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<!DOCTYPE html>
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<title>MABWiser Public API — MABWiser 2.7.4 documentation</title>
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MABWiser
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<li class="toctree-l1"><a class="reference internal" href="about.html">About Multi-Armed Bandits</a></li>
<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="quick.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="examples.html">Usage Examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a></li>
<li class="toctree-l1"><a class="reference internal" href="new_bandit.html">Adding a New Bandit</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">MABWiser Public API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#module-mabwiser.base_mab">base_mab</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB"><code class="docutils literal notranslate"><span class="pre">BaseMAB</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.rng"><code class="docutils literal notranslate"><span class="pre">BaseMAB.rng</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.arms"><code class="docutils literal notranslate"><span class="pre">BaseMAB.arms</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.n_jobs"><code class="docutils literal notranslate"><span class="pre">BaseMAB.n_jobs</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.backend"><code class="docutils literal notranslate"><span class="pre">BaseMAB.backend</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.arm_to_expectation"><code class="docutils literal notranslate"><span class="pre">BaseMAB.arm_to_expectation</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.arm_to_status"><code class="docutils literal notranslate"><span class="pre">BaseMAB.arm_to_status</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.add_arm"><code class="docutils literal notranslate"><span class="pre">BaseMAB.add_arm()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.cold_arms"><code class="docutils literal notranslate"><span class="pre">BaseMAB.cold_arms</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.fit"><code class="docutils literal notranslate"><span class="pre">BaseMAB.fit()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.partial_fit"><code class="docutils literal notranslate"><span class="pre">BaseMAB.partial_fit()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.predict"><code class="docutils literal notranslate"><span class="pre">BaseMAB.predict()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.predict_expectations"><code class="docutils literal notranslate"><span class="pre">BaseMAB.predict_expectations()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.remove_arm"><code class="docutils literal notranslate"><span class="pre">BaseMAB.remove_arm()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.trained_arms"><code class="docutils literal notranslate"><span class="pre">BaseMAB.trained_arms</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.base_mab.BaseMAB.warm_start"><code class="docutils literal notranslate"><span class="pre">BaseMAB.warm_start()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#module-mabwiser.mab">mab</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.mab.LearningPolicy"><code class="docutils literal notranslate"><span class="pre">LearningPolicy</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.EpsilonGreedy"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.EpsilonGreedy</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.LinGreedy"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.LinGreedy</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.LinTS"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.LinTS</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.LinUCB"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.LinUCB</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.Popularity"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.Popularity</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.Random"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.Random</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.Softmax"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.Softmax</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.ThompsonSampling"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.ThompsonSampling</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.LearningPolicy.UCB1"><code class="docutils literal notranslate"><span class="pre">LearningPolicy.UCB1</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.mab.MAB"><code class="docutils literal notranslate"><span class="pre">MAB</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.arms"><code class="docutils literal notranslate"><span class="pre">MAB.arms</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.learning_policy"><code class="docutils literal notranslate"><span class="pre">MAB.learning_policy</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.neighborhood_policy"><code class="docutils literal notranslate"><span class="pre">MAB.neighborhood_policy</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.is_contextual"><code class="docutils literal notranslate"><span class="pre">MAB.is_contextual</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.seed"><code class="docutils literal notranslate"><span class="pre">MAB.seed</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.n_jobs"><code class="docutils literal notranslate"><span class="pre">MAB.n_jobs</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.backend"><code class="docutils literal notranslate"><span class="pre">MAB.backend</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.add_arm"><code class="docutils literal notranslate"><span class="pre">MAB.add_arm()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.cold_arms"><code class="docutils literal notranslate"><span class="pre">MAB.cold_arms</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.fit"><code class="docutils literal notranslate"><span class="pre">MAB.fit()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#id13"><code class="docutils literal notranslate"><span class="pre">MAB.learning_policy</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#id14"><code class="docutils literal notranslate"><span class="pre">MAB.neighborhood_policy</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.partial_fit"><code class="docutils literal notranslate"><span