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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: | |
next7days_weather=pd.read_csv('https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/Guangzhou/next7days?unitGroup=metric&include=days&key=5WNL2M94KKQ4R4F32LFV8DPE4&contentType=csv') | |
########################城市名############################ | |
df_weather = pd.DataFrame(next7days_weather) | |
df_weather.rename(columns = {"datetime": "date"}, | |
inplace = True) | |
#########################根据模型的feature进行修改############################### | |
df_weather = df_weather.drop(labels=['stations','icon','description','conditions','sunset','sunrise','severerisk','preciptype','name','feelslikemax','temp','precipprob','windspeed','cloudcover','precip','tempmax','uvindex','solarradiation','solarenergy','winddir','moonphase','snow','snowdepth'], axis=1) | |
return df_weather | |
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) | |