如何在scikit-learn中用管道调整自定义内核函数的参数

目前我已经使用def函数成功定义了一个自定义内核函数（预先计算内核矩阵），现在我使用GridSearchCV函数来获取最佳参数 .

因此，在自定义内核函数中，总共有2个参数将被调整（即下例中的gamm和sea_gamma），而且对于SVR模型，还必须调整cost c参数 . 但到目前为止，我可以使用GridSearchCV调整成本c参数 - >请参考下面的第一部分：示例 .

我搜索了一些类似的解决方案，例如：

Is it possible to tune parameters with grid search for custom kernels in scikit-learn?

它说“实现这一目标的一种方法是使用Pipeline，SVC（kernel = 'precomputed'）并将自定义内核函数包装为sklearn估计器（BaseEstimator和TransformerMixin的子类） . ”但是这仍然不同于我的案例和问题但是，我尝试基于此解决方案解决问题，但到目前为止它没有打印任何输出，甚至是任何错误 . - >请参考第二部分：管道解决方案 .

第一部分：示例 - >我在网格搜索中的原始自定义内核和评分方法是：

import numpy as np
    import pandas as pd
    import sklearn.svm as svm
    from sklearn import preprocessing,svm, datasets
    from sklearn.preprocessing import StandardScaler,  MaxAbsScaler
    from sklearn.metrics.pairwise import rbf_kernel
    from sklearn.grid_search import GridSearchCV
    from sklearn.svm import SVR
    from sklearn.pipeline import Pipeline
    from sklearn.metrics.scorer import make_scorer

    # weighting the vectors
    def distance_scale(X,Y):
        K = np.zeros((X.shape[0],Y.shape[0]))
        gamma_sea =192

        for i in range(X.shape[0]):
            for j in range(Y.shape[0]):
                dis = min(np.abs(X[i]-Y[j]),1-np.abs(X[i]-Y[j]))
                K[i,j] = np.exp(-gamma_sea*dis**2)
        return K

    # custom RBF kernel : kernel matrix calculation 
    def sea_rbf(X,Y):
        gam=1
        t1 = X[:, 5:6]
        t2 = Y[:, 5:6]
        X = X[:, 0:5]
        Y = Y[:, 0:5]
        d = distance_scale(t1,t2)
        return rbf_kernel(X,Y,gamma=gam)*d

    def my_custom_loss_func(y_true, y_pred):
        error=np.abs((y_true - y_pred)/y_true)
        return np.mean(error)*100

    my_scorer = make_scorer(my_custom_loss_func,greater_is_better=False)


    # Generate sample data 
    X_train=np.random.random((100,6))
    y_train=np.random.random((100,1))
    X_test=np.random.random((40,6))
    y_test=np.random.random((40,1))
    y_train=np.ravel(y_train)
    y_test=np.ravel(y_test)

    # scale the input and output in training data set, also scale the input                                         
    #in testing data set
    max_scale = preprocessing.MaxAbsScaler().fit(X_train)
    X_train_max = max_scale.transform(X_train)
    X_test_max = max_scale.transform(X_test)
    max_scale_y = preprocessing.MaxAbsScaler().fit(y_train)
    y_train_max = max_scale_y.transform(y_train)

    #precompute the kernel matrix
    gam=sea_rbf(X_train_max,X_train_max)

    #grid search for the model with the custom scoring method, but can only tune the *cost c* parameter in this case.
    clf= GridSearchCV(SVR(kernel='precomputed'),
                       scoring=my_scorer,
                       cv=5,
                       param_grid={"C": [0.1,1,2,3,4,5]
                                   })

    clf.fit(gam, y_train_max)
    print(clf.best_params_)
    print(clf.best_score_)
    print(clf.grid_scores_)

第二部分：管道解决方案

from __future__ import print_function
from __future__ import division

import sys

import sklearn
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline

# Wrapper class for the custom kernel RBF_kernel
class RBF2Kernel(BaseEstimator,TransformerMixin):

    def __init__(self, gamma=1,sea_gamma=20):
        super(RBF2Kernel,self).__init__()
        self.gamma = gamma
        self.sea_gamma = sea_gamma

        def fit(self, X, y=None, **fit_params):
        return self
   #calculate the kernel matrix
    def transform(self, X):
        self.a_train_ = X[:, 0:5]
        self.b_train_ = X[:, 0:5]
        self.t1_train_ = X[:, 5:6]
        self.t2_train_ = X[:, 5:6]
        sea=16
        K = np.zeros((t1.shape[0],t2.shape[0]))

        for i in range(self.t1_train_.shape[0]):
             for j in range(self.t2_train_.shape[0]):
                    dis = min(np.abs(self.t1_train_[i]*sea-        self.t2_train_[j]*sea),sea-np.abs(self.t1_train_[i]*sea-self.t2_train_[j]*sea))
                    K[i,j] = np.exp(-self.gamma_sea *dis**2)
        return K

        return rbf_kernel(self.a_train_ , self.b_train_, gamma=self.gamma)*K

def main():

    print('python: {}'.format(sys.version))
    print('numpy: {}'.format(np.__version__))
    print('sklearn: {}'.format(sklearn.__version__))

    # Generate sample data
    X_train=np.random.random((100,6))
    y_train=np.random.random((100,1))
    X_test=np.random.random((40,6))
    y_test=np.random.random((40,1))
    y_train=np.ravel(y_train)
    y_test=np.ravel(y_test)


    # Create a pipeline where our custom predefined kernel RBF2Kernel
    # is run before SVR.

    pipe = Pipeline([
        ('sc', MaxAbsScaler()),    
        ('rbf2', RBF2Kernel()),
        ('svm', SVR()),
    ])

    # Set the parameter 'gamma' of our custom kernel by
    # using the 'estimator__param' syntax.
    cv_params = dict([
        ('rbf2__gamma', 10.0**np.arange(-2,2)),
        ('rbf2__sea_gamma', 10.0**np.arange(-2,2)),
        ('svm__kernel', ['precomputed']),
        ('svm__C', 10.0**np.arange(-2,2)),
    ])

    # Do grid search to get the best parameter value of 'gamma'.
    # here i am also trying to tune the parameters of the custom kernel
    model = GridSearchCV(pipe, cv_params, verbose=1, n_jobs=-1,scoring=my_scorer)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)

    acc_test = mean_absolute_error(y_test, y_pred)
    mape_100 =  my_custom_loss_func (y_test, y_pred)

    print("Test accuracy: {}".format(acc_test))
    print("mape_100: {}".format(mape_100))
    print("Best params:")
    print(model.best_params_)
    print(model.grid_scores_)

if __name__ == '__main__':
    main()

总结如下：

• 该示例运行良好，但它可以调整默认参数（在这种情况下成本参数）

• 我想调整自定义内核中的额外参数，我已将其定义为第一部分中的函数 .

• scikit-learn或python对我来说仍然是新的，如果解释不清楚，如果您对细节有任何疑问，请告诉我 .

非常感谢您的阅读，希望长篇大论会让您更加清晰，欢迎所有建议:)