XGboost python - classifier class weight option?

Question

Is there a way to set different class weights for xgboost classifier? For example in sklearn RandomForestClassifier this is done by the "class_weight" parameter.

Answer 1

For sklearn version < 0.19

Just assign each entry of your train data its class weight. First get the class weights with class_weight.compute_class_weight of sklearn then assign each row of the train data its appropriate weight.

I assume here that the train data has the column class containing the class number. I assumed also that there are nb_classes that are from 1 to nb_classes.

from sklearn.utils import class_weight
classes_weights = list(class_weight.compute_class_weight('balanced',
                                             np.unique(train_df['class']),
                                             train_df['class']))

weights = np.ones(y_train.shape[0], dtype = 'float')
for i, val in enumerate(y_train):
    weights[i] = classes_weights[val-1]

xgb_classifier.fit(X, y, sample_weight=weights)

Update for sklearn version >= 0.19

There is simpler solution

from sklearn.utils import class_weight
classes_weights = class_weight.compute_sample_weight(
    class_weight='balanced',
    y=train_df['class']
)

xgb_classifier.fit(X, y, sample_weight=classes_weights)

Answer 2

from sklearn.utils.class_weight import compute_sample_weight
xgb_classifier.fit(X, y, sample_weight=compute_sample_weight("balanced", y))