Exception occured in Recorder when calling event after_validate: multi_class must be in ('ovo', 'ovr')

[jerronl]

Hi, I tried InceptionTime with following code and got error as in the title. I also enclosed the stack frames below. How to fix? Thanks.

print(X_.shape,y_.shape)
bs = 64
n_epochs = 100
tfms  = [None, [Categorize()]]
dsets = TSDatasets(X_, y_, tfms=tfms, splits=splits, inplace=True)
dls   = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=bs, batch_tfms=TSStandardize())
dls.show_batch()
from tsai.models.InceptionTime import *
model = InceptionTime(dls.vars, dls.c)
learn = Learner(dls, model, metrics=[RocAucBinary(), accuracy], cbs=ShowGraphCallback2())
learn.lr_find()
start = time.time()
learn.fit_one_cycle(n_epochs, lr_max=6e-4)
print('\nElapsed time:', time.time() - start)

(100000, 21, 6) (100000,)


InceptionTime(
  (inceptionblock): InceptionBlock(
    (inception): ModuleList(
      (0): InceptionModule(
        (bottleneck): Conv1d(21, 32, kernel_size=(1,), stride=(1,), bias=False)
        (convs): ModuleList(
          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
        )
        (maxconvpool): Sequential(
          (0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
          (1): Conv1d(21, 32, kernel_size=(1,), stride=(1,), bias=False)
        )
        (concat): Concat(dim=1)
        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act): ReLU()
      )
      (1-5): 5 x InceptionModule(
        (bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
        (convs): ModuleList(
          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
        )
        (maxconvpool): Sequential(
          (0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
          (1): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
        )
        (concat): Concat(dim=1)
        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act): ReLU()
      )
    )
    (shortcut): ModuleList(
      (0): ConvBlock(
        (0): Conv1d(21, 128, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (add): Add
    (act): ReLU()
  )
  (gap): GAP1d(
    (gap): AdaptiveAvgPool1d(output_size=1)
    (flatten): Reshape(bs)
  )
  (fc): Linear(in_features=128, out_features=22, bias=True)
)

SuggestedLRs(valley=0.0006918309954926372)

epoch 	train_loss 	valid_loss 	roc_auc_score 	accuracy 	time

100.00% [313/313 00:02<00:00 2.2765]

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

[<ipython-input-18-840a403fe4f0>](https://localhost:8080/#) in <cell line: 2>()
      1 start = time.time()
----> 2 learn.fit_one_cycle(n_epochs, lr_max=6e-4)
      3 print('\nElapsed time:', time.time() - start)

21 frames

[/usr/local/lib/python3.10/dist-packages/fastai/callback/schedule.py](https://localhost:8080/#) in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt, start_epoch)
    117     scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
    118               'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 119     self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd, start_epoch=start_epoch)
    120 
    121 # %% ../../nbs/14_callback.schedule.ipynb 50

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in fit(self, n_epoch, lr, wd, cbs, reset_opt, start_epoch)
    262             self.opt.set_hypers(lr=self.lr if lr is None else lr)
    263             self.n_epoch = n_epoch
--> 264             self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
    265 
    266     def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _with_events(self, f, event_type, ex, final)
    197 
    198     def _with_events(self, f, event_type, ex, final=noop):
--> 199         try: self(f'before_{event_type}');  f()
    200         except ex: self(f'after_cancel_{event_type}')
    201         self(f'after_{event_type}');  final()

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _do_fit(self)
    251         for epoch in range(self.n_epoch):
    252             self.epoch=epoch
--> 253             self._with_events(self._do_epoch, 'epoch', CancelEpochException)
    254 
    255     def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False, start_epoch=0):

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _with_events(self, f, event_type, ex, final)
    197 
    198     def _with_events(self, f, event_type, ex, final=noop):
--> 199         try: self(f'before_{event_type}');  f()
    200         except ex: self(f'after_cancel_{event_type}')
    201         self(f'after_{event_type}');  final()

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _do_epoch(self)
    246     def _do_epoch(self):
    247         self._do_epoch_train()
--> 248         self._do_epoch_validate()
    249 
    250     def _do_fit(self):

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _do_epoch_validate(self, ds_idx, dl)
    242         if dl is None: dl = self.dls[ds_idx]
    243         self.dl = dl
--> 244         with torch.no_grad(): self._with_events(self.all_batches, 'validate', CancelValidException)
    245 
    246     def _do_epoch(self):

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _with_events(self, f, event_type, ex, final)
    199         try: self(f'before_{event_type}');  f()
    200         except ex: self(f'after_cancel_{event_type}')
--> 201         self(f'after_{event_type}');  final()
    202 
    203     def all_batches(self):

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in __call__(self, event_name)
    170 
    171     def ordered_cbs(self, event): return [cb for cb in self.cbs.sorted('order') if hasattr(cb, event)]
--> 172     def __call__(self, event_name): L(event_name).map(self._call_one)
    173 
    174     def _call_one(self, event_name):

