You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
610 lines
28 KiB
610 lines
28 KiB
<!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 0.7.5" />
|
|
<title>miplearn.components.primal 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.primal</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
|
|
from typing import Union, Dict, Any
|
|
|
|
import numpy as np
|
|
from tqdm.auto import tqdm
|
|
|
|
from miplearn.classifiers import Classifier
|
|
from miplearn.classifiers.adaptive import AdaptiveClassifier
|
|
from miplearn.classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
|
|
from miplearn.components import classifier_evaluation_dict
|
|
from miplearn.components.component import Component
|
|
from miplearn.extractors import VariableFeaturesExtractor, SolutionExtractor, Extractor
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class PrimalSolutionComponent(Component):
|
|
"""
|
|
A component that predicts primal solutions.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
classifier: Classifier = AdaptiveClassifier(),
|
|
mode: str = "exact",
|
|
threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
|
|
) -> None:
|
|
self.mode = mode
|
|
self.classifiers: Dict[Any, Classifier] = {}
|
|
self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
|
|
self.threshold_prototype = threshold
|
|
self.classifier_prototype = classifier
|
|
|
|
def before_solve(self, solver, instance, model):
|
|
logger.info("Predicting primal solution...")
|
|
solution = self.predict(instance)
|
|
if self.mode == "heuristic":
|
|
solver.internal_solver.fix(solution)
|
|
else:
|
|
solver.internal_solver.set_warm_start(solution)
|
|
|
|
def after_solve(
|
|
self,
|
|
solver,
|
|
instance,
|
|
model,
|
|
stats,
|
|
training_data,
|
|
):
|
|
pass
|
|
|
|
def x(self, training_instances):
|
|
return VariableFeaturesExtractor().extract(training_instances)
|
|
|
|
def y(self, training_instances):
|
|
return SolutionExtractor().extract(training_instances)
|
|
|
|
def fit(self, training_instances, n_jobs=1):
|
|
logger.debug("Extracting features...")
|
|
features = VariableFeaturesExtractor().extract(training_instances)
|
|
solutions = SolutionExtractor().extract(training_instances)
|
|
|
|
for category in tqdm(
|
|
features.keys(),
|
|
desc="Fit (primal)",
|
|
):
|
|
x_train = features[category]
|
|
for label in [0, 1]:
|
|
y_train = solutions[category][:, label].astype(int)
|
|
|
|
# If all samples are either positive or negative, make constant
|
|
# predictions
|
|
y_avg = np.average(y_train)
|
|
if y_avg < 0.001 or y_avg >= 0.999:
|
|
self.classifiers[category, label] = round(y_avg)
|
|
self.thresholds[category, label] = 0.50
|
|
continue
|
|
|
|
# Create a copy of classifier prototype and train it
|
|
if isinstance(self.classifier_prototype, list):
|
|
clf = deepcopy(self.classifier_prototype[label])
|
|
else:
|
|
clf = deepcopy(self.classifier_prototype)
|
|
clf.fit(x_train, y_train)
|
|
|
|
# Find threshold (dynamic or static)
|
|
if isinstance(self.threshold_prototype, DynamicThreshold):
|
|
self.thresholds[category, label] = self.threshold_prototype.find(
|
|
clf,
|
|
x_train,
|
|
y_train,
|
|
)
|
|
else:
|
|
self.thresholds[category, label] = deepcopy(
|
|
self.threshold_prototype
|
|
)
|
|
|
|
self.classifiers[category, label] = clf
|
|
|
|
def predict(self, instance):
|
|
solution = {}
|
|
x_test = VariableFeaturesExtractor().extract([instance])
|
|
var_split = Extractor.split_variables(instance)
|
|
for category in var_split.keys():
