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<body>
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.steps.convert_tight</code></h1>
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</header>
|
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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import random
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from copy import deepcopy
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import numpy as np
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from tqdm import tqdm
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components import classifier_evaluation_dict
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from miplearn.components.component import Component
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from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
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from miplearn.extractors import InstanceIterator
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logger = logging.getLogger(__name__)
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class ConvertTightIneqsIntoEqsStep(Component):
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"""
|
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Component that predicts which inequality constraints are likely to be binding in
|
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the LP relaxation of the problem and converts them into equality constraints.
|
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|
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This component always makes sure that the conversion process does not affect the
|
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feasibility of the problem. It can also, optionally, make sure that it does not affect
|
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the optimality, but this may be expensive.
|
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|
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This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
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"""
|
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def __init__(
|
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self,
|
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classifier=CountingClassifier(),
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threshold=0.95,
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slack_tolerance=0.0,
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check_optimality=False,
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):
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self.classifiers = {}
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self.classifier_prototype = classifier
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self.threshold = threshold
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self.slack_tolerance = slack_tolerance
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self.check_optimality = check_optimality
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self.converted = []
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self.original_sense = {}
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def before_solve(self, solver, instance, _):
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logger.info("Predicting tight LP constraints...")
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x, constraints = DropRedundantInequalitiesStep._x_test(
|
||||
instance,
|
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constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
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y = self.predict(x)
|
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|
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self.n_converted = 0
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self.n_restored = 0
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self.n_kept = 0
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self.n_infeasible_iterations = 0
|
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self.n_suboptimal_iterations = 0
|
||||
for category in y.keys():
|
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for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
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cid = constraints[category][i]
|
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s = solver.internal_solver.get_constraint_sense(cid)
|
||||
self.original_sense[cid] = s
|
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solver.internal_solver.set_constraint_sense(cid, "=")
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self.converted += [cid]
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||||
self.n_converted += 1
|
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else:
|
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self.n_kept += 1
|
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|
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logger.info(f"Converted {self.n_converted} inequalities")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
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model,
|
||||
stats,
|
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training_data,
|
||||
):
|
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if "slacks" not in training_data.keys():
|
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training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
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stats["ConvertTight: Kept"] = self.n_kept
|
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stats["ConvertTight: Converted"] = self.n_converted
|
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stats["ConvertTight: Restored"] = self.n_restored
|
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stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
|
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stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
|
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|
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def fit(self, training_instances):
|
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logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
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y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
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for category in tqdm(x.keys(), desc="Fit (rlx:conv_ineqs)"):
|
||||
if category not in self.classifiers:
|
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self.classifiers[category] = deepcopy(self.classifier_prototype)
|
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self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
def x(self, instances):
|
||||
return DropRedundantInequalitiesStep._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:conv_ineqs:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if 0 <= slack <= self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
is_infeasible, is_suboptimal = False, False
|
||||
restored = []
|
||||
|
||||
def check_pi(msense, csense, pi):
|
||||
if csense == "=":
|
||||
return True
|
||||
if msense == "max":
|
||||
if csense == "<":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
else:
|
||||
if csense == ">":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
|
||||
def restore(cid):
|
||||
nonlocal restored
|
||||
csense = self.original_sense[cid]
|
||||
solver.internal_solver.set_constraint_sense(cid, csense)
|
||||
restored += [cid]
|
||||
|
||||
if solver.internal_solver.is_infeasible():
|
||||
for cid in self.converted:
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
if abs(pi) > 0:
|
||||
is_infeasible = True
|
||||
restore(cid)
|
||||
elif self.check_optimality:
|
||||
random.shuffle(self.converted)
|
||||
n_restored = 0
|
||||
for cid in self.converted:
|
||||
if n_restored >= 100:
|
||||
break
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
csense = self.original_sense[cid]
|
||||
msense = solver.internal_solver.get_sense()
|
||||
if not check_pi(msense, csense, pi):
|
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is_suboptimal = True
|
||||
restore(cid)
|
||||
n_restored += 1
|
||||
|
||||
for cid in restored:
|
||||
self.converted.remove(cid)
|
||||
|
||||
if len(restored) > 0:
|
||||
self.n_restored += len(restored)
|
||||
if is_infeasible:
|
||||
self.n_infeasible_iterations += 1
|
||||
if is_suboptimal:
|
||||
self.n_suboptimal_iterations += 1
|
||||
logger.info(f"Restored {len(restored)} inequalities")
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep"><code class="flex name class">
|
||||
<span>class <span class="ident">ConvertTightIneqsIntoEqsStep</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=0.0, check_optimality=False)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Component that predicts which inequality constraints are likely to be binding in
|
||||
the LP relaxation of the problem and converts them into equality constraints.</p>
|
||||
<p>This component always makes sure that the conversion process does not affect the
|
||||
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
||||
the optimality, but this may be expensive.</p>
|
||||
<p>This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ConvertTightIneqsIntoEqsStep(Component):
|
||||
"""
|
||||
Component that predicts which inequality constraints are likely to be binding in
|
||||
the LP relaxation of the problem and converts them into equality constraints.
|
||||
|
||||
This component always makes sure that the conversion process does not affect the
|
||||
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
||||
the optimality, but this may be expensive.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=0.0,
|
||||
check_optimality=False,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.check_optimality = check_optimality
|
||||
self.converted = []
|
||||
self.original_sense = {}
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Predicting tight LP constraints...")
|
||||
x, constraints = DropRedundantInequalitiesStep._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.n_converted = 0
|
||||
self.n_restored = 0
|
||||
self.n_kept = 0
|
||||
self.n_infeasible_iterations = 0
|
||||
self.n_suboptimal_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
s = solver.internal_solver.get_constraint_sense(cid)
|
||||
self.original_sense[cid] = s
|
||||
solver.internal_solver.set_constraint_sense(cid, "=")
|
||||
self.converted += [cid]
|
||||
self.n_converted += 1
|
||||
else:
|
||||
self.n_kept += 1
|
||||
|
||||
logger.info(f"Converted {self.n_converted} inequalities")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats["ConvertTight: Kept"] = self.n_kept
|
||||
stats["ConvertTight: Converted"] = self.n_converted
|
||||
stats["ConvertTight: Restored"] = self.n_restored
|
||||
stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
|
||||
stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:conv_ineqs)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
def x(self, instances):
|
||||
return DropRedundantInequalitiesStep._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:conv_ineqs:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if 0 <= slack <= self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
is_infeasible, is_suboptimal = False, False
|
||||
restored = []
|
||||
|
||||
def check_pi(msense, csense, pi):
|
||||
if csense == "=":
|
||||
return True
|
||||
if msense == "max":
|
||||
if csense == "<":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
else:
|
||||
if csense == ">":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
|
||||
def restore(cid):
|
||||
nonlocal restored
|
||||
csense = self.original_sense[cid]
|
||||
solver.internal_solver.set_constraint_sense(cid, csense)
|
||||
restored += [cid]
|
||||
|
||||
if solver.internal_solver.is_infeasible():
|
||||
for cid in self.converted:
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
if abs(pi) > 0:
|
||||
is_infeasible = True
|
||||
restore(cid)
|
||||
elif self.check_optimality:
|
||||
random.shuffle(self.converted)
|
||||
n_restored = 0
|
||||
for cid in self.converted:
|
||||
if n_restored >= 100:
|
||||
break
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
csense = self.original_sense[cid]
|
||||
msense = solver.internal_solver.get_sense()
|
||||
if not check_pi(msense, csense, pi):
|
||||
is_suboptimal = True
|
||||
restore(cid)
|
||||
n_restored += 1
|
||||
|
||||
for cid in restored:
|
||||
self.converted.remove(cid)
|
||||
|
||||
if len(restored) > 0:
|
||||
self.n_restored += len(restored)
|
||||
if is_infeasible:
|
||||
self.n_infeasible_iterations += 1
|
||||
if is_suboptimal:
|
||||
self.n_suboptimal_iterations += 1
|
||||
logger.info(f"Restored {len(restored)} inequalities")
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:conv_ineqs)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x"><code class="name flex">
|
||||
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def x(self, instances):
|
||||
return DropRedundantInequalitiesStep._x_train(instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y"><code class="name flex">
|
||||
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:conv_ineqs:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if 0 <= slack <= self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x">x</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y">y</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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<script>hljs.initHighlightingOnLoad()</script>
|
||||
</body>
|
||||
</html>
|
||||
663
0.2/api/miplearn/components/steps/drop_redundant.html
Normal file
663
0.2/api/miplearn/components/steps/drop_redundant.html
Normal file
@@ -0,0 +1,663 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
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<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
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<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.components.steps.drop_redundant API documentation</title>
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<meta name="description" content="" />
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
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|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.drop_redundant</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components import classifier_evaluation_dict
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.components.lazy_static import LazyConstraint
|
||||
from miplearn.extractors import InstanceIterator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DropRedundantInequalitiesStep(Component):
|
||||
"""
|
||||
Component that predicts which inequalities are likely loose in the LP and removes
|
||||
them. Optionally, double checks after the problem is solved that all dropped
|
||||
inequalities were in fact redundant, and, if not, re-adds them to the problem.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=1e-5,
|
||||
check_feasibility=False,
|
||||
violation_tolerance=1e-5,
|
||||
max_iterations=3,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.pool = []
|
||||
self.check_feasibility = check_feasibility
|
||||
self.violation_tolerance = violation_tolerance
|
||||
self.max_iterations = max_iterations
|
||||
self.current_iteration = 0
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
self.current_iteration = 0
|
||||
|
||||
logger.info("Predicting redundant LP constraints...")
