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Below are the error messages:
Environment: Python 3.7
ValueError Traceback (most recent call last)
/tmp/ipykernel_5240/2454607948.py in
35 # print('starting active labeling...')
36
---> 37 dedupe.convenience.console_label(gazetteer)
38
39 gazetteer.train()
~/anaconda3/envs/conda_python37_env/lib/python3.7/site-packages/dedupe/convenience.py in console_label(deduper)
148 try:
149 if not unlabeled:
--> 150 unlabeled = deduper.uncertain_pairs()
151
152 record_pair = unlabeled.pop()
~/anaconda3/envs/conda_python37_env/lib/python3.7/site-packages/dedupe/api.py in uncertain_pairs(self)
1166 self.active_learner is not None
1167 ), "Please initialize with the prepare_training method"
-> 1168 return [self.active_learner.pop()]
1169
1170 def mark_pairs(self, labeled_pairs: TrainingData) -> None:
~/anaconda3/envs/conda_python37_env/lib/python3.7/site-packages/dedupe/labeler.py in pop(self)
329
330 prob_l = [learner.candidate_scores() for learner in self._learners]
--> 331 probs = numpy.concatenate(prob_l, axis=1)
332
333 # where do the classifers disagree?
<array_function internals> in concatenate(*args, **kwargs)
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 4998 and the array at index 1 has size 4996
The text was updated successfully, but these errors were encountered:
I had the same error while running the record_linkage_example on Python 3.10.6.
The error appears when responding to the second question in the active learning phase.
The Traceback is:
Traceback (most recent call last):
File "/mnt/74225F01225EC82E/Archivos/FIUBA/Trabajo Profesional/dedupe_examples_[exclude]/record_linkage_example/record_linkage_example.py", line 140, in <module>
dedupe.console_label(linker)
File "/mnt/74225F01225EC82E/Archivos/FIUBA/Trabajo Profesional/dedupe_examples_[exclude]/record_linkage_example/venv/lib/python3.10/site-packages/dedupe/convenience.py", line 150, in console_label
unlabeled = deduper.uncertain_pairs()
File "/mnt/74225F01225EC82E/Archivos/FIUBA/Trabajo Profesional/dedupe_examples_[exclude]/record_linkage_example/venv/lib/python3.10/site-packages/dedupe/api.py", line 1168, in uncertain_pairs
return [self.active_learner.pop()]
File "/mnt/74225F01225EC82E/Archivos/FIUBA/Trabajo Profesional/dedupe_examples_[exclude]/record_linkage_example/venv/lib/python3.10/site-packages/dedupe/labeler.py", line 331, in pop
probs = numpy.concatenate(prob_l, axis=1)
File "<__array_function__ internals>", line 180, in concatenate
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 4998 and the array at index 1 has size 4996
Below are the error messages:
Environment: Python 3.7
ValueError Traceback (most recent call last)
/tmp/ipykernel_5240/2454607948.py in
35 # print('starting active labeling...')
36
---> 37 dedupe.convenience.console_label(gazetteer)
38
39 gazetteer.train()
~/anaconda3/envs/conda_python37_env/lib/python3.7/site-packages/dedupe/convenience.py in console_label(deduper)
148 try:
149 if not unlabeled:
--> 150 unlabeled = deduper.uncertain_pairs()
151
152 record_pair = unlabeled.pop()
~/anaconda3/envs/conda_python37_env/lib/python3.7/site-packages/dedupe/api.py in uncertain_pairs(self)
1166 self.active_learner is not None
1167 ), "Please initialize with the prepare_training method"
-> 1168 return [self.active_learner.pop()]
1169
1170 def mark_pairs(self, labeled_pairs: TrainingData) -> None:
~/anaconda3/envs/conda_python37_env/lib/python3.7/site-packages/dedupe/labeler.py in pop(self)
329
330 prob_l = [learner.candidate_scores() for learner in self._learners]
--> 331 probs = numpy.concatenate(prob_l, axis=1)
332
333 # where do the classifers disagree?
<array_function internals> in concatenate(*args, **kwargs)
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 4998 and the array at index 1 has size 4996
The text was updated successfully, but these errors were encountered: