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

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pull/3/head
Alinson S. Xavier 5 years ago
parent 3823931382
commit d99600f101
49 changed files with 1294 additions and 975 deletions
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11
.github/workflows/lint.yml vendored
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@ -0,0 +1,11 @@
name: Lint
on: [push, pull_request]
jobs:
lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
- uses: psf/black@stable

6
.pre-commit-config.yaml
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@ -0,0 +1,6 @@
repos:
- repo: https://github.com/ambv/black
rev: stable
hooks:
- id: black
args: ["--check"]

3
Makefile
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@ -34,6 +34,9 @@ install:
uninstall:
$(PIP) uninstall miplearn
reformat:
$(PYTHON) -m black miplearn
test:
$(PYTEST) $(PYTEST_ARGS)

10
miplearn/__init__.py
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@ -2,10 +2,12 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from .extractors import (SolutionExtractor,
InstanceFeaturesExtractor,
ObjectiveValueExtractor,
VariableFeaturesExtractor)
from .extractors import (
SolutionExtractor,
InstanceFeaturesExtractor,
ObjectiveValueExtractor,
VariableFeaturesExtractor,
)
from .components.component import Component
from .components.objective import ObjectiveValueComponent

212
miplearn/benchmark.py
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@ -24,27 +24,37 @@ class BenchmarkRunner:
for (solver_name, solver) in self.solvers.items():
for i in tqdm(range(len((instances)))):
results = solver.solve(deepcopy(instances[i]), tee=tee)
self._push_result(results, solver=solver, solver_name=solver_name, instance=i)
def parallel_solve(self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
self._push_result(
results,
solver=solver,
solver_name=solver_name,
instance=i,
)
def parallel_solve(
self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
results = solver.parallel_solve(trials,
n_jobs=n_jobs,
label="Solve (%s)" % solver_name,
output=None)
results = solver.parallel_solve(
trials,
n_jobs=n_jobs,
label="Solve (%s)" % solver_name,
output=None,
)
for i in range(len(trials)):
idx = (i % len(instances)) + index_offset
self._push_result(results[i],
solver=solver,
solver_name=solver_name,
instance=idx)
self._push_result(
results[i],
solver=solver,
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger()
def raw_results(self):
@ -66,53 +76,60 @@ class BenchmarkRunner:
def _push_result(self, result, solver, solver_name, instance):
if self.results is None:
self.results = pd.DataFrame(columns=["Solver",
"Instance",
"Wallclock Time",
"Lower Bound",
"Upper Bound",
"Gap",
"Nodes",
"Mode",
"Sense",
"Predicted LB",
"Predicted UB",
])
self.results = pd.DataFrame(
columns=[
"Solver",
"Instance",
"Wallclock Time",
"Lower Bound",
"Upper Bound",
"Gap",
"Nodes",
"Mode",
"Sense",
"Predicted LB",
"Predicted UB",
]
)
lb = result["Lower bound"]
ub = result["Upper bound"]
gap = (ub - lb) / lb
if "Predicted LB" not in result:
result["Predicted LB"] = float("nan")
result["Predicted UB"] = float("nan")
self.results = self.results.append({
"Solver": solver_name,
"Instance": instance,
"Wallclock Time": result["Wallclock time"],
"Lower Bound": lb,
"Upper Bound": ub,
"Gap": gap,
"Nodes": result["Nodes"],
"Mode": solver.mode,
"Sense": result["Sense"],
"Predicted LB": result["Predicted LB"],
"Predicted UB": result["Predicted UB"],
}, ignore_index=True)
self.results = self.results.append(
{
"Solver": solver_name,
"Instance": instance,
"Wallclock Time": result["Wallclock time"],
"Lower Bound": lb,
"Upper Bound": ub,
"Gap": gap,
"Nodes": result["Nodes"],
"Mode": solver.mode,
"Sense": result["Sense"],
"Predicted LB": result["Predicted LB"],
"Predicted UB": result["Predicted UB"],
},
ignore_index=True,
)
groups = self.results.groupby("Instance")
best_lower_bound = groups["Lower Bound"].transform("max")
best_upper_bound = groups["Upper Bound"].transform("min")
best_gap = groups["Gap"].transform("min")
best_nodes = np.maximum(1, groups["Nodes"].transform("min"))
best_wallclock_time = groups["Wallclock Time"].transform("min")
self.results["Relative Lower Bound"] = \
self.results["Lower Bound"] / best_lower_bound
self.results["Relative Upper Bound"] = \
self.results["Upper Bound"] / best_upper_bound
self.results["Relative Wallclock Time"] = \
self.results["Wallclock Time"] / best_wallclock_time
self.results["Relative Gap"] = \
self.results["Gap"] / best_gap
self.results["Relative Nodes"] = \
self.results["Nodes"] / best_nodes
self.results["Relative Lower Bound"] = (
self.results["Lower Bound"] / best_lower_bound
)
self.results["Relative Upper Bound"] = (
self.results["Upper Bound"] / best_upper_bound
)
self.results["Relative Wallclock Time"] = (
self.results["Wallclock Time"] / best_wallclock_time
)
self.results["Relative Gap"] = self.results["Gap"] / best_gap
self.results["Relative Nodes"] = self.results["Nodes"] / best_nodes
def save_chart(self, filename):
import matplotlib.pyplot as plt
@ -134,63 +151,70 @@ class BenchmarkRunner:
obj_column = "Lower Bound"
predicted_obj_column = "Predicted LB"
fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
ncols=4,
figsize=(12,4),
gridspec_kw={'width_ratios': [2, 1, 1, 2]})
fig, (ax1, ax2, ax3, ax4) = plt.subplots(
nrows=1,
ncols=4,
figsize=(12, 4),
gridspec_kw={"width_ratios": [2, 1, 1, 2]},
)
# Figure 1: Solver x Wallclock Time
sns.stripplot(x="Solver",
y="Wallclock Time",
data=results,
ax=ax1,
jitter=0.25,
size=4.0,
)
sns.barplot(x="Solver",
y="Wallclock Time",
data=results,
ax=ax1,
errwidth=0.,
alpha=0.4,
estimator=median,
)
ax1.set(ylabel='Wallclock Time (s)')
sns.stripplot(
x="Solver",
y="Wallclock Time",
data=results,
ax=ax1,
jitter=0.25,
size=4.0,
)
sns.barplot(
x="Solver",
y="Wallclock Time",
data=results,
ax=ax1,
errwidth=0.0,
alpha=0.4,
estimator=median,
)
ax1.set(ylabel="Wallclock Time (s)")
# Figure 2: Solver x Gap (%)
ax2.set_ylim(-0.5, 5.5)
sns.stripplot(x="Solver",
y="Gap (%)",
jitter=0.25,
data=results[results["Mode"] != "heuristic"],
ax=ax2,
size=4.0,
)
sns.stripplot(
x="Solver",
y="Gap (%)",
jitter=0.25,
data=results[results["Mode"] != "heuristic"],
ax=ax2,
size=4.0,
)
# Figure 3: Solver x Primal Value
ax3.set_ylim(0.95,1.05)
sns.stripplot(x="Solver",
y=primal_column,
jitter=0.25,
data=results[results["Mode"] == "heuristic"],
ax=ax3,
)
ax3.set_ylim(0.95, 1.05)
sns.stripplot(
x="Solver",
y=primal_column,
jitter=0.25,
data=results[results["Mode"] == "heuristic"],
ax=ax3,
)
# Figure 4: Predicted vs Actual Objective Value
sns.scatterplot(x=obj_column,
y=predicted_obj_column,
hue="Solver",
data=results[results["Mode"] != "heuristic"],
ax=ax4,
)
sns.scatterplot(
x=obj_column,
y=predicted_obj_column,
hue="Solver",
data=results[results["Mode"] != "heuristic"],
ax=ax4,
)
xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls='-', color="#cccccc")
ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls="-", color="#cccccc")
ax4.set_xlim(xlim)
ax4.set_ylim(ylim)
ax4.get_legend().remove()
fig.tight_layout()
plt.savefig(filename, bbox_inches='tight', dpi=150)
plt.savefig(filename, bbox_inches="tight", dpi=150)
def _silence_miplearn_logger(self):
miplearn_logger = logging.getLogger("miplearn")
@ -200,5 +224,3 @@ class BenchmarkRunner:
def _restore_miplearn_logger(self):
miplearn_logger = logging.getLogger("miplearn")
miplearn_logger.setLevel(self.prev_log_level)

13
miplearn/classifiers/adaptive.py
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@ -22,9 +22,11 @@ class AdaptiveClassifier(Classifier):
based on its cross-validation score on a particular training data set.
"""
def __init__(self,
candidates=None,
evaluator=ClassifierEvaluator()):
def __init__(
self,
candidates=None,
evaluator=ClassifierEvaluator(),
):
"""
Initializes the meta-classifier.
"""
@ -35,14 +37,13 @@ class AdaptiveClassifier(Classifier):
"min samples": 100,
},
"logistic": {
"classifier": make_pipeline(StandardScaler(),
LogisticRegression()),
"classifier": make_pipeline(StandardScaler(), LogisticRegression()),
"min samples": 30,
},
"counting": {
"classifier": CountingClassifier(),
"min samples": 0,
}
},
}
self.candidates = candidates
self.evaluator = evaluator

3
miplearn/classifiers/counting.py
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@ -21,8 +21,7 @@ class CountingClassifier(Classifier):
self.mean = np.mean(y_train)
def predict_proba(self, x_test):
return np.array([[1 - self.mean, self.mean]
for _ in range(x_test.shape[0])])
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
def __repr__(self):
return "CountingClassifier(mean=%s)" % self.mean

