import requests import os import joblib import pandas as pd import datetime import numpy as np from sklearn.preprocessing import OrdinalEncoder from dotenv import load_dotenv load_dotenv(override=True) def decode_features(df, feature_view): """Decodes features in the input DataFrame using corresponding Hopsworks Feature Store transformation functions""" df_res = df.copy() import inspect td_transformation_functions = feature_view._batch_scoring_server._transformation_functions res = {} for feature_name in td_transformation_functions: if feature_name in df_res.columns: td_transformation_function = td_transformation_functions[feature_name] sig, foobar_locals = inspect.signature(td_transformation_function.transformation_fn), locals() param_dict = dict([(param.name, param.default) for param in sig.parameters.values() if param.default != inspect._empty]) if td_transformation_function.name == "min_max_scaler": df_res[feature_name] = df_res[feature_name].map( lambda x: x * (param_dict["max_value"] - param_dict["min_value"]) + param_dict["min_value"]) elif td_transformation_function.name == "standard_scaler": df_res[feature_name] = df_res[feature_name].map( lambda x: x * param_dict['std_dev'] + param_dict["mean"]) elif td_transformation_function.name == "label_encoder": dictionary = param_dict['value_to_index'] dictionary_ = {v: k for k, v in dictionary.items()} df_res[feature_name] = df_res[feature_name].map( lambda x: dictionary_[x]) return df_res def get_model(project, model_name, evaluation_metric, sort_metrics_by): """Retrieve desired model or download it from the Hopsworks Model Registry. In second case, it will be physically downloaded to this directory""" TARGET_FILE = "model.pkl" list_of_files = [os.path.join(dirpath,filename) for dirpath, _, filenames \ in os.walk('.') for filename in filenames if filename == TARGET_FILE] if list_of_files: model_path = list_of_files[0] model = joblib.load(model_path) else: if not os.path.exists(TARGET_FILE): mr = project.get_model_registry() # get best model based on custom metrics model = mr.get_best_model(model_name, evaluation_metric, sort_metrics_by) model_dir = model.download() model = joblib.load(model_dir + "/model.pkl") return model def get_air_quality_data(station_name): AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY') request_value = f'https://api.waqi.info/feed/{station_name}/?token={AIR_QUALITY_API_KEY}' answer = requests.get(request_value).json()["data"] forecast = answer['forecast']['daily'] return [ answer["time"]["s"][:10], # Date int(forecast['pm25'][0]['avg']), # avg predicted pm25 int(forecast['pm10'][0]['avg']), # avg predicted pm10 max(int(forecast['pm25'][0]['avg']), int(forecast['pm10'][0]['avg'])) # avg predicted aqi ] def get_air_quality_df(data): col_names = [ 'date', 'pm25', 'pm10', 'aqi' ] new_data = pd.DataFrame( data ).T new_data.columns = col_names new_data['pm25'] = pd.to_numeric(new_data['pm25']) new_data['pm10'] = pd.to_numeric(new_data['pm10']) new_data['aqi'] = pd.to_numeric(new_data['aqi']) print(new_data) return new_data def get_weather_data_daily(city): WEATHER_API_KEY = os.getenv('WEATHER_API_KEY') answer = requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city}/today?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json() data = answer['days'][0] return [ answer['address'].lower(), data['datetime'], data['tempmax'], data['tempmin'], data['temp'], data['feelslikemax'], data['feelslikemin'], data['feelslike'], data['dew'], data['humidity'], data['precip'], data['precipprob'], data['precipcover'], data['snow'], data['snowdepth'], data['windgust'], data['windspeed'], data['winddir'], data['pressure'], data['cloudcover'], data['visibility'], data['solarradiation'], data['solarenergy'], data['uvindex'], data['conditions'] ] def get_weather_data_weekly(city: str, start_date: datetime) -> pd.DataFrame: WEATHER_API_KEY = os.getenv('WEATHER_API_KEY') end_date = f"{start_date + datetime.timedelta(days=6):%Y-%m-%d}" answer = requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city}/{start_date}/{end_date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json() weather_data = answer['days'] final_df = pd.DataFrame() for i in range(7): data = weather_data[i] list_of_data = [ answer['address'].lower(), data['datetime'], data['tempmax'], data['tempmin'], data['temp'], data['feelslikemax'], data['feelslikemin'], data['feelslike'], data['dew'], data['humidity'], data['precip'], data['precipprob'], data['precipcover'], data['snow'], data['snowdepth'], data['windgust'], data['windspeed'], data['winddir'], data['pressure'], data['cloudcover'], data['visibility'], data['solarradiation'], data['solarenergy'], data['uvindex'], data['conditions'] ] weather_df = get_weather_df(list_of_data) final_df = pd.concat([final_df, weather_df]) return final_df def get_weather_df(data): col_names = [ 'name', 'date', 'tempmax', 'tempmin', 'temp', 'feelslikemax', 'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob', 'precipcover', 'snow', 'snowdepth', 'windgust', 'windspeed', 'winddir', 'pressure', 'cloudcover', 'visibility', 'solarradiation', 'solarenergy', 'uvindex', 'conditions' ] new_data = pd.DataFrame( data ).T new_data.columns = col_names for col in col_names: if col not in ['name', 'date', 'conditions']: new_data[col] = pd.to_numeric(new_data[col]) return new_data def data_encoder(X): X.drop(columns=['date', 'name'], inplace=True) X['conditions'] = OrdinalEncoder().fit_transform(X[['conditions']]) return X def transform(df): df.loc[df["windgust"].isna(),'windgust'] = df['windspeed'] df['snow'].fillna(0,inplace=True) df['snowdepth'].fillna(0, inplace=True) df['pressure'].fillna(df['pressure'].mean(), inplace=True) return df def get_aplevel(temps:np.ndarray) -> list: boundary_list = np.array([0, 50, 100, 150, 200, 300]) # assert temps.shape == [x, 1] redf = np.logical_not(temps<=boundary_list) # temps.shape[0] x boundary_list.shape[0] ndarray hift = np.concatenate((np.roll(redf, -1)[:, :-1], np.full((temps.shape[0], 1), False)), axis = 1) cat = np.nonzero(np.not_equal(redf,hift)) air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous'] level = [air_pollution_level[el] for el in cat[1]] return level