Random Over-sampling

Random Over-sampling is an over-sampling method that synthesizes new samples by randomly copying minority samples.

ro(data, y, samp_method='balance', drop_na_col=True, drop_na_row=True, replace=True, manual_perc=False, perc_o=-1, 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.

  • 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.

  • replace (bool) – Randomly select sample to duplicate: with or without replacement.

  • manual_perc (bool) – Keep the same percentage of re-sampling for all bins. If True, perc_o is required to be a positive real number.

  • perc_o (float) – User-specified fixed percentage of over-sampling for all bins. Must be a positive real number if manual_perc = True.

  • 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] G Menardi, N. Torelli, “Training and assessing classification rules with imbalanced data,” Data Mining and Knowledge Discovery, 28(1), pp.92-122, 2014.

[2] P. Branco, L. Torgo, R. P. Ribeiro, “Pre-processing approaches for imbalanced distributions in regression,” Neurocomputing, 343, pp. 76-99, 2019.

Examples

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