Automl#

AutoMLSearch and related modules.

Subpackages#

Submodules#

Package Contents#

Classes Summary#

`AutoMLSearch`	Automated Pipeline search.
`EngineBase`	Base class for EvalML engines.
`Progress`	Progress object holding stopping criteria and progress information.
`SequentialEngine`	The default engine for the AutoML search.

Functions#

`get_default_primary_search_objective`	Get the default primary search objective for a problem type.
`get_threshold_tuning_info`	Determine for a given automl config and pipeline what the threshold tuning objective should be and whether or not training data should be further split to achieve proper threshold tuning.
`make_data_splitter`	Given the training data and ML problem parameters, compute a data splitting method to use during AutoML search.
`resplit_training_data`	Further split the training data for a given pipeline. This is needed for binary pipelines in order to properly tune the threshold.
`search`	Given data and configuration, run an automl search.
`search_iterative`	Given data and configuration, run an automl search.
`tune_binary_threshold`	Tunes the threshold of a binary pipeline to the X and y thresholding data.

Contents#

class evalml.automl.AutoMLSearch(X_train=None, y_train=None, X_holdout=None, y_holdout=None, problem_type=None, objective='auto', max_iterations=None, max_time=None, patience=None, tolerance=None, data_splitter=None, allowed_component_graphs=None, allowed_model_families=None, excluded_model_families=None, features=None, run_feature_selection=True, start_iteration_callback=None, add_result_callback=None, error_callback=None, additional_objectives=None, alternate_thresholding_objective='F1', random_seed=0, n_jobs=-1, tuner_class=None, optimize_thresholds=True, ensembling=False, max_batches=None, problem_configuration=None, train_best_pipeline=True, search_parameters=None, sampler_method='auto', sampler_balanced_ratio=0.25, allow_long_running_models=False, _pipelines_per_batch=5, automl_algorithm='default', engine='sequential', verbose=False, timing=False, exclude_featurizers=None, holdout_set_size=0, use_recommendation=False, include_recommendation=None, exclude_recommendation=None)[source]#

Automated Pipeline search.

Parameters

X_train (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Required.
y_train (pd.Series) – The target training data of length [n_samples]. Required for supervised learning tasks.
X_holdout (pd.DataFrame) – The input holdout data of shape [n_samples, n_features].
y_holdout (pd.Series) – The target holdout data of length [n_samples].
problem_type (str or ProblemTypes) – Type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
objective (str, ObjectiveBase) – The objective to optimize for. Used to propose and rank pipelines, but not for optimizing each pipeline during fit-time. When set to ‘auto’, chooses: - LogLossBinary for binary classification problems, - LogLossMulticlass for multiclass classification problems, and - R2 for regression problems.
max_iterations (int) – Maximum number of iterations to search. If max_iterations and max_time is not set, then max_iterations will default to max_iterations of 5.
max_time (int, str) – Maximum time to search for pipelines. This will not start a new pipeline search after the duration has elapsed. If it is an integer, then the time will be in seconds. For strings, time can be specified as seconds, minutes, or hours.
patience (int) – Number of iterations without improvement to stop search early. Must be positive. If None, early stopping is disabled. Defaults to None.
tolerance (float) – Minimum percentage difference to qualify as score improvement for early stopping. Only applicable if patience is not None. Defaults to None.
allowed_component_graphs (dict) –
A dictionary of lists or ComponentGraphs indicating the component graphs allowed in the search. The format should follow { “Name_0”: [list_of_components], “Name_1”: ComponentGraph(…) }

The default of None indicates all pipeline component graphs for this problem type are allowed. Setting this field will cause allowed_model_families to be ignored.