[/usr/local/lib/python3.10/dist-packages/fastcore/foundation.py](https://localhost:8080/#) in map(self, f, *args, **kwargs)
    154     def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
    155 
--> 156     def map(self, f, *args, **kwargs): return self._new(map_ex(self, f, *args, gen=False, **kwargs))
    157     def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
    158     def argfirst(self, f, negate=False):

[/usr/local/lib/python3.10/dist-packages/fastcore/basics.py](https://localhost:8080/#) in map_ex(iterable, f, gen, *args, **kwargs)
    838     res = map(g, iterable)
    839     if gen: return res
--> 840     return list(res)
    841 
    842 # %% ../nbs/01_basics.ipynb 336

[/usr/local/lib/python3.10/dist-packages/fastcore/basics.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
    823             if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
    824         fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 825         return self.func(*fargs, **kwargs)
    826 
    827 # %% ../nbs/01_basics.ipynb 326

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _call_one(self, event_name)
    174     def _call_one(self, event_name):
    175         if not hasattr(event, event_name): raise Exception(f'missing {event_name}')
--> 176         for cb in self.cbs.sorted('order'): cb(event_name)
    177 
    178     def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)

[/usr/local/lib/python3.10/dist-packages/fastai/callback/core.py](https://localhost:8080/#) in __call__(self, event_name)
     60             try: res = getcallable(self, event_name)()
     61             except (CancelBatchException, CancelBackwardException, CancelEpochException, CancelFitException, CancelStepException, CancelTrainException, CancelValidException): raise
---> 62             except Exception as e: raise modify_exception(e, f'Exception occured in `{self.__class__.__name__}` when calling event `{event_name}`:\n\t{e.args[0]}', replace=True)
     63         if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
     64         return res

[/usr/local/lib/python3.10/dist-packages/fastai/callback/core.py](https://localhost:8080/#) in __call__(self, event_name)
     58         res = None
     59         if self.run and _run:
---> 60             try: res = getcallable(self, event_name)()
     61             except (CancelBatchException, CancelBackwardException, CancelEpochException, CancelFitException, CancelStepException, CancelTrainException, CancelValidException): raise
     62             except Exception as e: raise modify_exception(e, f'Exception occured in `{self.__class__.__name__}` when calling event `{event_name}`:\n\t{e.args[0]}', replace=True)

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in after_validate(self)
    573     def before_validate(self): self._valid_mets.map(Self.reset())
    574     def after_train   (self): self.log += self._train_mets.map(_maybe_item)
--> 575     def after_validate(self): self.log += self._valid_mets.map(_maybe_item)
    576     def after_cancel_train(self):    self.cancel_train = True
    577     def after_cancel_validate(self): self.cancel_valid = True

[/usr/local/lib/python3.10/dist-packages/fastcore/foundation.py](https://localhost:8080/#) in map(self, f, *args, **kwargs)
    154     def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
    155 
--> 156     def map(self, f, *args, **kwargs): return self._new(map_ex(self, f, *args, gen=False, **kwargs))
    157     def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
    158     def argfirst(self, f, negate=False):

[/usr/local/lib/python3.10/dist-packages/fastcore/basics.py](https://localhost:8080/#) in map_ex(iterable, f, gen, *args, **kwargs)
    838     res = map(g, iterable)
    839     if gen: return res
--> 840     return list(res)
    841 
    842 # %% ../nbs/01_basics.ipynb 336

[/usr/local/lib/python3.10/dist-packages/fastcore/basics.py](https://localhost:8080/#) in __call__(self, *args, **kwargs)
    823             if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
    824         fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 825         return self.func(*fargs, **kwargs)
    826 
    827 # %% ../nbs/01_basics.ipynb 326

[/usr/local/lib/python3.10/dist-packages/fastai/learner.py](https://localhost:8080/#) in _maybe_item(t)
    527 # %% ../nbs/13a_learner.ipynb 134
    528 def _maybe_item(t):
--> 529     t = t.value
    530     try: return t.item()
    531     except: return t

[/usr/local/lib/python3.10/dist-packages/fastai/metrics.py](https://localhost:8080/#) in value(self)
     68         preds,targs = torch.cat(self.preds),torch.cat(self.targs)
     69         if self.to_np: preds,targs = preds.numpy(),targs.numpy()
---> 70         return self.func(targs, preds, **self.kwargs) if self.invert_args else self.func(preds, targs, **self.kwargs)
     71 
     72     @property

[/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_ranking.py](https://localhost:8080/#) in roc_auc_score(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)
    563             )
    564         if multi_class == "raise":
--> 565             raise ValueError("multi_class must be in ('ovo', 'ovr')")
    566         return _multiclass_roc_auc_score(
    567             y_true, y_score, labels, multi_class, average, sample_weight

ValueError: Exception occured in `Recorder` when calling event `after_validate`:
	multi_class must be in ('ovo', 'ovr')

[jerronl]

The problem seems related to the metric RocAucBinary(). After I removed it, it seems running.