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Reformat source code with Black; add pre-commit hooks and CI checks

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Alinson S. Xavier
2020-12-05 10:59:33 -06:00
parent 3823931382
commit d99600f101
49 changed files with 1291 additions and 972 deletions
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135
miplearn/components/tests/test_primal.py
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@@ -25,71 +25,82 @@ def test_predict():
def test_evaluate():
instances, models = get_test_pyomo_instances()
clf_zero = Mock(spec=Classifier)
clf_zero.predict_proba = Mock(return_value=np.array([
[0., 1.], # x[0]
[0., 1.], # x[1]
[1., 0.], # x[2]
[1., 0.], # x[3]
]))
clf_zero.predict_proba = Mock(
return_value=np.array(
[
[0.0, 1.0], # x[0]
[0.0, 1.0], # x[1]
[1.0, 0.0], # x[2]
[1.0, 0.0], # x[3]
]
)
)
clf_one = Mock(spec=Classifier)
clf_one.predict_proba = Mock(return_value=np.array([
[1., 0.], # x[0] instances[0]
[1., 0.], # x[1] instances[0]
[0., 1.], # x[2] instances[0]
[1., 0.], # x[3] instances[0]
]))
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one],
threshold=0.50)
clf_one.predict_proba = Mock(
return_value=np.array(
[
[1.0, 0.0], # x[0] instances[0]
[1.0, 0.0], # x[1] instances[0]
[0.0, 1.0], # x[2] instances[0]
[1.0, 0.0], # x[3] instances[0]
]
)
)
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
comp.fit(instances[:1])
assert comp.predict(instances[0]) == {"x": {0: 0,
1: 0,
2: 1,
3: None}}
assert instances[0].solution == {"x": {0: 1,
1: 0,
2: 1,
3: 1}}
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
assert instances[0].solution == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
ev = comp.evaluate(instances[:1])
assert ev == {'Fix one': {0: {'Accuracy': 0.5,
'Condition negative': 1,
'Condition negative (%)': 25.0,
'Condition positive': 3,
'Condition positive (%)': 75.0,
'F1 score': 0.5,
'False negative': 2,
'False negative (%)': 50.0,
'False positive': 0,
'False positive (%)': 0.0,
'Precision': 1.0,
'Predicted negative': 3,
'Predicted negative (%)': 75.0,
'Predicted positive': 1,
'Predicted positive (%)': 25.0,
'Recall': 0.3333333333333333,
'True negative': 1,
'True negative (%)': 25.0,
'True positive': 1,
'True positive (%)': 25.0}},
'Fix zero': {0: {'Accuracy': 0.75,
'Condition negative': 3,
'Condition negative (%)': 75.0,
'Condition positive': 1,
'Condition positive (%)': 25.0,
'F1 score': 0.6666666666666666,
'False negative': 0,
'False negative (%)': 0.0,
'False positive': 1,
'False positive (%)': 25.0,
'Precision': 0.5,
'Predicted negative': 2,
'Predicted negative (%)': 50.0,
'Predicted positive': 2,
'Predicted positive (%)': 50.0,
'Recall': 1.0,
'True negative': 2,
'True negative (%)': 50.0,
'True positive': 1,
'True positive (%)': 25.0}}}
assert ev == {
"Fix one": {
0: {
"Accuracy": 0.5,
"Condition negative": 1,
"Condition negative (%)": 25.0,
"Condition positive": 3,
"Condition positive (%)": 75.0,
"F1 score": 0.5,
"False negative": 2,
"False negative (%)": 50.0,
"False positive": 0,
"False positive (%)": 0.0,
"Precision": 1.0,
"Predicted negative": 3,
"Predicted negative (%)": 75.0,
"Predicted positive": 1,
"Predicted positive (%)": 25.0,
"Recall": 0.3333333333333333,
"True negative": 1,
"True negative (%)": 25.0,
"True positive": 1,
"True positive (%)": 25.0,
}
},
"Fix zero": {
0: {
"Accuracy": 0.75,
"Condition negative": 3,
"Condition negative (%)": 75.0,
"Condition positive": 1,
"Condition positive (%)": 25.0,
"F1 score": 0.6666666666666666,
"False negative": 0,
"False negative (%)": 0.0,
"False positive": 1,
"False positive (%)": 25.0,
"Precision": 0.5,
"Predicted negative": 2,
"Predicted negative (%)": 50.0,
"Predicted positive": 2,
"Predicted positive (%)": 50.0,
"Recall": 1.0,
"True negative": 2,
"True negative (%)": 50.0,
"True positive": 1,
"True positive (%)": 25.0,
}
},
}
def test_primal_parallel_fit():