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.
316 lines
15 KiB
316 lines
15 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 0.7.0" />
|
|
<title>miplearn.classifiers.cv 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.classifiers.cv</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 sklearn.dummy import DummyClassifier
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.model_selection import cross_val_score
|
|
|
|
from miplearn.classifiers import Classifier
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class CrossValidatedClassifier(Classifier):
|
|
"""
|
|
A meta-classifier that, upon training, evaluates the performance of another
|
|
classifier on the training data set using k-fold cross validation, then
|
|
either adopts the other classifier it if the cv-score is high enough, or
|
|
returns a constant label for every x_test otherwise.
|
|
|
|
The threshold is specified in comparison to a dummy classifier trained
|
|
on the same dataset. For example, a threshold of 0.0 indicates that any
|
|
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
|
|
indicates that only classifier with a perfect cross-validation score are
|
|
acceptable. Other numbers are a linear interpolation of these two extremes.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
classifier=LogisticRegression(),
|
|
threshold=0.75,
|
|
constant=0.0,
|
|
cv=5,
|
|
scoring="accuracy",
|
|
):
|
|
self.classifier = None
|
|
self.classifier_prototype = classifier
|
|
self.constant = constant
|
|
self.threshold = threshold
|
|
self.cv = cv
|
|
self.scoring = scoring
|
|
|
|
def fit(self, x_train, y_train):
|
|
# Calculate dummy score and absolute score threshold
|
|
y_train_avg = np.average(y_train)
|
|
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
|
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
|
|
|
|
# Calculate cross validation score and decide which classifier to use
|
|
clf = deepcopy(self.classifier_prototype)
|
|
cv_score = float(
|
|
np.mean(
|
|
cross_val_score(
|
|
clf,
|
|
x_train,
|
|
y_train,
|
|
cv=self.cv,
|
|
scoring=self.scoring,
|
|
)
|
|
)
|
|
)
|
|
if cv_score >= absolute_threshold:
|
|
logger.debug(
|
|
"cv_score is above threshold (%.2f >= %.2f); keeping"
|
|
% (cv_score, absolute_threshold)
|
|
)
|
|
self.classifier = clf
|
|
else:
|
|
logger.debug(
|
|
"cv_score is below threshold (%.2f < %.2f); discarding"
|
|
% (cv_score, absolute_threshold)
|
|
)
|
|
self.classifier = DummyClassifier(
|
|
strategy="constant",
|
|
constant=self.constant,
|
|
)
|
|
|
|
# Train chosen classifier
|
|
self.classifier.fit(x_train, y_train)
|
|
|
|
def predict_proba(self, x_test):
|
|
return self.classifier.predict_proba(x_test)</code></pre>
|
|
</details>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
<h2 class="section-title" id="header-classes">Classes</h2>
|
|
<dl>
|
|
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier"><code class="flex name class">
|
|
<span>class <span class="ident">CrossValidatedClassifier</span></span>
|
|
<span>(</span><span>classifier=LogisticRegression(), threshold=0.75, constant=0.0, cv=5, scoring='accuracy')</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>A meta-classifier that, upon training, evaluates the performance of another
|
|
classifier on the training data set using k-fold cross validation, then
|
|
either adopts the other classifier it if the cv-score is high enough, or
|
|
returns a constant label for every x_test otherwise.</p>
|
|
<p>The threshold is specified in comparison to a dummy classifier trained
|
|
on the same dataset. For example, a threshold of 0.0 indicates that any
|
|
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
|
|
indicates that only classifier with a perfect cross-validation score are
|
|
acceptable. Other numbers are a linear interpolation of these two extremes.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class CrossValidatedClassifier(Classifier):
|
|
"""
|
|
A meta-classifier that, upon training, evaluates the performance of another
|
|
classifier on the training data set using k-fold cross validation, then
|
|
either adopts the other classifier it if the cv-score is high enough, or
|
|
returns a constant label for every x_test otherwise.
|
|
|
|
The threshold is specified in comparison to a dummy classifier trained
|
|
on the same dataset. For example, a threshold of 0.0 indicates that any
|
|
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
|
|
indicates that only classifier with a perfect cross-validation score are
|
|
acceptable. Other numbers are a linear interpolation of these two extremes.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
classifier=LogisticRegression(),
|
|
threshold=0.75,
|
|
constant=0.0,
|
|
cv=5,
|
|
scoring="accuracy",
|
|
):
|
|
self.classifier = None
|
|
self.classifier_prototype = classifier
|
|
self.constant = constant
|
|
self.threshold = threshold
|
|
self.cv = cv
|
|
self.scoring = scoring
|
|
|
|
def fit(self, x_train, y_train):
|
|
# Calculate dummy score and absolute score threshold
|
|
y_train_avg = np.average(y_train)
|
|
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
|
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
|
|
|
|
# Calculate cross validation score and decide which classifier to use
|
|
clf = deepcopy(self.classifier_prototype)
|
|
cv_score = float(
|
|
np.mean(
|
|
cross_val_score(
|
|
clf,
|
|
x_train,
|
|
y_train,
|
|
cv=self.cv,
|
|
scoring=self.scoring,
|
|
)
|
|
)
|
|
)
|
|
if cv_score >= absolute_threshold:
|
|
logger.debug(
|
|
"cv_score is above threshold (%.2f >= %.2f); keeping"
|
|
% (cv_score, absolute_threshold)
|
|
)
|
|
self.classifier = clf
|
|
else:
|
|
logger.debug(
|
|
"cv_score is below threshold (%.2f < %.2f); discarding"
|
|
% (cv_score, absolute_threshold)
|
|
)
|
|
self.classifier = DummyClassifier(
|
|
strategy="constant",
|
|
constant=self.constant,
|
|
)
|
|
|
|
# Train chosen classifier
|
|
self.classifier.fit(x_train, y_train)
|
|
|
|
def predict_proba(self, x_test):
|
|
return self.classifier.predict_proba(x_test)</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier.fit"><code class="name flex">
|
|
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</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, x_train, y_train):
|
|
# Calculate dummy score and absolute score threshold
|
|
y_train_avg = np.average(y_train)
|
|
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
|
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
|
|
|
|
# Calculate cross validation score and decide which classifier to use
|
|
clf = deepcopy(self.classifier_prototype)
|
|
cv_score = float(
|
|
np.mean(
|
|
cross_val_score(
|
|
clf,
|
|
x_train,
|
|
y_train,
|
|
cv=self.cv,
|
|
scoring=self.scoring,
|
|
)
|
|
)
|
|
)
|
|
if cv_score >= absolute_threshold:
|
|
logger.debug(
|
|
"cv_score is above threshold (%.2f >= %.2f); keeping"
|
|
% (cv_score, absolute_threshold)
|
|
)
|
|
self.classifier = clf
|
|
else:
|
|
logger.debug(
|
|
"cv_score is below threshold (%.2f < %.2f); discarding"
|
|
% (cv_score, absolute_threshold)
|
|
)
|
|
self.classifier = DummyClassifier(
|
|
strategy="constant",
|
|
constant=self.constant,
|
|
)
|
|
|
|
# Train chosen classifier
|
|
self.classifier.fit(x_train, y_train)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba"><code class="name flex">
|
|
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def predict_proba(self, x_test):
|
|
return self.classifier.predict_proba(x_test)</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</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.classifiers" href="index.html">miplearn.classifiers</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier.fit" href="#miplearn.classifiers.cv.CrossValidatedClassifier.fit">fit</a></code></li>
|
|
<li><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba" href="#miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba">predict_proba</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> |