设置分类器的参数并使用它而不进行拟合

Question

我正在使用python并scikit-learn做一些分类.

是否可以重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()

让我们把它params作为字符串字典存储在某处,甚至写入文件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？我怎么做？

Answer 1

请阅读SKLearn中关于模型持久性的内容:

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

后来:

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