e.g. allowed_component_graphs = { “My_Graph”: [“Imputer”, “One Hot Encoder”, “Random Forest Classifier”] }
allowed_model_families (list(str, ModelFamily)) – The model families to search. The default of None searches over all model families. Run evalml.pipelines.components.utils.allowed_model_families(“binary”) to see options. Change binary to multiclass or regression depending on the problem type. Note that if allowed_pipelines is provided, this parameter will be ignored. For default algorithm, this only applies to estimators in the non-naive batches.
features (list) – List of features to run DFS on AutoML pipelines. Defaults to None. Features will only be computed if the columns used by the feature exist in the search input and if the feature itself is not in search input. If features is an empty list, the DFS Transformer will not be included in pipelines.
run_feature_selection (bool) – If True, will run a separate feature selection pipeline and only use selected features in subsequent batches. If False, will use all of the features for every pipeline. Only used for default algorithm, setting is no-op for iterative algorithm.
data_splitter (sklearn.model_selection.BaseCrossValidator) – Data splitting method to use. Defaults to StratifiedKFold.
tuner_class – The tuner class to use. Defaults to SKOptTuner.
optimize_thresholds (bool) – Whether or not to optimize the binary pipeline threshold. Defaults to True.
start_iteration_callback (callable) – Function called before each pipeline training iteration. Callback function takes three positional parameters: The pipeline instance and the AutoMLSearch object.
add_result_callback (callable) – Function called after each pipeline training iteration. Callback function takes three positional parameters: A dictionary containing the training results for the new pipeline, an untrained_pipeline containing the parameters used during training, and the AutoMLSearch object.
error_callback (callable) – Function called when search() errors and raises an Exception. Callback function takes three positional parameters: the Exception raised, the traceback, and the AutoMLSearch object. Must also accepts kwargs, so AutoMLSearch is able to pass along other appropriate parameters by default. Defaults to None, which will call log_error_callback.
additional_objectives (list) – Custom set of objectives to score on. Will override default objectives for problem type if not empty.
alternate_thresholding_objective (str) – The objective to use for thresholding binary classification pipelines if the main objective provided isn’t tuneable. Defaults to F1.
random_seed (int) – Seed for the random number generator. Defaults to 0.
n_jobs (int or None) – Non-negative integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
ensembling (boolean) – If True, runs ensembling in a separate batch after every allowed pipeline class has been iterated over. If the number of unique pipelines to search over per batch is one, ensembling will not run. Defaults to False.
max_batches (int) – The maximum number of batches of pipelines to search. Parameters max_time, and max_iterations have precedence over stopping the search.
problem_configuration (dict, None) – Additional parameters needed to configure the search. For example, in time series problems, values should be passed in for the time_index, gap, forecast_horizon, and max_delay variables. For multiseries time series problems, the values passed in should also include the name of a series_id column.
train_best_pipeline (boolean) – Whether or not to train the best pipeline before returning it. Defaults to True.
search_parameters (dict) –
A dict of the hyperparameter ranges or pipeline parameters used to iterate over during search. Keys should consist of the component names and values should specify a singular value/list for pipeline parameters, or skopt.Space for hyperparameter ranges. In the example below, the Imputer parameters would be passed to the hyperparameter ranges, and the Label Encoder parameters would be used as the component parameter.

