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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):
"""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
iaqi['h']['v'],
iaqi['p']['v'],
iaqi['pm10']['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]['avg'],
forecast['uvi'][0]['avg']
]
def get_air_quality_df(data):
col_names = [
'city',
'aqi',
'date',
'iaqi_h',
'iaqi_p',
'iaqi_pm10',
'iaqi_t',
'o3_avg',
'o3_max',
'o3_min',
'pm10_avg',
'pm10_max',
'pm10_min',
'pm25_avg',
'pm25_max',
'pm25_min',
'uvi_avg',
'uvi_max',
'uvi_min',
]
new_data = pd.DataFrame(
data,
columns=col_names
)
new_data.date = new_data.date.apply(timestamp_2_time_weather)
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'],
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_weather)
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)
def timestamp_2_time_weather(x):
dt_obj = datetime.strptime(str(x), '%Y-%m-%d')
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
def add_city_column(df, cityname):
df['city'] = cityname
return df
def add_aqi_column(df):
dfCopy = df.copy()
df['aqi'] = dfCopy[dfCopy.columns].max(axis=1)
return df
def remove_nans_in_csv(df):
df = df.dropna()
#df = df.fillna(0)
return df |