class dask_ml.model_selection.InverseDecaySearchCV(estimator, parameters, n_initial_parameters=10, test_size=None, patience=False, tol=0.001, fits_per_score=1, max_iter=100, random_state=None, scoring=None, verbose=False, prefix='', decay_rate=1.0)

Incrementally search for hyper-parameters on models that support partial_fit

This incremental hyper-parameter optimization class starts training the model on many hyper-parameters on a small amount of data, and then only continues training those models that seem to be performing well.

This class will decay the number of parameters over time. At time step k, this class will retain 1 / (k + 1) fraction of the highest performing models.

estimatorestimator object.

A object of that type is instantiated for each initial hyperparameter combination. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score` function, or scoring must be passed. The estimator must implement partial_fit, set_params, and work well with clone.


Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly.

n_initial_parametersint, default=10

Number of parameter settings that are sampled. This trades off runtime vs quality of the solution.

Alternatively, you can set this to "grid" to do a full grid search.

patienceint, default False

If specified, training stops when the score does not increase by tol after patience calls to partial_fit. Off by default.

fits_per_scoresint, optional, default=1

If patience is used the maximum number of partial_fit calls between score calls.

scores_per_fitint, default 1

If patience is used the maximum number of partial_fit calls between score calls.

tolfloat, default 0.001

The required level of improvement to consider stopping training on that model. The most recent score must be at at most tol better than the all of the previous patience scores for that model. Increasing tol will tend to reduce training time, at the cost of worse models.

max_iterint, default 100

Maximum number of partial fit calls per model.


Fraction of the dataset to hold out for computing test scores. Defaults to the size of a single partition of the input training set


The training dataset should fit in memory on a single machine. Adjust the test_size parameter as necessary to achieve this.

random_stateint, RandomState instance or None, optional, default: None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

scoringstring, callable, list/tuple, dict or None, default: None

A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set.

For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values.

NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.

See Specifying multiple metrics for evaluation for an example.

If None, the estimator’s default scorer (if available) is used.

verbosebool, float, int, optional, default: False

If False (default), don’t print logs (or pipe them to stdout). However, standard logging will still be used.

If True, print logs and use standard logging.

If float, print/log approximately verbose fraction of the time.

prefixstr, optional, default=””

While logging, add prefix to each message.

decay_ratefloat, default 1.0

How quickly to decrease the number partial future fit calls. Higher decay_rate will result in lower training times, at the cost of worse models.

The default decay_rate=1.0 is chosen because it has some theoretical motivation [1].

cv_results_dict of np.ndarrays

This dictionary has keys

  • mean_partial_fit_time

  • mean_score_time

  • std_partial_fit_time

  • std_score_time

  • test_score

  • rank_test_score

  • model_id

  • partial_fit_calls

  • params

  • param_{key}, where key is every key in params.

The values in the test_score key correspond to the last score a model received on the hold out dataset. The key model_id corresponds with history_. This dictionary can be imported into Pandas.

model_history_dict of lists of dict

A dictionary of each models history. This is a reorganization of history_: the same information is present but organized per model.

This data has the structure {model_id: hist} where hist is a subset of history_ and model_id are model identifiers.

history_list of dicts

Information about each model after each partial_fit call. Each dict the keys

  • partial_fit_time

  • score_time

  • score

  • model_id

  • params

  • partial_fit_calls

  • elapsed_wall_time

The key model_id corresponds to the model_id in cv_results_. This list of dicts can be imported into Pandas.


The model with the highest validation score among all the models retained by the “inverse decay” algorithm.


Score achieved by best_estimator_ on the validation set after the final call to partial_fit.


Index indicating which estimator in cv_results_ corresponds to the highest score.


Dictionary of best parameters found on the hold-out data.


The function used to score models, which has a call signature of scorer_(estimator, X, y).


Number of cross validation splits.


Whether this cross validation search uses multiple metrics.


When decay_rate==1, this class approximates the number of partial_fit calls that SuccessiveHalvingSearchCV performs. If n_initial_parameters is configured properly with decay_rate=1, it’s possible this class will mirror the most aggressive bracket of HyperbandSearchCV. This might yield good results and/or find good models, but is untested.



Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2017). Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), 6765-6816. http://www.jmlr.org/papers/volume18/16-558/16-558.pdf



fit(X[, y])

Find the best parameters for a particular model.


Get metadata routing of this object.


Get parameters for this estimator.



Predict for X.


Log of probability estimates.


Probability estimates.

score(X[, y])

Returns the score on the given data.


Set the parameters of this estimator.

set_score_request(*[, compute])

Request metadata passed to the score method.


Transform block or partition-wise for dask inputs.


__init__(estimator, parameters, n_initial_parameters=10, test_size=None, patience=False, tol=0.001, fits_per_score=1, max_iter=100, random_state=None, scoring=None, verbose=False, prefix='', decay_rate=1.0)