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from math import ceil
import numpy as np
import pytest
from scipy.stats import expon, norm, randint
from sklearn.datasets import make_classification
from sklearn.dummy import DummyClassifier
from sklearn.experimental import enable_halving_search_cv # noqa
from sklearn.model_selection import (
GroupKFold,
GroupShuffleSplit,
HalvingGridSearchCV,
HalvingRandomSearchCV,
KFold,
LeaveOneGroupOut,
LeavePGroupsOut,
ShuffleSplit,
StratifiedKFold,
StratifiedShuffleSplit,
)
from sklearn.model_selection._search_successive_halving import (
_SubsampleMetaSplitter,
_top_k,
)
from sklearn.model_selection.tests.test_search import (
check_cv_results_array_types,
check_cv_results_keys,
)
from sklearn.svm import SVC, LinearSVC
class FastClassifier(DummyClassifier):
"""Dummy classifier that accepts parameters a, b, ... z.
These parameter don't affect the predictions and are useful for fast
grid searching."""
# update the constraints such that we accept all parameters from a to z
_parameter_constraints: dict = {
**DummyClassifier._parameter_constraints,
**{
chr(key): "no_validation" # type: ignore
for key in range(ord("a"), ord("z") + 1)
},
}
def __init__(
self, strategy="stratified", random_state=None, constant=None, **kwargs
):
super().__init__(
strategy=strategy, random_state=random_state, constant=constant
)
def get_params(self, deep=False):
params = super().get_params(deep=deep)
for char in range(ord("a"), ord("z") + 1):
params[chr(char)] = "whatever"
return params
class SometimesFailClassifier(DummyClassifier):
def __init__(
self,
strategy="stratified",
random_state=None,
constant=None,
n_estimators=10,
fail_fit=False,
fail_predict=False,
a=0,
):
self.fail_fit = fail_fit
self.fail_predict = fail_predict
self.n_estimators = n_estimators
self.a = a
super().__init__(
strategy=strategy, random_state=random_state, constant=constant
)
def fit(self, X, y):
if self.fail_fit:
raise Exception("fitting failed")
return super().fit(X, y)
def predict(self, X):
if self.fail_predict:
raise Exception("predict failed")
return super().predict(X)
@pytest.mark.filterwarnings("ignore::sklearn.exceptions.FitFailedWarning")
@pytest.mark.filterwarnings("ignore:Scoring failed:UserWarning")
@pytest.mark.filterwarnings("ignore:One or more of the:UserWarning")
@pytest.mark.parametrize("HalvingSearch", (HalvingGridSearchCV, HalvingRandomSearchCV))
@pytest.mark.parametrize("fail_at", ("fit", "predict"))
def test_nan_handling(HalvingSearch, fail_at):
"""Check the selection of the best scores in presence of failure represented by
NaN values."""
n_samples = 1_000
X, y = make_classification(n_samples=n_samples, random_state=0)
search = HalvingSearch(
SometimesFailClassifier(),
{f"fail_{fail_at}": [False, True], "a": range(3)},
resource="n_estimators",
max_resources=6,
min_resources=1,
factor=2,
)
search.fit(X, y)
# estimators that failed during fit/predict should always rank lower
# than ones where the fit/predict succeeded
assert not search.best_params_[f"fail_{fail_at}"]
scores = search.cv_results_["mean_test_score"]
ranks = search.cv_results_["rank_test_score"]
# some scores should be NaN
assert np.isnan(scores).any()
unique_nan_ranks = np.unique(ranks[np.isnan(scores)])
# all NaN scores should have the same rank
assert unique_nan_ranks.shape[0] == 1
# NaNs should have the lowest rank
assert (unique_nan_ranks[0] >= ranks).all()
@pytest.mark.parametrize("Est", (HalvingGridSearchCV, HalvingRandomSearchCV))
@pytest.mark.parametrize(
(
"aggressive_elimination,"
"max_resources,"
"expected_n_iterations,"
"expected_n_required_iterations,"
"expected_n_possible_iterations,"
"expected_n_remaining_candidates,"
"expected_n_candidates,"
"expected_n_resources,"
