from math import ceil, floor, log
from abc import abstractmethod
import numpy as np
from sklearn.model_selection._search import _check_param_grid
from sklearn.model_selection import ParameterGrid, ParameterSampler
from sklearn.utils import check_random_state
from sklearn.utils.validation import _num_samples
from sklearn.base import is_classifier
from sklearn.model_selection._split import check_cv
from ._search import CustomBaseSearchCV
from ._resample import resample
__all__ = ['GridSuccessiveHalving', 'RandomSuccessiveHalving']
def _refit_callable(results):
# Custom refit callable to return the index of the best candidate. We want
# the best candidate out of the last iteration. By default BaseSearchCV
# would return the best candidate out of all iterations.
last_iter = np.max(results['iter'])
sorted_indices = np.argsort(results['mean_test_score'])[::-1]
best_index = next(i for i in sorted_indices
if results['iter'][i] == last_iter)
return best_index
class BaseSuccessiveHalving(CustomBaseSearchCV):
"""Implements successive halving.
Ref:
Almost optimal exploration in multi-armed bandits, ICML 13
Zohar Karnin, Tomer Koren, Oren Somekh
"""
def __init__(self, estimator, scoring=None,
n_jobs=None, refit=True, cv=5, verbose=0,
pre_dispatch='2*n_jobs', random_state=None,
error_score=np.nan, return_train_score=True,
max_budget='auto', budget_on='n_samples', ratio=3,
r_min='auto', aggressive_elimination=False,
force_exhaust_budget=False):
refit = _refit_callable if refit else False
super().__init__(estimator, scoring=scoring,
n_jobs=n_jobs, refit=refit, cv=cv,
verbose=verbose, pre_dispatch=pre_dispatch,
error_score=error_score,
return_train_score=return_train_score, iid=False)
self.random_state = random_state
self.max_budget = max_budget
self.budget_on = budget_on
self.ratio = ratio
self.r_min = r_min
self.aggressive_elimination = aggressive_elimination
self.force_exhaust_budget = force_exhaust_budget
def _check_input_parameters(self, X, y, groups):
if self.scoring is not None and not (isinstance(self.scoring, str)
or callable(self.scoring)):
raise ValueError('scoring parameter must be a string, '
'a callable or None.')
if (self.budget_on != 'n_samples'
and self.budget_on not in self.estimator.get_params()):
raise ValueError(
'Cannot budget on parameter {} which is not supported '
'by estimator {}'.format(self.budget_on,
self.estimator.__class__.__name__))
if isinstance(self.max_budget, str) and self.max_budget != 'auto':
raise ValueError(
"max_budget must be either 'auto' or a positive number"
)
if self.max_budget != 'auto' and self.max_budget <= 0:
raise ValueError(
"max_budget must be either 'auto' or a positive number"
)
if isinstance(self.r_min, str) and self.r_min != 'auto':
raise ValueError(
"r_min must be either 'auto' or a positive number no greater "
"than max_budget."
)
if self.r_min != 'auto' and self.r_min <= 0:
raise ValueError(
"r_min must be either 'auto' or a positive number no greater "
"than max_budget."
)
if self.force_exhaust_budget and self.r_min != 'auto':
raise ValueError(
'r_min must be set to auto if force_exhaust_budget is True.'
)
self.r_min_ = self.r_min
if self.r_min_ == 'auto':
if self.budget_on == 'n_samples':
cv = check_cv(self.cv, y,
classifier=is_classifier(self.estimator))
n_splits = cv.get_n_splits(X, y, groups)
# please see https://gph.is/1KjihQe for a justification
magic_factor = 2
self.r_min_ = n_splits * magic_factor
if is_classifier(self.estimator):
n_classes = np.unique(y).shape[0]
self.r_min_ *= n_classes
else:
self.r_min_ = 1
self.max_budget_ = self.max_budget
if self.max_budget_ == 'auto':
if not self.budget_on == 'n_samples':
raise ValueError(
"max_budget can only be 'auto' if budget_on='n_samples'")
self.max_budget_ = _num_samples(X)
if self.r_min_ > self.max_budget_:
raise ValueError(
'r_min_={} is greater than max_budget_={}.'