|
||||
x, constraints = self._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.total_dropped = 0
|
||||
self.total_restored = 0
|
||||
self.total_kept = 0
|
||||
self.total_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
c = LazyConstraint(
|
||||
cid=cid,
|
||||
obj=solver.internal_solver.extract_constraint(cid),
|
||||
)
|
||||
self.pool += [c]
|
||||
self.total_dropped += 1
|
||||
else:
|
||||
self.total_kept += 1
|
||||
logger.info(f"Extracted {self.total_dropped} predicted constraints")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats.update(
|
||||
{
|
||||
"DropRedundant: Kept": self.total_kept,
|
||||
"DropRedundant: Dropped": self.total_dropped,
|
||||
"DropRedundant: Restored": self.total_restored,
|
||||
"DropRedundant: Iterations": self.total_iterations,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
@staticmethod
|
||||
def _x_test(instance, constraint_ids):
|
||||
x = {}
|
||||
constraints = {}
|
||||
cids = constraint_ids
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
constraints[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
constraints[category] += [cid]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x, constraints
|
||||
|
||||
@staticmethod
|
||||
def _x_train(instances):
|
||||
x = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:x)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
cids = training_data["slacks"].keys()
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x
|
||||
|
||||
def x(self, instances):
|
||||
return self._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
for (cid, slack) in training_data["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if slack > self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
if not self.check_feasibility:
|
||||
return False
|
||||
if self.current_iteration >= self.max_iterations:
|
||||
return False
|
||||
self.current_iteration += 1
|
||||
logger.debug("Checking that dropped constraints are satisfied...")
|
||||
constraints_to_add = []
|
||||
for c in self.pool:
|
||||
if not solver.internal_solver.is_constraint_satisfied(
|
||||
c.obj,
|
||||
self.violation_tolerance,
|
||||
):
|
||||
constraints_to_add.append(c)
|
||||
for c in constraints_to_add:
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
if len(constraints_to_add) > 0:
|
||||
self.total_restored += len(constraints_to_add)
|
||||
logger.info(
|
||||
"%8d constraints %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
self.total_iterations += 1
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep"><code class="flex name class">
|
||||
<span>class <span class="ident">DropRedundantInequalitiesStep</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_iterations=3)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Component that predicts which inequalities are likely loose in the LP and removes
|
||||
them. Optionally, double checks after the problem is solved that all dropped
|
||||
inequalities were in fact redundant, and, if not, re-adds them to the problem.</p>
|
||||
<p>This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class DropRedundantInequalitiesStep(Component):
|
||||
"""
|
||||
Component that predicts which inequalities are likely loose in the LP and removes
|
||||
them. Optionally, double checks after the problem is solved that all dropped
|
||||
inequalities were in fact redundant, and, if not, re-adds them to the problem.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=1e-5,
|
||||
check_feasibility=False,
|
||||
violation_tolerance=1e-5,
|
||||
max_iterations=3,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.pool = []
|
||||
self.check_feasibility = check_feasibility
|
||||
self.violation_tolerance = violation_tolerance
|
||||
self.max_iterations = max_iterations
|
||||
self.current_iteration = 0
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
self.current_iteration = 0
|
||||
|
||||
logger.info("Predicting redundant LP constraints...")