),
[
# notice how it loops at the beginning
# also, the number of candidates evaluated at the last iteration is
# <= factor
(True, "limited", 4, 4, 3, 1, [60, 20, 7, 3], [20, 20, 60, 180]),
# no aggressive elimination: we end up with less iterations, and
# the number of candidates at the last iter is > factor, which isn't
# ideal
(False, "limited", 3, 4, 3, 3, [60, 20, 7], [20, 60, 180]),
# # When the amount of resource isn't limited, aggressive_elimination
# # has no effect. Here the default min_resources='exhaust' will take
# # over.
(True, "unlimited", 4, 4, 4, 1, [60, 20, 7, 3], [37, 111, 333, 999]),
(False, "unlimited", 4, 4, 4, 1, [60, 20, 7, 3], [37, 111, 333, 999]),
],
)
def test_aggressive_elimination(
Est,
aggressive_elimination,
max_resources,
expected_n_iterations,
expected_n_required_iterations,
expected_n_possible_iterations,
expected_n_remaining_candidates,
expected_n_candidates,
expected_n_resources,
):
# Test the aggressive_elimination parameter.
n_samples = 1000
X, y = make_classification(n_samples=n_samples, random_state=0)
param_grid = {"a": ("l1", "l2"), "b": list(range(30))}
base_estimator = FastClassifier()
if max_resources == "limited":
max_resources = 180
else:
max_resources = n_samples
sh = Est(
base_estimator,
param_grid,
aggressive_elimination=aggressive_elimination,
max_resources=max_resources,
factor=3,
)
sh.set_params(verbose=True) # just for test coverage
if Est is HalvingRandomSearchCV:
# same number of candidates as with the grid
sh.set_params(n_candidates=2 * 30, min_resources="exhaust")
sh.fit(X, y)
assert sh.n_iterations_ == expected_n_iterations
assert sh.n_required_iterations_ == expected_n_required_iterations
assert sh.n_possible_iterations_ == expected_n_possible_iterations
assert sh.n_resources_ == expected_n_resources
assert sh.n_candidates_ == expected_n_candidates
assert sh.n_remaining_candidates_ == expected_n_remaining_candidates
assert ceil(sh.n_candidates_[-1] / sh.factor) == sh.n_remaining_candidates_
@pytest.mark.parametrize("Est", (HalvingGridSearchCV, HalvingRandomSearchCV))
@pytest.mark.parametrize(
(
"min_resources,"
"max_resources,"
"expected_n_iterations,"
"expected_n_possible_iterations,"
"expected_n_resources,"
),
[
# with enough resources
("smallest", "auto", 2, 4, [20, 60]),
# with enough resources but min_resources set manually
(50, "auto", 2, 3, [50, 150]),
# without enough resources, only one iteration can be done
("smallest", 30, 1, 1, [20]),
# with exhaust: use as much resources as possible at the last iter
("exhaust", "auto", 2, 2, [333, 999]),
("exhaust", 1000, 2, 2, [333, 999]),
("exhaust", 999, 2, 2, [333, 999]),
("exhaust", 600, 2, 2, [200, 600]),
("exhaust", 599, 2, 2, [199, 597]),
("exhaust", 300, 2, 2, [100, 300]),
("exhaust", 60, 2, 2, [20, 60]),
("exhaust", 50, 1, 1, [20]),
("exhaust", 20, 1, 1, [20]),
],
)
def test_min_max_resources(
Est,
min_resources,
max_resources,
expected_n_iterations,
expected_n_possible_iterations,
expected_n_resources,
):
# Test the min_resources and max_resources parameters, and how they affect
# the number of resources used at each iteration
n_samples = 1000
X, y = make_classification(n_samples=n_samples, random_state=0)
param_grid = {"a": [1, 2], "b": [1, 2, 3]}
base_estimator = FastClassifier()
sh = Est(
base_estimator,
param_grid,
factor=3,
min_resources=min_resources,
max_resources=max_resources,
)
if Est is HalvingRandomSearchCV:
sh.set_params(n_candidates=6) # same number as with the grid
sh.fit(X, y)
expected_n_required_iterations = 2 # given 6 combinations and factor = 3
assert sh.n_iterations_ == expected_n_iterations
assert sh.n_required_iterations_ == expected_n_required_iterations
assert sh.n_possible_iterations_ == expected_n_possible_iterations
assert sh.n_resources_ == expected_n_resources
if min_resources == "exhaust":
assert sh.n_possible_iterations_ == sh.n_iterations_ == len(sh.n_resources_)
