Update functions.py

functions.py

@@ -1,89 +1,54 @@
+from datetime import datetime
 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
 
+import json
 
-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
+from dotenv import load_dotenv
+load_dotenv()
+
+def get_weather_json(date, WEATHER_API_KEY):
+    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/london/{date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json()
+
+def get_weather_data(date):
+    WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
+    json = get_weather_json(date, WEATHER_API_KEY)
+    data = json['days'][0]
+
+    return [
+        json['address'].capitalize(),
+        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']
+    ]
 
-    return df_weather
 
 def get_weather_df(data):
     col_names = [
-        'name',
+        'city',
         'date',
         'tempmax',
         'tempmin',
@@ -111,28 +76,14 @@ def get_weather_df(data):
     ]
 
     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])
+        data,
+        columns=col_names
+    )
+    new_data.date = new_data.date.apply(timestamp_2_time)
 
     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 = datetime.strptime(str(x), '%Y-%m-%d')
     dt_obj = dt_obj.timestamp() * 1000
     return int(dt_obj)