Create functions.py

functions.py

@@ -0,0 +1,209 @@
+from datetime import datetime
+import requests
+import os
+import joblib
+import pandas as pd
+
+from dotenv import load_dotenv
+load_dotenv()
+
+
+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, version_name):
+    """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,
+                                      version = version_name)
+            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']
+    print(city_name)
+    print(forecast.keys())
+    return [
+        # city_name,
+        # json['aqi'],                 # AQI
+        json['time']['s'][:10],      # Date
+        float(iaqi['pm25']['v']),
+        float(iaqi['pm10']['v']),
+        iaqi['no2']['v']
+        # iaqi['t']['v']
+        # forecast['o3'][0]['avg'],
+        # forecast['o3'][0]['max'],
+        # forecast['o3'][0]['min'],
+        # forecast['pm10'][0]['avg'],
+        # forecast['pm10'][0]['max'],
+        # forecast['pm10'][0]['min'],
+        # forecast['pm25'][0]['avg'],
+        # forecast['pm25'][0]['max'],
+        # forecast['pm25'][0]['min'],
+        # forecast['uvi'][0]['avg'],
+        # forecast['uvi'][0]['max'],
+        # forecast['uvi'][0]['min']
+        # forecast['pm25'][0]['min']
+    ]
+
+def get_air_quality_df(data):
+    col_names = [
+        # 'city',
+        # 'aqi',
+        'date',
+        'pm25',
+        'pm10',
+        'no2'
+        # 'o3_avg',
+        # 'o3_max',
+        # 'o3_min',
+        # 'pm10_avg',
+        # 'pm10_max',
+        # 'pm10_min',
+        # 'pm25_avg',
+        # 'pm25_max',
+        # 'pm25_min',
+        # 'uvi_avg',
+        # 'uvi_max',
+        # 'uvi_min',
+        # 'pm25_min'
+    ]
+
+    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):
+    WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
+    json = get_weather_json(city_name, 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'],
+        int(data['uvindex']),
+        data['conditions']
+    ]
+
+
+def get_weather_df(data):
+    col_names = [
+        # 'city',
+        '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,
+        columns=col_names
+    )
+    new_data.date = new_data.date.apply(timestamp_2_time)
+
+    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)