@pytest.mark.parametrize("Est", (HalvingRandomSearchCV, HalvingGridSearchCV))
@pytest.mark.parametrize(
"max_resources, n_iterations, n_possible_iterations",
[
("auto", 5, 9), # all resources are used
(1024, 5, 9),
(700, 5, 8),
(512, 5, 8),
(511, 5, 7),
(32, 4, 4),
(31, 3, 3),
(16, 3, 3),
(4, 1, 1), # max_resources == min_resources, only one iteration is
# possible
],
)
def test_n_iterations(Est, max_resources, n_iterations, n_possible_iterations):
# test the number of actual iterations that were run depending on
# max_resources
n_samples = 1024
X, y = make_classification(n_samples=n_samples, random_state=1)
param_grid = {"a": [1, 2], "b": list(range(10))}
base_estimator = FastClassifier()
factor = 2
sh = Est(
base_estimator,
param_grid,
cv=2,
factor=factor,
max_resources=max_resources,
min_resources=4,
)
if Est is HalvingRandomSearchCV:
sh.set_params(n_candidates=20) # same as for HalvingGridSearchCV
sh.fit(X, y)
assert sh.n_required_iterations_ == 5
assert sh.n_iterations_ == n_iterations
assert sh.n_possible_iterations_ == n_possible_iterations
@pytest.mark.parametrize("Est", (HalvingRandomSearchCV, HalvingGridSearchCV))
def test_resource_parameter(Est):
# Test the resource parameter
n_samples = 1000
X, y = make_classification(n_samples=n_samples, random_state=0)
param_grid = {"a": [1, 2], "b": list(range(10))}
base_estimator = FastClassifier()
sh = Est(base_estimator, param_grid, cv=2, resource="c", max_resources=10, factor=3)
sh.fit(X, y)
assert set(sh.n_resources_) == set([1, 3, 9])
for r_i, params, param_c in zip(
sh.cv_results_["n_resources"],
sh.cv_results_["params"],
sh.cv_results_["param_c"],
):
assert r_i == params["c"] == param_c
with pytest.raises(
ValueError, match="Cannot use resource=1234 which is not supported "
):
sh = HalvingGridSearchCV(
base_estimator, param_grid, cv=2, resource="1234", max_resources=10
)
sh.fit(X, y)
with pytest.raises(
ValueError,
match=(
"Cannot use parameter c as the resource since it is part "
"of the searched parameters."
),
):
param_grid = {"a": [1, 2], "b": [1, 2], "c": [1, 3]}
sh = HalvingGridSearchCV(
base_estimator, param_grid, cv=2, resource="c", max_resources=10
)
sh.fit(X, y)
@pytest.mark.parametrize(
"max_resources, n_candidates, expected_n_candidates",
[
(512, "exhaust", 128), # generate exactly as much as needed
(32, "exhaust", 8),
(32, 8, 8),
(32, 7, 7), # ask for less than what we could
(32, 9, 9), # ask for more than 'reasonable'
],
)
def test_random_search(max_resources, n_candidates, expected_n_candidates):
# Test random search and make sure the number of generated candidates is
# as expected
n_samples = 1024
X, y = make_classification(n_samples=n_samples, random_state=0)
param_grid = {"a": norm, "b": norm}
base_estimator = FastClassifier()
sh = HalvingRandomSearchCV(
base_estimator,
param_grid,
n_candidates=n_candidates,
cv=2,
max_resources=max_resources,
factor=2,
min_resources=4,
)
sh.fit(X, y)
assert sh.n_candidates_[0] == expected_n_candidates
if n_candidates == "exhaust":
# Make sure 'exhaust' makes the last iteration use as much resources as
# we can
assert sh.n_resources_[-1] == max_resources
@pytest.mark.parametrize(
"param_distributions, expected_n_candidates",
[
({"a": [1, 2]}, 2), # all lists, sample less than n_candidates
({"a": randint(1, 3)}, 10), # not all list, respect n_candidates
],
)
def test_random_search_discrete_distributions(
param_distributions, expected_n_candidates
):
# Make sure random search samples the appropriate number of candidates when
# we ask for more than what's possible. How many parameters are sampled
# depends whether the distributions are 'all lists' or not (see
# ParameterSampler for details). This is somewhat redundant with the checks
# in ParameterSampler but interaction bugs were discovered during
# development of SH
n_samples = 1024
X, y = make_classification(n_samples=n_samples, random_state=0)
