From af05060b632c8ca37b2679313f1bff242e0fade4 Mon Sep 17 00:00:00 2001
From: Franziska Oschmann
<franziskaoschmann@staff-net-oct-dock-1-a-dhcp-100.intern.ethz.ch>
Date: Thu, 13 Jul 2023 15:12:47 +0200
Subject: [PATCH] Adjust averaging of precision, recall, f1
---
moderation_classifier/eval_BERT.py | 10 +++++-----
moderation_classifier/eval_MNB.py | 5 +++--
2 files changed, 8 insertions(+), 7 deletions(-)
diff --git a/moderation_classifier/eval_BERT.py b/moderation_classifier/eval_BERT.py
index b72a9d5..3868d7f 100644
--- a/moderation_classifier/eval_BERT.py
+++ b/moderation_classifier/eval_BERT.py
@@ -49,16 +49,16 @@ def main(train_logs: Union[str, os.PathLike]):
text_proc = tp.fit_transform(df_de.text)
df_de.text = text_proc
comon_topics = tl.get_comments_per_topic(df_de)
-
+
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("bert-base-german-cased")
model = TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=path_model)
# Split text into batches
text_list = list(df_de.text.values)
- n = 500
+ n = 100
results=[text_list[idx:idx+n] for idx in range(0, len(text_list), n)]
-
+ import pdb; pdb.set_trace()
# eval all
y_pred_all = []
for batch in tqdm(results):
@@ -70,7 +70,7 @@ def main(train_logs: Union[str, os.PathLike]):
y_pred_all = np.concatenate(y_pred_all)
precision, recall, f1, _ = precision_recall_fscore_support(
- df_de.label, y_pred_all, average="weighted"
+ df_de.label, y_pred_all, average="binary", pos_label=0
)
accuracy = accuracy_score(df_de.label, y_pred_all)
@@ -89,7 +89,7 @@ def main(train_logs: Union[str, os.PathLike]):
y_pred_t = y_pred_all[df_de.topic == t]
precision, recall, f1, _ = precision_recall_fscore_support(
- y_test_t, y_pred_t, average="weighted"
+ y_test_t, y_pred_t, average="binary", pos_label=0
)
accuracy = accuracy_score(y_test_t, y_pred_t)
results_t[t] = dict()
diff --git a/moderation_classifier/eval_MNB.py b/moderation_classifier/eval_MNB.py
index 7867c33..622e12d 100644
--- a/moderation_classifier/eval_MNB.py
+++ b/moderation_classifier/eval_MNB.py
@@ -45,6 +45,7 @@ def main(train_logs: Union[str, os.PathLike]):
remove_duplicates=False,
min_num_words=3,
)
+
X_test = df_test.text
y_test = df_test.label
@@ -53,7 +54,7 @@ def main(train_logs: Union[str, os.PathLike]):
y_pred_t = pipe.predict(X_test)
precision, recall, f1, _ = precision_recall_fscore_support(
- y_test, y_pred, average="weighted"
+ y_test, y_pred, average='weighted'
)
accuracy = pipe.score(X_test, y_test)
@@ -76,7 +77,7 @@ def main(train_logs: Union[str, os.PathLike]):
y_pred_t = pipe.predict(X_test_t)
precision, recall, f1, _ = precision_recall_fscore_support(
- y_test_t, y_pred_t, average="weighted"
+ y_test_t, y_pred_t, average='weighted'
)
#f1 = f1_score(y_test_t, y_pred_t)
accuracy = pipe.score(X_test_t, y_test_t)
--
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