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Rename methods that use TrainingSample to _old

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master
Alinson S. Xavier 5 years ago
parent 5fd13981d4
commit 2da60dd293
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GPG Key ID: DCA0DAD4D2F58624
13 changed files with 63 additions and 63 deletions
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16
miplearn/components/component.py
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@ -25,7 +25,7 @@ class Component:
strategy. strategy.
""" """
def before_solve_lp( def before_solve_lp_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -62,7 +62,7 @@ class Component:
""" """
return return
def after_solve_lp( def after_solve_lp_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -77,7 +77,7 @@ class Component:
""" """
return return
def before_solve_mip( def before_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -92,7 +92,7 @@ class Component:
""" """
return return
def after_solve_mip( def after_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -107,7 +107,7 @@ class Component:
""" """
return return
def sample_xy( def sample_xy_old(
self, self,
instance: Instance, instance: Instance,
sample: TrainingSample, sample: TrainingSample,
@ -128,7 +128,7 @@ class Component:
for instance in instances: for instance in instances:
instance.load() instance.load()
for sample in instance.training_data: for sample in instance.training_data:
xy = self.sample_xy(instance, sample) xy = self.sample_xy_old(instance, sample)
if xy is None: if xy is None:
continue continue
x_sample, y_sample = xy x_sample, y_sample = xy
@ -213,11 +213,11 @@ class Component:
for instance in instances: for instance in instances:
instance.load() instance.load()
for sample in instance.training_data: for sample in instance.training_data:
ev += [self.sample_evaluate(instance, sample)] ev += [self.sample_evaluate_old(instance, sample)]
instance.free() instance.free()
return ev return ev
def sample_evaluate( def sample_evaluate_old(
self, self,
instance: Instance, instance: Instance,
sample: TrainingSample, sample: TrainingSample,

4
miplearn/components/dynamic_common.py
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@ -79,7 +79,7 @@ class DynamicConstraintsComponent(Component):
return x, y, cids return x, y, cids
@overrides @overrides
def sample_xy( def sample_xy_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,
@ -139,7 +139,7 @@ class DynamicConstraintsComponent(Component):
self.thresholds[category].fit(self.classifiers[category], npx, npy) self.thresholds[category].fit(self.classifiers[category], npx, npy)
@overrides @overrides
def sample_evaluate( def sample_evaluate_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,

10
miplearn/components/dynamic_lazy.py
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@ -54,7 +54,7 @@ class DynamicLazyConstraintsComponent(Component):
instance.enforce_lazy_constraint(solver.internal_solver, model, cid) instance.enforce_lazy_constraint(solver.internal_solver, model, cid)
@overrides @overrides
def before_solve_mip( def before_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -93,12 +93,12 @@ class DynamicLazyConstraintsComponent(Component):
# Delegate ML methods to self.dynamic # Delegate ML methods to self.dynamic
# ------------------------------------------------------------------- # -------------------------------------------------------------------
@overrides @overrides
def sample_xy( def sample_xy_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,
) -> Tuple[Dict, Dict]: ) -> Tuple[Dict, Dict]:
return self.dynamic.sample_xy(instance, sample) return self.dynamic.sample_xy_old(instance, sample)
def sample_predict( def sample_predict(
self, self,
@ -120,9 +120,9 @@ class DynamicLazyConstraintsComponent(Component):
self.dynamic.fit_xy(x, y) self.dynamic.fit_xy(x, y)
@overrides @overrides
def sample_evaluate( def sample_evaluate_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,
) -> Dict[Hashable, Dict[str, float]]: ) -> Dict[Hashable, Dict[str, float]]:
return self.dynamic.sample_evaluate(instance, sample) return self.dynamic.sample_evaluate_old(instance, sample)

12
miplearn/components/dynamic_user_cuts.py
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@ -37,7 +37,7 @@ class UserCutsComponent(Component):
self.n_added_in_callback = 0 self.n_added_in_callback = 0
@overrides @overrides
def before_solve_mip( def before_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: "Instance", instance: "Instance",
@ -79,7 +79,7 @@ class UserCutsComponent(Component):
logger.debug(f"Added {len(cids)} violated user cuts") logger.debug(f"Added {len(cids)} violated user cuts")
@overrides @overrides
def after_solve_mip( def after_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: "Instance", instance: "Instance",
@ -96,12 +96,12 @@ class UserCutsComponent(Component):
# Delegate ML methods to self.dynamic # Delegate ML methods to self.dynamic
# ------------------------------------------------------------------- # -------------------------------------------------------------------
@overrides @overrides
def sample_xy( def sample_xy_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,
) -> Tuple[Dict, Dict]: ) -> Tuple[Dict, Dict]:
return self.dynamic.sample_xy(instance, sample) return self.dynamic.sample_xy_old(instance, sample)
def sample_predict( def sample_predict(
self, self,
@ -123,9 +123,9 @@ class UserCutsComponent(Component):
self.dynamic.fit_xy(x, y) self.dynamic.fit_xy(x, y)
@overrides @overrides
def sample_evaluate( def sample_evaluate_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,
) -> Dict[Hashable, Dict[str, float]]: ) -> Dict[Hashable, Dict[str, float]]:
return self.dynamic.sample_evaluate(instance, sample) return self.dynamic.sample_evaluate_old(instance, sample)

8
miplearn/components/objective.py
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@ -36,7 +36,7 @@ class ObjectiveValueComponent(Component):
self.regressor_prototype = regressor self.regressor_prototype = regressor
@overrides @overrides
def before_solve_mip( def before_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -68,7 +68,7 @@ class ObjectiveValueComponent(Component):
sample: TrainingSample, sample: TrainingSample,
) -> Dict[str, float]: ) -> Dict[str, float]:
pred: Dict[str, float] = {} pred: Dict[str, float] = {}
x, _ = self.sample_xy(instance, sample) x, _ = self.sample_xy_old(instance, sample)
for c in ["Upper bound", "Lower bound"]: for c in ["Upper bound", "Lower bound"]:
if c in self.regressors is not None: if c in self.regressors is not None:
pred[c] = self.regressors[c].predict(np.array(x[c]))[0, 0] pred[c] = self.regressors[c].predict(np.array(x[c]))[0, 0]
@ -77,7 +77,7 @@ class ObjectiveValueComponent(Component):
return pred return pred
@overrides @overrides
def sample_xy( def sample_xy_old(
self, self,
instance: Instance, instance: Instance,
sample: TrainingSample, sample: TrainingSample,
@ -99,7 +99,7 @@ class ObjectiveValueComponent(Component):
return x, y return x, y
@overrides @overrides
def sample_evaluate( def sample_evaluate_old(
self, self,
instance: Instance, instance: Instance,
sample: TrainingSample, sample: TrainingSample,

8
miplearn/components/primal.py
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@ -60,7 +60,7 @@ class PrimalSolutionComponent(Component):
self.classifier_prototype = classifier self.classifier_prototype = classifier
@overrides @overrides
def before_solve_mip( def before_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: Instance, instance: Instance,
@ -110,7 +110,7 @@ class PrimalSolutionComponent(Component):
assert instance.features.variables is not None assert instance.features.variables is not None
# Compute y_pred # Compute y_pred
x, _ = self.sample_xy(instance, sample) x, _ = self.sample_xy_old(instance, sample)
y_pred = {} y_pred = {}
for category in x.keys(): for category in x.keys():
assert category in self.classifiers, ( assert category in self.classifiers, (
@ -144,7 +144,7 @@ class PrimalSolutionComponent(Component):
return solution return solution
@overrides @overrides
def sample_xy( def sample_xy_old(
self, self,
instance: Instance, instance: Instance,
sample: TrainingSample, sample: TrainingSample,
@ -180,7 +180,7 @@ class PrimalSolutionComponent(Component):
return x, y return x, y
@overrides @overrides
def sample_evaluate( def sample_evaluate_old(
self, self,
instance: Instance, instance: Instance,
sample: TrainingSample, sample: TrainingSample,

