///////////////////////////////////////////
// Running convGAN-majority-full on imblearn_ozone_level
///////////////////////////////////////////

Load 'data_input/imblearn_ozone_level'
from imblearn
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Traceback (most recent call last):
  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
    runExercise(dataset, None, name, generators[name])
  File "/benchmark/data/library/analysis.py", line 169, in runExercise
    avg = exercise.run(gan, data, resultsFileName=resultsFileName)
  File "/benchmark/data/library/exercise.py", line 127, in run
    self._exerciseWithDataSlice(gan, sliceData, imageFileName=imageFileName)
  File "/benchmark/data/library/exercise.py", line 170, in _exerciseWithDataSlice
    gan.train(dataSlice.train)
  File "/benchmark/data/library/generators/convGAN.py", line 94, in train
    self.nmbMin = NNSearch(self.neb).fit(haystack=dataSet.data1)
  File "/benchmark/data/library/NNSearch.py", line 74, in fit
    self.neighbourhoods = [
  File "/benchmark/data/library/NNSearch.py", line 75, in <listcomp>
    (neigh.kneighbors([x], nebSize, return_distance=False))[0]
  File "/usr/local/lib/python3.8/dist-packages/sklearn/neighbors/_base.py", line 727, in kneighbors
    raise ValueError(
ValueError: Expected n_neighbors <= n_samples,  but n_samples = 58, n_neighbors = 72