First Example¶

Once Sklong is installed, a good first milestone is to run a complete longitudinal classification workflow end to end.

The example below uses LongitudinalDataset to prepare the data and a longitudinal-aware LexicoGradientBoostingClassifier to train on wave-aware feature groups.

from scikit_longitudinal.data_preparation import LongitudinalDataset
from scikit_longitudinal.estimators.ensemble.lexicographical.lexico_gradient_boosting import (
    LexicoGradientBoostingClassifier,
)
from sklearn.metrics import classification_report

# Load your dataset (replace 'stroke.csv' with your actual dataset path)
dataset = LongitudinalDataset("./stroke.csv")

# Set up the target column and split the data into training and testing sets
dataset.load_data_target_train_test_split(
    target_column="class_stroke_wave_4",
)

# Set up feature groups (temporal dependencies)
# Use a pre-set for English Longitudinal Study of Ageing (ELSA) data or define manually
dataset.setup_features_group(input_data="elsa")

# Initialise the classifier with feature groups
model = LexicoGradientBoostingClassifier(
    features_group=dataset.feature_groups(),
    threshold_gain=0.00015,  # Adjust hyperparameters as needed
)

# Fit the model to the training data
model.fit(dataset.X_train, dataset.y_train)

# Make predictions on the test data
y_pred = model.predict(dataset.X_test)

# Print the classification report
print(classification_report(dataset.y_test, y_pred))

What is the LexicoGradientBoostingClassifier?

It is a longitudinal variant of Gradient Boosting that uses a lexicographical strategy to prioritise more recent waves when evaluating candidate splits. If you want the theory behind that design, see the API reference and the broader paper on the lexicographical split strategy.

Where is the data?

Scikit-longitudinal does not ship datasets by default, mainly for privacy and redistribution reasons. You can use your own longitudinal datasets or start with publicly available ones such as the ELSA dataset. If you would like a bundled synthetic example dataset in the future, open an issue.