|
||||
x, constraints = self._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.total_dropped = 0
|
||||
self.total_restored = 0
|
||||
self.total_kept = 0
|
||||
self.total_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
c = LazyConstraint(
|
||||
cid=cid,
|
||||
obj=solver.internal_solver.extract_constraint(cid),
|
||||
)
|
||||
self.pool += [c]
|
||||
self.total_dropped += 1
|
||||
else:
|
||||
self.total_kept += 1
|
||||
logger.info(f"Extracted {self.total_dropped} predicted constraints")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats.update(
|
||||
{
|
||||
"DropRedundant: Kept": self.total_kept,
|
||||
"DropRedundant: Dropped": self.total_dropped,
|
||||
"DropRedundant: Restored": self.total_restored,
|
||||
"DropRedundant: Iterations": self.total_iterations,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
@staticmethod
|
||||
def _x_test(instance, constraint_ids):
|
||||
x = {}
|
||||
constraints = {}
|
||||
cids = constraint_ids
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
constraints[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
constraints[category] += [cid]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x, constraints
|
||||
|
||||
@staticmethod
|
||||
def _x_train(instances):
|
||||
x = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:x)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
cids = training_data["slacks"].keys()
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x
|
||||
|
||||
def x(self, instances):
|
||||
return self._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
for (cid, slack) in training_data["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if slack > self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
if not self.check_feasibility:
|
||||
return False
|
||||
if self.current_iteration >= self.max_iterations:
|
||||
return False
|
||||
self.current_iteration += 1
|
||||
logger.debug("Checking that dropped constraints are satisfied...")
|
||||
constraints_to_add = []
|
||||
for c in self.pool:
|
||||
if not solver.internal_solver.is_constraint_satisfied(
|
||||
c.obj,
|
||||
self.violation_tolerance,
|
||||
):
|
||||
constraints_to_add.append(c)
|
||||
for c in constraints_to_add:
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
if len(constraints_to_add) > 0:
|
||||
self.total_restored += len(constraints_to_add)
|
||||
logger.info(
|
||||
"%8d constraints %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
self.total_iterations += 1
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x"><code class="name flex">
|
||||
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def x(self, instances):
|
||||
return self._x_train(instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y"><code class="name flex">
|
||||
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
for (cid, slack) in training_data["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if slack > self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x">x</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y">y</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
80
0.2/api/miplearn/components/steps/index.html
Normal file
80
0.2/api/miplearn/components/steps/index.html
Normal file
@@ -0,0 +1,80 @@
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.convert_tight" href="convert_tight.html">miplearn.components.steps.convert_tight</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.drop_redundant" href="drop_redundant.html">miplearn.components.steps.drop_redundant</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.relax_integrality" href="relax_integrality.html">miplearn.components.steps.relax_integrality</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.tests" href="tests/index.html">miplearn.components.steps.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="../index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight" href="convert_tight.html">miplearn.components.steps.convert_tight</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant" href="drop_redundant.html">miplearn.components.steps.drop_redundant</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.relax_integrality" href="relax_integrality.html">miplearn.components.steps.relax_integrality</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="tests/index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
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||||
142
0.2/api/miplearn/components/steps/relax_integrality.html
Normal file
142
0.2/api/miplearn/components/steps/relax_integrality.html
Normal file
@@ -0,0 +1,142 @@
|
||||
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||||
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||||
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|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.relax_integrality</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from miplearn.components.component import Component
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RelaxIntegralityStep(Component):
|
||||
"""
|
||||
Component that relaxes all integrality constraints before the problem is solved.
|
||||
"""
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Relaxing integrality...")
|
||||
solver.internal_solver.relax()
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
return</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.relax_integrality.RelaxIntegralityStep"><code class="flex name class">
|
||||
<span>class <span class="ident">RelaxIntegralityStep</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Component that relaxes all integrality constraints before the problem is solved.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class RelaxIntegralityStep(Component):
|
||||
"""
|
||||
Component that relaxes all integrality constraints before the problem is solved.
|
||||
"""
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Relaxing integrality...")
