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Use compact variable features everywhere

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This commit is contained in:
Alinson S. Xavier
2021-04-15 09:49:35 -05:00
parent fec0113722
commit 95e326f5f6
11 changed files with 147 additions and 374 deletions
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44
miplearn/components/primal.py
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@@ -104,7 +104,7 @@ class PrimalSolutionComponent(Component):
def sample_predict(self, sample: Sample) -> Solution:
assert sample.after_load is not None
assert sample.after_load.variables_old is not None
assert sample.after_load.variables is not None
# Compute y_pred
x, _ = self.sample_xy(None, sample)
@@ -125,10 +125,12 @@ class PrimalSolutionComponent(Component):
).T
# Convert y_pred into solution
solution: Solution = {v: None for v in sample.after_load.variables_old.keys()}
assert sample.after_load.variables.names is not None
assert sample.after_load.variables.categories is not None
solution: Solution = {v: None for v in sample.after_load.variables.names}
category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
for (var_name, var_features) in sample.after_load.variables_old.items():
category = var_features.category
for (i, var_name) in enumerate(sample.after_load.variables.names):
category = sample.after_load.variables.categories[i]
if category not in category_offset:
continue
offset = category_offset[category]
@@ -150,10 +152,13 @@ class PrimalSolutionComponent(Component):
y: Dict = {}
assert sample.after_load is not None
assert sample.after_load.instance is not None
assert sample.after_load.variables_old is not None
for (var_name, var) in sample.after_load.variables_old.items():
assert sample.after_load.variables is not None
assert sample.after_load.variables.names is not None
assert sample.after_load.variables.categories is not None
for (i, var_name) in enumerate(sample.after_load.variables.names):
# Initialize categories
category = var.category
category = sample.after_load.variables.categories[i]
if category is None:
continue
if category not in x.keys():
@@ -162,17 +167,17 @@ class PrimalSolutionComponent(Component):
# Features
features = list(sample.after_load.instance.to_list())
features.extend(sample.after_load.variables_old[var_name].to_list())
features.extend(sample.after_load.variables.to_list(i))
if sample.after_lp is not None:
assert sample.after_lp.variables_old is not None
features.extend(sample.after_lp.variables_old[var_name].to_list())
assert sample.after_lp.variables is not None
features.extend(sample.after_lp.variables.to_list(i))
x[category].append(features)
# Labels
if sample.after_mip is not None:
assert sample.after_mip.variables_old is not None
assert sample.after_mip.variables_old[var_name] is not None
opt_value = sample.after_mip.variables_old[var_name].value
assert sample.after_mip.variables is not None
assert sample.after_mip.variables.values is not None
opt_value = sample.after_mip.variables.values[i]
assert opt_value is not None
assert 0.0 - 1e-5 <= opt_value <= 1.0 + 1e-5, (
f"Variable {var_name} has non-binary value {opt_value} in the "
@@ -190,15 +195,18 @@ class PrimalSolutionComponent(Component):
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
assert sample.after_mip is not None
assert sample.after_mip.variables_old is not None
assert sample.after_mip.variables is not None
assert sample.after_mip.variables.values is not None
assert sample.after_mip.variables.names is not None
solution_actual = sample.after_mip.variables_old
solution_actual = {
var_name: sample.after_mip.variables.values[i]
for (i, var_name) in enumerate(sample.after_mip.variables.names)
}
solution_pred = self.sample_predict(sample)
vars_all, vars_one, vars_zero = set(), set(), set()
pred_one_positive, pred_zero_positive = set(), set()
for (var_name, var) in solution_actual.items():
assert var.value is not None
value_actual = var.value
for (var_name, value_actual) in solution_actual.items():
vars_all.add(var_name)
if value_actual > 0.5:
vars_one.add(var_name)