e.g. search_parameters = { ‘Imputer’{ ‘numeric_impute_strategy’: Categorical([‘most_frequent’, ‘median’]) },
’Label Encoder’: {‘positive_label’: True} }
sampler_method (str) – The data sampling component to use in the pipelines if the problem type is classification and the target balance is smaller than the sampler_balanced_ratio. Either ‘auto’, which will use our preferred sampler for the data, ‘Undersampler’, ‘Oversampler’, or None. Defaults to ‘auto’.
sampler_balanced_ratio (float) – The minority:majority class ratio that we consider balanced, so a 1:4 ratio would be equal to 0.25. If the class balance is larger than this provided value, then we will not add a sampler since the data is then considered balanced. Overrides the sampler_ratio of the samplers. Defaults to 0.25.
allow_long_running_models (bool) – Whether or not to allow longer-running models for large multiclass problems. If False and no pipelines, component graphs, or model families are provided, AutoMLSearch will not use Elastic Net or XGBoost when there are more than 75 multiclass targets and will not use CatBoost when there are more than 150 multiclass targets. Defaults to False.
_ensembling_split_size (float) – The amount of the training data we’ll set aside for training ensemble metalearners. Only used when ensembling is True. Must be between 0 and 1, exclusive. Defaults to 0.2
_pipelines_per_batch (int) – The number of pipelines to train for every batch after the first one. The first batch will train a baseline pipline + one of each pipeline family allowed in the search.
automl_algorithm (str) – The automl algorithm to use. Currently the two choices are ‘iterative’ and ‘default’. Defaults to default.
engine (EngineBase or str) – The engine instance used to evaluate pipelines. Dask or concurrent.futures engines can also be chosen by providing a string from the list [“sequential”, “cf_threaded”, “cf_process”, “dask_threaded”, “dask_process”]. If a parallel engine is selected this way, the maximum amount of parallelism, as determined by the engine, will be used. Defaults to “sequential”.
verbose (boolean) – Whether or not to display semi-real-time updates to stdout while search is running. Defaults to False.
timing (boolean) – Whether or not to write pipeline search times to the logger. Defaults to False.
exclude_featurizers (list[str]) – A list of featurizer components to exclude from the pipelines built by search. Valid options are “DatetimeFeaturizer”, “EmailFeaturizer”, “URLFeaturizer”, “NaturalLanguageFeaturizer”, “TimeSeriesFeaturizer”
excluded_model_families (list(str, ModelFamily)) – A list of model families to exclude from the estimators used when building pipelines. For default algorithm, this only excludes estimators in the non-naive batches.
holdout_set_size (float) – The size of the holdout set that AutoML search will take for datasets larger than 500 rows. If set to 0, holdout set will not be taken regardless of number of rows. Must be between 0 and 1, exclusive. Defaults to 0.1.
use_recommendation (bool) – Whether or not to use a recommendation score to rank pipelines instead of optimization objective. Defaults to False.
include_recommendation (list[str]) – A list of objectives to include beyond the defaults in the recommendation score. Defaults to None.
exclude_recommendation (list[str]) – A list of objectives to exclude from the defaults in the recommendation score. Defaults to None.

Methods

`add_to_rankings`	Fits and evaluates a given pipeline then adds the results to the automl rankings with the requirement that automl search has been run.
`best_pipeline`	Returns a trained instance of the best pipeline and parameters found during automl search. If train_best_pipeline is set to False, returns an untrained pipeline instance.
`close_engine`	Function to explicitly close the engine, client, parallel resources.
`describe_pipeline`	Describe a pipeline.
`full_rankings`	Returns a pandas.DataFrame with scoring results from all pipelines searched.
`get_ensembler_input_pipelines`	Returns a list of input pipeline IDs given an ensembler pipeline ID.
`get_pipeline`	Given the ID of a pipeline training result, returns an untrained instance of the specified pipeline initialized with the parameters used to train that pipeline during automl search.
`get_recommendation_score_breakdown`	Reports the scores for the objectives used in the given pipeline's recommendation score calculation.
`get_recommendation_scores`	Calculates recommendation scores for all pipelines in the search results.
`load`	Loads AutoML object at file path.
`plot`	Return an instance of the plot with the latest scores.
`rankings`	Returns a pandas.DataFrame with scoring results from the highest-scoring set of parameters used with each pipeline.
`results`	Class that allows access to a copy of the results from automl_search.
`save`	Saves AutoML object at file path.
`score_pipelines`	Score a list of pipelines on the given holdout data.
`search`	Find the best pipeline for the data set.
`train_pipelines`	Train a list of pipelines on the training data.

add_to_rankings(self, pipeline)[source]#

Fits and evaluates a given pipeline then adds the results to the automl rankings with the requirement that automl search has been run.

Parameters: pipeline (PipelineBase) – pipeline to train and evaluate.

property best_pipeline(self)#

Returns a trained instance of the best pipeline and parameters found during automl search. If train_best_pipeline is set to False, returns an untrained pipeline instance.

Returns: A trained instance of the best pipeline and parameters found during automl search. If train_best_pipeline is set to False, returns an untrained pipeline instance.
Return type: PipelineBase
Raises: PipelineNotFoundError – If this is called before .search() is called.

close_engine(self)[source]#: Function to explicitly close the engine, client, parallel resources.

describe_pipeline(self, pipeline_id, return_dict=False)[source]#

Describe a pipeline.

Parameters

pipeline_id (int) – pipeline to describe
return_dict (bool) – If True, return dictionary of information about pipeline. Defaults to False.

Returns

Description of specified pipeline. Includes information such as type of pipeline components, problem, training time, cross validation, etc.