|
||||
solver.internal_solver.relax()
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
return</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.relax_integrality.RelaxIntegralityStep" href="#miplearn.components.steps.relax_integrality.RelaxIntegralityStep">RelaxIntegralityStep</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
<footer id="footer">
|
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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<script>hljs.initHighlightingOnLoad()</script>
|
||||
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|
||||
</html>
|
||||
70
0.2/api/miplearn/components/steps/tests/index.html
Normal file
70
0.2/api/miplearn/components/steps/tests/index.html
Normal file
@@ -0,0 +1,70 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
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<title>miplearn.components.steps.tests API documentation</title>
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<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
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||||
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|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.tests</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.tests.test_convert_tight" href="test_convert_tight.html">miplearn.components.steps.tests.test_convert_tight</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.tests.test_drop_redundant" href="test_drop_redundant.html">miplearn.components.steps.tests.test_drop_redundant</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="../index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight" href="test_convert_tight.html">miplearn.components.steps.tests.test_convert_tight</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant" href="test_drop_redundant.html">miplearn.components.steps.tests.test_drop_redundant</a></code></li>
|
||||
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|
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|
||||
385
0.2/api/miplearn/components/steps/tests/test_convert_tight.html
Normal file
385
0.2/api/miplearn/components/steps/tests/test_convert_tight.html
Normal file
@@ -0,0 +1,385 @@
|
||||
<!doctype html>
|
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<html lang="en">
|
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<head>
|
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|
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|
||||
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|
||||
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|
||||
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|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.tests.test_convert_tight</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">from unittest.mock import Mock
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
|
||||
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.problems.knapsack import GurobiKnapsackInstance
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_convert_tight_usage():
|
||||
instance = GurobiKnapsackInstance(
|
||||
weights=[3.0, 5.0, 10.0],
|
||||
prices=[1.0, 1.0, 1.0],
|
||||
capacity=16.0,
|
||||
)
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
ConvertTightIneqsIntoEqsStep(),
|
||||
],
|
||||
)
|
||||
|
||||
# Solve original problem
|
||||
stats = solver.solve(instance)
|
||||
original_upper_bound = stats["Upper bound"]
|
||||
|
||||
# Should collect training data
|
||||
assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
|
||||
|
||||
# Fit and resolve
|
||||
solver.fit([instance])
|
||||
stats = solver.solve(instance)
|
||||
|
||||
# Objective value should be the same
|
||||
assert stats["Upper bound"] == original_upper_bound
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
|
||||
|
||||
class SampleInstance(Instance):
|
||||
def to_model(self):
|
||||
import gurobipy as grb
|
||||
|
||||
m = grb.Model("model")
|
||||
x1 = m.addVar(name="x1")
|
||||
x2 = m.addVar(name="x2")
|
||||
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
|
||||
m.addConstr(x1 <= 2, name="c1")
|
||||
m.addConstr(x2 <= 2, name="c2")
|
||||
m.addConstr(x1 + x2 <= 3, name="c2")
|
||||
return m
|
||||
|
||||
|
||||
def test_convert_tight_infeasibility():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 1
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
|
||||
|
||||
def test_convert_tight_suboptimality():
|
||||
comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 1
|
||||
|
||||
|
||||
def test_convert_tight_optimal():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_infeasibility</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_infeasibility():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 1
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_optimal</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_optimal():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_suboptimality</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_suboptimality():
|
||||
comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 1</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_usage</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_usage():
|
||||
instance = GurobiKnapsackInstance(
|
||||
weights=[3.0, 5.0, 10.0],
|
||||
prices=[1.0, 1.0, 1.0],
|
||||
capacity=16.0,
|
||||
)
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
ConvertTightIneqsIntoEqsStep(),
|
||||
],
|
||||
)
|
||||
|
||||
# Solve original problem
|
||||
stats = solver.solve(instance)
|
||||
original_upper_bound = stats["Upper bound"]
|
||||
|
||||
# Should collect training data
|
||||
assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
|
||||
|
||||
# Fit and resolve
|
||||
solver.fit([instance])
|
||||
stats = solver.solve(instance)
|
||||
|
||||
# Objective value should be the same
|
||||
assert stats["Upper bound"] == original_upper_bound
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.SampleInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">SampleInstance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class SampleInstance(Instance):
|
||||
def to_model(self):
|
||||
import gurobipy as grb
|
||||
|
||||
m = grb.Model("model")
|
||||
x1 = m.addVar(name="x1")
|
||||
x2 = m.addVar(name="x2")
|
||||
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
|
||||
m.addConstr(x1 <= 2, name="c1")
|
||||
m.addConstr(x2 <= 2, name="c2")
|
||||
m.addConstr(x1 + x2 <= 3, name="c2")
|
