stackingNT 0.0.2

Stacking algorithm optimisation

Prostate_cancer_related_research

介绍

研究前列腺癌的低中高风险分层问题

对stacking算法进行优化

安装教程

1. pip install stackingNT

使用说明

1. 您需要传入您的base model pool、meta model

classifiers_ = {

    'RandomForest': RandomForestClassifier(random_state=random_seed, n_estimators=100, max_depth=None),

    'DecisionTree': DecisionTreeClassifier(random_state=random_seed),

    'XGBoost': XGBClassifier(reg_lambda=0.5,

                             max_depth=8,

                             learning_rate=0.93,

                             n_estimators=100, 

                             min_child_weight=1,

                             gamma=0.3,

                             # min_weight=5,

                             colsample_bytree=0.8,

                             verbosity=0,

                             num_class=len(n_classes),

                             objective='multi:softmax',

                             random_state=random_seed),

    # 'AdaBoost': AdaBoostClassifier(n_estimators=100, learning_rate=0.9, random_state=random_seed),

    'LogisticRegression':LogisticRegression(random_state=random_seed),

    'SVM': SVC(kernel='linear', C=1, probability=True, tol=1.e-4, random_state=random_seed),

}



base_models = classifiers_

meta_model = {'SVM': SVC(kernel='linear', C=1, probability=True, tol=1.e-4, random_state=random_seed)}

具体的传入模型过程如下：

from stackingNT import StackingNonlinearTransformations

meta_model = {'SVM': SVC(kernel='linear', C=1, probability=True, tol=1.e-4, random_state=random_seed)}

base_models = classifiers_

SNT = StackingNonlinearTransformations(base_models, meta_model)

2. SNT.fit()使用说明：

传入参数：

train_X=train_df_lassoCV, train_y=Y, test_X=test_df_lassoCV, test_y=Y_test,NT="relu"

train_X、train_y、test_X、test_y分别代表训练集和测试集。NT代表一种非线性变换的方法。（有关非线性变换具体介绍可参考论文：Predictions of Prostate Cancer Risk Stratification Based on A Non-Linear Transformation Stacking Learning Strategy）

具体fit函数调用如下所示：

train_pred, test_pred, train_pred_prob, test_pred_prob = SNT.fit(train_X=train_df_lassoCV, train_y=Y, test_X=test_df_lassoCV, test_y=Y_test,NT="relu")

train_pred：训练集预测值

test_pred：测试集预测值

train_pred_prob：训练集预测概率

test_pred_prob ：测试集预测概率

相关论文：

Predictions of Prostate Cancer Risk Stratification Based on A Non-Linear Transformation Stacking Learning Strategy