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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Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline. |
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Returns a list of actions based on the defaults parameters of each option in the input DataCheckActionOption list. |
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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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Creates a pipeline of components to address the input DataCheckAction list. |
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Creates a pipeline of components to address warnings and errors output from running data checks. Uses all default suggestions. |
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Make a baseline pipeline for time series regression problems. |
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Get the row indices of the data that are closest to the threshold. Works only for binary classification problems and pipelines. |
Attributes Summary#
Contents#
- evalml.pipelines.utils.DECOMPOSER_PERIOD_CAP = 1000#
- evalml.pipelines.utils.generate_pipeline_code(element, features_path=None)[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.
features_path (str) – path to features json created from featuretools.save_features(). Defaults to None.
- 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.
ValueError – If features in features_path do not match the features on the pipeline.
- evalml.pipelines.utils.generate_pipeline_example(pipeline, path_to_train, path_to_holdout, target, path_to_features=None, path_to_mapping='', output_file_path=None)[source]#
Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
- Parameters
pipeline (pipeline instance) – The instance of the pipeline to generate string Python code.
path_to_train (str) – path to training data.
path_to_holdout (str) – path to holdout data.
target (str) – target variable.
path_to_features (str) – path to features json. Defaults to None.
path_to_mapping (str) – path to mapping json. Defaults to None.
output_file_path (str) – path to output python file. Defaults to None.
- 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
- evalml.pipelines.utils.get_actions_from_option_defaults(action_options)[source]#
Returns a list of actions based on the defaults parameters of each option in the input DataCheckActionOption list.
- Parameters
action_options (list[DataCheckActionOption]) – List of DataCheckActionOption objects
- Returns
List of actions based on the defaults parameters of each option in the input list.
- Return type
list[DataCheckAction]
- evalml.pipelines.utils.make_pipeline(X, y, estimator, problem_type, parameters=None, sampler_name=None, extra_components_before=None, extra_components_after=None, use_estimator=True, known_in_advance=None, features=False, exclude_featurizers=None, include_decomposer=True)[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_before (list[ComponentBase]) – List of extra components to be added before preprocessing components. Defaults to None.
extra_components_after (list[ComponentBase]) – List of extra components to be added after preprocessing components. Defaults to None.
use_estimator (bool) – Whether to add the provided estimator to the pipeline or not. Defaults to True.
known_in_advance (list[str], None) – List of features that are known in advance.
features (bool) – Whether to add a DFSTransformer component to this pipeline.
exclude_featurizers (list[str]) – A list of featurizer components to exclude from the pipeline. Valid options are “DatetimeFeaturizer”, “EmailFeaturizer”, “URLFeaturizer”, “NaturalLanguageFeaturizer”, “TimeSeriesFeaturizer”
include_decomposer (bool) – For time series regression problems, whether or not to include a decomposer in the generated pipeline. Defaults to True.
- 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_pipeline_from_actions(problem_type, actions, problem_configuration=None)[source]#
Creates a pipeline of components to address the input DataCheckAction list.
- Parameters
problem_type (str or ProblemType) – The problem type that the pipeline should address.
actions (list[DataCheckAction]) – List of DataCheckAction objects used to create list of components
problem_configuration (dict) – Required for time series problem types. Values should be passed in for time_index, gap, forecast_horizon, and max_delay.
- Returns
Pipeline which can be used to address data check actions.
- Return type
PipelineBase
- evalml.pipelines.utils.make_pipeline_from_data_check_output(problem_type, data_check_output, problem_configuration=None)[source]#
Creates a pipeline of components to address warnings and errors output from running data checks. Uses all default suggestions.
- Parameters
problem_type (str or ProblemType) – The problem type.
data_check_output (dict) – Output from calling
DataCheck.validate().problem_configuration (dict) – Required for time series problem types. Values should be passed in for time_index, gap, forecast_horizon, and max_delay.
- Returns
Pipeline which can be used to address data check outputs.
- Return type
PipelineBase
- Raises
ValueError – If problem_type is of type time series but an incorrect problem_configuration has been passed.
- evalml.pipelines.utils.make_timeseries_baseline_pipeline(problem_type, gap, forecast_horizon, time_index, exclude_featurizer=False)[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.
time_index (str) – Column name of time_index parameter.
exclude_featurizer (bool) – Whether or not to exclude the TimeSeriesFeaturizer from the baseline graph. Defaults to False.
- 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')[source]#
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].