TypeError: __init__() 得到了一个意外的关键字参数“评分”

Posted 2023-03-12

【中文标题】TypeError: __init__() 得到了一个意外的关键字参数“评分”

【英文标题】：TypeError: __init__() got an unexpected keyword argument 'scoring'

【发布时间】：2013-02-02 09:28:20

【问题描述】：

这个演示代码怎么可能（取自这里：http://scikit-learn.org/dev/auto_examples/grid_search_digits.html）TypeError: __init__() got an unexpected keyword argument 'scoring' 当明显的得分是一个参数时（http://scikit-learn.org/dev/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV）？

from __future__ import print_function

from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV  
from sklearn.metrics import classification_report
from sklearn.svm import SVC

print(__doc__)

# Loading the Digits dataset
digits = datasets.load_digits()

# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=0)

# Set the parameters by cross-validation
tuned_parameters = ['kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                 'C': [1, 10, 100, 1000],
                'kernel': ['linear'], 'C': [1, 10, 100, 1000]]

scores = ['precision', 'recall']

for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()

    clf = GridSearchCV(SVC(C=1), tuned_parameters, scoring=score)
    clf.fit(X_train, y_train, cv=5)

    print("Best parameters set found on development set:")
    print()
    print(clf.best_estimator_)
    print()
    print("Grid scores on development set:")
    print()
    for params, mean_score, scores in clf.grid_scores_:
        print("%0.3f (+/-%0.03f) for %r"
          % (mean_score, scores.std() / 2, params))
    print()

    print("Detailed classification report:")
    print()
    print("The model is trained on the full development set.")
    print("The scores are computed on the full evaluation set.")
    print()
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))
    print()

# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.

【问题讨论】：

能否给出scikit的完整回溯和版本号？

【参考方案1】：

scoring 参数是 0.14 开发版本中的新参数，示例代码适用于该版本。你安装的scikit可能是0.13或更早的版本，没有评分参数。

【讨论】：

对于 Windows，我发现只有 0.13：sourceforge.net/projects/scikit-learn/files。如果我在示例代码中不使用评分参数，结果会有多大变化？

@postgres：有一个corresponding example on the 0.13 documentation。

当前版本为 0.13。正如 Lie Ryan 指出的那样，0.14 版是开发版本，它没有二进制文件或任何东西，因为它每天都在变化。

【参考方案2】：

您正在运行开发版本吗？

例如0.12不支持该参数