||||
return m</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility">test_convert_tight_infeasibility</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal">test_convert_tight_optimal</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality">test_convert_tight_suboptimality</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage">test_convert_tight_usage</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.tests.test_convert_tight.SampleInstance" href="#miplearn.components.steps.tests.test_convert_tight.SampleInstance">SampleInstance</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
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||||
<footer id="footer">
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
</body>
|
||||
</html>
|
||||
764
0.2/api/miplearn/components/steps/tests/test_drop_redundant.html
Normal file
764
0.2/api/miplearn/components/steps/tests/test_drop_redundant.html
Normal file
@@ -0,0 +1,764 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.7.0" />
|
||||
<title>miplearn.components.steps.tests.test_drop_redundant API documentation</title>
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||||
<meta name="description" content="" />
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.tests.test_drop_redundant</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.relaxation import DropRedundantInequalitiesStep
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def _setup():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.get_inequality_slacks = Mock(
|
||||
side_effect=lambda: {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
)
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
return solver, internal, instance, classifiers
|
||||
|
||||
|
||||
def test_drop_redundant():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep()
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should query list of constraints
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should query category and features for each constraint in the model
|
||||
assert instance.get_constraint_category.call_count == 4
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For constraint with non-null categories, should ask for features
|
||||
assert instance.get_constraint_features.call_count == 3
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether constraint should be removed
|
||||
type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
|
||||
type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
|
||||
np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
|
||||
np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
|
||||
|
||||
# Should ask internal solver to remove constraints predicted as redundant
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls after_solve
|
||||
training_data = {}
|
||||
component.after_solve(solver, instance, None, {}, training_data)
|
||||
|
||||
# Should query slack for all inequalities
|
||||
internal.get_inequality_slacks.assert_called_once()
|
||||
|
||||
# Should store constraint slacks in instance object
|
||||
assert training_data["slacks"] == {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
|
||||
|
||||
def test_drop_redundant_with_check_feasibility():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep(
|
||||
check_feasibility=True,
|
||||
violation_tolerance=1e-3,
|
||||
)
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver call before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Assert constraints are extracted
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls iteration_cb (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
|
||||
# Should ask LearningSolver to repeat
|
||||
assert should_repeat
|
||||
|
||||
# Should ask solver if removed constraints are satisfied (mock always returns false)
|
||||
internal.is_constraint_satisfied.assert_has_calls(
|
||||
[
|
||||
call("<c3>", 1e-3),
|
||||
call("<c4>", 1e-3),
|
||||
]
|
||||
)
|
||||
|
||||
# Should add constraints back to LP relaxation
|
||||
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
||||
|
||||
# LearningSolver calls iteration_cb (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
|
||||
def test_x_y_fit_predict_evaluate():
|
||||
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
||||
component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
np.array([0.20, 0.80]),
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.50, 0.50],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
# First mock instance
|
||||
instances[0].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[0].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[0].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
# Second mock instance
|
||||
instances[1].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c3": 0.30,
|
||||
"c4": 0.00,
|
||||
"c5": 0.00,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[1].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[1].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c3": np.array([0.3, 0.4]),
|
||||
"c4": np.array([0.7]),
|
||||
"c5": np.array([0.8]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[0.3, 0.4],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[0.7],
|
||||
[0.8],
|
||||
]
|
||||
),
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": np.array([[0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
actual_x = component.x(instances)
|
||||
actual_y = component.y(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
# Should pass along X and Y matrices to classifiers
|
||||
component.fit(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
actual_x = component.classifiers[category].fit.call_args[0][0]
|
||||
actual_y = component.classifiers[category].fit.call_args[0][1]
|
||||
np.testing.assert_array_equal(actual_x, expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y, expected_y[category])
|
||||
|
||||
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
|
||||
|
||||
ev = component.evaluate(instances[1])
|
||||
assert ev["True positive"] == 1
|
||||
assert ev["True negative"] == 1
|
||||
assert ev["False positive"] == 1
|
||||
assert ev["False negative"] == 0
|
||||
|
||||
|
||||
def test_x_multiple_solves():
|
||||
instance = Mock(spec=Instance)
|
||||
instance.training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
},
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.00,
|
||||
"c3": 1.00,
|
||||
"c4": 0.0,
|
||||
}
|
||||
},
|
||||
]