51
miplearn/classifiers/cv.py
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@ -11,6 +11,7 @@ from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import logging
logger = logging.getLogger(__name__)
@ -28,12 +29,14 @@ class CrossValidatedClassifier(Classifier):
acceptable. Other numbers are a linear interpolation of these two extremes.
"""
def __init__(self,
classifier=LogisticRegression(),
threshold=0.75,
constant=0.0,
cv=5,
scoring='accuracy'):
def __init__(
self,
classifier=LogisticRegression(),
threshold=0.75,
constant=0.0,
cv=5,
scoring="accuracy",
):
self.classifier = None
self.classifier_prototype = classifier
self.constant = constant
@ -45,24 +48,36 @@ class CrossValidatedClassifier(Classifier):
# Calculate dummy score and absolute score threshold
y_train_avg = np.average(y_train)
dummy_score = max(y_train_avg, 1 - y_train_avg)
absolute_threshold = 1. * self.threshold + dummy_score * (1 - self.threshold)
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
# Calculate cross validation score and decide which classifier to use
clf = deepcopy(self.classifier_prototype)
cv_score = float(np.mean(cross_val_score(clf,
x_train,
y_train,
cv=self.cv,
scoring=self.scoring)))
cv_score = float(
np.mean(
cross_val_score(
clf,
x_train,
y_train,
cv=self.cv,
scoring=self.scoring,
)
)
)
if cv_score >= absolute_threshold:
logger.debug("cv_score is above threshold (%.2f >= %.2f); keeping" %
(cv_score, absolute_threshold))
logger.debug(
"cv_score is above threshold (%.2f >= %.2f); keeping"
% (cv_score, absolute_threshold)
)
self.classifier = clf
else:
logger.debug("cv_score is below threshold (%.2f < %.2f); discarding" %
(cv_score, absolute_threshold))
self.classifier = DummyClassifier(strategy="constant",
constant=self.constant)
logger.debug(
"cv_score is below threshold (%.2f < %.2f); discarding"
% (cv_score, absolute_threshold)
)
self.classifier = DummyClassifier(
strategy="constant",
constant=self.constant,
)
# Train chosen classifier
self.classifier.fit(x_train, y_train)

3
miplearn/classifiers/tests/test_counting.py
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@ -12,7 +12,6 @@ E = 0.1
def test_counting():
clf = CountingClassifier()
clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
expected_proba = np.array([[0.375, 0.625],
[0.375, 0.625]])
expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
actual_proba = clf.predict_proba(np.zeros((2, 25)))
assert norm(actual_proba - expected_proba) < E

36
miplearn/classifiers/tests/test_cv.py
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@ -13,34 +13,36 @@ E = 0.1
def test_cv():
# Training set: label is true if point is inside a 2D circle
x_train = np.array([[x1, x2]
for x1 in range(-10, 11)
for x2 in range(-10, 11)])
x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
x_train = StandardScaler().fit_transform(x_train)
n_samples = x_train.shape[0]
y_train = np.array([1.0 if x1*x1 + x2*x2 <= 100 else 0.0
for x1 in range(-10, 11)
for x2 in range(-10, 11)])
y_train = np.array(
[
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
for x1 in range(-10, 11)
for x2 in range(-10, 11)
]
)
# Support vector machines with linear kernels do not perform well on this
# data set, so predictor should return the given constant.
clf = CrossValidatedClassifier(classifier=SVC(probability=True,
random_state=42),
threshold=0.90,
constant=0.0,
cv=30)
clf = CrossValidatedClassifier(
classifier=SVC(probability=True, random_state=42),
threshold=0.90,
constant=0.0,
cv=30,
)
clf.fit(x_train, y_train)
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
# Support vector machines with quadratic kernels perform almost perfectly
# on this data set, so predictor should return their prediction.
clf = CrossValidatedClassifier(classifier=SVC(probability=True,
kernel='poly',
degree=2,
random_state=42),
threshold=0.90,
cv=30)
clf = CrossValidatedClassifier(
classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
threshold=0.90,
cv=30,
)
clf.fit(x_train, y_train)
print(y_train - clf.predict(x_train))
assert norm(y_train - clf.predict(x_train)) < E

1
miplearn/classifiers/tests/test_evaluator.py
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@ -17,4 +17,3 @@ def test_evaluator():
ev = ClassifierEvaluator()
assert ev.evaluate(clf_a, x_train, y_train) == 1.0
assert ev.evaluate(clf_b, x_train, y_train) == 0.5

17
miplearn/classifiers/tests/test_threshold.py
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@ -11,12 +11,16 @@ from miplearn.classifiers.threshold import MinPrecisionThreshold
def test_threshold_dynamic():
clf = Mock(spec=Classifier)
clf.predict_proba = Mock(return_value=np.array([
[0.10, 0.90],
[0.10, 0.90],
[0.20, 0.80],
[0.30, 0.70],
]))
clf.predict_proba = Mock(
return_value=np.array(
[
[0.10, 0.90],
[0.10, 0.90],
[0.20, 0.80],
[0.30, 0.70],
]
)
)
x_train = np.array([0, 1, 2, 3])
y_train = np.array([1, 1, 0, 0])
@ -31,4 +35,3 @@ def test_threshold_dynamic():
threshold = MinPrecisionThreshold(min_precision=0.00)
assert threshold.find(clf, x_train, y_train) == 0.70

14
miplearn/classifiers/threshold.py
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@ -30,11 +30,15 @@ class MinPrecisionThreshold(DynamicThreshold):
def find(self, clf, x_train, y_train):
proba = clf.predict_proba(x_train)
assert isinstance(proba, np.ndarray), \
"classifier should return numpy array"
assert proba.shape == (x_train.shape[0], 2), \
"classifier should return (%d,%d)-shaped array, not %s" % (
x_train.shape[0], 2, str(proba.shape))
assert isinstance(proba, np.ndarray), "classifier should return numpy array"
assert proba.shape == (
x_train.shape[0],
2,
), "classifier should return (%d,%d)-shaped array, not %s" % (
x_train.shape[0],
2,
str(proba.shape),
)
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
precision = tps / (tps + fps)

26
miplearn/components/cuts.py
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@ -19,9 +19,11 @@ class UserCutsComponent(Component):
A component that predicts which user cuts to enforce.
"""
def __init__(self,
classifier=CountingClassifier(),
threshold=0.05):
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.05,
):
self.violations = set()
self.count = {}
self.n_samples = 0
@ -56,10 +58,11 @@ class UserCutsComponent(Component):
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(self.classifiers.items(),
desc="Fit (user cuts)",
disable=not sys.stdout.isatty(),
):
for (v, classifier) in tqdm(
self.classifiers.items(),
desc="Fit (user cuts)",
disable=not sys.stdout.isatty(),
):
logger.debug("Training: %s" % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
@ -79,10 +82,11 @@ class UserCutsComponent(Component):
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_user_cuts)
for idx in tqdm(range(len(instances)),
desc="Evaluate (lazy)",
disable=not sys.stdout.isatty(),
):
for idx in tqdm(
range(len(instances)),
desc="Evaluate (lazy)",
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_user_cuts)
condition_negative = all_violations - condition_positive

26
miplearn/components/lazy_dynamic.py
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@ -19,9 +19,11 @@ class DynamicLazyConstraintsComponent(Component):
A component that predicts which lazy constraints to enforce.
"""
def __init__(self,
classifier=CountingClassifier(),
threshold=0.05):
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.05,
):
self.violations = set()
self.count = {}
self.n_samples = 0
@ -68,10 +70,11 @@ class DynamicLazyConstraintsComponent(Component):
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(self.classifiers.items(),
desc="Fit (lazy)",
disable=not sys.stdout.isatty(),
):
for (v, classifier) in tqdm(
self.classifiers.items(),
desc="Fit (lazy)",
disable=not sys.stdout.isatty(),
):
logger.debug("Training: %s" % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
@ -91,10 +94,11 @@ class DynamicLazyConstraintsComponent(Component):
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_lazy_constraints)
for idx in tqdm(range(len(instances)),
desc="Evaluate (lazy)",
disable=not sys.stdout.isatty(),
):
for idx in tqdm(
range(len(instances)),
desc="Evaluate (lazy)",
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_lazy_constraints)
condition_negative = all_violations - condition_positive

53
miplearn/components/lazy_static.py
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@ -19,13 +19,14 @@ class LazyConstraint:
class StaticLazyConstraintsComponent(Component):
def __init__(self,
classifier=CountingClassifier(),
threshold=0.05,
use_two_phase_gap=True,
large_gap=1e-2,
violation_tolerance=-0.5,
):
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.05,
use_two_phase_gap=True,
large_gap=1e-2,
violation_tolerance=-0.5,
):
self.threshold = threshold
self.classifier_prototype = classifier
self.classifiers = {}
@ -74,32 +75,38 @@ class StaticLazyConstraintsComponent(Component):
logger.debug("Finding violated lazy constraints...")
constraints_to_add = []
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(c.obj,
tol=self.violation_tolerance):
if not solver.internal_solver.is_constraint_satisfied(
c.obj, tol=self.violation_tolerance
):
constraints_to_add.append(c)
for c in constraints_to_add:
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
if len(constraints_to_add) > 0:
logger.info("%8d lazy constraints added %8d in the pool" % (len(constraints_to_add), len(self.pool)))
logger.info(
"%8d lazy constraints added %8d in the pool"
% (len(constraints_to_add), len(self.pool))
)
return True
else:
return False
def fit(self, training_instances):
training_instances = [t
for t in training_instances
if hasattr(t, "found_violated_lazy_constraints")]
training_instances = [
t
for t in training_instances
if hasattr(t, "found_violated_lazy_constraints")
]
logger.debug("Extracting x and y...")
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug("Fitting...")
for category in tqdm(x.keys(),
desc="Fit (lazy)",
disable=not sys.stdout.isatty()):
for category in tqdm(
x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
@ -121,8 +128,10 @@ class StaticLazyConstraintsComponent(Component):
x[category] = []
constraints[category] = []
x[category] += [instance.get_constraint_features(cid)]
c = LazyConstraint(cid=cid,
obj=solver.internal_solver.extract_constraint(cid))
c = LazyConstraint(
cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
constraints[category] += [c]
self.pool.append(c)
logger.info("%8d lazy constraints extracted" % len(self.pool))
@ -141,7 +150,13 @@ class StaticLazyConstraintsComponent(Component):
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
logger.info("%8d lazy constraints added %8d in the pool" % (n_added, len(self.pool)))
logger.info(
"%8d lazy constraints added %8d in the pool"
% (
n_added,
len(self.pool),
)
)
def _collect_constraints(self, train_instances):
constraints = {}

