Not able to use Stratified-K-Fold on multi label classifier

Question

The following code is used to do KFold Validation but I am to train the model as it is throwing the error

ValueError: Error when checking target: expected dense_14 to have shape (7,) but got array with shape (1,)

My target Variable has 7 classes. I am using LabelEncoder to encode the classes into numbers.

By seeing this error, If I am changing the into MultiLabelBinarizer to encode the classes. I am getting the following error

ValueError: Supported target types are: ('binary', 'multiclass'). Got 'multilabel-indicator' instead.

The following is the code for KFold validation

skf = StratifiedKFold(n_splits=10, shuffle=True)
scores = np.zeros(10)
idx = 0
for index, (train_indices, val_indices) in enumerate(skf.split(X, y)):
    print("Training on fold " + str(index+1) + "/10...")
    # Generate batches from indices
    xtrain, xval = X[train_indices], X[val_indices]
    ytrain, yval = y[train_indices], y[val_indices]
    model = None
    model = load_model() //defined above

    scores[idx] = train_model(model, xtrain, ytrain, xval, yval)
    idx+=1
print(scores)
print(scores.mean())

I don't know what to do. I want to use Stratified K Fold on my model. Please help me.

Answer 1

MultiLabelBinarizer returns a vector which is of the length of your number of classes.

If you look at how StratifiedKFold splits your dataset, you will see that it only accepts a one-dimensional target variable, whereas you are trying to pass a target variable with dimensions [n_samples, n_classes]

Stratefied split basically preserves your class distribution. And if you think about it, it does not make a lot of sense if you have a multi-label classification problem.

If you want to preserve the distribution in terms of the different combinations of classes in your target variable, then the answer here explains two ways in which you can define your own stratefied split function.

UPDATE:

The logic is something like this:

Assuming you have n classes and your target variable is a combination of these n classes. You will have (2^n) - 1 combinations (Not including all 0s). You can now create a new target variable considering each combination as a new label.

For example, if n=3, you will have 7 unique combinations:

 1. [1, 0, 0]
 2. [0, 1, 0]
 3. [0, 0, 1]
 4. [1, 1, 0]
 5. [1, 0, 1]
 6. [0, 1, 1]
 7. [1, 1, 1]

Map all your labels to this new target variable. You can now look at your problem as simple multi-class classification, instead of multi-label classification.

Now you can directly use StartefiedKFold using y_new as your target. Once the splits are done, you can map your labels back.

Code sample:

import numpy as np

np.random.seed(1)
y = np.random.randint(0, 2, (10, 7))
y = y[np.where(y.sum(axis=1) != 0)[0]]

OUTPUT:

array([[1, 1, 0, 0, 1, 1, 1],
       [1, 1, 0, 0, 1, 0, 1],
       [1, 0, 0, 1, 0, 0, 0],
       [1, 0, 0, 1, 0, 0, 0],
       [1, 0, 0, 0, 1, 1, 1],
       [1, 1, 0, 0, 0, 1, 1],
       [1, 1, 1, 1, 0, 1, 1],
       [0, 0, 1, 0, 0, 1, 1],
       [1, 0, 1, 0, 0, 1, 1],
       [0, 1, 1, 1, 1, 0, 0]])

Label encode your class vectors:

from sklearn.preprocessing import LabelEncoder

def get_new_labels(y):
    y_new = LabelEncoder().fit_transform([''.join(str(l)) for l in y])
    return y_new

y_new = get_new_labels(y)

OUTPUT:

array([7, 6, 3, 3, 2, 5, 8, 0, 4, 1])