Is there a good way to add test sets correctly?:狞笑:
If you are using MixedItemList, I could not make it work because it doesnt work like other ItemLists.
What I did in my code is that I trick fastai into thinking the test set is actually the validation set and then get predictions for the validation set.
Yes, thank you for your excellent work, which has helped me a lot. However, if I set the training set size to 0 in fastai, an error will be reported:哀思:
Hopefully fixed in master now (thanks to @sgugger 's quick response): https://github.com/fastai/fastai/pull/2107
3 Likes
Thanks @Herman!
Hi Etienne,
Thanks a lot for your wonderful work and also for kindly sharing it with the rest of us (and special thanks to sgugger. I coincidentally found myself following the exact path you followed. I was wondering if you were able to use learn.predict method after training?
I am using a model only with tabular and image data and successfully trained it. But no luck on predict. This was sort of an effort to compile a list of predictions on validation set and “plot_top_losses” manually for inspection 
But the funny thing is error I’m getting is a KeyError from pd.Categorical called by fastai’s Categorify proc on data. And below is what I think is causing the problem; the creation of a dataframe from two copies of TabularLines (fastai.tabular.data line 45). I tried to temporarily replace TabularLine inside my MixedItem with a custom object to make those two lines “happy” but ended up creating more problems that I understand less. I’ve included full traceback when I call predict.
Final note; I’m looking to see if I did some obvious mistake. Otherwise, I think the answer for my (and any) MixedItemList related Q is to wait for v2 Also, apologies for long post.
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/tabular/data.py in process_one(self, item)
44 def process_one(self, item):
45 df = pd.DataFrame([item,item])
---> 46 for proc in self.procs: proc(df, test=True)
Partial code (which is proven working with Images only) and full traceback below;
dup_valid_dl = copy.deepcopy(data.valid_dl)
for item in dup_valid_dl.dataset:
# print(f'{item}')
data_item, label = item
img_item, tab_item = data_item.obj[0], data_item.obj[1]
print(f'Tab_Item:\t{tab_item}')
print(f'Img_Item:\t{img_item}')
print(f'Label:\t{label}')
predicted = learn.predict( (data_item) )[0].data[0]
# print(f'{predicted}')
# truth = label.data
# error = truth-predicted
# predicted_list.append([item, np.abs(error), truth, predicted])
# print(f'{truth:.3f}-{predicted:.3f}={error:.3f}')
# print(f'======================================================================================')
predicted_list = sorted(predicted_list, key=lambda x: -x[1])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2656 try:
-> 2657 return self._engine.get_loc(key)
2658 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: 'port_id'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-175-383315a78990> in <module>()
20 print(my_df)
21
---> 22 predicted = learn.predict( (data_item) )[0].data[0]
23 # print(f'{predicted}')
24 # truth = label.data
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/basic_train.py in predict(self, item, return_x, batch_first, with_dropout, **kwargs)
372 def predict(self, item:ItemBase, return_x:bool=False, batch_first:bool=True, with_dropout:bool=False, **kwargs):
373 "Return predicted class, label and probabilities for `item`."
--> 374 batch = self.data.one_item(item)
375 res = self.pred_batch(batch=batch, with_dropout=with_dropout)
376 raw_pred,x = grab_idx(res,0,batch_first=batch_first),batch[0]
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/basic_data.py in one_item(self, item, detach, denorm, cpu)
178 "Get `item` into a batch. Optionally `detach` and `denorm`."
179 ds = self.single_ds
--> 180 with ds.set_item(item):
181 return self.one_batch(ds_type=DatasetType.Single, detach=detach, denorm=denorm, cpu=cpu)
182
~/anaconda3/envs/pytorch_p36/lib/python3.6/contextlib.py in __enter__(self)
79 def __enter__(self):
80 try:
---> 81 return next(self.gen)
82 except StopIteration:
83 raise RuntimeError("generator didn't yield") from None
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/data_block.py in set_item(self, item)
606 def set_item(self,item):
607 "For inference, will briefly replace the dataset with one that only contains `item`."
--> 608 self.item = self.x.process_one(item)
609 yield None
610 self.item = None
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/data_block.py in process_one(self, item, processor)
88 if processor is not None: self.processor = processor
89 self.processor = listify(self.processor)
---> 90 for p in self.processor: item = p.process_one(item)
91 return item
92
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/data_block.py in process_one(self, item)
754 res = []
755 for procs, i in zip(self.procs, item):
--> 756 for p in procs: i = p.process_one(i)
757 res.append(i)
758 return res
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/tabular/data.py in process_one(self, item)
44 def process_one(self, item):
45 df = pd.DataFrame([item,item])
---> 46 for proc in self.procs: proc(df, test=True)
47 if len(self.cat_names) != 0:
48 codes = np.stack([c.cat.codes.values for n,c in df[self.cat_names].items()], 1).astype(np.int64) + 1
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/tabular/transform.py in __call__(self, df, test)
122 "Apply the correct function to `df` depending on `test`."
123 func = self.apply_test if test else self.apply_train
--> 124 func(df)
125
126 def apply_train(self, df:DataFrame):
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai/tabular/transform.py in apply_test(self, df)
143 "Transform `self.cat_names` columns in categorical using the codes decided in `apply_train`."
144 for n in self.cat_names:
--> 145 df.loc[:,n] = pd.Categorical(df[n], categories=self.categories[n], ordered=True)
146
147 FillStrategy = IntEnum('FillStrategy', 'MEDIAN COMMON CONSTANT')
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
2925 if self.columns.nlevels > 1:
2926 return self._getitem_multilevel(key)
-> 2927 indexer = self.columns.get_loc(key)
2928 if is_integer(indexer):
2929 indexer = [indexer]
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2657 return self._engine.get_loc(key)
2658 except KeyError:
-> 2659 return self._engine.get_loc(self._maybe_cast_indexer(key))
2660 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2661 if indexer.ndim > 1 or indexer.size > 1:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: 'port_id'Hey @alpsayin, sorry I haven’t touched anything deep learning related since May. But from what I remember I did not try to make predict work. You can see my code here on how I went around the problem: https://github.com/EtienneT/fastai-petfinder/blob/master/Fastai%20PetFinder.ipynb. But really not ideal.
Can’t wait to see what v2 will yield for this kind of scenario too.
1 Like
??? If, in a Jupyter notebook, you enclose a block of code inside an
if name == 'main':
statement, you get a
NameError.
NameError Traceback (most recent call last)
in
NameError: name ‘name’ is not defined
Hi Elfayoumi,
I’m also wondering about how to join several imageLists in mixedItemList. Is there a straightforward way to do that?
Thanks
1 Like