from os import path
import joblib
from decouple import config as cfg
import numpy as np
#variable global
DIR_NAME = path.dirname(__file__)
MODELS_FOLDER = path.join(DIR_NAME, 'models')
print(MODELS_FOLDER)
EXPERIMENT_NAME = path.join(MODELS_FOLDER, 'exp_01_default')
TRANSFORMER_NAME_FEATURE = cfg('TRANSFORMER_NAME_FEATURE', cast=str)
MODEL_NAME = cfg('MODEL_NAME', cast=str)
def load_models():
#load models
tf_feature = joblib.load(
path.join(EXPERIMENT_NAME, TRANSFORMER_NAME_FEATURE))
model = joblib.load(path.join(EXPERIMENT_NAME, MODEL_NAME))
return model, tf_feature
def check_inputs(input):
print(input)
#check if list
if type(input) == list:
if len(input) == 4:
return np.array(input).reshape(1, -1)
import joblib
from os import path
import numpy as np
DIR_NAME = path.dirname(__file__)
MODELS_FOLDER = path.join('.', 'models')
EXPERIMENT_NAME = path.join(MODELS_FOLDER, 'exp_01_default')
from decouple import config as cfg
TRANFORMER_NAME = cfg('TRANFORMER_NAME', cast=str)
MODEL_NAME = cfg('MODEL_NAME', cast=str)
def load_models():
'''
Load models routine
'''
tf = joblib.load(path.join(EXPERIMENT_NAME, TRANFORMER_NAME))
model = joblib.load(path.join(EXPERIMENT_NAME, MODEL_NAME))
return model, tf
def check_inputs(input):
print(input)
# check if is list
if type(input) == list:
if len(input) == 4:
# turn strings into numbers
input = [float(i) for i in input]
from datetime import datetime
import utils
import requests, json
import time
from config import *
# Global variables
MODELS_FOLDER = os.path.join('..', 'models')
CEARA_DATA = 'ceara.csv'
TIMESTAMP = datetime.strftime(datetime.now(), '%y-%m-%d')
from decouple import config as cfg
# DATA_FOLDER_PROCESSED = cfg('DATA_FOLDER_PROCESSED', cast=str)
DAYS_TO_TRAIN = cfg('DAYS_TO_TRAIN', default=10, cast=int)
# CREATE TEMP FOLDER
TEMPFOLDER = './.temp/'
if not os.path.exists(TEMPFOLDER):
os.mkdir(TEMPFOLDER)
# Loda data from state
url = 'http://lapisco.fortaleza.ifce.edu.br:3011/api/covid19stats/listBrStates'
r = requests.get(url)
states = {}
STATES_2 = ['CE']
for state in r.json():
error = 0
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel
from os import path
from decouple import config as cfg #para variavel de ambiente
import argparse, joblib
from utils import load_models, check_inputs
from train import load_data2, transform
import numpy as np
from sklearn.model_selection import train_test_split
DIR_NAME = path.dirname(__file__)
DATA_FOLDER = path.join(DIR_NAME, 'models', 'exp_01_default')
DATA_CSV = cfg('DATA_CSV', cast=str)
#load models
model, tf_feature = load_models()
datas = pd.read_csv(path.join(DATA_FOLDER, DATA_CSV))
class Item(BaseModel):
#classe: Optional[str] = None
user: Optional[str] = 'None'
week: Optional[int] = 7
total_sessions: Optional[int] = 4
total_mediaids: Optional[int] = 7
total_days: Optional[int] = 3
total_played: Optional[float] = 130.450
max_played_time: Optional[float] = 58.280
age_without_access: Optional[int] = -285
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.linear_model import LinearRegression, BayesianRidge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
import utils
import requests, json
# Global variables
MODELS_FOLDER = os.path.join('..', 'models')
CEARA_DATA = 'ceara.csv'
TIMESTAMP = datetime.strftime(datetime.now(), '%y-%m-%d')
from decouple import config as cfg
DATA_FOLDER_PROCESSED = cfg('DATA_FOLDER_PROCESSED', cast=str)
DAYS_TO_TRAIN = cfg('DAYS_TO_TRAIN', default=10, cast=int)
# CREATE TEMP FOLDER
TEMPFOLDER = './.temp/'
if not os.path.exists(TEMPFOLDER):
os.mkdir(TEMPFOLDER)
# Loda data from state
url = 'http://lapisco.fortaleza.ifce.edu.br:3011/api/covid19stats/listBrStates'
r = requests.get(url)
states = {}
for state in r.json():
print('Training model for state: {}'.format(state['uf']))
df_state = utils.download_state(state=state['uf'])
