Simple Example using GroupKFold with Cross-Validate

groupkfold_example.py

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GroupKFold

# Loading the data
iris = datasets.load_iris()
design_matrix = np.concatenate((iris['data'], iris['target'].reshape(150,1)), axis=1)
df = pd.DataFrame(design_matrix, columns = ['sepal_length', 'sepal_width', 
                                            'petal_length', 'petal_width', 
                                            'species'])
X = df.drop(columns='species', axis=1)
y = df.species

# Defining validation schema and the groups to split the data
group_kfold = GroupKFold(n_splits=3)
groups = df.species

# Picking the model
model = DecisionTreeClassifier()

# Training the model with a GroupKFold validation schema
results = cross_validate(model, X, y, cv = group_kfold, groups = groups, return_train_score = True)

print(results)
print("Accuracy: %.2f (%.2f)" %(results['test_score'].mean(), results['test_score'].std()))