التحزيم والتعزيز والتكديس

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score

def stack_oof(models, X_train, y_train, X_test, n_folds=class="tok-num">5):
    class="tok-str">"""Returns OOF predictions + test predictions for all models."""
    skf = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=class="tok-num">42)
    oof_preds = np.zeros((len(X_train), len(models)))
    test_preds = np.zeros((len(X_test), len(models)))

    for m_idx, model in enumerate(models):
        fold_test_preds = np.zeros((len(X_test), n_folds))
        for f_idx, (tr, val) in enumerate(skf.split(X_train, y_train)):
            model.fit(X_train[tr], y_train[tr])
            oof_preds[val, m_idx] = model.predict_proba(X_train[val])[:,class="tok-num">1]
            fold_test_preds[:, f_idx] = model.predict_proba(X_test)[:,class="tok-num">1]
        test_preds[:, m_idx] = fold_test_preds.mean(axis=class="tok-num">1)
        print(fclass="tok-str">"Model {m_idx} OOF AUC: {roc_auc_score(y_train, oof_preds[:,m_idx]):.4f}")

    class="tok-comment"># Meta-learner on OOF predictions
    meta = LogisticRegression(C=class="tok-num">0.1)
    meta.fit(oof_preds, y_train)
    final_preds = meta.predict_proba(test_preds)[:,class="tok-num">1]
    return final_preds, meta.coef_