.format(self.r_min_, self.max_budget_)
)
def fit(self, X, y=None, groups=None, **fit_params):
self._check_input_parameters(
X=X,
y=y,
groups=groups,
)
super().fit(X, y=y, groups=groups, **fit_params)
# Set best_score_: BaseSearchCV does not set it, as refit is a callable
self.best_score_ = (
self.cv_results_['mean_test_score'][self.best_index_])
return self
def _run_search(self, evaluate_candidates, X, y, groups):
rng = check_random_state(self.random_state)
candidate_params = self._generate_candidate_params()
# Remove duplicates (may happen with random sampling)
candidate_params = set(tuple(d.items()) for d in candidate_params)
candidate_params = [dict(t) for t in candidate_params]
self.n_candidates_ = len(candidate_params)
if self.budget_on != 'n_samples' and any(
self.budget_on in candidate for candidate in candidate_params):
# Can only check this now since we need the candidates list
raise ValueError(
"Cannot budget on parameter {} since it is part of "
"the searched parameters.".format(self.budget_on))
# n_required_iterations is the number of iterations needed so that the
# last iterations evaluates less than `ratio` candidates.
n_required_iterations = 1 + floor(log(self.n_candidates_, self.ratio))
if self.force_exhaust_budget:
# To exhaust the budget, we want to start with the biggest r_min
# possible so that the last (required) iteration uses as many
# resources as possible
# We only force exhausting the budget if r_min wasn't specified by
# the user.
last_iteration = n_required_iterations - 1
self.r_min_ = max(self.r_min_,
self.max_budget_ // self.ratio**last_iteration)
# n_possible iterations is the number of iterations that we can
# actually do starting from r_min and without exceeding the budget.
# Depending on budget size the number of candidates, this may be higher
# or smaller than n_required_iterations.
n_possible_iterations = 1 + floor(log(self.max_budget_ // self.r_min_,
self.ratio))
if self.aggressive_elimination:
n_iterations = n_required_iterations
else:
n_iterations = min(n_possible_iterations, n_required_iterations)
if self.verbose:
print('n_iterations: {}'.format(n_iterations))
print('n_required_iterations: {}'.format(n_required_iterations))
print('n_possible_iterations: {}'.format(n_possible_iterations))
print('r_min_: {}'.format(self.r_min_))
print('max_budget_: {}'.format(self.max_budget_))
print('aggressive_elimination: {}'.format(
self.aggressive_elimination))
print('force_exhaust_budget: {}'.format(self.force_exhaust_budget))
print('ratio: {}'.format(self.ratio))
self._r_i_list = [] # list of r_i for each iteration, used in tests
for iter_i in range(n_iterations):
power = iter_i # default
if self.aggressive_elimination:
# this will set r_i to the initial value (i.e. the value of r_i
# at the first iteration) for as many iterations as needed
# (while candidates are being eliminated), and then go on as
# usual.
power = max(
0,
iter_i - n_required_iterations + n_possible_iterations
)
r_i = int(self.ratio**power * self.r_min_)
r_i = min(r_i, self.max_budget_) # guard, probably not needed
self._r_i_list.append(r_i)
n_candidates = len(candidate_params)
if self.verbose:
print('-' * 10)
print('iter_i: {}'.format(iter_i))
print('n_candidates: {}'.format(n_candidates))
print('r_i: {}'.format(r_i))
if self.budget_on == 'n_samples':
stratify = y if is_classifier(self.estimator) else None
X_iter, y_iter = resample(X, y, replace=False,
random_state=rng, stratify=stratify,
n_samples=r_i)
else:
# Need copy so that r_i of next iteration do not overwrite
candidate_params = [c.copy() for c in candidate_params]
for candidate in candidate_params:
candidate[self.budget_on] = r_i
X_iter, y_iter = X, y
more_results = {'iter': [iter_i] * n_candidates,
'r_i': [r_i] * n_candidates}
results = evaluate_candidates(candidate_params, X_iter, y_iter,
groups, more_results=more_results)
n_candidates_to_keep = ceil(n_candidates / self.ratio)
candidate_params = self._top_k(results,
n_candidates_to_keep,
iter_i)
self.n_remaining_candidates_ = len(candidate_params)
self.n_required_iterations_ = n_required_iterations
self.n_possible_iterations_ = n_possible_iterations
self.n_iterations_ = n_iterations
def _top_k(self, results, k, iter_i):
# Return the best candidates of a given iteration
# We need to filter out candidates from the previous iterations
# when sorting
best_candidates_indices = np.argsort(results['mean_test_score'])[::-1]
best_candidates_indices = [idx for idx in best_candidates_indices
if results['iter'][idx] == iter_i]
best_candidates_indices = best_candidates_indices[:k]
return [results['params'][idx] for idx in best_candidates_indices]
@abstractmethod
def _generate_candidate_params(self):
pass
[docs]class GridSuccessiveHalving(BaseSuccessiveHalving):
"""Grid-search with successive halving.
