Scikit set_params()

Question

我想使用 set_params() 设置 SVC 的参数，如以下示例代码所示。

from sklearn.svm import SVC

params = {'C': [.1, 1, 10]}

for k, v in params.items():
    for val in v:
        clf = SVC().set_params(k=val)
        print(clf)
        print()

如果我运行代码，我会得到以下错误：

ValueError: Invalid parameter k for estimator SVC

如何正确地将密钥放入 set_params()？

Answer 1

问题实际上是如何使用字符串作为关键字参数。您可以构造一个参数字典并使用 ** 语法将其传递给 set_params。

from sklearn.svm import SVC

params = {'C': [.1, 1, 10]}

for k, v in params.items():
    for val in v:
        clf = SVC().set_params(**{k: val})
        print(clf)
        print()

输出：

SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

Answer 2

虽然前一个答案工作得很好，但用多个参数来覆盖这种情况可能会有用。在这种情况下，sklearn 还有一个很好的便利函数来创建参数网格，使其更具可读性。

from sklearn.model_selection import ParameterGrid
from sklearn.svm import SVC
param_grid = ParameterGrid({'C': [.1, 1, 10], 'gamma':["auto", 0.01]})

for params in param_grid:
    svc_clf = SVC(**params)
    print (svc_clf)

给出了类似的结果：

SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,In [235]: 
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

Answer 3

你可以用很多超参数来做到这一点

from sklearn.svm import SVC

params = {'C': [.1, 1, 10],  'gamma':["auto", 0.01],'tol':[0.001,0.003]}
for k, v in params.items():
     For val in v:
         clf = SVC().set_params(**{k: val})
         print(clf)
         print()

Scikit set_params()

Scikit set_params()

python

scikit-learn