13
miplearn/components/objective.py
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@ -1,13 +1,20 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from sklearn.metrics import mean_squared_error, explained_variance_score, max_error, mean_absolute_error, r2_score
from sklearn.metrics import (
mean_squared_error,
explained_variance_score,
max_error,
mean_absolute_error,
r2_score,
)
from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
from sklearn.linear_model import LinearRegression
from copy import deepcopy
import numpy as np
import logging
logger = logging.getLogger(__name__)
@ -15,8 +22,8 @@ class ObjectiveValueComponent(Component):
"""
A Component which predicts the optimal objective value of the problem.
"""
def __init__(self,
regressor=LinearRegression()):
def __init__(self, regressor=LinearRegression()):
self.ub_regressor = None
self.lb_regressor = None
self.regressor_prototype = regressor

50
miplearn/components/primal.py
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@ -19,10 +19,12 @@ class PrimalSolutionComponent(Component):
A component that predicts primal solutions.
"""
def __init__(self,
classifier=AdaptiveClassifier(),
mode="exact",
threshold=MinPrecisionThreshold(0.98)):
def __init__(
self,
classifier=AdaptiveClassifier(),
mode="exact",
threshold=MinPrecisionThreshold(0.98),
):
self.mode = mode
self.classifiers = {}
self.thresholds = {}
@ -51,9 +53,10 @@ class PrimalSolutionComponent(Component):
features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances)
for category in tqdm(features.keys(),
desc="Fit (primal)",
):
for category in tqdm(
features.keys(),
desc="Fit (primal)",
):
x_train = features[category]
for label in [0, 1]:
y_train = solutions[category][:, label].astype(int)
@ -74,9 +77,15 @@ class PrimalSolutionComponent(Component):
# Find threshold (dynamic or static)
if isinstance(self.threshold_prototype, DynamicThreshold):
self.thresholds[category, label] = self.threshold_prototype.find(clf, x_train, y_train)
self.thresholds[category, label] = self.threshold_prototype.find(
clf,
x_train,
y_train,
)
else:
self.thresholds[category, label] = deepcopy(self.threshold_prototype)
self.thresholds[category, label] = deepcopy(
self.threshold_prototype
)
self.classifiers[category, label] = clf
@ -98,18 +107,21 @@ class PrimalSolutionComponent(Component):
ws = np.array([[1 - clf, clf] for _ in range(n)])
else:
ws = clf.predict_proba(x_test[category])
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (n, ws.shape)
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
n,
ws.shape,
)
for (i, (var, index)) in enumerate(var_split[category]):
if ws[i, 1] >= self.thresholds[category, label]:
solution[var][index] = label
return solution
def evaluate(self, instances):
ev = {"Fix zero": {},
"Fix one": {}}
for instance_idx in tqdm(range(len(instances)),
desc="Evaluate (primal)",
):
ev = {"Fix zero": {}, "Fix one": {}}
for instance_idx in tqdm(
range(len(instances)),
desc="Evaluate (primal)",
):
instance = instances[instance_idx]
solution_actual = instance.solution
solution_pred = self.predict(instance)
@ -143,6 +155,10 @@ class PrimalSolutionComponent(Component):
tn_one = len(pred_one_negative & vars_zero)
fn_one = len(pred_one_negative & vars_one)
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero)
ev["Fix one"][instance_idx] = classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one)
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
tp_zero, tn_zero, fp_zero, fn_zero
)
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
tp_one, tn_one, fp_one, fn_one
)
return ev

67
miplearn/components/relaxation.py
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@ -51,14 +51,15 @@ class RelaxationComponent(Component):
If `check_dropped` is true, set the maximum number of iterations in the lazy constraint loop.
"""
def __init__(self,
classifier=CountingClassifier(),
threshold=0.95,
slack_tolerance=1e-5,
check_dropped=False,
violation_tolerance=1e-5,
max_iterations=3,
):
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.95,
slack_tolerance=1e-5,
check_dropped=False,
violation_tolerance=1e-5,
max_iterations=3,
):
self.classifiers = {}
self.classifier_prototype = classifier
self.threshold = threshold
@ -77,16 +78,20 @@ class RelaxationComponent(Component):
logger.info("Predicting redundant LP constraints...")
cids = solver.internal_solver.get_constraint_ids()
x, constraints = self.x([instance],
constraint_ids=cids,
return_constraints=True)
x, constraints = self.x(
[instance],
constraint_ids=cids,
return_constraints=True,
)
y = self.predict(x)
for category in y.keys():
for i in range(len(y[category])):
if y[category][i][0] == 1:
cid = constraints[category][i]
c = LazyConstraint(cid=cid,
obj=solver.internal_solver.extract_constraint(cid))
c = LazyConstraint(
cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
self.pool += [c]
logger.info("Extracted %d predicted constraints" % len(self.pool))
@ -98,21 +103,19 @@ class RelaxationComponent(Component):
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug("Fitting...")
for category in tqdm(x.keys(),
desc="Fit (relaxation)"):
for category in tqdm(x.keys(), desc="Fit (relaxation)"):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
def x(self,
instances,
constraint_ids=None,
return_constraints=False):
def x(self, instances, constraint_ids=None, return_constraints=False):
x = {}
constraints = {}
for instance in tqdm(InstanceIterator(instances),
desc="Extract (relaxation:x)",
disable=len(instances) < 5):
for instance in tqdm(
InstanceIterator(instances),
desc="Extract (relaxation:x)",
disable=len(instances) < 5,
):
if constraint_ids is not None:
cids = constraint_ids
else:
@ -133,9 +136,11 @@ class RelaxationComponent(Component):
def y(self, instances):
y = {}
for instance in tqdm(InstanceIterator(instances),
desc="Extract (relaxation:y)",
disable=len(instances) < 5):
for instance in tqdm(
InstanceIterator(instances),
desc="Extract (relaxation:y)",
disable=len(instances) < 5,
):
for (cid, slack) in instance.slacks.items():
category = instance.get_constraint_category(cid)
if category is None:
@ -154,7 +159,7 @@ class RelaxationComponent(Component):
if category not in self.classifiers:
continue
y[category] = []
#x_cat = np.array(x_cat)
# x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] >= self.threshold:
@ -191,13 +196,19 @@ class RelaxationComponent(Component):
logger.debug("Checking that dropped constraints are satisfied...")
constraints_to_add = []
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(c.obj, self.violation_tolerance):
if not solver.internal_solver.is_constraint_satisfied(
c.obj,
self.violation_tolerance,
):
constraints_to_add.append(c)
for c in constraints_to_add:
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
if len(constraints_to_add) > 0:
logger.info("%8d constraints %8d in the pool" % (len(constraints_to_add), len(self.pool)))
logger.info(
"%8d constraints %8d in the pool"
% (len(constraints_to_add), len(self.pool))
)
return True
else:
return False

26
miplearn/components/tests/test_lazy_dynamic.py
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@ -28,9 +28,9 @@ def test_lazy_fit():
assert "c" in component.classifiers
# Should provide correct x_train to each classifier
expected_x_train_a = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
expected_x_train_b = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
expected_x_train_c = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
@ -56,16 +56,15 @@ def test_lazy_before():
solver = LearningSolver()
solver.internal_solver = Mock(spec=InternalSolver)
component = DynamicLazyConstraintsComponent(threshold=0.10)
component.classifiers = {"a": Mock(spec=Classifier),
"b": Mock(spec=Classifier)}
component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
component.before_solve(solver, instances[0], models[0])
# Should ask classifier likelihood of each constraint being violated
expected_x_test_a = np.array([[67., 21.75, 1287.92]])
expected_x_test_b = np.array([[67., 21.75, 1287.92]])
expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
assert norm(expected_x_test_a - actual_x_test_a) < E
@ -82,9 +81,11 @@ def test_lazy_before():
def test_lazy_evaluate():
instances, models = get_test_pyomo_instances()
component = DynamicLazyConstraintsComponent()
component.classifiers = {"a": Mock(spec=Classifier),
"b": Mock(spec=Classifier),
"c": Mock(spec=Classifier)}
component.classifiers = {
"a": Mock(spec=Classifier),
"b": Mock(spec=Classifier),
"c": Mock(spec=Classifier),
}
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
@ -96,7 +97,7 @@ def test_lazy_evaluate():
"Accuracy": 0.75,
"F1 score": 0.8,
"Precision": 1.0,
"Recall": 2/3.,
"Recall": 2 / 3.0,
"Predicted positive": 2,
"Predicted negative": 2,
"Condition positive": 3,
@ -135,6 +136,5 @@ def test_lazy_evaluate():
"False positive (%)": 25.0,
"True negative (%)": 25.0,
"True positive (%)": 25.0,
}
},
}

