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</head>
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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.classifiers.adaptive</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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from copy import deepcopy
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from typing import Any, Dict
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from sklearn.linear_model import LogisticRegression
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from miplearn.classifiers import Classifier
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.classifiers.evaluator import ClassifierEvaluator
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logger = logging.getLogger(__name__)
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class AdaptiveClassifier(Classifier):
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"""
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A meta-classifier which dynamically selects what actual classifier to use
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based on its cross-validation score on a particular training data set.
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"""
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def __init__(
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self,
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candidates: Dict[str, Any] = None,
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evaluator: ClassifierEvaluator = ClassifierEvaluator(),
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) -> None:
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"""
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Initializes the meta-classifier.
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"""
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if candidates is None:
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candidates = {
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"knn(100)": {
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"classifier": KNeighborsClassifier(n_neighbors=100),
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"min samples": 100,
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},
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"logistic": {
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"classifier": make_pipeline(StandardScaler(), LogisticRegression()),
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"min samples": 30,
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},
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"counting": {
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"classifier": CountingClassifier(),
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"min samples": 0,
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},
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}
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self.candidates = candidates
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self.evaluator = evaluator
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self.classifier = None
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def fit(self, x_train, y_train):
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best_name, best_clf, best_score = None, None, -float("inf")
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n_samples = x_train.shape[0]
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for (name, clf_dict) in self.candidates.items():
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if n_samples < clf_dict["min samples"]:
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continue
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clf = deepcopy(clf_dict["classifier"])
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clf.fit(x_train, y_train)
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score = self.evaluator.evaluate(clf, x_train, y_train)
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if score > best_score:
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best_name, best_clf, best_score = name, clf, score
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logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
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self.classifier = best_clf
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def predict_proba(self, x_test):
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return self.classifier.predict_proba(x_test)</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier"><code class="flex name class">
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<span>class <span class="ident">AdaptiveClassifier</span></span>
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<span>(</span><span>candidates=None, evaluator=<miplearn.classifiers.evaluator.ClassifierEvaluator object>)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>A meta-classifier which dynamically selects what actual classifier to use
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based on its cross-validation score on a particular training data set.</p>
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<p>Initializes the meta-classifier.</p></section>
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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">class AdaptiveClassifier(Classifier):
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"""
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A meta-classifier which dynamically selects what actual classifier to use
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based on its cross-validation score on a particular training data set.
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"""
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def __init__(
|
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self,
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candidates: Dict[str, Any] = None,
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evaluator: ClassifierEvaluator = ClassifierEvaluator(),
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) -> None:
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"""
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Initializes the meta-classifier.
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"""
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if candidates is None:
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candidates = {
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"knn(100)": {
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"classifier": KNeighborsClassifier(n_neighbors=100),
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"min samples": 100,
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},
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"logistic": {
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"classifier": make_pipeline(StandardScaler(), LogisticRegression()),
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"min samples": 30,
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},
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"counting": {
