utils¶
Utility methods for EvalML pipelines.
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, 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. |
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Make a baseline pipeline for time series regression problems. |
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.
- Return type
str
- Raises
ValueError – If element is not a pipeline, or if the pipeline is nonlinear.
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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[ComponentBase]) – List of extra components to be added after preprocessing components. Defaults to None.
- Returns
PipelineBase instance with dynamically generated preprocessing components and specified estimator.
- Return type
PipelineBase object
- Raises
ValueError – If estimator is not valid for the given problem type, or sampling is not supported for the given problem type.
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evalml.pipelines.utils.make_timeseries_baseline_pipeline(problem_type, gap, forecast_horizon)[source]¶ Make a baseline pipeline for time series regression problems.
- Parameters
problem_type – One of TIME_SERIES_REGRESSION, TIME_SERIES_MULTICLASS, TIME_SERIES_BINARY
gap (int) – Non-negative gap parameter.
forecast_horizon (int) – Positive forecast_horizon parameter.
- Returns
TimeSeriesPipelineBase, a time series pipeline corresponding to the problem type.