์ฌํ:
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'}
IterativeAlgorithm
๋ ์๋ํ ๋์์ด ์๋ columns
๋ชฉ๋ก์ ์ฒซ ๋ฒ์งธ ์์๋ฅผ ์ ํํฉ๋๋ค.
์ด ๋ฌธ์ ๋ IterativeAlgorithm
๊ฐ _transform_parameters
ํธ์ถํ๊ณ ๋งค๊ฐ๋ณ์์ ์์ถ์ ํ๋ ค๊ณ ํ ๋ ๋ฐ์ํฉ๋๋ค. ์ด ์ฝ๋๋ ์ฌ์ฉ์๊ฐ pipeline_parameters
๋ฅผ ์ ๋ฌํ์ฌ ํ์ดํผํ๋ผ๋ฏธํฐ๋ฅผ ํน์ ํ์ ์งํฉ์ผ๋ก ๊ณ ์ ํ๊ฑฐ๋ ์ค์ ํ๋ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ์ถ๊ฐ๋์์ต๋๋ค. ์๋ฅผ ๋ค์ด:
params = {'Imputer': {'numeric_impute_strategy': ['median', 'most_frequent']},
'Decision Tree Regressor': {'max_depth': [17, 18, 19], 'max_features': Categorical(['auto'])},
'Elastic Net Regressor': {"alpha": Real(0, 0.5), "l1_ratio": (0.01, 0.02, 0.03)}}
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='regression', pipeline_parameters=params, n_jobs=1)
automl.search()
_transform_parameters
์ ์ฒซ ๋ฒ์งธ ๋ฐฐ์น์์ max_depth
๋๋ numeric_impute_strategy
์ ๊ฐ์ ๋ชฉ๋ก ์
๋ ฅ์ ์ฒ๋ฆฌํ๋ ค๋ฉด ๋ชฉ๋ก์ ์ฒซ ๋ฒ์งธ ์์๋ฅผ ์ ํํ๊ฑฐ๋ ์ํ๋งํ๊ธฐ๋ง ํ๋ฉด ๋ฉ๋๋ค.
๋ฐ๋ผ์ ์ด ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๋ ํ ๊ฐ์ง ๋ฐฉ๋ฒ์ ์ด ์ค ์ ์ ๊ฑฐํ๊ณ ๋ชฉ๋ก์ด ํ์ฉ๋์ง ์๋๋ก ํ๋ ๊ฒ์ ๋๋ค.
@dsherry @freddyaboulton @bchen1116 @chukarsten FYI :)
@dsherry @chukarsten
#1862์์๋ ์ธ๋ฑ์ค ์ด์ด ์์ ๋ Drop Columns Transformer
๋ฅผ _get_preprocessing_components
์ถ๊ฐํ ๋ค์ ํด๋น ์ด์ self. pipeline_parameters
์๋ ์ถ๊ฐํ์ฌ ์ด ๋ฌธ์ ๊ฐ ์ด๋ฅผ ์ฐจ๋จํ ๊ณํ์
๋๋ค.
๊ฐ์ฅ ์ ์ฉํ ๋๊ธ
@dsherry @chukarsten
#1862์์๋ ์ธ๋ฑ์ค ์ด์ด ์์ ๋
Drop Columns Transformer
๋ฅผ_get_preprocessing_components
์ถ๊ฐํ ๋ค์ ํด๋น ์ด์self. pipeline_parameters
์๋ ์ถ๊ฐํ์ฌ ์ด ๋ฌธ์ ๊ฐ ์ด๋ฅผ ์ฐจ๋จํ ๊ณํ์ ๋๋ค.