[机器学习]Cascade结构 多粒度级联森林(GCForest)的简单使用

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下载gcforest

github下载地址

介绍gcforest算法原理的文章在网上有很多，这里不再赘述。

简单代码实现

把下载好的gcforest文件夹放在自己的项目里或者直接放到lib里，然后代码如下：

import numpy as np
from gcforest.gcforest import GCForest
from sklearn.datasets import load_iris, load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

iris = []
data = []

def get_toy_config():
    config = {}
    ca_config = {}
    ca_config["random_state"] = 0
    ca_config["max_layers"] = 100 #最大的层数，layer对应论文中的level
    ca_config["early_stopping_rounds"] = 3 #如果出现某层的三层以内的准确率都没有提升，层中止
    ca_config["n_classes"] = 3 # 类别数
    ca_config["estimators"] = []
    ca_config["estimators"].append(
            {"n_folds": 5, "type": "XGBClassifier", "n_estimators": 10, "max_depth": 5,
             "objective": "multi:softprob", "silent": True, "nthread": -1, "learning_rate": 0.1} )
    ca_config["estimators"].append({"n_folds": 5, "type": "RandomForestClassifier", "n_estimators": 10, "max_depth": None, "n_jobs": -1})
    ca_config["estimators"].append({"n_folds": 5, "type": "ExtraTreesClassifier", "n_estimators": 10, "max_depth": None, "n_jobs": -1})
    ca_config["estimators"].append({"n_folds": 5, "type": "LogisticRegression"})
    config["cascade"] = ca_config
    return config


# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    iris = load_iris()
    X = iris.data
    y = iris.target
    # print(y)
    X_train, X_test, y_train, y_truth = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=111,
                                                         stratify=y)
    model = GCForest(get_toy_config())  ##构建模型
    model.fit_transform(X_train, y_train)  ##训练
    y_predict = model.predict(np.array(X_test))  ##预测
    # joblib.dump(model, 'irisModel.sav')  ##保存模型
    print(y_predict)
    print("accuracy:", accuracy_score(y_truth, y_predict))

注意！ 针对不同的数据集要修改ca_config["n_classes"] = 3 # 类别数 的参数为对应类别数，否则会报AssertionError错误

运行结果