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Make get_variable_{categories,features} return np.ndarray

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This commit is contained in:
Alinson S. Xavier
2021-08-09 15:19:53 -05:00
parent 56b39b6c9c
commit 895cb962b6
13 changed files with 165 additions and 155 deletions
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57
tests/components/test_primal.py
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@@ -23,21 +23,28 @@ def sample() -> Sample:
sample = MemorySample(
{
"static_var_names": np.array(["x[0]", "x[1]", "x[2]", "x[3]"], dtype="S"),
"static_var_categories": ["default", None, "default", "default"],
"static_var_categories": np.array(
["default", "", "default", "default"],
dtype="S",
),
"mip_var_values": np.array([0.0, 1.0, 1.0, 0.0]),
"static_instance_features": [5.0],
"static_var_features": [
[0.0, 0.0],
None,
[1.0, 0.0],
[1.0, 1.0],
],
"lp_var_features": [
[0.0, 0.0, 2.0, 2.0],
None,
[1.0, 0.0, 3.0, 2.0],
[1.0, 1.0, 3.0, 3.0],
],
"static_instance_features": np.array([5.0]),
"static_var_features": np.array(
[
[0.0, 0.0],
[0.0, 0.0],
[1.0, 0.0],
[1.0, 1.0],
]
),
"lp_var_features": np.array(
[
[0.0, 0.0, 2.0, 2.0],
[0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 3.0, 2.0],
[1.0, 1.0, 3.0, 3.0],
]
),
},
)
return sample
@@ -45,14 +52,14 @@ def sample() -> Sample:
def test_xy(sample: Sample) -> None:
x_expected = {
"default": [
b"default": [
[5.0, 0.0, 0.0, 2.0, 2.0],
[5.0, 1.0, 0.0, 3.0, 2.0],
[5.0, 1.0, 1.0, 3.0, 3.0],
]
}
y_expected = {
"default": [
b"default": [
[True, False],
[False, True],
[True, False],
@@ -72,15 +79,15 @@ def test_fit_xy() -> None:
thr.clone = lambda: Mock(spec=Threshold)
comp = PrimalSolutionComponent(classifier=clf, threshold=thr)
x = {
"type-a": np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
"type-b": np.array([[7.0, 8.0, 9.0]]),
b"type-a": np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
b"type-b": np.array([[7.0, 8.0, 9.0]]),
}
y = {
"type-a": np.array([[True, False], [False, True]]),
"type-b": np.array([[True, False]]),
b"type-a": np.array([[True, False], [False, True]]),
b"type-b": np.array([[True, False]]),
}
comp.fit_xy(x, y)
for category in ["type-a", "type-b"]:
for category in [b"type-a", b"type-b"]:
assert category in comp.classifiers
assert category in comp.thresholds
clf = comp.classifiers[category] # type: ignore
@@ -142,13 +149,13 @@ def test_predict(sample: Sample) -> None:
thr.predict = Mock(return_value=[0.75, 0.75])
comp = PrimalSolutionComponent()
x, _ = comp.sample_xy(None, sample)
comp.classifiers = {"default": clf}
comp.thresholds = {"default": thr}
comp.classifiers = {b"default": clf}
comp.thresholds = {b"default": thr}
pred = comp.sample_predict(sample)
clf.predict_proba.assert_called_once()
thr.predict.assert_called_once()
assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
assert_array_equal(x["default"], thr.predict.call_args[0][0])
assert_array_equal(x[b"default"], clf.predict_proba.call_args[0][0])
assert_array_equal(x[b"default"], thr.predict.call_args[0][0])
assert pred == {
b"x[0]": 0.0,
b"x[1]": None,

4
tests/features/test_extractor.py
View File

@@ -49,8 +49,8 @@ def test_knapsack() -> None:
sample.get_vector("static_var_upper_bounds"), [1.0, 1.0, 1.0, 1.0, 67.0]
)
assert_equals(
sample.get_vector("static_var_categories"),
["default", "default", "default", "default", None],
sample.get_array("static_var_categories"),
np.array(["default", "default", "default", "default", ""], dtype="S"),
)
assert sample.get_vector_list("static_var_features") is not None
assert_equals(