Question

我使用python和scikit-learn进行分类。

是否可以重复使用分类器学习的参数？

例如：

from sklearn.svm import SVC

cl = SVC(...)    # create svm classifier with some hyperparameters
cl.fit(X_train, y_train)
params = cl.get_params()

让我们将这个params存储在字符串字典中，甚至可以写入文件json。假设，我们希望以后使用这种训练有素的分类器对某些数据做出一些预测。尝试恢复它：

params = ...  # retrieve these parameters stored somewhere as a dictionary
data = ...    # the data, we want make predictions on
cl = SVC(...)
cl.set_params(**params)
predictions = cl.predict(data)

如果我这样做，我会得到NonFittedError和以下的堆栈跟踪：

File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\svm\base.py", line 548, in predict
    y = super(BaseSVC, self).predict(X)
  File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\svm\base.py", line 308, in predict
    X = self._validate_for_predict(X)
  File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\svm\base.py", line 437, in _validate_for_predict
    check_is_fitted(self, 'support_')
  File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\utils\validation.py", line 768, in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.exceptions.NotFittedError: This SVC instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

是否可以将参数设置为分类器并进行预测而不适合？我该怎么做？

Answer 1

请阅读model persistence in SKLearn：

from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl')

以及后来：

clf = joblib.load('filename.pkl')

设置分类器的参数并使用它而不进行拟合

1 个答案: