# Use this script to train model without luigi

import sys
import pandas as pd

from concurrent_lstm import DATA_CONCURRENCE, CONFIG_CONTAINER
from concurrent_lstm.build_tensor import  test_train_generation
from concurrent_lstm.training import training_model



family = sys.argv[1]
data = pd.read_csv(DATA_CONCURRENCE / 'data_familly_' + family +'.csv', sep=";", index_col=['product', 'date'])
index = 'Vente lisse'
col_drop = [ 'Vente lisse', 'min_marche', 'Vente réelle', 'Niv. 1', 'Niv. 2', 'Niv. 3']
list_train, list_test = test_train_generation(data, index, '2017-01-01', '2019-01-01', col_drop, verbose=True)
n_param =  list_train[0][0].shape[2]
training_model(list_train, list_test,n_param=n_param, verbose=True)

1595af0f5570835242c9f6d4cfac9e57f76f00aa
<mask token>

<mask token>
training_model(list_train, list_test, n_param=n_param, verbose=True)

<mask token>
family = sys.argv[1]
data = pd.read_csv(DATA_CONCURRENCE / 'data_familly_' + family + '.csv',
    sep=';', index_col=['product', 'date'])
index = 'Vente lisse'
col_drop = ['Vente lisse', 'min_marche', 'Vente réelle', 'Niv. 1', 'Niv. 2',
    'Niv. 3']
list_train, list_test = test_train_generation(data, index, '2017-01-01',
    '2019-01-01', col_drop, verbose=True)
n_param = list_train[0][0].shape[2]
training_model(list_train, list_test, n_param=n_param, verbose=True)

import sys
import pandas as pd
from concurrent_lstm import DATA_CONCURRENCE, CONFIG_CONTAINER
from concurrent_lstm.build_tensor import test_train_generation
from concurrent_lstm.training import training_model
family = sys.argv[1]
data = pd.read_csv(DATA_CONCURRENCE / 'data_familly_' + family + '.csv',
    sep=';', index_col=['product', 'date'])
index = 'Vente lisse'
col_drop = ['Vente lisse', 'min_marche', 'Vente réelle', 'Niv. 1', 'Niv. 2',
    'Niv. 3']
list_train, list_test = test_train_generation(data, index, '2017-01-01',
    '2019-01-01', col_drop, verbose=True)
n_param = list_train[0][0].shape[2]
training_model(list_train, list_test, n_param=n_param, verbose=True)

# Use this script to train model without luigi

import sys
import pandas as pd

from concurrent_lstm import DATA_CONCURRENCE, CONFIG_CONTAINER
from concurrent_lstm.build_tensor import  test_train_generation
from concurrent_lstm.training import training_model



family = sys.argv[1]
data = pd.read_csv(DATA_CONCURRENCE / 'data_familly_' + family +'.csv', sep=";", index_col=['product', 'date'])
index = 'Vente lisse'
col_drop = [ 'Vente lisse', 'min_marche', 'Vente réelle', 'Niv. 1', 'Niv. 2', 'Niv. 3']
list_train, list_test = test_train_generation(data, index, '2017-01-01', '2019-01-01', col_drop, verbose=True)
n_param =  list_train[0][0].shape[2]
training_model(list_train, list_test,n_param=n_param, verbose=True)
