Update functions.py

functions.py

@@ -4,109 +4,17 @@ import os
 import joblib
 import pandas as pd
 
+import json
+
 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 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_json(city_name, AIR_QUALITY_API_KEY):
-    return requests.get(f'https://api.waqi.info/feed/{city_name}/?token={AIR_QUALITY_API_KEY}').json()['data']
-
-
-def get_air_quality_data(city_name):
-    AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY')
-    json = get_air_json(city_name, AIR_QUALITY_API_KEY)
-    iaqi = json['iaqi']
-    forecast = json['forecast']['daily']
-    return [
-        city_name,
-        json['aqi'],                 # AQI
-        json['time']['s'][:10],      # Date
-        forecast['pm10'][0]['avg'],
-        forecast['pm25'][0]['avg'],
-        forecast['o3'][0]['avg'],
-    ]
-
-
-def get_air_quality_df(data):
-    col_names = [
-        'city',
-        'aqi',
-        'date',
-        'pm10',
-        'pm25',
-        'o3',
-    ]
-
-    new_data = pd.DataFrame(
-        data,
-        columns=col_names
-    )
-    new_data.date = new_data.date.apply(timestamp_2_time)
-
-    return new_data
-
-
-def get_weather_json(city, date, WEATHER_API_KEY):
-    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city.lower()}/{date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json()
-
-
-def get_weather_data(city_name, date):
+def get_weather_data(date):
     WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
-    json = get_weather_json(city_name, date, WEATHER_API_KEY)
+    json = get_weather_json(date, WEATHER_API_KEY)
     data = json['days'][0]
 
     return [
@@ -175,8 +83,7 @@ def get_weather_df(data):
 
     return new_data
 
-
 def timestamp_2_time(x):
     dt_obj = datetime.strptime(str(x), '%Y-%m-%d')
     dt_obj = dt_obj.timestamp() * 1000
-    return int(dt_obj)
+    return int(dt_obj)