base_estimator = FastClassifier()
sh = HalvingRandomSearchCV(base_estimator, param_distributions, n_candidates=10)
sh.fit(X, y)
assert sh.n_candidates_[0] == expected_n_candidates
@pytest.mark.parametrize("Est", (HalvingGridSearchCV, HalvingRandomSearchCV))
@pytest.mark.parametrize(
"params, expected_error_message",
[
(
{"resource": "not_a_parameter"},
"Cannot use resource=not_a_parameter which is not supported",
),
(
{"resource": "a", "max_resources": 100},
"Cannot use parameter a as the resource since it is part of",
),
(
{"max_resources": "auto", "resource": "b"},
"resource can only be 'n_samples' when max_resources='auto'",
),
(
{"min_resources": 15, "max_resources": 14},
"min_resources_=15 is greater than max_resources_=14",
),
({"cv": KFold(shuffle=True)}, "must yield consistent folds"),
({"cv": ShuffleSplit()}, "must yield consistent folds"),
],
)
def test_input_errors(Est, params, expected_error_message):
base_estimator = FastClassifier()
param_grid = {"a": [1]}
X, y = make_classification(100)
sh = Est(base_estimator, param_grid, **params)
with pytest.raises(ValueError, match=expected_error_message):
sh.fit(X, y)
@pytest.mark.parametrize(
"params, expected_error_message",
[
(
{"n_candidates": "exhaust", "min_resources": "exhaust"},
"cannot be both set to 'exhaust'",
),
],
)
def test_input_errors_randomized(params, expected_error_message):
# tests specific to HalvingRandomSearchCV
base_estimator = FastClassifier()
param_grid = {"a": [1]}
X, y = make_classification(100)
sh = HalvingRandomSearchCV(base_estimator, param_grid, **params)
with pytest.raises(ValueError, match=expected_error_message):
sh.fit(X, y)
@pytest.mark.parametrize(
"fraction, subsample_test, expected_train_size, expected_test_size",
[
(0.5, True, 40, 10),
(0.5, False, 40, 20),
(0.2, True, 16, 4),
(0.2, False, 16, 20),
],
)
def test_subsample_splitter_shapes(
fraction, subsample_test, expected_train_size, expected_test_size
):
# Make sure splits returned by SubsampleMetaSplitter are of appropriate
# size
n_samples = 100
X, y = make_classification(n_samples)
cv = _SubsampleMetaSplitter(
base_cv=KFold(5),
fraction=fraction,
subsample_test=subsample_test,
random_state=None,
)
for train, test in cv.split(X, y):
assert train.shape[0] == expected_train_size
assert test.shape[0] == expected_test_size
if subsample_test:
assert train.shape[0] + test.shape[0] == int(n_samples * fraction)
else:
assert test.shape[0] == n_samples // cv.base_cv.get_n_splits()
@pytest.mark.parametrize("subsample_test", (True, False))
def test_subsample_splitter_determinism(subsample_test):
# Make sure _SubsampleMetaSplitter is consistent across calls to split():
# - we're OK having training sets differ (they're always sampled with a
# different fraction anyway)
# - when we don't subsample the test set, we want it to be always the same.
# This check is the most important. This is ensured by the determinism
# of the base_cv.
# Note: we could force both train and test splits to be always the same if
# we drew an int seed in _SubsampleMetaSplitter.__init__
n_samples = 100
X, y = make_classification(n_samples)
cv = _SubsampleMetaSplitter(
base_cv=KFold(5), fraction=0.5, subsample_test=subsample_test, random_state=None
)
folds_a = list(cv.split(X, y, groups=None))
folds_b = list(cv.split(X, y, groups=None))
for (train_a, test_a), (train_b, test_b) in zip(folds_a, folds_b):
assert not np.all(train_a == train_b)
if subsample_test:
assert not np.all(test_a == test_b)
else:
assert np.all(test_a == test_b)
assert np.all(X[test_a] == X[test_b])
@pytest.mark.parametrize(
"k, itr, expected",
[
(1, 0, ["c"]),
(2, 0, ["a", "c"]),
(4, 0, ["d", "b", "a", "c"]),
(10, 0, ["d", "b", "a", "c"]),
(1, 1, ["e"]),
(2, 1, ["f", "e"]),
(10, 1, ["f", "e"]),
(1, 2, ["i"]),
(10, 2, ["g", "h", "i"]),
],
)
def test_top_k(k, itr, expected):
results = { # this isn't a 'real world' result dict
"iter": [0, 0, 0, 0, 1, 1, 2, 2, 2],
