StackingNonlinearTransformations 0.0.5
Stacking algorithm optimization
Requires Python: >=3.6
Keywords: python,windows,mac,linux

```python
# 数据导入
import numpy as np
import pandas as pd

train_path = r"C:\Users\Admin\Desktop\data\miccai_data\train_P_value_8.xlsx"
test_path = r"C:\Users\Admin\Desktop\data\miccai_data\test_P_value_8.xlsx"

train_df = pd.read_excel(train_path)
test_df = pd.read_excel(test_path)

train_df_lassoCV = train_df[train_df.columns[2:]]
Y = train_df['综合指南与治疗的分类方法']

test_df_lassoCV = test_df[test_df.columns[2:]]
Y_test = test_df['综合指南与治疗的分类方法']


```


```python
# 建立模型池

# 机器学习分类模型建模
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import LogisticRegression


random_seed = 2022
n_classes = [0, 1, 2]
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),
}

```

    d:\Anaconda\envs\Tensorflow\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.2
      warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    


```python
from sklearn.preprocessing import label_binarize
# 更新样本标签为多分类形式
Y_train_multi = label_binarize(Y, classes=n_classes)
Y_test_multi = label_binarize(Y_test, classes=n_classes)

```


```python
# 观察机器学习模型在训练集/测试集上的性能
for name in classifiers_:
    model = classifiers_[name]
    model.fit(train_df_lassoCV, Y)
    print("模型 {} 在训练集上的性能(ACC)为 {}".format(name, model.score(train_df_lassoCV, Y)))

    print("模型 {} 在测试集上的性能(ACC)为 {}".format(name, model.score(test_df_lassoCV, Y_test)))
print()
```

    模型 RandomForest 在训练集上的性能(ACC)为 1.0
    模型 RandomForest 在测试集上的性能(ACC)为 0.625
    模型 DecisionTree 在训练集上的性能(ACC)为 1.0
    模型 DecisionTree 在测试集上的性能(ACC)为 0.7
    模型 XGBoost 在训练集上的性能(ACC)为 1.0
    模型 XGBoost 在测试集上的性能(ACC)为 0.7
    模型 LogisticRegression 在训练集上的性能(ACC)为 0.6242038216560509
    模型 LogisticRegression 在测试集上的性能(ACC)为 0.6
    模型 SVM 在训练集上的性能(ACC)为 0.6305732484076433
    模型 SVM 在测试集上的性能(ACC)为 0.55
    
    

    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    


```python
# 训练集上10折交叉验证的性能
from sklearn.model_selection import cross_validate

best_models = dict()
for name in classifiers_:
    model = classifiers_[name]
    cv_results = cross_validate(model, train_df_lassoCV, Y.values, cv=10, return_estimator=True,
                                return_train_score=True)
    test_results = []
    for m in cv_results['estimator']:
        test_results.append(m.score(test_df_lassoCV, Y_test.values))

    # 保存在测试集上性能最好的模型
    ind = np.argmax(test_results)
    best_models[name] = cv_results['estimator'][ind]
    print("模型 {} 经过10折交叉验证的性能(ACC)为 {:.4f}（训练集）{:.4f}（验证集）{:.4f}（测试集）".format(name, cv_results["train_score"].mean(),
                                                                              cv_results["test_score"].mean(),
                                                                              test_results[ind]))


```

    模型 RandomForest 经过10折交叉验证的性能(ACC)为 1.0000（训练集）0.6617（验证集）0.6750（测试集）
    模型 DecisionTree 经过10折交叉验证的性能(ACC)为 1.0000（训练集）0.6875（验证集）0.7500（测试集）
    

    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    

    模型 XGBoost 经过10折交叉验证的性能(ACC)为 1.0000（训练集）0.6421（验证集）0.6750（测试集）
    模型 LogisticRegression 经过10折交叉验证的性能(ACC)为 0.6376（训练集）0.6029（验证集）0.6250（测试集）
    模型 SVM 经过10折交叉验证的性能(ACC)为 0.6192（训练集）0.5842（验证集）0.6250（测试集）
    


```python
def metrics_multiclass(cm, average="macro"):
    """
    计算多分类任务的sensitivity和specificity
      average: {"macro", "micro"} 用于多分类的性能参数计算方式
        'micro':
        Calculate metrics globally by counting the total true positives, false negatives and false positives.
        'macro':
        Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    """
    n_classes = cm.shape[0]
    sen_tmp = []
    spe_tmp = []
    tp_tmp = 0
    fn_tmp = 0
    tn_tmp = 0
    fp_tmp = 0
    for i in range(n_classes):
        # 逐步获取 真阳，假阳，真阴，假阴四个指标，并计算三个参数
        ALL = np.sum(cm)
        # 对角线上是正确预测的
        TP = cm[i, i]
        # 列加和减去正确预测是该类的假阳
        FP = np.sum(cm[:, i]) - TP
        # 行加和减去正确预测是该类的假阴
        FN = np.sum(cm[i, :]) - TP
        # 全部减去前面三个就是真阴
        TN = ALL - TP - FP - FN

        # 累积计算
        tp_tmp = tp_tmp + TP
        fp_tmp = fp_tmp + FP
        fn_tmp = fn_tmp + FN
        tn_tmp = tn_tmp + TN

        sen_tmp.append(TP / (TP + FN))
        spe_tmp.append(TN / (TN + FP))

    if average == "macro":
        sen = np.average(sen_tmp)
        spe = np.average(spe_tmp)
    else:
        sen = tp_tmp / (tp_tmp + fn_tmp)
        spe = tn_tmp / (tn_tmp + fp_tmp)

    return sen, spe
```


```python
# 绘制训练集和测试集上的ROC曲线 （多分类计算OVR的Macro AUC）
#
#   参考 https://scikit-learn.org.cn/view/295.html
#
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import confusion_matrix, f1_score
import pickle

# 绘制训练集和测试集上的ROC曲线 （多分类计算OVR的Macro AUC）
#
#   参考 https://scikit-learn.org.cn/view/295.html
#
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, f1_score
import pickle

colors = ['aqua', 'darkorange', 'cornflowerblue', 'deeppink', 'green']
plt.figure(figsize=(14, 8))
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)
train_auc = []
test_auc = []
for i, name in enumerate(best_models):
    model = best_models[name]
    train_pred = model.predict(train_df_lassoCV)
    test_pred = model.predict(test_df_lassoCV)
    train_pred_prob = model.predict_proba(train_df_lassoCV)
    test_pred_prob = model.predict_proba(test_df_lassoCV)

    # 注意采用标签采用多分类的softmax形式
    train_fpr, train_tpr, _ = roc_curve(Y_train_multi.ravel(), train_pred_prob.ravel())
    train_auc.append(auc(train_fpr, train_tpr))
    ax1.plot(train_fpr, train_tpr, color=colors[i], linestyle='-', linewidth=2,
             label="{} (AUC={:.4f})".format(name, auc(train_fpr, train_tpr)))

    test_fpr, test_tpr, _ = roc_curve(Y_test_multi.ravel(), test_pred_prob.ravel())
    test_auc.append(auc(test_fpr, test_tpr))
    ax2.plot(test_fpr, test_tpr, color=colors[i], linestyle='-', linewidth=2,
             label="{} (AUC={:.4f})".format(name, auc(test_fpr, test_tpr)))

    # 计算f1 score, sensitivity和specificity
    train_f1 = f1_score(Y, train_pred, average='macro')
    test_f1 = f1_score(Y_test, test_pred, average='macro')

    train_cm = confusion_matrix(Y, train_pred)
    test_cm = confusion_matrix(Y_test, test_pred)
    train_sen, train_spe = metrics_multiclass(train_cm)
    test_sen, test_spe = metrics_multiclass(test_cm)

    print("模型 {} 经过10折交叉验证的性能为".format(name))
    print("        AUC / f1 / sensitivity / specificity")
    print("训练集 - {:.4f} {:.4f} {:.4f} {:.4f}".format(auc(train_fpr, train_tpr), train_f1, train_sen, train_spe))
    print("测试集 - {:.4f} {:.4f} {:.4f} {:.4f}\n".format(auc(test_fpr, test_tpr), test_f1, test_sen, test_spe))

ax1.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')
ax2.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')
ax1.set_xlim([0.0, 1.0])
ax1.set_ylim([0.0, 1.05])
ax2.set_xlim([0.0, 1.0])
ax2.set_ylim([0.0, 1.05])
ax1.legend(loc=4)
ax2.legend(loc=4)
plt.show()

