| import pandas as pd, numpy as np |
| from sklearn.model_selection import StratifiedKFold |
| from sklearn.metrics import roc_auc_score |
| import lightgbm as lgb |
|
|
| train = pd.read_csv('train.csv') |
| test = pd.read_csv('test.csv') |
|
|
| target = 'addicted_label' |
| cat_cols = ['gender','stress_level','academic_work_impact'] |
| num_cols = [c for c in train.columns if c not in cat_cols+['id',target]] |
|
|
| for c in cat_cols: |
| train[c] = train[c].astype('category') |
| test[c] = test[c].astype('category') |
| |
| cats = train[c].cat.categories |
| test[c] = test[c].cat.set_categories(cats) |
|
|
| X = train[num_cols+cat_cols] |
| y = train[target] |
| X_test = test[num_cols+cat_cols] |
|
|
| skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=42) |
| oof = np.zeros(len(X)) |
| preds = np.zeros(len(X_test)) |
| aucs=[] |
|
|
| params = dict( |
| objective='binary', |
| metric='auc', |
| learning_rate=0.03, |
| num_leaves=63, |
| max_depth=-1, |
| min_child_samples=30, |
| subsample=0.8, |
| colsample_bytree=0.8, |
| reg_alpha=0.1, |
| reg_lambda=0.1, |
| n_estimators=800, |
| verbosity=-1 |
| ) |
|
|
| for fold,(tr_idx,va_idx) in enumerate(skf.split(X,y)): |
| X_tr,X_va = X.iloc[tr_idx], X.iloc[va_idx] |
| y_tr,y_va = y.iloc[tr_idx], y.iloc[va_idx] |
| model = lgb.LGBMClassifier(**params) |
| model.fit(X_tr,y_tr, eval_set=[(X_va,y_va)], |
| callbacks=[lgb.early_stopping(100, verbose=False)], |
| categorical_feature=cat_cols) |
| va_pred = model.predict_proba(X_va)[:,1] |
| oof[va_idx] = va_pred |
| auc = roc_auc_score(y_va, va_pred) |
| aucs.append(auc) |
| print(f"Fold {fold} AUC: {auc:.5f}") |
| preds += model.predict_proba(X_test)[:,1] / skf.n_splits |
|
|
| print("Mean AUC:", np.mean(aucs)) |
| print("OOF AUC:", roc_auc_score(y, oof)) |
|
|
| sub = pd.DataFrame({'id': test['id'], 'addicted_label': preds}) |
| sub.to_csv('submission.csv', index=False) |
| print(sub.head()) |
|
|
