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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: uninstall:
$(PIP) uninstall miplearn $(PIP) uninstall miplearn
reformat:
$(PYTHON) -m black miplearn
test: test:
$(PYTEST) $(PYTEST_ARGS) $(PYTEST) $(PYTEST_ARGS)

6
miplearn/__init__.py
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@ -2,10 +2,12 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
from .extractors import (SolutionExtractor, from .extractors import (
SolutionExtractor,
InstanceFeaturesExtractor, InstanceFeaturesExtractor,
ObjectiveValueExtractor, ObjectiveValueExtractor,
VariableFeaturesExtractor) VariableFeaturesExtractor,
)
from .components.component import Component from .components.component import Component
from .components.objective import ObjectiveValueComponent from .components.objective import ObjectiveValueComponent

86
miplearn/benchmark.py
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@ -24,9 +24,15 @@ class BenchmarkRunner:
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
for i in tqdm(range(len((instances)))): for i in tqdm(range(len((instances)))):
results = solver.solve(deepcopy(instances[i]), tee=tee) results = solver.solve(deepcopy(instances[i]), tee=tee)
self._push_result(results, solver=solver, solver_name=solver_name, instance=i) self._push_result(
results,
solver=solver,
solver_name=solver_name,
instance=i,
)
def parallel_solve(self, def parallel_solve(
self,
instances, instances,
n_jobs=1, n_jobs=1,
n_trials=1, n_trials=1,
@ -35,16 +41,20 @@ class BenchmarkRunner:
self._silence_miplearn_logger() self._silence_miplearn_logger()
trials = instances * n_trials trials = instances * n_trials
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
results = solver.parallel_solve(trials, results = solver.parallel_solve(
trials,
n_jobs=n_jobs, n_jobs=n_jobs,
label="Solve (%s)" % solver_name, label="Solve (%s)" % solver_name,
output=None) output=None,
)
for i in range(len(trials)): for i in range(len(trials)):
idx = (i % len(instances)) + index_offset idx = (i % len(instances)) + index_offset
self._push_result(results[i], self._push_result(
results[i],
solver=solver, solver=solver,
solver_name=solver_name, solver_name=solver_name,
instance=idx) instance=idx,
)
self._restore_miplearn_logger() self._restore_miplearn_logger()
def raw_results(self): def raw_results(self):
@ -66,7 +76,9 @@ class BenchmarkRunner:
def _push_result(self, result, solver, solver_name, instance): def _push_result(self, result, solver, solver_name, instance):
if self.results is None: if self.results is None:
self.results = pd.DataFrame(columns=["Solver", self.results = pd.DataFrame(
columns=[
"Solver",
"Instance", "Instance",
"Wallclock Time", "Wallclock Time",
"Lower Bound", "Lower Bound",
@ -77,14 +89,16 @@ class BenchmarkRunner:
"Sense", "Sense",
"Predicted LB", "Predicted LB",
"Predicted UB", "Predicted UB",
]) ]
)
lb = result["Lower bound"] lb = result["Lower bound"]
ub = result["Upper bound"] ub = result["Upper bound"]
gap = (ub - lb) / lb gap = (ub - lb) / lb
if "Predicted LB" not in result: if "Predicted LB" not in result:
result["Predicted LB"] = float("nan") result["Predicted LB"] = float("nan")
result["Predicted UB"] = float("nan") result["Predicted UB"] = float("nan")
self.results = self.results.append({ self.results = self.results.append(
{
"Solver": solver_name, "Solver": solver_name,
"Instance": instance, "Instance": instance,
"Wallclock Time": result["Wallclock time"], "Wallclock Time": result["Wallclock time"],
@ -96,23 +110,26 @@ class BenchmarkRunner:
"Sense": result["Sense"], "Sense": result["Sense"],
"Predicted LB": result["Predicted LB"], "Predicted LB": result["Predicted LB"],
"Predicted UB": result["Predicted UB"], "Predicted UB": result["Predicted UB"],
}, ignore_index=True) },
ignore_index=True,
)
groups = self.results.groupby("Instance") groups = self.results.groupby("Instance")
best_lower_bound = groups["Lower Bound"].transform("max") best_lower_bound = groups["Lower Bound"].transform("max")
best_upper_bound = groups["Upper Bound"].transform("min") best_upper_bound = groups["Upper Bound"].transform("min")
best_gap = groups["Gap"].transform("min") best_gap = groups["Gap"].transform("min")
best_nodes = np.maximum(1, groups["Nodes"].transform("min")) best_nodes = np.maximum(1, groups["Nodes"].transform("min"))
best_wallclock_time = groups["Wallclock Time"].transform("min") best_wallclock_time = groups["Wallclock Time"].transform("min")
self.results["Relative Lower Bound"] = \ self.results["Relative Lower Bound"] = (
self.results["Lower Bound"] / best_lower_bound self.results["Lower Bound"] / best_lower_bound
self.results["Relative Upper Bound"] = \ )
self.results["Relative Upper Bound"] = (
self.results["Upper Bound"] / best_upper_bound self.results["Upper Bound"] / best_upper_bound
self.results["Relative Wallclock Time"] = \ )
self.results["Relative Wallclock Time"] = (
self.results["Wallclock Time"] / best_wallclock_time self.results["Wallclock Time"] / best_wallclock_time
self.results["Relative Gap"] = \ )
self.results["Gap"] / best_gap self.results["Relative Gap"] = self.results["Gap"] / best_gap
self.results["Relative Nodes"] = \ self.results["Relative Nodes"] = self.results["Nodes"] / best_nodes
self.results["Nodes"] / best_nodes
def save_chart(self, filename): def save_chart(self, filename):
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -134,32 +151,37 @@ class BenchmarkRunner:
obj_column = "Lower Bound" obj_column = "Lower Bound"
predicted_obj_column = "Predicted LB" predicted_obj_column = "Predicted LB"
fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1, fig, (ax1, ax2, ax3, ax4) = plt.subplots(
nrows=1,
ncols=4, ncols=4,
figsize=(12,4), figsize=(12, 4),
gridspec_kw={'width_ratios': [2, 1, 1, 2]}) gridspec_kw={"width_ratios": [2, 1, 1, 2]},
)
# Figure 1: Solver x Wallclock Time # Figure 1: Solver x Wallclock Time
sns.stripplot(x="Solver", sns.stripplot(
x="Solver",
y="Wallclock Time", y="Wallclock Time",
data=results, data=results,
ax=ax1, ax=ax1,
jitter=0.25, jitter=0.25,
size=4.0, size=4.0,
) )
sns.barplot(x="Solver", sns.barplot(
x="Solver",
y="Wallclock Time", y="Wallclock Time",
data=results, data=results,
ax=ax1, ax=ax1,
errwidth=0., errwidth=0.0,
alpha=0.4, alpha=0.4,
estimator=median, estimator=median,
) )
ax1.set(ylabel='Wallclock Time (s)') ax1.set(ylabel="Wallclock Time (s)")
# Figure 2: Solver x Gap (%) # Figure 2: Solver x Gap (%)
ax2.set_ylim(-0.5, 5.5) ax2.set_ylim(-0.5, 5.5)
sns.stripplot(x="Solver", sns.stripplot(
x="Solver",
y="Gap (%)", y="Gap (%)",
jitter=0.25, jitter=0.25,
data=results[results["Mode"] != "heuristic"], data=results[results["Mode"] != "heuristic"],
@ -168,8 +190,9 @@ class BenchmarkRunner:
) )
# Figure 3: Solver x Primal Value # Figure 3: Solver x Primal Value
ax3.set_ylim(0.95,1.05) ax3.set_ylim(0.95, 1.05)
sns.stripplot(x="Solver", sns.stripplot(
x="Solver",
y=primal_column, y=primal_column,
jitter=0.25, jitter=0.25,
data=results[results["Mode"] == "heuristic"], data=results[results["Mode"] == "heuristic"],
@ -177,20 +200,21 @@ class BenchmarkRunner:
) )
# Figure 4: Predicted vs Actual Objective Value # Figure 4: Predicted vs Actual Objective Value
sns.scatterplot(x=obj_column, sns.scatterplot(
x=obj_column,
y=predicted_obj_column, y=predicted_obj_column,
hue="Solver", hue="Solver",
data=results[results["Mode"] != "heuristic"], data=results[results["Mode"] != "heuristic"],
ax=ax4, ax=ax4,
) )
xlim, ylim = ax4.get_xlim(), ax4.get_ylim() 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_xlim(xlim)
ax4.set_ylim(ylim) ax4.set_ylim(ylim)
ax4.get_legend().remove() ax4.get_legend().remove()
fig.tight_layout() fig.tight_layout()
plt.savefig(filename, bbox_inches='tight', dpi=150) plt.savefig(filename, bbox_inches="tight", dpi=150)
def _silence_miplearn_logger(self): def _silence_miplearn_logger(self):
miplearn_logger = logging.getLogger("miplearn") miplearn_logger = logging.getLogger("miplearn")
@ -200,5 +224,3 @@ class BenchmarkRunner:
def _restore_miplearn_logger(self): def _restore_miplearn_logger(self):
miplearn_logger = logging.getLogger("miplearn") miplearn_logger = logging.getLogger("miplearn")
miplearn_logger.setLevel(self.prev_log_level) miplearn_logger.setLevel(self.prev_log_level)

11
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. based on its cross-validation score on a particular training data set.
""" """
def __init__(self, def __init__(
self,
candidates=None, candidates=None,
evaluator=ClassifierEvaluator()): evaluator=ClassifierEvaluator(),
):
""" """
Initializes the meta-classifier. Initializes the meta-classifier.
""" """
@ -35,14 +37,13 @@ class AdaptiveClassifier(Classifier):
"min samples": 100, "min samples": 100,
}, },
"logistic": { "logistic": {
"classifier": make_pipeline(StandardScaler(), "classifier": make_pipeline(StandardScaler(), LogisticRegression()),
LogisticRegression()),
"min samples": 30, "min samples": 30,
}, },
"counting": { "counting": {
"classifier": CountingClassifier(), "classifier": CountingClassifier(),
"min samples": 0, "min samples": 0,
} },
} }
self.candidates = candidates self.candidates = candidates
self.evaluator = evaluator 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) self.mean = np.mean(y_train)
def predict_proba(self, x_test): def predict_proba(self, x_test):
return np.array([[1 - self.mean, self.mean] return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
for _ in range(x_test.shape[0])])
def __repr__(self): def __repr__(self):
return "CountingClassifier(mean=%s)" % self.mean return "CountingClassifier(mean=%s)" % self.mean