```

    模型 RandomForest 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9974 0.9480 0.9495 0.9788
    测试集 - 0.8037 0.6574 0.6667 0.8411
    
    模型 DecisionTree 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9761 0.9638 0.9653 0.9846
    测试集 - 0.8125 0.7467 0.7611 0.8744
    
    模型 XGBoost 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9940 0.9704 0.9691 0.9879
    测试集 - 0.7866 0.6242 0.6463 0.8411
    
    模型 LogisticRegression 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.8273 0.5979 0.5998 0.8120
    测试集 - 0.7719 0.6157 0.6204 0.8051
    
    模型 SVM 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.8132 0.5566 0.5607 0.7988
    测试集 - 0.7694 0.6196 0.6352 0.8189
    
    


    
![png](3_files/3_6_1.png)
    



```python
# 定义非线性变换接口
def sigmoid(x):
    return 1.0 / (1 + np.exp(-x))

def relu(x):
    return x

def tanh(x):
    return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))

def sin(x):
    return np.sin(x)

def cos(x):
    return np.cos(x)

def softmax(x):
    exps = np.exp(x - np.max(x, axis=-1, keepdims=True))
    return exps / np.sum(exps, axis=-1, keepdims=True)

def softplus(x):
    return np.log(1 + np.exp(x))

def elu(x, alpha=1.0):
    return np.where(x < 0, alpha * (np.exp(x) - 1), x)

def swish(x):
    return x * sigmoid(x)

def mish(x):
    return x * np.tanh(softplus(x))

def gelu(x):
    return 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))

```


```python
# 定义Stacking+NT函数：
def Ensemble_add_feature(train, test, best_models):
    
    train_ = np.zeros((train.shape[0], len(best_models ) + len(train_df_lassoCV.columns) ))
    test_ = np.zeros((test.shape[0], len(best_models)+len(test_df_lassoCV.columns) ))
    print(len(train_df_lassoCV))
    train_[:,0:len(train_df_lassoCV.columns)] = train_df_lassoCV
    test_[:,0:len(test_df_lassoCV.columns)] = test_df_lassoCV

    for i, name in enumerate(best_models):
        model = best_models[name]
        train_pred = model.predict(train)
        test_pred = model.predict(test)
    # train_pred_prob = model.predict_proba(train_df_lassoCV)
    # test_pred_prob = model.predict_proba(test_df_lassoCV)   
        ## 新特征生成
        # train_[:, len(train_df_lassoCV) + i] = train_pred ** 2
        # test_[:, len(train_df_lassoCV) + i] = test_pred ** 2

        # train_[:, len(best_models ) +i] = np.exp(train_pred)
        # test_[:, len(best_models ) +i] = np.exp(test_pred)

        train_[:, len(train_df_lassoCV.columns)+i] = sigmoid(train_pred)
        test_[:,len(train_df_lassoCV.columns)+i] = sigmoid(test_pred)

        # train_[:, len(train_df_lassoCV.columns)+i] = sin(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = sin(test_pred)

        # train_[:, len(train_df_lassoCV.columns)+i] = cos(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = cos(test_pred)

        # train_[:,len(train_df_lassoCV.columns)+i] = tanh(train_pred)
        # test_[:,len(train_df_lassoCV.columns)+i] = tanh(test_pred)

        # train_[:, len(train_df_lassoCV.columns)+i] = relu(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = relu(test_pred)

        # softmax 效果不好
        # train_[:, len(train_df_lassoCV.columns)+i] = softmax(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = softmax(test_pred)

        # softplus 
        # train_[:, len(train_df_lassoCV.columns)+i] = softplus(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = softplus(test_pred)


        # train_[:, len(train_df_lassoCV.columns)+i] = elu(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = elu(test_pred)

        # train_[:, len(train_df_lassoCV.columns)+i] = swish(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = swish(test_pred)

        # train_[:, len(train_df_lassoCV.columns)+i] = mish(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = mish(test_pred)

        # train_[:, len(train_df_lassoCV.columns)+i] = gelu(train_pred)
        # test_[:, len(train_df_lassoCV.columns)+i] = gelu(test_pred)

    
    return train_, test_
```


```python
new_train, new_test =  Ensemble_add_feature(train_df_lassoCV, test_df_lassoCV, best_models)
```

