text stringlengths 1 93.6k |
|---|
train_feature = train_feature.toarray()
|
test_feature = test_feature.toarray()
|
elif method == "charlength":
|
train_feature = np.asarray(
|
[len(s) for s in train["processed_text"].values]
|
).reshape(-1, 1)
|
test_feature = np.asarray(
|
[len(s) for s in test["processed_text"].values]
|
).reshape(-1, 1)
|
elif method == "wordlength":
|
train_feature = np.asarray(
|
[len(s.split(" ")) for s in train["processed_text"].values]
|
).reshape(-1, 1)
|
test_feature = np.asarray(
|
[len(s.split(" ")) for s in test["processed_text"].values]
|
).reshape(-1, 1)
|
return train_feature, test_feature
|
def parse_arguments():
|
parser = argparse.ArgumentParser(description="Run classification for memorization.")
|
parser.add_argument(
|
"--train_split", type=float, default=0.8, help="Fraction for training"
|
)
|
parser.add_argument(
|
"--method",
|
type=str,
|
choices=["tfidf", "bow", "wordlength", "charlength", "combine",],
|
default="charlength",
|
help="Vectorization method",
|
)
|
parser.add_argument(
|
"--text_field",
|
type=str,
|
choices=[
|
"quiz",
|
"names",
|
"solution",
|
"solution_text",
|
"solution_text_format",
|
"cot_steps",
|
"cot_repeat_steps",
|
"statements",
|
"response",
|
"all_fields",
|
"state_quiz",
|
"state_quiz_resp",
|
"quiz_resp",
|
"state_resp",
|
],
|
default="quiz",
|
help="The field to featurize",
|
)
|
parser.add_argument(
|
"--input_file",
|
type=str,
|
default="",
|
help="Path to data jsonl file",
|
)
|
parser.add_argument(
|
"--output_dir", type=str, default="result/", help="Directory to save output CSV"
|
)
|
parser.add_argument("--no_balance_label", action="store_true")
|
return parser.parse_args()
|
def prepare_cls_data(df, train_split=0.8):
|
return train_test_split(
|
df,
|
test_size=1 - train_split,
|
stratify=df["label"],
|
random_state=42,
|
)
|
def train_and_evaluate(train_feature, test_feature, train_label, test_label):
|
model = LogisticRegression(random_state=42,max_iter=10000)
|
model.fit(train_feature, train_label)
|
train_pred = model.predict(train_feature)
|
test_pred = model.predict(test_feature)
|
# Predict probabilities instead of labels
|
train_probs = model.predict_proba(train_feature)
|
test_probs = model.predict_proba(test_feature)
|
evaluation= {
|
"train_accuracy": accuracy_score(train_label, train_pred),
|
"test_accuracy": accuracy_score(test_label, test_pred),
|
"train_auc": roc_auc_score(train_label, train_probs[:, 1]),
|
"test_auc":roc_auc_score(test_label, test_probs[:, 1]),
|
}
|
report= classification_report(test_label, test_pred,output_dict=True)
|
evaluation.update(report)
|
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