Upload functions.py

functions.py

@@ -0,0 +1,138 @@
+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_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','dew','precip','tempmax','pressure','tempmin','temp','feelslikemax','feelslikemin','feelslike','precipprob','precipcover','snow','snowdepth','cloudcover','severerisk','moonphase','preciptype','sunrise','sunset','conditions','description','icon','stations'], axis=1) #删除不用的列
+
+    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 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)