Fix docstring about sampling strategy for under and oversampling

doc/whats_new/v0.5.rst

@@ -6,6 +6,13 @@ Version 0.5 (under development)
 Changelog
 ---------
 
+Documentation
+.............
+
+- Correct the definition of the ratio when using a ``float`` in sampling
+  strategy for the over-sampling and under-sampling.
+  :issue:`525` by :user:`Ariel Rossanigo <arielrossanigo>`.
+
 Maintenance
 ...........
 

examples/plot_sampling_strategy_usage.py

@@ -96,7 +96,7 @@ def my_autopct(pct):
 
 ###############################################################################
 # For **over-sampling methods**, it correspond to the ratio
-# :math:`\\alpha_{os}` defined by :math:`N_{rm} = \\alpha_{os} \\times N_{m}`
+# :math:`\\alpha_{os}` defined by :math:`N_{rm} = \\alpha_{os} \\times N_{M}`
 # where :math:`N_{rm}` and :math:`N_{M}` are the number of samples in the
 # minority class after resampling and the number of samples in the majority
 # class, respectively.

imblearn/over_sampling/base.py

@@ -22,12 +22,11 @@ class BaseOverSampler(BaseSampler):
         Sampling information to resample the data set.
 
         - When ``float``, it corresponds to the desired ratio of the number of
-          samples in the majority class over the number of samples in the
-          minority class after resampling. Therefore, the ratio is expressed as
-          :math:`\\alpha_{os} = N_{M} / N_{rm}` where :math:`N_{rm}` and
-          :math:`N_{M}` are the number of samples in the minority class after
-          resampling and the number of samples in the majority class,
-          respectively.
+          samples in the minority class over the number of samples in the
+          majority class after resampling. Therefore, the ratio is expressed as
+          :math:`\\alpha_{os} = N_{rm} / N_{M}` where :math:`N_{rm}` is the
+          number of samples in the minority class after resampling and
+          :math:`N_{M}` is the number of samples in the majority class.
 
             .. warning::
                ``float`` is only available for **binary** classification. An

imblearn/under_sampling/base.py

@@ -20,12 +20,12 @@ class BaseUnderSampler(BaseSampler):
         Sampling information to sample the data set.
 
         - When ``float``, it corresponds to the desired ratio of the number of
-          samples in the majority class over the number of samples in the
-          minority class after resampling. Therefore, the ratio is expressed as
-          :math:`\\alpha_{us} = N_{rM} / N_{m}` where :math:`N_{rM}` and
-          :math:`N_{m}` are the number of samples in the majority class after
-          resampling and the number of samples in the minority class,
-          respectively.
+          samples in the minority class over the number of samples in the
+          majority class after resampling. Therefore, the ratio is expressed as
+          :math:`\\alpha_{us} = N_{m} / N_{rM}` where :math:`N_{m}` is the
+          number of samples in the minority class and
+          :math:`N_{rM}` is the number of samples in the majority class
+          after resampling.
 
           .. warning::
              ``float`` is only available for **binary** classification. An