Raises

PipelineNotFoundError – If pipeline_id is not a valid ID.

property full_rankings(self)#: Returns a pandas.DataFrame with scoring results from all pipelines searched.

get_ensembler_input_pipelines(self, ensemble_pipeline_id)[source]#

Returns a list of input pipeline IDs given an ensembler pipeline ID.

Parameters: ensemble_pipeline_id (id) – Ensemble pipeline ID to get input pipeline IDs from.
Returns: A list of ensemble input pipeline IDs.
Return type: list[int]
Raises: ValueError – If ensemble_pipeline_id does not correspond to a valid ensemble pipeline ID.

get_pipeline(self, pipeline_id)[source]#

Given the ID of a pipeline training result, returns an untrained instance of the specified pipeline initialized with the parameters used to train that pipeline during automl search.

Parameters: pipeline_id (int) – Pipeline to retrieve.
Returns: Untrained pipeline instance associated with the provided ID.
Return type: PipelineBase
Raises: PipelineNotFoundError – if pipeline_id is not a valid ID.

get_recommendation_score_breakdown(self, pipeline_id)[source]#

Reports the scores for the objectives used in the given pipeline’s recommendation score calculation.

Note that these scores are reported in their raw form, not scaled to be between 0 and 1.

Parameters: pipeline_id (int) – The id of the pipeline to get the recommendation score breakdown for.
Returns: A dictionary of the scores for each objective used in the recommendation score calculation.
Return type: dict

get_recommendation_scores(self, priority=None, custom_weights=None, use_pipeline_names=False)[source]#

Calculates recommendation scores for all pipelines in the search results.

Parameters

priority (str) – An optional name of a priority objective that should be given heavier weight (of 0.5) than the other objectives contributing to the score. Defaults to None, where all objectives are weighted equally.
custom_weights (dict[str,float]) – A dictionary mapping objective names to corresponding weights between 0 and 1. Should not be used at the same time as prioritized_objective. Defaults to None.
use_pipeline_names (bool) – Whether or not to return the pipeline names instead of ids as the keys to the recommendation score dictionary. Defaults to False.

Returns

A dictionary mapping pipeline IDs to recommendation scores

static load(file_path, pickle_type='cloudpickle')[source]#

Loads AutoML object at file path.

Parameters

file_path (str) – Location to find file to load
pickle_type ({"pickle", "cloudpickle"}) – The pickling library to use. Currently not used since the standard pickle library can handle cloudpickles.

Returns

AutoSearchBase object

property plot(self)#: Return an instance of the plot with the latest scores.

property rankings(self)#: Returns a pandas.DataFrame with scoring results from the highest-scoring set of parameters used with each pipeline.

property results(self)#

Class that allows access to a copy of the results from automl_search.

Returns

Dictionary containing pipeline_results, a dict with results from each pipeline,: and search_order, a list describing the order the pipelines were searched.

Return type

dict

save(self, file_path, pickle_type='cloudpickle', pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)[source]#

Saves AutoML object at file path.

Parameters

file_path (str) – Location to save file.
pickle_type ({"pickle", "cloudpickle"}) – The pickling library to use.
pickle_protocol (int) – The pickle data stream format.

Raises

ValueError – If pickle_type is not “pickle” or “cloudpickle”.

score_pipelines(self, pipelines, X_holdout, y_holdout, objectives)[source]#

Score a list of pipelines on the given holdout data.

Parameters

pipelines (list[PipelineBase]) – List of pipelines to train.
X_holdout (pd.DataFrame) – Holdout features.
y_holdout (pd.Series) – Holdout targets for scoring.
objectives (list[str], list[ObjectiveBase]) – Objectives used for scoring.

Returns

Dictionary keyed by pipeline name that maps to a dictionary of scores. Note that the any pipelines that error out during scoring will not be included in the dictionary but the exception and stacktrace will be displayed in the log.

Return type

dict[str, Dict[str, float]]

search(self, interactive_plot=True)[source]#

Find the best pipeline for the data set.