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"classifier": CountingClassifier(),
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"min samples": 0,
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},
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}
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self.candidates = candidates
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self.evaluator = evaluator
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self.classifier = None
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def fit(self, x_train, y_train):
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best_name, best_clf, best_score = None, None, -float("inf")
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n_samples = x_train.shape[0]
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for (name, clf_dict) in self.candidates.items():
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if n_samples < clf_dict["min samples"]:
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continue
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clf = deepcopy(clf_dict["classifier"])
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clf.fit(x_train, y_train)
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score = self.evaluator.evaluate(clf, x_train, y_train)
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if score > best_score:
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best_name, best_clf, best_score = name, clf, score
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logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
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self.classifier = best_clf
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def predict_proba(self, x_test):
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return self.classifier.predict_proba(x_test)</code></pre>
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</details>
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<h3>Ancestors</h3>
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<ul class="hlist">
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<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
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<li>abc.ABC</li>
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</ul>
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<h3>Methods</h3>
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<dl>
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<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier.fit"><code class="name flex">
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<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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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">def fit(self, x_train, y_train):
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best_name, best_clf, best_score = None, None, -float("inf")
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n_samples = x_train.shape[0]
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for (name, clf_dict) in self.candidates.items():
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if n_samples < clf_dict["min samples"]:
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continue
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clf = deepcopy(clf_dict["classifier"])
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clf.fit(x_train, y_train)
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score = self.evaluator.evaluate(clf, x_train, y_train)
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if score > best_score:
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best_name, best_clf, best_score = name, clf, score
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logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
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self.classifier = best_clf</code></pre>
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</details>
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</dd>
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<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba"><code class="name flex">
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<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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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">def predict_proba(self, x_test):
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return self.classifier.predict_proba(x_test)</code></pre>
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</details>
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</dd>
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</dl>
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</dd>
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</dl>
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</section>
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</article>
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
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<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<ul>
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<li>
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<h4><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier" href="#miplearn.classifiers.adaptive.AdaptiveClassifier">AdaptiveClassifier</a></code></h4>
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<ul class="">
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<li><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier.fit" href="#miplearn.classifiers.adaptive.AdaptiveClassifier.fit">fit</a></code></li>
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<li><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba" href="#miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba">predict_proba</a></code></li>
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</ul>
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</li>
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</ul>
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</li>
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0.2/api/miplearn/classifiers/counting.html
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|
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<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.counting</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 numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
|
||||
|
||||
class CountingClassifier(Classifier):
|
||||
"""
|
||||
A classifier that generates constant predictions, based only on the
|
||||
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
|
||||
this classifier always returns [0.66 0.33] for any x_test. It essentially
|
||||
counts how many times each label appeared, hence the name.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.mean = None
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
self.mean = np.mean(y_train)
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
|
||||
|
||||
def __repr__(self):
|
||||
return "CountingClassifier(mean=%s)" % self.mean</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.counting.CountingClassifier"><code class="flex name class">
