simple_tabular_classification.py

# -*- encoding: utf-8 -*-

"""Simple example for binary classification on tabular data."""

import pandas as pd

from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task


# load and prepare data
data = pd.read_csv("./data/sampled_app_train.csv")
train_data, test_data = train_test_split(data, test_size=0.2, stratify=data["TARGET"], random_state=42)

# run automl
automl = TabularAutoML(task=Task("binary"))
oof_predictions = automl.fit_predict(train_data, roles={"target": "TARGET", "drop": ["SK_ID_CURR"]})
te_pred = automl.predict(test_data)

# calculate scores
print(f"Score for out-of-fold predictions: {roc_auc_score(train_data['TARGET'].values, oof_predictions.data[:, 0])}")
print(f"Score for hold-out: {roc_auc_score(test_data['TARGET'].values, te_pred.data[:, 0])}")