class hana_ml.algorithms.pal.preprocessing.SMOTE(smote_amount=None, k_nearest_neighbours=None, minority_class=None, thread_ratio=None, random_seed=None, method=None, search_method=None, category_weights=None)

This class is to handle imbalanced dataset. Synthetic minority over-sampling technique (SMOTE) proposes an over-sampling approach in which the minority class is over-sampled by creating "synthetic" examples in "feature space".

smote_amountint, optional

Amount of SMOTE N%. E.g. 200 means 200%, so each minority class sample will generate 2 synthetic samples.

The synthetic samples are generated until the minority class sample amount matches the majority class sample amount.

k_nearest_neighboursint, optional

Number of nearest neighbors (k).

Defaults to 1.

minority_classstr, optional(deprecated)

Specifies the minority class value in dependent variable column.

All classes except majority class are re-sampled to match the majority class sample amount.

thread_ratiofloat, optional

Specifies the ratio of total number of threads that can be used by this function.

The value range is from 0 to 1, where 0 means only using 1 thread, and 1 means using at most all the currently available threads.

Values outside the range [0, 1] will be ignored and this function heuristically determines the number of threads to use.

Default to 0.

random_seedint, optional

Specifies the seed for random number generator.

  • 0: Uses the current time (in seconds) as seed

  • Others: Uses the specified value as seed

Defaults to 0.

methodint, optional(deprecated)

Searching method when finding K nearest neighbour.

  • 0: Brute force searching

  • 1: KD-tree searching

Defaults to 0.

search_methodstr, optional

Specifies the searching method for finding the k nearest-neighbors.

  • 'brute-force'

  • 'kd-tree'

Defaults to 'brute-force'.

category_weightsfloat, optional

Represents the weight of category attributes. The value must be greater or equal to 0.


>>> smote = SMOTE(smote_amount=200, k_nearest_neighbours=2,
>>> res = smote.fit_transform(data=df, label = 'TYPE', minority_class=2)


fit_transform(data[, label, minority_class, ...])

Upsampling given datasets using SMOTE with specified configuration.

fit_transform(data, label=None, minority_class=None, categorical_variable=None, variable_weight=None, key=None)

Upsampling given datasets using SMOTE with specified configuration.


Dataframe containing the data for upsampling via SMOTE.

keystr, optional

Name of the ID column in data.

If data is indexed by a single column, then key defaults to that index column; otherwise no default value, so data is assumed having no ID column.


Specifies the dependent variable by name.

If not specified, defaults to the 1st column in data.

minority_classstr/int, optional

Specifies the minority class value in dependent variable column.

If not specified, all but the majority classes are resampled to match the majority class sample amount.

categorical_variablestr/ListOfStrings, optional

Specifies the list of INTEGER columns that should be treated as categorical.

By default, only VARCHAR and NVARCHAR columns are treated as categorical, while numerical (i.e. INTEGER or DOUBLE) columns are treated as continuous.

No default value.

variable_weightdict, optional

Specifies the weights of variables participating in distance calculation in a dictionary, illustrated as follows:

{variable_name0 : value0, variable_name1 : value1, ...}.

The values must be no less than 0.

Weights default to 1 for variables not specified.

  • SMOTE result, the same structure as defined in the input data.

property fit_hdbprocedure

Returns the generated hdbprocedure for fit.

property predict_hdbprocedure

Returns the generated hdbprocedure for predict.

Inherited Methods from PALBase

Besides those methods mentioned above, the SMOTE class also inherits methods from PALBase class, please refer to PAL Base for more details.