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fix: update train.py to fix a bug in ensemble methods #283

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Sep 20, 2024
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fix: update train.py to fix a bug in ensemble methods #283

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2 changes: 1 addition & 1 deletion rdagent/scenarios/kaggle/experiment/meta_tpl/model/model_xgb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_v
params = {
"nthred": -1,
}
num_round = 200
num_round = 100

evallist = [(dtrain, "train"), (dvalid, "eval")]
bst = xgb.train(params, dtrain, num_round, evallist)
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12 changes: 4 additions & 8 deletions rdagent/scenarios/kaggle/experiment/meta_tpl/train.py
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Expand Up @@ -102,18 +102,14 @@ def import_module_from_path(module_name, module_path):
pd.Series(data=[mcc], index=["MCC"]).to_csv("submission_score.csv")

# 7) Make predictions on the test set and save them
label_encoder = LabelEncoder()
label_encoder.fit(y_train)
y_test_pred_bool_l = []
y_test_pred_l = []
for m, m_pred in model_l:
y_test_pred_bool_l.append(
m_pred(m, X_test).astype(int)
) # TODO Make this an ensemble. Currently it uses the last prediction
y_test_pred_l.append(m_pred(m, X_test)) # TODO Make this an ensemble. Currently it uses the last prediction

y_test_pred = np.mean(y_test_pred_bool_l, axis=0)
y_test_pred = np.mean(y_test_pred_l, axis=0)
y_test_pred = (y_test_pred > 0.5).astype(int)

y_test_pred_labels = label_encoder.inverse_transform(y_test_pred) # 将整数转换回 'e' 或 'p'
y_test_pred_labels = np.where(y_test_pred == 1, "p", "e") # 将整数转换回 'e' 或 'p'

submission_result = pd.DataFrame({"id": passenger_ids, "class": y_test_pred_labels})

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