()
#
# start saving stuff !!!
dict_results = {
'log_likelihood': {
'seq': log_likelihood_final,
'type': log_likelihood_type_final,
'time': log_likelihood_time_final
},
'model': input_test['path_pre_train'],
'data': input_test['path_rawdata'],
'tag_split': tag_split,
'lists': {
'log_likelihood_seq': list_log_likelihood_seq,
'log_likelihood_type': list_log_likelihood_type,
'log_likelihood_time': list_log_likelihood_time,
'num_of_events': list_num_of_events
}
}
#
#TODO: add more info about this model
if input_test['path_logs'] != None:
log_org = organizers.LogOrg(
{
'path_tracks': os.path.abspath(
input_test['path_logs']
)
}
)
log_org.read_logs()
dict_log_org = log_org.get_one_log()
dict_results['log_info'] = dict_log_org
else:
dict_results['log_info'] = None
#
with open(input_test['file_to_save_results'], 'wb') as f:
pickle.dump(dict_results, f)
#
#
print "the model is : ", input_test['path_pre_train']
print "the dataset is : ", input_test['path_rawdata']
print "the tag split is : ", tag_split
print "the log-likelihood of seq is : ", log_likelihood_final
print "the log-likelihood of type is : ", log_likelihood_type_final
print "the log-likelihood of time is : ", log_likelihood_time_final
print "the three values in the order of seq / type / time : is " + str(log_likelihood_final) + ' / ' + str(log_likelihood_type_final) + ' / ' + str(log_likelihood_time_final)
#
#
print "finish testing"
#
#
def test_intensity_and_save(input_test):
'''
this function is called to test hawkes
evaluation on intensity prediciton
and save results
'''
#TODO: pre-settings like random states
numpy.random.seed(
input_test['seed_random']
)
#
log_dict = {
'log_file': None,
'save_file_path': None,
'mode': 'create', 'compile_time': None,
'max_dev_log_likelihood': -1e6,
#
'args': input_test['args'],
#
'iteration': 0,
'track_period': None,
'max_epoch': input_test['max_epoch'],
'size_batch': input_test['size_batch'],
# this size batch has to be 1 !!!
'tracked': {
'track_cnt': None,
'train_log_likelihood': None,
'dev_log_likelihood': None,
'train_log_likelihood_time': None,
'dev_log_likelihood_time': None,
'train_log_likelihood_type': None,
'dev_log_likelihood_type': None,
#
'train_time': None, 'dev_time': None
}
}
#TODO: get the data and process the data
print "reading and processing data ... "
data_process = data_processers.DataProcesser(
{
'path_rawdata': input_test['path_rawdata'],
'size_batch': input_test['size_batch'],
'ratio_train': numpy.float32(0.0),
'to_read': [input_test['tag_split']],
'partial_predict': input_test['partial_predict']
}
)
#
prune_stream = numpy.int32(
input_test['prune_stream']
)
assert(prune_stream >= 0)
if prune_stream > 0:
data_process.prune_stream(prune_stream)
#
#TODO: build the model
print "building model ... "
compile_start = time.time()
# in testing, regularizer is not used,
# but we still pass it in, to make sure the model is built
model_settings = {
'model': input_test['model'],
'dim_process': data_process.dim_process,
'coef_l2': input_test['coef_l2'],
'size_batch': input_test['size_batch'],
#'size_batch': numpy.int32(1),
'optimizer': input_test['optimizer'],
'path_pre_train': input_test['path_pre_train'],
'predict_lambda': input_test['predict_lambda'],
'loss_type': 'loglikehood'
}
#
model_settings_gold = {
'model': input_test['model_gold'],
'dim_process': data_process.dim_process,
