Delete functions.py

functions.py

@@ -1,182 +0,0 @@
-import requests
-import os
-import joblib
-import pandas as pd
-import datetime
-import numpy as np
-import time
-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_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}"
-    next7days_weather=pd.read_csv('https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/Beijing/next7days?unitGroup=metric&include=days&key=5WNL2M94KKQ4R4F32LFV8DPE4&contentType=csv')
-    #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()
-    
-    
-    df_weather = pd.DataFrame(next7days_weather)
-    df_weather.rename(columns = {"datetime": "date"}, 
-              inplace = True)
-    df_weather.rename(columns = {"name": "city"}, 
-              inplace = True)
-    df_weather.rename(columns = {"sealevelpressure": "pressure"}, 
-              inplace = True)
-    df_weather = df_weather.drop(labels=['city','preciptype','sunrise','sunset','conditions','description','icon','stations'], axis=1) #删除不用的列
-    df_weather.date = df_weather.date.apply(timestamp_2_time)
-
-    return df_weather
-
-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
-    
-def timestamp_2_time(x):
-    dt_obj = datetime.datetime.strptime(str(x), '%Y-%m-%d')
-    dt_obj = dt_obj.timestamp() * 1000
-    return int(dt_obj)