TypeError: classification_report() got an unexpected keyword argument 'target_names' about farm

[Timoeller]

Hey @guggio thanks for reporting the issue and also for finding the problematic code.

We already fixed this bug and are merging quite some changes into master this minute. Please update your farm installation with a git pull if you installed through "pip install --editable ." Please keep me updated if that resolved your issue. Thanks!

[guggio]

Hi @Timoeller thanks a lot for the fix! I can run my BIO-classification model now. However, there is an additional issues I faced:
My NERProcessor settings from above do not work anymore, since setting a dev_split != 0.0 throws the following error message:

/usr/local/lib/python3.6/dist-packages/farm/data_handler/data_silo.py in _get_dataset(self, filename)
     65                     dicts = random.shuffle(dicts)
     66 
---> 67         dict_batches_to_process = int(len(dicts) / self.multiprocessing_chunk_size)
     68         num_cpus = min(mp.cpu_count(), self.max_processes,  dict_batches_to_process) or 1
     69 
TypeError: object of type 'NoneType' has no len()

[Timoeller]

We changed the way the dev set is splitted away from train set, there might be some issues there that didn't come up during our test pipeline. I will look into that next.

There also seems to be a small mistake in the classification report. I am currently working on that, too.

[Timoeller]

Ok, we fixed the bug with the classification report.

The issue you reported on a dev_split != 0.0 seems to be coming from a error in _file_to_dicts() in processor.py file, because the dicts returned from that function is None.
In _file_to_dicts is a function called read_ner_file() that has quite some limitations on what it can read. Please have a look here to see examples of how the input format should look like.
Otherwise you could adjust the _file_to_dicts() and create a own data processor.

Maybe you can post the input format of your NER data, so we could help you out.

[guggio]

Thanks a lot for the bugfix and your help :) I had to write two textfiles based on my dataframe (probably not the most efficient solution).

I was able to run the model on my data and it seems like I am achieving very promising results.

Thanks again for your great work!

[guggio]

I guess the problem lies in the eval.py file. Since I am doing "per_token" classification, it takes the seqeval.metrics.classification_report which does not take target_names as input.

from seqeval.metrics import classification_report as token_classification_report
from sklearn.metrics import classification_report

...

if self.classification_report:
                if head.ph_output_type == "per_token":
                    report_fn = token_classification_report
                elif head.ph_output_type == "per_sequence":
                    report_fn = classification_report
                elif head.ph_output_type == "per_token_squad":
                    report_fn = lambda *args, **kwargs: "not Implemented"
                elif head.ph_output_type == "per_sequence_continuous":
                    report_fn = r2_score
                else:
                    raise NotImplementedError

                # CHANGE PARAMETERS, not all report_fn accept digits
                if head.ph_output_type == "per_sequence_continuous":
                    result["report"] = report_fn(
                        label_all[head_num], preds_all[head_num]
                    )
                else:
                    result["report"] = report_fn(
                        label_all[head_num], preds_all[head_num], digits=4, target_names=head.label_list)

[Timoeller]

Nice! If you have good results to share we are more than happy to celebrate with you : )