The search strategy for hyper-parameter optimization starts evaluating all
the candidates with a small amount a resource and iteratively selects the
best candidates, using more and more resources.
Read more in the :ref:`User guide<successive_halving_user_guide>`.
Parameters
----------
estimator : estimator object.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of
parameter settings to try as values, or a list of such
dictionaries, in which case the grids spanned by each dictionary
in the list are explored. This enables searching over any sequence
of parameter settings.
scoring : string, callable, or None, default: None
A single string (see :ref:`scoring_parameter`) or a callable
(see :ref:`scoring`) to evaluate the predictions on the test set.
If None, the estimator's score method is used.
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
cv : int, cross-validation generator or an iterable, optional (default=5)
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
refit : boolean, default=True
If True, refit an estimator using the best found parameters on the
whole dataset.
The refitted estimator is made available at the ``best_estimator_``
attribute and permits using ``predict`` directly on this
``GridSearchCV`` instance.
verbose : integer
Controls the verbosity: the higher, the more messages.
error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error. Default is ``np.nan``
return_train_score : boolean, default=False
If ``False``, the ``cv_results_`` attribute will not include training
scores.
Computing training scores is used to get insights on how different
parameter settings impact the overfitting/underfitting trade-off.
However computing the scores on the training set can be computationally
expensive and is not strictly required to select the parameters that
yield the best generalization performance.
max_budget : int, optional(default='auto')
The maximum number of resources that any candidate is allowed to use
for a given iteration. By default, this is set ``n_samples`` when
``budget_on='n_samples'`` (default), else an error is raised.
budget_on : `n_samples` or str, optional(default='n_samples')
Defines the nature of the budget. By default, the budget is the number
of samples. It can also be set to any parameter of the base estimator
that accepts positive integer values, e.g. 'n_iterations' or
'n_estimators' for a gradient boosting estimator. In this case
``max_budget`` cannot be 'auto'.
ratio : int or float, optional(default=3)
The 'halving' parameter, which determines the proportion of candidates
that are selected for the next iteration. For example, ``ratio=3``
means that only one third of the candidates are selected.
r_min : int, optional(default='auto')
The minimum amount of resource that any candidate is allowed to use for
a given iteration. Equivalently, this defines the amount of resources
that are allocated for each candidate at the first iteration. By
default, this is set to:
- ``n_splits * 2`` when ``budget_on='n_samples'`` for a regression
problem
- ``n_classes * n_splits * 2`` when ``budget_on='n_samples'`` for a
regression problem
- The highest possible value satisfying the constraint
``force_exhaust_budget=True``.
- ``1`` when ``budget_on!='n_samples'``
Note that the amount of resources used at each iteration is always a
multiple of ``r_min``.
aggressive_elimination : bool, optional(default=False)
This is only relevant in cases where there isn't enough budget to
eliminate enough candidates at the last iteration. If ``True``, then
the search process will 'replay' the first iteration for as long as
needed until the number of candidates is small enough. This is
``False`` by default, which means that the last iteration may evaluate
more than ``ratio`` candidates.
force_exhaust_budget : bool, optional(default=False)
If True, then ``r_min`` is set to a specific value such that the
last iteration uses as much budget as possible. Namely, the last
iteration uses the highest value smaller than ``max_budget`` that is a
multiple of both ``r_min`` and ``ratio``.