8
miplearn/components/static_lazy.py
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@ -51,7 +51,7 @@ class StaticLazyConstraintsComponent(Component):
self.n_iterations: int = 0 self.n_iterations: int = 0
@overrides @overrides
def before_solve_mip( def before_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: "Instance", instance: "Instance",
@ -85,7 +85,7 @@ class StaticLazyConstraintsComponent(Component):
self.n_iterations = 0 self.n_iterations = 0
@overrides @overrides
def after_solve_mip( def after_solve_mip_old(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: "Instance", instance: "Instance",
@ -151,7 +151,7 @@ class StaticLazyConstraintsComponent(Component):
) -> List[Hashable]: ) -> List[Hashable]:
assert instance.features.constraints is not None assert instance.features.constraints is not None
x, y = self.sample_xy(instance, sample) x, y = self.sample_xy_old(instance, sample)
category_to_cids: Dict[Hashable, List[Hashable]] = {} category_to_cids: Dict[Hashable, List[Hashable]] = {}
for (cid, cfeatures) in instance.features.constraints.items(): for (cid, cfeatures) in instance.features.constraints.items():
if cfeatures.category is None: if cfeatures.category is None:
@ -174,7 +174,7 @@ class StaticLazyConstraintsComponent(Component):
return enforced_cids return enforced_cids
@overrides @overrides
def sample_xy( def sample_xy_old(
self, self,
instance: "Instance", instance: "Instance",
sample: TrainingSample, sample: TrainingSample,

10
miplearn/solvers/learning.py
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@ -162,7 +162,7 @@ class LearningSolver:
instance.features.__dict__ = features.__dict__ instance.features.__dict__ = features.__dict__
sample.after_load = features sample.after_load = features
callback_args = ( callback_args_old = (
self, self,
instance, instance,
model, model,
@ -177,7 +177,7 @@ class LearningSolver:
if self.solve_lp: if self.solve_lp:
logger.debug("Running before_solve_lp callbacks...") logger.debug("Running before_solve_lp callbacks...")
for component in self.components.values(): for component in self.components.values():
component.before_solve_lp(*callback_args) component.before_solve_lp_old(*callback_args_old)
logger.info("Solving root LP relaxation...") logger.info("Solving root LP relaxation...")
lp_stats = self.internal_solver.solve_lp(tee=tee) lp_stats = self.internal_solver.solve_lp(tee=tee)
@ -188,7 +188,7 @@ class LearningSolver:
logger.debug("Running after_solve_lp callbacks...") logger.debug("Running after_solve_lp callbacks...")
for component in self.components.values(): for component in self.components.values():
component.after_solve_lp(*callback_args) component.after_solve_lp_old(*callback_args_old)
# Extract features (after-lp) # Extract features (after-lp)
# ------------------------------------------------------- # -------------------------------------------------------
@ -232,7 +232,7 @@ class LearningSolver:
# ------------------------------------------------------- # -------------------------------------------------------
logger.debug("Running before_solve_mip callbacks...") logger.debug("Running before_solve_mip callbacks...")
for component in self.components.values(): for component in self.components.values():
component.before_solve_mip(*callback_args) component.before_solve_mip_old(*callback_args_old)
# Solve MIP # Solve MIP
# ------------------------------------------------------- # -------------------------------------------------------
@ -269,7 +269,7 @@ class LearningSolver:
# ------------------------------------------------------- # -------------------------------------------------------
logger.debug("Calling after_solve_mip callbacks...") logger.debug("Calling after_solve_mip callbacks...")
for component in self.components.values(): for component in self.components.values():
component.after_solve_mip(*callback_args) component.after_solve_mip_old(*callback_args_old)
# Flush # Flush
# ------------------------------------------------------- # -------------------------------------------------------

4
tests/components/test_component.py
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@ -10,7 +10,7 @@ from miplearn.instance.base import Instance
def test_xy_instance() -> None: def test_xy_instance() -> None:
def _sample_xy(features: Features, sample: str) -> Tuple[Dict, Dict]: def _sample_xy_old(features: Features, sample: str) -> Tuple[Dict, Dict]:
x = { x = {
"s1": { "s1": {
"category_a": [ "category_a": [
@ -60,7 +60,7 @@ def test_xy_instance() -> None:
instance_2 = Mock(spec=Instance) instance_2 = Mock(spec=Instance)
instance_2.training_data = ["s3"] instance_2.training_data = ["s3"]
instance_2.features = {} instance_2.features = {}
comp.sample_xy = _sample_xy # type: ignore comp.sample_xy_old = _sample_xy_old # type: ignore
x_expected = { x_expected = {
"category_a": [ "category_a": [
[1, 2, 3], [1, 2, 3],

2
tests/components/test_dynamic_lazy.py
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@ -160,7 +160,7 @@ def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
training_instances[0].training_data[0], training_instances[0].training_data[0],
) )
assert pred == ["c1", "c4"] assert pred == ["c1", "c4"]
ev = comp.sample_evaluate( ev = comp.sample_evaluate_old(
training_instances[0], training_instances[0],
training_instances[0].training_data[0], training_instances[0].training_data[0],
) )

30
tests/components/test_objective.py
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@ -33,7 +33,7 @@ def features() -> Features:
@pytest.fixture @pytest.fixture
def sample() -> TrainingSample: def sample_old() -> TrainingSample:
return TrainingSample( return TrainingSample(
lower_bound=1.0, lower_bound=1.0,
upper_bound=2.0, upper_bound=2.0,
@ -50,7 +50,7 @@ def sample_without_lp() -> TrainingSample:
@pytest.fixture @pytest.fixture
def sample_without_ub() -> TrainingSample: def sample_without_ub_old() -> TrainingSample:
return TrainingSample( return TrainingSample(
lower_bound=1.0, lower_bound=1.0,
lp_value=3.0, lp_value=3.0,
@ -59,7 +59,7 @@ def sample_without_ub() -> TrainingSample:
def test_sample_xy( def test_sample_xy(
instance: Instance, instance: Instance,
sample: TrainingSample, sample_old: TrainingSample,
) -> None: ) -> None:
x_expected = { x_expected = {
"Lower bound": [[1.0, 2.0, 3.0]], "Lower bound": [[1.0, 2.0, 3.0]],
@ -69,7 +69,7 @@ def test_sample_xy(
"Lower bound": [[1.0]], "Lower bound": [[1.0]],
"Upper bound": [[2.0]], "Upper bound": [[2.0]],
} }
xy = ObjectiveValueComponent().sample_xy(instance, sample) xy = ObjectiveValueComponent().sample_xy_old(instance, sample_old)
assert xy is not None assert xy is not None
x_actual, y_actual = xy x_actual, y_actual = xy
assert x_actual == x_expected assert x_actual == x_expected
@ -88,7 +88,7 @@ def test_sample_xy_without_lp(
"Lower bound": [[1.0]], "Lower bound": [[1.0]],
"Upper bound": [[2.0]], "Upper bound": [[2.0]],
} }
xy = ObjectiveValueComponent().sample_xy(instance, sample_without_lp) xy = ObjectiveValueComponent().sample_xy_old(instance, sample_without_lp)
assert xy is not None assert xy is not None
x_actual, y_actual = xy x_actual, y_actual = xy
assert x_actual == x_expected assert x_actual == x_expected
@ -97,14 +97,14 @@ def test_sample_xy_without_lp(
def test_sample_xy_without_ub( def test_sample_xy_without_ub(
instance: Instance, instance: Instance,
sample_without_ub: TrainingSample, sample_without_ub_old: TrainingSample,
) -> None: ) -> None:
x_expected = { x_expected = {
"Lower bound": [[1.0, 2.0, 3.0]], "Lower bound": [[1.0, 2.0, 3.0]],
"Upper bound": [[1.0, 2.0, 3.0]], "Upper bound": [[1.0, 2.0, 3.0]],
} }
y_expected = {"Lower bound": [[1.0]]} y_expected = {"Lower bound": [[1.0]]}
xy = ObjectiveValueComponent().sample_xy(instance, sample_without_ub) xy = ObjectiveValueComponent().sample_xy_old(instance, sample_without_ub_old)
assert xy is not None assert xy is not None
x_actual, y_actual = xy x_actual, y_actual = xy
assert x_actual == x_expected assert x_actual == x_expected
@ -179,9 +179,9 @@ def test_fit_xy_without_ub() -> None:
def test_sample_predict( def test_sample_predict(
instance: Instance, instance: Instance,
sample: TrainingSample, sample_old: TrainingSample,
) -> None: ) -> None:
x, y = ObjectiveValueComponent().sample_xy(instance, sample) x, y = ObjectiveValueComponent().sample_xy_old(instance, sample_old)
comp = ObjectiveValueComponent() comp = ObjectiveValueComponent()
comp.regressors["Lower bound"] = Mock(spec=Regressor) comp.regressors["Lower bound"] = Mock(spec=Regressor)
comp.regressors["Upper bound"] = Mock(spec=Regressor) comp.regressors["Upper bound"] = Mock(spec=Regressor)
@ -191,7 +191,7 @@ def test_sample_predict(
comp.regressors["Upper bound"].predict = Mock( # type: ignore comp.regressors["Upper bound"].predict = Mock( # type: ignore
side_effect=lambda _: np.array([[60.0]]) side_effect=lambda _: np.array([[60.0]])
) )
pred = comp.sample_predict(instance, sample) pred = comp.sample_predict(instance, sample_old)
assert pred == { assert pred == {
"Lower bound": 50.0, "Lower bound": 50.0,
"Upper bound": 60.0, "Upper bound": 60.0,
@ -208,15 +208,15 @@ def test_sample_predict(
def test_sample_predict_without_ub( def test_sample_predict_without_ub(
instance: Instance, instance: Instance,
sample_without_ub: TrainingSample, sample_without_ub_old: TrainingSample,
) -> None: ) -> None:
x, y = ObjectiveValueComponent().sample_xy(instance, sample_without_ub) x, y = ObjectiveValueComponent().sample_xy_old(instance, sample_without_ub_old)
comp = ObjectiveValueComponent() comp = ObjectiveValueComponent()
comp.regressors["Lower bound"] = Mock(spec=Regressor) comp.regressors["Lower bound"] = Mock(spec=Regressor)
comp.regressors["Lower bound"].predict = Mock( # type: ignore comp.regressors["Lower bound"].predict = Mock( # type: ignore
side_effect=lambda _: np.array([[50.0]]) side_effect=lambda _: np.array([[50.0]])
) )
pred = comp.sample_predict(instance, sample_without_ub) pred = comp.sample_predict(instance, sample_without_ub_old)
assert pred == { assert pred == {
"Lower bound": 50.0, "Lower bound": 50.0,
} }
@ -226,13 +226,13 @@ def test_sample_predict_without_ub(
) )
def test_sample_evaluate(instance: Instance, sample: TrainingSample) -> None: def test_sample_evaluate(instance: Instance, sample_old: TrainingSample) -> None:
comp = ObjectiveValueComponent() comp = ObjectiveValueComponent()
comp.regressors["Lower bound"] = Mock(spec=Regressor) comp.regressors["Lower bound"] = Mock(spec=Regressor)
comp.regressors["Lower bound"].predict = lambda _: np.array([[1.05]]) # type: ignore comp.regressors["Lower bound"].predict = lambda _: np.array([[1.05]]) # type: ignore
comp.regressors["Upper bound"] = Mock(spec=Regressor) comp.regressors["Upper bound"] = Mock(spec=Regressor)
comp.regressors["Upper bound"].predict = lambda _: np.array([[2.50]]) # type: ignore comp.regressors["Upper bound"].predict = lambda _: np.array([[2.50]]) # type: ignore
ev = comp.sample_evaluate(instance, sample) ev = comp.sample_evaluate_old(instance, sample_old)
assert ev == { assert ev == {
"Lower bound": { "Lower bound": {
"Actual value": 1.0, "Actual value": 1.0,