204
miplearn/components/tests/test_lazy_static.py
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@ -4,10 +4,12 @@
from unittest.mock import Mock, call
from miplearn import (StaticLazyConstraintsComponent,
LearningSolver,
Instance,
InternalSolver)
from miplearn import (
StaticLazyConstraintsComponent,
LearningSolver,
Instance,
InternalSolver,
)
from miplearn.classifiers import Classifier
@ -23,39 +25,47 @@ def test_usage_with_solver():
instance = Mock(spec=Instance)
instance.has_static_lazy_constraints = Mock(return_value=True)
instance.is_constraint_lazy = Mock(side_effect=lambda cid: {
"c1": False,
"c2": True,
"c3": True,
"c4": True,
}[cid])
instance.get_constraint_features = Mock(side_effect=lambda cid: {
"c2": [1.0, 0.0],
"c3": [0.5, 0.5],
"c4": [1.0],
}[cid])
instance.get_constraint_category = Mock(side_effect=lambda cid: {
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
}[cid])
component = StaticLazyConstraintsComponent(threshold=0.90,
use_two_phase_gap=False,
violation_tolerance=1.0)
instance.is_constraint_lazy = Mock(
side_effect=lambda cid: {
"c1": False,
"c2": True,
"c3": True,
"c4": True,
}[cid]
)
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
"c2": [1.0, 0.0],
"c3": [0.5, 0.5],
"c4": [1.0],
}[cid]
)
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
}[cid]
)
component = StaticLazyConstraintsComponent(
threshold=0.90, use_two_phase_gap=False, violation_tolerance=1.0
)
component.classifiers = {
"type-a": Mock(spec=Classifier),
"type-b": Mock(spec=Classifier),
}
component.classifiers["type-a"].predict_proba = \
Mock(return_value=[
component.classifiers["type-a"].predict_proba = Mock(
return_value=[
[0.20, 0.80],
[0.05, 0.95],
])
component.classifiers["type-b"].predict_proba = \
Mock(return_value=[
]
)
component.classifiers["type-b"].predict_proba = Mock(
return_value=[
[0.02, 0.98],
])
]
)
# LearningSolver calls before_solve
component.before_solve(solver, instance, None)
@ -67,37 +77,59 @@ def test_usage_with_solver():
internal.get_constraint_ids.assert_called_once()
# Should ask if each constraint in the model is lazy
instance.is_constraint_lazy.assert_has_calls([
call("c1"), call("c2"), call("c3"), call("c4"),
])
instance.is_constraint_lazy.assert_has_calls(
[
call("c1"),
call("c2"),
call("c3"),
call("c4"),
]
)
# For the lazy ones, should ask for features
instance.get_constraint_features.assert_has_calls([
call("c2"), call("c3"), call("c4"),
])
instance.get_constraint_features.assert_has_calls(
[
call("c2"),
call("c3"),
call("c4"),
]
)
# Should also ask for categories
assert instance.get_constraint_category.call_count == 3
instance.get_constraint_category.assert_has_calls([
call("c2"), call("c3"), call("c4"),
])
instance.get_constraint_category.assert_has_calls(
[
call("c2"),
call("c3"),
call("c4"),
]
)
# Should ask internal solver to remove constraints identified as lazy
assert internal.extract_constraint.call_count == 3
internal.extract_constraint.assert_has_calls([
call("c2"), call("c3"), call("c4"),
])
internal.extract_constraint.assert_has_calls(
[
call("c2"),
call("c3"),
call("c4"),
]
)
# Should ask ML to predict whether each lazy constraint should be enforced
component.classifiers["type-a"].predict_proba.assert_called_once_with([[1.0, 0.0], [0.5, 0.5]])
component.classifiers["type-a"].predict_proba.assert_called_once_with(
[[1.0, 0.0], [0.5, 0.5]]
)
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
# For the ones that should be enforced, should ask solver to re-add them
# to the formulation. The remaining ones should remain in the pool.
assert internal.add_constraint.call_count == 2
internal.add_constraint.assert_has_calls([
call("<c3>"), call("<c4>"),
])
internal.add_constraint.assert_has_calls(
[
call("<c3>"),
call("<c4>"),
]
)
internal.add_constraint.reset_mock()
# LearningSolver calls after_iteration (first time)
@ -126,37 +158,45 @@ def test_usage_with_solver():
def test_fit():
instance_1 = Mock(spec=Instance)
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
instance_1.get_constraint_category = Mock(side_effect=lambda cid: {
"c1": "type-a",
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
"c5": "type-b",
}[cid])
instance_1.get_constraint_features = Mock(side_effect=lambda cid: {
"c1": [1, 1],
"c2": [1, 2],
"c3": [1, 3],
"c4": [1, 4, 0],
"c5": [1, 5, 0],
}[cid])
instance_1.get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": "type-a",
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
"c5": "type-b",
}[cid]
)
instance_1.get_constraint_features = Mock(
side_effect=lambda cid: {
"c1": [1, 1],
"c2": [1, 2],
"c3": [1, 3],
"c4": [1, 4, 0],
"c5": [1, 5, 0],
}[cid]
)
instance_2 = Mock(spec=Instance)
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
instance_2.get_constraint_category = Mock(side_effect=lambda cid: {
"c1": "type-a",
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
"c5": "type-b",
}[cid])
instance_2.get_constraint_features = Mock(side_effect=lambda cid: {
"c1": [2, 1],
"c2": [2, 2],
"c3": [2, 3],
"c4": [2, 4, 0],
"c5": [2, 5, 0],
}[cid])
instance_2.get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": "type-a",
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
"c5": "type-b",
}[cid]
)
instance_2.get_constraint_features = Mock(
side_effect=lambda cid: {
"c1": [2, 1],
"c2": [2, 2],
"c3": [2, 3],
"c4": [2, 4, 0],
"c5": [2, 5, 0],
}[cid]
)
instances = [instance_1, instance_2]
component = StaticLazyConstraintsComponent()
@ -171,18 +211,22 @@ def test_fit():
}
expected_x = {
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]]
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
}
expected_y = {
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]]
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
}
assert component._collect_constraints(instances) == expected_constraints
assert component.x(instances) == expected_x
assert component.y(instances) == expected_y
component.fit(instances)
component.classifiers["type-a"].fit.assert_called_once_with(expected_x["type-a"],
expected_y["type-a"])
component.classifiers["type-b"].fit.assert_called_once_with(expected_x["type-b"],
expected_y["type-b"])
component.classifiers["type-a"].fit.assert_called_once_with(
expected_x["type-a"],
expected_y["type-a"],
)
component.classifiers["type-b"].fit.assert_called_once_with(
expected_x["type-b"],
expected_y["type-b"],
)

36
miplearn/components/tests/test_objective.py
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@ -16,8 +16,10 @@ def test_usage():
comp.fit(instances)
assert instances[0].lower_bound == 1183.0
assert instances[0].upper_bound == 1183.0
assert np.round(comp.predict(instances), 2).tolist() == [[1183.0, 1183.0],
[1070.0, 1070.0]]
assert np.round(comp.predict(instances), 2).tolist() == [
[1183.0, 1183.0],
[1070.0, 1070.0],
]
def test_obj_evaluate():
@ -28,20 +30,20 @@ def test_obj_evaluate():
comp.fit(instances)
ev = comp.evaluate(instances)
assert ev == {
'Lower bound': {
'Explained variance': 0.0,
'Max error': 183.0,
'Mean absolute error': 126.5,
'Mean squared error': 19194.5,
'Median absolute error': 126.5,
'R2': -5.012843605607331,
"Lower bound": {
"Explained variance": 0.0,
"Max error": 183.0,
"Mean absolute error": 126.5,
"Mean squared error": 19194.5,
"Median absolute error": 126.5,
"R2": -5.012843605607331,
},
"Upper bound": {
"Explained variance": 0.0,
"Max error": 183.0,
"Mean absolute error": 126.5,
"Mean squared error": 19194.5,
"Median absolute error": 126.5,
"R2": -5.012843605607331,
},
'Upper bound': {
'Explained variance': 0.0,
'Max error': 183.0,
'Mean absolute error': 126.5,
'Mean squared error': 19194.5,
'Median absolute error': 126.5,
'R2': -5.012843605607331,
}
}

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():

