VUVclassification.py 文件源码

def rf_cv(fv_train,target_train,fv_test,target_test):

    ####---- cross validation of train dataset, gridsearch the best parameters for random forest

    # Set the parameters by cross-validation
    tuned_parameters = {'n_estimators': [1000, 2000],
                        "max_depth": [3, 6, 9, None],
                        "max_features": ["auto","log2",None],
                        "class_weight": [None, 'balanced']}

    scores = ['recall_macro']

    n_iter_search   = 20

    for score in scores:
        print("# Tuning hyper-parameters for %s" % score)
        print()

        mycv = StratifiedKFold(target_train, n_folds = 5)

        clf = RandomizedSearchCV(RandomForestClassifier(n_jobs=-1), tuned_parameters, cv=mycv, n_iter=n_iter_search,
                           scoring='%s' % score)

        clf.fit(fv_train, target_train)

        report_cv(clf,fv_test,target_test)