lale.lib.lale.grid_search_cv module

class lale.lib.lale.grid_search_cv.GridSearchCV(*, estimator=None, scoring, cv=5, verbose=0, n_jobs=None, lale_num_samples=None, lale_num_grids=None, param_grid=None, pgo=None, observer=None, max_opt_time=None)

Bases: PlannedIndividualOp

GridSearchCV performs an exhaustive search over a discretized space.

This documentation is auto-generated from JSON schemas.

Parameters

• estimator (union type, default None) –

  Planned Lale individual operator or pipeline.

  • operator

  • or None

    lale.lib.sklearn.LogisticRegression

• scoring (union type, not for optimizer) –

  Scorer object, or known scorer named by string.

  • None

    When not specified, use accuracy for classification tasks and r2 for regression.

  • or union type

    Scorer object, or known scorer named by string.

    • callable

      Callable with signature scoring(estimator, X, y) as documented in sklearn scoring.

      The callable has to return a scalar value, such that a higher score is better. This may be created from one of the sklearn metrics using make_scorer. Or it can be one of the scoring callables returned by the factory functions in lale.lib.aif360 metrics, for example, symmetric_disparate_impact(**fairness_info). Or it can be a completely custom user-written Python callable.

    • or ‘accuracy’, ‘explained_variance’, ‘max_error’, ‘roc_auc’, ‘roc_auc_ovr’, ‘roc_auc_ovo’, ‘roc_auc_ovr_weighted’, ‘roc_auc_ovo_weighted’, ‘balanced_accuracy’, ‘average_precision’, ‘neg_log_loss’, or ‘neg_brier_score’

      Known scorer for classification task.

    • or ‘r2’, ‘neg_mean_squared_error’, ‘neg_mean_absolute_error’, ‘neg_root_mean_squared_error’, ‘neg_mean_squared_log_error’, or ‘neg_median_absolute_error’

      Known scorer for regression task.

• cv (union type, not for optimizer, default 5) –

  Cross-validation as integer or as object that has a split function.

  The fit method performs cross validation on the input dataset for per trial, and uses the mean cross validation performance for optimization. This behavior is also impacted by the handle_cv_failure flag.

  • union type

    • integer, >=2, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 5

      Number of folds for cross-validation.

    • or None, not for optimizer

      to use the default 5-fold cross validation

  • or CrossvalGenerator, not for optimizer

    Object with split function: generator yielding (train, test) splits as arrays of indices. Can use any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators

• verbose (integer, not for optimizer, default 0) – Controls the verbosity: the higher, the more messages.

• n_jobs (union type, not for optimizer, default None) –

  Number of jobs to run in parallel.

  • None

    1 unless in joblib.parallel_backend context.

  • or -1

    Use all processors.

  • or integer, >=1

    Number of jobs to run in parallel.

• lale_num_samples (union type, not for optimizer, default None) –

  How many samples to draw when discretizing a continuous hyperparameter.

  • integer, >=1

  • or None

    lale.search.lale_grid_search_cv.DEFAULT_SAMPLES_PER_DISTRIBUTION

• lale_num_grids (union type, not for optimizer, default None) –

  How many top-level disjuncts to explore.

  • None

    If not set, keep all grids.

  • or float, >0.0, <1.0

    Fraction of grids to keep.

  • or integer, >=1

    Number of grids to keep.

• param_grid (union type, optional, not for optimizer, default None) –

  • None

    Generated automatically.

  • or any type

    Dictionary of hyperparameter ranges in the grid.

• pgo (union type, not for optimizer, default None) –

  • any type

    lale.search.PGO

  • or None

• observer (Any, optional, not for optimizer, default None) – a class or object with callbacks for observing the state of the optimization

• max_opt_time (union type, not for optimizer, default None) –

  Maximum amount of time in seconds for the optimization.

  • float, >=0.0

  • or None

    No runtime bound.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (any type) –

• y (any type) –

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (any type) –

Returns

result

Return type

any type