Question

我正在尝试使用Pipeline和GridSearchCV以及自定义回归函数。

我通过设置类来使用sklearn.svm.SVR工作，然后在设置GridSearchCV时从字典中传递参数。

使用我自己的回归函数（RegFn）时，我似乎无法做同样的事情。特别是我想在设置时将参数字典中的值传递给RegFn。

感谢有关如何使其发挥作用的任何指示。

from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline   
from sklearn.base import BaseEstimator,RegressorMixin                                                  


parameters = dict(regfn__i=i, regfn__j=j, regfn__k=k)  
regfn = RegFn(numInputs=noinputs, numOutputs=1, i = 100, j = 1, k =0.5)
scaler = StandardScaler()   

steps = [ ('scaler', scaler), ('regfn', regfn   ) ]  

grid = GridSearchCV(Pipeline(steps), param_grid=parameters, cv=splits, refit=True, verbose=3, n_jobs=1)

更新了课程结构

class RBF(BaseEstimator,RegressorMixin):

    def __init__(self, i,j,k,numInputs=10,numOutputs=1):
        self.numInputs = numInputs
        self.numOutputs = numOutputs
        self.i = i
        self.j = j
        self.k = k
        .....

    def fit(self, x, y):
      ....

    def predict(self, x):
      ....

我仍然无法从字典中传递i，j，k值。我是以正确的方式提及它们吗？

Answer 1

如果您想使用自定义估算工具it should be inherited from sklearn.base.BaseEstimator，则应实施方法predict()和__init__()。

如果您希望能够将参数传递给估算工具，则还应实施>>> import numpy as np >>> from sklearn.base import BaseEstimator, RegressorMixin >>> from sklearn.utils.validation import check_X_y, check_array, check_is_fitted >>> from sklearn.utils.multiclass import unique_labels >>> from sklearn.metrics import euclidean_distances >>> class TemplateClassifier(BaseEstimator, RegressorMixin): ... ... def __init__(self, demo_param='demo'): ... self.demo_param = demo_param ... ... def fit(self, X, y): ... ... # Check that X and y have correct shape ... X, y = check_X_y(X, y) ... # Store the classes seen during fit ... self.classes_ = unique_labels(y) ... ... self.X_ = X ... self.y_ = y ... # Return the classifier ... return self ... ... def predict(self, X): ... ... # Check is fit had been called ... check_is_fitted(self, ['X_', 'y_']) ... ... # Input validation ... X = check_array(X) ... ... closest = np.argmin(euclidean_distances(X, self.X_), axis=1) ... return self.y_[closest]方法。

Example from sklearn docs：

fp.cron.expr=0 15 0 ? * *

PS请注意，上面的示例显示了如何创建自定义分类器，因此它可能包含一个代码，这对于回归量来说不太合适

具有自定义回归函数的Pipeline和GridSearchCV - 传递参数？

1 个答案: