utils

Utility methods for EvalML pipelines.

Module Contents

Functions

generate_pipeline_code	Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
make_pipeline	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.
make_timeseries_baseline_pipeline	Make a baseline pipeline for time series regression problems.
rows_of_interest	Get the row indices of the data that are closest to the threshold. Works only for binary classification problems and pipelines.

Attributes Summary

logger

Contents

evalml.pipelines.utils.generate_pipeline_code(element)

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.

evalml.pipelines.utils.logger

evalml.pipelines.utils.make_pipeline(X, y, estimator, problem_type, parameters=None, sampler_name=None, extra_components=None)

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.

evalml.pipelines.utils.make_timeseries_baseline_pipeline(problem_type, gap, forecast_horizon)

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.

evalml.pipelines.utils.rows_of_interest(pipeline, X, y=None, threshold=None, epsilon=0.1, sort_values=True, types='all')

Get the row indices of the data that are closest to the threshold. Works only for binary classification problems and pipelines.

Parameters

• pipeline (PipelineBase) – The fitted binary pipeline.

• X (ww.DataTable, pd.DataFrame) – The input features to predict on.

• y (ww.DataColumn, pd.Series, None) – The input target data, if available. Defaults to None.

• threshold (float) – The threshold value of interest to separate positive and negative predictions. If None, uses the pipeline threshold if set, else 0.5. Defaults to None.

• epsilon (epsilon) – The difference between the probability and the threshold that would make the row interesting for us. For instance, epsilon=0.1 and threhsold=0.5 would mean we consider all rows in [0.4, 0.6] to be of interest. Defaults to 0.1.

• sort_values (bool) – Whether to return the indices sorted by the distance from the threshold, such that the first values are closer to the threshold and the later values are further. Defaults to True.

• types (str) –

  The type of rows to keep and return. Can be one of [‘incorrect’, ‘correct’, ‘true_positive’, ‘true_negative’, ‘all’]. Defaults to ‘all’.

  ’incorrect’ - return only the rows where the predictions are incorrect. This means that, given the threshold and target y, keep only the rows which are labeled wrong. ‘correct’ - return only the rows where the predictions are correct. This means that, given the threshold and target y, keep only the rows which are correctly labeled. ‘true_positive’ - return only the rows which are positive, as given by the targets. ‘true_negative’ - return only the rows which are negative, as given by the targets. ‘all’ - return all rows. This is the only option available when there is no target data provided.

Returns

The indices corresponding to the rows of interest.

Raises

• ValueError – If pipeline is not a fitted Binary Classification pipeline.

• ValueError – If types is invalid or y is not provided when types is not ‘all’.

• ValueError – If the threshold is provided and is exclusive of [0, 1].

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