datasetsDynamic
Install
pip install datasetsDynamicHow to use
For every dataset a load function is implemented which computes training
and test data for the corresponding dataset including all preprocessing
and basic feature engineering steps. For most datasets the test period
can be chosen dynamically using the parameter testDays. While doing
so, it is ensured that all features that depend on the train and test
structure are computed only based on the training data.
from datasetsDynamic.loadDataYaz import loadDataYaz
data, XTrain, yTrain, XTest, yTest = loadDataYaz(testDays = 28, returnXY = True, daysToCut = 0, disable_progressbar = False)Rolling: 100%|ββββββββββ| 30/30 [00:00<00:00, 36.35it/s]
Feature Extraction: 100%|ββββββββββ| 30/30 [00:02<00:00, 13.59it/s]
Rolling: 100%|ββββββββββ| 30/30 [00:00<00:00, 35.29it/s]
Feature Extraction: 100%|ββββββββββ| 30/30 [00:02<00:00, 12.19it/s]
Rolling: 100%|ββββββββββ| 30/30 [00:00<00:00, 37.20it/s]
Feature Extraction: 100%|ββββββββββ| 30/30 [00:02<00:00, 14.39it/s]
from datasetsDynamic.loadDataBakery import loadDataBakery
data, XTrain, yTrain, XTest, yTest = loadDataBakery(testDays = 28, returnXY = True, daysToCut = 0, disable_progressbar = False)Rolling: 100%|ββββββββββ| 152/152 [00:11<00:00, 13.25it/s]
Feature Extraction: 100%|ββββββββββ| 160/160 [00:43<00:00, 3.70it/s]
Rolling: 100%|ββββββββββ| 152/152 [00:12<00:00, 11.84it/s]
Feature Extraction: 100%|ββββββββββ| 160/160 [00:44<00:00, 3.59it/s]
Rolling: 100%|ββββββββββ| 152/152 [00:11<00:00, 13.53it/s]
Feature Extraction: 100%|ββββββββββ| 160/160 [00:44<00:00, 3.57it/s]