class="pre">MAB.partial_fit()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.predict"><code class="docutils literal notranslate"><span class="pre">MAB.predict()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.predict_expectations"><code class="docutils literal notranslate"><span class="pre">MAB.predict_expectations()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.remove_arm"><code class="docutils literal notranslate"><span class="pre">MAB.remove_arm()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.MAB.warm_start"><code class="docutils literal notranslate"><span class="pre">MAB.warm_start()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.mab.NeighborhoodPolicy"><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.NeighborhoodPolicy.Clusters"><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy.Clusters</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.NeighborhoodPolicy.KNearest"><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy.KNearest</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.NeighborhoodPolicy.LSHNearest"><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy.LSHNearest</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.NeighborhoodPolicy.Radius"><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy.Radius</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.mab.NeighborhoodPolicy.TreeBandit"><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy.TreeBandit</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#module-mabwiser.simulator">simulator</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.simulator.Simulator"><code class="docutils literal notranslate"><span class="pre">Simulator</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandits"><code class="docutils literal notranslate"><span class="pre">Simulator.bandits</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.decisions"><code class="docutils literal notranslate"><span class="pre">Simulator.decisions</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.rewards"><code class="docutils literal notranslate"><span class="pre">Simulator.rewards</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.contexts"><code class="docutils literal notranslate"><span class="pre">Simulator.contexts</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.scaler"><code class="docutils literal notranslate"><span class="pre">Simulator.scaler</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.test_size"><code class="docutils literal notranslate"><span class="pre">Simulator.test_size</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.is_ordered"><code class="docutils literal notranslate"><span class="pre">Simulator.is_ordered</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.batch_size"><code class="docutils literal notranslate"><span class="pre">Simulator.batch_size</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.evaluator"><code class="docutils literal notranslate"><span class="pre">Simulator.evaluator</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.is_quick"><code class="docutils literal notranslate"><span class="pre">Simulator.is_quick</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.logger"><code class="docutils literal notranslate"><span class="pre">Simulator.logger</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.arms"><code class="docutils literal notranslate"><span class="pre">Simulator.arms</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.arm_to_stats_total"><code class="docutils literal notranslate"><span class="pre">Simulator.arm_to_stats_total</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.arm_to_stats_train"><code class="docutils literal notranslate"><span class="pre">Simulator.arm_to_stats_train</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.arm_to_stats_test"><code class="docutils literal notranslate"><span class="pre">Simulator.arm_to_stats_test</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_arm_to_stats_avg"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_arm_to_stats_avg</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_arm_to_stats_min"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_arm_to_stats_min</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_arm_to_stats_max"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_arm_to_stats_max</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_confusion_matrices"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_confusion_matrices</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_predictions"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_predictions</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_expectations"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_expectations</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_neighborhood_size"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_neighborhood_size</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.bandit_to_arm_to_stats_neighborhoods"><code class="docutils literal notranslate"><span class="pre">Simulator.bandit_to_arm_to_stats_neighborhoods</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.test_indices"><code class="docutils literal notranslate"><span class="pre">Simulator.test_indices</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.get_arm_stats"><code class="docutils literal notranslate"><span class="pre">Simulator.get_arm_stats()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.get_stats"><code class="docutils literal notranslate"><span class="pre">Simulator.get_stats()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.plot"><code class="docutils literal notranslate"><span class="pre">Simulator.plot()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.simulator.Simulator.run"><code class="docutils literal notranslate"><span class="pre">Simulator.run()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.simulator.default_evaluator"><code class="docutils literal notranslate"><span class="pre">default_evaluator()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#module-mabwiser.utils">utils</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.Arm"><code class="docutils literal notranslate"><span class="pre">Arm</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.Constants"><code class="docutils literal notranslate"><span class="pre">Constants</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.utils.Constants.default_seed"><code class="docutils literal notranslate"><span class="pre">Constants.default_seed</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#mabwiser.utils.Constants.distance_metrics"><code