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- TrainTestSplit(data=None, id_column=None, stratify_column=None, seed=None, train_size=0.75, test_size=0.25, **generic_arguments)
- DESCRIPTION:
The TrainTestSplit() function simulates how a model would
perform on new data. The function divides the dataset into
train and test subsets to evaluate machine learning algorithms
and validate processes. The first subset is used to train
the model. The second subset is used to make predictions and
compare the predictions to actual values.
Notes:
* The TrainTestSplit() function gives consistent results
across multiple runs on same machine. With different machines,
it might produce different train and test datasets.
* Requires the UTF8 client character set for UNICODE data.
* Does not support Pass Through Characters (PTCs).
* Does not support KanjiSJIS or graphic data types.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame on which split
to be performed.
Types: teradataml DataFrame
id_column:
Optional Argument.
Specifies the input data column name that has the
unique identifier for each row in the input.
Notes:
* Mandatory when "seed" argument is present so that the
output of TrainTestSplit() function is deterministic
across multiple function calls.
Types: str
stratify_column:
Optional Argument.
Specifies column name that contains the labels indicating
which data needs to be stratified.
Types: str
seed:
Optional Argument.
Specifies the seed value that controls the shuffling applied
to the data before applying the split. Pass an int for reproducible
output across multiple function calls.
Notes:
* When the argument is not specified, different
runs of the query generate different outputs.
* It must be in the range [0, 2147483647]
Types: int
train_size:
Optional Argument.
Specifies the size of the train dataset.
Notes:
* If both "train_size" and "test_size" arguments are specified,
then their sum must be equal to 1.
* "train_size" and "test_size" should be greater than the number
of classes when using stratify.
* If the input data does not have an identifier column, then
FillRowId() function can be used to generate one.
* It must be in the range (0, 1)
Default Value: 0.75
Types: float
test_size:
Optional Argument.
Specifies the size of the test dataset.
Note:
* It must be in the range (0, 1)
Default Value: 0.25
Types: float
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept. Below
are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile table or not. When set to
True, results are stored in a volatile table,
otherwise not.
Default Value: False
Types: bool
Function allows the user to partition, hash, order or local
order the input data. These generic arguments are available
for each argument that accepts teradataml DataFrame as
input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or
list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list
of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list
of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if
the underlying SQL Engine function supports, else an
exception is raised.
RETURNS:
Instance of TrainTestSplit.
Output teradataml DataFrames can be accessed using attribute
references, such as TrainTestSplitObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Load the example data.
load_example_data("teradataml", ["titanic"])
# Create teradataml DataFrame objects.
data_input = DataFrame.from_table("titanic")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1 : Split the input data to test dataset and train dataset,
# with ratio of test:train is 20:80. Note that output
# of TrainTestSplit() function contains 'TD_IsTrainRow'
# column in which '0' represent test data and '1'
# represent train data.
TrainTestSplit_out = TrainTestSplit(data = data_input,
id_column="passenger",
train_size=0.80,
test_size=0.20,
seed=42)
# Print the result DataFrame.
print(TrainTestSplit_out.result)
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