Attributes
----------
n_candidates_ : int
The number of candidate parameters that were evaluated at the first
iteartion.
n_remaining_candidates_ : int
The number of candidate parameters that are left after the last
iteration.
max_budget_ : int
The maximum number of resources that any candidate is allowed to use
for a given iteration. Note that since the number of resources used at
each iteration must be a multiple of ``r_min_``, the actual number of
resources used at the last iteartion may be smaller than
``max_budget_``.
r_min_ : int
The amount of resources that are allocated for each candidate at the
first iteration.
n_iterations_ : int
The actual number of iterations that were run. This is equal to
``n_required_iterations_`` if ``aggressive_elimination`` is ``True``.
Else, this is equal to ``min(n_possible_iterations_,
n_required_iterations_)``.
n_possible_iterations_ : int
The number of iterations that are possible starting with ``r_min_``
resources and without exceeding ``max_budget_``.
n_required_iterations_ : int
The number of iterations that are required to end up with less than
``ratio`` candidates at the last iteration, starting with ``r_min_``
resources. This will be smaller than ``n_possible_iterations_`` when
there isn't enough budget.
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.
For instance the below given table
+--------------+-------------+-------------------+---+---------------+
| param_kernel | param_gamma | split0_test_score |...|rank_test_score|
+==============+=============+===================+===+===============+
| 'rbf' | 0.1 | 0.80 |...| 2 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.2 | 0.90 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.3 | 0.70 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
will be represented by a ``cv_results_`` dict of::
{
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
mask = False),
'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score' : [0.80, 0.90, 0.70],
'split1_test_score' : [0.82, 0.50, 0.70],
'mean_test_score' : [0.81, 0.70, 0.70],
'std_test_score' : [0.01, 0.20, 0.00],
'rank_test_score' : [3, 1, 1],
'split0_train_score' : [0.80, 0.92, 0.70],
'split1_train_score' : [0.82, 0.55, 0.70],
'mean_train_score' : [0.81, 0.74, 0.70],
'std_train_score' : [0.01, 0.19, 0.00],
'mean_fit_time' : [0.73, 0.63, 0.43],
'std_fit_time' : [0.01, 0.02, 0.01],
'mean_score_time' : [0.01, 0.06, 0.04],
'std_score_time' : [0.00, 0.00, 0.00],
'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}
NOTE
The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.
The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.
best_estimator_ : estimator or dict
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if ``refit=False``.
best_score_ : float
Mean cross-validated score of the best_estimator.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
best_index_ : int
The index (of the ``cv_results_`` arrays) which corresponds to the best
candidate parameter setting.
The dict at ``search.cv_results_['params'][search.best_index_]`` gives
the parameter setting for the best model, that gives the highest
mean score (``search.best_score_``).
scorer_ : function or a dict
Scorer function used on the held out data to choose the best
parameters for the model.
n_splits_ : int
The number of cross-validation splits (folds/iterations).
refit_time_ : float
Seconds used for refitting the best model on the whole dataset.
This is present only if ``refit`` is not False.
Notes
-----
The parameters selected are those that maximize the score of the held-out
data, according to the scoring parameter.
If `n_jobs` was set to a value higher than one, the data is copied for each
parameter setting(and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
--------
:class:`RandomSuccessiveHalving`:
Random search over a set of parameters using successive halving.
"""
def __init__(self, estimator, param_grid, scoring=None,
n_jobs=None, refit=True, verbose=0, cv=5,
pre_dispatch='2*n_jobs', random_state=None,
error_score=np.nan, return_train_score=True,
max_budget='auto', budget_on='n_samples', ratio=3,
r_min='auto', aggressive_elimination=False,
force_exhaust_budget=False):
super().__init__(estimator, scoring=scoring,
n_jobs=n_jobs, refit=refit, verbose=verbose, cv=cv,
pre_dispatch=pre_dispatch,
random_state=random_state, error_score=error_score,
return_train_score=return_train_score,
max_budget=max_budget, budget_on=budget_on,
ratio=ratio, r_min=r_min,
aggressive_elimination=aggressive_elimination,
force_exhaust_budget=force_exhaust_budget)
self.param_grid = param_grid
_check_param_grid(self.param_grid)
def _generate_candidate_params(self):
return ParameterGrid(self.param_grid)
[docs]class RandomSuccessiveHalving(BaseSuccessiveHalving):
"""Randomized search with successive halving.
The search strategy for hyper-parameter optimization starts evaluating all
the candidates with a small amount a resource and iteratively selects the
best candidates, using more and more resources.
Read more in the :ref:`User guide<successive_halving_user_guide>`.
Parameters
----------
estimator : estimator object.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_distributions : dict
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_candidates: int, optional(default='auto')
The number of candidate parameters to sample. By default this will
sample enough candidates so that the last iteration uses as many
resources as possible. Note that ``force_exhaust_budget`` has no
effect in this case.
scoring : string, callable, or None, default: None
A single string (see :ref:`scoring_parameter`) or a callable
(see :ref:`scoring`) to evaluate the predictions on the test set.
If None, the estimator's score method is used.
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
cv : int, cross-validation generator or an iterable, optional (default=5)
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
refit : boolean, default=True
If True, refit an estimator using the best found parameters on the
whole dataset.
The refitted estimator is made available at the ``best_estimator_``
attribute and permits using ``predict`` directly on this
``GridSearchCV`` instance.
verbose : integer
Controls the verbosity: the higher, the more messages.
error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error. Default is ``np.nan``
return_train_score : boolean, default=False
If ``False``, the ``cv_results_`` attribute will not include training
scores.
Computing training scores is used to get insights on how different
parameter settings impact the overfitting/underfitting trade-off.
However computing the scores on the training set can be computationally
expensive and is not strictly required to select the parameters that
yield the best generalization performance.
max_budget : int, optional(default='auto')
The maximum number of resources that any candidate is allowed to use
for a given iteration. By default, this is set ``n_samples`` when
``budget_on='n_samples'`` (default), else an error is raised.
budget_on : `n_samples` or str, optional(default='n_samples')
Defines the nature of the budget. By default, the budget is the number
of samples. It can also be set to any parameter of the base estimator
that accepts positive integer values, e.g. 'n_iterations' or
'n_estimators' for a gradient boosting estimator. In this case
``max_budget`` cannot be 'auto'.
ratio : int or float, optional(default=3)
The 'halving' parameter, which determines the proportion of candidates
that are selected for the next iteration. For example, ``ratio=3``
means that only one third of the candidates are selected.
r_min : int, optional(default='auto')
The minimum amount of resource that any candidate is allowed to use for
a given iteration. Equivalently, this defines the amount of resources
that are allocated for each candidate at the first iteration. By
default, this is set to:
- ``n_splits * 2`` when ``budget_on='n_samples'`` for a regression
problem
- ``n_classes * n_splits * 2`` when ``budget_on='n_samples'`` for a
regression problem
- The highest possible value satisfying the constraint
``force_exhaust_budget=True``.
- ``1`` when ``budget_on!='n_samples'``
Note that the amount of resources used at each iteration is always a
multiple of ``r_min``.
aggressive_elimination : bool, optional(default=False)
This is only relevant in cases where there isn't enough budget to
eliminate enough candidates at the last iteration. If ``True``, then
the search process will 'replay' the first iteration for as long as
needed until the number of candidates is small enough. This is
``False`` by default, which means that the last iteration may evaluate
more than ``ratio`` candidates.
force_exhaust_budget : bool, optional(default=False)
If True, then ``r_min`` is set to a specific value such that the
last iteration uses as much budget as possible. Namely, the last
iteration uses the highest value smaller than ``max_budget`` that is a
multiple of both ``r_min`` and ``ratio``.
Attributes
----------
n_candidates_ : int
The number of candidate parameters that were evaluated at the first
iteartion.
n_remaining_candidates_ : int
The number of candidate parameters that are left after the last
iteration.
max_budget_ : int
The maximum number of resources that any candidate is allowed to use
for a given iteration. Note that since the number of resources used at
each iteration must be a multiple of ``r_min_``, the actual number of
resources used at the last iteartion may be smaller than
``max_budget_``.
r_min_ : int
The amount of resources that are allocated for each candidate at the
first iteration.
n_iterations_ : int
The actual number of iterations that were run. This is equal to
``n_required_iterations_`` if ``aggressive_elimination`` is ``True``.