210
miplearn/components/tests/test_relaxation.py
Unescape View File

@ -4,10 +4,7 @@
from unittest.mock import Mock, call
from miplearn import (RelaxationComponent,
LearningSolver,
Instance,
InternalSolver)
from miplearn import RelaxationComponent, LearningSolver, Instance, InternalSolver
from miplearn.classifiers import Classifier
@ -16,41 +13,49 @@ def _setup():
internal = solver.internal_solver = Mock(spec=InternalSolver)
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
internal.get_constraint_slacks = Mock(side_effect=lambda: {
"c1": 0.5,
"c2": 0.0,
"c3": 0.0,
"c4": 1.4,
})
internal.get_constraint_slacks = Mock(
side_effect=lambda: {
"c1": 0.5,
"c2": 0.0,
"c3": 0.0,
"c4": 1.4,
}
)
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
internal.is_constraint_satisfied = Mock(return_value=False)
instance = Mock(spec=Instance)
instance.get_constraint_features = Mock(side_effect=lambda cid: {
"c2": [1.0, 0.0],
"c3": [0.5, 0.5],
"c4": [1.0],
}[cid])
instance.get_constraint_category = Mock(side_effect=lambda cid: {
"c1": None,
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
}[cid])
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
"c2": [1.0, 0.0],
"c3": [0.5, 0.5],
"c4": [1.0],
}[cid]
)
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": None,
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
}[cid]
)
classifiers = {
"type-a": Mock(spec=Classifier),
"type-b": Mock(spec=Classifier),
}
classifiers["type-a"].predict_proba = \
Mock(return_value=[
classifiers["type-a"].predict_proba = Mock(
return_value=[
[0.20, 0.80],
[0.05, 0.95],
])
classifiers["type-b"].predict_proba = \
Mock(return_value=[
]
)
classifiers["type-b"].predict_proba = Mock(
return_value=[
[0.02, 0.98],
])
]
)
return solver, internal, instance, classifiers
@ -72,25 +77,39 @@ def test_usage():
# Should query category and features for each constraint in the model
assert instance.get_constraint_category.call_count == 4
instance.get_constraint_category.assert_has_calls([
call("c1"), call("c2"), call("c3"), call("c4"),
])
instance.get_constraint_category.assert_has_calls(
[
call("c1"),
call("c2"),
call("c3"),
call("c4"),
]
)
# For constraint with non-null categories, should ask for features
assert instance.get_constraint_features.call_count == 3
instance.get_constraint_features.assert_has_calls([
call("c2"), call("c3"), call("c4"),
])
instance.get_constraint_features.assert_has_calls(
[
call("c2"),
call("c3"),
call("c4"),
]
)
# Should ask ML to predict whether constraint should be removed
component.classifiers["type-a"].predict_proba.assert_called_once_with([[1.0, 0.0], [0.5, 0.5]])
component.classifiers["type-a"].predict_proba.assert_called_once_with(
[[1.0, 0.0], [0.5, 0.5]]
)
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
# Should ask internal solver to remove constraints predicted as redundant
assert internal.extract_constraint.call_count == 2
internal.extract_constraint.assert_has_calls([
call("c3"), call("c4"),
])
internal.extract_constraint.assert_has_calls(
[
call("c3"),
call("c4"),
]
)
# LearningSolver calls after_solve
component.after_solve(solver, instance, None, None)
@ -111,8 +130,7 @@ def test_usage():
def test_usage_with_check_dropped():
solver, internal, instance, classifiers = _setup()
component = RelaxationComponent(check_dropped=True,
violation_tolerance=1e-3)
component = RelaxationComponent(check_dropped=True, violation_tolerance=1e-3)
component.classifiers = classifiers
# LearningSolver call before_solve
@ -120,9 +138,12 @@ def test_usage_with_check_dropped():
# Assert constraints are extracted
assert internal.extract_constraint.call_count == 2
internal.extract_constraint.assert_has_calls([
call("c3"), call("c4"),
])
internal.extract_constraint.assert_has_calls(
[
call("c3"),
call("c4"),
]
)
# LearningSolver calls iteration_cb (first time)
should_repeat = component.iteration_cb(solver, instance, None)
@ -131,15 +152,15 @@ def test_usage_with_check_dropped():
assert should_repeat
# Should ask solver if removed constraints are satisfied (mock always returns false)
internal.is_constraint_satisfied.assert_has_calls([
call("<c3>", 1e-3),
call("<c4>", 1e-3),
])
internal.is_constraint_satisfied.assert_has_calls(
[
call("<c3>", 1e-3),
call("<c4>", 1e-3),
]
)
# Should add constraints back to LP relaxation
internal.add_constraint.assert_has_calls([
call("<c3>"), call("<c4>")
])
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
# LearningSolver calls iteration_cb (second time)
should_repeat = component.iteration_cb(solver, instance, None)
@ -148,21 +169,22 @@ def test_usage_with_check_dropped():
def test_x_y_fit_predict_evaluate():
instances = [Mock(spec=Instance), Mock(spec=Instance)]
component = RelaxationComponent(slack_tolerance=0.05,
threshold=0.80)
component = RelaxationComponent(slack_tolerance=0.05, threshold=0.80)
component.classifiers = {
"type-a": Mock(spec=Classifier),
"type-b": Mock(spec=Classifier),
}
component.classifiers["type-a"].predict_proba = \
Mock(return_value=[
component.classifiers["type-a"].predict_proba = Mock(
return_value=[
[0.20, 0.80],
])
component.classifiers["type-b"].predict_proba = \
Mock(return_value=[
]
)
component.classifiers["type-b"].predict_proba = Mock(
return_value=[
[0.50, 0.50],
[0.05, 0.95],
])
]
)
# First mock instance
instances[0].slacks = {
@ -171,17 +193,21 @@ def test_x_y_fit_predict_evaluate():
"c3": 0.00,
"c4": 30.0,
}
instances[0].get_constraint_category = Mock(side_effect=lambda cid: {
"c1": None,
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
}[cid])
instances[0].get_constraint_features = Mock(side_effect=lambda cid: {
"c2": [1.0, 0.0],
"c3": [0.5, 0.5],
"c4": [1.0],
}[cid])
instances[0].get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": None,
"c2": "type-a",
"c3": "type-a",
"c4": "type-b",
}[cid]
)
instances[0].get_constraint_features = Mock(
side_effect=lambda cid: {
"c2": [1.0, 0.0],
"c3": [0.5, 0.5],
"c4": [1.0],
}[cid]
)
# Second mock instance
instances[1].slacks = {
@ -190,26 +216,27 @@ def test_x_y_fit_predict_evaluate():
"c4": 0.00,
"c5": 0.00,
}
instances[1].get_constraint_category = Mock(side_effect=lambda cid: {
"c1": None,
"c3": "type-a",
"c4": "type-b",
"c5": "type-b",
}[cid])
instances[1].get_constraint_features = Mock(side_effect=lambda cid: {
"c3": [0.3, 0.4],
"c4": [0.7],
"c5": [0.8],
}[cid])
instances[1].get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": None,
"c3": "type-a",
"c4": "type-b",
"c5": "type-b",
}[cid]
)
instances[1].get_constraint_features = Mock(
side_effect=lambda cid: {
"c3": [0.3, 0.4],
"c4": [0.7],
"c5": [0.8],
}[cid]
)
expected_x = {
"type-a": [[1.0, 0.0], [0.5, 0.5], [0.3, 0.4]],
"type-b": [[1.0], [0.7], [0.8]],
}
expected_y = {
"type-a": [[0], [0], [1]],
"type-b": [[1], [0], [0]]
}
expected_y = {"type-a": [[0], [0], [1]], "type-b": [[1], [0], [0]]}
# Should build X and Y matrices correctly
assert component.x(instances) == expected_x
@ -217,13 +244,16 @@ def test_x_y_fit_predict_evaluate():
# Should pass along X and Y matrices to classifiers
component.fit(instances)
component.classifiers["type-a"].fit.assert_called_with(expected_x["type-a"], expected_y["type-a"])
component.classifiers["type-b"].fit.assert_called_with(expected_x["type-b"], expected_y["type-b"])
assert component.predict(expected_x) == {
"type-a": [[1]],
"type-b": [[0], [1]]
}
component.classifiers["type-a"].fit.assert_called_with(
expected_x["type-a"],
expected_y["type-a"],
)
component.classifiers["type-b"].fit.assert_called_with(
expected_x["type-b"],
expected_y["type-b"],
)
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
ev = component.evaluate(instances[1])
assert ev["True positive"] == 1

57
miplearn/extractors.py
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@ -40,7 +40,7 @@ class InstanceIterator:
class Extractor(ABC):
@abstractmethod
def extract(self, instances,):
def extract(self, instances):
pass
@staticmethod
@ -61,9 +61,11 @@ class Extractor(ABC):
class VariableFeaturesExtractor(Extractor):
def extract(self, instances):
result = {}
for instance in tqdm(InstanceIterator(instances),
desc="Extract (vars)",
disable=len(instances) < 5):
for instance in tqdm(
InstanceIterator(instances),
desc="Extract (vars)",
disable=len(instances) < 5,
):
instance_features = instance.get_instance_features()
var_split = self.split_variables(instance)
for (category, var_index_pairs) in var_split.items():
@ -71,9 +73,9 @@ class VariableFeaturesExtractor(Extractor):
result[category] = []
for (var_name, index) in var_index_pairs:
result[category] += [
instance_features.tolist() + \
instance.get_variable_features(var_name, index).tolist() + \
[instance.lp_solution[var_name][index]]
instance_features.tolist()
+ instance.get_variable_features(var_name, index).tolist()
+ [instance.lp_solution[var_name][index]]
]
for category in result:
result[category] = np.array(result[category])
@ -86,9 +88,11 @@ class SolutionExtractor(Extractor):
def extract(self, instances):
result = {}
for instance in tqdm(InstanceIterator(instances),
desc="Extract (solution)",
disable=len(instances) < 5):
for instance in tqdm(
InstanceIterator(instances),
desc="Extract (solution)",
disable=len(instances) < 5,
):
var_split = self.split_variables(instance)
for (category, var_index_pairs) in var_split.items():
if category not in result:
@ -109,13 +113,17 @@ class SolutionExtractor(Extractor):
class InstanceFeaturesExtractor(Extractor):
def extract(self, instances):
return np.vstack([
np.hstack([
instance.get_instance_features(),
instance.lp_value,
])
for instance in InstanceIterator(instances)
])
return np.vstack(
[
np.hstack(
[
instance.get_instance_features(),
instance.lp_value,
]
)
for instance in InstanceIterator(instances)
]
)
class ObjectiveValueExtractor(Extractor):
@ -125,11 +133,14 @@ class ObjectiveValueExtractor(Extractor):
def extract(self, instances):
if self.kind == "lower bound":
return np.array([[instance.lower_bound]
for instance in InstanceIterator(instances)])
return np.array(
[[instance.lower_bound] for instance in InstanceIterator(instances)]
)
if self.kind == "upper bound":
return np.array([[instance.upper_bound]
for instance in InstanceIterator(instances)])
return np.array(
[[instance.upper_bound] for instance in InstanceIterator(instances)]
)
if self.kind == "lp":
return np.array([[instance.lp_value]
for instance in InstanceIterator(instances)])
return np.array(
[[instance.lp_value] for instance in InstanceIterator(instances)]
)

8
miplearn/instance.py
Unescape View File

@ -141,11 +141,11 @@ class Instance(ABC):
pass
def load(self, filename):
with gzip.GzipFile(filename, 'r') as f:
data = json.loads(f.read().decode('utf-8'))
with gzip.GzipFile(filename, "r") as f:
data = json.loads(f.read().decode("utf-8"))
self.__dict__ = data
def dump(self, filename):
data = json.dumps(self.__dict__, indent=2).encode('utf-8')
with gzip.GzipFile(filename, 'w') as f:
data = json.dumps(self.__dict__, indent=2).encode("utf-8")
with gzip.GzipFile(filename, "w") as f:
f.write(data)

25
miplearn/log.py
Unescape View File

@ -7,7 +7,8 @@ import logging
import time
import sys
class TimeFormatter():
class TimeFormatter:
def __init__(self, start_time, log_colors):
self.start_time = start_time
self.log_colors = log_colors
@ -19,21 +20,23 @@ class TimeFormatter():
color = self.log_colors["yellow"]
else:
color = self.log_colors["green"]
return "%s[%12.3f]%s %s" % (color,
record.created - self.start_time,
self.log_colors["reset"],
record.getMessage())
return "%s[%12.3f]%s %s" % (
color,
record.created - self.start_time,
self.log_colors["reset"],
record.getMessage(),
)
def setup_logger(start_time=None,
force_color=False):
def setup_logger(start_time=None, force_color=False):
if start_time is None:
start_time = time.time()
if sys.stdout.isatty() or force_color:
log_colors = {
"green": '\033[92m',
"yellow": '\033[93m',
"red": '\033[91m',
"reset": '\033[0m',
"green": "\033[92m",
"yellow": "\033[93m",
"red": "\033[91m",
"reset": "\033[0m",
}
else:
log_colors = {