|
|
n = len(var_split[category])
|
|
for (i, (var, index)) in enumerate(var_split[category]):
|
|
if var not in solution.keys():
|
|
solution[var] = {}
|
|
solution[var][index] = None
|
|
for label in [0, 1]:
|
|
if (category, label) not in self.classifiers.keys():
|
|
continue
|
|
clf = self.classifiers[category, label]
|
|
if isinstance(clf, float) or isinstance(clf, int):
|
|
ws = np.array([[1 - clf, clf] for _ in range(n)])
|
|
else:
|
|
ws = clf.predict_proba(x_test[category])
|
|
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
|
|
n,
|
|
ws.shape,
|
|
)
|
|
for (i, (var, index)) in enumerate(var_split[category]):
|
|
if ws[i, 1] >= self.thresholds[category, label]:
|
|
solution[var][index] = label
|
|
return solution
|
|
|
|
def evaluate(self, instances):
|
|
ev = {"Fix zero": {}, "Fix one": {}}
|
|
for instance_idx in tqdm(
|
|
range(len(instances)),
|
|
desc="Evaluate (primal)",
|
|
):
|
|
instance = instances[instance_idx]
|
|
solution_actual = instance.training_data[0]["Solution"]
|
|
solution_pred = self.predict(instance)
|
|
|
|
vars_all, vars_one, vars_zero = set(), set(), set()
|
|
pred_one_positive, pred_zero_positive = set(), set()
|
|
for (varname, var_dict) in solution_actual.items():
|
|
if varname not in solution_pred.keys():
|
|
continue
|
|
for (idx, value) in var_dict.items():
|
|
vars_all.add((varname, idx))
|
|
if value > 0.5:
|
|
vars_one.add((varname, idx))
|
|
else:
|
|
vars_zero.add((varname, idx))
|
|
if solution_pred[varname][idx] is not None:
|
|
if solution_pred[varname][idx] > 0.5:
|
|
pred_one_positive.add((varname, idx))
|
|
else:
|
|
pred_zero_positive.add((varname, idx))
|
|
pred_one_negative = vars_all - pred_one_positive
|
|
pred_zero_negative = vars_all - pred_zero_positive
|
|
|
|
tp_zero = len(pred_zero_positive & vars_zero)
|
|
fp_zero = len(pred_zero_positive & vars_one)
|
|
tn_zero = len(pred_zero_negative & vars_one)
|
|
fn_zero = len(pred_zero_negative & vars_zero)
|
|
|
|
tp_one = len(pred_one_positive & vars_one)
|
|
fp_one = len(pred_one_positive & vars_zero)
|
|
tn_one = len(pred_one_negative & vars_zero)
|
|
fn_one = len(pred_one_negative & vars_one)
|
|
|
|
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
|
|
tp_zero, tn_zero, fp_zero, fn_zero
|
|
)
|
|
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
|
|
tp_one, tn_one, fp_one, fn_one
|
|
)
|
|
return ev</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.primal.PrimalSolutionComponent"><code class="flex name class">
|
|
<span>class <span class="ident">PrimalSolutionComponent</span></span>
|
|
<span>(</span><span>classifier=<miplearn.classifiers.adaptive.AdaptiveClassifier object>, mode='exact', threshold=<miplearn.classifiers.threshold.MinPrecisionThreshold object>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>A component that predicts primal solutions.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class PrimalSolutionComponent(Component):
|
|
"""
|
|
A component that predicts primal solutions.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
classifier: Classifier = AdaptiveClassifier(),
|
|
mode: str = "exact",
|
|
threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
|
|
) -> None:
|
|
self.mode = mode
|
|
self.classifiers: Dict[Any, Classifier] = {}
|
|
self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
|
|
self.threshold_prototype = threshold
|
|
self.classifier_prototype = classifier
|
|
|
|
def before_solve(self, solver, instance, model):
|
|
logger.info("Predicting primal solution...")
