Evalml: لا يمكن تحديد معلمات تشعبية بقائمة في AutoMLSearch

ريبرو:

from evalml.demos import load_breast_cancer
from evalml.pipelines import BinaryClassificationPipeline
from evalml.automl import AutoMLSearch

class PipeLine(BinaryClassificationPipeline):
    component_graph = ["Drop Columns Transformer", "Random Forest Classifier"]

X , y = load_breast_cancer()

automl = AutoMLSearch(X, y, problem_type="binary", allowed_pipelines=[PipeLine],
                      pipeline_parameters={"Drop Columns Transformer": {"columns": ["mean texture"]}})
automl.search()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~/sources/evalml/evalml/pipelines/component_graph.py in instantiate(self, parameters)
     77             try:
---> 78                 new_component = component_class(**component_parameters, random_seed=self.random_seed)
     79             except (ValueError, TypeError) as e:

~/sources/evalml/evalml/pipelines/components/transformers/column_selectors.py in __init__(self, columns, random_seed, **kwargs)
     15         if columns and not isinstance(columns, list):
---> 16             raise ValueError(f"Parameter columns must be a list. Received {type(columns)}.")
     17 

ValueError: Parameter columns must be a list. Received <class 'str'>.

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
<ipython-input-21-b4819258a317> in <module>
     10 automl = AutoMLSearch(X, y, problem_type="binary", allowed_pipelines=[PipeLine],
     11                       pipeline_parameters={"Drop Columns Transformer": {"columns": ["mean texture"]}})
---> 12 automl.search()

~/sources/evalml/evalml/automl/automl_search.py in search(self, show_iteration_plot)
    490         logger.info("Allowed model families: %s\n" % ", ".join([model.value for model in self.allowed_model_families]))
    491         self.search_iteration_plot = None
--> 492         if self.plot:
    493             self.search_iteration_plot = self.plot.search_iteration_plot(interactive_plot=show_iteration_plot)
    494 

~/sources/evalml/evalml/automl/automl_algorithm/iterative_algorithm.py in next_batch(self)
     63         next_batch = []
     64         if self._batch_number == 0:
---> 65             next_batch = [pipeline_class(parameters=self._transform_parameters(pipeline_class, {}), random_seed=self.random_seed)
     66                           for pipeline_class in self.allowed_pipelines]
     67 

~/sources/evalml/evalml/automl/automl_algorithm/iterative_algorithm.py in <listcomp>(.0)
     63         next_batch = []
     64         if self._batch_number == 0:
---> 65             next_batch = [pipeline_class(parameters=self._transform_parameters(pipeline_class, {}), random_seed=self.random_seed)
     66                           for pipeline_class in self.allowed_pipelines]
     67 

~/sources/evalml/evalml/pipelines/classification_pipeline.py in __init__(self, parameters, random_seed)
     23         """
     24         self._encoder = LabelEncoder()
---> 25         super().__init__(parameters, random_seed=random_seed)
     26 
     27     def fit(self, X, y):

~/sources/evalml/evalml/pipelines/pipeline_base.py in __init__(self, parameters, random_seed)
     77         else:
     78             self._component_graph = ComponentGraph(component_dict=self.component_graph, random_seed=self.random_seed)
---> 79         self._component_graph.instantiate(parameters)
     80 
     81         self.input_feature_names = {}

~/sources/evalml/evalml/pipelines/component_graph.py in instantiate(self, parameters)
     80                 self._is_instantiated = False
     81                 err = "Error received when instantiating component {} with the following arguments {}".format(component_name, component_parameters)
---> 82                 raise ValueError(err) from e
     83 
     84             component_instances[component_name] = new_component

ValueError: Error received when instantiating component Drop Columns Transformer with the following arguments {'columns': 'mean texture'}

يحدد IterativeAlgorithm العنصر الأول من قائمة columns وهو ليس السلوك المقصود.