37
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 from sklearn.model_selection import cross_val_score
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -28,12 +29,14 @@ class CrossValidatedClassifier(Classifier):
acceptable. Other numbers are a linear interpolation of these two extremes. acceptable. Other numbers are a linear interpolation of these two extremes.
""" """
def __init__(self, def __init__(
self,
classifier=LogisticRegression(), classifier=LogisticRegression(),
threshold=0.75, threshold=0.75,
constant=0.0, constant=0.0,
cv=5, cv=5,
scoring='accuracy'): scoring="accuracy",
):
self.classifier = None self.classifier = None
self.classifier_prototype = classifier self.classifier_prototype = classifier
self.constant = constant self.constant = constant
@ -45,24 +48,36 @@ class CrossValidatedClassifier(Classifier):
# Calculate dummy score and absolute score threshold # Calculate dummy score and absolute score threshold
y_train_avg = np.average(y_train) y_train_avg = np.average(y_train)
dummy_score = max(y_train_avg, 1 - y_train_avg) 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 # Calculate cross validation score and decide which classifier to use
clf = deepcopy(self.classifier_prototype) clf = deepcopy(self.classifier_prototype)
cv_score = float(np.mean(cross_val_score(clf, cv_score = float(
np.mean(
cross_val_score(
clf,
x_train, x_train,
y_train, y_train,
cv=self.cv, cv=self.cv,
scoring=self.scoring))) scoring=self.scoring,
)
)
)
if cv_score >= absolute_threshold: if cv_score >= absolute_threshold:
logger.debug("cv_score is above threshold (%.2f >= %.2f); keeping" % logger.debug(
(cv_score, absolute_threshold)) "cv_score is above threshold (%.2f >= %.2f); keeping"
% (cv_score, absolute_threshold)
)
self.classifier = clf self.classifier = clf
else: else:
logger.debug("cv_score is below threshold (%.2f < %.2f); discarding" % logger.debug(
(cv_score, absolute_threshold)) "cv_score is below threshold (%.2f < %.2f); discarding"
self.classifier = DummyClassifier(strategy="constant", % (cv_score, absolute_threshold)
constant=self.constant) )
self.classifier = DummyClassifier(
strategy="constant",
constant=self.constant,
)
# Train chosen classifier # Train chosen classifier
self.classifier.fit(x_train, y_train) 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(): def test_counting():
clf = CountingClassifier() clf = CountingClassifier()
clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]) 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], expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
[0.375, 0.625]])
actual_proba = clf.predict_proba(np.zeros((2, 25))) actual_proba = clf.predict_proba(np.zeros((2, 25)))
assert norm(actual_proba - expected_proba) < E assert norm(actual_proba - expected_proba) < E

28
miplearn/classifiers/tests/test_cv.py
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@ -13,34 +13,36 @@ E = 0.1
def test_cv(): def test_cv():
# Training set: label is true if point is inside a 2D circle # Training set: label is true if point is inside a 2D circle
x_train = np.array([[x1, x2] x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
for x1 in range(-10, 11)
for x2 in range(-10, 11)])
x_train = StandardScaler().fit_transform(x_train) x_train = StandardScaler().fit_transform(x_train)
n_samples = x_train.shape[0] n_samples = x_train.shape[0]
y_train = np.array([1.0 if x1*x1 + x2*x2 <= 100 else 0.0 y_train = np.array(
[
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
for x1 in range(-10, 11) for x1 in range(-10, 11)
for x2 in range(-10, 11)]) for x2 in range(-10, 11)
]
)
# Support vector machines with linear kernels do not perform well on this # Support vector machines with linear kernels do not perform well on this
# data set, so predictor should return the given constant. # data set, so predictor should return the given constant.
clf = CrossValidatedClassifier(classifier=SVC(probability=True, clf = CrossValidatedClassifier(
random_state=42), classifier=SVC(probability=True, random_state=42),
threshold=0.90, threshold=0.90,
constant=0.0, constant=0.0,
cv=30) cv=30,
)
clf.fit(x_train, y_train) clf.fit(x_train, y_train)
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
# Support vector machines with quadratic kernels perform almost perfectly # Support vector machines with quadratic kernels perform almost perfectly
# on this data set, so predictor should return their prediction. # on this data set, so predictor should return their prediction.
clf = CrossValidatedClassifier(classifier=SVC(probability=True, clf = CrossValidatedClassifier(
kernel='poly', classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
degree=2,
random_state=42),
threshold=0.90, threshold=0.90,
cv=30) cv=30,
)
clf.fit(x_train, y_train) clf.fit(x_train, y_train)
print(y_train - clf.predict(x_train)) print(y_train - clf.predict(x_train))
assert norm(y_train - clf.predict(x_train)) < E 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() ev = ClassifierEvaluator()
assert ev.evaluate(clf_a, x_train, y_train) == 1.0 assert ev.evaluate(clf_a, x_train, y_train) == 1.0
assert ev.evaluate(clf_b, x_train, y_train) == 0.5 assert ev.evaluate(clf_b, x_train, y_train) == 0.5

9
miplearn/classifiers/tests/test_threshold.py
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@ -11,12 +11,16 @@ from miplearn.classifiers.threshold import MinPrecisionThreshold
def test_threshold_dynamic(): def test_threshold_dynamic():
clf = Mock(spec=Classifier) clf = Mock(spec=Classifier)
clf.predict_proba = Mock(return_value=np.array([ clf.predict_proba = Mock(
return_value=np.array(
[
[0.10, 0.90], [0.10, 0.90],
[0.10, 0.90], [0.10, 0.90],
[0.20, 0.80], [0.20, 0.80],
[0.30, 0.70], [0.30, 0.70],
])) ]
)
)
x_train = np.array([0, 1, 2, 3]) x_train = np.array([0, 1, 2, 3])
y_train = np.array([1, 1, 0, 0]) y_train = np.array([1, 1, 0, 0])
@ -31,4 +35,3 @@ def test_threshold_dynamic():
threshold = MinPrecisionThreshold(min_precision=0.00) threshold = MinPrecisionThreshold(min_precision=0.00)
assert threshold.find(clf, x_train, y_train) == 0.70 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): def find(self, clf, x_train, y_train):
proba = clf.predict_proba(x_train) proba = clf.predict_proba(x_train)
assert isinstance(proba, np.ndarray), \ assert isinstance(proba, np.ndarray), "classifier should return numpy array"
"classifier should return numpy array" assert proba.shape == (
assert proba.shape == (x_train.shape[0], 2), \ x_train.shape[0],
"classifier should return (%d,%d)-shaped array, not %s" % ( 2,
x_train.shape[0], 2, str(proba.shape)) ), "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]) fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
precision = tps / (tps + fps) precision = tps / (tps + fps)

12
miplearn/components/cuts.py
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@ -19,9 +19,11 @@ class UserCutsComponent(Component):
A component that predicts which user cuts to enforce. A component that predicts which user cuts to enforce.
""" """
def __init__(self, def __init__(
self,
classifier=CountingClassifier(), classifier=CountingClassifier(),
threshold=0.05): threshold=0.05,
):
self.violations = set() self.violations = set()
self.count = {} self.count = {}
self.n_samples = 0 self.n_samples = 0
@ -56,7 +58,8 @@ class UserCutsComponent(Component):
violation_to_instance_idx[v] = [] violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx] violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(self.classifiers.items(), for (v, classifier) in tqdm(
self.classifiers.items(),
desc="Fit (user cuts)", desc="Fit (user cuts)",
disable=not sys.stdout.isatty(), disable=not sys.stdout.isatty(),
): ):
@ -79,7 +82,8 @@ class UserCutsComponent(Component):
all_violations = set() all_violations = set()
for instance in instances: for instance in instances:
all_violations |= set(instance.found_violated_user_cuts) all_violations |= set(instance.found_violated_user_cuts)
for idx in tqdm(range(len(instances)), for idx in tqdm(
range(len(instances)),
desc="Evaluate (lazy)", desc="Evaluate (lazy)",
disable=not sys.stdout.isatty(), disable=not sys.stdout.isatty(),
): ):

12
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. A component that predicts which lazy constraints to enforce.
""" """
def __init__(self, def __init__(
self,
classifier=CountingClassifier(), classifier=CountingClassifier(),
threshold=0.05): threshold=0.05,
):
self.violations = set() self.violations = set()
self.count = {} self.count = {}
self.n_samples = 0 self.n_samples = 0
@ -68,7 +70,8 @@ class DynamicLazyConstraintsComponent(Component):
violation_to_instance_idx[v] = [] violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx] violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(self.classifiers.items(), for (v, classifier) in tqdm(
self.classifiers.items(),
desc="Fit (lazy)", desc="Fit (lazy)",
disable=not sys.stdout.isatty(), disable=not sys.stdout.isatty(),
): ):
@ -91,7 +94,8 @@ class DynamicLazyConstraintsComponent(Component):
all_violations = set() all_violations = set()
for instance in instances: for instance in instances:
all_violations |= set(instance.found_violated_lazy_constraints) all_violations |= set(instance.found_violated_lazy_constraints)
for idx in tqdm(range(len(instances)), for idx in tqdm(
range(len(instances)),
desc="Evaluate (lazy)", desc="Evaluate (lazy)",
disable=not sys.stdout.isatty(), disable=not sys.stdout.isatty(),
): ):

39
miplearn/components/lazy_static.py
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@ -19,7 +19,8 @@ class LazyConstraint:
class StaticLazyConstraintsComponent(Component): class StaticLazyConstraintsComponent(Component):
def __init__(self, def __init__(
self,
classifier=CountingClassifier(), classifier=CountingClassifier(),
threshold=0.05, threshold=0.05,
use_two_phase_gap=True, use_two_phase_gap=True,
@ -74,32 +75,38 @@ class StaticLazyConstraintsComponent(Component):
logger.debug("Finding violated lazy constraints...") logger.debug("Finding violated lazy constraints...")
constraints_to_add = [] constraints_to_add = []
for c in self.pool: for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(c.obj, if not solver.internal_solver.is_constraint_satisfied(
tol=self.violation_tolerance): c.obj, tol=self.violation_tolerance
):
constraints_to_add.append(c) constraints_to_add.append(c)
for c in constraints_to_add: for c in constraints_to_add:
self.pool.remove(c) self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj) solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid] instance.found_violated_lazy_constraints += [c.cid]
if len(constraints_to_add) > 0: 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 return True
else: else:
return False return False
def fit(self, training_instances): def fit(self, training_instances):
training_instances = [t training_instances = [
t
for t in training_instances for t in training_instances
if hasattr(t, "found_violated_lazy_constraints")] if hasattr(t, "found_violated_lazy_constraints")
]
logger.debug("Extracting x and y...") logger.debug("Extracting x and y...")
x = self.x(training_instances) x = self.x(training_instances)
y = self.y(training_instances) y = self.y(training_instances)
logger.debug("Fitting...") logger.debug("Fitting...")
for category in tqdm(x.keys(), for category in tqdm(
desc="Fit (lazy)", x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
disable=not sys.stdout.isatty()): ):
if category not in self.classifiers: if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype) self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category]) self.classifiers[category].fit(x[category], y[category])
@ -121,8 +128,10 @@ class StaticLazyConstraintsComponent(Component):
x[category] = [] x[category] = []
constraints[category] = [] constraints[category] = []
x[category] += [instance.get_constraint_features(cid)] x[category] += [instance.get_constraint_features(cid)]
c = LazyConstraint(cid=cid, c = LazyConstraint(
obj=solver.internal_solver.extract_constraint(cid)) cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
constraints[category] += [c] constraints[category] += [c]
self.pool.append(c) self.pool.append(c)
logger.info("%8d lazy constraints extracted" % len(self.pool)) logger.info("%8d lazy constraints extracted" % len(self.pool))
@ -141,7 +150,13 @@ class StaticLazyConstraintsComponent(Component):
self.pool.remove(c) self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj) solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid] 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): def _collect_constraints(self, train_instances):
constraints = {} constraints = {}

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

38
miplearn/components/primal.py
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@ -19,10 +19,12 @@ class PrimalSolutionComponent(Component):
A component that predicts primal solutions. A component that predicts primal solutions.
""" """
def __init__(self, def __init__(
self,
classifier=AdaptiveClassifier(), classifier=AdaptiveClassifier(),
mode="exact", mode="exact",
threshold=MinPrecisionThreshold(0.98)): threshold=MinPrecisionThreshold(0.98),
):
self.mode = mode self.mode = mode
self.classifiers = {} self.classifiers = {}
self.thresholds = {} self.thresholds = {}
@ -51,7 +53,8 @@ class PrimalSolutionComponent(Component):
features = VariableFeaturesExtractor().extract(training_instances) features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances) solutions = SolutionExtractor().extract(training_instances)
for category in tqdm(features.keys(), for category in tqdm(
features.keys(),
desc="Fit (primal)", desc="Fit (primal)",
): ):
x_train = features[category] x_train = features[category]
@ -74,9 +77,15 @@ class PrimalSolutionComponent(Component):
# Find threshold (dynamic or static) # Find threshold (dynamic or static)
if isinstance(self.threshold_prototype, DynamicThreshold): 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: else:
self.thresholds[category, label] = deepcopy(self.threshold_prototype) self.thresholds[category, label] = deepcopy(
self.threshold_prototype
)
self.classifiers[category, label] = clf self.classifiers[category, label] = clf
@ -98,16 +107,19 @@ class PrimalSolutionComponent(Component):
ws = np.array([[1 - clf, clf] for _ in range(n)]) ws = np.array([[1 - clf, clf] for _ in range(n)])
else: else:
ws = clf.predict_proba(x_test[category]) 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]): for (i, (var, index)) in enumerate(var_split[category]):
if ws[i, 1] >= self.thresholds[category, label]: if ws[i, 1] >= self.thresholds[category, label]:
solution[var][index] = label solution[var][index] = label
return solution return solution
def evaluate(self, instances): def evaluate(self, instances):
ev = {"Fix zero": {}, ev = {"Fix zero": {}, "Fix one": {}}
"Fix one": {}} for instance_idx in tqdm(
for instance_idx in tqdm(range(len(instances)), range(len(instances)),
desc="Evaluate (primal)", desc="Evaluate (primal)",
): ):
instance = instances[instance_idx] instance = instances[instance_idx]
@ -143,6 +155,10 @@ class PrimalSolutionComponent(Component):
tn_one = len(pred_one_negative & vars_zero) tn_one = len(pred_one_negative & vars_zero)
fn_one = len(pred_one_negative & vars_one) 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 zero"][instance_idx] = classifier_evaluation_dict(
ev["Fix one"][instance_idx] = classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one) 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 return ev

47
miplearn/components/relaxation.py
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@ -51,7 +51,8 @@ class RelaxationComponent(Component):
If `check_dropped` is true, set the maximum number of iterations in the lazy constraint loop. If `check_dropped` is true, set the maximum number of iterations in the lazy constraint loop.
""" """
def __init__(self, def __init__(
self,
classifier=CountingClassifier(), classifier=CountingClassifier(),
threshold=0.95, threshold=0.95,
slack_tolerance=1e-5, slack_tolerance=1e-5,
@ -77,16 +78,20 @@ class RelaxationComponent(Component):
logger.info("Predicting redundant LP constraints...") logger.info("Predicting redundant LP constraints...")
cids = solver.internal_solver.get_constraint_ids() cids = solver.internal_solver.get_constraint_ids()
x, constraints = self.x([instance], x, constraints = self.x(
[instance],
constraint_ids=cids, constraint_ids=cids,
return_constraints=True) return_constraints=True,
)
y = self.predict(x) y = self.predict(x)
for category in y.keys(): for category in y.keys():
for i in range(len(y[category])): for i in range(len(y[category])):
if y[category][i][0] == 1: if y[category][i][0] == 1:
cid = constraints[category][i] cid = constraints[category][i]
c = LazyConstraint(cid=cid, c = LazyConstraint(
obj=solver.internal_solver.extract_constraint(cid)) cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
self.pool += [c] self.pool += [c]
logger.info("Extracted %d predicted constraints" % len(self.pool)) logger.info("Extracted %d predicted constraints" % len(self.pool))
@ -98,21 +103,19 @@ class RelaxationComponent(Component):
x = self.x(training_instances) x = self.x(training_instances)
y = self.y(training_instances) y = self.y(training_instances)
logger.debug("Fitting...") logger.debug("Fitting...")
for category in tqdm(x.keys(), for category in tqdm(x.keys(), desc="Fit (relaxation)"):
desc="Fit (relaxation)"):
if category not in self.classifiers: if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype) self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category]) self.classifiers[category].fit(x[category], y[category])
def x(self, def x(self, instances, constraint_ids=None, return_constraints=False):
instances,
constraint_ids=None,
return_constraints=False):
x = {} x = {}
constraints = {} constraints = {}
for instance in tqdm(InstanceIterator(instances), for instance in tqdm(
InstanceIterator(instances),
desc="Extract (relaxation:x)", desc="Extract (relaxation:x)",
disable=len(instances) < 5): disable=len(instances) < 5,
):
if constraint_ids is not None: if constraint_ids is not None:
cids = constraint_ids cids = constraint_ids
else: else:
@ -133,9 +136,11 @@ class RelaxationComponent(Component):
def y(self, instances): def y(self, instances):
y = {} y = {}
for instance in tqdm(InstanceIterator(instances), for instance in tqdm(
InstanceIterator(instances),
desc="Extract (relaxation:y)", desc="Extract (relaxation:y)",
disable=len(instances) < 5): disable=len(instances) < 5,
):
for (cid, slack) in instance.slacks.items(): for (cid, slack) in instance.slacks.items():
category = instance.get_constraint_category(cid) category = instance.get_constraint_category(cid)
if category is None: if category is None:
@ -154,7 +159,7 @@ class RelaxationComponent(Component):
if category not in self.classifiers: if category not in self.classifiers:
continue continue
y[category] = [] y[category] = []
#x_cat = np.array(x_cat) # x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat) proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)): for i in range(len(proba)):
if proba[i][1] >= self.threshold: if proba[i][1] >= self.threshold:
@ -191,13 +196,19 @@ class RelaxationComponent(Component):
logger.debug("Checking that dropped constraints are satisfied...") logger.debug("Checking that dropped constraints are satisfied...")
constraints_to_add = [] constraints_to_add = []
for c in self.pool: 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) constraints_to_add.append(c)
for c in constraints_to_add: for c in constraints_to_add:
self.pool.remove(c) self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj) solver.internal_solver.add_constraint(c.obj)
if len(constraints_to_add) > 0: 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 return True
else: else:
return False return False

24
miplearn/components/tests/test_lazy_dynamic.py
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@ -28,9 +28,9 @@ def test_lazy_fit():
assert "c" in component.classifiers assert "c" in component.classifiers
# Should provide correct x_train to each classifier # 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_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
expected_x_train_b = np.array([[67., 21.75, 1287.92], [70., 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., 21.75, 1287.92], [70., 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_a = component.classifiers["a"].fit.call_args[0][0]
actual_x_train_b = component.classifiers["b"].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] actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
@ -56,16 +56,15 @@ def test_lazy_before():
solver = LearningSolver() solver = LearningSolver()
solver.internal_solver = Mock(spec=InternalSolver) solver.internal_solver = Mock(spec=InternalSolver)
component = DynamicLazyConstraintsComponent(threshold=0.10) component = DynamicLazyConstraintsComponent(threshold=0.10)
component.classifiers = {"a": Mock(spec=Classifier), component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
"b": Mock(spec=Classifier)}
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]]) 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.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
component.before_solve(solver, instances[0], models[0]) component.before_solve(solver, instances[0], models[0])
# Should ask classifier likelihood of each constraint being violated # Should ask classifier likelihood of each constraint being violated
expected_x_test_a = 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., 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_a = component.classifiers["a"].predict_proba.call_args[0][0]
actual_x_test_b = component.classifiers["b"].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 assert norm(expected_x_test_a - actual_x_test_a) < E
@ -82,9 +81,11 @@ def test_lazy_before():
def test_lazy_evaluate(): def test_lazy_evaluate():
instances, models = get_test_pyomo_instances() instances, models = get_test_pyomo_instances()
component = DynamicLazyConstraintsComponent() component = DynamicLazyConstraintsComponent()
component.classifiers = {"a": Mock(spec=Classifier), component.classifiers = {
"a": Mock(spec=Classifier),
"b": Mock(spec=Classifier), "b": Mock(spec=Classifier),
"c": Mock(spec=Classifier)} "c": Mock(spec=Classifier),
}
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]]) 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["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
component.classifiers["c"].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, "Accuracy": 0.75,
"F1 score": 0.8, "F1 score": 0.8,
"Precision": 1.0, "Precision": 1.0,
"Recall": 2/3., "Recall": 2 / 3.0,
"Predicted positive": 2, "Predicted positive": 2,
"Predicted negative": 2, "Predicted negative": 2,
"Condition positive": 3, "Condition positive": 3,
@ -135,6 +136,5 @@ def test_lazy_evaluate():
"False positive (%)": 25.0, "False positive (%)": 25.0,
"True negative (%)": 25.0, "True negative (%)": 25.0,
"True positive (%)": 25.0, "True positive (%)": 25.0,
},
} }
}