"mean_test_score": [4, 3, 5, 1, 11, 10, 5, 6, 9],
"params": ["a", "b", "c", "d", "e", "f", "g", "h", "i"],
}
got = _top_k(results, k=k, itr=itr)
assert np.all(got == expected)
@pytest.mark.parametrize("Est", (HalvingRandomSearchCV, HalvingGridSearchCV))
def test_cv_results(Est):
# test that the cv_results_ matches correctly the logic of the
# tournament: in particular that the candidates continued in each
# successive iteration are those that were best in the previous iteration
pd = pytest.importorskip("pandas")
rng = np.random.RandomState(0)
n_samples = 1000
X, y = make_classification(n_samples=n_samples, random_state=0)
param_grid = {"a": ("l1", "l2"), "b": list(range(30))}
base_estimator = FastClassifier()
# generate random scores: we want to avoid ties, which would otherwise
# mess with the ordering and make testing harder
def scorer(est, X, y):
return rng.rand()
sh = Est(base_estimator, param_grid, factor=2, scoring=scorer)
if Est is HalvingRandomSearchCV:
# same number of candidates as with the grid
sh.set_params(n_candidates=2 * 30, min_resources="exhaust")
sh.fit(X, y)
# non-regression check for
# https://github.com/scikit-learn/scikit-learn/issues/19203
assert isinstance(sh.cv_results_["iter"], np.ndarray)
assert isinstance(sh.cv_results_["n_resources"], np.ndarray)
cv_results_df = pd.DataFrame(sh.cv_results_)
# just make sure we don't have ties
assert len(cv_results_df["mean_test_score"].unique()) == len(cv_results_df)
cv_results_df["params_str"] = cv_results_df["params"].apply(str)
table = cv_results_df.pivot(
index="params_str", columns="iter", values="mean_test_score"
)
# table looks like something like this:
# iter 0 1 2 3 4 5
# params_str
# {'a': 'l2', 'b': 23} 0.75 NaN NaN NaN NaN NaN
# {'a': 'l1', 'b': 30} 0.90 0.875 NaN NaN NaN NaN
# {'a': 'l1', 'b': 0} 0.75 NaN NaN NaN NaN NaN
# {'a': 'l2', 'b': 3} 0.85 0.925 0.9125 0.90625 NaN NaN
# {'a': 'l1', 'b': 5} 0.80 NaN NaN NaN NaN NaN
# ...
# where a NaN indicates that the candidate wasn't evaluated at a given
# iteration, because it wasn't part of the top-K at some previous
# iteration. We here make sure that candidates that aren't in the top-k at
# any given iteration are indeed not evaluated at the subsequent
# iterations.
nan_mask = pd.isna(table)
n_iter = sh.n_iterations_
for it in range(n_iter - 1):
already_discarded_mask = nan_mask[it]
# make sure that if a candidate is already discarded, we don't evaluate
# it later
assert (
already_discarded_mask & nan_mask[it + 1] == already_discarded_mask
).all()
# make sure that the number of discarded candidate is correct
discarded_now_mask = ~already_discarded_mask & nan_mask[it + 1]
kept_mask = ~already_discarded_mask & ~discarded_now_mask
assert kept_mask.sum() == sh.n_candidates_[it + 1]
# make sure that all discarded candidates have a lower score than the
# kept candidates
discarded_max_score = table[it].where(discarded_now_mask).max()
kept_min_score = table[it].where(kept_mask).min()
assert discarded_max_score < kept_min_score
# We now make sure that the best candidate is chosen only from the last
# iteration.
# We also make sure this is true even if there were higher scores in
# earlier rounds (this isn't generally the case, but worth ensuring it's
# possible).
last_iter = cv_results_df["iter"].max()
idx_best_last_iter = cv_results_df[cv_results_df["iter"] == last_iter][
"mean_test_score"
].idxmax()
idx_best_all_iters = cv_results_df["mean_test_score"].idxmax()
assert sh.best_params_ == cv_results_df.iloc[idx_best_last_iter]["params"]
assert (
cv_results_df.iloc[idx_best_last_iter]["mean_test_score"]
< cv_results_df.iloc[idx_best_all_iters]["mean_test_score"]
)
assert (
cv_results_df.iloc[idx_best_last_iter]["params"]
!= cv_results_df.iloc[idx_best_all_iters]["params"]
)
@pytest.mark.parametrize("Est", (HalvingGridSearchCV, HalvingRandomSearchCV))
def test_base_estimator_inputs(Est):
