utils¶
Module Contents¶
Functions¶
Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline. |
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Given input data, target data, an estimator class and the problem type, |
Contents¶
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evalml.pipelines.utils.generate_pipeline_code(element)[source]¶ Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
- Parameters
element (pipeline instance) – The instance of the pipeline to generate string Python code
- Returns
String representation of Python code that can be run separately in order to recreate the pipeline instance. Does not include code for custom component implementation.
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evalml.pipelines.utils.logger¶
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evalml.pipelines.utils.make_pipeline(X, y, estimator, problem_type, parameters=None, sampler_name=None, extra_components=None)[source]¶ - Given input data, target data, an estimator class and the problem type,
generates a pipeline class with a preprocessing chain which was recommended based on the inputs. The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type.
- Parameters
X (pd.DataFrame) – The input data of shape [n_samples, n_features]
y (pd.Series) – The target data of length [n_samples]
estimator (Estimator) – Estimator for pipeline
problem_type (ProblemTypes or str) – Problem type for pipeline to generate
parameters (dict) – Dictionary with component names as keys and dictionary of that component’s parameters as values. An empty dictionary or None implies using all default values for component parameters.
sampler_name (str) – The name of the sampler component to add to the pipeline. Only used in classification problems. Defaults to None
extra_components – List of extra components to be added after preprocessing components. Defaults to None.