class="docutils literal notranslate"><span class="pre">Constants.distance_metrics</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.Num"><code class="docutils literal notranslate"><span class="pre">Num</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.argmax"><code class="docutils literal notranslate"><span class="pre">argmax()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.argmin"><code class="docutils literal notranslate"><span class="pre">argmin()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.check_false"><code class="docutils literal notranslate"><span class="pre">check_false()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.check_true"><code class="docutils literal notranslate"><span class="pre">check_true()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.create_rng"><code class="docutils literal notranslate"><span class="pre">create_rng()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#mabwiser.utils.reset"><code class="docutils literal notranslate"><span class="pre">reset()</span></code></a></li>
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<section id="mabwiser-public-api">
<span id="mabwiser-api"></span><h1>MABWiser Public API<a class="headerlink" href="#mabwiser-public-api" title="Link to this heading"></a></h1>
<section id="module-mabwiser.base_mab">
<span id="base-mab"></span><h2>base_mab<a class="headerlink" href="#module-mabwiser.base_mab" title="Link to this heading"></a></h2>
<p>This module defines the abstract base class for contextual multi-armed bandit algorithms.</p>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mabwiser.base_mab.</span></span><span class="sig-name descname"><span class="pre">BaseMAB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rng</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">_BaseRNG</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arms</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Abstract base class for multi-armed bandits.</p>
<p>This module is not intended to be used directly, instead it declares
the basic skeleton of multi-armed bandits together with a set of parameters
that are common to every bandit algorithm.</p>
<p>It declares abstract methods that sub-classes can override to
implement specific bandit policies using:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">__init__</span></code> constructor to initialize the bandit</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">add_arm</span></code> method to add a new arm</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">fit</span></code> method for training</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method for _online learning</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">predict_expectations</span></code> method to retrieve the expectation of each arm</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">predict</span></code> method for testing to retrieve the best arm based on the policy</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">remove_arm</span></code> method for removing an arm</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> method for warm starting untrained (cold) arms</p></li>
</ul>
<p>To ensure this is the case, alpha and l2_lambda are required to be greater than zero.</p>
</div></blockquote>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.rng">
<span class="sig-name descname"><span class="pre">rng</span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.rng" title="Link to this definition"></a></dt>
<dd><p>The random number generator.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>_BaseRNG</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.arms">
<span class="sig-name descname"><span class="pre">arms</span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.arms" title="Link to this definition"></a></dt>
<dd><p>The list of all arms.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.n_jobs">
<span class="sig-name descname"><span class="pre">n_jobs</span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.n_jobs" title="Link to this definition"></a></dt>
<dd><p>This is used to specify how many concurrent processes/threads should be used for parallelized routines.
Default value is set to 1.
If set to -1, all CPUs are used.
If set to -2, all CPUs but one are used, and so on.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.backend">
<span class="sig-name descname"><span class="pre">backend</span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.backend" title="Link to this definition"></a></dt>
<dd><p>Specify a parallelization backend implementation supported in the joblib library. Supported options are:
- “loky” used by default, can induce some communication and memory overhead when exchanging input and output.
- “multiprocessing” previous process-based backend based on multiprocessing.Pool. Less robust than loky.
- “threading” is a very low-overhead backend but it suffers from the Python Global Interpreter Lock if the</p>
<blockquote>
<div><p>called function relies a lot on Python objects.</p>
</div></blockquote>
<p>Default value is None. In this case the default backend selected by joblib will be used.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>str, optional</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.arm_to_expectation">
<span class="sig-name descname"><span class="pre">arm_to_expectation</span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.arm_to_expectation" title="Link to this definition"></a></dt>
<dd><p>The dictionary of arms (keys) to their expected rewards (values).</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Dict[<a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm">Arm</a>, float]</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.arm_to_status">
<span class="sig-name descname"><span class="pre">arm_to_status</span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.arm_to_status" title="Link to this definition"></a></dt>
<dd><p>The dictionary of arms (keys) to their status (values), where the status consists of
- <code class="docutils literal notranslate"><span class="pre">is_trained</span></code>, which indicates whether an arm was <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>;
- <code class="docutils literal notranslate"><span class="pre">is_warm</span></code>, which indicates whether an arm was warm started, and therefore has a trained model associated;
- and <code class="docutils literal notranslate"><span class="pre">warm_started_by</span></code>, which indicates the arm that originally warm started this arm.
Arms that were initially warm-started and then updated with <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> will retain <code class="docutils literal notranslate"><span class="pre">is_warm</span></code> as True
with the relevant <code class="docutils literal notranslate"><span class="pre">warm_started_by</span></code> arm for tracking purposes.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Dict[<a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm">Arm</a>, dict]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.add_arm">
<span class="sig-name descname"><span class="pre">add_arm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">binarizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.add_arm" title="Link to this definition"></a></dt>
<dd><p>Introduces a new arm to the bandit.</p>
<p>Adds the new arm with zero expectations and
calls the <code class="docutils literal notranslate"><span class="pre">_uptake_new_arm()</span></code> function of the sub-class.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.cold_arms">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">cold_arms</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.cold_arms" title="Link to this definition"></a></dt>
<dd><p>List of cold arms</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.fit">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decisions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rewards</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contexts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.fit" title="Link to this definition"></a></dt>
<dd><p>Abstract method.</p>
<p>Fits the multi-armed bandit to the given
decision and reward history and corresponding contexts if any.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.partial_fit">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decisions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rewards</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contexts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.partial_fit" title="Link to this definition"></a></dt>
<dd><p>Abstract method.</p>
<p>Updates the multi-armed bandit with the given
decision and reward history and corresponding contexts if any.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.predict">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">contexts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.predict" title="Link to this definition"></a></dt>
<dd><p>Abstract method.</p>
<p>Returns the predicted arm.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.predict_expectations">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predict_expectations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">contexts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.predict_expectations" title="Link to this definition"></a></dt>
<dd><p>Abstract method.</p>
<p>Returns a dictionary from arms (keys) to their expected rewards (values).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.remove_arm">
<span class="sig-name descname"><span class="pre">remove_arm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.remove_arm" title="Link to this definition"></a></dt>
<dd><p>Removes arm from the bandit.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.trained_arms">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">trained_arms</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.trained_arms" title="Link to this definition"></a></dt>
<dd><p>List of trained arms.</p>
<p>Arms for which at least one decision has been observed are deemed trained.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="mabwiser.base_mab.BaseMAB.warm_start">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">warm_start</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arm_to_features</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_quantile</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#mabwiser.base_mab.BaseMAB.warm_start" title="Link to this definition"></a></dt>
<dd><p>Abstract method.</p>
<p>Warm starts cold arms using similar warm arms based on distances between arm features.
Only implemented for Learning Policies that make use of <code class="docutils literal notranslate"><span class="pre">_warm_start</span></code> method to copy arm information.</p>
</dd></dl>
</dd></dl>
</section>
<section id="module-mabwiser.mab">
<span id="mab"></span><h2>mab<a class="headerlink" href="#module-mabwiser.mab" title="Link to this heading"></a></h2>
<p>This module defines the public interface of the <strong>MABWiser Library</strong> providing access to the following modules:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">MAB</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LearningPolicy</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">NeighborhoodPolicy</span></code></p></li>
</ul>
</div></blockquote>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mabwiser.mab.</span></span><span class="sig-name descname"><span class="pre">LearningPolicy</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.EpsilonGreedy">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">EpsilonGreedy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.EpsilonGreedy" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>Epsilon Greedy Learning Policy.</p>
<p>This policy selects the arm with the highest expected reward with probability 1 - <span class="math notranslate nohighlight">\(\epsilon\)</span>,
and with probability <span class="math notranslate nohighlight">\(\epsilon\)</span> it selects an arm at random for exploration.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.EpsilonGreedy.epsilon">
<span class="sig-name descname"><span class="pre">epsilon</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.EpsilonGreedy.epsilon" title="Link to this definition"></a></dt>
<dd><p>The probability of selecting a random arm for exploration.
Integer or float. Must be between 0 and 1.
Default value is 0.1.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">EpsilonGreedy</span><span class="p">(</span><span class="n">epsilon</span><span class="o">=</span><span class="mf">0.25</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123456</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm1'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id0">
<span class="sig-name descname"><span class="pre">epsilon</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id0" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinGreedy">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LinGreedy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_lambda</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinGreedy" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>LinGreedy Learning Policy.</p>
<p>This policy trains a ridge regression for each arm.
Then, given a given context, it predicts a regression value.
This policy selects the arm with the highest regression value with probability 1 - <span class="math notranslate nohighlight">\(\epsilon\)</span>,
and with probability <span class="math notranslate nohighlight">\(\epsilon\)</span> it selects an arm at random for exploration.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinGreedy.epsilon">
<span class="sig-name descname"><span class="pre">epsilon</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinGreedy.epsilon" title="Link to this definition"></a></dt>
<dd><p>The probability of selecting a random arm for exploration.
Integer or float. Must be between 0 and 1.
Default value is 0.1.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinGreedy.l2_lambda">
<span class="sig-name descname"><span class="pre">l2_lambda</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinGreedy.l2_lambda" title="Link to this definition"></a></dt>
<dd><p>The regularization strength.
Integer or float. Cannot be negative.
Default value is 1.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinGreedy.scale">
<span class="sig-name descname"><span class="pre">scale</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinGreedy.scale" title="Link to this definition"></a></dt>
<dd><p>Whether to scale features to have zero mean and unit variance.
Uses StandardScaler in sklearn.preprocessing.
Default value is False.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">contexts</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">LinGreedy</span><span class="p">(</span><span class="n">epsilon</span><span class="o">=</span><span class="mf">0.5</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">,</span> <span class="n">contexts</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id1">
<span class="sig-name descname"><span class="pre">epsilon</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id1" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id2">
<span class="sig-name descname"><span class="pre">l2_lambda</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id2" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 1</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id3">
<span class="sig-name descname"><span class="pre">scale</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#id3" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 2</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinTS">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LinTS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_lambda</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinTS" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>LinTS Learning Policy</p>
<p>For each arm LinTS trains a ridge regression and
creates a multivariate normal distribution for the coefficients using the
calculated coefficients as the mean and the covariance as:</p>
<div class="math notranslate nohighlight">
\[\alpha^{2} (x_i^{T}x_i + \lambda * I_d)^{-1}\]</div>
<p>The normal distribution is randomly sampled to obtain
expected coefficients for the ridge regression for each
prediction.</p>
<p><span class="math notranslate nohighlight">\(\alpha\)</span> is a factor used to adjust how conservative the estimate is.
Higher <span class="math notranslate nohighlight">\(\alpha\)</span> values promote more exploration.</p>
<p>The multivariate normal distribution uses Cholesky decomposition to guarantee deterministic behavior.
This method requires that the covariance is a positive definite matrix.
To ensure this is the case, alpha and l2_lambda are required to be greater than zero.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinTS.alpha">
<span class="sig-name descname"><span class="pre">alpha</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinTS.alpha" title="Link to this definition"></a></dt>
<dd><p>The multiplier to determine the degree of exploration.
Integer or float. Must be greater than zero.
Default value is 1.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinTS.l2_lambda">
<span class="sig-name descname"><span class="pre">l2_lambda</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinTS.l2_lambda" title="Link to this definition"></a></dt>
<dd><p>The regularization strength.
Integer or float. Must be greater than zero.
Default value is 1.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinTS.scale">
<span class="sig-name descname"><span class="pre">scale</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinTS.scale" title="Link to this definition"></a></dt>
<dd><p>Whether to scale features to have zero mean and unit variance.
Uses StandardScaler in sklearn.preprocessing.
Default value is False.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">contexts</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">LinTS</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">,</span> <span class="n">contexts</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id4">
<span class="sig-name descname"><span class="pre">alpha</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id4" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id5">
<span class="sig-name descname"><span class="pre">l2_lambda</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id5" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 1</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id6">
<span class="sig-name descname"><span class="pre">scale</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#id6" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 2</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinUCB">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LinUCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_lambda</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinUCB" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>LinUCB Learning Policy.</p>
<p>This policy trains a ridge regression for each arm.
Then, given a given context, it predicts a regression value
and calculates the upper confidence bound of that prediction.
The arm with the highest highest upper bound is selected.</p>
<p>The UCB for each arm is calculated as:</p>
<div class="math notranslate nohighlight">
\[UCB = x_i \beta + \alpha \sqrt{(x_i^{T}x_i + \lambda * I_d)^{-1}x_i}\]</div>
<p>Where <span class="math notranslate nohighlight">\(\beta\)</span> is the matrix of the ridge regression coefficients, <span class="math notranslate nohighlight">\(\lambda\)</span> is the regularization
strength, and I_d is a dxd identity matrix where d is the number of features in the context data.</p>
<p><span class="math notranslate nohighlight">\(\alpha\)</span> is a factor used to adjust how conservative the estimate is.
Higher <span class="math notranslate nohighlight">\(\alpha\)</span> values promote more exploration.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinUCB.alpha">
<span class="sig-name descname"><span class="pre">alpha</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinUCB.alpha" title="Link to this definition"></a></dt>
<dd><p>The parameter to control the exploration.
Integer or float. Cannot be negative.
Default value is 1.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinUCB.l2_lambda">
<span class="sig-name descname"><span class="pre">l2_lambda</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinUCB.l2_lambda" title="Link to this definition"></a></dt>
<dd><p>The regularization strength.
Integer or float. Cannot be negative.
Default value is 1.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.LinUCB.scale">
<span class="sig-name descname"><span class="pre">scale</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.LinUCB.scale" title="Link to this definition"></a></dt>
<dd><p>Whether to scale features to have zero mean and unit variance.
Uses StandardScaler in sklearn.preprocessing.
Default value is False.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">contexts</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">LinUCB</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1.25</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">,</span> <span class="n">contexts</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id7">
<span class="sig-name descname"><span class="pre">alpha</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id7" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id8">
<span class="sig-name descname"><span class="pre">l2_lambda</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id8" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 1</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id9">
<span class="sig-name descname"><span class="pre">scale</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#id9" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 2</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.Popularity">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Popularity</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.Popularity" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>Randomized Popularity Learning Policy.</p>
<p>Returns a randomized popular arm for each prediction.
The probability of selection for each arm is weighted by their mean reward.
It assumes that the rewards are non-negative.</p>
<p>The probability of selection is calculated as:</p>
<div class="math notranslate nohighlight">
\[P(arm) = \frac{ \mu_i } { \Sigma{ \mu } }\]</div>
<p>where <span class="math notranslate nohighlight">\(\mu_i\)</span> is the mean reward for that arm.</p>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">Popularity</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm1'</span>
</pre></div>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.Random">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Random</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.Random" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>Random Learning Policy.</p>
<p>Returns a random arm for each prediction.
The probability of selection for each arm is uniformly at random.</p>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">Random</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.Softmax">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.Softmax" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>Softmax Learning Policy.</p>
<p>This policy selects each arm with a probability proportionate to its average reward.
The average reward is calculated as a logistic function with each probability as:</p>
<div class="math notranslate nohighlight">
\[P(arm) = \frac{ e ^ \frac{\mu_i - \max{\mu}}{ \tau } }
{ \Sigma{e ^ \frac{\mu - \max{\mu}}{ \tau }} }\]</div>
<p>where <span class="math notranslate nohighlight">\(\mu_i\)</span> is the mean reward for that arm and <span class="math notranslate nohighlight">\(\tau\)</span> is the “temperature” to determine
the degree of exploration.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.Softmax.tau">
<span class="sig-name descname"><span class="pre">tau</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.Softmax.tau" title="Link to this definition"></a></dt>
<dd><p>The temperature to control the exploration.
Integer or float. Must be greater than zero.
Default value is 1.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">tau</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id10">
<span class="sig-name descname"><span class="pre">tau</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id10" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.ThompsonSampling">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ThompsonSampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binarizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.ThompsonSampling" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>Thompson Sampling Learning Policy.</p>
<p>This policy creates a beta distribution for each arm and
then randomly samples from these distributions.
