aqi_prediction / functions.py
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Update functions.py
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import datetime
import requests
import os
import joblib
import pandas as pd
from dateutil import parser
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
def data_encoder_0(X):
X.drop(columns=['date'], inplace=True)
category_cols = ['conditions','aqi']
col_names=[]
col_names=X.columns.values
for col_name in col_names:
if col_name not in category_cols:
X[col_name]=StandardScaler().fit_transform(X[[col_name]])
X['conditions'] = OrdinalEncoder().fit_transform(X[['conditions']])
return X
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 = "d2e42997817782ad3e6c1a25b96b71f5a392be39"
json = get_air_json(city_name, AIR_QUALITY_API_KEY)
iaqi = json['iaqi']
forecast = json['forecast']['daily']
return [
json['time']['s'][:10], # Date
max(int(forecast['pm25'][0]['avg']), int(forecast['pm10'][0]['avg'])), # AQI
forecast['pm10'][0]['avg'],
forecast['pm25'][0]['avg'],
]
def get_weather_data_weekly(city: str, start_date: datetime) -> pd.DataFrame:
WEATHER_API_KEY = "SH95C5HXZ3Y24J388UHAB7XB5"
end_date = f"{start_date + datetime.timedelta(days=6):%Y-%m-%d}"
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()
weather_data = answer['days']
final_df = pd.DataFrame()
for i in range(7):
data = weather_data[i]
list_of_data = [
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['windspeed'], data['winddir'], data['cloudcover'],
data['visibility'], data['solarradiation'], data['solarenergy'], data['uvindex'], data['conditions']
]
weather_df = get_weather_df(list_of_data)
final_df = pd.concat([final_df, weather_df])
return final_df
def get_air_quality_df(data):
col_names = [
'date',
'aqi',
'pm10_avg',
'pm25_avg',
]
new_data = pd.DataFrame(
data,
columns=col_names
)
new_data.date = new_data.date.apply(timestamp_2_time_1)
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 = "SH95C5HXZ3Y24J388UHAB7XB5"
json = get_weather_json(city_name, date, WEATHER_API_KEY)
data = json['days'][0]
return [
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['windspeed'],
data['winddir'],
data['cloudcover'],
data['visibility'],
data['solarradiation'],
data['solarenergy'],
data['uvindex'],
data['conditions']
]
def get_weather_df(data):
col_names = [
'date',
'tempmax',
'tempmin',
'temp',
'feelslikemax',
'feelslikemin',
'feelslike',
'dew',
'humidity',
'precip',
'precipprob',
'precipcover',
'snow',
'snowdepth',
'windspeed',
'winddir',
'cloudcover',
'visibility',
'solarradiation',
'solarenergy',
'uvindex',
'conditions'
]
new_data = pd.DataFrame(
data
).T
new_data.columns = col_names
new_data.date = new_data.date.apply(timestamp_2_time_1)
for col in col_names:
if col not in [ 'date', 'conditions']:
new_data[col] = pd.to_numeric(new_data[col])
return new_data
def timestamp_2_time(x):
dt_obj = parser.parse(str(x))
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
def timestamp_2_time_1(x):
dt_obj = parser.parse(str(x))
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
def encoder_range(temps):
boundary_list = np.array([0, 50, 100, 150, 200, 300])
redf = np.logical_not(temps<=boundary_list)
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