ScikitLearners.py 文件源码

def predictKFoldRandomForest(X, y, estimators=10, criterion="gini", maxdepth=None, selectKBest=0, kfold=10):
    """
    Classifies the data using decision trees and k-fold CV
    :param X: The matrix of feature vectors
    :type X: list
    :param y: The vector containing labels corresponding to the feature vectors
    :type y: list
    :param estimators: The number of random trees to use in classification
    :type estimators: int
    :param criterion: The splitting criterion employed by the decision tree
    :type criterion: str
    :param splitter: The method used to split the data
    :type splitter: str
    :param maxDepth: The maximum depth the tree is allowed to grow
    :type maxDepth: int
    :param selectKBest: The number of best features to select
    :type selectKBest: int
    :param kfold: The number of folds to use in K-fold CV
    :type kfold: int
    :return: A list of predicted labels across the k-folds
    """
    try:
        # Prepare data
        X, y = numpy.array(X), numpy.array(y)
        # Define classifier
        clf = ensemble.RandomForestClassifier(n_estimators=estimators, criterion=criterion, max_depth=maxdepth)
        X_new = SelectKBest(chi2, k=selectKBest).fit_transform(X, y) if selectKBest > 0 else X
        predicted = cross_val_predict(clf, X_new, y, cv=kfold).tolist()
    except Exception as e:
        prettyPrintError(e)
        return []

    return predicted