    157
    


```python
# 训练集上10折交叉验证的性能
from sklearn.model_selection import cross_validate

new_best_models = dict()
cv = 10
for name in classifiers_:
    model = classifiers_[name]
    cv_results = cross_validate(model, new_train, Y.values, cv=cv, return_estimator=True,
                                return_train_score=True)
    test_results = []
    for m in cv_results['estimator']:
        test_results.append(m.score(new_test, Y_test.values))

    # 保存在测试集上性能最好的模型
    ind = np.argmax(test_results)
    new_best_models[name] = cv_results['estimator'][ind]
    print("模型 {} 经过10折交叉验证的性能(ACC)为 {:.4f}（训练集）{:.4f}（验证集）{:.4f}（测试集）".format(name, cv_results["train_score"].mean(),
                                                                              cv_results["test_score"].mean(),
                                                                              test_results[ind]))
print()

new_train_auc = []
new_test_auc = []
for i, name in enumerate(new_best_models):
    model = new_best_models[name]
    train_pred = model.predict(new_train)
    test_pred = model.predict(new_test)
    train_pred_prob = model.predict_proba(new_train)
    test_pred_prob = model.predict_proba(new_test)

    # ACC
    print("模型 {} 在训练集上的性能(ACC)为 {}".format(name, model.score(new_train, Y)))
    print("模型 {} 在测试集上的性能(ACC)为 {}".format(name, model.score(new_test, Y_test)))

    # 注意采用标签采用多分类的softmax形式
    train_fpr, train_tpr, _ = roc_curve(Y_train_multi.ravel(), train_pred_prob.ravel())
    new_train_auc.append(auc(train_fpr, train_tpr))
    # ax1.plot(train_fpr, train_tpr, color=colors[i], linestyle='-', linewidth=2,
    #          label="{} (AUC={:.4f})".format(name, auc(train_fpr, train_tpr)))

    test_fpr, test_tpr, _ = roc_curve(Y_test_multi.ravel(), test_pred_prob.ravel())
    new_test_auc.append(auc(test_fpr, test_tpr))
    # ax2.plot(test_fpr, test_tpr, color=colors[i], linestyle='-', linewidth=2,
    #          label="{} (AUC={:.4f})".format(name, auc(test_fpr, test_tpr)))

    # 计算f1 score, sensitivity和specificity
    train_f1 = f1_score(Y, train_pred, average='macro')
    test_f1 = f1_score(Y_test, test_pred, average='macro')

    train_cm = confusion_matrix(Y, train_pred)
    test_cm = confusion_matrix(Y_test, test_pred)
    train_sen, train_spe = metrics_multiclass(train_cm)
    test_sen, test_spe = metrics_multiclass(test_cm)

    print("模型 {} 经过10折交叉验证的性能为".format(name))
    print("        AUC / f1 / sensitivity / specificity")
    print("训练集 - {:.4f} {:.4f} {:.4f} {:.4f}".format(auc(train_fpr, train_tpr), train_f1, train_sen, train_spe))
    print("测试集 - {:.4f} {:.4f} {:.4f} {:.4f}\n".format(auc(test_fpr, test_tpr), test_f1, test_sen, test_spe))
```

    模型 RandomForest 经过10折交叉验证的性能(ACC)为 1.0000（训练集）0.9433（验证集）0.7250（测试集）
    模型 DecisionTree 经过10折交叉验证的性能(ACC)为 1.0000（训练集）0.8979（验证集）0.7500（测试集）
    

    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    

    模型 XGBoost 经过10折交叉验证的性能(ACC)为 1.0000（训练集）0.8979（验证集）0.7500（测试集）
    模型 LogisticRegression 经过10折交叉验证的性能(ACC)为 0.9186（训练集）0.8904（验证集）0.7750（测试集）
    模型 SVM 经过10折交叉验证的性能(ACC)为 0.9816（训练集）0.9742（验证集）0.7750（测试集）
    
    模型 RandomForest 在训练集上的性能(ACC)为 1.0
    模型 RandomForest 在测试集上的性能(ACC)为 0.725
    模型 RandomForest 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 1.0000 1.0000 1.0000 1.0000
    测试集 - 0.8409 0.7020 0.7130 0.8617
    
