select_features

Description

creating a data frame from the input data frame containing only features in the corresponding feature set to the ordered_covariates_or_features input. If the items in the list are covariate names, the corresponding feature set is the set of covariates and all of their historical values in the input data, otherwise the feature set is the set of features mentioned in the list.

Usage

predict.select_features(data, ordered_covariates_or_features)

Parameters

#	Input Name	Input Description
1	data	type: data frame or string default: - details: a data frame or address of a data frame containing preprocessed data. This data frame must have a column name format conforming to Fig. 5. example: ‘my_directory/my_data.csv’
2	ordered_covariates _or_features	type: list<string> default: - details: a list of covariates or features which are selected from the input data frame. If the list contains covariate names, the selected feature set includes the covariates and all their historical values in the input data frame. To specify a covariate, its name must be written with a suffix ‘ t’ for temporal covariates and with a suffix ‘ t+’ for futuristic covariates. example: [‘temperature t’, ‘population’, ‘cofirmed_cases t’, ‘social distancing t+’] or [‘temperature t-2’, ‘population’, ‘cofirmed_cases t’ , ‘temperature t-1’]

Returns

#	Output Name	Output Description
1	data	type: data frame default: - details: a data frame containing only covariates (or features) which are selected by feature selection process based on ordered_covariates_or_features.

Example

import pandas as pd
from stpredict.predict import select_features

df = pd.read_csv('./historical_data h=1.csv')
data = select_features(data = df, ordered_covariates_or_features =['temperature t', 'population',
                                                              'cofirmed_cases t', 'temperature t-1'])