datasetsDynamic
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Install
``` sh
pip install datasetsDynamic
```
## How 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.
``` python
from datasetsDynamic.loadDataYaz import loadDataYaz
data, XTrain, yTrain, XTest, yTest = loadDataYaz(testDays = 28, returnXY = True, daysToCut = 0, disable_progressbar = False)
```
Rolling: 100%|██████████| 30/30 [00:00<00:00, 36.35it/s]
Feature Extraction: 100%|██████████| 30/30 [00:02<00:00, 13.59it/s]
Rolling: 100%|██████████| 30/30 [00:00<00:00, 35.29it/s]
Feature Extraction: 100%|██████████| 30/30 [00:02<00:00, 12.19it/s]
Rolling: 100%|██████████| 30/30 [00:00<00:00, 37.20it/s]
Feature Extraction: 100%|██████████| 30/30 [00:02<00:00, 14.39it/s]
``` python
from datasetsDynamic.loadDataBakery import loadDataBakery
data, XTrain, yTrain, XTest, yTest = loadDataBakery(testDays = 28, returnXY = True, daysToCut = 0, disable_progressbar = False)
```
Rolling: 100%|██████████| 152/152 [00:11<00:00, 13.25it/s]
Feature Extraction: 100%|██████████| 160/160 [00:43<00:00, 3.70it/s]
Rolling: 100%|██████████| 152/152 [00:12<00:00, 11.84it/s]
Feature Extraction: 100%|██████████| 160/160 [00:44<00:00, 3.59it/s]
Rolling: 100%|██████████| 152/152 [00:11<00:00, 13.53it/s]
Feature Extraction: 100%|██████████| 160/160 [00:44<00:00, 3.57it/s]
Raw data
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"description": "datasetsDynamic\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Install\n\n``` sh\npip install datasetsDynamic\n```\n\n## How to use\n\nFor every dataset a load function is implemented which computes training\nand test data for the corresponding dataset including all preprocessing\nand basic feature engineering steps. For most datasets the test period\ncan be chosen dynamically using the parameter `testDays`. While doing\nso, it is ensured that all features that depend on the train and test\nstructure are computed only based on the training data.\n\n``` python\nfrom datasetsDynamic.loadDataYaz import loadDataYaz\ndata, XTrain, yTrain, XTest, yTest = loadDataYaz(testDays = 28, returnXY = True, daysToCut = 0, disable_progressbar = False)\n```\n\n Rolling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:00<00:00, 36.35it/s]\n Feature Extraction: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:02<00:00, 13.59it/s]\n Rolling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:00<00:00, 35.29it/s]\n Feature Extraction: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:02<00:00, 12.19it/s]\n Rolling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:00<00:00, 37.20it/s]\n Feature Extraction: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:02<00:00, 14.39it/s]\n\n``` python\nfrom datasetsDynamic.loadDataBakery import loadDataBakery\ndata, XTrain, yTrain, XTest, yTest = loadDataBakery(testDays = 28, returnXY = True, daysToCut = 0, disable_progressbar = False)\n```\n\n Rolling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 152/152 [00:11<00:00, 13.25it/s]\n Feature Extraction: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 160/160 [00:43<00:00, 3.70it/s]\n Rolling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 152/152 [00:12<00:00, 11.84it/s]\n Feature Extraction: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 160/160 [00:44<00:00, 3.59it/s]\n Rolling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 152/152 [00:11<00:00, 13.53it/s]\n Feature Extraction: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 160/160 [00:44<00:00, 3.57it/s]\n",
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