'coef_l2': input_test['coef_l2'],
'size_batch': input_test['size_batch'],
'optimizer': input_test['optimizer'],
'path_pre_train': input_test['path_gold'],
'predict_lambda': input_test['predict_lambda'],
'loss_type': 'loglikehood'
}
#
#TODO: make controller given model
if input_test['model'] == 'hawkes':
control = controllers.ControlHawkesCTSM(
model_settings
)
elif input_test['model'] == 'neuralreduce':
control = controllers.ControlNeuralHawkesAdaptiveBaseCTSM_time(
model_settings
)
elif input_test['model'] == 'conttime':
control = controllers.ControlNeuralHawkesAdaptiveBaseCTSM_time(
model_settings
)
else:
print "wrong model, it is gonna crash"
#
if input_test['model_gold'] == 'hawkes':
control_gold = controllers.ControlHawkesCTSM(
model_settings_gold
)
elif input_test['model_gold'] == 'neuralreduce':
control_gold = controllers.ControlNeuralHawkesAdaptiveBaseCTSM_time(
model_settings_gold
)
elif input_test['model_gold'] == 'conttime':
control_gold = controllers.ControlNeuralHawkesAdaptiveBaseCTSM_time(
model_settings_gold
)
else:
print "wrong model it is gonna crash"
#
compile_end = time.time()
compile_time = compile_end - compile_start
#'''
print "model finished, comilation time is ", round(compile_time, 0)
#TODO: start training, define the training functions
tag_split = input_test['tag_split']
for epi in range(log_dict['max_epoch']):
#
list_lambda_samples = []
list_lambda_samples_gold = []
list_num_of_samples = []
#
dev_start = time.time()
print "validating for ", tag_split
#
#TODO: get the dev loss values
for step_dev in range(data_process.max_nums[tag_split]):
#
data_process.process_data_lambda(
tag_split, step_dev,
predict_first = input_test['predict_first']
)
#
#print "validating for ", tag_split
if input_test['model'] == 'hawkes':
lambda_samples_numpy, num_of_samples_numpy = control.model_dev_lambda(
data_process.seq_type_event_hawkes_numpy,
data_process.seq_sims_time_to_current_hawkes_numpy,
data_process.seq_sims_mask_numpy,
data_process.seq_sims_mask_to_current_hawkes_numpy
)
else:
'''
by default it is neural
can add others like hawkesinhib
'''
lambda_samples_numpy, num_of_samples_numpy = control.model_dev_lambda(
data_process.seq_type_event_numpy,
data_process.seq_time_values_numpy,
data_process.seq_sims_time_to_current_numpy,
data_process.seq_sims_index_in_hidden_numpy,
data_process.seq_sims_mask_numpy
)
#
if input_test['model_gold']=='hawkes':
lambda_samples_gold_numpy, num_of_samples_gold_numpy = control_gold.model_dev_lambda(
data_process.seq_type_event_hawkes_numpy,
data_process.seq_sims_time_to_current_hawkes_numpy,
data_process.seq_sims_mask_numpy,
data_process.seq_sims_mask_to_current_hawkes_numpy
)
else:
'''
by default it is neural
can add others like hawkesinhib
'''
lambda_samples_gold_numpy, num_of_samples_gold_numpy = control_gold.model_dev_lambda(
data_process.seq_type_event_numpy,
data_process.seq_time_values_numpy,
data_process.seq_sims_time_to_current_numpy,
data_process.seq_sims_index_in_hidden_numpy,
data_process.seq_sims_mask_numpy
)
#
list_lambda_samples.append(
numpy.copy(lambda_samples_numpy)
)
list_lambda_samples_gold.append(
numpy.copy(
lambda_samples_gold_numpy
)
)
list_num_of_samples.append(
numpy.copy(num_of_samples_numpy)
)
#
if step_dev % 100 == 99:
print "in validation, the step is out of ", step_dev, data_process.max_nums[tag_split]
#
#
dev_end = time.time()