|
|
solution = self.predict(instance)
|
|
if self.mode == "heuristic":
|
|
solver.internal_solver.fix(solution)
|
|
else:
|
|
solver.internal_solver.set_warm_start(solution)
|
|
|
|
def after_solve(
|
|
self,
|
|
solver,
|
|
instance,
|
|
model,
|
|
stats,
|
|
training_data,
|
|
):
|
|
pass
|
|
|
|
def x(self, training_instances):
|
|
return VariableFeaturesExtractor().extract(training_instances)
|
|
|
|
def y(self, training_instances):
|
|
return SolutionExtractor().extract(training_instances)
|
|
|
|
def fit(self, training_instances, n_jobs=1):
|
|
logger.debug("Extracting features...")
|
|
features = VariableFeaturesExtractor().extract(training_instances)
|
|
solutions = SolutionExtractor().extract(training_instances)
|
|
|
|
for category in tqdm(
|
|
features.keys(),
|
|
desc="Fit (primal)",
|
|
):
|
|
x_train = features[category]
|
|
for label in [0, 1]:
|
|
y_train = solutions[category][:, label].astype(int)
|
|
|
|
# If all samples are either positive or negative, make constant
|
|
# predictions
|
|
y_avg = np.average(y_train)
|
|
if y_avg < 0.001 or y_avg >= 0.999:
|
|
self.classifiers[category, label] = round(y_avg)
|
|
self.thresholds[category, label] = 0.50
|
|
continue
|
|
|
|
# Create a copy of classifier prototype and train it
|
|
if isinstance(self.classifier_prototype, list):
|
|
clf = deepcopy(self.classifier_prototype[label])
|
|
else:
|
|
clf = deepcopy(self.classifier_prototype)
|
|
clf.fit(x_train, y_train)
|
|
|
|
# Find threshold (dynamic or static)
|
|
if isinstance(self.threshold_prototype, DynamicThreshold):
|
|
self.thresholds[category, label] = self.threshold_prototype.find(
|
|
clf,
|
|
x_train,
|
|
y_train,
|
|
)
|
|
else:
|
|
self.thresholds[category, label] = deepcopy(
|
|
self.threshold_prototype
|
|
)
|
|
|
|
self.classifiers[category, label] = clf
|
|
|
|
def predict(self, instance):
|
|
solution = {}
|
|
x_test = VariableFeaturesExtractor().extract([instance])
|
|
var_split = Extractor.split_variables(instance)
|
|
for category in var_split.keys():
|
|
n = len(var_split[category])
|
|
for (i, (var, index)) in enumerate(var_split[category]):
|
|
if var not in solution.keys():
|
|
solution[var] = {}
|
|
solution[var][index] = None
|
|
for label in [0, 1]:
|
|
if (category, label) not in self.classifiers.keys():
|
|
continue
|
|
clf = self.classifiers[category, label]
|
|
if isinstance(clf, float) or isinstance(clf, int):
|
|
ws = np.array([[1 - clf, clf] for _ in range(n)])
|
|
else:
|
|
ws = clf.predict_proba(x_test[category])
|
|
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
|
|
n,
|
|
ws.shape,
|
|
)
|
|
for (i, (var, index)) in enumerate(var_split[category]):
|
|
if ws[i, 1] >= self.thresholds[category, label]:
|
|
solution[var][index] = label
|
|
return solution
|
|
|
|
def evaluate(self, instances):
|
|
ev = {"Fix zero": {}, "Fix one": {}}
|
|
for instance_idx in tqdm(
|
|
range(len(instances)),
|
|
desc="Evaluate (primal)",
|
|
):
|
|
instance = instances[instance_idx]
|
|
solution_actual = instance.training_data[0]["Solution"]
|
|
solution_pred = self.predict(instance)
|
|
|
|
vars_all, vars_one, vars_zero = set(), set(), set()
|
|
pred_one_positive, pred_zero_positive = set(), set()
|
|
for (varname, var_dict) in solution_actual.items():
|
|