Parameters: interactive_plot (boolean, True) – Shows an iteration vs. score plot in Jupyter notebook. Disabled by default in non-Jupyter enviroments.
Raises: AutoMLSearchException – If all pipelines in the current AutoML batch produced a score of np.nan on the primary objective.
Returns: Dictionary keyed by batch number that maps to the timings for pipelines run in that batch, as well as the total time for each batch. Pipelines within a batch are labeled by pipeline name.
Return type: Dict[int, Dict[str, Timestamp]]

train_pipelines(self, pipelines)[source]#

Train a list of pipelines on the training data.

This can be helpful for training pipelines once the search is complete.

Parameters: pipelines (list[PipelineBase]) – List of pipelines to train.
Returns: Dictionary keyed by pipeline name that maps to the fitted pipeline. Note that the any pipelines that error out during training will not be included in the dictionary but the exception and stacktrace will be displayed in the log.
Return type: Dict[str, PipelineBase]

class evalml.automl.EngineBase[source]#

Base class for EvalML engines.

Methods

`setup_job_log`	Set up logger for job.
`submit_evaluation_job`	Submit job for pipeline evaluation during AutoMLSearch.
`submit_scoring_job`	Submit job for pipeline scoring.
`submit_training_job`	Submit job for pipeline training.

static setup_job_log()[source]#: Set up logger for job.

abstract submit_evaluation_job(self, automl_config, pipeline, X, y, X_holdout=None, y_holdout=None)[source]#: Submit job for pipeline evaluation during AutoMLSearch.

abstract submit_scoring_job(self, automl_config, pipeline, X, y, objectives, X_train=None, y_train=None)[source]#: Submit job for pipeline scoring.

abstract submit_training_job(self, automl_config, pipeline, X, y, X_holdout=None, y_holdout=None)[source]#: Submit job for pipeline training.

evalml.automl.get_default_primary_search_objective(problem_type)[source]#

Get the default primary search objective for a problem type.

Parameters: problem_type (str or ProblemType) – Problem type of interest.
Returns: primary objective instance for the problem type.
Return type: ObjectiveBase

evalml.automl.get_threshold_tuning_info(automl_config, pipeline)[source]#

Determine for a given automl config and pipeline what the threshold tuning objective should be and whether or not training data should be further split to achieve proper threshold tuning.

Can also be used after automl search has been performed to determine whether the full training data was used to train the pipeline.

Parameters

automl_config (AutoMLConfig) – The AutoMLSearch’s config object. Used to determine threshold tuning objective and whether data needs resplitting.
pipeline (Pipeline) – The pipeline instance to Threshold.

Returns

threshold_tuning_objective, data_needs_resplitting (str, bool)

evalml.automl.make_data_splitter(X, y, problem_type, problem_configuration=None, n_splits=3, shuffle=True, random_seed=0)[source]#

Given the training data and ML problem parameters, compute a data splitting method to use during AutoML search.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
problem_type (ProblemType) – The type of machine learning problem.
problem_configuration (dict, None) – Additional parameters needed to configure the search. For example, in time series problems, values should be passed in for the time_index, gap, and max_delay variables. Defaults to None.
n_splits (int, None) – The number of CV splits, if applicable. Defaults to 3.
shuffle (bool) – Whether or not to shuffle the data before splitting, if applicable. Defaults to True.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Returns

Data splitting method.

Return type

sklearn.model_selection.BaseCrossValidator

Raises

ValueError – If problem_configuration is not given for a time-series problem.

class evalml.automl.Progress(max_time=None, max_batches=None, max_iterations=None, patience=None, tolerance=None, automl_algorithm=None, objective=None, verbose=False)[source]#

Progress object holding stopping criteria and progress information.

Parameters

max_time (int) – Maximum time to search for pipelines.
max_iterations (int) – Maximum number of iterations to search.
max_batches (int) – The maximum number of batches of pipelines to search. Parameters max_time, and max_iterations have precedence over stopping the search.
patience (int) – Number of iterations without improvement to stop search early.
tolerance (float) – Minimum percentage difference to qualify as score improvement for early stopping.
automl_algorithm (str) – The automl algorithm to use. Used to calculate iterations if max_batches is selected as stopping criteria.
objective (str, ObjectiveBase) – The objective used in search.
verbose (boolean) – Whether or not to log out stopping information.

