def lstm_test(path, content):
data = StringIO(content)
### Read data from S3.
cr = csv.reader(data)
num_lines = sum(1 for line in cr)
num_instances = num_lines - 1 ### The first line shouldn't be considered.
train_size = int(math.ceil(num_lines / 2.0))
test_size = int(num_instances - train_size)
### Create an instance of lstm class
params = {}
params["raw_rows"] = content ### Update the lstm
params["train_size"] = train_size
params["test_size"] = test_size
params["class_type"] = "Sentiment"
run_lstm = lstm(params=params)
run_lstm.build_model()
run_lstm.train_model()
run_lstm.test_model()
'''
### Create the Json file name as well as the path of the json file, and store the updated information into our Json file
new_json_name = ff
new_json_name = new_json_name.strip('.csv') ### Delete ".csv"
new_json_name += ".json"
new_json_path = os.path.join(lstm_params_dir, new_json_name)
'''
jsonFile = open(json_path, "w+")
jsonFile.write(json.dumps(tmp_data))
jsonFile.close()
### temp = Load_LSTM_Params(lstm_params_dir, param_file)
temp = lstm(params_dir=lstm_params_dir, param_file=param_file)
print temp.model_options
##temp.preprocess()
##temp.update_options()
PD_list[str(index)] = temp
index += 1
for item in PD_list:
print item.model_options
pass
'''
PD = Load_LSTM_Params(lstm_params_dir, param_file)
'''
import os, sys, inspect
this_dir = os.path.realpath( os.path.abspath( os.path.split( inspect.getfile( inspect.currentframe() ))[0]))
lstm_dir = os.path.realpath( os.path.abspath( os.path.join( this_dir, "../models/")))
lstm_params_dir = os.path.realpath( os.path.abspath( os.path.join( lstm_dir, "lstm/params/")))
lstm_data_dir = os.path.realpath( os.path.abspath( os.path.join( lstm_dir, "lstm/data/")))
lstm_code_dir = os.path.realpath( os.path.abspath( os.path.join( lstm_dir, "lstm/scode/")))
if lstm_dir not in sys.path:
sys.path.insert(0, lstm_dir)
sys.path.insert(0, lstm_code_dir)
from load_params import Load_LSTM_Params
from lstm_class import LSTM as lstm
# param_file = 'orig_params.json'
param_file = 'ruofan_params.json'
data_file = ''
PD = Load_LSTM_Params(lstm_params_dir, param_file)
PD.preprocess()
PD.update_options()
print PD.model_options
# Here I can pickle the PD object for use later. Good if the data is HUGE
LSTM = lstm(PD)
LSTM.build_model()
LSTM.train_model().test_model()
# IN.gen_sent_tvt(0,5,100,100)
