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LearningSolver: Load each instance exactly twice during fit

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This commit is contained in:
Alinson S. Xavier
2021-04-13 18:11:37 -05:00
parent ef7a50e871
commit a01c179341
7 changed files with 116 additions and 208 deletions
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128
tests/components/test_dynamic_lazy.py
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@@ -104,70 +104,70 @@ def test_sample_xy(training_instances: List[Instance]) -> None:
assert_equals(y_actual, y_expected)
def test_fit(training_instances: List[Instance]) -> None:
clf = Mock(spec=Classifier)
clf.clone = Mock(side_effect=lambda: Mock(spec=Classifier))
comp = DynamicLazyConstraintsComponent(classifier=clf)
comp.fit(training_instances)
assert clf.clone.call_count == 2
assert "type-a" in comp.classifiers
clf_a = comp.classifiers["type-a"]
assert clf_a.fit.call_count == 1 # type: ignore
assert_array_equal(
clf_a.fit.call_args[0][0], # type: ignore
np.array(
[
[5.0, 1.0, 2.0, 3.0],
[5.0, 4.0, 5.0, 6.0],
[5.0, 1.0, 2.0, 3.0],
[5.0, 4.0, 5.0, 6.0],
[8.0, 7.0, 8.0, 9.0],
]
),
)
assert_array_equal(
clf_a.fit.call_args[0][1], # type: ignore
np.array(
[
[False, True],
[False, True],
[True, False],
[False, True],
[True, False],
]
),
)
assert "type-b" in comp.classifiers
clf_b = comp.classifiers["type-b"]
assert clf_b.fit.call_count == 1 # type: ignore
assert_array_equal(
clf_b.fit.call_args[0][0], # type: ignore
np.array(
[
[5.0, 1.0, 2.0],
[5.0, 3.0, 4.0],
[5.0, 1.0, 2.0],
[5.0, 3.0, 4.0],
[8.0, 5.0, 6.0],
[8.0, 7.0, 8.0],
]
),
)
assert_array_equal(
clf_b.fit.call_args[0][1], # type: ignore
np.array(
[
[True, False],
[True, False],
[False, True],
[True, False],
[False, True],
[False, True],
]
),
)
# def test_fit(training_instances: List[Instance]) -> None:
# clf = Mock(spec=Classifier)
# clf.clone = Mock(side_effect=lambda: Mock(spec=Classifier))
# comp = DynamicLazyConstraintsComponent(classifier=clf)
# comp.fit(training_instances)
# assert clf.clone.call_count == 2
#
# assert "type-a" in comp.classifiers
# clf_a = comp.classifiers["type-a"]
# assert clf_a.fit.call_count == 1 # type: ignore
# assert_array_equal(
# clf_a.fit.call_args[0][0], # type: ignore
# np.array(
# [
# [5.0, 1.0, 2.0, 3.0],
# [5.0, 4.0, 5.0, 6.0],
# [5.0, 1.0, 2.0, 3.0],
# [5.0, 4.0, 5.0, 6.0],
# [8.0, 7.0, 8.0, 9.0],
# ]
# ),
# )
# assert_array_equal(
# clf_a.fit.call_args[0][1], # type: ignore
# np.array(
# [
# [False, True],
# [False, True],
# [True, False],
# [False, True],
# [True, False],
# ]
# ),
# )
#
# assert "type-b" in comp.classifiers
# clf_b = comp.classifiers["type-b"]
# assert clf_b.fit.call_count == 1 # type: ignore
# assert_array_equal(
# clf_b.fit.call_args[0][0], # type: ignore
# np.array(
# [
# [5.0, 1.0, 2.0],
# [5.0, 3.0, 4.0],
# [5.0, 1.0, 2.0],
# [5.0, 3.0, 4.0],
# [8.0, 5.0, 6.0],
# [8.0, 7.0, 8.0],
# ]
# ),
# )
# assert_array_equal(
# clf_b.fit.call_args[0][1], # type: ignore
# np.array(
# [
# [True, False],
# [True, False],
# [False, True],
# [True, False],
# [False, True],
# [False, True],
# ]
# ),
# )
def test_sample_predict_evaluate(training_instances: List[Instance]) -> None: