mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 09:28:51 -06:00
Refer to variables by varname instead of (vname, index)
This commit is contained in:
@@ -10,6 +10,7 @@ import networkx as nx
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import pytest
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from gurobipy import GRB
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from networkx import Graph
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from overrides import overrides
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from miplearn.components.dynamic_user_cuts import UserCutsComponent
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from miplearn.instance.base import Instance
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@@ -24,6 +25,7 @@ class GurobiStableSetProblem(Instance):
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super().__init__()
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self.graph: Graph = graph
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@overrides
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def to_model(self) -> Any:
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model = gp.Model()
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x = [model.addVar(vtype=GRB.BINARY) for _ in range(len(self.graph.nodes))]
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@@ -32,9 +34,11 @@ class GurobiStableSetProblem(Instance):
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model.addConstr(x[e[0]] + x[e[1]] <= 1)
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return model
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@overrides
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def has_user_cuts(self) -> bool:
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return True
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@overrides
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def find_violated_user_cuts(self, model):
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assert isinstance(model, gp.Model)
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vals = model.cbGetNodeRel(model.getVars())
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@@ -44,6 +48,7 @@ class GurobiStableSetProblem(Instance):
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violations += [frozenset(clique)]
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return violations
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@overrides
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def build_user_cut(self, model: Any, cid: Hashable) -> Any:
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assert isinstance(cid, FrozenSet)
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x = model.getVars()
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@@ -20,43 +20,37 @@ from miplearn.solvers.learning import LearningSolver
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def test_xy() -> None:
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features = Features(
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variables={
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"x": {
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0: VariableFeatures(
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category="default",
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user_features=[0.0, 0.0],
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),
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1: VariableFeatures(
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category=None,
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),
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2: VariableFeatures(
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category="default",
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user_features=[1.0, 0.0],
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),
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3: VariableFeatures(
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category="default",
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user_features=[1.0, 1.0],
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),
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}
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"x[0]": VariableFeatures(
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category="default",
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user_features=[0.0, 0.0],
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),
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"x[1]": VariableFeatures(
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category=None,
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),
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"x[2]": VariableFeatures(
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category="default",
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user_features=[1.0, 0.0],
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),
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"x[3]": VariableFeatures(
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category="default",
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user_features=[1.0, 1.0],
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),
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}
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)
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instance = Mock(spec=Instance)
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instance.features = features
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sample = TrainingSample(
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solution={
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"x": {
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0: 0.0,
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1: 1.0,
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2: 1.0,
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3: 0.0,
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}
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"x[0]": 0.0,
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"x[1]": 1.0,
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"x[2]": 1.0,
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"x[3]": 0.0,
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},
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lp_solution={
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"x": {
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0: 0.1,
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1: 0.1,
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2: 0.1,
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3: 0.1,
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}
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"x[0]": 0.1,
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"x[1]": 0.1,
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"x[2]": 0.1,
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"x[3]": 0.1,
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},
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)
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x_expected = {
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@@ -73,7 +67,7 @@ def test_xy() -> None:
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[True, False],
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]
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}
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xy = PrimalSolutionComponent.sample_xy(instance, sample)
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xy = PrimalSolutionComponent().sample_xy(instance, sample)
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assert xy is not None
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x_actual, y_actual = xy
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assert x_actual == x_expected
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@@ -83,35 +77,31 @@ def test_xy() -> None:
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def test_xy_without_lp_solution() -> None:
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features = Features(
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variables={
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"x": {
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0: VariableFeatures(
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category="default",
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user_features=[0.0, 0.0],
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),
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1: VariableFeatures(
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category=None,
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),
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2: VariableFeatures(
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category="default",
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user_features=[1.0, 0.0],
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),
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3: VariableFeatures(
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category="default",
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user_features=[1.0, 1.0],
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),
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}
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"x[0]": VariableFeatures(
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category="default",
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user_features=[0.0, 0.0],
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),
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"x[1]": VariableFeatures(
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category=None,
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),
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"x[2]": VariableFeatures(
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category="default",
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user_features=[1.0, 0.0],
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),
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"x[3]": VariableFeatures(
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category="default",
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user_features=[1.0, 1.0],
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),
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}
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)
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instance = Mock(spec=Instance)
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instance.features = features
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sample = TrainingSample(
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solution={
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"x": {
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0: 0.0,
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1: 1.0,
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2: 1.0,
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3: 0.0,
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}
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"x[0]": 0.0,
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"x[1]": 1.0,
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"x[2]": 1.0,
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"x[3]": 0.0,
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},
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)
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x_expected = {
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@@ -128,7 +118,7 @@ def test_xy_without_lp_solution() -> None:
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[True, False],
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]
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}
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xy = PrimalSolutionComponent.sample_xy(instance, sample)
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xy = PrimalSolutionComponent().sample_xy(instance, sample)
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assert xy is not None
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x_actual, y_actual = xy
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assert x_actual == x_expected
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@@ -150,48 +140,42 @@ def test_predict() -> None:
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thr.predict = Mock(return_value=[0.75, 0.75])
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features = Features(
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variables={
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"x": {
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0: VariableFeatures(
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category="default",
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user_features=[0.0, 0.0],
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),
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1: VariableFeatures(
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category="default",
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user_features=[0.0, 2.0],
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),
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2: VariableFeatures(
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category="default",
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user_features=[2.0, 0.0],
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),
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}
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"x[0]": VariableFeatures(
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category="default",
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user_features=[0.0, 0.0],
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),
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"x[1]": VariableFeatures(
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category="default",
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user_features=[0.0, 2.0],
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),
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"x[2]": VariableFeatures(
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category="default",
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user_features=[2.0, 0.0],
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),
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}
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)
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instance = Mock(spec=Instance)
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instance.features = features
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sample = TrainingSample(
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lp_solution={
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"x": {
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0: 0.1,
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1: 0.5,
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2: 0.9,
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}
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"x[0]": 0.1,
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"x[1]": 0.5,
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"x[2]": 0.9,
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}
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)
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x, _ = PrimalSolutionComponent.sample_xy(instance, sample)
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x, _ = PrimalSolutionComponent().sample_xy(instance, sample)
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comp = PrimalSolutionComponent()
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comp.classifiers = {"default": clf}
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comp.thresholds = {"default": thr}
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solution_actual = comp.sample_predict(instance, sample)
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pred = comp.sample_predict(instance, sample)
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clf.predict_proba.assert_called_once()
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assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
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thr.predict.assert_called_once()
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assert_array_equal(x["default"], thr.predict.call_args[0][0])
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assert solution_actual == {
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"x": {
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0: 0.0,
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1: None,
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2: 1.0,
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}
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assert pred == {
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"x[0]": 0.0,
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"x[1]": None,
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"x[2]": 1.0,
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}
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@@ -242,36 +226,30 @@ def test_usage():
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def test_evaluate() -> None:
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comp = PrimalSolutionComponent()
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comp.sample_predict = lambda _, __: { # type: ignore
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"x": {
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0: 1.0,
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1: 0.0,
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2: 0.0,
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3: None,
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4: 1.0,
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}
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"x[0]": 1.0,
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"x[1]": 0.0,
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"x[2]": 0.0,
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"x[3]": None,
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"x[4]": 1.0,
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}
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features: Features = Features(
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variables={
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"x": {
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0: VariableFeatures(),
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1: VariableFeatures(),
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2: VariableFeatures(),
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3: VariableFeatures(),
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4: VariableFeatures(),
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}
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"x[0]": VariableFeatures(),
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"x[1]": VariableFeatures(),
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"x[2]": VariableFeatures(),
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"x[3]": VariableFeatures(),
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"x[4]": VariableFeatures(),
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}
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)
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instance = Mock(spec=Instance)
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instance.features = features
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sample: TrainingSample = TrainingSample(
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solution={
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"x": {
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0: 1.0,
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1: 1.0,
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2: 0.0,
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3: 1.0,
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4: 1.0,
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}
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"x[0]": 1.0,
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"x[1]": 1.0,
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"x[2]": 0.0,
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"x[3]": 1.0,
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"x[4]": 1.0,
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}
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)
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ev = comp.sample_evaluate(instance, sample)
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