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<article id="content">
<header>
<h1 class="title">Module <code>selection.router</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.router.Router"><code class="flex name class">
<span>class <span class="ident">Router</span></span>
<span>(</span><span>quality_computer, cost_computer, models, max_expected_cost, rounding_digits=8, strategies=[<selection.lambda_strategy.ConstantStrategy object>])</span>
</code></dt>
<dd>
<div class="desc"><p>Initialize the Router object.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>quality_computer</code></strong></dt>
<dd>The quality computer object used for computing model qualities.</dd>
<dt><strong><code>cost_computer</code></strong></dt>
<dd>The cost computer object used for computing model costs.</dd>
<dt><strong><code>models</code></strong></dt>
<dd>A list of model objects.</dd>
<dt><strong><code>max_expected_cost</code></strong></dt>
<dd>The maximum expected cost.</dd>
<dt><strong><code>rounding_digits</code></strong></dt>
<dd>The number of digits to round the results to (default: 8).</dd>
<dt><strong><code>strategies</code></strong></dt>
<dd>A list of strategy objects for hyperparameter optimization
(default: [ConstantStrategy(10000, 50)]).</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Router(Algorithm):
def __init__(self, quality_computer, cost_computer, models, max_expected_cost,
rounding_digits=8, strategies=[ConstantStrategy(10000, 50)]):
"""
Initialize the Router object.
Args:
quality_computer: The quality computer object used for computing model qualities.
cost_computer: The cost computer object used for computing model costs.
models: A list of model objects.
max_expected_cost: The maximum expected cost.
rounding_digits: The number of digits to round the results to (default: 8).
strategies: A list of strategy objects for hyperparameter optimization
(default: [ConstantStrategy(10000, 50)]).
"""
super().__init__(quality_computer, cost_computer, models,
max_expected_cost, strategies, rounding_digits)
self.lambda_ = None
self.qualities = None
self.costs = None
def compute_budget(self, qualities, costs, ground_truth_qualities=None, ground_truth_costs=None):
"""
Computes the optimal lambdas for model selection based on the given qualities and costs.
Args:
qualities (list): List of qualities for each model.
costs (list): List of costs for each model.
ground_truth_qualities (list, optional): List of ground truth qualities for each model.
Defaults to None.
ground_truth_costs (list, optional): List of ground truth costs for each model.
Defaults to None.
Returns:
None
"""
best_lambda = None
best_quality = None
for strategy in self.strategies:
lambdas, cost, quality = strategy.compute_lambdas([0], self._execute, self.max_expected_cost,
(qualities, costs, ground_truth_qualities, ground_truth_costs))
if best_lambda is None or cost < self.max_expected_cost and (best_lambda is None or quality > best_quality):
best_lambda = lambdas[0]
best_quality = quality
self.lambda_ = best_lambda
output_cheap = self._execute(self.lambda_, qualities, costs, ground_truth_qualities,
ground_truth_costs, cheapest=True, most_expensive=False)
output_expensive = self._execute(self.lambda_, qualities, costs, ground_truth_qualities,
ground_truth_costs, cheapest=False, most_expensive=True)
cost_cheap = output_cheap['cost']
cost_expensive = output_expensive['cost']
if cost_cheap == cost_expensive:
self.gamma = 1
else:
self.gamma = (cost_expensive - self.max_expected_cost) / (cost_expensive - cost_cheap)
self.gamma = min(1, max(0, self.gamma))
def get_lambdas(self):
return [self.lambda_]
def fit(self, questions, model_answers, ground_truth_qualities=None, ground_truth_costs=None):
self.quality_computer.trigger_training(True)
self.cost_computer.trigger_training(True)
qualities, _ = self.quality_computer.predict(questions, [[None] * len(self.models)] * len(questions))
costs = self.cost_computer.predict(questions, [[None] * len(self.models)] * len(questions))
self.compute_budget(qualities, costs, ground_truth_qualities, ground_truth_costs)
self.quality_computer.trigger_training(False)
self.cost_computer.trigger_training(False)
def predict(self, questions, model_answers=None):
qualities_question, _ = self.quality_computer.predict(questions, model_answers)
costs_question = self.cost_computer.predict(questions, model_answers)
return [self._predict_model(qualities_question[i], costs_question[i], cheapest=False, most_expensive=False) for i in range(len(questions))]
def select_answer(self, questions, model_answers):
models_selected = []
for i, model_answer in enumerate(model_answers):
indices_with_answer = [j for j in range(len(model_answer)) if model_answer[j] is not None]
if len(indices_with_answer) == 0:
models_selected.append(None)
else:
models_selected.append(self.models[indices_with_answer[0]])
return models_selected
def _predict_model(self, qualities_question, costs_model,
lambda_=None, cheapest=True, most_expensive=False):
"""
Predicts the best model based on the qualities of the question and the costs of the models.
