import inspect from operator import itemgetter from .automl_algorithm import AutoMLAlgorithm, AutoMLAlgorithmException from evalml.pipelines.components.utils import handle_component_class [docs]class IterativeAlgorithm(AutoMLAlgorithm): """An automl algorithm which first fits a base round of pipelines with default parameters, then does a round of parameter tuning on each pipeline in order of performance.""" [docs] def __init__(self, allowed_pipelines=None, max_pipelines=None, tuner_class=None, random_state=0, pipelines_per_batch=5, n_jobs=-1, # TODO remove number_features=None): # TODO remove """An automl algorithm which first fits a base round of pipelines with default parameters, then does a round of parameter tuning on each pipeline in order of performance. Arguments: allowed_pipelines (list(class)): A list of PipelineBase subclasses indicating the pipelines allowed in the search. The default of None indicates all pipelines for this problem type are allowed. max_pipelines (int): The maximum number of pipelines to be evaluated. tuner_class (class): A subclass of Tuner, to be used to find parameters for each pipeline. The default of None indicates the SKOptTuner will be used. random_state (int, np.random.RandomState): The random seed/state. Defaults to 0. pipelines_per_batch (int): the number of pipelines to be evaluated in each batch, after the first batch. n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines. number_features (int): The number of columns in the input features. """ super().__init__(allowed_pipelines=allowed_pipelines, max_pipelines=max_pipelines, tuner_class=tuner_class, random_state=random_state) self.pipelines_per_batch = pipelines_per_batch self.n_jobs = n_jobs self.number_features = number_features self._first_batch_results = [] [docs] def next_batch(self): """Get the next batch of pipelines to evaluate Returns: list(PipelineBase): a list of instances of PipelineBase subclasses, ready to be trained and evaluated. """ if self._batch_number == 1: if len(self._first_batch_results) == 0: raise AutoMLAlgorithmException('No results were reported from the first batch') self._first_batch_results = sorted(self._first_batch_results, key=itemgetter(0)) next_batch = [] if self._batch_number == 0: next_batch = [pipeline_class(parameters=self._transform_parameters(pipeline_class, {})) for pipeline_class in self.allowed_pipelines] else: idx = (self._batch_number - 1) % len(self._first_batch_results) pipeline_class = self._first_batch_results[idx][1] for i in range(self.pipelines_per_batch): proposed_parameters = self._tuners[pipeline_class.name].propose() next_batch.append(pipeline_class(parameters=self._transform_parameters(pipeline_class, proposed_parameters))) self._pipeline_number += len(next_batch) self._batch_number += 1 return next_batch [docs] def add_result(self, score_to_minimize, pipeline): """Register results from evaluating a pipeline Arguments: score_to_minimize (float): The score obtained by this pipeline on the primary objective, converted so that lower values indicate better pipelines. pipeline (PipelineBase): The trained pipeline object which was used to compute the score. """ super().add_result(score_to_minimize, pipeline) if self.batch_number == 1: self._first_batch_results.append((score_to_minimize, pipeline.__class__)) def _transform_parameters(self, pipeline_class, proposed_parameters): """Given a pipeline parameters dict, make sure n_jobs and number_features are set.""" parameters = {} component_graph = [handle_component_class(c) for c in pipeline_class.component_graph] for component_class in component_graph: component_parameters = proposed_parameters.get(component_class.name, {}) init_params = inspect.signature(component_class.__init__).parameters # Inspects each component and adds the following parameters when needed if 'n_jobs' in init_params: component_parameters['n_jobs'] = self.n_jobs if 'number_features' in init_params: component_parameters['number_features'] = self.number_features parameters[component_class.name] = component_parameters return parameters