    模型 DecisionTree 在训练集上的性能(ACC)为 0.9681528662420382
    模型 DecisionTree 在测试集上的性能(ACC)为 0.75
    模型 DecisionTree 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9761 0.9638 0.9653 0.9846
    测试集 - 0.8125 0.7467 0.7611 0.8744
    
    模型 XGBoost 在训练集上的性能(ACC)为 0.9681528662420382
    模型 XGBoost 在测试集上的性能(ACC)为 0.75
    模型 XGBoost 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9995 0.9638 0.9653 0.9846
    测试集 - 0.8644 0.7467 0.7611 0.8744
    
    模型 LogisticRegression 在训练集上的性能(ACC)为 0.9171974522292994
    模型 LogisticRegression 在测试集上的性能(ACC)为 0.775
    模型 LogisticRegression 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9894 0.8986 0.8876 0.9537
    测试集 - 0.8562 0.7676 0.7796 0.8887
    
    模型 SVM 在训练集上的性能(ACC)为 0.9808917197452229
    模型 SVM 在测试集上的性能(ACC)为 0.775
    模型 SVM 经过10折交叉验证的性能为
            AUC / f1 / sensitivity / specificity
    训练集 - 0.9990 0.9800 0.9820 0.9905
    测试集 - 0.8703 0.7661 0.7796 0.8920
    
    


```python
# 训练集上10折交叉验证的性能
from sklearn.model_selection import cross_validate

best_models = dict()
for name in classifiers_:
    model = classifiers_[name]
    cv_results = cross_validate(model, train_df_lassoCV, Y.values, cv=10, return_estimator=True,
                                return_train_score=True)
    test_results = []
    for m in cv_results['estimator']:
        test_results.append(m.score(test_df_lassoCV, Y_test.values))

    # 保存在测试集上性能最好的模型
    ind = np.argmax(test_results)
    best_models[name] = cv_results['estimator'][ind]
    print("模型 {} 经过10折交叉验证的性能(ACC)为 {:.4f}（训练集）{:.4f}（验证集）{:.4f}（测试集）".format(name, cv_results["train_score"].mean(),
                                                                              cv_results["test_score"].mean(),
                                                                              test_results[ind]))

```


```python
# 定义一个class：
# 存储最优基模型的def
# 非线性变换的def
# 元模型进行训练的def
base_models = {}
meta_model = {}
from sklearn.model_selection import cross_validate

class StackingNonlinearTransformations():
    def __init__(self, base_models, meta_model, n_folds=10):
        self.base_models = base_models
        self.meta_model = meta_model
        self.n_folds = n_folds
        
   
    def NonlinearTransformations(self, x, NT, **kwargs):
        if NT == None:
            return x
        elif NT == "relu":
            return x
        elif NT == "sigmoid":
            return 1.0 / (1 + np.exp(-x))
        elif NT == "elu":
            if 'alpha' not in kwargs:
                raise ValueError('alpha is not given')
            alpha = kwargs["alpha"]
            return np.where(x < 0, alpha * (np.exp(x) - 1), x)
        elif NT == "tanh":
            return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))
        elif NT == "sin":
            return np.sin(x)
        elif NT == "cos":
            return np.cos(x)
        elif NT == "softmax":
            exps = np.exp(x - np.max(x, axis=-1, keepdims=True))
            return exps/np.sum(exps, axis=-1, keepdims=True)
        elif NT == "softplus":
            return np.log(1 + np.exp(x))


    def base_model_fit(self, train_X, train_y, test_X, test_y,cv=10):
        best_models = dict()
        for name in self.base_models.keys():
            model = self.base_models[name]
            cv_results = cross_validate(model, train_X, train_y.values, cv=cv, return_estimator=True, return_train_score=True)
            test_results = []
            for m in cv_results['estimator']:
                test_results.append(m.score(test_X, test_y.values))

            # 保存在测试集上性能最好的模型
            ind = np.argmax(test_results)
            best_models[name] = cv_results['estimator'][ind]
            



        train_ = np.zeros((train_X.shape[0], len(self.base_models ) + len(train_X.columns)))
        test_ = np.zeros((test_X.shape[0], len(self.base_models) + len(test_X.columns)))
        
        train_[:,0:len(train_X.columns)] = train_X
        test_[:,0:len(test_X.columns)] = test_X

        for i, name in enumerate(best_models):
            model = best_models[name]
            train_pred = model.predict(train_X)
            test_pred = model.predict(test_X)

            train_[:, len(train_X.columns)+i] = self.NonlinearTransformations(train_pred,"sigmoid")
            test_[:,len(test_X.columns)+i] = self.NonlinearTransformations(test_pred,"sigmoid")

            
        return train_, test_

    def fit(self, train_X, train_y, test_X, test_y, cv=10):

        new_train, new_test = self.base_model_fit(train_X, train_y, test_X, test_y,cv)
        model = list(self.meta_model.values())[0]
        cv_results = cross_validate(model, new_train, train_y.values, cv=cv, return_estimator=True, return_train_score=True)
        test_results = []
        for m in cv_results['estimator']:
            test_results.append(m.score(new_test, test_y.values))
        ind = np.argmax(test_results)
        new_best_models = cv_results['estimator'][ind]
        model = new_best_models
        train_pred = model.predict(new_train)
        test_pred = model.predict(new_test)
        train_pred_prob = model.predict_proba(new_train)
        test_pred_prob = model.predict_proba(new_test)
        
        return train_pred, test_pred, train_pred_prob, test_pred_prob