The arm with the highest sample value is selected.</p>
<p>Notice that rewards must be binary to create beta distributions.
If rewards are not binary, see the <code class="docutils literal notranslate"><span class="pre">binarizer</span></code> function.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.ThompsonSampling.binarizer">
<span class="sig-name descname"><span class="pre">binarizer</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.ThompsonSampling.binarizer" title="Link to this definition"></a></dt>
<dd><p>If rewards are not binary, a binarizer function is required.
Given an arm decision and its corresponding reward, the binarizer function
returns <cite>True/False</cite> or <cite>0/1</cite> to denote whether the decision counts
as a success, i.e., <cite>True/1</cite> based on the reward or <cite>False/0</cite> otherwise.</p>
<p>The function signature of the binarizer is:</p>
<p><code class="docutils literal notranslate"><span class="pre">binarize(arm:</span> <span class="pre">Arm,</span> <span class="pre">reward:</span> <span class="pre">Num)</span> <span class="pre">-></span> <span class="pre">True/False</span> <span class="pre">or</span> <span class="pre">0/1</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Callable</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">ThompsonSampling</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">arm_to_threshold</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Arm1'</span><span class="p">:</span><span class="mi">10</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">:</span><span class="mi">10</span><span class="p">}</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">binarize</span><span class="p">(</span><span class="n">arm</span><span class="p">,</span> <span class="n">reward</span><span class="p">):</span> <span class="k">return</span> <span class="n">reward</span> <span class="o">></span> <span class="n">arm_to_threshold</span><span class="p">[</span><span class="n">arm</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">ThompsonSampling</span><span class="p">(</span><span class="n">binarizer</span><span class="o">=</span><span class="n">binarize</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id11">
<span class="sig-name descname"><span class="pre">binarizer</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Callable</span></em><a class="headerlink" href="#id11" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.UCB1">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">UCB1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.UCB1" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">NamedTuple</span></code></p>
<p>Upper Confidence Bound1 Learning Policy.</p>
<p>This policy calculates an upper confidence bound for the mean reward of each arm.
It greedily selects the arm with the highest upper confidence bound.</p>
<p>The UCB for each arm is calculated as:</p>
<div class="math notranslate nohighlight">
\[UCB = \mu_i + \alpha \times \sqrt[]{\frac{2 \times log(N)}{n_i}}\]</div>
<p>Where <span class="math notranslate nohighlight">\(\mu_i\)</span> is the mean for that arm,
<span class="math notranslate nohighlight">\(N\)</span> is the total number of trials, and
<span class="math notranslate nohighlight">\(n_i\)</span> is the number of times the arm has been selected.</p>
<p><span class="math notranslate nohighlight">\(\alpha\)</span> is a factor used to adjust how conservative the estimate is.
Higher <span class="math notranslate nohighlight">\(\alpha\)</span> values promote more exploration.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.LearningPolicy.UCB1.alpha">
<span class="sig-name descname"><span class="pre">alpha</span></span><a class="headerlink" href="#mabwiser.mab.LearningPolicy.UCB1.alpha" title="Link to this definition"></a></dt>
<dd><p>The parameter to control the exploration.
Integer of float. Cannot be negative.
Default value is 1.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Num</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">list_of_arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">list_of_arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">UCB1</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1.25</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm2'</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="id12">
<span class="sig-name descname"><span class="pre">alpha</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#id12" title="Link to this definition"></a></dt>
<dd><p>Alias for field number 0</p>
</dd></dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="mabwiser.mab.MAB">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mabwiser.mab.</span></span><span class="sig-name descname"><span class="pre">MAB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arms</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#mabwiser.utils.Arm" title="mabwiser.utils.Arm"><span class="pre">Arm</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_policy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LearningPolicyType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighborhood_policy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">NeighborhoodPolicyType</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">123456</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mabwiser.mab.MAB" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p><strong>MABWiser: Contextual Multi-Armed Bandit Library</strong></p>
<p>MABWiser is a research library for fast prototyping of multi-armed bandit algorithms.
