| import pandas as pd | |
| from autogluon.tabular import TabularPredictor | |
| if __name__ == '__main__': | |
| train_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv') | |
| subsample_size = 5000 | |
| if subsample_size is not None and subsample_size < len(train_data): | |
| train_data = train_data.sample(n=subsample_size, random_state=0) | |
| test_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv') | |
| tabpfnmix_default = { | |
| "model_path_classifier": "autogluon/tabpfn-mix-1.0-classifier", | |
| "model_path_regressor": "autogluon/tabpfn-mix-1.0-regressor", | |
| "n_ensembles": 1, | |
| "max_epochs": 30, | |
| } | |
| hyperparameters = { | |
| "TABPFNMIX": [ | |
| tabpfnmix_default, | |
| ], | |
| } | |
| label = "class" | |
| predictor = TabularPredictor(label=label) | |
| predictor = predictor.fit( | |
| train_data=train_data, | |
| hyperparameters=hyperparameters, | |
| verbosity=3, | |
| presets="best", | |
| num_cpus=2, | |
| ) | |
| predictor.leaderboard(test_data, display=True) |