dask_ml.model_selection.SuccessiveHalvingSearchCV
dask_ml.model_selection
.SuccessiveHalvingSearchCV¶
- class dask_ml.model_selection.SuccessiveHalvingSearchCV(estimator, parameters, n_initial_parameters=10, n_initial_iter=None, max_iter=None, aggressiveness=3, test_size=None, patience=False, tol=0.001, random_state=None, scoring=None, verbose=False, prefix='')¶
Perform the successive halving algorithm [1].
This algorithm trains estimators for a certain number
partial_fit
calls topartial_fit
, then kills the worst performing half. It trains the surviving estimators for twice as long, and repeats this until one estimator survives.The value of \(1/2\) above is used for a clear explanation. This class defaults to killing the worst performing
1 - 1 // aggressiveness
fraction of models, and trains estimators foraggressiveness
times longer, and waits until the number of models left is less thanaggressiveness
.- Parameters
- 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, orscoring
must be passed. The estimator must implementpartial_fit
,set_params
, and work well withclone
.- parametersdict
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.- aggressivenessfloat, default=3
How aggressive to be in culling off the different estimators. Higher values imply higher confidence in scoring (or that the hyperparameters influence the
estimator.score
more than the data).- n_initial_parametersint, default=10
Number of parameter settings that are sampled. This trades off runtime vs quality of the solution.
- n_initial_iterint
Number of times to call partial fit initially before scoring. Estimators are trained for
n_initial_iter
calls topartial_fit
initially. Higher values ofn_initial_iter
train the estimators longer before making a decision. Metadata on the number of calls topartial_fit
is inmetadata
(andmetadata_
).- max_iterint, default None
Maximum number of partial fit calls per model. If None, will allow SuccessiveHalvingSearchCV to run until (about) one model survives. If specified, models will stop being trained when
max_iter
calls topartial_fit
are reached.- test_sizefloat
Fraction of the dataset to hold out for computing test scores. Defaults to the size of a single partition of the input training set
Note
The training dataset should fit in memory on a single machine. Adjust the
test_size
parameter as necessary to achieve this.- patienceint, default False
If specified, training stops when the score does not increase by
tol
afterpatience
calls topartial_fit
. Off by default.- 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 previouspatience
scores for that model. Increasingtol
will tend to reduce training time, at the cost of worse models.- scoringstring, callable, 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.
If None, the estimator’s default scorer (if available) is used.
- 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.
- 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.
- Attributes
- 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}
, wherekey
is every key inparams
.
The values in the
test_score
key correspond to the last score a model received on the hold out dataset. The keymodel_id
corresponds withhistory_
. This dictionary can be imported into Pandas.- metadata and metadata_dict[key, int]
Dictionary describing the computation.
metadata
describes the computation that will be performed, andmetadata_
describes the computation that has been performed. Both dictionaries have keysn_models
: the number of models for this run of successive halvingmax_iter
: the maximum number of timespartial_fit
is called. At least one model will have this manypartial_fit
calls.partial_fit_calls
: the total number ofpartial_fit
calls. All models together will receive this manypartial_fit
calls.
When
patience
is specified, the reduced computation will be reflected inmetadata_
but notmetadata
.- 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}
wherehist
is a subset ofhistory_
andmodel_id
are model identifiers.- history_list of dicts
Information about each model after each
partial_fit
call. Each dict the keyspartial_fit_time
score_time
score
model_id
params
partial_fit_calls
The key
model_id
corresponds to themodel_id
incv_results_
. This list of dicts can be imported into Pandas.- best_estimator_BaseEstimator
The model with the highest validation score among all the models retained by the “inverse decay” algorithm.
- best_score_float
Score achieved by
best_estimator_
on the validation set after the final call topartial_fit
.- best_index_int
Index indicating which estimator in
cv_results_
corresponds to the highest score.- best_params_dict
Dictionary of best parameters found on the hold-out data.
- scorer_
The function used to score models, which has a call signature of
scorer_(estimator, X, y)
.- n_splits_int
Number of cross validation splits.
- multimetric_bool
Whether this cross validation search uses multiple metrics.
References
- 1
“Non-stochastic best arm identification and hyperparameter optimization” by Jamieson, Kevin and Talwalkar, Ameet. 2016. https://arxiv.org/abs/1502.07943
Methods
decision_function
(X)fit
(X[, y])Find the best parameters for a particular model.
get_params
([deep])Get parameters for this estimator.
inverse_transform
(Xt)predict
(X)Predict for X.
predict_log_proba
(X)Log of probability estimates.
predict_proba
(X)Probability estimates.
score
(X[, y])Returns the score on the given data.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Transform block or partition-wise for dask inputs.
partial_fit
- __init__(estimator, parameters, n_initial_parameters=10, n_initial_iter=None, max_iter=None, aggressiveness=3, test_size=None, patience=False, tol=0.001, random_state=None, scoring=None, verbose=False, prefix='')¶