138
miplearn/components/tests/test_lazy_static.py
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@ -4,10 +4,12 @@
from unittest.mock import Mock, call from unittest.mock import Mock, call
from miplearn import (StaticLazyConstraintsComponent, from miplearn import (
StaticLazyConstraintsComponent,
LearningSolver, LearningSolver,
Instance, Instance,
InternalSolver) InternalSolver,
)
from miplearn.classifiers import Classifier from miplearn.classifiers import Classifier
@ -23,39 +25,47 @@ def test_usage_with_solver():
instance = Mock(spec=Instance) instance = Mock(spec=Instance)
instance.has_static_lazy_constraints = Mock(return_value=True) instance.has_static_lazy_constraints = Mock(return_value=True)
instance.is_constraint_lazy = Mock(side_effect=lambda cid: { instance.is_constraint_lazy = Mock(
side_effect=lambda cid: {
"c1": False, "c1": False,
"c2": True, "c2": True,
"c3": True, "c3": True,
"c4": True, "c4": True,
}[cid]) }[cid]
instance.get_constraint_features = Mock(side_effect=lambda cid: { )
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
"c2": [1.0, 0.0], "c2": [1.0, 0.0],
"c3": [0.5, 0.5], "c3": [0.5, 0.5],
"c4": [1.0], "c4": [1.0],
}[cid]) }[cid]
instance.get_constraint_category = Mock(side_effect=lambda cid: { )
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
"c2": "type-a", "c2": "type-a",
"c3": "type-a", "c3": "type-a",
"c4": "type-b", "c4": "type-b",
}[cid]) }[cid]
)
component = StaticLazyConstraintsComponent(threshold=0.90, component = StaticLazyConstraintsComponent(
use_two_phase_gap=False, threshold=0.90, use_two_phase_gap=False, violation_tolerance=1.0
violation_tolerance=1.0) )
component.classifiers = { component.classifiers = {
"type-a": Mock(spec=Classifier), "type-a": Mock(spec=Classifier),
"type-b": Mock(spec=Classifier), "type-b": Mock(spec=Classifier),
} }
component.classifiers["type-a"].predict_proba = \ component.classifiers["type-a"].predict_proba = Mock(
Mock(return_value=[ return_value=[
[0.20, 0.80], [0.20, 0.80],
[0.05, 0.95], [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], [0.02, 0.98],
]) ]
)
# LearningSolver calls before_solve # LearningSolver calls before_solve
component.before_solve(solver, instance, None) component.before_solve(solver, instance, None)
@ -67,37 +77,59 @@ def test_usage_with_solver():
internal.get_constraint_ids.assert_called_once() internal.get_constraint_ids.assert_called_once()
# Should ask if each constraint in the model is lazy # Should ask if each constraint in the model is lazy
instance.is_constraint_lazy.assert_has_calls([ instance.is_constraint_lazy.assert_has_calls(
call("c1"), call("c2"), call("c3"), call("c4"), [
]) call("c1"),
call("c2"),
call("c3"),
call("c4"),
]
)
# For the lazy ones, should ask for features # For the lazy ones, should ask for features
instance.get_constraint_features.assert_has_calls([ instance.get_constraint_features.assert_has_calls(
call("c2"), call("c3"), call("c4"), [
]) call("c2"),
call("c3"),
call("c4"),
]
)
# Should also ask for categories # Should also ask for categories
assert instance.get_constraint_category.call_count == 3 assert instance.get_constraint_category.call_count == 3
instance.get_constraint_category.assert_has_calls([ instance.get_constraint_category.assert_has_calls(
call("c2"), call("c3"), call("c4"), [
]) call("c2"),
call("c3"),
call("c4"),
]
)
# Should ask internal solver to remove constraints identified as lazy # Should ask internal solver to remove constraints identified as lazy
assert internal.extract_constraint.call_count == 3 assert internal.extract_constraint.call_count == 3
internal.extract_constraint.assert_has_calls([ internal.extract_constraint.assert_has_calls(
call("c2"), call("c3"), call("c4"), [
]) call("c2"),
call("c3"),
call("c4"),
]
)
# Should ask ML to predict whether each lazy constraint should be enforced # 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]]) 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 # 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. # to the formulation. The remaining ones should remain in the pool.
assert internal.add_constraint.call_count == 2 assert internal.add_constraint.call_count == 2
internal.add_constraint.assert_has_calls([ internal.add_constraint.assert_has_calls(
call("<c3>"), call("<c4>"), [
]) call("<c3>"),
call("<c4>"),
]
)
internal.add_constraint.reset_mock() internal.add_constraint.reset_mock()
# LearningSolver calls after_iteration (first time) # LearningSolver calls after_iteration (first time)
@ -126,37 +158,45 @@ def test_usage_with_solver():
def test_fit(): def test_fit():
instance_1 = Mock(spec=Instance) instance_1 = Mock(spec=Instance)
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"] instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
instance_1.get_constraint_category = Mock(side_effect=lambda cid: { instance_1.get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": "type-a", "c1": "type-a",
"c2": "type-a", "c2": "type-a",
"c3": "type-a", "c3": "type-a",
"c4": "type-b", "c4": "type-b",
"c5": "type-b", "c5": "type-b",
}[cid]) }[cid]
instance_1.get_constraint_features = Mock(side_effect=lambda cid: { )
instance_1.get_constraint_features = Mock(
side_effect=lambda cid: {
"c1": [1, 1], "c1": [1, 1],
"c2": [1, 2], "c2": [1, 2],
"c3": [1, 3], "c3": [1, 3],
"c4": [1, 4, 0], "c4": [1, 4, 0],
"c5": [1, 5, 0], "c5": [1, 5, 0],
}[cid]) }[cid]
)
instance_2 = Mock(spec=Instance) instance_2 = Mock(spec=Instance)
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"] instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
instance_2.get_constraint_category = Mock(side_effect=lambda cid: { instance_2.get_constraint_category = Mock(
side_effect=lambda cid: {
"c1": "type-a", "c1": "type-a",
"c2": "type-a", "c2": "type-a",
"c3": "type-a", "c3": "type-a",
"c4": "type-b", "c4": "type-b",
"c5": "type-b", "c5": "type-b",
}[cid]) }[cid]
instance_2.get_constraint_features = Mock(side_effect=lambda cid: { )
instance_2.get_constraint_features = Mock(
side_effect=lambda cid: {
"c1": [2, 1], "c1": [2, 1],
"c2": [2, 2], "c2": [2, 2],
"c3": [2, 3], "c3": [2, 3],
"c4": [2, 4, 0], "c4": [2, 4, 0],
"c5": [2, 5, 0], "c5": [2, 5, 0],
}[cid]) }[cid]
)
instances = [instance_1, instance_2] instances = [instance_1, instance_2]
component = StaticLazyConstraintsComponent() component = StaticLazyConstraintsComponent()
@ -171,18 +211,22 @@ def test_fit():
} }
expected_x = { expected_x = {
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]], "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 = { expected_y = {
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]], "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._collect_constraints(instances) == expected_constraints
assert component.x(instances) == expected_x assert component.x(instances) == expected_x
assert component.y(instances) == expected_y assert component.y(instances) == expected_y
component.fit(instances) component.fit(instances)
component.classifiers["type-a"].fit.assert_called_once_with(expected_x["type-a"], component.classifiers["type-a"].fit.assert_called_once_with(
expected_y["type-a"]) expected_x["type-a"],
component.classifiers["type-b"].fit.assert_called_once_with(expected_x["type-b"], expected_y["type-a"],
expected_y["type-b"]) )
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) comp.fit(instances)
assert instances[0].lower_bound == 1183.0 assert instances[0].lower_bound == 1183.0
assert instances[0].upper_bound == 1183.0 assert instances[0].upper_bound == 1183.0
assert np.round(comp.predict(instances), 2).tolist() == [[1183.0, 1183.0], assert np.round(comp.predict(instances), 2).tolist() == [
[1070.0, 1070.0]] [1183.0, 1183.0],
[1070.0, 1070.0],
]
def test_obj_evaluate(): def test_obj_evaluate():
@ -28,20 +30,20 @@ def test_obj_evaluate():
comp.fit(instances) comp.fit(instances)
ev = comp.evaluate(instances) ev = comp.evaluate(instances)
assert ev == { assert ev == {
'Lower bound': { "Lower bound": {
'Explained variance': 0.0, "Explained variance": 0.0,
'Max error': 183.0, "Max error": 183.0,
'Mean absolute error': 126.5, "Mean absolute error": 126.5,
'Mean squared error': 19194.5, "Mean squared error": 19194.5,
'Median absolute error': 126.5, "Median absolute error": 126.5,
'R2': -5.012843605607331, "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(): def test_evaluate():
instances, models = get_test_pyomo_instances() instances, models = get_test_pyomo_instances()
clf_zero = Mock(spec=Classifier) clf_zero = Mock(spec=Classifier)
clf_zero.predict_proba = Mock(return_value=np.array([ clf_zero.predict_proba = Mock(
[0., 1.], # x[0] return_value=np.array(
[0., 1.], # x[1] [
[1., 0.], # x[2] [0.0, 1.0], # x[0]
[1., 0.], # x[3] [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 = Mock(spec=Classifier)
clf_one.predict_proba = Mock(return_value=np.array([ clf_one.predict_proba = Mock(
[1., 0.], # x[0] instances[0] return_value=np.array(
[1., 0.], # x[1] instances[0] [
[0., 1.], # x[2] instances[0] [1.0, 0.0], # x[0] instances[0]
[1., 0.], # x[3] instances[0] [1.0, 0.0], # x[1] instances[0]
])) [0.0, 1.0], # x[2] instances[0]
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], [1.0, 0.0], # x[3] instances[0]
threshold=0.50) ]
)
)
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
comp.fit(instances[:1]) comp.fit(instances[:1])
assert comp.predict(instances[0]) == {"x": {0: 0, assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
1: 0, assert instances[0].solution == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
2: 1,
3: None}}
assert instances[0].solution == {"x": {0: 1,
1: 0,
2: 1,
3: 1}}
ev = comp.evaluate(instances[:1]) ev = comp.evaluate(instances[:1])
assert ev == {'Fix one': {0: {'Accuracy': 0.5, assert ev == {
'Condition negative': 1, "Fix one": {
'Condition negative (%)': 25.0, 0: {
'Condition positive': 3, "Accuracy": 0.5,
'Condition positive (%)': 75.0, "Condition negative": 1,
'F1 score': 0.5, "Condition negative (%)": 25.0,
'False negative': 2, "Condition positive": 3,
'False negative (%)': 50.0, "Condition positive (%)": 75.0,
'False positive': 0, "F1 score": 0.5,
'False positive (%)': 0.0, "False negative": 2,
'Precision': 1.0, "False negative (%)": 50.0,
'Predicted negative': 3, "False positive": 0,
'Predicted negative (%)': 75.0, "False positive (%)": 0.0,
'Predicted positive': 1, "Precision": 1.0,
'Predicted positive (%)': 25.0, "Predicted negative": 3,
'Recall': 0.3333333333333333, "Predicted negative (%)": 75.0,
'True negative': 1, "Predicted positive": 1,
'True negative (%)': 25.0, "Predicted positive (%)": 25.0,
'True positive': 1, "Recall": 0.3333333333333333,
'True positive (%)': 25.0}}, "True negative": 1,
'Fix zero': {0: {'Accuracy': 0.75, "True negative (%)": 25.0,
'Condition negative': 3, "True positive": 1,
'Condition negative (%)': 75.0, "True positive (%)": 25.0,
'Condition positive': 1, }
'Condition positive (%)': 25.0, },
'F1 score': 0.6666666666666666, "Fix zero": {
'False negative': 0, 0: {
'False negative (%)': 0.0, "Accuracy": 0.75,
'False positive': 1, "Condition negative": 3,
'False positive (%)': 25.0, "Condition negative (%)": 75.0,
'Precision': 0.5, "Condition positive": 1,
'Predicted negative': 2, "Condition positive (%)": 25.0,
'Predicted negative (%)': 50.0, "F1 score": 0.6666666666666666,
'Predicted positive': 2, "False negative": 0,
'Predicted positive (%)': 50.0, "False negative (%)": 0.0,
'Recall': 1.0, "False positive": 1,
'True negative': 2, "False positive (%)": 25.0,
'True negative (%)': 50.0, "Precision": 0.5,
'True positive': 1, "Predicted negative": 2,
'True positive (%)': 25.0}}} "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(): def test_primal_parallel_fit():