150
miplearn/problems/knapsack.py
Unescape View File

@ -17,21 +17,25 @@ class ChallengeA:
- K = 500, u ~ U(0., 1.)
- alpha = 0.25
"""
def __init__(self,
seed=42,
n_training_instances=500,
n_test_instances=50):
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.gen = MultiKnapsackGenerator(n=randint(low=250, high=251),
m=randint(low=10, high=11),
w=uniform(loc=0.0, scale=1000.0),
K=uniform(loc=500.0, scale=0.0),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=True,
w_jitter=uniform(loc=0.95, scale=0.1),
)
self.gen = MultiKnapsackGenerator(
n=randint(low=250, high=251),
m=randint(low=10, high=11),
w=uniform(loc=0.0, scale=1000.0),
K=uniform(loc=500.0, scale=0.0),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=True,
w_jitter=uniform(loc=0.95, scale=0.1),
)
np.random.seed(seed + 1)
self.training_instances = self.gen.generate(n_training_instances)
@ -51,10 +55,7 @@ class MultiKnapsackInstance(Instance):
same size and items don't shuffle around.
"""
def __init__(self,
prices,
capacities,
weights):
def __init__(self, prices, capacities, weights):
assert isinstance(prices, np.ndarray)
assert isinstance(capacities, np.ndarray)
assert isinstance(weights, np.ndarray)
@ -69,44 +70,53 @@ class MultiKnapsackInstance(Instance):
def to_model(self):
model = pe.ConcreteModel()
model.x = pe.Var(range(self.n), domain=pe.Binary)
model.OBJ = pe.Objective(rule=lambda model: sum(model.x[j] * self.prices[j]
for j in range(self.n)),
sense=pe.maximize)
model.OBJ = pe.Objective(
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
sense=pe.maximize,
)
model.eq_capacity = pe.ConstraintList()
for i in range(self.m):
model.eq_capacity.add(sum(model.x[j] * self.weights[i,j]
for j in range(self.n)) <= self.capacities[i])
model.eq_capacity.add(
sum(model.x[j] * self.weights[i, j] for j in range(self.n))
<= self.capacities[i]
)
return model
def get_instance_features(self):
return np.hstack([
np.mean(self.prices),
self.capacities,
])
return np.hstack(
[
np.mean(self.prices),
self.capacities,
]
)
def get_variable_features(self, var, index):
return np.hstack([
self.prices[index],
self.weights[:, index],
])
return np.hstack(
[
self.prices[index],
self.weights[:, index],
]
)
# def get_variable_category(self, var, index):
# return index
class MultiKnapsackGenerator:
def __init__(self,
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=500),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=False,
w_jitter=uniform(loc=1.0, scale=0.0),
round=True,
):
def __init__(
self,
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=500),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=False,
w_jitter=uniform(loc=1.0, scale=0.0),
round=True,
):
"""Initialize the problem generator.
Instances have a random number of items (or variables) and a random number of knapsacks
@ -168,10 +178,13 @@ class MultiKnapsackGenerator:
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
assert isinstance(K, rv_frozen), "K should be a SciPy probability distribution"
assert isinstance(u, rv_frozen), "u should be a SciPy probability distribution"
assert isinstance(alpha, rv_frozen), "alpha should be a SciPy probability distribution"
assert isinstance(
alpha, rv_frozen
), "alpha should be a SciPy probability distribution"
assert isinstance(fix_w, bool), "fix_w should be boolean"
assert isinstance(w_jitter, rv_frozen), \
"w_jitter should be a SciPy probability distribution"
assert isinstance(
w_jitter, rv_frozen
), "w_jitter should be a SciPy probability distribution"
self.n = n
self.m = m
@ -211,13 +224,14 @@ class MultiKnapsackGenerator:
K = self.K.rvs()
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
alpha = self.alpha.rvs(m)
p = np.array([w[:,j].sum() / m + K * u[j] for j in range(n)])
b = np.array([w[i,:].sum() * alpha[i] for i in range(m)])
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
if self.round:
p = p.round()
b = b.round()
w = w.round()
return MultiKnapsackInstance(p, b, w)
return [_sample() for _ in range(n_samples)]
@ -225,6 +239,7 @@ class KnapsackInstance(Instance):
"""
Simpler (one-dimensional) Knapsack Problem, used for testing.
"""
def __init__(self, weights, prices, capacity):
self.weights = weights
self.prices = prices
@ -234,23 +249,29 @@ class KnapsackInstance(Instance):
model = pe.ConcreteModel()
items = range(len(self.weights))
model.x = pe.Var(items, domain=pe.Binary)
model.OBJ = pe.Objective(expr=sum(model.x[v] * self.prices[v] for v in items),
sense=pe.maximize)
model.eq_capacity = pe.Constraint(expr=sum(model.x[v] * self.weights[v]
for v in items) <= self.capacity)
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
)
model.eq_capacity = pe.Constraint(
expr=sum(model.x[v] * self.weights[v] for v in items) <= self.capacity
)
return model
def get_instance_features(self):
return np.array([
self.capacity,
np.average(self.weights),
])
return np.array(
[
self.capacity,
np.average(self.weights),
]
)
def get_variable_features(self, var, index):
return np.array([
self.weights[index],
self.prices[index],
])
return np.array(
[
self.weights[index],
self.prices[index],
]
)
class GurobiKnapsackInstance(KnapsackInstance):
@ -258,6 +279,7 @@ class GurobiKnapsackInstance(KnapsackInstance):
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
instead of Pyomo, used for testing.
"""
def __init__(self, weights, prices, capacity):
super().__init__(weights, prices, capacity)
@ -268,9 +290,11 @@ class GurobiKnapsackInstance(KnapsackInstance):
model = gp.Model("Knapsack")
n = len(self.weights)
x = model.addVars(n, vtype=GRB.BINARY, name="x")
model.addConstr(gp.quicksum(x[i] * self.weights[i]
for i in range(n)) <= self.capacity,
"eq_capacity")
model.setObjective(gp.quicksum(x[i] * self.prices[i]
for i in range(n)), GRB.MAXIMIZE)
model.addConstr(
gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
"eq_capacity",
)
model.setObjective(
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
)
return model

39
miplearn/problems/stab.py
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@ -12,17 +12,20 @@ from scipy.stats.distributions import rv_frozen
class ChallengeA:
def __init__(self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
n=randint(low=200, high=201),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True)
self.generator = MaxWeightStableSetGenerator(
w=uniform(loc=100.0, scale=50.0),
n=randint(low=200, high=201),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
@ -43,11 +46,13 @@ class MaxWeightStableSetGenerator:
parameters are sampled in the same way.
"""
def __init__(self,
w=uniform(loc=10.0, scale=1.0),
n=randint(low=250, high=251),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True):
def __init__(
self,
w=uniform(loc=10.0, scale=1.0),
n=randint(low=250, high=251),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
):
"""Initialize the problem generator.
Parameters
@ -78,6 +83,7 @@ class MaxWeightStableSetGenerator:
graph = self._generate_graph()
weights = self.w.rvs(graph.number_of_nodes())
return MaxWeightStableSetInstance(graph, weights)
return [_sample() for _ in range(n_samples)]
def _generate_graph(self):
@ -102,8 +108,9 @@ class MaxWeightStableSetInstance(Instance):
nodes = list(self.graph.nodes)
model = pe.ConcreteModel()
model.x = pe.Var(nodes, domain=pe.Binary)
model.OBJ = pe.Objective(expr=sum(model.x[v] * self.weights[v] for v in nodes),
sense=pe.maximize)
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
)
model.clique_eqs = pe.ConstraintList()
for clique in nx.find_cliques(self.graph):
model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1)

1
miplearn/problems/tests/__init__.py
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@ -1,4 +1,3 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.

19
miplearn/problems/tests/test_knapsack.py
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@ -9,17 +9,18 @@ import numpy as np
def test_knapsack_generator():
gen = MultiKnapsackGenerator(n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=1.0),
alpha=uniform(loc=0.50, scale=0.0),
)
gen = MultiKnapsackGenerator(
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=1.0),
alpha=uniform(loc=0.50, scale=0.0),
)
instances = gen.generate(100)
w_sum = sum(instance.weights for instance in instances) / len(instances)
p_sum = sum(instance.prices for instance in instances) / len(instances)
b_sum = sum(instance.capacities for instance in instances) / len(instances)
assert round(np.mean(w_sum), -1) == 500.
assert round(np.mean(w_sum), -1) == 500.0
# assert round(np.mean(p_sum), -1) == 1200. # flaky
assert round(np.mean(b_sum), -3) == 25000.
assert round(np.mean(b_sum), -3) == 25000.0

30
miplearn/problems/tests/test_stab.py
Unescape View File

@ -11,20 +11,23 @@ from scipy.stats import uniform, randint
def test_stab():
graph = nx.cycle_graph(5)
weights = [1., 1., 1., 1., 1.]
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver()
solver.solve(instance)
assert instance.lower_bound == 2.
assert instance.lower_bound == 2.0
def test_stab_generator_fixed_graph():
np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=0.),
fix_graph=True)
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
)
instances = gen.generate(1_000)
weights = np.array([instance.weights for instance in instances])
weights_avg_actual = np.round(np.average(weights, axis=0))
@ -35,12 +38,15 @@ def test_stab_generator_fixed_graph():
def test_stab_generator_random_graph():
np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=0.),
fix_graph=False)
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=0.0),
fix_graph=False,
)
instances = gen.generate(1_000)
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
n_edges = [instance.graph.number_of_edges() for instance in instances]
assert np.round(np.mean(n_nodes)) == 35.
assert np.round(np.mean(n_edges), -1) == 300.
assert np.round(np.mean(n_nodes)) == 35.0
assert np.round(np.mean(n_edges), -1) == 300.0

