| 12345678910111213141516171819202122232425262728293031323334353637 |
- ///////////////////////////////////////////
- // Running convGAN-proxymary-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
|