156
miplearn/components/tests/test_relaxation.py
Unescape View File

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

47
miplearn/extractors.py
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@ -40,7 +40,7 @@ class InstanceIterator:
class Extractor(ABC): class Extractor(ABC):
@abstractmethod @abstractmethod
def extract(self, instances,): def extract(self, instances):
pass pass
@staticmethod @staticmethod
@ -61,9 +61,11 @@ class Extractor(ABC):
class VariableFeaturesExtractor(Extractor): class VariableFeaturesExtractor(Extractor):
def extract(self, instances): def extract(self, instances):
result = {} result = {}
for instance in tqdm(InstanceIterator(instances), for instance in tqdm(
InstanceIterator(instances),
desc="Extract (vars)", desc="Extract (vars)",
disable=len(instances) < 5): disable=len(instances) < 5,
):
instance_features = instance.get_instance_features() instance_features = instance.get_instance_features()
var_split = self.split_variables(instance) var_split = self.split_variables(instance)
for (category, var_index_pairs) in var_split.items(): for (category, var_index_pairs) in var_split.items():
@ -71,9 +73,9 @@ class VariableFeaturesExtractor(Extractor):
result[category] = [] result[category] = []
for (var_name, index) in var_index_pairs: for (var_name, index) in var_index_pairs:
result[category] += [ result[category] += [
instance_features.tolist() + \ instance_features.tolist()
instance.get_variable_features(var_name, index).tolist() + \ + instance.get_variable_features(var_name, index).tolist()
[instance.lp_solution[var_name][index]] + [instance.lp_solution[var_name][index]]
] ]
for category in result: for category in result:
result[category] = np.array(result[category]) result[category] = np.array(result[category])
@ -86,9 +88,11 @@ class SolutionExtractor(Extractor):
def extract(self, instances): def extract(self, instances):
result = {} result = {}
for instance in tqdm(InstanceIterator(instances), for instance in tqdm(
InstanceIterator(instances),
desc="Extract (solution)", desc="Extract (solution)",
disable=len(instances) < 5): disable=len(instances) < 5,
):
var_split = self.split_variables(instance) var_split = self.split_variables(instance)
for (category, var_index_pairs) in var_split.items(): for (category, var_index_pairs) in var_split.items():
if category not in result: if category not in result:
@ -109,13 +113,17 @@ class SolutionExtractor(Extractor):
class InstanceFeaturesExtractor(Extractor): class InstanceFeaturesExtractor(Extractor):
def extract(self, instances): def extract(self, instances):
return np.vstack([ return np.vstack(
np.hstack([ [
np.hstack(
[
instance.get_instance_features(), instance.get_instance_features(),
instance.lp_value, instance.lp_value,
]) ]
)
for instance in InstanceIterator(instances) for instance in InstanceIterator(instances)
]) ]
)
class ObjectiveValueExtractor(Extractor): class ObjectiveValueExtractor(Extractor):
@ -125,11 +133,14 @@ class ObjectiveValueExtractor(Extractor):
def extract(self, instances): def extract(self, instances):
if self.kind == "lower bound": if self.kind == "lower bound":
return np.array([[instance.lower_bound] return np.array(
for instance in InstanceIterator(instances)]) [[instance.lower_bound] for instance in InstanceIterator(instances)]
)
if self.kind == "upper bound": if self.kind == "upper bound":
return np.array([[instance.upper_bound] return np.array(
for instance in InstanceIterator(instances)]) [[instance.upper_bound] for instance in InstanceIterator(instances)]
)
if self.kind == "lp": if self.kind == "lp":
return np.array([[instance.lp_value] return np.array(
for instance in InstanceIterator(instances)]) [[instance.lp_value] for instance in InstanceIterator(instances)]
)

8
miplearn/instance.py
Unescape View File

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

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

94
miplearn/problems/knapsack.py
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@ -17,13 +17,17 @@ class ChallengeA:
- K = 500, u ~ U(0., 1.) - K = 500, u ~ U(0., 1.)
- alpha = 0.25 - alpha = 0.25
""" """
def __init__(self,
def __init__(
self,
seed=42, seed=42,
n_training_instances=500, n_training_instances=500,
n_test_instances=50): n_test_instances=50,
):
np.random.seed(seed) np.random.seed(seed)
self.gen = MultiKnapsackGenerator(n=randint(low=250, high=251), self.gen = MultiKnapsackGenerator(
n=randint(low=250, high=251),
m=randint(low=10, high=11), m=randint(low=10, high=11),
w=uniform(loc=0.0, scale=1000.0), w=uniform(loc=0.0, scale=1000.0),
K=uniform(loc=500.0, scale=0.0), K=uniform(loc=500.0, scale=0.0),
@ -51,10 +55,7 @@ class MultiKnapsackInstance(Instance):
same size and items don't shuffle around. same size and items don't shuffle around.
""" """
def __init__(self, def __init__(self, prices, capacities, weights):
prices,
capacities,
weights):
assert isinstance(prices, np.ndarray) assert isinstance(prices, np.ndarray)
assert isinstance(capacities, np.ndarray) assert isinstance(capacities, np.ndarray)
assert isinstance(weights, np.ndarray) assert isinstance(weights, np.ndarray)
@ -69,34 +70,43 @@ class MultiKnapsackInstance(Instance):
def to_model(self): def to_model(self):
model = pe.ConcreteModel() model = pe.ConcreteModel()
model.x = pe.Var(range(self.n), domain=pe.Binary) model.x = pe.Var(range(self.n), domain=pe.Binary)
model.OBJ = pe.Objective(rule=lambda model: sum(model.x[j] * self.prices[j] model.OBJ = pe.Objective(
for j in range(self.n)), rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
sense=pe.maximize) sense=pe.maximize,
)
model.eq_capacity = pe.ConstraintList() model.eq_capacity = pe.ConstraintList()
for i in range(self.m): for i in range(self.m):
model.eq_capacity.add(sum(model.x[j] * self.weights[i,j] model.eq_capacity.add(
for j in range(self.n)) <= self.capacities[i]) sum(model.x[j] * self.weights[i, j] for j in range(self.n))
<= self.capacities[i]
)
return model return model
def get_instance_features(self): def get_instance_features(self):
return np.hstack([ return np.hstack(
[
np.mean(self.prices), np.mean(self.prices),
self.capacities, self.capacities,
]) ]
)
def get_variable_features(self, var, index): def get_variable_features(self, var, index):
return np.hstack([ return np.hstack(
[
self.prices[index], self.prices[index],
self.weights[:, index], self.weights[:, index],
]) ]
)
# def get_variable_category(self, var, index): # def get_variable_category(self, var, index):
# return index # return index
class MultiKnapsackGenerator: class MultiKnapsackGenerator:
def __init__(self, def __init__(
self,
n=randint(low=100, high=101), n=randint(low=100, high=101),
m=randint(low=30, high=31), m=randint(low=30, high=31),
w=randint(low=0, high=1000), w=randint(low=0, high=1000),
@ -168,10 +178,13 @@ class MultiKnapsackGenerator:
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution" 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(K, rv_frozen), "K should be a SciPy probability distribution"
assert isinstance(u, rv_frozen), "u 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(fix_w, bool), "fix_w should be boolean"
assert isinstance(w_jitter, rv_frozen), \ assert isinstance(
"w_jitter should be a SciPy probability distribution" w_jitter, rv_frozen
), "w_jitter should be a SciPy probability distribution"
self.n = n self.n = n
self.m = m self.m = m
@ -211,13 +224,14 @@ class MultiKnapsackGenerator:
K = self.K.rvs() K = self.K.rvs()
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)]) w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
alpha = self.alpha.rvs(m) alpha = self.alpha.rvs(m)
p = np.array([w[:,j].sum() / m + K * u[j] for j in range(n)]) 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)]) b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
if self.round: if self.round:
p = p.round() p = p.round()
b = b.round() b = b.round()
w = w.round() w = w.round()
return MultiKnapsackInstance(p, b, w) return MultiKnapsackInstance(p, b, w)
return [_sample() for _ in range(n_samples)] return [_sample() for _ in range(n_samples)]
@ -225,6 +239,7 @@ class KnapsackInstance(Instance):
""" """
Simpler (one-dimensional) Knapsack Problem, used for testing. Simpler (one-dimensional) Knapsack Problem, used for testing.
""" """
def __init__(self, weights, prices, capacity): def __init__(self, weights, prices, capacity):
self.weights = weights self.weights = weights
self.prices = prices self.prices = prices
@ -234,23 +249,29 @@ class KnapsackInstance(Instance):
model = pe.ConcreteModel() model = pe.ConcreteModel()
items = range(len(self.weights)) items = range(len(self.weights))
model.x = pe.Var(items, domain=pe.Binary) model.x = pe.Var(items, domain=pe.Binary)
model.OBJ = pe.Objective(expr=sum(model.x[v] * self.prices[v] for v in items), model.OBJ = pe.Objective(
sense=pe.maximize) 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.eq_capacity = pe.Constraint(
expr=sum(model.x[v] * self.weights[v] for v in items) <= self.capacity
)
return model return model
def get_instance_features(self): def get_instance_features(self):
return np.array([ return np.array(
[
self.capacity, self.capacity,
np.average(self.weights), np.average(self.weights),
]) ]
)
def get_variable_features(self, var, index): def get_variable_features(self, var, index):
return np.array([ return np.array(
[
self.weights[index], self.weights[index],
self.prices[index], self.prices[index],
]) ]
)
class GurobiKnapsackInstance(KnapsackInstance): class GurobiKnapsackInstance(KnapsackInstance):
@ -258,6 +279,7 @@ class GurobiKnapsackInstance(KnapsackInstance):
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
instead of Pyomo, used for testing. instead of Pyomo, used for testing.
""" """
def __init__(self, weights, prices, capacity): def __init__(self, weights, prices, capacity):
super().__init__(weights, prices, capacity) super().__init__(weights, prices, capacity)
@ -268,9 +290,11 @@ class GurobiKnapsackInstance(KnapsackInstance):
model = gp.Model("Knapsack") model = gp.Model("Knapsack")
n = len(self.weights) n = len(self.weights)
x = model.addVars(n, vtype=GRB.BINARY, name="x") x = model.addVars(n, vtype=GRB.BINARY, name="x")
model.addConstr(gp.quicksum(x[i] * self.weights[i] model.addConstr(
for i in range(n)) <= self.capacity, gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
"eq_capacity") "eq_capacity",
model.setObjective(gp.quicksum(x[i] * self.prices[i] )
for i in range(n)), GRB.MAXIMIZE) model.setObjective(
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
)
return model return model

