Bagging_regression.py 文件源码

def featureImp(dataset1):
    import numpy as np
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.ensemble import ExtraTreesRegressor
    import collections

    #f = open('F:\kaggle\Final Project\\Book.txt')
    # f.readline()  # skip the header
    #dataset = np.loadtxt(fname=f, delimiter=',')
    # dataset = datasets.load_iris()
    # fit an Extra Trees model to the data
    # print(dataset)
    mapElement = {}
    X = dataset1[:, 1:406]
    Y = dataset1[:, 0]
    num_trees = 10
    max_feature = 7
    model = ExtraTreesRegressor(n_estimators=num_trees, max_features=max_feature)
    model.fit(X, Y)
    z = model.feature_importances_
    #print("first", z.item(0))

    for i in range(len(z)):
        mapElement[z.item(i)] = (i + 1)
    # od = collections.OrderedDict(sorted(mapElement.items()))
    p = sorted(mapElement)
    #print(p)
    result = []
    for i in range(len(p)):
        result.append(mapElement.get(p[(len(p) - 1) - i]))

    return (result)
    #print(result)
    # print(type(od))
    #print(mapElement)
    # print(od)
    # model.fit(dataset.data, dataset.target)
    # display the relative importance of each attribute
    #print(model.feature_importances_)