autogen/test/test_xgboost2d_sample_size.py

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import unittest
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
import numpy as np
from flaml.automl import AutoML
from flaml.model import XGBoostSklearnEstimator
from flaml import tune
dataset = "credit-g"
class XGBoost2D(XGBoostSklearnEstimator):
@classmethod
def search_space(cls, data_size, task):
upper = min(32768, int(data_size))
return {
'n_estimators': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
},
'max_leaves': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
},
}
def _test_simple(method=None, size_ratio=1.0):
automl = AutoML()
automl.add_learner(learner_name='XGBoost2D',
learner_class=XGBoost2D)
X, y = fetch_openml(name=dataset, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
random_state=42)
final_size = int(len(y_train) * size_ratio)
X_train = X_train[:final_size]
y_train = y_train[:final_size]
automl_settings = {
"estimator_list": ['XGBoost2D'],
# "metric": 'accuracy',
"task": 'classification',
"log_file_name": f"test/xgboost2d_{dataset}_{method}_{final_size}.log",
# "model_history": True,
# "log_training_metric": True,
# "split_type": split_type,
"n_jobs": 1,
"hpo_method": method,
"log_type": "all",
"time_budget": 3600,
}
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
def _test_grid_1():
_test_simple(method="grid", size_ratio=1.0 / 3.0)
def _test_grid_2():
_test_simple(method="grid", size_ratio=2.0 / 3.0)
def _test_grid_4():
_test_simple(method="grid", size_ratio=0.5)
def _test_grid_3():
_test_simple(method="grid", size_ratio=1.0)
if __name__ == "__main__":
unittest.main()