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[1.0],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
expected_y = {
|
||||
"type-a": np.array([[1], [0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
component = DropRedundantInequalitiesStep()
|
||||
actual_x = component.x([instance])
|
||||
actual_y = component.y([instance])
|
||||
print(actual_x)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant"><code class="name flex">
|
||||
<span>def <span class="ident">test_drop_redundant</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_drop_redundant():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep()
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should query list of constraints
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should query category and features for each constraint in the model
|
||||
assert instance.get_constraint_category.call_count == 4
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For constraint with non-null categories, should ask for features
|
||||
assert instance.get_constraint_features.call_count == 3
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether constraint should be removed
|
||||
type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
|
||||
type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
|
||||
np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
|
||||
np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
|
||||
|
||||
# Should ask internal solver to remove constraints predicted as redundant
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls after_solve
|
||||
training_data = {}
|
||||
component.after_solve(solver, instance, None, {}, training_data)
|
||||
|
||||
# Should query slack for all inequalities
|
||||
internal.get_inequality_slacks.assert_called_once()
|
||||
|
||||
# Should store constraint slacks in instance object
|
||||
assert training_data["slacks"] == {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility"><code class="name flex">
|
||||
<span>def <span class="ident">test_drop_redundant_with_check_feasibility</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_drop_redundant_with_check_feasibility():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep(
|
||||
check_feasibility=True,
|
||||
violation_tolerance=1e-3,
|
||||
)
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver call before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Assert constraints are extracted
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls iteration_cb (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
|
||||
# Should ask LearningSolver to repeat
|
||||
assert should_repeat
|
||||
|
||||
# Should ask solver if removed constraints are satisfied (mock always returns false)
|
||||
internal.is_constraint_satisfied.assert_has_calls(
|
||||
[
|
||||
call("<c3>", 1e-3),
|
||||
call("<c4>", 1e-3),
|
||||
]
|
||||
)
|
||||
|
||||
# Should add constraints back to LP relaxation
|
||||
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
||||
|
||||
# LearningSolver calls iteration_cb (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves"><code class="name flex">
|
||||
<span>def <span class="ident">test_x_multiple_solves</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_x_multiple_solves():
|
||||
instance = Mock(spec=Instance)
|
||||
instance.training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
},
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.00,
|
||||
"c3": 1.00,
|
||||
"c4": 0.0,
|
||||
}
|
||||
},
|
||||
]
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[1.0],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
expected_y = {
|
||||
"type-a": np.array([[1], [0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
component = DropRedundantInequalitiesStep()
|
||||
actual_x = component.x([instance])
|
||||
actual_y = component.y([instance])
|
||||
print(actual_x)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_x_y_fit_predict_evaluate</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_x_y_fit_predict_evaluate():
|
||||
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
||||
component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
np.array([0.20, 0.80]),
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.50, 0.50],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
# First mock instance
|
||||
instances[0].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[0].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[0].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
# Second mock instance
|
||||
instances[1].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c3": 0.30,
|
||||
"c4": 0.00,
|
||||
"c5": 0.00,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[1].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[1].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c3": np.array([0.3, 0.4]),
|
||||
"c4": np.array([0.7]),
|
||||
"c5": np.array([0.8]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[0.3, 0.4],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[0.7],
|
||||
[0.8],
|
||||
]
|
||||
),
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": np.array([[0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
actual_x = component.x(instances)
|
||||
actual_y = component.y(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
# Should pass along X and Y matrices to classifiers
|
||||
component.fit(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
actual_x = component.classifiers[category].fit.call_args[0][0]
|
||||
actual_y = component.classifiers[category].fit.call_args[0][1]
|
||||
np.testing.assert_array_equal(actual_x, expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y, expected_y[category])
|
||||
|
||||
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
|
||||
|
||||
ev = component.evaluate(instances[1])
|
||||
assert ev["True positive"] == 1
|
||||
assert ev["True negative"] == 1
|
||||
assert ev["False positive"] == 1
|
||||
assert ev["False negative"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant">test_drop_redundant</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility">test_drop_redundant_with_check_feasibility</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves">test_x_multiple_solves</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate">test_x_y_fit_predict_evaluate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
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