Methods

`elapsed`	Return time elapsed using the start time and current time.
`return_progress`	Return information about current and end state of each stopping criteria in order of priority.
`should_continue`	Given AutoML Results, return whether or not the search should continue.
`start_timing`	Sets start time to current time.

elapsed(self)[source]#: Return time elapsed using the start time and current time.

return_progress(self)[source]#

Return information about current and end state of each stopping criteria in order of priority.

Returns: list of dictionaries containing information of each stopping criteria.
Return type: List[Dict[str, unit]]

should_continue(self, results, interrupted=False, mid_batch=False)[source]#

Given AutoML Results, return whether or not the search should continue.

Parameters

results (dict) – AutoMLSearch results.
interrupted (bool) – whether AutoMLSearch was given an keyboard interrupt. Defaults to False.
mid_batch (bool) – whether this method was called while in the middle of a batch or not. Defaults to False.

Returns

True if search should continue, False otherwise.

Return type

bool

start_timing(self)[source]#: Sets start time to current time.

evalml.automl.resplit_training_data(pipeline, X_train, y_train)[source]#

Further split the training data for a given pipeline. This is needed for binary pipelines in order to properly tune the threshold.

Can be used after automl search has been performed to recreate the data that was used to train a pipeline.

Parameters

pipeline (PipelineBase) – the pipeline whose training data we are splitting
X_train (pd.DataFrame or np.ndarray) – training data of shape [n_samples, n_features]
y_train (pd.Series, or np.ndarray) – training target data of length [n_samples]

Returns

Feature and target data each split into train and threshold tuning sets.

Return type

pd.DataFrame, pd.DataFrame, pd.Series, pd.Series

evalml.automl.search(X_train=None, y_train=None, problem_type=None, objective='auto', mode='fast', max_time=None, patience=None, tolerance=None, problem_configuration=None, n_splits=3, verbose=False, timing=False)[source]#

Given data and configuration, run an automl search.

This method will run EvalML’s default suite of data checks. If the data checks produce errors, the data check results will be returned before running the automl search. In that case we recommend you alter your data to address these errors and try again. This method is provided for convenience. If you’d like more control over when each of these steps is run, consider making calls directly to the various pieces like the data checks and AutoMLSearch, instead of using this method.

Parameters

X_train (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Required.
y_train (pd.Series) – The target training data of length [n_samples]. Required for supervised learning tasks.
problem_type (str or ProblemTypes) – Type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
objective (str, ObjectiveBase) – The objective to optimize for. Used to propose and rank pipelines, but not for optimizing each pipeline during fit-time. When set to ‘auto’, chooses: - LogLossBinary for binary classification problems, - LogLossMulticlass for multiclass classification problems, and - R2 for regression problems.
mode (str) – mode for DefaultAlgorithm. There are two modes: fast and long, where fast is a subset of long. Please look at DefaultAlgorithm for more details.
max_time (int, str) – Maximum time to search for pipelines. This will not start a new pipeline search after the duration has elapsed. If it is an integer, then the time will be in seconds. For strings, time can be specified as seconds, minutes, or hours.
patience (int) – Number of iterations without improvement to stop search early. Must be positive. If None, early stopping is disabled. Defaults to None.
tolerance (float) – Minimum percentage difference to qualify as score improvement for early stopping. Only applicable if patience is not None. Defaults to None.
problem_configuration (dict) – Additional parameters needed to configure the search. For example, in time series problems, values should be passed in for the time_index, gap, forecast_horizon, and max_delay variables.
n_splits (int) – Number of splits to use with the default data splitter.
verbose (boolean) – Whether or not to display semi-real-time updates to stdout while search is running. Defaults to False.
timing (boolean) – Whether or not to write pipeline search times to the logger. Defaults to False.

Returns

The automl search object containing pipelines and rankings, and the results from running the data checks. If the data check results contain errors, automl search will not be run and an automl search object will not be returned.

Return type

(AutoMLSearch, dict)

Raises

ValueError – If search configuration is not valid.

evalml.automl.search_iterative(X_train=None, y_train=None, problem_type=None, objective='auto', problem_configuration=None, n_splits=3, timing=False, **kwargs)[source]#

Given data and configuration, run an automl search.

