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Always pass (n,)-shaped arrays to regressors instead of (n,1)

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Alinson S. Xavier
2020-04-15 08:42:06 -05:00
parent 8d2d9873cb
commit 659ef4cf9e
2 changed files with 8 additions and 4 deletions
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10
src/python/miplearn/components/objective.py
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@@ -41,18 +41,22 @@ class ObjectiveValueComponent(Component):
features = InstanceFeaturesExtractor().extract(training_instances) features = InstanceFeaturesExtractor().extract(training_instances)
ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances) ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances) lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
assert ub.shape == (len(training_instances), 1)
assert lb.shape == (len(training_instances), 1)
self.ub_regressor = deepcopy(self.regressor_prototype) self.ub_regressor = deepcopy(self.regressor_prototype)
self.lb_regressor = deepcopy(self.regressor_prototype) self.lb_regressor = deepcopy(self.regressor_prototype)
logger.debug("Fitting ub_regressor...") logger.debug("Fitting ub_regressor...")
self.ub_regressor.fit(features, ub) self.ub_regressor.fit(features, ub.ravel())
logger.debug("Fitting ub_regressor...") logger.debug("Fitting ub_regressor...")
self.lb_regressor.fit(features, lb) self.lb_regressor.fit(features, lb.ravel())
def predict(self, instances): def predict(self, instances):
features = InstanceFeaturesExtractor().extract(instances) features = InstanceFeaturesExtractor().extract(instances)
lb = self.lb_regressor.predict(features) lb = self.lb_regressor.predict(features)
ub = self.ub_regressor.predict(features) ub = self.ub_regressor.predict(features)
return np.hstack([lb, ub]) assert lb.shape == (len(instances),)
assert ub.shape == (len(instances),)
return np.array([lb, ub]).T
def evaluate(self, instances): def evaluate(self, instances):
y_pred = self.predict(instances) y_pred = self.predict(instances)

2
src/python/miplearn/components/tests/test_objective.py
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@@ -23,7 +23,7 @@ def test_usage():
def test_obj_evaluate(): def test_obj_evaluate():
instances, models = get_training_instances_and_models() instances, models = get_training_instances_and_models()
reg = Mock(spec=Regressor) reg = Mock(spec=Regressor)
reg.predict = Mock(return_value=np.array([[1000.0], [1000.0]])) reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
comp = ObjectiveValueComponent(regressor=reg) comp = ObjectiveValueComponent(regressor=reg)
comp.fit(instances) comp.fit(instances)
ev = comp.evaluate(instances) ev = comp.evaluate(instances)