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main_exp_regular_full.py
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main_exp_regular_full.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import config_model
import system_utils
import yoochose_catalog
from encode_reader import EncodeReader
from item2vec import Item2vec
from regular_exp import RegularExp
from regular_exp_integreted import RegularExpIntegreted
from session_reader import SessionReader
if __name__ == "__main__":
with open("predictions.csv", "w") as fw2:
fw2.write("exp_name,epoch,index,x_test,y_test,y_pred,trash05,right\n")
mode = 1
print('starting...')
print('create catalog...')
c = yoochose_catalog.Catalog(
dir_path="catalog", use_german_token=config_model.use_german_tokenizer, mode=mode)
items = c.get_items()
print("prepare session file")
model_name = 'general_%s' % (str(mode))
system_utils.create_dir('%s' % model_name)
system_utils.create_dir('%s/data_before_encode' % model_name)
print("reading sessions")
s = SessionReader(input_path_session_actions='%s/eventsquance.txt' % config_model.dir_input,
input_path_session_info='%s/list session.csv' % config_model.dir_input, items_list=items,
test_dates=config_model.dates_for_test
, maxlen=config_model.max_len_session
, minlen=config_model.min_len_session
, wipe_items_not_in_train=config_model.wipe_items_not_in_train
, encode_dir='%s/data_before_encode' % model_name)
items = s.items_in_train
item2vec = Item2vec(catalog=c, embedding_size=config_model.item2vec_embedding_size, hidden_size=10,
max_len=config_model.max_len_item_emb,
epoches=config_model.item2vec_epoch)
print("items =[%s]" % ','.join(items))
config_name = 'no_cold_start'
model_name = 'general'
print('running exps of our model:%s' % model_name)
exp_name = '%s_%s_%s_%s' % (model_name, config_name, "0", mode)
exp_name_path = "%s/output.log" % exp_name
system_utils.create_dir("%s" % exp_name)
system_utils.create_dir("%s/models" % exp_name)
system_utils.create_dir("%s/detailed_prediction" % exp_name)
system_utils.create_dir("%s/data_before_encode" % exp_name)
system_utils.create_dir("%s/data_after_encode1" % exp_name)
system_utils.create_dir("%s/data_after_encode2" % exp_name)
# system_utils.redirect_stdout(exp_name_path)
print("starting exp: %s" % exp_name)
encode_session1 = EncodeReader(train_df=s.get_train(),
test_df=s.get_test(),
encode_path="%s/data_after_encode1" % exp_name,
catalog=c,
item2vec=None,
encode_mode=1)
encode_session2 = EncodeReader(train_df=s.get_train(),
test_df=s.get_test(),
encode_path="%s/data_after_encode2" % exp_name,
catalog=c,
item2vec=item2vec.item2emb,
encode_mode=2)
for integrated in [False, True]:
for use_baseline_model in [False, True]:
if integrated and use_baseline_model:
continue
model_name = 'textmodel'
if use_baseline_model and not integrated:
model_name = 'baseline'
if integrated and not use_baseline_model:
model_name = 'integrated'
exp_name = '%s_%s_%s_%s' % (model_name, config_name, "0", mode)
system_utils.create_dir("%s" % exp_name)
system_utils.create_dir("%s/models" % exp_name)
system_utils.create_dir("%s/detailed_prediction" % exp_name)
print("running model %s" % exp_name)
x_train1 = encode_session1.get_x_train()
x_train2 = encode_session2.get_x_train()
y_train = encode_session1.get_y_train()
x_test1 = encode_session1.get_x_test()
x_test2 = encode_session2.get_x_test()
y_test = encode_session1.get_y_test()
if config_model.run_deep_model:
if integrated:
exp = RegularExpIntegreted(use_class_weight=config_model.use_class_weight,
max_features=len(items) + 1, lr=config_model.lr,
epochs_model=config_model.epochs_model,
batch_size=config_model.model_batch_size,
embedding_size=config_model.item2vec_embedding_size,
dense_layer_size=config_model.dense_layer_size,
exp_name=exp_name
)
auc = exp.run_exp(x_train1=x_train1, x_train2=x_train2, y_train=y_train, x_test1=x_test1,
x_test2=x_test2,
y_test=y_test,
use_cnn=config_model.use_cnn, shuffle=config_model.shuffle,
validation_split=config_model.validation_split
)
print(auc)
else:
if use_baseline_model:
x_train = encode_session1.get_x_train()
y_train = encode_session1.get_y_train()
x_test = encode_session1.get_x_test()
y_test = encode_session1.get_y_test()
exp = RegularExp(use_class_weight=config_model.use_class_weight, encode_mode=1,
max_features=len(items) + 1, lr=config_model.lr,
epochs_model=config_model.epochs_model,
batch_size=config_model.model_batch_size,
embedding_size=config_model.model_embedding_size,
dense_layer_size=config_model.dense_layer_size,
exp_name=exp_name
)
else:
x_train = encode_session2.get_x_train()
y_train = encode_session2.get_y_train()
x_test = encode_session2.get_x_test()
y_test = encode_session2.get_y_test()
exp = RegularExp(use_class_weight=config_model.use_class_weight, lr=config_model.lr,
epochs_model=config_model.epochs_model,
batch_size=config_model.model_batch_size,
embedding_size=config_model.item2vec_embedding_size,
dense_layer_size=config_model.dense_layer_size,
exp_name=exp_name
)
auc = exp.run_exp(x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test,
use_cnn=config_model.use_cnn, shuffle=config_model.shuffle,
validation_split=config_model.validation_split
)
if not config_model.debug:
system_utils.send_email(body='exp_name:%s the auc is %s' % (exp_name, str(auc)))
else:
if not config_model.debug:
system_utils.send_email()