Parameters

X_train (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Required.
y_train (pd.Series) – The target training data of length [n_samples]. Required for supervised learning tasks.
problem_type (str or ProblemTypes) – Type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
objective (str, ObjectiveBase) – The objective to optimize for. Used to propose and rank pipelines, but not for optimizing each pipeline during fit-time. When set to ‘auto’, chooses: - LogLossBinary for binary classification problems, - LogLossMulticlass for multiclass classification problems, and - R2 for regression problems.
problem_configuration (dict) – Additional parameters needed to configure the search. For example, in time series problems, values should be passed in for the time_index, gap, forecast_horizon, and max_delay variables.
n_splits (int) – Number of splits to use with the default data splitter.
timing (boolean) – Whether or not to write pipeline search times to the logger. Defaults to False.
**kwargs – Other keyword arguments which are provided will be passed to AutoMLSearch.

Returns

the automl search object containing pipelines and rankings, and the results from running the data checks. If the data check results contain errors, automl search will not be run and an automl search object will not be returned.

Return type

(AutoMLSearch, dict)

Raises

ValueError – If the search configuration is invalid.

class evalml.automl.SequentialEngine[source]#

The default engine for the AutoML search.

Trains and scores pipelines locally and sequentially.

Methods

`close`	No-op.
`setup_job_log`	Set up logger for job.
`submit_evaluation_job`	Submit a job to evaluate a pipeline.
`submit_scoring_job`	Submit a job to score a pipeline.
`submit_training_job`	Submit a job to train a pipeline.

close(self)[source]#: No-op.

static setup_job_log()#: Set up logger for job.

submit_evaluation_job(self, automl_config, pipeline, X, y, X_holdout=None, y_holdout=None)[source]#

Submit a job to evaluate a pipeline.

Parameters

automl_config – Structure containing data passed from AutoMLSearch instance.
pipeline (pipeline.PipelineBase) – Pipeline to evaluate.
X (pd.DataFrame) – Input data for modeling.
y (pd.Series) – Target data for modeling.
X_holdout (pd.Series) – Holdout input data for holdout scoring.
y_holdout (pd.Series) – Holdout target data for holdout scoring.

Returns

Computation result.

Return type

SequentialComputation

submit_scoring_job(self, automl_config, pipeline, X, y, objectives, X_train=None, y_train=None)[source]#

Submit a job to score a pipeline.

Parameters

automl_config – Structure containing data passed from AutoMLSearch instance.
pipeline (pipeline.PipelineBase) – Pipeline to train.
X (pd.DataFrame) – Input data for modeling.
y (pd.Series) – Target data for modeling.
X_train (pd.DataFrame) – Training features. Used for feature engineering in time series.
y_train (pd.Series) – Training target. Used for feature engineering in time series.
objectives (list[ObjectiveBase]) – List of objectives to score on.

Returns

Computation result.

Return type

SequentialComputation

submit_training_job(self, automl_config, pipeline, X, y)[source]#

Submit a job to train a pipeline.

Parameters

automl_config – Structure containing data passed from AutoMLSearch instance.
pipeline (pipeline.PipelineBase) – Pipeline to evaluate.
X (pd.DataFrame) – Input data for modeling.
y (pd.Series) – Target data for modeling.

Returns

Computation result.

Return type

SequentialComputation

evalml.automl.tune_binary_threshold(pipeline, objective, problem_type, X_threshold_tuning, y_threshold_tuning, X=None, y=None)[source]#

Tunes the threshold of a binary pipeline to the X and y thresholding data.

Parameters

pipeline (Pipeline) – Pipeline instance to threshold.
objective (ObjectiveBase) – The objective we want to tune with. If not tuneable and best_pipeline is True, will use F1.
problem_type (ProblemType) – The problem type of the pipeline.
X_threshold_tuning (pd.DataFrame) – Features to which the pipeline will be tuned.
y_threshold_tuning (pd.Series) – Target data to which the pipeline will be tuned.
X (pd.DataFrame) – Features to which the pipeline will be trained (used for time series binary). Defaults to None.
y (pd.Series) – Target to which the pipeline will be trained (used for time series binary). Defaults to None.