cross_val_score 不符合实际输入模型？

Question

我正在处理一个处理大型数据集的项目。

我需要在 Sklearn 的 KFold 交叉验证库中训练 SVM 分类器。

import pandas as pd
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score


x__df_chunk_synth = pd.read_csv('C:/Users/anujp/Desktop/sort/semester 4/ATML/Sem project/atml_proj/Data/x_train_syn.csv')
y_df_chunk_synth = pd.read_csv('C:/Users/anujp/Desktop/sort/semester 4/ATML/Sem project/atml_proj/Data/y_train_syn.csv')

svm_clf = svm.SVC(kernel='poly', gamma=1, class_weight=None, max_iter=20000, C = 100, tol=1e-5)
X = x__df_chunk_synth
Y = y_df_chunk_synth
scores = cross_val_score(svm_clf, X, Y,cv = 5, scoring = 'f1_weighted')
print(scores)
    
pred = svm_clf.predict(chunk_test_x)
accuracy = accuracy_score(chunk_test_y,pred)

print(accuracy)

我正在使用上述代码。我知道我正在 cross_val_score 的函数内训练我的分类器，因此每当我试图在外部调用分类器以预测测试数据时，我都会收到错误消息：

sklearn.exceptions.NotFittedError: This SVC instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

是否有任何其他选择以正确的方式做同样的事情？

请帮我解决这个问题。

Answer 1

确实model_selection.cross_val_score使用输入模型来拟合数据，所以不一定要拟合。但是，它不适合用作输入的实际对象，而是它的 copy，因此在尝试预测时出现错误 This SVC instance is not fitted yet...。

首先查看 cross_validate which is called in cross_val_score, in the scoring step, the estimator goes through clone 中的源代码：

scores = parallel(
    delayed(_fit_and_score)(
        clone(estimator), X, y, scorers, train, test, verbose, None,
        fit_params, return_train_score=return_train_score,
        return_times=True, return_estimator=return_estimator,
        error_score=error_score)
    for train, test in cv.split(X, y, groups))

创建模型的深层副本（这就是未拟合实际输入模型的原因）：

def clone(estimator, *, safe=True):
    """Constructs a new estimator with the same parameters.
    Clone does a deep copy of the model in an estimator
    without actually copying attached data. It yields a new estimator
    with the same parameters that has not been fit on any data.
    ...

cross_val_score 不符合实际输入模型？

Does cross_val_score not fit the actual input model?

python

machine-learning

scikit-learn

svm

k-fold