21
miplearn/problems/stab.py
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@ -12,17 +12,20 @@ from scipy.stats.distributions import rv_frozen
class ChallengeA: class ChallengeA:
def __init__(self, def __init__(
self,
seed=42, seed=42,
n_training_instances=500, n_training_instances=500,
n_test_instances=50, n_test_instances=50,
): ):
np.random.seed(seed) np.random.seed(seed)
self.generator = MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), self.generator = MaxWeightStableSetGenerator(
w=uniform(loc=100.0, scale=50.0),
n=randint(low=200, high=201), n=randint(low=200, high=201),
p=uniform(loc=0.05, scale=0.0), p=uniform(loc=0.05, scale=0.0),
fix_graph=True) fix_graph=True,
)
np.random.seed(seed + 1) np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances) self.training_instances = self.generator.generate(n_training_instances)
@ -43,11 +46,13 @@ class MaxWeightStableSetGenerator:
parameters are sampled in the same way. parameters are sampled in the same way.
""" """
def __init__(self, def __init__(
self,
w=uniform(loc=10.0, scale=1.0), w=uniform(loc=10.0, scale=1.0),
n=randint(low=250, high=251), n=randint(low=250, high=251),
p=uniform(loc=0.05, scale=0.0), p=uniform(loc=0.05, scale=0.0),
fix_graph=True): fix_graph=True,
):
"""Initialize the problem generator. """Initialize the problem generator.
Parameters Parameters
@ -78,6 +83,7 @@ class MaxWeightStableSetGenerator:
graph = self._generate_graph() graph = self._generate_graph()
weights = self.w.rvs(graph.number_of_nodes()) weights = self.w.rvs(graph.number_of_nodes())
return MaxWeightStableSetInstance(graph, weights) return MaxWeightStableSetInstance(graph, weights)
return [_sample() for _ in range(n_samples)] return [_sample() for _ in range(n_samples)]
def _generate_graph(self): def _generate_graph(self):
@ -102,8 +108,9 @@ class MaxWeightStableSetInstance(Instance):
nodes = list(self.graph.nodes) nodes = list(self.graph.nodes)
model = pe.ConcreteModel() model = pe.ConcreteModel()
model.x = pe.Var(nodes, domain=pe.Binary) model.x = pe.Var(nodes, domain=pe.Binary)
model.OBJ = pe.Objective(expr=sum(model.x[v] * self.weights[v] for v in nodes), model.OBJ = pe.Objective(
sense=pe.maximize) expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
)
model.clique_eqs = pe.ConstraintList() model.clique_eqs = pe.ConstraintList()
for clique in nx.find_cliques(self.graph): for clique in nx.find_cliques(self.graph):
model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1) 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 # MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.

7
miplearn/problems/tests/test_knapsack.py
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@ -9,7 +9,8 @@ import numpy as np
def test_knapsack_generator(): def test_knapsack_generator():
gen = MultiKnapsackGenerator(n=randint(low=100, high=101), gen = MultiKnapsackGenerator(
n=randint(low=100, high=101),
m=randint(low=30, high=31), m=randint(low=30, high=31),
w=randint(low=0, high=1000), w=randint(low=0, high=1000),
K=randint(low=500, high=501), K=randint(low=500, high=501),
@ -20,6 +21,6 @@ def test_knapsack_generator():
w_sum = sum(instance.weights for instance in instances) / len(instances) w_sum = sum(instance.weights for instance in instances) / len(instances)
p_sum = sum(instance.prices 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) 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(p_sum), -1) == 1200. # flaky
assert round(np.mean(b_sum), -3) == 25000. assert round(np.mean(b_sum), -3) == 25000.0

26
miplearn/problems/tests/test_stab.py
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@ -11,20 +11,23 @@ from scipy.stats import uniform, randint
def test_stab(): def test_stab():
graph = nx.cycle_graph(5) 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) instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver() solver = LearningSolver()
solver.solve(instance) solver.solve(instance)
assert instance.lower_bound == 2. assert instance.lower_bound == 2.0
def test_stab_generator_fixed_graph(): def test_stab_generator_fixed_graph():
np.random.seed(42) np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=10, high=11), n=randint(low=10, high=11),
p=uniform(loc=0.05, scale=0.), p=uniform(loc=0.05, scale=0.0),
fix_graph=True) fix_graph=True,
)
instances = gen.generate(1_000) instances = gen.generate(1_000)
weights = np.array([instance.weights for instance in instances]) weights = np.array([instance.weights for instance in instances])
weights_avg_actual = np.round(np.average(weights, axis=0)) 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(): def test_stab_generator_random_graph():
np.random.seed(42) np.random.seed(42)
from miplearn.problems.stab import MaxWeightStableSetGenerator from miplearn.problems.stab import MaxWeightStableSetGenerator
gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
gen = MaxWeightStableSetGenerator(
w=uniform(loc=50.0, scale=10.0),
n=randint(low=30, high=41), n=randint(low=30, high=41),
p=uniform(loc=0.5, scale=0.), p=uniform(loc=0.5, scale=0.0),
fix_graph=False) fix_graph=False,
)
instances = gen.generate(1_000) instances = gen.generate(1_000)
n_nodes = [instance.graph.number_of_nodes() for instance in instances] n_nodes = [instance.graph.number_of_nodes() for instance in instances]
n_edges = [instance.graph.number_of_edges() 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_nodes)) == 35.0
assert np.round(np.mean(n_edges), -1) == 300. assert np.round(np.mean(n_edges), -1) == 300.0

42
miplearn/problems/tests/test_tsp.py
Unescape View File

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

47
miplearn/problems/tsp.py
Unescape View File

@ -13,14 +13,16 @@ import random
class ChallengeA: class ChallengeA:
def __init__(self, def __init__(
self,
seed=42, seed=42,
n_training_instances=500, n_training_instances=500,
n_test_instances=50, n_test_instances=50,
): ):
np.random.seed(seed) np.random.seed(seed)
self.generator = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0), self.generator = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0), y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351), n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1), gamma=uniform(loc=0.95, scale=0.1),
@ -38,7 +40,8 @@ class ChallengeA:
class TravelingSalesmanGenerator: class TravelingSalesmanGenerator:
"""Random generator for the Traveling Salesman Problem.""" """Random generator for the Traveling Salesman Problem."""
def __init__(self, def __init__(
self,
x=uniform(loc=0.0, scale=1000.0), x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0), y=uniform(loc=0.0, scale=1000.0),
n=randint(low=100, high=101), n=randint(low=100, high=101),
@ -79,7 +82,10 @@ class TravelingSalesmanGenerator:
assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution" 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(y, rv_frozen), "y should be a SciPy probability distribution"
assert isinstance(n, rv_frozen), "n 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.x = x
self.y = y self.y = y
self.n = n self.n = n
@ -103,6 +109,7 @@ class TravelingSalesmanGenerator:
if self.round: if self.round:
distances = distances.round() distances = distances.round()
return TravelingSalesmanInstance(n, distances) return TravelingSalesmanInstance(n, distances)
return [_sample() for _ in range(n_samples)] return [_sample() for _ in range(n_samples)]
def _generate_cities(self): def _generate_cities(self):
@ -128,18 +135,25 @@ class TravelingSalesmanInstance(Instance):
def to_model(self): def to_model(self):
model = pe.ConcreteModel() model = pe.ConcreteModel()
model.edges = edges = [(i,j) model.edges = edges = [
for i in range(self.n_cities) (i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
for j in range(i+1, self.n_cities)] ]
model.x = pe.Var(edges, domain=pe.Binary) model.x = pe.Var(edges, domain=pe.Binary)
model.obj = pe.Objective(expr=sum(model.x[i,j] * self.distances[i,j] model.obj = pe.Objective(
for (i,j) in edges), expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
sense=pe.minimize) sense=pe.minimize,
)
model.eq_degree = pe.ConstraintList() model.eq_degree = pe.ConstraintList()
model.eq_subtour = pe.ConstraintList() model.eq_subtour = pe.ConstraintList()
for i in range(self.n_cities): for i in range(self.n_cities):
model.eq_degree.add(sum(model.x[min(i,j), max(i,j)] model.eq_degree.add(
for j in range(self.n_cities) if i != j) == 2) sum(
model.x[min(i, j), max(i, j)]
for j in range(self.n_cities)
if i != j
)
== 2
)
return model return model
def get_instance_features(self): def get_instance_features(self):
@ -163,9 +177,12 @@ class TravelingSalesmanInstance(Instance):
return violations return violations
def build_lazy_constraint(self, model, component): def build_lazy_constraint(self, model, component):
cut_edges = [e for e in model.edges cut_edges = [
if (e[0] in component and e[1] not in component) or e
(e[0] not in component and e[1] in component)] 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) return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)
def find_violated_user_cuts(self, model): def find_violated_user_cuts(self, model):

