mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 09:28:51 -06:00
Update 0.2 docs
This commit is contained in:
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">
|
||||
<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.drop_redundant API documentation</title>
|
||||
<meta name="description" content="" />
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<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>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
|
||||
</footer>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user