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- SMOTE(data=None, encoding_data=None, id_column=None, response_column=None, input_columns=None, categorical_columns=None, median_standard_deviation=None, minority_class=None, oversampling_factor=None, sampling_strategy='smote', fill_sampleid=True, noninput_columns_value='sample', n_neighbors=5, seed=None, **generic_arguments)
- DESCRIPTION:
SMOTE() function generates data by oversampling a minority class using
smote, adasyn, borderline-2 or smote-nc algorithms.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
encoding_data:
Optional Argument, Required when "sampling_strategy" is set to 'smotenc' algorithm.
Specifies the teradataml dataframe containing the ordinal encoding information.
Types: teradataml DataFrame
id_column:
Required Argument.
Specifies the name of the column in "data" that
uniquely identifies a data sample.
Types: str
response_column:
Optional Argument.
Specifies the name of the column in "data" that contains the
numeric value to be used as the response value for a sample.
Types: str
input_columns:
Required Argument.
Specifies the name of the input columns in "data" for oversampling.
Types: str OR list of Strings (str)
categorical_columns:
Optional Argument, Required when "sampling_strategy" is set to 'smotenc' algorithm.
Specifies the name of the categorical columns in the "data" that
the function uses for oversampling with smotenc.
Types: str OR list of Strings (str)
median_standard_deviation:
Optional Argument, Required when "sampling_strategy" is set to 'smotenc' algorithm.
Specifies the median of the standard deviations computed over the
numerical input columns.
Types: float
minority_class:
Required Argument.
Specifies the minority class for which synthetic samples need to be
generated.
Note:
* The label for minority class under response column must be numeric integer.
Types: str
oversampling_factor:
Required Argument.
Specifies the factor for oversampling the minority class.
Types: float
sampling_strategy:
Optional Argument.
Specifies the oversampling algorithm to be used to create synthetic samples.
Default Value: "smote"
Permitted Values: 'smote', 'adasyn', 'borderline', 'smotenc'
Types: str
fill_sampleid:
Optional Argument.
Specifies whether to include the id of the original observation used
to generate each synthetic observation.
Default Value: True
Types: bool
noninput_columns_value:
Optional Argument.
Specifies the value to put in a sample column for columns not
specified as input columns.
Default Value: "sample"
Permitted Values: 'sample', 'neighbor', 'null'
Types: str
n_neighbors:
Optional Argument.
Specifies the number of nearest neighbors for choosing the sample to
be used in oversampling.
Default Value: 5
Types: int
seed:
Optional Argument.
Specifies the random seed the algorithm uses for repeatable results.
The function uses the seed for random interpolation and generate the
synthetic sample.
Types: int
**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 SMOTE.
Output teradataml DataFrames can be accessed using attribute
references, such as SMOTEObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage, before importing the
# function in user space.
# 2. User can import the function, if it is available on
# Vantage user is connected to.
# 3. To check the list of analytic functions available on
# Vantage user connected to, use
# "display_analytic_functions()".
# Load the example data.
load_example_data("dataframe", "iris_test")
load_example_data("teradataml", "titanic")
# Create teradataml DataFrame objects.
iris_input = DataFrame.from_table("iris_test").iloc[:25]
titanic_input = DataFrame("titanic").iloc[:50]
# Create Encoding DataFrame objects.
encoded_data = OrdinalEncodingFit(data=titanic_input,
target_column=['sex','embarked'],
approach="AUTO")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function SMOTE.
from teradataml import SMOTE
# Example 1 : Generate synthetic samples using smote algorithm.
smote_out = SMOTE(data = iris_input,
n_neighbors = 5,
id_column='id',
minority_class='3',
response_column='species',
input_columns =['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
oversampling_factor=2,
sampling_strategy='smote',
seed=10)
# Print the result DataFrame.
print(smote_out.result)
# Example 2 : Generate synthetic samples using smotenc algorithm with categorical columns.
smote_out2 = SMOTE(data = titanic_input,
encoding_data = encoded_data.result,
id_column = 'passenger',
response_column = 'survived',
input_columns = ['parch', 'age', 'sibsp'],
categorical_columns = ['sex', 'embarked'],
median_standard_deviation = 31.47806044604718,
minority_class = '1',
oversampling_factor = 5,
sampling_strategy = "smotenc",
noninput_columns_value = "null",
n_neighbors = 5)
# Print the result DataFrame.
print(smote_out2.result)
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