8
tests/components/test_primal.py
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@ -68,7 +68,7 @@ def test_xy() -> None:
[True, False], [True, False],
] ]
} }
xy = PrimalSolutionComponent().sample_xy(instance, sample) xy = PrimalSolutionComponent().sample_xy_old(instance, sample)
assert xy is not None assert xy is not None
x_actual, y_actual = xy x_actual, y_actual = xy
assert x_actual == x_expected assert x_actual == x_expected
@ -119,7 +119,7 @@ def test_xy_without_lp_solution() -> None:
[True, False], [True, False],
] ]
} }
xy = PrimalSolutionComponent().sample_xy(instance, sample) xy = PrimalSolutionComponent().sample_xy_old(instance, sample)
assert xy is not None assert xy is not None
x_actual, y_actual = xy x_actual, y_actual = xy
assert x_actual == x_expected assert x_actual == x_expected
@ -164,7 +164,7 @@ def test_predict() -> None:
"x[2]": 0.9, "x[2]": 0.9,
} }
) )
x, _ = PrimalSolutionComponent().sample_xy(instance, sample) x, _ = PrimalSolutionComponent().sample_xy_old(instance, sample)
comp = PrimalSolutionComponent() comp = PrimalSolutionComponent()
comp.classifiers = {"default": clf} comp.classifiers = {"default": clf}
comp.thresholds = {"default": thr} comp.thresholds = {"default": thr}
@ -253,7 +253,7 @@ def test_evaluate() -> None:
"x[4]": 1.0, "x[4]": 1.0,
} }
) )
ev = comp.sample_evaluate(instance, sample) ev = comp.sample_evaluate_old(instance, sample)
assert ev == { assert ev == {
0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0), 0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0),
1: classifier_evaluation_dict(tp=2, fp=0, tn=1, fn=2), 1: classifier_evaluation_dict(tp=2, fp=0, tn=1, fn=2),

6
tests/components/test_static_lazy.py
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@ -116,7 +116,7 @@ def test_usage_with_solver(instance: Instance) -> None:
stats: LearningSolveStats = {} stats: LearningSolveStats = {}
# LearningSolver calls before_solve_mip # LearningSolver calls before_solve_mip
component.before_solve_mip( component.before_solve_mip_old(
solver=solver, solver=solver,
instance=instance, instance=instance,
model=None, model=None,
@ -154,7 +154,7 @@ def test_usage_with_solver(instance: Instance) -> None:
internal.add_constraint.assert_not_called() internal.add_constraint.assert_not_called()
# LearningSolver calls after_solve_mip # LearningSolver calls after_solve_mip
component.after_solve_mip( component.after_solve_mip_old(
solver=solver, solver=solver,
instance=instance, instance=instance,
model=None, model=None,
@ -250,7 +250,7 @@ def test_sample_xy(
"type-a": [[False, True], [False, True], [True, False]], "type-a": [[False, True], [False, True], [True, False]],
"type-b": [[False, True]], "type-b": [[False, True]],
} }
xy = StaticLazyConstraintsComponent().sample_xy(instance, sample) xy = StaticLazyConstraintsComponent().sample_xy_old(instance, sample)
assert xy is not None assert xy is not None
x_actual, y_actual = xy x_actual, y_actual = xy
assert x_actual == x_expected assert x_actual == x_expected

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