SMOTE

Synthetic Minority Oversampling Technique. SMOTE is an over-sampling method that synthesizes new samples by using one of the neighbors of a seed sample.

smote(data, y, k=5, samp_method='balance', drop_na_col=True, drop_na_row=True, rel_thres=0.5, rel_method='auto', rel_xtrm_type='both', rel_coef=1.5, rel_ctrl_pts_rg=None)
Parameters:

  • data (Pandas dataframe) – Pandas dataframe, the dataset to re-sample.

  • y (str) – Column name of the target variable in the Pandas dataframe.

  • k (int) – The number of neighbors considered. Must be a positive integer.

  • samp_method (str) – Method to determine re-sampling percentage. Either balance or extreme.

  • drop_na_col (bool) – Determine whether or not automatically drop columns containing NaN values. The data frame should not contain any missing values, so it is suggested to keep it as default.

  • drop_na_row (bool) – Determine whether or not automatically drop rows containing NaN values. The data frame should not contain any missing values, so it is suggested to keep it as default.

  • rel_thres (float) – Relevance threshold, above which a sample is considered rare. Must be a real number between 0 and 1 (0, 1].

  • rel_method (str) – Method to define the relevance function, either auto or manual. If manual, must specify rel_ctrl_pts_rg.

  • rel_xtrm_type (str) – Distribution focus, high, low, or both. If high, rare cases having small y values will be considerd as normal, and vise versa.

  • rel_coef (float) – Coefficient for box plot.

  • rel_ctrl_pts_rg (2D array) – Manually specify the regions of interest. See SMOGN advanced example for more details.

Returns:

Re-sampled dataset.

Return type:

Pandas dataframe

Raises:

ValueError – If an input attribute has wrong data type or invalid value, or relevance values are all zero or all one, or synthetic data contains missing values.

References

[1] L. Torgo, R. P. Ribeiro, B. Pfahringer, P. Branco, “Smote for regression,” In Portuguese conference on artificial intelligence, pp. 378-389, 2013. Springer, Berlin, Heidelberg.

[2] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of artificial intelligence research, 321-357, 2002.

Examples

>>> from ImbalancedLearningRegression import smote
>>> housing = pandas.read_csv("https://raw.githubusercontent.com/paobranco/ImbalancedLearningRegression/master/data/housing.csv")
>>> housing_smote = smote(data = housing, y = "SalePrice")