Random Under-sampling
Random Under-sampling is an under-sampling method that randomly select a subset of majority samples.
- random_under(data, y, samp_method='balance', drop_na_col=True, drop_na_row=True, replacement=False, manual_perc=False, perc_u=-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
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.
replacement (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_u
is required to be a real number between 0 and 1 (0, 1).perc_u (float) – User-specified fixed percentage of under-sampling for all bins. Must be a real number between 0 and 1 (0, 1) 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
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 under_sampled data contains missing values.
Examples
>>> from ImbalancedLearningRegression import random_under
>>> housing = pandas.read_csv("https://raw.githubusercontent.com/paobranco/ImbalancedLearningRegression/master/data/housing.csv")
>>> housing_ru = random_under(data = housing, y = "SalePrice")