#
# start saving stuff !!!
dict_results = {
'model': input_test['model'],
'model_gold': input_test['model_gold'],
'path_pre_train': input_test['path_pre_train'],
'path_gold': input_test['path_gold'],
'data': input_test['path_rawdata'],
'tag_split': tag_split,
'lists': {
'lambda_samples': list_lambda_samples,
'lambda_samples_gold': list_lambda_samples_gold,
'num_of_samples': list_num_of_samples
}
}
#
#TODO: add more info about this model
#if input_test['path_logs'] != None:
# log_org = organizers.LogOrg(
# {
# 'path_tracks': os.path.abspath(
# input_test['path_logs']
# )
# }
# )
# log_org.read_logs()
# dict_log_org = log_org.get_one_log()
# dict_results['log_info'] = dict_log_org
#else:
# dict_results['log_info'] = None
#
with open(input_test['file_to_save_results'], 'wb') as f:
pickle.dump(dict_results, f)
#
#
print "the model is : ", input_test['path_pre_train']
print "the gold model is : ", input_test['path_gold']
print "the dataset is : ", input_test['path_rawdata']
print "the tag split is : ", tag_split
#
print "finish testing"
#
#
# Hawkes process with inhibition
def train_hawkesinhib_ctsm(input_train):
'''
this function is called to train hawkes ctsm model with inhibition
'''
#TODO: pre-settings like random states
numpy.random.seed(
input_train['seed_random']
)
#
#command_mkdir = 'mkdir -p ' + os.path.abspath(
# input_train['save_file_path']
#)
#os.system(command_mkdir)
#
log_dict = {
'log_file': input_train['log_file'],
'save_file_path': input_train['save_file_path'],
'mode': 'create', 'compile_time': None,
'max_dev_log_likelihood': -1e6,
'min_dev_error_rate': 1e6,
'min_dev_rmse': 1e6,
#
'what_to_track': input_train['what_to_track'],
#
'args': input_train['args'],
#
'iteration': 0,
'track_period': input_train['track_period'],
'max_epoch': input_train['max_epoch'],
'size_batch': input_train['size_batch'],
#
'tracked_best': {},
#
'tracked': {
'track_cnt': None,
'train_log_likelihood': None,
'dev_log_likelihood': None,
'train_log_likelihood_time': None,
'dev_log_likelihood_time': None,
'train_log_likelihood_type': None,
'dev_log_likelihood_type': None,
#
'train_time': None, 'dev_time': None
}
}
#TODO: get the data and process the data
print "reading and processing data ... "
data_process = data_processers.DataProcesser(
{
'path_rawdata': input_train['path_rawdata'],
'size_batch': input_train['size_batch'],
'ratio_train': input_train['ratio_train'],
'to_read': [
'train', 'dev'
],
'partial_predict': input_train['partial_predict']
}
)
#
prune_stream = numpy.int32(
input_train['prune_stream']
)
assert(prune_stream >= 0)
if prune_stream > 0:
data_process.prune_stream(prune_stream)
#
#TODO: build the model
print "building model ... "
compile_start = time.time()
model_settings = {
'model': input_train['model'],
'dim_process': data_process.dim_process,
'coef_l2': input_train['coef_l2'],
'size_batch': input_train['size_batch'],
'optimizer': input_train['optimizer'],
'path_pre_train': input_train['path_pre_train'],
'predict_lambda': input_train['predict_lambda']
}
control = controllers.ControlHawkesInhibCTSM(
model_settings
)
compile_end = time.time()
compile_time = compile_end - compile_start
#'''
print "model finished, comilation time is ", round(compile_time, 0)
#TODO: start training, define the training functions
print "building training log ... "
log_dict['compile_time'] = round(compile_time, 0)
data_process.track_log(log_dict)
log_dict['mode'] = 'continue'
for epi in range(log_dict['max_epoch']):
#
print "training epoch ", epi
#
total_log_likelihood, total_log_likelihood_time, total_log_likelihood_type = 0.0, 0.0, 0.0
total_num_of_events = 0.0
#TODO: shuffle the training data and train this epoch
data_process.shuffle_train_data()
#
for step_train in range(data_process.max_nums['train'] ):
#
train_start = time.time()
#print "the step is ", step
#
data_process.process_data(
tag_batch = 'train',
idx_batch_current = step_train,
tag_model = 'hawkesinhib',
multiple = numpy.int32(
input_train['multiple_sample_for_train']
),
predict_first = input_train['predict_first']
)
#
#print "training ... "
log_likelihood_numpy, log_likelihood_time_numpy, log_likelihood_type_numpy, num_of_events_numpy = control.model_learn(
#data_process.seq_time_to_end_numpy,
data_process.seq_time_to_current_numpy,
data_process.seq_type_event_numpy,
data_process.time_since_start_to_end_numpy,
data_process.num_sims_start_to_end_numpy,
data_process.seq_mask_numpy,
