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<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>selection.cost_computer</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.cost_computer.BaseCostComputer"><code class="flex name class">
<span>class <span class="ident">BaseCostComputer</span></span>
</code></dt>
<dd>
<div class="desc"><p>Initializes the BaseComputer object. This is the base class for all computer objects.</p>
<h2 id="parameters">Parameters</h2>
<p>None</p>
<h2 id="returns">Returns</h2>
<p>None</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class BaseCostComputer(BaseComputer):
def predict(self, questions, model_answers):
"""
Predict the cost of the given model answers.
Args:
questions (list): List of questions.
model_answers (list): List of model answers.
Returns:
list: A list of predictions. Each question should have a corresponding prediction for each model.
"""
raise NotImplementedError</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="selection.base_computer.BaseComputer" href="base_computer.html#selection.base_computer.BaseComputer">BaseComputer</a></li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="selection.classification.ClassificationCostComputer" href="classification.html#selection.classification.ClassificationCostComputer">ClassificationCostComputer</a></li>
<li><a title="selection.cost_computer.GroundTruthCostComputer" href="#selection.cost_computer.GroundTruthCostComputer">GroundTruthCostComputer</a></li>
<li><a title="selection.open_form.OpenFormCostComputer" href="open_form.html#selection.open_form.OpenFormCostComputer">OpenFormCostComputer</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="selection.cost_computer.BaseCostComputer.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, questions, model_answers)</span>
</code></dt>
<dd>
<div class="desc"><p>Predict the cost of the given model answers.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>questions</code></strong> : <code>list</code></dt>
<dd>List of questions.</dd>
<dt><strong><code>model_answers</code></strong> : <code>list</code></dt>
<dd>List of model answers.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>list</code></dt>
<dd>A list of predictions. Each question should have a corresponding prediction for each model.</dd>
</dl></div>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="selection.base_computer.BaseComputer" href="base_computer.html#selection.base_computer.BaseComputer">BaseComputer</a></b></code>:
<ul class="hlist">
<li><code><a title="selection.base_computer.BaseComputer.fit" href="base_computer.html#selection.base_computer.BaseComputer.fit">fit</a></code></li>
<li><code><a title="selection.base_computer.BaseComputer.is_independent" href="base_computer.html#selection.base_computer.BaseComputer.is_independent">is_independent</a></code></li>
<li><code><a title="selection.base_computer.BaseComputer.trigger_training" href="base_computer.html#selection.base_computer.BaseComputer.trigger_training">trigger_training</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="selection.cost_computer.GroundTruthCostComputer"><code class="flex name class">
<span>class <span class="ident">GroundTruthCostComputer</span></span>
<span>(</span><span>noise_before_run, noise_after_run, assume_constant=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Initialize the CostComputer object.
Computes the cost by adding noise to the ground truth cost values and then fitting a linear model
to the noisy values.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>noise_before_run</code></strong> : <code>float</code></dt>
<dd>The noise value before running model computation.</dd>
<dt><strong><code>noise_after_run</code></strong> : <code>float</code></dt>
<dd>The noise value after running model computation.</dd>
<dt><strong><code>assume_constant</code></strong> : <code>bool</code>, optional</dt>
<dd>Flag indicating whether to set computed cost to a constant for each model.
Defaults to False.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GroundTruthCostComputer(BaseCostComputer):
def __init__(self, noise_before_run, noise_after_run, assume_constant=False):
"""
Initialize the CostComputer object.
Computes the cost by adding noise to the ground truth cost values and then fitting a linear model
to the noisy values.
Args:
noise_before_run (float): The noise value before running model computation.
noise_after_run (float): The noise value after running model computation.
assume_constant (bool, optional): Flag indicating whether to set computed cost to a constant for each model.
Defaults to False.
"""
super().__init__()
self.noise_before_run = noise_before_run
self.noise_after_run = noise_after_run
self.assume_constant = assume_constant
self.cost_mapping = None
def fit(self, questions, model_answers, measure):
self.cost_mapping = dict()
noisy_values = []
for measure_value in measure:
value = [
[float(measure_value[i] + np.random.normal(0, self.noise_before_run)),
float(measure_value[i] + np.random.normal(0, self.noise_after_run))]
for i in range(len(measure_value))
]
noisy_values.append(value)
if self.assume_constant:
self.average_costs = np.mean(measure, axis=0)
noisy_values = np.array(noisy_values)
actual_values = np.zeros(noisy_values.shape)
for model in range(noisy_values.shape[1]):
for i in range(noisy_values.shape[2]):
linear_model = LinearRegression()
linear_model.fit(noisy_values[:, model, i].reshape(-1, 1), measure[:, model])
actual_values[:, model, i] = linear_model.predict(noisy_values[:, model, i].reshape(-1, 1))
for q, a in zip(questions, actual_values):
self.cost_mapping[q] = a
def predict(self, questions, model_answers):
qualities = []
for question, model_answer in zip(questions, model_answers):
if not self.assume_constant:
value = self.cost_mapping[question]
value = np.array([
value[i][0] if answer is None else value[i][1] for i, answer in enumerate(model_answer)
])
else:
value = self.average_costs
qualities.append(value)
return np.array(qualities)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="selection.cost_computer.BaseCostComputer" href="#selection.cost_computer.BaseCostComputer">BaseCostComputer</a></li>
<li><a title="selection.base_computer.BaseComputer" href="base_computer.html#selection.base_computer.BaseComputer">BaseComputer</a></li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="selection.cost_computer.BaseCostComputer" href="#selection.cost_computer.BaseCostComputer">BaseCostComputer</a></b></code>:
<ul class="hlist">
<li><code><a title="selection.cost_computer.BaseCostComputer.fit" href="base_computer.html#selection.base_computer.BaseComputer.fit">fit</a></code></li>
<li><code><a title="selection.cost_computer.BaseCostComputer.is_independent" href="base_computer.html#selection.base_computer.BaseComputer.is_independent">is_independent</a></code></li>
<li><code><a title="selection.cost_computer.BaseCostComputer.predict" href="#selection.cost_computer.BaseCostComputer.predict">predict</a></code></li>
<li><code><a title="selection.cost_computer.BaseCostComputer.trigger_training" href="base_computer.html#selection.base_computer.BaseComputer.trigger_training">trigger_training</a></code></li>
</ul>
</li>
</ul>
</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.cost_computer.BaseCostComputer" href="#selection.cost_computer.BaseCostComputer">BaseCostComputer</a></code></h4>
<ul class="">
<li><code><a title="selection.cost_computer.BaseCostComputer.predict" href="#selection.cost_computer.BaseCostComputer.predict">predict</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="selection.cost_computer.GroundTruthCostComputer" href="#selection.cost_computer.GroundTruthCostComputer">GroundTruthCostComputer</a></code></h4>
</li>
</ul>
</li>
</ul>
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