ADASYN
ADASYN is an over-sampling method that is similar to SMOTE, but more samples are generated for seed samples that are difficult to be learned.
- adasyn(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
orextreme
.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
ormanual
. Ifmanual
, must specifyrel_ctrl_pts_rg
.rel_xtrm_type (str) – Distribution focus,
high
,low
, orboth
. Ifhigh
, 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:
- 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.
AssertionError – If normalized ratio ri does not sum up to one.
References
[1] He, Haibo, Yang Bai, Edwardo A. Garcia, and Shutao Li. “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322-1328, 2008.
Examples
>>> from ImbalancedLearningRegression import adasyn
>>> housing = pandas.read_csv("https://raw.githubusercontent.com/paobranco/ImbalancedLearningRegression/master/data/housing.csv")
>>> housing_adasyn = adasyn(data = housing, y = "SalePrice")