Question

我想将PCA和SVM结合到一个管道中，以便在GridSearch中找到最佳的超参数组合。

以下代码

from sklearn.svm import SVC
from sklearn import decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

digits = datasets.load_digits()
X_train = digits.data
y_train = digits.target

#Use Principal Component Analysis to reduce dimensionality
# and improve generalization
pca = decomposition.PCA()
# Use a linear SVC 
svm = SVC()
# Combine PCA and SVC to a pipeline
pipe = Pipeline(steps=[('pca', pca), ('svm', svm)])
# Check the training time for the SVC
n_components = [20, 40, 64]
svm_grid = [
  {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
  {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
 ]
estimator = GridSearchCV(pipe,
                         dict(pca__n_components=n_components,
                              svm=svm_grid))
estimator.fit(X_train, y_train)

结果

AttributeError: 'dict' object has no attribute 'get_params'

我定义和使用svm_grid的方式可能有问题。如何正确地将此参数组合传递给GridSearchCV？

Answer 1

问题在于GridSearchCV试图给估算器提供参数：

if parameters is not None:
    estimator.set_params(**parameters)

这里的估算器是一个Pipeline对象，而不是实际的svm，因为你的参数网格中有命名。

我相信它应该是这样的：

from sklearn.svm import SVC
from sklearn import decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

digits = datasets.load_digits()
X_train = digits.data
y_train = digits.target

# Use Principal Component Analysis to reduce dimensionality
# and improve generalization
pca = decomposition.PCA()
# Use a linear SVC
svm = SVC()
# Combine PCA and SVC to a pipeline
pipe = Pipeline(steps=[('pca', pca), ('svm', svm)])
# Check the training time for the SVC
n_components = [20, 40, 64]

params_grid = {
    'svm__C': [1, 10, 100, 1000],
    'svm__kernel': ['linear', 'rbf'],
    'svm__gamma': [0.001, 0.0001],
    'pca__n_components': n_components,
}

estimator = GridSearchCV(pipe, params_grid)
estimator.fit(X_train, y_train)

print estimator.best_params_, estimator.best_score_

输出：

{'pca__n_components': 64, 'svm__C': 10, 'svm__kernel': 'rbf', 'svm__gamma': 0.001} 0.976071229827

将所有参数合并到params_grid中，并将其命名为与指定步骤相对应。

希望这有帮助！祝你好运！

在管道中结合主成分分析和支持向量机

1 个答案: