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MetaCAT fixes and upgrades #495

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merged 6 commits into from
Oct 14, 2024
Merged

MetaCAT fixes and upgrades #495

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26642a4
MetaCAT fixes and upgrades
shubham-s-agarwal Oct 9, 2024
210741c
Pushing type fix
shubham-s-agarwal Oct 9, 2024
b9cdf03
Pushing for type fix
shubham-s-agarwal Oct 9, 2024
994a11a
Fixing type issues
shubham-s-agarwal Oct 9, 2024
3be0dc1
Pushing change
shubham-s-agarwal Oct 9, 2024
fa8d321
Pushing update w/o try except block
shubham-s-agarwal Oct 14, 2024
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17 changes: 0 additions & 17 deletions medcat/utils/meta_cat/data_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,23 +180,6 @@ def encode_category_values(data: Dict, existing_category_value2id: Optional[Dict
category_value2id = {}

category_values = set([x[2] for x in data])
# Ensuring that each label has data and checking for class imbalance

label_data = {key: 0 for key in category_value2id}
for i in range(len(data)):
if data[i][2] in category_value2id:
label_data[data[i][2]] = label_data[data[i][2]] + 1

# If a label has no data, changing the mapping
if 0 in label_data.values():
category_value2id_: Dict = {}
keys_ls = [key for key, value in category_value2id.items() if value != 0]
for k in keys_ls:
category_value2id_[k] = len(category_value2id_)

logger.warning("Labels found with 0 data; updates made\nFinal label encoding mapping: %s",category_value2id_)
category_value2id = category_value2id_

for c in category_values:
if c not in category_value2id:
category_value2id[c] = len(category_value2id)
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12 changes: 9 additions & 3 deletions medcat/utils/meta_cat/ml_utils.py
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Expand Up @@ -66,7 +66,7 @@ class label of the data

x = torch.tensor(x, dtype=torch.long).to(device)
# cpos = torch.tensor(cpos, dtype=torch.long).to(device)
attention_masks = (x != 0).type(torch.int)
attention_masks = (x != pad_id).type(torch.int)
return x, cpos, attention_masks, y


Expand Down Expand Up @@ -412,10 +412,16 @@ def eval_model(model: nn.Module, data: List, config: ConfigMetaCAT, tokenizer: T
precision, recall, f1, support = precision_recall_fscore_support(y_eval, predictions, average=score_average)

labels = [name for (name, _) in sorted(config.general['category_value2id'].items(), key=lambda x: x[1])]
labels_present_: set = set(predictions)
labels_present: List[str] = [str(x) for x in labels_present_]

if len(labels) != len(labels_present):
logger.warning(
"The evaluation dataset does not contain all the labels, some labels are missing. Performance displayed for labels found...")
confusion = pd.DataFrame(
data=confusion_matrix(y_eval, predictions, ),
columns=["true " + label for label in labels],
index=["predicted " + label for label in labels],
columns=["true " + label for label in labels_present],
index=["predicted " + label for label in labels_present],
)

examples: Dict = {'FP': {}, 'FN': {}, 'TP': {}}
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