|
||||
<span>class <span class="ident">CountingClassifier</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A classifier that generates constant predictions, based only on the
|
||||
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
|
||||
this classifier always returns [0.66 0.33] for any x_test. It essentially
|
||||
counts how many times each label appeared, hence the name.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class CountingClassifier(Classifier):
|
||||
"""
|
||||
A classifier that generates constant predictions, based only on the
|
||||
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
|
||||
this classifier always returns [0.66 0.33] for any x_test. It essentially
|
||||
counts how many times each label appeared, hence the name.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.mean = None
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
self.mean = np.mean(y_train)
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
|
||||
|
||||
def __repr__(self):
|
||||
return "CountingClassifier(mean=%s)" % self.mean</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.counting.CountingClassifier.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):
|
||||
self.mean = np.mean(y_train)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.counting.CountingClassifier.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 np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])</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.counting.CountingClassifier" href="#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.counting.CountingClassifier.fit" href="#miplearn.classifiers.counting.CountingClassifier.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.counting.CountingClassifier.predict_proba" href="#miplearn.classifiers.counting.CountingClassifier.predict_proba">predict_proba</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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316
0.2/api/miplearn/classifiers/cv.html
Normal file
316
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Normal file
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|
||||
</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>
|
||||
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|
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123
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Normal file
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Normal file
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.evaluator</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 sklearn.metrics import roc_auc_score
|
||||
|
||||
|
||||
class ClassifierEvaluator:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def evaluate(self, clf, x_train, y_train):
|
||||
# FIXME: use cross-validation
|
||||
proba = clf.predict_proba(x_train)
|
||||
return roc_auc_score(y_train, proba[:, 1])</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.evaluator.ClassifierEvaluator"><code class="flex name class">
|
||||
<span>class <span class="ident">ClassifierEvaluator</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ClassifierEvaluator:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def evaluate(self, clf, x_train, y_train):
|
||||
# FIXME: use cross-validation
|
||||
proba = clf.predict_proba(x_train)
|
||||
return roc_auc_score(y_train, proba[:, 1])</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, clf, 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 evaluate(self, clf, x_train, y_train):
|
||||
# FIXME: use cross-validation
|
||||
proba = clf.predict_proba(x_train)
|
||||
return roc_auc_score(y_train, proba[:, 1])</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.evaluator.ClassifierEvaluator" href="#miplearn.classifiers.evaluator.ClassifierEvaluator">ClassifierEvaluator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate" href="#miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate">evaluate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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290
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Normal file
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Normal file
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|
||||
<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 abc import ABC, abstractmethod
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Classifier(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict_proba(self, x_test):
|
||||
pass
|
||||
|
||||
def predict(self, x_test):
|
||||
proba = self.predict_proba(x_test)
|
||||
assert isinstance(proba, np.ndarray)
|
||||
assert proba.shape == (x_test.shape[0], 2)
|
||||
return (proba[:, 1] > 0.5).astype(float)
|
||||
|
||||
|
||||
class Regressor(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict(self):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.adaptive" href="adaptive.html">miplearn.classifiers.adaptive</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.counting" href="counting.html">miplearn.classifiers.counting</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.cv" href="cv.html">miplearn.classifiers.cv</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.evaluator" href="evaluator.html">miplearn.classifiers.evaluator</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.tests" href="tests/index.html">miplearn.classifiers.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.threshold" href="threshold.html">miplearn.classifiers.threshold</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.Classifier"><code class="flex name class">
|
||||
<span>class <span class="ident">Classifier</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Classifier(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict_proba(self, x_test):
|
||||
pass
|
||||
|
||||
def predict(self, x_test):
|
||||
proba = self.predict_proba(x_test)
|
||||
assert isinstance(proba, np.ndarray)
|
||||
assert proba.shape == (x_test.shape[0], 2)
|
||||
return (proba[:, 1] > 0.5).astype(float)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.counting.CountingClassifier" href="counting.html#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></li>
|
||||
<li><a title="miplearn.classifiers.adaptive.AdaptiveClassifier" href="adaptive.html#miplearn.classifiers.adaptive.AdaptiveClassifier">AdaptiveClassifier</a></li>
|
||||
<li><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="cv.html#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.Classifier.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">@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Classifier.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</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(self, x_test):
|
||||
proba = self.predict_proba(x_test)
|
||||
assert isinstance(proba, np.ndarray)
|
||||
assert proba.shape == (x_test.shape[0], 2)
|
||||
return (proba[:, 1] > 0.5).astype(float)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Classifier.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">@abstractmethod
|
||||
def predict_proba(self, x_test):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Regressor"><code class="flex name class">
|
||||
<span>class <span class="ident">Regressor</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Regressor(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict(self):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.Regressor.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">@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Regressor.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def predict(self):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers.adaptive" href="adaptive.html">miplearn.classifiers.adaptive</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.counting" href="counting.html">miplearn.classifiers.counting</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.cv" href="cv.html">miplearn.classifiers.cv</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.evaluator" href="evaluator.html">miplearn.classifiers.evaluator</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.tests" href="tests/index.html">miplearn.classifiers.tests</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.threshold" href="threshold.html">miplearn.classifiers.threshold</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.Classifier" href="#miplearn.classifiers.Classifier">Classifier</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.Classifier.fit" href="#miplearn.classifiers.Classifier.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.Classifier.predict" href="#miplearn.classifiers.Classifier.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.Classifier.predict_proba" href="#miplearn.classifiers.Classifier.predict_proba">predict_proba</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.Regressor" href="#miplearn.classifiers.Regressor">Regressor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.Regressor.fit" href="#miplearn.classifiers.Regressor.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.Regressor.predict" href="#miplearn.classifiers.Regressor.predict">predict</a></code></li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
<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.</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.tests.test_counting" href="test_counting.html">miplearn.classifiers.tests.test_counting</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.tests.test_cv" href="test_cv.html">miplearn.classifiers.tests.test_cv</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.tests.test_evaluator" href="test_evaluator.html">miplearn.classifiers.tests.test_evaluator</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.tests.test_threshold" href="test_threshold.html">miplearn.classifiers.tests.test_threshold</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
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|
||||
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|
||||
<section>
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
<li><h3>Super-module</h3>
|
||||
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||||
<h1 class="title">Module <code>miplearn.classifiers.tests.test_counting</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 numpy as np
|
||||
from numpy.linalg import norm
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_counting():
|
||||
clf = CountingClassifier()
|
||||
clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
||||
expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
|
||||
actual_proba = clf.predict_proba(np.zeros((2, 25)))
|
||||
assert norm(actual_proba - expected_proba) < E</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_counting.test_counting"><code class="name flex">
|
||||
<span>def <span class="ident">test_counting</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_counting():
|
||||
clf = CountingClassifier()
|
||||
clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
||||
expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
|
||||
actual_proba = clf.predict_proba(np.zeros((2, 25)))
|
||||
assert norm(actual_proba - expected_proba) < E</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
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||||
</li>
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||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_counting.test_counting" href="#miplearn.classifiers.tests.test_counting.test_counting">test_counting</a></code></li>
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<header>
|
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<h1 class="title">Module <code>miplearn.classifiers.tests.test_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 numpy as np
|
||||
from numpy.linalg import norm
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.svm import SVC
|
||||
|
||||
from miplearn.classifiers.cv import CrossValidatedClassifier
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_cv():
|
||||
# Training set: label is true if point is inside a 2D circle
|
||||
x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
|
||||
x_train = StandardScaler().fit_transform(x_train)
|
||||
n_samples = x_train.shape[0]
|
||||
|
||||
y_train = np.array(
|
||||
[
|
||||
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
|
||||
for x1 in range(-10, 11)
|
||||
for x2 in range(-10, 11)
|
||||
]
|
||||
)
|
||||
|
||||
# Support vector machines with linear kernels do not perform well on this
|
||||
# data set, so predictor should return the given constant.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, random_state=42),
|
||||
threshold=0.90,
|
||||
constant=0.0,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
|
||||
|
||||
# Support vector machines with quadratic kernels perform almost perfectly
|
||||
# on this data set, so predictor should return their prediction.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
|
||||
threshold=0.90,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
print(y_train - clf.predict(x_train))
|
||||
assert norm(y_train - clf.predict(x_train)) < E</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_cv.test_cv"><code class="name flex">
|
||||
<span>def <span class="ident">test_cv</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_cv():
|
||||
# Training set: label is true if point is inside a 2D circle
|
||||
x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
|
||||
x_train = StandardScaler().fit_transform(x_train)
|
||||
n_samples = x_train.shape[0]
|
||||
|
||||
y_train = np.array(
|
||||
[
|
||||
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
|
||||
for x1 in range(-10, 11)
|
||||
for x2 in range(-10, 11)
|
||||
]
|
||||
)
|
||||
|
||||
# Support vector machines with linear kernels do not perform well on this
|
||||
# data set, so predictor should return the given constant.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, random_state=42),
|
||||
threshold=0.90,
|
||||
constant=0.0,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
|
||||
|
||||
# Support vector machines with quadratic kernels perform almost perfectly
|
||||
# on this data set, so predictor should return their prediction.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
|
||||
threshold=0.90,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
print(y_train - clf.predict(x_train))
|
||||
assert norm(y_train - clf.predict(x_train)) < E</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
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|
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|
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<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers.tests" href="index.html">miplearn.classifiers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_cv.test_cv" href="#miplearn.classifiers.tests.test_cv.test_cv">test_cv</a></code></li>
|
||||
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|
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107
0.2/api/miplearn/classifiers/tests/test_evaluator.html
Normal file
107
0.2/api/miplearn/classifiers/tests/test_evaluator.html
Normal file
@@ -0,0 +1,107 @@
|
||||
<!doctype html>
|
||||
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|
||||
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|
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<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>
|
||||
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<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>
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|
||||
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|
||||
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||||
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|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.tests.test_evaluator</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 numpy as np
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
|
||||
from miplearn.classifiers.evaluator import ClassifierEvaluator
|
||||
|
||||
|
||||
def test_evaluator():
|
||||
clf_a = KNeighborsClassifier(n_neighbors=1)
|
||||
clf_b = KNeighborsClassifier(n_neighbors=2)
|
||||
x_train = np.array([[0, 0], [1, 0]])
|
||||
y_train = np.array([0, 1])
|
||||
clf_a.fit(x_train, y_train)
|
||||
clf_b.fit(x_train, y_train)
|
||||
ev = ClassifierEvaluator()
|
||||
assert ev.evaluate(clf_a, x_train, y_train) == 1.0
|
||||
assert ev.evaluate(clf_b, x_train, y_train) == 0.5</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_evaluator.test_evaluator"><code class="name flex">
|
||||
<span>def <span class="ident">test_evaluator</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_evaluator():
|
||||
clf_a = KNeighborsClassifier(n_neighbors=1)
|
||||
clf_b = KNeighborsClassifier(n_neighbors=2)
|
||||
x_train = np.array([[0, 0], [1, 0]])
|
||||
y_train = np.array([0, 1])
|
||||
clf_a.fit(x_train, y_train)
|
||||
clf_b.fit(x_train, y_train)
|
||||
ev = ClassifierEvaluator()
|
||||
assert ev.evaluate(clf_a, x_train, y_train) == 1.0
|
||||
assert ev.evaluate(clf_b, x_train, y_train) == 0.5</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.classifiers.tests" href="index.html">miplearn.classifiers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_evaluator.test_evaluator" href="#miplearn.classifiers.tests.test_evaluator.test_evaluator">test_evaluator</a></code></li>
|
||||
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|
||||
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|
||||
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||||
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|
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|
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141
0.2/api/miplearn/classifiers/tests/test_threshold.html
Normal file
141
0.2/api/miplearn/classifiers/tests/test_threshold.html
Normal file
@@ -0,0 +1,141 @@
|
||||
<!doctype html>
|
||||
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|
||||
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|
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<title>miplearn.classifiers.tests.test_threshold API documentation</title>
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<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>
|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.tests.test_threshold</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
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.classifiers.threshold import MinPrecisionThreshold
|
||||
|
||||
|
||||
def test_threshold_dynamic():
|
||||
clf = Mock(spec=Classifier)
|
||||
clf.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.10, 0.90],
|
||||
[0.10, 0.90],
|
||||
[0.20, 0.80],
|
||||
[0.30, 0.70],
|
||||
]
|
||||
)
|
||||
)
|
||||
x_train = np.array([0, 1, 2, 3])
|
||||
y_train = np.array([1, 1, 0, 0])
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=1.0)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.90
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.65)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.80
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.50)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.00)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_threshold.test_threshold_dynamic"><code class="name flex">
|
||||
<span>def <span class="ident">test_threshold_dynamic</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_threshold_dynamic():
|
||||
clf = Mock(spec=Classifier)
|
||||
clf.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.10, 0.90],
|
||||
[0.10, 0.90],
|
||||
[0.20, 0.80],
|
||||
[0.30, 0.70],
|
||||
]
|
||||
)
|
||||
)
|
||||
x_train = np.array([0, 1, 2, 3])
|
||||
y_train = np.array([1, 1, 0, 0])
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=1.0)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.90
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.65)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.80
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.50)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.00)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70</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.classifiers.tests" href="index.html">miplearn.classifiers.tests</a></code></li>
|
||||
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||||
</li>
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||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_threshold.test_threshold_dynamic" href="#miplearn.classifiers.tests.test_threshold.test_threshold_dynamic">test_threshold_dynamic</a></code></li>
|
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0.2/api/miplearn/classifiers/threshold.html
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246
0.2/api/miplearn/classifiers/threshold.html
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|
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.threshold</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 abc import abstractmethod, ABC
|
||||
|
||||
import numpy as np
|
||||
from sklearn.metrics._ranking import _binary_clf_curve
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
|
||||
|
||||
class DynamicThreshold(ABC):
|
||||
@abstractmethod
|
||||
def find(
|
||||
self,
|
||||
clf: Classifier,
|
||||
x_train: np.ndarray,
|
||||
y_train: np.ndarray,
|
||||
) -> float:
|
||||
"""
|
||||
Given a trained binary classifier `clf` and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class MinPrecisionThreshold(DynamicThreshold):
|
||||
"""
|
||||
The smallest possible threshold satisfying a minimum acceptable true
|
||||
positive rate (also known as precision).
