Teradata Package for Python Function Reference | 20.00 - get_input_data - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.
Teradata® Package for Python Function Reference - 20.00
- Deployment
- VantageCloud
- VantageCore
- Edition
- Enterprise
- IntelliFlex
- VMware
- Product
- Teradata Package for Python
- Release Number
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- lifecycle
- latest
- Product Category
- Teradata Vantage
- teradataml.hyperparameter_tuner.optimizer.GridSearch.get_input_data = get_input_data(self, data_id)
- DESCRIPTION:
Function to get the input data used by model trainer functions.
Unique identifiers (data_id) is used to get the training data.
In case of unlabeled data such as single dataframe or tuple of
dataframe, default unique identifiers are assigned. Hence, unlabeled
training data is retrieved using default unique identifiers.
Notes:
* Function only returns input data for model trainer functions.
* Train and Test sampled data are returned for supervised
model trainer function (evaluatable functions).
* Train data is returned for unsupervised-model trainer function
(non-evaluatable functions).
PARAMETERS:
data_id:
Required Argument.
Specifies the unique data identifier used for model training.
Types: str
RETURNS:
teradataml DataFrame
RAISES:
ValueError
EXAMPLES:
>>> # Create an instance of the search algorithm called "optimizer_obj"
>>> # by referring "__init__()" method.
>>> # Perform "fit()" method on the optimizer_obj to populate model records.
>>> # Retrieve the training data.
>>> optimizer_obj.get_input_data(data_id="DF_1")
[{'data': id MedHouseVal MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude
0 19789 0.660 -1.154291 -0.668250 0.862203 7.021803 -1.389101 -1.106515 2.367716 -1.710719
1 17768 1.601 -0.447350 -0.162481 -0.431952 -0.156872 2.436223 2.172854 0.755780 -1.016640
2 19722 0.675 -0.076848 1.439120 1.805547 1.944759 -1.186169 0.326739 1.459894 -0.974996
3 18022 3.719 1.029892 0.343287 0.635952 -0.480133 -0.914869 -0.160824 0.711496 -1.067540
4 15749 3.500 -0.182247 1.776299 -0.364226 0.035715 -0.257239 -0.970166 0.941772 -1.294272
5 11246 2.028 -0.294581 -0.583955 -0.265916 -0.270654 0.182266 -0.703494 -0.807444 0.764827
6 16736 3.152 0.943735 1.439120 -0.747066 -1.036053 -1.071138 -0.678411 0.906345 -1.234118
7 12242 0.775 -1.076758 -0.752545 -0.424517 0.460470 0.742228 -0.597809 -0.838443 1.241428
8 14365 2.442 -0.704218 1.017646 -0.428965 -0.367301 -1.014707 -1.333045 -1.294568 1.121121
9 18760 1.283 0.019018 -1.258313 0.754993 0.013994 0.094365 0.222254 2.195008 -1.201728},
{'newdata': id MedHouseVal MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude
0 16102 2.841 0.206284 1.270530 -0.248620 -0.224210 -0.059733 -0.242386 0.937344 -1.317408
1 15994 3.586 0.306050 1.439120 0.255448 -0.334613 -0.160657 -0.426510 0.937344 -1.303526
2 15391 2.541 0.423107 -1.595492 0.951807 -0.061005 1.955480 0.517572 -1.055434 1.236801
3 18799 0.520 -0.677565 -0.415366 0.548756 1.254406 -0.883398 -0.534060 2.358859 -1.035149
4 19172 1.964 0.247152 -0.162481 0.428766 -0.427459 -0.175849 -0.451380 1.238475 -1.396070
5 18164 3.674 0.295345 -1.258313 -1.078181 0.175885 0.045531 -1.298667 0.760208 -1.099930
6 13312 1.598 0.484475 -1.342608 0.767557 -0.229585 0.113899 0.361520 -0.692306 0.949915
7 12342 1.590 -0.520029 -0.246776 0.973345 1.407755 2.325532 -0.406887 -0.798587 1.445024}]