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TST Add TODO for global_dtype in sklearn/tree/tests/test_tree.py #22926

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TST Add TODO for global_dtype in sklearn/tree/tests/test_tree.py #22926

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adam2392 Mar 23, 2022
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Merge branch 'globaltree' of github.com:neurodata/scikit-learn into g…
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Update sklearn/tree/tests/test_tree.py
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Merge branch 'main' into globaltree
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Update sklearn/tree/tests/test_tree.py
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30 changes: 22 additions & 8 deletions sklearn/tree/tests/test_tree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@

import pytest
import numpy as np
from numpy.testing import assert_allclose
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
Expand All @@ -34,6 +33,7 @@
from sklearn.utils._testing import create_memmap_backed_data
from sklearn.utils._testing import ignore_warnings
from sklearn.utils._testing import skip_if_32bit
from sklearn.utils._testing import assert_allclose

from sklearn.utils.estimator_checks import check_sample_weights_invariance
from sklearn.utils.validation import check_random_state
Expand Down Expand Up @@ -260,9 +260,11 @@ def test_weighted_classification_toy():
assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))


# TODO: Tree-based model do not preserve dtype on their fitted attribute
# Add a test using `global_dtype` when this is the case.
@pytest.mark.parametrize("Tree", REG_TREES.values())
@pytest.mark.parametrize("criterion", REG_CRITERIONS)
def test_regression_toy(Tree, criterion):
def test_regression_toy(Tree, criterion, global_dtype):
# Check regression on a toy dataset.
if criterion == "poisson":
# make target positive while not touching the original y and
Expand All @@ -274,13 +276,18 @@ def test_regression_toy(Tree, criterion):
y_train = y
y_test = true_result

y_train = np.array(y_train).astype(global_dtype, copy=False)
y_test = np.array(y_test).astype(global_dtype, copy=False)

reg = Tree(criterion=criterion, random_state=1)
reg.fit(X, y_train)
assert_allclose(reg.predict(T), y_test)
reg.fit(np.array(X).astype(global_dtype), y_train)
y_pred = reg.predict(np.array(T).astype(global_dtype))
assert_allclose(y_pred, y_test)

clf = Tree(criterion=criterion, max_features=1, random_state=1)
clf.fit(X, y_train)
assert_allclose(reg.predict(T), y_test)
clf.fit(np.array(X).astype(global_dtype), y_train)
y_pred = reg.predict(np.array(T).astype(global_dtype))
assert_allclose(y_pred, y_test)


def test_xor():
Expand Down Expand Up @@ -2143,8 +2150,10 @@ def test_poisson_vs_mse():
assert metric_poi < 0.75 * metric_dummy


# TODO: Tree-based model do not preserve dtype on their fitted attribute
# Add a test using `global_dtype` when this is the case.
@pytest.mark.parametrize("criterion", REG_CRITERIONS)
def test_decision_tree_regressor_sample_weight_consistency(criterion):
def test_decision_tree_regressor_sample_weight_consistentcy(criterion, global_dtype):
"""Test that the impact of sample_weight is consistent."""
tree_params = dict(criterion=criterion)
tree = DecisionTreeRegressor(**tree_params, random_state=42)
Expand All @@ -2161,6 +2170,9 @@ def test_decision_tree_regressor_sample_weight_consistency(criterion):
# make it positive in order to work also for poisson criterion
y += np.min(y) + 0.1

X = X.astype(global_dtype, copy=False)
y = y.astype(global_dtype, copy=False)

# check that multiplying sample_weight by 2 is equivalent
# to repeating corresponding samples twice
X2 = np.concatenate([X, X[: n_samples // 2]], axis=0)
Expand All @@ -2178,7 +2190,9 @@ def test_decision_tree_regressor_sample_weight_consistency(criterion):
# Thresholds, tree.tree_.threshold, and values, tree.tree_.value, are not
# exactly the same, but on the training set, those differences do not
# matter and thus predictions are the same.
assert_allclose(tree1.predict(X), tree2.predict(X))
y_pred1 = tree1.predict(X)
y_pred2 = tree2.predict(X)
assert_allclose(y_pred1, y_pred2)


# TODO: Remove in v1.2
Expand Down