48
miplearn/problems/tests/test_tsp.py
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@ -11,11 +11,13 @@ from scipy.stats import uniform, randint
def test_generator():
instances = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True).generate(100)
instances = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
).generate(100)
assert len(instances) == 100
assert instances[0].n_cities == 100
assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
@ -25,14 +27,16 @@ def test_generator():
def test_instance():
n_cities = 4
distances = np.array([
[0., 1., 2., 1.],
[1., 0., 1., 2.],
[2., 1., 0., 1.],
[1., 2., 1., 0.],
])
distances = np.array(
[
[0.0, 1.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 2.0],
[2.0, 1.0, 0.0, 1.0],
[1.0, 2.0, 1.0, 0.0],
]
)
instance = TravelingSalesmanInstance(n_cities, distances)
for solver_name in ['gurobi']:
for solver_name in ["gurobi"]:
solver = LearningSolver(solver=solver_name)
solver.solve(instance)
x = instance.solution["x"]
@ -48,17 +52,19 @@ def test_instance():
def test_subtour():
n_cities = 6
cities = np.array([
[0., 0.],
[1., 0.],
[2., 0.],
[3., 0.],
[0., 1.],
[3., 1.],
])
cities = np.array(
[
[0.0, 0.0],
[1.0, 0.0],
[2.0, 0.0],
[3.0, 0.0],
[0.0, 1.0],
[3.0, 1.0],
]
)
distances = squareform(pdist(cities))
instance = TravelingSalesmanInstance(n_cities, distances)
for solver_name in ['gurobi']:
for solver_name in ["gurobi"]:
solver = LearningSolver(solver=solver_name)
solver.solve(instance)
assert hasattr(instance, "found_violated_lazy_constraints")

81
miplearn/problems/tsp.py
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@ -13,20 +13,22 @@ import random
class ChallengeA:
def __init__(self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
round=True,
)
self.generator = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
round=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
@ -38,14 +40,15 @@ class ChallengeA:
class TravelingSalesmanGenerator:
"""Random generator for the Traveling Salesman Problem."""
def __init__(self,
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=1.0, scale=0.0),
fix_cities=True,
round=True,
):
def __init__(
self,
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101),
gamma=uniform(loc=1.0, scale=0.0),
fix_cities=True,
round=True,
):
"""Initializes the problem generator.
Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
@ -79,7 +82,10 @@ class TravelingSalesmanGenerator:
assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution"
assert isinstance(y, rv_frozen), "y should be a SciPy probability distribution"
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
assert isinstance(gamma, rv_frozen), "gamma should be a SciPy probability distribution"
assert isinstance(
gamma,
rv_frozen,
), "gamma should be a SciPy probability distribution"
self.x = x
self.y = y
self.n = n
@ -103,6 +109,7 @@ class TravelingSalesmanGenerator:
if self.round:
distances = distances.round()
return TravelingSalesmanInstance(n, distances)
return [_sample() for _ in range(n_samples)]
def _generate_cities(self):
@ -128,18 +135,25 @@ class TravelingSalesmanInstance(Instance):
def to_model(self):
model = pe.ConcreteModel()
model.edges = edges = [(i,j)
for i in range(self.n_cities)
for j in range(i+1, self.n_cities)]
model.edges = edges = [
(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
]
model.x = pe.Var(edges, domain=pe.Binary)
model.obj = pe.Objective(expr=sum(model.x[i,j] * self.distances[i,j]
for (i,j) in edges),
sense=pe.minimize)
model.obj = pe.Objective(
expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
sense=pe.minimize,
)
model.eq_degree = pe.ConstraintList()
model.eq_subtour = pe.ConstraintList()
for i in range(self.n_cities):
model.eq_degree.add(sum(model.x[min(i,j), max(i,j)]
for j in range(self.n_cities) if i != j) == 2)
model.eq_degree.add(
sum(
model.x[min(i, j), max(i, j)]
for j in range(self.n_cities)
if i != j
)
== 2
)
return model
def get_instance_features(self):
@ -163,9 +177,12 @@ class TravelingSalesmanInstance(Instance):
return violations
def build_lazy_constraint(self, model, component):
cut_edges = [e for e in model.edges
if (e[0] in component and e[1] not in component) or
(e[0] not in component and e[1] in component)]
cut_edges = [
e
for e in model.edges
if (e[0] in component and e[1] not in component)
or (e[0] not in component and e[1] in component)
]
return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)
def find_violated_user_cuts(self, model):

48
miplearn/solvers/gurobi.py
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@ -13,10 +13,11 @@ logger = logging.getLogger(__name__)
class GurobiSolver(InternalSolver):
def __init__(self,
params=None,
lazy_cb_frequency=1,
):
def __init__(
self,
params=None,
lazy_cb_frequency=1,
):
"""
An InternalSolver backed by Gurobi's Python API (without Pyomo).
@ -33,6 +34,7 @@ class GurobiSolver(InternalSolver):
if params is None:
params = {}
from gurobipy import GRB
self.GRB = GRB
self.instance = None
self.model = None
@ -44,8 +46,7 @@ class GurobiSolver(InternalSolver):
if lazy_cb_frequency == 1:
self.lazy_cb_where = [self.GRB.Callback.MIPSOL]
else:
self.lazy_cb_where = [self.GRB.Callback.MIPSOL,
self.GRB.Callback.MIPNODE]
self.lazy_cb_where = [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]
def set_instance(self, instance, model=None):
self._raise_if_callback()
@ -70,14 +71,15 @@ class GurobiSolver(InternalSolver):
idx = [0]
else:
name = m.group(1)
idx = tuple(int(k) if k.isdecimal() else k
for k in m.group(2).split(","))
idx = tuple(
int(k) if k.isdecimal() else k for k in m.group(2).split(",")
)
if len(idx) == 1:
idx = idx[0]
if name not in self._all_vars:
self._all_vars[name] = {}
self._all_vars[name][idx] = var
if var.vtype != 'C':
if var.vtype != "C":
if name not in self._bin_vars:
self._bin_vars[name] = {}
self._bin_vars[name][idx] = var
@ -103,15 +105,9 @@ class GurobiSolver(InternalSolver):
for (idx, var) in vardict.items():
var.vtype = self.GRB.BINARY
log = streams[0].getvalue()
return {
"Optimal value": self.model.objVal,
"Log": log
}
return {"Optimal value": self.model.objVal, "Log": log}
def solve(self,
tee=False,
iteration_cb=None,
lazy_cb=None):
def solve(self, tee=False, iteration_cb=None, lazy_cb=None):
self._raise_if_callback()
def cb_wrapper(cb_model, cb_where):
@ -133,7 +129,7 @@ class GurobiSolver(InternalSolver):
if tee:
streams += [sys.stdout]
if iteration_cb is None:
iteration_cb = lambda : False
iteration_cb = lambda: False
while True:
logger.debug("Solving MIP...")
with RedirectOutput(streams):
@ -187,7 +183,9 @@ class GurobiSolver(InternalSolver):
elif self.cb_where is None:
return var.x
else:
raise Exception("get_value cannot be called from cb_where=%s" % self.cb_where)
raise Exception(
"get_value cannot be called from cb_where=%s" % self.cb_where
)
def get_variables(self):
self._raise_if_callback()
@ -220,8 +218,10 @@ class GurobiSolver(InternalSolver):
if value is not None:
count_fixed += 1
self._all_vars[varname][idx].start = value
logger.info("Setting start values for %d variables (out of %d)" %
(count_fixed, count_total))
logger.info(
"Setting start values for %d variables (out of %d)"
% (count_fixed, count_total)
)
def clear_warm_start(self):
self._raise_if_callback()
@ -248,10 +248,7 @@ class GurobiSolver(InternalSolver):
def extract_constraint(self, cid):
self._raise_if_callback()
constr = self.model.getConstrByName(cid)
cobj = (self.model.getRow(constr),
constr.sense,
constr.RHS,
constr.ConstrName)
cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
self.model.remove(constr)
return cobj
@ -324,6 +321,7 @@ class GurobiSolver(InternalSolver):
def __setstate__(self, state):
from gurobipy import GRB
self.params = state["params"]
self.lazy_cb_where = state["lazy_cb_where"]
self.GRB = GRB

1
miplearn/solvers/internal.py
Unescape View File

@ -222,4 +222,3 @@ class InternalSolver(ABC):
for idx in indices:
solution[var][idx] = 0.0
return solution

62
miplearn/solvers/learning.py
Unescape View File

@ -12,10 +12,12 @@ from copy import deepcopy
from typing import Optional, List
from p_tqdm import p_map
from .. import (ObjectiveValueComponent,
PrimalSolutionComponent,
DynamicLazyConstraintsComponent,
UserCutsComponent)
from .. import (
ObjectiveValueComponent,
PrimalSolutionComponent,
DynamicLazyConstraintsComponent,
UserCutsComponent,
)
from .pyomo.cplex import CplexPyomoSolver
from .pyomo.gurobi import GurobiPyomoSolver
@ -43,16 +45,18 @@ def _parallel_solve(idx):
class LearningSolver:
def __init__(self,
components=None,
gap_tolerance=1e-4,
mode="exact",
solver="gurobi",
threads=None,
time_limit=None,
node_limit=None,
solve_lp_first=True,
use_lazy_cb=False):
def __init__(
self,
components=None,
gap_tolerance=1e-4,
mode="exact",
solver="gurobi",
threads=None,
time_limit=None,
node_limit=None,
solve_lp_first=True,
use_lazy_cb=False,
):
"""
Mixed-Integer Linear Programming (MIP) solver that extracts information
from previous runs and uses Machine Learning methods to accelerate the
@ -142,11 +146,13 @@ class LearningSolver:
solver.set_node_limit(self.node_limit)
return solver
def solve(self,
instance,
model=None,
output="",
tee=False):
def solve(
self,
instance,
model=None,
output="",
tee=False,
):
"""
Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process.
@ -248,9 +254,11 @@ class LearningSolver:
lazy_cb = lazy_cb_wrapper
logger.info("Solving MILP...")
results = self.internal_solver.solve(tee=tee,
iteration_cb=iteration_cb,
lazy_cb=lazy_cb)
results = self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb,
lazy_cb=lazy_cb,
)
results["LP value"] = instance.lp_value
# Read MIP solution and bounds
@ -306,10 +314,12 @@ class LearningSolver:
SOLVER[0] = self
OUTPUTS[0] = output
INSTANCES[0] = instances
results = p_map(_parallel_solve,
list(range(len(instances))),
num_cpus=n_jobs,
desc=label)
results = p_map(
_parallel_solve,
list(range(len(instances))),
num_cpus=n_jobs,
desc=label,
)
stats = []
for (idx, (s, instance)) in enumerate(results):
stats.append(s)

