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Typehint for Sklearn. (#6799)

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This commit is contained in:
Jiaming Yuan
2021-04-14 06:55:21 +08:00
committed by GitHub
parent 3d919db0c0
commit dee5ef2dfd
11 changed files with 335 additions and 262 deletions
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4
tests/python-gpu/test_gpu_with_dask.py
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@@ -269,7 +269,9 @@ class TestDistributedGPU:
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.parametrize("model", ["boosting"])
def test_dask_classifier(self, model, local_cuda_cluster: LocalCUDACluster) -> None:
def test_dask_classifier(
self, model: str, local_cuda_cluster: LocalCUDACluster
) -> None:
import dask_cudf
with Client(local_cuda_cluster) as client:
X_, y_, w_ = generate_array(with_weights=True)

23
tests/python/test_interaction_constraints.py
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@@ -60,25 +60,25 @@ class TestInteractionConstraints:
def test_interaction_constraints_feature_names(self):
with pytest.raises(ValueError):
constraints = [('feature_0', 'feature_1')]
self.run_interaction_constraints(tree_method='exact',
self.run_interaction_constraints(tree_method='exact',
interaction_constraints=constraints)
with pytest.raises(ValueError):
constraints = [('feature_0', 'feature_3')]
feature_names = ['feature_0', 'feature_1', 'feature_2']
self.run_interaction_constraints(tree_method='exact',
feature_names=feature_names,
self.run_interaction_constraints(tree_method='exact',
feature_names=feature_names,
interaction_constraints=constraints)
constraints = [('feature_0', 'feature_1')]
feature_names = ['feature_0', 'feature_1', 'feature_2']
self.run_interaction_constraints(tree_method='exact',
feature_names=feature_names,
self.run_interaction_constraints(tree_method='exact',
feature_names=feature_names,
interaction_constraints=constraints)
@pytest.mark.skipif(**tm.no_sklearn())
def training_accuracy(self, tree_method):
"""Test accuracy, reused by GPU tests."""
from sklearn.metrics import accuracy_score
dtrain = xgboost.DMatrix(dpath + 'agaricus.txt.train?indexing_mode=1')
dtest = xgboost.DMatrix(dpath + 'agaricus.txt.test?indexing_mode=1')
@@ -101,11 +101,6 @@ class TestInteractionConstraints:
pred_dtest = (bst.predict(dtest) < 0.5)
assert accuracy_score(dtest.get_label(), pred_dtest) < 0.1
def test_hist_training_accuracy(self):
self.training_accuracy(tree_method='hist')
def test_exact_training_accuracy(self):
self.training_accuracy(tree_method='exact')
def test_approx_training_accuracy(self):
self.training_accuracy(tree_method='approx')
@pytest.mark.parametrize("tree_method", ["hist", "approx", "exact"])
def test_hist_training_accuracy(self, tree_method):
self.training_accuracy(tree_method=tree_method)

12
tests/python/test_monotone_constraints.py
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@@ -22,14 +22,14 @@ def is_correctly_constrained(learner, feature_names=None):
for i in range(n):
fixed_x = fixed_xs_values[i] * np.ones((n, 1))
monotonically_increasing_x = np.column_stack((variable_x, fixed_x))
monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x,
monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x,
feature_names=feature_names)
monotonically_increasing_y = learner.predict(
monotonically_increasing_dset
)
monotonically_decreasing_x = np.column_stack((fixed_x, variable_x))
monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x,
monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x,
feature_names=feature_names)
monotonically_decreasing_y = learner.predict(
monotonically_decreasing_dset
@@ -105,7 +105,7 @@ class TestMonotoneConstraints:
@pytest.mark.parametrize('format', [dict, list])
def test_monotone_constraints_feature_names(self, format):
# next check monotonicity when initializing monotone_constraints by feature names
params = {
'tree_method': 'hist', 'verbosity': 1,
@@ -119,13 +119,13 @@ class TestMonotoneConstraints:
with pytest.raises(ValueError):
xgb.train(params, training_dset)
feature_names =[ 'feature_0', 'feature_2']
feature_names = ['feature_0', 'feature_2']
training_dset_w_feature_names = xgb.DMatrix(x, label=y, feature_names=feature_names)
with pytest.raises(ValueError):
xgb.train(params, training_dset_w_feature_names)
feature_names =[ 'feature_0', 'feature_1']
feature_names = ['feature_0', 'feature_1']
training_dset_w_feature_names = xgb.DMatrix(x, label=y, feature_names=feature_names)
constrained_learner = xgb.train(