if varname not in solution_pred.keys():
|
|
continue
|
|
for (idx, value) in var_dict.items():
|
|
vars_all.add((varname, idx))
|
|
if value > 0.5:
|
|
vars_one.add((varname, idx))
|
|
else:
|
|
vars_zero.add((varname, idx))
|
|
if solution_pred[varname][idx] is not None:
|
|
if solution_pred[varname][idx] > 0.5:
|
|
pred_one_positive.add((varname, idx))
|
|
else:
|
|
pred_zero_positive.add((varname, idx))
|
|
pred_one_negative = vars_all - pred_one_positive
|
|
pred_zero_negative = vars_all - pred_zero_positive
|
|
|
|
tp_zero = len(pred_zero_positive & vars_zero)
|
|
fp_zero = len(pred_zero_positive & vars_one)
|
|
tn_zero = len(pred_zero_negative & vars_one)
|
|
fn_zero = len(pred_zero_negative & vars_zero)
|
|
|
|
tp_one = len(pred_one_positive & vars_one)
|
|
fp_one = len(pred_one_positive & vars_zero)
|
|
tn_one = len(pred_one_negative & vars_zero)
|
|
fn_one = len(pred_one_negative & vars_one)
|
|
|
|
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
|
|
tp_zero, tn_zero, fp_zero, fn_zero
|
|
)
|
|
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
|
|
tp_one, tn_one, fp_one, fn_one
|
|
)
|
|
return ev</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.primal.PrimalSolutionComponent.evaluate"><code class="name flex">
|
|
<span>def <span class="ident">evaluate</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 evaluate(self, instances):
|
|
ev = {"Fix zero": {}, "Fix one": {}}
|
|
for instance_idx in tqdm(
|
|
range(len(instances)),
|
|
desc="Evaluate (primal)",
|
|
):
|
|
instance = instances[instance_idx]
|
|
solution_actual = instance.training_data[0]["Solution"]
|
|
solution_pred = self.predict(instance)
|
|
|
|
vars_all, vars_one, vars_zero = set(), set(), set()
|
|
pred_one_positive, pred_zero_positive = set(), set()
|
|
for (varname, var_dict) in solution_actual.items():
|
|
if varname not in solution_pred.keys():
|
|
continue
|
|
for (idx, value) in var_dict.items():
|
|
vars_all.add((varname, idx))
|
|
if value > 0.5:
|
|
vars_one.add((varname, idx))
|
|
else:
|
|
vars_zero.add((varname, idx))
|
|
if solution_pred[varname][idx] is not None:
|
|
if solution_pred[varname][idx] > 0.5:
|
|
pred_one_positive.add((varname, idx))
|
|
else:
|
|
pred_zero_positive.add((varname, idx))
|
|
pred_one_negative = vars_all - pred_one_positive
|
|
pred_zero_negative = vars_all - pred_zero_positive
|
|
|
|
tp_zero = len(pred_zero_positive & vars_zero)
|
|
fp_zero = len(pred_zero_positive & vars_one)
|
|
tn_zero = len(pred_zero_negative & vars_one)
|
|
fn_zero = len(pred_zero_negative & vars_zero)
|
|
|
|
tp_one = len(pred_one_positive & vars_one)
|
|
fp_one = len(pred_one_positive & vars_zero)
|
|
tn_one = len(pred_one_negative & vars_zero)
|
|
fn_one = len(pred_one_negative & vars_one)
|
|
|
|
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
|
|
tp_zero, tn_zero, fp_zero, fn_zero
|
|
)
|
|
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
|
|
tp_one, tn_one, fp_one, fn_one
|
|
)
|
|
return ev</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.primal.PrimalSolutionComponent.fit"><code class="name flex">
|
|
<span>def <span class="ident">fit</span></span>(<span>self, training_instances, n_jobs=1)</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, n_jobs=1):
|
|
logger.debug("Extracting features...")
|
|
features = VariableFeaturesExtractor().extract(training_instances)
|
|
solutions = SolutionExtractor().extract(training_instances)
|
|
|
|
for category in tqdm(
|
|
features.keys(),
|
|
desc="Fit (primal)",
|
|
):
|
|
x_train = features[category]
|
|
for label in [0, 1]:
|
|
y_train = solutions[category][:, label].astype(int)
|
|
|
|
# If all samples are either positive or negative, make constant
|
|
# predictions
|
|
y_avg = np.average(y_train)
|
|
if y_avg < 0.001 or y_avg >= 0.999:
|
|
self.classifiers[category, label] = round(y_avg)
|
|
self.thresholds[category, label] = 0.50
|
|
continue
|
|
|
|
# Create a copy of classifier prototype and train it
|
|
if isinstance(self.classifier_prototype, list):
|
|
clf = deepcopy(self.classifier_prototype[label])
|
|
else:
|
|
clf = deepcopy(self.classifier_prototype)
|
|
clf.fit(x_train, y_train)
|
|
|
|
# Find threshold (dynamic or static)
|
|
if isinstance(self.threshold_prototype, DynamicThreshold):
|
|
self.thresholds[category, label] = self.threshold_prototype.find(
|
|
clf,
|
|
x_train,
|
|
y_train,
|
|
)
|
|
else:
|
|
self.thresholds[category, label] = deepcopy(
|
|
self.threshold_prototype
|
|
)
|
|
|
|
self.classifiers[category, label] = clf</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.primal.PrimalSolutionComponent.predict"><code class="name flex">
|
|
<span>def <span class="ident">predict</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 predict(self, instance):
|
|
solution = {}
|
|
x_test = VariableFeaturesExtractor().extract([instance])
|
|
var_split = Extractor.split_variables(instance)
|
|
for category in var_split.keys():
|
|
n = len(var_split[category])
|
|
for (i, (var, index)) in enumerate(var_split[category]):
|
|
if var not in solution.keys():
|
|
solution[var] = {}
|
|
solution[var][index] = None
|
|
for label in [0, 1]:
|
|
if (category, label) not in self.classifiers.keys():
|
|
continue
|
|
clf = self.classifiers[category, label]
|
|
if isinstance(clf, float) or isinstance(clf, int):
|
|
ws = np.array([[1 - clf, clf] for _ in range(n)])
|
|
else:
|
|
ws = clf.predict_proba(x_test[category])
|
|
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
|
|
n,
|
|
ws.shape,
|
|
)
|
|
for (i, (var, index)) in enumerate(var_split[category]):
|
|
if ws[i, 1] >= self.thresholds[category, label]:
|
|
solution[var][index] = label
|
|
return solution</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.primal.PrimalSolutionComponent.x"><code class="name flex">
|
|
<span>def <span class="ident">x</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 x(self, training_instances):
|
|
return VariableFeaturesExtractor().extract(training_instances)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.primal.PrimalSolutionComponent.y"><code class="name flex">
|
|
<span>def <span class="ident">y</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 y(self, training_instances):
|
|
return SolutionExtractor().extract(training_instances)</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" href="index.html">miplearn.components</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.components.primal.PrimalSolutionComponent" href="#miplearn.components.primal.PrimalSolutionComponent">PrimalSolutionComponent</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.evaluate" href="#miplearn.components.primal.PrimalSolutionComponent.evaluate">evaluate</a></code></li>
|
|
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.fit" href="#miplearn.components.primal.PrimalSolutionComponent.fit">fit</a></code></li>
|
|
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.predict" href="#miplearn.components.primal.PrimalSolutionComponent.predict">predict</a></code></li>
|
|
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.x" href="#miplearn.components.primal.PrimalSolutionComponent.x">x</a></code></li>
|
|
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.y" href="#miplearn.components.primal.PrimalSolutionComponent.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> 0.7.5</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> |