Model training is incompatible with sklearn >=1.7: " AttributeError: '<model_class>' object has no attribute '_validate_data' "

[pedrobslima]

I tried running a code similar to example_heterogeneous.py with only the META-DES model:

import numpy as np
from sklearn.calibration import CalibratedClassifierCV
# Importing dataset and preprocessing routines
from sklearn.datasets import fetch_openml
# Base classifier models:
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

from deslib.des import METADES

rng = np.random.RandomState(42)
data = fetch_openml(name='phoneme', cache=False, as_frame=False)
X = data.data
y = data.target

# split the data into training and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
                                                    random_state=rng)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Split the data into training and DSEL for DS techniques
X_train, X_dsel, y_train, y_dsel = train_test_split(X_train, y_train,
                                                    test_size=0.5,
                                                    random_state=rng)

model_perceptron = CalibratedClassifierCV(Perceptron(max_iter=100,
                                                     random_state=rng),
                                          cv=3)

model_perceptron.fit(X_train, y_train)
model_svc = SVC(probability=True, gamma='auto',
                random_state=rng).fit(X_train, y_train)
model_bayes = GaussianNB().fit(X_train, y_train)
model_tree = DecisionTreeClassifier(random_state=rng,
                                    max_depth=10).fit(X_train, y_train)
model_knn = KNeighborsClassifier(n_neighbors=7).fit(X_train, y_train)

pool_classifiers = [model_perceptron,
                    model_svc,
                    model_bayes,
                    model_tree,
                    model_knn]


# Initializing the techniques
metades = METADES(pool_classifiers).fit(X_dsel, y_dsel)

But got the following error:
AttributeError: 'METADES' object has no attribute '_validate_data'

Later I tried running the same code on Google Colab and it worked, but I received this warning:

/usr/local/lib/python3.12/dist-packages/sklearn/base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.

I assume this is a compatibility issue with version 1.7 of scikit-learn. I also tried the other models on the example, but the same thing happened, on my local environment and on Google Colab.

---

• Local environment info:

  • Linux-6.17.4-76061704-generic-x86_64-with-glibc2.35
  • Python 3.10.12 (main, Nov 4 2025, 08:48:33) [GCC 11.4.0]
  • NumPy 2.2.6
  • SciPy 1.15.3
  • Scikit-Learn 1.7.2

• Google Colab environment info:

  • Linux-6.6.105+-x86_64-with-glibc2.35
  • Python 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0]
  • NumPy 2.0.2
  • SciPy 1.16.3
  • Scikit-Learn 1.6.1