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<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>selection.base_algorithm</code></h1>
</header>
<section id="section-intro">
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="selection.base_algorithm.Algorithm"><code class="flex name class">
<span>class <span class="ident">Algorithm</span></span>
<span>(</span><span>quality_computer, cost_computer, models, max_expected_cost, strategies, rounding_digits=8)</span>
</code></dt>
<dd>
<div class="desc"><p>Initializes the BaseAlgorithm object. This serves as the base class for all selection strategies.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>quality_computer</code></strong></dt>
<dd>The quality computer object used for evaluating model quality.</dd>
<dt><strong><code>cost_computer</code></strong></dt>
<dd>The cost computer object used for evaluating model cost.</dd>
<dt><strong><code>models</code></strong></dt>
<dd>A list of models to be considered for selection.</dd>
<dt><strong><code>max_expected_cost</code></strong></dt>
<dd>The maximum expected cost allowed for the selection strategy.</dd>
<dt><strong><code>strategies</code></strong></dt>
<dd>The strategies to be used for hyperparameter optimization.</dd>
<dt><strong><code>rounding_digits</code></strong></dt>
<dd>The number of digits to round the results to (default: 8).</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Algorithm:
def __init__(self, quality_computer, cost_computer, models,
max_expected_cost, strategies, rounding_digits=8):
"""
Initializes the BaseAlgorithm object. This serves as the base class for all selection strategies.
Args:
quality_computer: The quality computer object used for evaluating model quality.
cost_computer: The cost computer object used for evaluating model cost.
models: A list of models to be considered for selection.
max_expected_cost: The maximum expected cost allowed for the selection strategy.
strategies: The strategies to be used for hyperparameter optimization.
rounding_digits: The number of digits to round the results to (default: 8).
"""
self.quality_computer = quality_computer
self.cost_computer = cost_computer
self.models = models
self.max_expected_cost = max_expected_cost
self.strategies = strategies
self.rounding_digits = rounding_digits
def predict(self, questions, model_answers=None):
"""
Predicts the model to run for the given questions.
Args:
questions (list): A list representing the questions.
model_answers (list, optional): A list representing the model answers. Defaults to None.
Returns:
list: A list of models to run for the given questions.
"""
raise NotImplementedError
def select_answer(self, questions, model_answers):
"""
Selects the best answer from a list of model answers based on the given questions.
Args:
questions (list): A list representing the questions.
model_answers (list): A list representing the model answers.
Raises:
NotImplementedError: This method is meant to be overridden by subclasses.
Returns:
List[str]: The name of the selected model for each question.
"""
raise NotImplementedError
def fit(self, questions, model_answers,
ground_truth_qualities=None, ground_truth_costs=None):
"""
Fit the algorithm to the given data.
Args:
questions: A list of questions.
model_answers: A list of model answers corresponding to the questions.
ground_truth_qualities: (optional) A list of ground truth qualities for the model answers.
ground_truth_costs: (optional) A list of ground truth costs for the model answers.
"""
raise NotImplementedError</code></pre>
</details>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="selection.baseline_cascader.BaselineCascader" href="baseline_cascader.html#selection.baseline_cascader.BaselineCascader">BaselineCascader</a></li>
<li><a title="selection.cascade_router.CascadeRouter" href="cascade_router.html#selection.cascade_router.CascadeRouter">CascadeRouter</a></li>
<li><a title="selection.router.Router" href="router.html#selection.router.Router">Router</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="selection.base_algorithm.Algorithm.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, questions, model_answers, ground_truth_qualities=None, ground_truth_costs=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Fit the algorithm to the given data.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>questions</code></strong></dt>
<dd>A list of questions.</dd>
<dt><strong><code>model_answers</code></strong></dt>
<dd>A list of model answers corresponding to the questions.</dd>
<dt><strong><code>ground_truth_qualities</code></strong></dt>
<dd>(optional) A list of ground truth qualities for the model answers.</dd>
<dt><strong><code>ground_truth_costs</code></strong></dt>
<dd>(optional) A list of ground truth costs for the model answers.</dd>
</dl></div>
</dd>
<dt id="selection.base_algorithm.Algorithm.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, questions, model_answers=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Predicts the model to run for the given questions.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>questions</code></strong> : <code>list</code></dt>
<dd>A list representing the questions.</dd>
<dt><strong><code>model_answers</code></strong> : <code>list</code>, optional</dt>
<dd>A list representing the model answers. Defaults to None.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>list</code></dt>
<dd>A list of models to run for the given questions.</dd>
</dl></div>
</dd>
<dt id="selection.base_algorithm.Algorithm.select_answer"><code class="name flex">
<span>def <span class="ident">select_answer</span></span>(<span>self, questions, model_answers)</span>
</code></dt>
<dd>
<div class="desc"><p>Selects the best answer from a list of model answers based on the given questions.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>questions</code></strong> : <code>list</code></dt>
<dd>A list representing the questions.</dd>
<dt><strong><code>model_answers</code></strong> : <code>list</code></dt>
<dd>A list representing the model answers.</dd>
</dl>
<h2 id="raises">Raises</h2>
<dl>
<dt><code>NotImplementedError</code></dt>
<dd>This method is meant to be overridden by subclasses.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>List[str]</code></dt>
<dd>The name of the selected model for each question.</dd>
</dl></div>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="selection" href="index.html">selection</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="selection.base_algorithm.Algorithm" href="#selection.base_algorithm.Algorithm">Algorithm</a></code></h4>
<ul class="">
<li><code><a title="selection.base_algorithm.Algorithm.fit" href="#selection.base_algorithm.Algorithm.fit">fit</a></code></li>
<li><code><a title="selection.base_algorithm.Algorithm.predict" href="#selection.base_algorithm.Algorithm.predict">predict</a></code></li>
<li><code><a title="selection.base_algorithm.Algorithm.select_answer" href="#selection.base_algorithm.Algorithm.select_answer">select_answer</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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