Getting feature vectors from CatBoost pool

Question

I have a dataset with some numerical and categorical features and I am trying to apply CatBoost for categorical encoding and classification.

Since my dataset is highly imbalanced, with a large number of data samples with label 0 compared to those with label 1, I'm also trying to use SMOTE to synthesize label 1 data samples before CatBoost classification.

My code -

# train_categorical_features is a list of columns that have categorical values
train_pool = Pool(data = X,
                  label = y,
                  cat_features = train_categorical_cols)

X_enc = train_pool.get_features()
print(X_enc)
y_enc = train_pool.get_label()
print(y_enc)

smote = SMOTE()
X_res, y_res = smote.fit_resample(X_enc, y_enc)
print('Resampled dataset samples per class {}'.format(Counter(y_res)))

predictions = []
for i in range(10):
    clf = CatBoostClassifier(learning_rate=0.08,
                         depth = 10,
                         loss_function='Logloss',
                         l2_leaf_reg = 4,
                         iterations=1000,
                         task_type="GPU",
                         random_seed=i,
                         logging_level='Silent')
    clf.fit(train_pool, plot=True,silent=True)
    predictions.append(clf.predict_proba(test[inputcols])[:,1])
    print(clf.get_best_score())

I get an error on X_enc = train_pool.get_features() that says -

CatBoostError: Pool has non-numeric features, get_features supports only numeric features

My questions are -

1. Is my approach towards applying SMOTE with CatBoost correct?
2. I've diligently followed the catboost documentation, and I am not able to understand or fix the error I've mentioned above. Would appreciate any help.

Answer 1

The reason you are getting the error is that your pool contains categorical features. If all of your features were numerical, it would work fine.

SMOTE is irrelevant here.