Tabular Learner: IndexError: Target -1 is out of bounds.

[rdugue]

Link to notebook for full reproduce example. Only need to run the steps up to where train_loop() gets called. Full stacktrace:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
    182             self.dl = dl;                                    self('begin_validate')
--> 183             with torch.no_grad(): self.all_batches()
    184         except CancelValidException:                         self('after_cancel_validate')

45 frames
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in all_batches(self)
    152         self.n_iter = len(self.dl)
--> 153         for o in enumerate(self.dl): self.one_batch(*o)
    154 

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in one_batch(self, i, b)
    160             if len(self.yb) == 0: return
--> 161             self.loss = self.loss_func(self.pred, *self.yb); self('after_loss')
    162             if not self.training: return

/usr/local/lib/python3.6/dist-packages/fastai2/layers.py in __call__(self, inp, targ, **kwargs)
    293         if self.flatten: inp = inp.view(-1,inp.shape[-1]) if self.is_2d else inp.view(-1)
--> 294         return self.func.__call__(inp, targ.view(-1) if self.flatten else targ, **kwargs)
    295 

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
    931         return F.cross_entropy(input, target, weight=self.weight,
--> 932                                ignore_index=self.ignore_index, reduction=self.reduction)
    933 

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
   2316         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2317     return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
   2318 

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
   2114     if dim == 2:
-> 2115         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
   2116     elif dim == 4:

IndexError: Target -1 is out of bounds.

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
    182             self.dl = dl;                                    self('begin_validate')
--> 183             with torch.no_grad(): self.all_batches()
    184         except CancelValidException:                         self('after_cancel_validate')

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in all_batches(self)
    152         self.n_iter = len(self.dl)
--> 153         for o in enumerate(self.dl): self.one_batch(*o)
    154 

/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in __iter__(self)
     97         self.before_iter()
---> 98         for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
     99             if self.device is not None: b = to_device(b, self.device)

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
    344     def __next__(self):
--> 345         data = self._next_data()
    346         self._num_yielded += 1

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
    384         index = self._next_index()  # may raise StopIteration
--> 385         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    386         if self._pin_memory:

/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     33         else:
---> 34             data = next(self.dataset_iter)
     35         return self.collate_fn(data)

/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in create_batches(self, samps)
    106         res = filter(lambda o:o is not None, map(self.do_item, samps))
--> 107         yield from map(self.do_batch, self.chunkify(res))
    108 

/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in do_batch(self, b)
    127     def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
--> 128     def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
    129     def to(self, device): self.device = device

/usr/local/lib/python3.6/dist-packages/fastai2/tabular/core.py in create_batch(self, b)
    351 
--> 352     def create_batch(self, b): return self.dataset.iloc[b]
    353 

AttributeError: 'list' object has no attribute 'iloc'

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-19-6590b82f66d8> in <module>()
----> 1 train_loop()

<ipython-input-18-49b3c0a66381> in train_loop()
      1 def train_loop():
----> 2   learn.fit(1, lr.lr_steep)
      3   for sr in range(1, 8):
      4     for sj in range(2, 13):
      5       train = load_one_series(sj, sr)

/usr/local/lib/python3.6/dist-packages/fastcore/utils.py in _f(*args, **kwargs)
    429         init_args.update(log)
    430         setattr(inst, 'init_args', init_args)
--> 431         return inst if to_return else f(*args, **kwargs)
    432     return _f
    433 

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
    199                         self.epoch=epoch;          self('begin_epoch')
    200                         self._do_epoch_train()
--> 201                         self._do_epoch_validate()
    202                     except CancelEpochException:   self('after_cancel_epoch')
    203                     finally:                       self('after_epoch')

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
    183             with torch.no_grad(): self.all_batches()
    184         except CancelValidException:                         self('after_cancel_validate')
--> 185         finally:                                             self('after_validate')
    186 
    187     @log_args(but='cbs')

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in __call__(self, event_name)
    132     def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
    133 
--> 134     def __call__(self, event_name): L(event_name).map(self._call_one)
    135     def _call_one(self, event_name):
    136         assert hasattr(event, event_name)

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
    374              else f.format if isinstance(f,str)
    375              else f.__getitem__)
--> 376         return self._new(map(g, self))
    377 
    378     def filter(self, f, negate=False, **kwargs):

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
    325     @property
    326     def _xtra(self): return None
--> 327     def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
    328     def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
    329     def copy(self): return self._new(self.items.copy())

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
     45             return x
     46 
---> 47         res = super().__call__(*((x,) + args), **kwargs)
     48         res._newchk = 0
     49         return res

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
    316         if items is None: items = []
    317         if (use_list is not None) or not _is_array(items):
--> 318             items = list(items) if use_list else _listify(items)
    319         if match is not None:
    320             if is_coll(match): match = len(match)

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in _listify(o)
    252     if isinstance(o, list): return o
    253     if isinstance(o, str) or _is_array(o): return [o]
--> 254     if is_iter(o): return list(o)
    255     return [o]
    256 

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
    218             if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
    219         fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 220         return self.fn(*fargs, **kwargs)
    221 
    222 # Cell

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _call_one(self, event_name)
    135     def _call_one(self, event_name):
    136         assert hasattr(event, event_name)
--> 137         [cb(event_name) for cb in sort_by_run(self.cbs)]
    138 
    139     def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in <listcomp>(.0)
    135     def _call_one(self, event_name):
    136         assert hasattr(event, event_name)
--> 137         [cb(event_name) for cb in sort_by_run(self.cbs)]
    138 
    139     def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)

/usr/local/lib/python3.6/dist-packages/fastai2/callback/core.py in __call__(self, event_name)
     22         _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
     23                (self.run_valid and not getattr(self, 'training', False)))
---> 24         if self.run and _run: getattr(self, event_name, noop)()
     25         if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
     26 

/usr/local/lib/python3.6/dist-packages/fastai2/callback/core.py in after_validate(self)
    116         with self.learn.removed_cbs(to_rm + self.cbs) as learn:
    117             self.preds = learn.get_preds(ds_idx=self.ds_idx, dl=self.dl,
--> 118                 with_input=self.with_input, with_decoded=self.with_decoded, inner=True)
    119 
    120 # Cell

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, reorder, **kwargs)
    227             for mgr in ctx_mgrs: stack.enter_context(mgr)
    228             self(event.begin_epoch if inner else _before_epoch)
--> 229             self._do_epoch_validate(dl=dl)
    230             self(event.after_epoch if inner else _after_epoch)
    231             if act is None: act = getattr(self.loss_func, 'activation', noop)

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
    183             with torch.no_grad(): self.all_batches()
    184         except CancelValidException:                         self('after_cancel_validate')
--> 185         finally:                                             self('after_validate')
    186 
    187     @log_args(but='cbs')

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in __call__(self, event_name)
    132     def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
    133 
--> 134     def __call__(self, event_name): L(event_name).map(self._call_one)
    135     def _call_one(self, event_name):
    136         assert hasattr(event, event_name)

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
    374              else f.format if isinstance(f,str)
    375              else f.__getitem__)
--> 376         return self._new(map(g, self))
    377 
    378     def filter(self, f, negate=False, **kwargs):

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
    325     @property
    326     def _xtra(self): return None
--> 327     def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
    328     def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
    329     def copy(self): return self._new(self.items.copy())

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
     45             return x
     46 
---> 47         res = super().__call__(*((x,) + args), **kwargs)
     48         res._newchk = 0
     49         return res

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
    316         if items is None: items = []
    317         if (use_list is not None) or not _is_array(items):
--> 318             items = list(items) if use_list else _listify(items)
    319         if match is not None:
    320             if is_coll(match): match = len(match)

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in _listify(o)
    252     if isinstance(o, list): return o
    253     if isinstance(o, str) or _is_array(o): return [o]
--> 254     if is_iter(o): return list(o)
    255     return [o]
    256 

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
    218             if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
    219         fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 220         return self.fn(*fargs, **kwargs)
    221 
    222 # Cell

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _call_one(self, event_name)
    135     def _call_one(self, event_name):
    136         assert hasattr(event, event_name)
--> 137         [cb(event_name) for cb in sort_by_run(self.cbs)]
    138 
    139     def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)

/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in <listcomp>(.0)
    135     def _call_one(self, event_name):
    136         assert hasattr(event, event_name)
--> 137         [cb(event_name) for cb in sort_by_run(self.cbs)]
    138 
    139     def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)

/usr/local/lib/python3.6/dist-packages/fastai2/callback/core.py in __call__(self, event_name)
     22         _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
     23                (self.run_valid and not getattr(self, 'training', False)))
---> 24         if self.run and _run: getattr(self, event_name, noop)()
     25         if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
     26 

/usr/local/lib/python3.6/dist-packages/fastai2/callback/core.py in after_validate(self)
     93     def after_validate(self):
     94         "Concatenate all recorded tensors"
---> 95         if self.with_input:     self.inputs  = detuplify(to_concat(self.inputs, dim=self.concat_dim))
     96         if not self.save_preds: self.preds   = detuplify(to_concat(self.preds, dim=self.concat_dim))
     97         if not self.save_targs: self.targets = detuplify(to_concat(self.targets, dim=self.concat_dim))

/usr/local/lib/python3.6/dist-packages/fastai2/torch_core.py in to_concat(xs, dim)
    211 def to_concat(xs, dim=0):
    212     "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213     if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
    214     if isinstance(xs[0],dict):  return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs[0].keys()}
    215     #We may receives xs that are not concatenatable (inputs of a text classifier for instance),

IndexError: list index out of range 

[sgugger]

It looks like there is something wrong with your validation data loader. Maybe a category in the labels of your validation set that does not appear in the training set? In any case, please use the forum to debug your code, we keep issues for the bugs inside the library only.