In fastbook/09_tabular.ipynb, while tabular_learner()'s get_preds() works fine on a batch of inputs, I run into problems when using predict() on a single row, one input.
df_final_nn is the Dataframe used to create the TabularPandas, which in turn is used to create the DataLoaders of the learner object.
Wonder if anyone has seen this, and what I might have missed/done wrong here … 
Would appreciate any idea…
-
df_nn_final.iloc[0]is:
YearMade 2004
Coupler_System NaN
ProductSize NaN
fiProductClassDesc Wheel Loader - 110.0 to 120.0 Horsepower
ModelID 3157
fiSecondaryDesc D
saleElapsed 1163635200
Enclosure EROPS w AC
Hydraulics_Flow NaN
fiModelDesc 521D
fiModelDescriptor NaN
ProductGroup WL
Drive_System NaN
Hydraulics 2 Valve
Tire_Size None or Unspecified
SalePrice 11.0974
Name: 0, dtype: object
- The error when invoking in a notebook cell:
learn.predict(df_nn_final.iloc[0]):
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Dropbox/fastai2/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')
~/Dropbox/fastai2/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
~/Dropbox/fastai2/fastai2/data/load.py in __iter__(self)
99 if self.device is not None: b = to_device(b, self.device)
--> 100 yield self.after_batch(b)
101 self.after_iter()
~/Dropbox/fastcore/fastcore/transform.py in __call__(self, o)
186
--> 187 def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)
188 def __repr__(self): return f"Pipeline: {' -> '.join([f.name for f in self.fs if f.name != 'noop'])}"
~/Dropbox/fastcore/fastcore/transform.py in compose_tfms(x, tfms, is_enc, reverse, **kwargs)
139 if not is_enc: f = f.decode
--> 140 x = f(x, **kwargs)
141 return x
~/Dropbox/fastcore/fastcore/transform.py in __call__(self, x, **kwargs)
102 _retain = True
--> 103 def __call__(self, x, **kwargs): return self._call1(x, '__call__', **kwargs)
104 def decode(self, x, **kwargs): return self._call1(x, 'decode', **kwargs)
~/Dropbox/fastcore/fastcore/transform.py in _call1(self, x, name, **kwargs)
105 def _call1(self, x, name, **kwargs):
--> 106 if not _is_tuple(x): return getattr(super(), name)(x, **kwargs)
107 y = getattr(super(), name)(list(x), **kwargs)
~/Dropbox/fastcore/fastcore/transform.py in __call__(self, x, **kwargs)
71 def name(self): return getattr(self, '_name', _get_name(self))
---> 72 def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)
73 def decode (self, x, **kwargs): return self._call('decodes', x, **kwargs)
~/Dropbox/fastcore/fastcore/transform.py in _call(self, fn, x, split_idx, **kwargs)
81 if split_idx!=self.split_idx and self.split_idx is not None: return x
---> 82 return self._do_call(getattr(self, fn), x, **kwargs)
83
~/Dropbox/fastcore/fastcore/transform.py in _do_call(self, f, x, **kwargs)
85 if not _is_tuple(x):
---> 86 return x if f is None else retain_type(f(x, **kwargs), x, f.returns_none(x))
87 res = tuple(self._do_call(f, x_, **kwargs) for x_ in x)
~/Dropbox/fastcore/fastcore/dispatch.py in __call__(self, *args, **kwargs)
97 if self.inst is not None: f = MethodType(f, self.inst)
---> 98 return f(*args, **kwargs)
99
~/Dropbox/fastai2/fastai2/tabular/core.py in encodes(self, to)
288 ys = [n for n in to.y_names if n in to.items.columns]
--> 289 if len(ys) == len(to.y_names): res = res + (tensor(to.targ),)
290 if to.device is not None: res = to_device(res, to.device)
~/Dropbox/fastai2/fastai2/torch_core.py in tensor(x, *rest, **kwargs)
111 else _array2tensor(x) if isinstance(x, ndarray)
--> 112 else as_tensor(x.values, **kwargs) if isinstance(x, (pd.Series, pd.DataFrame))
113 else as_tensor(x, **kwargs) if hasattr(x, '__array__') or is_iter(x)
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, int64, int32, int16, int8, uint8, and bool.
During handling of the above exception, another exception occurred:
IndexError Traceback (most recent call last)
<ipython-input-109-71ce0638ebf9> in <module>
----> 1 learn.predict(df_nn_final.iloc[0])
~/Dropbox/fastai2/fastai2/tabular/learner.py in predict(self, row)
17 tst_to.conts = tst_to.conts.astype(np.float32)
18 dl = self.dls.valid.new(tst_to)
---> 19 inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)
20 i = getattr(self.dls, 'n_inp', -1)
21 b = (*tuplify(inp),*tuplify(dec_preds))
~/Dropbox/fastai2/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)
~/Dropbox/fastai2/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')
~/Dropbox/fastai2/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)
~/Dropbox/fastcore/fastcore/foundation.py in map(self, f, *args, **kwargs)
373 else f.format if isinstance(f,str)
374 else f.__getitem__)
--> 375 return self._new(map(g, self))
376
377 def filter(self, f, negate=False, **kwargs):
~/Dropbox/fastcore/fastcore/foundation.py in _new(self, items, *args, **kwargs)
324 @property
325 def _xtra(self): return None
--> 326 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
327 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
328 def copy(self): return self._new(self.items.copy())
~/Dropbox/fastcore/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
~/Dropbox/fastcore/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
315 if items is None: items = []
316 if (use_list is not None) or not _is_array(items):
--> 317 items = list(items) if use_list else _listify(items)
318 if match is not None:
319 if is_coll(match): match = len(match)
~/Dropbox/fastcore/fastcore/foundation.py in _listify(o)
251 if isinstance(o, list): return o
252 if isinstance(o, str) or _is_array(o): return [o]
--> 253 if is_iter(o): return list(o)
254 return [o]
255
~/Dropbox/fastcore/fastcore/foundation.py in __call__(self, *args, **kwargs)
217 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
218 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 219 return self.fn(*fargs, **kwargs)
220
221 # Cell
~/Dropbox/fastai2/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)
~/Dropbox/fastai2/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)
~/Dropbox/fastai2/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
~/Dropbox/fastai2/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))
~/Dropbox/fastai2/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