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'])