Sci-kit学习SGD分类器Partial_Fit错误

Question

I'm using scikit-learn and the SGD classifier to train an SVM in mini-batches. Here's a little code snippet:

for row in reader:
        if row[0] in model.docvecs:
            TRAINING_X.append(model.docvecs[row[0]])
            TRAINING_Y.append(row[2])
        if count % 10000 == 0:
            np_x = np.asarray(TRAINING_X)
            np_y = np.asarray(TRAINING_Y)
            clf.partial_fit(np_x,np_y, np.unique(np.asarray))
            TRAINING_X = []
            TRAINING_Y = []
        count += 1

I'm using the partial_fit function to read in every 1000 data points and using np.unique() to generate class labels as per the documentation .

However, when I run this, I get the following error:

raise ValueError("The number of class labels must be " ValueError: The number of class labels must be greater than one.

I'm a little confused. Am I generating class labels incorrectly?

Answer 1

The documentation for partial_fit says, Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. .

You seem to be passing np.unique(np.asarray) which does seem incorrect.

Answer 2

Going by the error thrown by the program, I think there is only one unique class in your target variable. Please use np.unique(np_y) and get the number of unique classes that you are feeding into the model and ensure that it is more than one.

Also, your value to the classes argument seem to be incorrect, it should have been np.unique(np_y) instead of np.unique(np.asarray)

Hope this helps!