It supports <strong>context-free</strong>, <strong>parametric</strong> and <strong>non-parametric</strong> <strong>contextual</strong> bandit models.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.arms">
<span class="sig-name descname"><span class="pre">arms</span></span><a class="headerlink" href="#mabwiser.mab.MAB.arms" title="Link to this definition"></a></dt>
<dd><p>The list of all the arms available for decisions. Arms can be integers, strings, etc.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>list</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.learning_policy">
<span class="sig-name descname"><span class="pre">learning_policy</span></span><a class="headerlink" href="#mabwiser.mab.MAB.learning_policy" title="Link to this definition"></a></dt>
<dd><p>The learning policy.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>LearningPolicyType</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.neighborhood_policy">
<span class="sig-name descname"><span class="pre">neighborhood_policy</span></span><a class="headerlink" href="#mabwiser.mab.MAB.neighborhood_policy" title="Link to this definition"></a></dt>
<dd><p>The neighborhood policy.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>NeighborhoodPolicyType</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.is_contextual">
<span class="sig-name descname"><span class="pre">is_contextual</span></span><a class="headerlink" href="#mabwiser.mab.MAB.is_contextual" title="Link to this definition"></a></dt>
<dd><p>True if contextual policy is given, false otherwise. This is a read-only data field.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.seed">
<span class="sig-name descname"><span class="pre">seed</span></span><a class="headerlink" href="#mabwiser.mab.MAB.seed" title="Link to this definition"></a></dt>
<dd><p>The random seed to initialize the internal random number generator. This is a read-only data field.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>numbers.Rational</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.n_jobs">
<span class="sig-name descname"><span class="pre">n_jobs</span></span><a class="headerlink" href="#mabwiser.mab.MAB.n_jobs" title="Link to this definition"></a></dt>
<dd><p>This is used to specify how many concurrent processes/threads should be used for parallelized routines.
Default value is set to 1.
If set to -1, all CPUs are used.
If set to -2, all CPUs but one are used, and so on.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="mabwiser.mab.MAB.backend">
<span class="sig-name descname"><span class="pre">backend</span></span><a class="headerlink" href="#mabwiser.mab.MAB.backend" title="Link to this definition"></a></dt>
<dd><p>Specify a parallelization backend implementation supported in the joblib library. Supported options are:
- “loky” used by default, can induce some communication and memory overhead when exchanging input and</p>
<blockquote>
<div><p>output data with the worker Python processes.</p>
</div></blockquote>
<ul class="simple">
<li><p>“multiprocessing” previous process-based backend based on multiprocessing.Pool. Less robust than loky.</p></li>
<li><p>“threading” is a very low-overhead backend but, it suffers from the Python Global Interpreter Lock if the
called function relies a lot on Python objects.</p></li>
</ul>
<p>Default value is None. In this case the default backend selected by joblib will be used.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>str, optional</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span>
<span class="gp">>>> </span><span class="n">arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">EpsilonGreedy</span><span class="p">(</span><span class="n">epsilon</span><span class="o">=</span><span class="mf">0.25</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123456</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm1'</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">add_arm</span><span class="p">(</span><span class="s1">'Arm3'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">([</span><span class="s1">'Arm3'</span><span class="p">],</span> <span class="p">[</span><span class="mi">30</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">mab</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="go">'Arm3'</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="p">,</span> <span class="n">NeighborhoodPolicy</span>
<span class="gp">>>> </span><span class="n">arms</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">decisions</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">,</span> <span class="s1">'Arm1'</span><span class="p">,</span> <span class="s1">'Arm2'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">contexts</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">contextual_mab</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">arms</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="o">.</span><span class="n">EpsilonGreedy</span><span class="p">(),</span> <span class="n">NeighborhoodPolicy</span><span class="o">.</span><span class="n">KNearest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">contextual_mab</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="p">,</span> <span class="n">rewards</span><span class="p">,</span> <span class="n">contexts</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">contextual_mab</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="go">['Arm2', 'Arm2', 'Arm2']</span>