machine-learning - 在管道中结合主成分分析和支持向量机

Question

我想将 PCA 和 SVM 组合到一个管道中，以在 GridSearch 中找到超参数的最佳组合。

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。如何正确将此参数组合传递给 GridSearchCV？

Answer 1

问题是当 GridSearchCV 尝试为估计器提供参数时:

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

这里的估计器是一个 Pipeline 对象，而不是实际的 svm，因为参数网格中的命名。

我认为应该是这样的:

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，并根据指定步骤对它们进行相应命名。

希望这有帮助!祝你好运!