|
||||
"""
|
||||
|
||||
def __init__(self, min_precision: float) -> None:
|
||||
self.min_precision = min_precision
|
||||
|
||||
def find(self, clf, x_train, y_train):
|
||||
proba = clf.predict_proba(x_train)
|
||||
|
||||
assert isinstance(proba, np.ndarray), "classifier should return numpy array"
|
||||
assert proba.shape == (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
), "classifier should return (%d,%d)-shaped array, not %s" % (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
str(proba.shape),
|
||||
)
|
||||
|
||||
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
|
||||
precision = tps / (tps + fps)
|
||||
|
||||
for k in reversed(range(len(precision))):
|
||||
if precision[k] >= self.min_precision:
|
||||
return thresholds[k]
|
||||
return 2.0</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.threshold.DynamicThreshold"><code class="flex name class">
|
||||
<span>class <span class="ident">DynamicThreshold</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class DynamicThreshold(ABC):
|
||||
@abstractmethod
|
||||
def find(
|
||||
self,
|
||||
clf: Classifier,
|
||||
x_train: np.ndarray,
|
||||
y_train: np.ndarray,
|
||||
) -> float:
|
||||
"""
|
||||
Given a trained binary classifier `clf` and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.threshold.DynamicThreshold.find"><code class="name flex">
|
||||
<span>def <span class="ident">find</span></span>(<span>self, clf, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Given a trained binary classifier <code>clf</code> and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def find(
|
||||
self,
|
||||
clf: Classifier,
|
||||
x_train: np.ndarray,
|
||||
y_train: np.ndarray,
|
||||
) -> float:
|
||||
"""
|
||||
Given a trained binary classifier `clf` and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.threshold.MinPrecisionThreshold"><code class="flex name class">
|
||||
<span>class <span class="ident">MinPrecisionThreshold</span></span>
|
||||
<span>(</span><span>min_precision)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>The smallest possible threshold satisfying a minimum acceptable true
|
||||
positive rate (also known as precision).</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MinPrecisionThreshold(DynamicThreshold):
|
||||
"""
|
||||
The smallest possible threshold satisfying a minimum acceptable true
|
||||
positive rate (also known as precision).
|
||||
"""
|
||||
|
||||
def __init__(self, min_precision: float) -> None:
|
||||
self.min_precision = min_precision
|
||||
|
||||
def find(self, clf, x_train, y_train):
|
||||
proba = clf.predict_proba(x_train)
|
||||
|
||||
assert isinstance(proba, np.ndarray), "classifier should return numpy array"
|
||||
assert proba.shape == (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
), "classifier should return (%d,%d)-shaped array, not %s" % (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
str(proba.shape),
|
||||
)
|
||||
|
||||
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
|
||||
precision = tps / (tps + fps)
|
||||
|
||||
for k in reversed(range(len(precision))):
|
||||
if precision[k] >= self.min_precision:
|
||||
return thresholds[k]
|
||||
return 2.0</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</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.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.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
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