Else, this is equal to ``min(n_possible_iterations_,
n_required_iterations_)``.
n_possible_iterations_ : int
The number of iterations that are possible starting with ``r_min_``
resources and without exceeding ``max_budget_``.
n_required_iterations_ : int
The number of iterations that are required to end up with less than
``ratio`` candidates at the last iteration, starting with ``r_min_``
resources. This will be smaller than ``n_possible_iterations_`` when
there isn't enough budget.
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.
For instance the below given table
+--------------+-------------+-------------------+---+---------------+
| param_kernel | param_gamma | split0_test_score |...|rank_test_score|
+==============+=============+===================+===+===============+
| 'rbf' | 0.1 | 0.80 |...| 2 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.2 | 0.90 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.3 | 0.70 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
will be represented by a ``cv_results_`` dict of::
{
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
mask = False),
'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score' : [0.80, 0.90, 0.70],
'split1_test_score' : [0.82, 0.50, 0.70],
'mean_test_score' : [0.81, 0.70, 0.70],
'std_test_score' : [0.01, 0.20, 0.00],
'rank_test_score' : [3, 1, 1],
'split0_train_score' : [0.80, 0.92, 0.70],
'split1_train_score' : [0.82, 0.55, 0.70],
'mean_train_score' : [0.81, 0.74, 0.70],
'std_train_score' : [0.01, 0.19, 0.00],
'mean_fit_time' : [0.73, 0.63, 0.43],
'std_fit_time' : [0.01, 0.02, 0.01],
'mean_score_time' : [0.01, 0.06, 0.04],
'std_score_time' : [0.00, 0.00, 0.00],
'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}
NOTE
The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.
The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.
best_estimator_ : estimator or dict
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if ``refit=False``.
best_score_ : float
Mean cross-validated score of the best_estimator.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
best_index_ : int
The index (of the ``cv_results_`` arrays) which corresponds to the best
candidate parameter setting.
The dict at ``search.cv_results_['params'][search.best_index_]`` gives
the parameter setting for the best model, that gives the highest
mean score (``search.best_score_``).
scorer_ : function or a dict
Scorer function used on the held out data to choose the best
parameters for the model.
n_splits_ : int
The number of cross-validation splits (folds/iterations).
refit_time_ : float
Seconds used for refitting the best model on the whole dataset.
This is present only if ``refit`` is not False.
Notes
-----
The parameters selected are those that maximize the score of the held-out
data, according to the scoring parameter.
If `n_jobs` was set to a value higher than one, the data is copied for each
parameter setting(and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
--------
:class:`GridSuccessiveHalving`:
Search over a grid of parameters using successive halving.
"""
def __init__(self, estimator, param_distributions,
n_candidates='auto', scoring=None, n_jobs=None, refit=True,
verbose=0, cv=5, pre_dispatch='2*n_jobs',
random_state=None, error_score=np.nan,
return_train_score=True, max_budget='auto',
budget_on='n_samples', ratio=3, r_min='auto',
aggressive_elimination=False, force_exhaust_budget=False):
super().__init__(estimator, scoring=scoring,
n_jobs=n_jobs, refit=refit, verbose=verbose, cv=cv,
random_state=random_state, error_score=error_score,
return_train_score=return_train_score,
max_budget=max_budget, budget_on=budget_on,
ratio=ratio, r_min=r_min,
aggressive_elimination=aggressive_elimination,
force_exhaust_budget=force_exhaust_budget)
self.param_distributions = param_distributions
self.n_candidates = n_candidates
def _generate_candidate_params(self):
n_candidates_ = self.n_candidates
if n_candidates_ == 'auto':
# This will generate enough candidate so that the last iteration
# uses as much budget as possible
n_candidates_ = self.max_budget_ // self.r_min_
return ParameterSampler(self.param_distributions, n_candidates_,
self.random_state)