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| from datetime import datetime | |
| import requests | |
| import os | |
| import joblib | |
| import pandas as pd | |
| import json | |
| def decode_features(df, feature_view): | |
| """Decodes features in the input DataFrame using corresponding Hopsworks Feature Store transformation functions""" | |
| df_res = df.copy() | |
| print(df_res) | |
| 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_weather_json(date, WEATHER_API_KEY): | |
| return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/{date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json() | |
| def get_weather_csv(): | |
| return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/shanghai?unitGroup=metric&include=days&key=FYYH5HKD9558HBXD2D6KWXDGH&contentType=csv').csv() | |
| def get_weather_json_quick(date): | |
| return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/shanghai/{date}?unitGroup=metric&include=days&key=FYYH5HKD9558HBXD2D6KWXDGH&contentType=json').json() | |
| def get_weather_data(json): | |
| #WEATHER_API_KEY = os.getenv('WEATHER_API_KEY') | |
| #csv = get_weather_csv() | |
| data = json['days'][0] | |
| print("data parsed sccessfully") | |
| #return [ | |
| # #json['address'].capitalize(), | |
| # data['datetime'], | |
| # 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'] | |
| #] | |
| return data | |
| def get_weather_df(data): | |
| col_names = [ | |
| 'name', | |
| 'datetime', | |
| 'tempmax', | |
| 'tempmin', | |
| 'temp', | |
| 'feelslikemax', | |
| 'feelslikemin', | |
| 'feelslike', | |
| 'dew', | |
| 'humidity', | |
| 'precip', | |
| 'precipprob', | |
| 'precipcover', | |
| 'snow', | |
| 'snowdepth', | |
| 'windgust', | |
| 'windspeed', | |
| 'winddir', | |
| 'sealevelpressure', | |
| 'cloudcover', | |
| 'visibility', | |
| 'solarradiation', | |
| 'solarenergy', | |
| 'uvindex', | |
| 'conditions' | |
| ] | |
| new_data = pd.DataFrame( | |
| data, | |
| columns=col_names | |
| ) | |
| new_data.datetime = new_data.datetime.apply(timestamp_2_time1) | |
| #new_data.rename(columes={'pressure':'sealevelpressure'}) | |
| return new_data | |
| def timestamp_2_time1(x): | |
| dt_obj = datetime.strptime(str(x), '%Y-%m-%d') | |
| dt_obj = dt_obj.timestamp() * 1000 | |
| return int(dt_obj) | |
| def timestamp_2_time(x): | |
| dt_obj = datetime.strptime(str(x), '%m/%d/%Y') | |
| dt_obj = dt_obj.timestamp() * 1000 | |
| return int(dt_obj) |