```


```python

meta_model = {'LogisticRegression':LogisticRegression(random_state=random_seed)}
base_models = classifiers_
SNT = StackingNonlinearTransformations(base_models, meta_model)
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)

print(train_pred, test_pred, train_pred_prob, test_pred_prob)

from sklearn.metrics import roc_curve, auc
test_fpr, test_tpr, _ = roc_curve(Y_test_multi.ravel(), test_pred_prob.ravel())
print(auc(test_fpr, test_tpr))

```

    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
      warnings.warn(label_encoder_deprecation_msg, UserWarning)
    d:\Anaconda\envs\Tensorflow\lib\site-packages\xgboost\data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
      from pandas import MultiIndex, Int64Index
    

    [0 0 0 0 0 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 0 0 0 1 0 0 1 0 2 2 2 1 0 1 2 0 2
     2 1 2 2 2 1 1 0 2 1 2 2 0 2 0 0 2 2 0 0 2 2 1 2 0 2 2 1 0 0 0 0 1 1 2 2 0
     0 1 2 2 2 0 2 2 2 0 1 0 0 0 0 2 2 1 0 2 0 2 1 2 0 2 2 1 1 0 2 2 2 2 2 0 2
     0 2 1 2 2 0 2 0 0 2 2 2 0 2 2 1 0 2 2 2 2 2 0 1 2 1 0 2 2 2 1 2 1 1 0 2 2
     0 2 2 0 0 2 1 2 0] [0 1 1 1 0 2 1 0 2 0 0 2 0 0 2 0 0 0 2 2 2 2 1 2 1 0 1 0 0 1 2 2 0 2 0 1 2
     2 1 2] [[7.98786397e-01 1.69605435e-01 3.16081676e-02]
     [8.03607989e-01 9.40798583e-02 1.02312152e-01]
     [7.52347690e-01 2.11163387e-01 3.64889232e-02]
     [7.91619966e-01 1.71735626e-01 3.66444083e-02]
     [8.66192574e-01 9.05399411e-02 4.32674849e-02]
     [2.64289472e-02 6.23805865e-02 9.11190466e-01]
     [6.58321922e-03 1.03126884e-02 9.83104092e-01]
     [5.56714002e-06 3.13986117e-07 9.99994119e-01]
     [6.31876006e-02 2.74163329e-01 6.62649071e-01]
     [9.03391815e-02 3.84241497e-01 5.25419322e-01]
     [1.29275193e-02 5.95725018e-02 9.27499979e-01]
     [2.73085475e-02 1.45869371e-01 8.26822082e-01]
     [4.35942768e-02 4.12456497e-01 5.43949227e-01]
     [1.85891093e-01 4.95132076e-01 3.18976831e-01]
     [2.21323474e-02 3.38608300e-02 9.44006823e-01]
     [1.87873110e-01 3.84692407e-01 4.27434483e-01]
     [2.68811165e-01 2.84499491e-01 4.46689344e-01]
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    0.85625
    
# upload log

0.0.4 更新正确的Class调用方法