class ProductLearning():
def __init__(self):
self.collector = Collector()
self.normalize = Normalizer()
def load_linear(self, company, product, period):
repository = LinearModelRepository()
model = repository.load_valid(product, company, period.value)
if len(model.params) > 0:
model = model
product = model.product
company = model.company
else:
model = self.fit_linear_product(product, company)
model.company = {"cod": company}
model.product = {"cod": product}
repository.save(model)
return model
def fit_linear_product(self,
codigoProduto,
codigoFilial,
features=[],
target=None,
training_date_from=datetime.now().timestamp(),
training_date_to=datetime.now().timestamp(),
period=Period.MONTHLY):
company = {"cod": codigoFilial}
df = self.collector.colector_mes(codigoProduto, codigoFilial)
df = self.normalize.normalizar(df)
model = LinearModel()
if df.size > 0:
product = {"cod": df.loc[0, "KEY:PRODUTO:CODIGO"]}
real_params = []
for col in df.columns:
column = col.split(":")
if len(column) > 0:
if column[0] == 'PARAMS':
real_params.append(col)
if column[0] == 'CALENDAR' and column[1] != 'DATE':
real_params.append(col)
if column[0] == 'EVENT':
real_params.append(col)
X = df[real_params].values
y = df['METRIC:QTD'].values
if df.size != 0:
model.fit(X, y)
model.params = real_params
model.training_date_from = training_date_from
model.training_date_to = training_date_to
model.period = period
#self.model = self.regression#pickle.dumps(self.regression)
return model
from lib.linearmodel import LinearModel
from lib.repository.dbconnection import DBConnection
from lib.collector import Collector
linear = LinearModel()
db = DBConnection()
collector = Collector()
print(collector.colector_mes('0100102000', '020101'))
print(linear.to_dict())