data_process.seq_mask_to_current_numpy,
data_process.seq_sims_time_to_current_numpy,
data_process.seq_sims_mask_to_current_numpy,
data_process.seq_sims_mask_numpy
)
#
#
log_dict['iteration'] += 1
#
total_log_likelihood += log_likelihood_numpy
total_log_likelihood_time += log_likelihood_time_numpy
total_log_likelihood_type += log_likelihood_type_numpy
total_num_of_events += num_of_events_numpy
#
#
log_dict['tracked']['train_log_likelihood'] = round(
total_log_likelihood / total_num_of_events, 4
)
log_dict['tracked']['train_log_likelihood_time'] = round(
total_log_likelihood_time / total_num_of_events, 4
)
log_dict['tracked']['train_log_likelihood_type'] = round(
total_log_likelihood_type / total_num_of_events, 4
)
train_end = time.time()
log_dict['tracked']['train_time'] = round(
(train_end - train_start)*log_dict['track_period'], 0
)
#
if step_train % 10 == 9:
print "in training, the step is out of ", step_train, data_process.max_nums['train']
########
# Now we track the performance and save the model for every # batches, so that we do not miss the convergence within the epoch -- one epoch is too large sometimes
########
if log_dict['iteration'] % log_dict['track_period'] == 0:
#TODO: go through the dev data and calculate the dev metrics
print "Now we start validating after batches ", log_dict['track_period']
dev_start = time.time()
#
#TODO: get the dev loss values
total_log_likelihood_dev = 0.0
total_log_likelihood_time_dev = 0.0
total_log_likelihood_type_dev = 0.0
total_num_of_events_dev = 0.0
for step_dev in range(data_process.max_nums['dev']):
#
data_process.process_data(
tag_batch = 'dev',
idx_batch_current = step_dev,
tag_model = 'hawkesinhib',
multiple = numpy.int32(
input_train[
'multiple_sample_for_dev'
]
),
predict_first = input_train['predict_first']
)
#
#print "validating ... "
log_likelihood_numpy, log_likelihood_time_numpy, log_likelihood_type_numpy, num_of_events_numpy = control.model_dev(
#data_process.seq_time_to_end_numpy,
data_process.seq_time_to_current_numpy,
data_process.seq_type_event_numpy,
data_process.time_since_start_to_end_numpy,
data_process.num_sims_start_to_end_numpy,
data_process.seq_mask_numpy,
data_process.seq_mask_to_current_numpy,
data_process.seq_sims_time_to_current_numpy,
data_process.seq_sims_mask_to_current_numpy,
data_process.seq_sims_mask_numpy
)
#
total_log_likelihood_dev += log_likelihood_numpy
total_log_likelihood_time_dev += log_likelihood_time_numpy
total_log_likelihood_type_dev += log_likelihood_type_numpy
total_num_of_events_dev += num_of_events_numpy
#
if step_dev % 10 == 9:
print "in dev, the step is out of ", step_dev, data_process.max_nums['dev']
#
#
log_dict['tracked']['dev_log_likelihood'] = round(
total_log_likelihood_dev / total_num_of_events_dev, 4
)
log_dict['tracked']['dev_log_likelihood_time'] = round(
total_log_likelihood_time_dev / total_num_of_events_dev, 4
)
log_dict['tracked']['dev_log_likelihood_type'] = round(
total_log_likelihood_type_dev / total_num_of_events_dev, 4
)
#
dev_end = time.time()
log_dict['tracked']['dev_time'] = round( dev_end - dev_start, 0 )
#
log_dict['tracked']['track_cnt'] = int(
log_dict['iteration']/log_dict['track_period']
)
#
#
if log_dict['tracked']['dev_log_likelihood'] > log_dict['max_dev_log_likelihood']:
#
#name_file = 'model_'+str(log_dict['tracked']['track_cnt'])+'.pkl'
name_file = 'model.pkl'
save_file = os.path.abspath(
log_dict['save_file_path']
) + '/'+name_file
#
control.save_model(save_file)
#
data_process.track_log(log_dict)
########
data_process.finish_log(log_dict)
print "finish training"
#
#
#
def test_hawkesinhib_ctsm_and_save(input_test):
'''
this function is called to test hawkes ctsm model with inhibition
'''
#TODO: pre-settings like random states
numpy.random.seed(
input_test['seed_random']
)
#
log_dict = {
'log_file': None,
'save_file_path': None,
'mode': 'create', 'compile_time': None,
'max_dev_log_likelihood': -1e6,
#
'args': input_test['args'],
#
'iteration': 0,
'track_period': None,
'max_epoch': input_test['max_epoch'],
'size_batch': input_test['size_batch'],
'tracked': {
'track_cnt': None,
'train_log_likelihood': None,
'dev_log_likelihood': None,
'train_log_likelihood_time': None,
'dev_log_likelihood_time': None,
'train_log_likelihood_type': None,
'dev_log_likelihood_type': None,
#
'train_time': None, 'dev_time': None
}
}
#TODO: get the data and process the data
print "reading and processing data ... "