def log(self, file, batch, tag_predictions, segment_length_predictions):
unfold_predictions, unfold_ground_truth = self._process_predictions(
tag_predictions=tag_predictions,
segment_length_predictions=segment_length_predictions,
ground_truth=batch.ground_truth,
ground_truth_segmentation_length=batch.ground_truth_segmentation_length,
ground_truth_segment_length=batch.ground_truth_segment_length,
question_length=batch.questions_length
)
with open(file, "a") as f:
string = ""
for tt, ts, pt, ps, qid, cv, table_id, unfold_p, unfold_t in zip(
batch.ground_truth,
batch.ground_truth_segment_length,
tag_predictions,
segment_length_predictions,
batch.questions_ids,
batch.cell_value_length,
batch.table_map_ids,
unfold_predictions,
unfold_ground_truth
):
result = np.sum(np.abs(np.array(unfold_p) - np.array(unfold_t)), axis=-1)
string += "=======================\n"
string += ("id: " + str(qid) + "\n")
string += ("tid: " + str(table_id) + "\n")
string += ("max_column: " + str(len(cv)) + "\n")
string += ("max_cell_value_per_col: " + str(len(cv[0])) + "\n")
string += ("unfold_t: " + (', '.join([str(i) for i in unfold_t])) + "\n")
string += ("unfold_p: " + (', '.join([str(i) for i in unfold_p])) + "\n")
string += ("ts: " + (', '.join([str(i) for i in ts])) + "\n")
string += ("tt: " + (', '.join([str(i) for i in tt])) + "\n")
string += ("pt: " + (', '.join([str(i) for i in pt])) + "\n")
string += ("ps: " + (', '.join([str(i) for i in ps])) + "\n")
string += ("Result: " + str(result == 0) + "\n")
# string += ("s: " + str(scores) + "\n")
f.write(string)
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