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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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</ul>
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