30
miplearn/solvers/pyomo/base.py
Unescape View File

@ -81,8 +81,10 @@ class BasePyomoSolver(InternalSolver):
count_fixed += 1
if count_fixed > 0:
self._is_warm_start_available = True
logger.info("Setting start values for %d variables (out of %d)" %
(count_fixed, count_total))
logger.info(
"Setting start values for %d variables (out of %d)"
% (count_fixed, count_total)
)
def clear_warm_start(self):
for var in self._all_vars:
@ -134,17 +136,19 @@ class BasePyomoSolver(InternalSolver):
count_fixed += 1
var[index].fix(solution[varname][index])
self._pyomo_solver.update_var(var[index])
logger.info("Fixing values for %d variables (out of %d)" %
(count_fixed, count_total))
logger.info(
"Fixing values for %d variables (out of %d)"
% (
count_fixed,
count_total,
)
)
def add_constraint(self, constraint):
self._pyomo_solver.add_constraint(constraint)
self._update_constrs()
def solve(self,
tee=False,
iteration_cb=None,
lazy_cb=None):
def solve(self, tee=False, iteration_cb=None, lazy_cb=None):
if lazy_cb is not None:
raise Exception("lazy callback not supported")
total_wallclock_time = 0
@ -158,8 +162,10 @@ class BasePyomoSolver(InternalSolver):
while True:
logger.debug("Solving MIP...")
with RedirectOutput(streams):
results = self._pyomo_solver.solve(tee=True,
warmstart=self._is_warm_start_available)
results = self._pyomo_solver.solve(
tee=True,
warmstart=self._is_warm_start_available,
)
total_wallclock_time += results["Solver"][0]["Wallclock time"]
should_repeat = iteration_cb()
if not should_repeat:
@ -192,9 +198,7 @@ class BasePyomoSolver(InternalSolver):
return value
def _extract_node_count(self, log):
return int(self.__extract(log,
self._get_node_count_regexp(),
default=1))
return int(self.__extract(log, self._get_node_count_regexp(), default=1))
def set_threads(self, threads):
key = self._get_threads_option_name()

2
miplearn/solvers/pyomo/cplex.py
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@ -20,7 +20,7 @@ class CplexPyomoSolver(BasePyomoSolver):
{"mip_display": 5} to increase the log verbosity.
"""
super().__init__()
self._pyomo_solver = pe.SolverFactory('cplex_persistent')
self._pyomo_solver = pe.SolverFactory("cplex_persistent")
self._pyomo_solver.options["randomseed"] = randint(low=0, high=1000).rvs()
self._pyomo_solver.options["mip_display"] = 4
if options is not None:

6
miplearn/solvers/pyomo/gurobi.py
Unescape View File

@ -15,8 +15,7 @@ logger = logging.getLogger(__name__)
class GurobiPyomoSolver(BasePyomoSolver):
def __init__(self,
options=None):
def __init__(self, options=None):
"""
Creates a new Gurobi solver, accessed through Pyomo.
@ -27,7 +26,7 @@ class GurobiPyomoSolver(BasePyomoSolver):
{"Threads": 4} to set the number of threads.
"""
super().__init__()
self._pyomo_solver = pe.SolverFactory('gurobi_persistent')
self._pyomo_solver = pe.SolverFactory("gurobi_persistent")
self._pyomo_solver.options["Seed"] = randint(low=0, high=1000).rvs()
if options is not None:
for (key, value) in options.items():
@ -56,6 +55,7 @@ class GurobiPyomoSolver(BasePyomoSolver):
def set_branching_priorities(self, priorities):
from gurobipy import GRB
for varname in priorities.keys():
var = self._varname_to_var[varname]
for (index, priority) in priorities[varname].items():

16
miplearn/solvers/tests/__init__.py
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@ -9,20 +9,22 @@ from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
def _get_instance(solver):
def _is_subclass_or_instance(solver, parentClass):
return isinstance(solver, parentClass) or (isclass(solver) and issubclass(solver, parentClass))
return isinstance(solver, parentClass) or (
isclass(solver) and issubclass(solver, parentClass)
)
if _is_subclass_or_instance(solver, BasePyomoSolver):
return KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
)
if _is_subclass_or_instance(solver, GurobiSolver):
return GurobiKnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
)
assert False

49
miplearn/solvers/tests/test_internal_solver.py
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@ -16,6 +16,7 @@ logger = logging.getLogger(__name__)
def test_redirect_output():
import sys
original_stdout = sys.stdout
io = StringIO()
with RedirectOutput([io]):
@ -31,36 +32,42 @@ def test_internal_solver_warm_starts():
model = instance.to_model()
solver = solver_class()
solver.set_instance(instance, model)
solver.set_warm_start({
"x": {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
solver.set_warm_start(
{
"x": {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
}
}
})
)
stats = solver.solve(tee=True)
assert stats["Warm start value"] == 725.0
solver.set_warm_start({
"x": {
0: 1.0,
1: 1.0,
2: 1.0,
3: 1.0,
solver.set_warm_start(
{
"x": {
0: 1.0,
1: 1.0,
2: 1.0,
3: 1.0,
}
}
})
)
stats = solver.solve(tee=True)
assert stats["Warm start value"] is None
solver.fix({
"x": {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
solver.fix(
{
"x": {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
}
}
})
)
stats = solver.solve(tee=True)
assert stats["Lower bound"] == 725.0
assert stats["Upper bound"] == 725.0

15
miplearn/solvers/tests/test_learning_solver.py
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@ -20,11 +20,13 @@ def test_learning_solver():
for internal_solver in _get_internal_solvers():
logger.info("Solver: %s" % internal_solver)
instance = _get_instance(internal_solver)
solver = LearningSolver(time_limit=300,
gap_tolerance=1e-3,
threads=1,
solver=internal_solver,
mode=mode)
solver = LearningSolver(
time_limit=300,
gap_tolerance=1e-3,
threads=1,
solver=internal_solver,
mode=mode,
)
solver.solve(instance)
assert instance.solution["x"][0] == 1.0
@ -74,8 +76,7 @@ def test_solve_fit_from_disk():
filenames = []
for k in range(3):
instance = _get_instance(internal_solver)
with tempfile.NamedTemporaryFile(suffix=".pkl",
delete=False) as file:
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
filenames += [file.name]
pickle.dump(instance, file)

12
miplearn/tests/__init__.py
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@ -8,14 +8,14 @@ from miplearn.problems.knapsack import KnapsackInstance
def get_test_pyomo_instances():
instances = [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
),
KnapsackInstance(
weights=[25., 30., 22., 18.],
prices=[500., 365., 420., 150.],
capacity=70.,
weights=[25.0, 30.0, 22.0, 18.0],
prices=[500.0, 365.0, 420.0, 150.0],
capacity=70.0,
),
]
models = [instance.to_model() for instance in instances]

9
miplearn/tests/test_benchmark.py
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@ -11,8 +11,9 @@ from scipy.stats import randint
def test_benchmark():
# Generate training and test instances
train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5)
test_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(3)
generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
train_instances = generator.generate(5)
test_instances = generator.generate(3)
# Training phase...
training_solver = LearningSolver()
@ -26,11 +27,11 @@ def test_benchmark():
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
assert benchmark.raw_results().values.shape == (12,16)
assert benchmark.raw_results().values.shape == (12, 16)
benchmark.save_results("/tmp/benchmark.csv")
assert os.path.isfile("/tmp/benchmark.csv")
benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results("/tmp/benchmark.csv")
assert benchmark.raw_results().values.shape == (12,16)
assert benchmark.raw_results().values.shape == (12, 16)

52
miplearn/tests/test_extractors.py
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@ -3,25 +3,28 @@
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn.problems.knapsack import KnapsackInstance
from miplearn import (LearningSolver,
SolutionExtractor,
InstanceFeaturesExtractor,
VariableFeaturesExtractor,
)
from miplearn import (
LearningSolver,
SolutionExtractor,
InstanceFeaturesExtractor,
VariableFeaturesExtractor,
)
import numpy as np
import pyomo.environ as pe
def _get_instances():
instances = [
KnapsackInstance(weights=[1., 2., 3.],
prices=[10., 20., 30.],
capacity=2.5,
),
KnapsackInstance(weights=[3., 4., 5.],
prices=[20., 30., 40.],
capacity=4.5,
),
KnapsackInstance(
weights=[1.0, 2.0, 3.0],
prices=[10.0, 20.0, 30.0],
capacity=2.5,
),
KnapsackInstance(
weights=[3.0, 4.0, 5.0],
prices=[20.0, 30.0, 40.0],
capacity=4.5,
),
]
models = [instance.to_model() for instance in instances]
solver = LearningSolver()
@ -38,19 +41,25 @@ def test_solution_extractor():
assert isinstance(features["default"], np.ndarray)
assert features["default"].shape == (6, 2)
assert features["default"].ravel().tolist() == [
1., 0.,
0., 1.,
1., 0.,
1., 0.,
0., 1.,
1., 0.,
1.0,
0.0,
0.0,
1.0,
1.0,
0.0,
1.0,
0.0,
0.0,
1.0,
1.0,
0.0,
]
def test_instance_features_extractor():
instances, models = _get_instances()
features = InstanceFeaturesExtractor().extract(instances)
assert features.shape == (2,3)
assert features.shape == (2, 3)
def test_variable_features_extractor():
@ -58,5 +67,4 @@ def test_variable_features_extractor():
features = VariableFeaturesExtractor().extract(instances)
assert isinstance(features, dict)
assert "default" in features
assert features["default"].shape == (6,5)
assert features["default"].shape == (6, 5)

3
pyproject.toml
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@ -0,0 +1,3 @@
[tool.black]
py36 = true
include = '\.pyi?$'

2
requirements.txt
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@ -12,3 +12,5 @@ python-markdown-math~=0.8
seaborn~=0.11
scikit-learn~=0.23
tqdm~=4.54
black==20.8b1
pre-commit~=2.9

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