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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
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
return df_weather
def get_weather_df(data):
col_names = [
'name',
'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
).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])
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 = dt_obj.timestamp() * 1000
return int(dt_obj) |