42
miplearn/solvers/gurobi.py
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@ -13,7 +13,8 @@ logger = logging.getLogger(__name__)
class GurobiSolver(InternalSolver): class GurobiSolver(InternalSolver):
def __init__(self, def __init__(
self,
params=None, params=None,
lazy_cb_frequency=1, lazy_cb_frequency=1,
): ):
@ -33,6 +34,7 @@ class GurobiSolver(InternalSolver):
if params is None: if params is None:
params = {} params = {}
from gurobipy import GRB from gurobipy import GRB
self.GRB = GRB self.GRB = GRB
self.instance = None self.instance = None
self.model = None self.model = None
@ -44,8 +46,7 @@ class GurobiSolver(InternalSolver):
if lazy_cb_frequency == 1: if lazy_cb_frequency == 1:
self.lazy_cb_where = [self.GRB.Callback.MIPSOL] self.lazy_cb_where = [self.GRB.Callback.MIPSOL]
else: else:
self.lazy_cb_where = [self.GRB.Callback.MIPSOL, self.lazy_cb_where = [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]
self.GRB.Callback.MIPNODE]
def set_instance(self, instance, model=None): def set_instance(self, instance, model=None):
self._raise_if_callback() self._raise_if_callback()
@ -70,14 +71,15 @@ class GurobiSolver(InternalSolver):
idx = [0] idx = [0]
else: else:
name = m.group(1) name = m.group(1)
idx = tuple(int(k) if k.isdecimal() else k idx = tuple(
for k in m.group(2).split(",")) int(k) if k.isdecimal() else k for k in m.group(2).split(",")
)
if len(idx) == 1: if len(idx) == 1:
idx = idx[0] idx = idx[0]
if name not in self._all_vars: if name not in self._all_vars:
self._all_vars[name] = {} self._all_vars[name] = {}
self._all_vars[name][idx] = var self._all_vars[name][idx] = var
if var.vtype != 'C': if var.vtype != "C":
if name not in self._bin_vars: if name not in self._bin_vars:
self._bin_vars[name] = {} self._bin_vars[name] = {}
self._bin_vars[name][idx] = var self._bin_vars[name][idx] = var
@ -103,15 +105,9 @@ class GurobiSolver(InternalSolver):
for (idx, var) in vardict.items(): for (idx, var) in vardict.items():
var.vtype = self.GRB.BINARY var.vtype = self.GRB.BINARY
log = streams[0].getvalue() log = streams[0].getvalue()
return { return {"Optimal value": self.model.objVal, "Log": log}
"Optimal value": self.model.objVal,
"Log": log
}
def solve(self, def solve(self, tee=False, iteration_cb=None, lazy_cb=None):
tee=False,
iteration_cb=None,
lazy_cb=None):
self._raise_if_callback() self._raise_if_callback()
def cb_wrapper(cb_model, cb_where): def cb_wrapper(cb_model, cb_where):
@ -133,7 +129,7 @@ class GurobiSolver(InternalSolver):
if tee: if tee:
streams += [sys.stdout] streams += [sys.stdout]
if iteration_cb is None: if iteration_cb is None:
iteration_cb = lambda : False iteration_cb = lambda: False
while True: while True:
logger.debug("Solving MIP...") logger.debug("Solving MIP...")
with RedirectOutput(streams): with RedirectOutput(streams):
@ -187,7 +183,9 @@ class GurobiSolver(InternalSolver):
elif self.cb_where is None: elif self.cb_where is None:
return var.x return var.x
else: 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): def get_variables(self):
self._raise_if_callback() self._raise_if_callback()
@ -220,8 +218,10 @@ class GurobiSolver(InternalSolver):
if value is not None: if value is not None:
count_fixed += 1 count_fixed += 1
self._all_vars[varname][idx].start = value self._all_vars[varname][idx].start = value
logger.info("Setting start values for %d variables (out of %d)" % logger.info(
(count_fixed, count_total)) "Setting start values for %d variables (out of %d)"
% (count_fixed, count_total)
)
def clear_warm_start(self): def clear_warm_start(self):
self._raise_if_callback() self._raise_if_callback()
@ -248,10 +248,7 @@ class GurobiSolver(InternalSolver):
def extract_constraint(self, cid): def extract_constraint(self, cid):
self._raise_if_callback() self._raise_if_callback()
constr = self.model.getConstrByName(cid) constr = self.model.getConstrByName(cid)
cobj = (self.model.getRow(constr), cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
constr.sense,
constr.RHS,
constr.ConstrName)
self.model.remove(constr) self.model.remove(constr)
return cobj return cobj
@ -324,6 +321,7 @@ class GurobiSolver(InternalSolver):
def __setstate__(self, state): def __setstate__(self, state):
from gurobipy import GRB from gurobipy import GRB
self.params = state["params"] self.params = state["params"]
self.lazy_cb_where = state["lazy_cb_where"] self.lazy_cb_where = state["lazy_cb_where"]
self.GRB = GRB self.GRB = GRB

1
miplearn/solvers/internal.py
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@ -222,4 +222,3 @@ class InternalSolver(ABC):
for idx in indices: for idx in indices:
solution[var][idx] = 0.0 solution[var][idx] = 0.0
return solution return solution

30
miplearn/solvers/learning.py
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@ -12,10 +12,12 @@ from copy import deepcopy
from typing import Optional, List from typing import Optional, List
from p_tqdm import p_map from p_tqdm import p_map
from .. import (ObjectiveValueComponent, from .. import (
ObjectiveValueComponent,
PrimalSolutionComponent, PrimalSolutionComponent,
DynamicLazyConstraintsComponent, DynamicLazyConstraintsComponent,
UserCutsComponent) UserCutsComponent,
)
from .pyomo.cplex import CplexPyomoSolver from .pyomo.cplex import CplexPyomoSolver
from .pyomo.gurobi import GurobiPyomoSolver from .pyomo.gurobi import GurobiPyomoSolver
@ -43,7 +45,8 @@ def _parallel_solve(idx):
class LearningSolver: class LearningSolver:
def __init__(self, def __init__(
self,
components=None, components=None,
gap_tolerance=1e-4, gap_tolerance=1e-4,
mode="exact", mode="exact",
@ -52,7 +55,8 @@ class LearningSolver:
time_limit=None, time_limit=None,
node_limit=None, node_limit=None,
solve_lp_first=True, solve_lp_first=True,
use_lazy_cb=False): use_lazy_cb=False,
):
""" """
Mixed-Integer Linear Programming (MIP) solver that extracts information Mixed-Integer Linear Programming (MIP) solver that extracts information
from previous runs and uses Machine Learning methods to accelerate the from previous runs and uses Machine Learning methods to accelerate the
@ -142,11 +146,13 @@ class LearningSolver:
solver.set_node_limit(self.node_limit) solver.set_node_limit(self.node_limit)
return solver return solver
def solve(self, def solve(
self,
instance, instance,
model=None, model=None,
output="", output="",
tee=False): tee=False,
):
""" """
Solves the given instance. If trained machine-learning models are Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process. available, they will be used to accelerate the solution process.
@ -248,9 +254,11 @@ class LearningSolver:
lazy_cb = lazy_cb_wrapper lazy_cb = lazy_cb_wrapper
logger.info("Solving MILP...") logger.info("Solving MILP...")
results = self.internal_solver.solve(tee=tee, results = self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb, iteration_cb=iteration_cb,
lazy_cb=lazy_cb) lazy_cb=lazy_cb,
)
results["LP value"] = instance.lp_value results["LP value"] = instance.lp_value
# Read MIP solution and bounds # Read MIP solution and bounds
@ -306,10 +314,12 @@ class LearningSolver:
SOLVER[0] = self SOLVER[0] = self
OUTPUTS[0] = output OUTPUTS[0] = output
INSTANCES[0] = instances INSTANCES[0] = instances
results = p_map(_parallel_solve, results = p_map(
_parallel_solve,
list(range(len(instances))), list(range(len(instances))),
num_cpus=n_jobs, num_cpus=n_jobs,
desc=label) desc=label,
)
stats = [] stats = []
for (idx, (s, instance)) in enumerate(results): for (idx, (s, instance)) in enumerate(results):
stats.append(s) stats.append(s)

30
miplearn/solvers/pyomo/base.py
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@ -81,8 +81,10 @@ class BasePyomoSolver(InternalSolver):
count_fixed += 1 count_fixed += 1
if count_fixed > 0: if count_fixed > 0:
self._is_warm_start_available = True self._is_warm_start_available = True
logger.info("Setting start values for %d variables (out of %d)" % logger.info(
(count_fixed, count_total)) "Setting start values for %d variables (out of %d)"
% (count_fixed, count_total)
)
def clear_warm_start(self): def clear_warm_start(self):
for var in self._all_vars: for var in self._all_vars:
@ -134,17 +136,19 @@ class BasePyomoSolver(InternalSolver):
count_fixed += 1 count_fixed += 1
var[index].fix(solution[varname][index]) var[index].fix(solution[varname][index])
self._pyomo_solver.update_var(var[index]) self._pyomo_solver.update_var(var[index])
logger.info("Fixing values for %d variables (out of %d)" % logger.info(
(count_fixed, count_total)) "Fixing values for %d variables (out of %d)"
% (
count_fixed,
count_total,
)
)
def add_constraint(self, constraint): def add_constraint(self, constraint):
self._pyomo_solver.add_constraint(constraint) self._pyomo_solver.add_constraint(constraint)
self._update_constrs() self._update_constrs()
def solve(self, def solve(self, tee=False, iteration_cb=None, lazy_cb=None):
tee=False,
iteration_cb=None,
lazy_cb=None):
if lazy_cb is not None: if lazy_cb is not None:
raise Exception("lazy callback not supported") raise Exception("lazy callback not supported")
total_wallclock_time = 0 total_wallclock_time = 0
@ -158,8 +162,10 @@ class BasePyomoSolver(InternalSolver):
while True: while True:
logger.debug("Solving MIP...") logger.debug("Solving MIP...")
with RedirectOutput(streams): with RedirectOutput(streams):
results = self._pyomo_solver.solve(tee=True, results = self._pyomo_solver.solve(
warmstart=self._is_warm_start_available) tee=True,
warmstart=self._is_warm_start_available,
)
total_wallclock_time += results["Solver"][0]["Wallclock time"] total_wallclock_time += results["Solver"][0]["Wallclock time"]
should_repeat = iteration_cb() should_repeat = iteration_cb()
if not should_repeat: if not should_repeat:
@ -192,9 +198,7 @@ class BasePyomoSolver(InternalSolver):
return value return value
def _extract_node_count(self, log): def _extract_node_count(self, log):
return int(self.__extract(log, return int(self.__extract(log, self._get_node_count_regexp(), default=1))
self._get_node_count_regexp(),
default=1))
def set_threads(self, threads): def set_threads(self, threads):
key = self._get_threads_option_name() 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. {"mip_display": 5} to increase the log verbosity.
""" """
super().__init__() 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["randomseed"] = randint(low=0, high=1000).rvs()
self._pyomo_solver.options["mip_display"] = 4 self._pyomo_solver.options["mip_display"] = 4
if options is not None: if options is not None:

