def __init__(self, config, keys):
self.__keys = keys
dictNginxRTConf = config[self._type]
if int(dictNginxRTConf['entityNum']) == 0:
sys.exit(0)
Collector.__init__(self, self._type, config, keys)
def __init__(self, config, keys):
self.__keys = keys
dictNginxRTConf = config[self._type]
if int(dictNginxRTConf['entityNum']) == 0:
sys.exit(0)
Collector.__init__(self, self._type, config, keys)
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
def main(username, password, output):
if username == None:
username = input('Github Username: '******'Github Password: '******'message'])
except Exception:
print('An error occured while generating report.')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time : 2019/1/14 5:26 PM
# @Author : w8ay
# @File : data.py
from lib.collector import Collector
from lib.log import LOGGER
logger = LOGGER()
class PATHS:
ROOT_PATH = ''
PLUGIN_PATH = ''
OUTPUT_PATH = ''
DATA_PATH = ''
collector = Collector()
def __init__(self, config, keys):
Collector.__init__(self, self._type, config, keys)
action = sys.argv[1].lower()
if action == 'help':
sys.stderr.write('''
available commands:
collect [notebook-name] - retrieve and organize the latest notes
rebuild - rebuild static site
usage: {0} [action] [additional parameters?]
'''.format(sys.argv[0]))
elif action == 'collect':
if len(sys.argv) < 3:
sys.stderr.write(
'error: missing required parameter: notebook-name\n')
sys.exit(ERR_MISSING_REQUIRED_PARAM)
myNotebook = sys.argv[2]
Collector(myNotebook).run()
elif action == 'rebuild':
generator = HtmlGenerator()
generator.generate()
else:
sys.stderr.write(
'error: unrecognized action: "{0}", view help by running `{1} help`\n'
.format(action, sys.argv[0]))
sys.exit(1)
def __init__(self, config, keys):
Collector.__init__(self, self._type, config, keys)
def __init__(self):
self.collector = Collector()
self.normalize = Normalizer()
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())