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cleanup

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This commit is contained in:
Peter Baumgartner 2022-09-07 10:30:58 -04:00
parent 64b57204e2
commit c09d99a069
1 changed files with 17 additions and 13 deletions
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spacy/cli/debug_data.py
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@ -676,6 +676,19 @@ def debug_data(
trees_train: Set[str] = gold_train_data["lemmatizer_trees"] trees_train: Set[str] = gold_train_data["lemmatizer_trees"]
trees_dev: Set[str] = gold_dev_data["lemmatizer_trees"] trees_dev: Set[str] = gold_dev_data["lemmatizer_trees"]
# This is necessary context if someone is attempting to interpret whether the
# number of trees exclusively in the dev set is meaningful.
msg.info(f"{len(trees_train)} lemmatizer trees generated from training data.")
msg.info(f"{len(trees_dev)} lemmatizer trees generated from dev data.")
dev_not_train = trees_dev - trees_train
if len(dev_not_train) != 0:
msg.warn(
f"{len(dev_not_train)} lemmatizer trees were found exclusively in the dev data."
)
else:
# Would we ever expect this case? It seems like it would be pretty rare.
msg.good("All trees in dev data present in training data.")
if gold_train_data["n_low_cardinality_lemmas"] > 0: if gold_train_data["n_low_cardinality_lemmas"] > 0:
msg.warn( msg.warn(
f"{gold_train_data['n_low_cardinality_lemmas']} docs with 1 or 0 unique lemmas." f"{gold_train_data['n_low_cardinality_lemmas']} docs with 1 or 0 unique lemmas."
@ -683,12 +696,6 @@ def debug_data(
else: else:
msg.good("Training docs have sufficient unique lemmas") msg.good("Training docs have sufficient unique lemmas")
train_not_dev = trees_train - trees_dev
if len(train_not_dev) != 0:
msg.warn(f"{len(train_not_dev)} labels were found only in the train data.")
else:
msg.good("Training data contains all lemmatizer trees in dev set.")
if gold_train_data["n_low_cardinality_lemmas"] > 0: if gold_train_data["n_low_cardinality_lemmas"] > 0:
msg.warn( msg.warn(
f"{gold_dev_data['n_low_cardinality_lemmas']} docs with 1 or 0 unique lemmas." f"{gold_dev_data['n_low_cardinality_lemmas']} docs with 1 or 0 unique lemmas."
@ -696,12 +703,6 @@ def debug_data(
else: else:
msg.good("Dev docs have sufficient unique lemmas") msg.good("Dev docs have sufficient unique lemmas")
dev_not_train = trees_dev - trees_train
if len(dev_not_train) != 0:
msg.warn(f"{len(dev_not_train)} labels were found only in the dev data.")
else:
msg.good("Trees in dev data present in training data.")
msg.divider("Summary") msg.divider("Summary")
good_counts = msg.counts[MESSAGES.GOOD] good_counts = msg.counts[MESSAGES.GOOD]
warn_counts = msg.counts[MESSAGES.WARN] warn_counts = msg.counts[MESSAGES.WARN]
@ -906,7 +907,10 @@ def _compile_gold(
tree_id = trees.add(token.text, token.lemma_) tree_id = trees.add(token.text, token.lemma_)
tree_str = trees.tree_to_str(tree_id) tree_str = trees.tree_to_str(tree_id)
data["lemmatizer_trees"].add(tree_str) data["lemmatizer_trees"].add(tree_str)
if len(lemma_set) < 2: # We want to identify cases where lemmas aren't assigned
# or are all assigned the same value, as this would indicate
# an issue since we're expecting a large set of lemmas
if len(lemma_set) < 2 and len(gold) > 1:
data["n_low_cardinality_lemmas"] += 1 data["n_low_cardinality_lemmas"] += 1
return data return data