Args:
qualities_question (numpy.ndarray): An array representing the qualities of the question.
costs_model (numpy.ndarray): An array representing the costs of the models.
lambda_ (float, optional): A parameter used to adjust the importance of qualities and costs. If not provided, the default value from the class instance will be used.
cheapest (bool, optional): If True, the cheapest model among the best models will be selected. Default is True.
most_expensive (bool, optional): If True, the most expensive model among the best models will be selected. Default is False.
Returns:
best_model: The best model based on the given qualities and costs.
"""
if lambda_ is None:
lambda_ = self.lambda_
highest = np.round(qualities_question - lambda_ * costs_model, self.rounding_digits)
# check which model has the highest value. If multiple, return the cheapest
max_value = np.max(highest)
# collect all models with the highest value
best_models = np.where(highest == max_value)[0]
# select the cheapest model
if len(best_models) > 1:
best_model_cheapest = best_models[np.argmin(costs_model[best_models])]
best_model_expensive = best_models[np.argmax(costs_model[best_models])]
if cheapest:
best_model = best_model_cheapest
elif most_expensive:
best_model = best_model_expensive
elif np.random.rand() < self.gamma:
best_model = best_model_expensive
else:
best_model = best_model_cheapest
else:
best_model = best_models[0]
best_model = self.models[best_model]
return best_model
def _execute(self, lambda_, qualities, costs, ground_truth_qualities=None, ground_truth_costs=None,
cheapest=True, most_expensive=False):
"""
Executes the model selection process based on the given parameters.
Args:
lambda_ (float or list): The regularization parameter(s) for the model selection.
qualities (list): The list of quality values for each model.
costs (list): The list of cost values for each model.
ground_truth_qualities (list, optional): The list of ground truth quality values for each model. Default is None.
ground_truth_costs (list, optional): The list of ground truth cost values for each model. Default is None.
cheapest (bool, optional): Flag indicating whether to select the cheapest model. Default is True.
most_expensive (bool, optional): Flag indicating whether to select the most expensive model. Default is False.
Returns:
- dict: A dictionary containing the following keys:
- 'cost': The mean cost value of the selected models.
- 'quality': The mean quality value of the selected models.
- 'models_run': A Counter object containing the count of each model selected.
"""
if isinstance(lambda_, list):
lambda_ = lambda_[0]
all_costs = []
all_qualities = []
models_run = []
for i in range(len(qualities)):
best_model = self._predict_model(qualities[i], costs[i], lambda_, cheapest, most_expensive)
best_model_index = self.models.index(best_model)
models_run.append(best_model_index)
if ground_truth_costs is not None:
all_costs.append(ground_truth_costs[i][best_model_index])
else:
all_costs.append(costs[i][best_model_index])
if ground_truth_qualities is not None:
all_qualities.append(ground_truth_qualities[i][best_model_index])
else:
all_qualities.append(qualities[i][best_model_index])
return {
'cost': np.mean(all_costs),
'quality': np.mean(all_qualities),
'models_run': Counter(models_run)
}</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="selection.base_algorithm.Algorithm" href="base_algorithm.html#selection.base_algorithm.Algorithm">Algorithm</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="selection.router.Router.compute_budget"><code class="name flex">
<span>def <span class="ident">compute_budget</span></span>(<span>self, qualities, costs, ground_truth_qualities=None, ground_truth_costs=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Computes the optimal lambdas for model selection based on the given qualities and costs.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>qualities</code></strong> : <code>list</code></dt>
<dd>List of qualities for each model.</dd>
<dt><strong><code>costs</code></strong> : <code>list</code></dt>
<dd>List of costs for each model.</dd>
<dt><strong><code>ground_truth_qualities</code></strong> : <code>list</code>, optional</dt>
<dd>List of ground truth qualities for each model.
Defaults to None.</dd>
<dt><strong><code>ground_truth_costs</code></strong> : <code>list</code>, optional</dt>
<dd>List of ground truth costs for each model.
Defaults to None.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>None</p></div>
</dd>
<dt id="selection.router.Router.get_lambdas"><code class="name flex">
<span>def <span class="ident">get_lambdas</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="selection.base_algorithm.Algorithm" href="base_algorithm.html#selection.base_algorithm.Algorithm">Algorithm</a></b></code>:
<ul class="hlist">
<li><code><a title="selection.base_algorithm.Algorithm.fit" href="base_algorithm.html#selection.base_algorithm.Algorithm.fit">fit</a></code></li>
<li><code><a title="selection.base_algorithm.Algorithm.predict" href="base_algorithm.html#selection.base_algorithm.Algorithm.predict">predict</a></code></li>
<li><code><a title="selection.base_algorithm.Algorithm.select_answer" href="base_algorithm.html#selection.base_algorithm.Algorithm.select_answer">select_answer</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.router.Router" href="#selection.router.Router">Router</a></code></h4>
<ul class="">
<li><code><a title="selection.router.Router.compute_budget" href="#selection.router.Router.compute_budget">compute_budget</a></code></li>
<li><code><a title="selection.router.Router.get_lambdas" href="#selection.router.Router.get_lambdas">get_lambdas</a></code></li>
</ul>
</li>
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</li>
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