Use hypothesis (#5759)
* Use hypothesis * Allow int64 array interface for groups * Add packages to Windows CI * Add to travis * Make sure device index is set correctly * Fix dask-cudf test * appveyor
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Rory Mitchell
GitHub
@@ -1,87 +1,72 @@
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import numpy as np
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import testing as tm
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import unittest
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import pytest
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from hypothesis import strategies, given, settings, note
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import xgboost as xgb
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try:
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from sklearn.linear_model import ElasticNet
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from sklearn.preprocessing import scale
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from regression_test_utilities import run_suite, parameter_combinations
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except ImportError:
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None
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parameter_strategy = strategies.fixed_dictionaries({
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'booster': strategies.just('gblinear'),
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'eta': strategies.floats(0.01, 0.25),
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'tolerance': strategies.floats(1e-5, 1e-2),
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'nthread': strategies.integers(1, 4),
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})
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coord_strategy = strategies.fixed_dictionaries({
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'feature_selector': strategies.sampled_from(['cyclic', 'shuffle',
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'greedy', 'thrifty']),
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'top_k': strategies.integers(1, 10),
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})
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def is_float(s):
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try:
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float(s)
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return 1
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except ValueError:
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return 0
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def train_result(param, dmat, num_rounds):
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result = {}
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xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
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evals_result=result)
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return result
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def xgb_get_weights(bst):
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return np.array([float(s) for s in bst.get_dump()[0].split() if
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is_float(s)])
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class TestLinear:
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy, coord_strategy)
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@settings(deadline=None)
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def test_coordinate(self, param, num_rounds, dataset, coord_param):
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param['updater'] = 'coord_descent'
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param.update(coord_param)
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
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assert tm.non_increasing(result)
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# Loss is not guaranteed to always decrease because of regularisation parameters
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# We test a weaker condition that the loss has not increased between the first and last
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# iteration
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy, coord_strategy, strategies.floats(1e-5, 2.0),
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strategies.floats(1e-5, 2.0))
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@settings(deadline=None)
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def test_coordinate_regularised(self, param, num_rounds, dataset, coord_param, alpha, lambd):
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param['updater'] = 'coord_descent'
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param['alpha'] = alpha
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param['lambda'] = lambd
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param.update(coord_param)
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
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assert tm.non_increasing([result[0], result[-1]])
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def assert_regression_result(results, tol):
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regression_results = [r for r in results if
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r["param"]["objective"] == "reg:squarederror"]
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for res in regression_results:
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X = scale(res["dataset"].X,
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with_mean=isinstance(res["dataset"].X, np.ndarray))
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y = res["dataset"].y
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reg_alpha = res["param"]["alpha"]
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reg_lambda = res["param"]["lambda"]
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pred = res["bst"].predict(xgb.DMatrix(X))
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weights = xgb_get_weights(res["bst"])[1:]
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enet = ElasticNet(alpha=reg_alpha + reg_lambda,
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l1_ratio=reg_alpha / (reg_alpha + reg_lambda))
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enet.fit(X, y)
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enet_pred = enet.predict(X)
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assert np.isclose(weights, enet.coef_, rtol=tol,
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atol=tol).all(), (weights, enet.coef_)
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assert np.isclose(enet_pred, pred, rtol=tol, atol=tol).all(), (
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res["dataset"].name, enet_pred[:5], pred[:5])
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy)
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@settings(deadline=None)
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def test_shotgun(self, param, num_rounds, dataset):
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param['updater'] = 'shotgun'
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
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assert tm.non_increasing(result)
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# TODO: More robust classification tests
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def assert_classification_result(results):
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classification_results = [r for r in results if
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r["param"]["objective"] != "reg:squarederror"]
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for res in classification_results:
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# Check accuracy is reasonable
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assert res["eval"][-1] < 2.0, (res["dataset"].name, res["eval"][-1])
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class TestLinear(unittest.TestCase):
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datasets = ["Boston", "Digits", "Cancer", "Sparse regression",
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"Boston External Memory"]
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_coordinate(self):
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variable_param = {'booster': ['gblinear'], 'updater':
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['coord_descent'], 'eta': [0.5], 'top_k':
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[10], 'tolerance': [1e-5], 'nthread': [2],
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'alpha': [.005, .1], 'lambda': [.005],
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'feature_selector': ['cyclic', 'shuffle',
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'greedy', 'thrifty']}
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for param in parameter_combinations(variable_param):
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results = run_suite(param, 150, self.datasets, scale_features=True)
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assert_regression_result(results, 1e-2)
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assert_classification_result(results)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_shotgun(self):
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variable_param = {'booster': ['gblinear'], 'updater':
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['shotgun'], 'eta': [0.5], 'top_k': [10],
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'tolerance': [1e-5], 'nthread': [2],
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'alpha': [.005, .1], 'lambda': [.005],
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'feature_selector': ['cyclic', 'shuffle']}
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for param in parameter_combinations(variable_param):
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results = run_suite(param, 200, self.datasets, True)
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assert_regression_result(results, 1e-2)
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assert_classification_result(results)
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy, strategies.floats(1e-5, 2.0),
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strategies.floats(1e-5, 2.0))
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@settings(deadline=None)
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def test_shotgun_regularised(self, param, num_rounds, dataset, alpha, lambd):
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param['updater'] = 'shotgun'
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param['alpha'] = alpha
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param['lambda'] = lambd
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
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assert tm.non_increasing([result[0], result[-1]])
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