6
miplearn/solvers/pyomo/gurobi.py
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@ -15,8 +15,7 @@ logger = logging.getLogger(__name__)
class GurobiPyomoSolver(BasePyomoSolver): class GurobiPyomoSolver(BasePyomoSolver):
def __init__(self, def __init__(self, options=None):
options=None):
""" """
Creates a new Gurobi solver, accessed through Pyomo. Creates a new Gurobi solver, accessed through Pyomo.
@ -27,7 +26,7 @@ class GurobiPyomoSolver(BasePyomoSolver):
{"Threads": 4} to set the number of threads. {"Threads": 4} to set the number of threads.
""" """
super().__init__() 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() self._pyomo_solver.options["Seed"] = randint(low=0, high=1000).rvs()
if options is not None: if options is not None:
for (key, value) in options.items(): for (key, value) in options.items():
@ -56,6 +55,7 @@ class GurobiPyomoSolver(BasePyomoSolver):
def set_branching_priorities(self, priorities): def set_branching_priorities(self, priorities):
from gurobipy import GRB from gurobipy import GRB
for varname in priorities.keys(): for varname in priorities.keys():
var = self._varname_to_var[varname] var = self._varname_to_var[varname]
for (index, priority) in priorities[varname].items(): 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 _get_instance(solver):
def _is_subclass_or_instance(solver, parentClass): 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): if _is_subclass_or_instance(solver, BasePyomoSolver):
return KnapsackInstance( return KnapsackInstance(
weights=[23., 26., 20., 18.], weights=[23.0, 26.0, 20.0, 18.0],
prices=[505., 352., 458., 220.], prices=[505.0, 352.0, 458.0, 220.0],
capacity=67., capacity=67.0,
) )
if _is_subclass_or_instance(solver, GurobiSolver): if _is_subclass_or_instance(solver, GurobiSolver):
return GurobiKnapsackInstance( return GurobiKnapsackInstance(
weights=[23., 26., 20., 18.], weights=[23.0, 26.0, 20.0, 18.0],
prices=[505., 352., 458., 220.], prices=[505.0, 352.0, 458.0, 220.0],
capacity=67., capacity=67.0,
) )
assert False assert False

19
miplearn/solvers/tests/test_internal_solver.py
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@ -16,6 +16,7 @@ logger = logging.getLogger(__name__)
def test_redirect_output(): def test_redirect_output():
import sys import sys
original_stdout = sys.stdout original_stdout = sys.stdout
io = StringIO() io = StringIO()
with RedirectOutput([io]): with RedirectOutput([io]):
@ -31,36 +32,42 @@ def test_internal_solver_warm_starts():
model = instance.to_model() model = instance.to_model()
solver = solver_class() solver = solver_class()
solver.set_instance(instance, model) solver.set_instance(instance, model)
solver.set_warm_start({ solver.set_warm_start(
{
"x": { "x": {
0: 1.0, 0: 1.0,
1: 0.0, 1: 0.0,
2: 0.0, 2: 0.0,
3: 1.0, 3: 1.0,
} }
}) }
)
stats = solver.solve(tee=True) stats = solver.solve(tee=True)
assert stats["Warm start value"] == 725.0 assert stats["Warm start value"] == 725.0
solver.set_warm_start({ solver.set_warm_start(
{
"x": { "x": {
0: 1.0, 0: 1.0,
1: 1.0, 1: 1.0,
2: 1.0, 2: 1.0,
3: 1.0, 3: 1.0,
} }
}) }
)
stats = solver.solve(tee=True) stats = solver.solve(tee=True)
assert stats["Warm start value"] is None assert stats["Warm start value"] is None
solver.fix({ solver.fix(
{
"x": { "x": {
0: 1.0, 0: 1.0,
1: 0.0, 1: 0.0,
2: 0.0, 2: 0.0,
3: 1.0, 3: 1.0,
} }
}) }
)
stats = solver.solve(tee=True) stats = solver.solve(tee=True)
assert stats["Lower bound"] == 725.0 assert stats["Lower bound"] == 725.0
assert stats["Upper bound"] == 725.0 assert stats["Upper bound"] == 725.0

9
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(): for internal_solver in _get_internal_solvers():
logger.info("Solver: %s" % internal_solver) logger.info("Solver: %s" % internal_solver)
instance = _get_instance(internal_solver) instance = _get_instance(internal_solver)
solver = LearningSolver(time_limit=300, solver = LearningSolver(
time_limit=300,
gap_tolerance=1e-3, gap_tolerance=1e-3,
threads=1, threads=1,
solver=internal_solver, solver=internal_solver,
mode=mode) mode=mode,
)
solver.solve(instance) solver.solve(instance)
assert instance.solution["x"][0] == 1.0 assert instance.solution["x"][0] == 1.0
@ -74,8 +76,7 @@ def test_solve_fit_from_disk():
filenames = [] filenames = []
for k in range(3): for k in range(3):
instance = _get_instance(internal_solver) instance = _get_instance(internal_solver)
with tempfile.NamedTemporaryFile(suffix=".pkl", with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
delete=False) as file:
filenames += [file.name] filenames += [file.name]
pickle.dump(instance, file) 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(): def get_test_pyomo_instances():
instances = [ instances = [
KnapsackInstance( KnapsackInstance(
weights=[23., 26., 20., 18.], weights=[23.0, 26.0, 20.0, 18.0],
prices=[505., 352., 458., 220.], prices=[505.0, 352.0, 458.0, 220.0],
capacity=67., capacity=67.0,
), ),
KnapsackInstance( KnapsackInstance(
weights=[25., 30., 22., 18.], weights=[25.0, 30.0, 22.0, 18.0],
prices=[500., 365., 420., 150.], prices=[500.0, 365.0, 420.0, 150.0],
capacity=70., capacity=70.0,
), ),
] ]
models = [instance.to_model() for instance in instances] 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(): def test_benchmark():
# Generate training and test instances # Generate training and test instances
train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5) generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
test_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(3) train_instances = generator.generate(5)
test_instances = generator.generate(3)
# Training phase... # Training phase...
training_solver = LearningSolver() training_solver = LearningSolver()
@ -26,11 +27,11 @@ def test_benchmark():
benchmark = BenchmarkRunner(test_solvers) benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances) benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2) 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") benchmark.save_results("/tmp/benchmark.csv")
assert os.path.isfile("/tmp/benchmark.csv") assert os.path.isfile("/tmp/benchmark.csv")
benchmark = BenchmarkRunner(test_solvers) benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results("/tmp/benchmark.csv") benchmark.load_results("/tmp/benchmark.csv")
assert benchmark.raw_results().values.shape == (12,16) assert benchmark.raw_results().values.shape == (12, 16)

38
miplearn/tests/test_extractors.py
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@ -3,23 +3,26 @@
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
from miplearn.problems.knapsack import KnapsackInstance from miplearn.problems.knapsack import KnapsackInstance
from miplearn import (LearningSolver, from miplearn import (
LearningSolver,
SolutionExtractor, SolutionExtractor,
InstanceFeaturesExtractor, InstanceFeaturesExtractor,
VariableFeaturesExtractor, VariableFeaturesExtractor,
) )
import numpy as np import numpy as np
import pyomo.environ as pe import pyomo.environ as pe
def _get_instances(): def _get_instances():
instances = [ instances = [
KnapsackInstance(weights=[1., 2., 3.], KnapsackInstance(
prices=[10., 20., 30.], weights=[1.0, 2.0, 3.0],
prices=[10.0, 20.0, 30.0],
capacity=2.5, capacity=2.5,
), ),
KnapsackInstance(weights=[3., 4., 5.], KnapsackInstance(
prices=[20., 30., 40.], weights=[3.0, 4.0, 5.0],
prices=[20.0, 30.0, 40.0],
capacity=4.5, capacity=4.5,
), ),
] ]
@ -38,19 +41,25 @@ def test_solution_extractor():
assert isinstance(features["default"], np.ndarray) assert isinstance(features["default"], np.ndarray)
assert features["default"].shape == (6, 2) assert features["default"].shape == (6, 2)
assert features["default"].ravel().tolist() == [ assert features["default"].ravel().tolist() == [
1., 0., 1.0,
0., 1., 0.0,
1., 0., 0.0,
1., 0., 1.0,
0., 1., 1.0,
1., 0., 0.0,
1.0,
0.0,
0.0,
1.0,
1.0,
0.0,
] ]
def test_instance_features_extractor(): def test_instance_features_extractor():
instances, models = _get_instances() instances, models = _get_instances()
features = InstanceFeaturesExtractor().extract(instances) features = InstanceFeaturesExtractor().extract(instances)
assert features.shape == (2,3) assert features.shape == (2, 3)
def test_variable_features_extractor(): def test_variable_features_extractor():
@ -58,5 +67,4 @@ def test_variable_features_extractor():
features = VariableFeaturesExtractor().extract(instances) features = VariableFeaturesExtractor().extract(instances)
assert isinstance(features, dict) assert isinstance(features, dict)
assert "default" in features 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 seaborn~=0.11
scikit-learn~=0.23 scikit-learn~=0.23
tqdm~=4.54 tqdm~=4.54
black==20.8b1
pre-commit~=2.9

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