# make sure that the base estimators are passed the correct parameters and
# number of samples at each iteration.
pd = pytest.importorskip("pandas")
passed_n_samples_fit = []
passed_n_samples_predict = []
passed_params = []
class FastClassifierBookKeeping(FastClassifier):
def fit(self, X, y):
passed_n_samples_fit.append(X.shape[0])
return super().fit(X, y)
def predict(self, X):
passed_n_samples_predict.append(X.shape[0])
return super().predict(X)
def set_params(self, **params):
passed_params.append(params)
return super().set_params(**params)
n_samples = 1024
n_splits = 2
X, y = make_classification(n_samples=n_samples, random_state=0)
param_grid = {"a": ("l1", "l2"), "b": list(range(30))}
base_estimator = FastClassifierBookKeeping()
sh = Est(
base_estimator,
param_grid,
factor=2,
cv=n_splits,
return_train_score=False,
refit=False,
)
if Est is HalvingRandomSearchCV:
# same number of candidates as with the grid
sh.set_params(n_candidates=2 * 30, min_resources="exhaust")
sh.fit(X, y)
assert len(passed_n_samples_fit) == len(passed_n_samples_predict)
passed_n_samples = [
x + y for (x, y) in zip(passed_n_samples_fit, passed_n_samples_predict)
]
# Lists are of length n_splits * n_iter * n_candidates_at_i.
# Each chunk of size n_splits corresponds to the n_splits folds for the
# same candidate at the same iteration, so they contain equal values. We
# subsample such that the lists are of length n_iter * n_candidates_at_it
passed_n_samples = passed_n_samples[::n_splits]
passed_params = passed_params[::n_splits]
cv_results_df = pd.DataFrame(sh.cv_results_)
assert len(passed_params) == len(passed_n_samples) == len(cv_results_df)
uniques, counts = np.unique(passed_n_samples, return_counts=True)
assert (sh.n_resources_ == uniques).all()
assert (sh.n_candidates_ == counts).all()
assert (cv_results_df["params"] == passed_params).all()
assert (cv_results_df["n_resources"] == passed_n_samples).all()
@pytest.mark.parametrize("Est", (HalvingGridSearchCV, HalvingRandomSearchCV))
def test_groups_support(Est):
# Check if ValueError (when groups is None) propagates to
# HalvingGridSearchCV and HalvingRandomSearchCV
# And also check if groups is correctly passed to the cv object
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=50, n_classes=2, random_state=0)
groups = rng.randint(0, 3, 50)
clf = LinearSVC(dual="auto", random_state=0)
grid = {"C": [1]}
group_cvs = [
LeaveOneGroupOut(),
LeavePGroupsOut(2),
GroupKFold(n_splits=3),
GroupShuffleSplit(random_state=0),
]
error_msg = "The 'groups' parameter should not be None."
for cv in group_cvs:
gs = Est(clf, grid, cv=cv, random_state=0)
with pytest.raises(ValueError, match=error_msg):
gs.fit(X, y)
gs.fit(X, y, groups=groups)
non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit(random_state=0)]
for cv in non_group_cvs:
gs = Est(clf, grid, cv=cv)
# Should not raise an error
gs.fit(X, y)
@pytest.mark.parametrize("SearchCV", [HalvingRandomSearchCV, HalvingGridSearchCV])
def test_min_resources_null(SearchCV):
"""Check that we raise an error if the minimum resources is set to 0."""
base_estimator = FastClassifier()
param_grid = {"a": [1]}
X = np.empty(0).reshape(0, 3)
search = SearchCV(base_estimator, param_grid, min_resources="smallest")
err_msg = "min_resources_=0: you might have passed an empty dataset X."
with pytest.raises(ValueError, match=err_msg):
search.fit(X, [])
@pytest.mark.parametrize("SearchCV", [HalvingGridSearchCV, HalvingRandomSearchCV])
def test_select_best_index(SearchCV):
"""Check the selection strategy of the halving search."""
results = { # this isn't a 'real world' result dict
"iter": np.array([0, 0, 0, 0, 1, 1, 2, 2, 2]),
"mean_test_score": np.array([4, 3, 5, 1, 11, 10, 5, 6, 9]),
"params": np.array(["a", "b", "c", "d", "e", "f", "g", "h", "i"]),
}
# we expect the index of 'i'
best_index = SearchCV._select_best_index(None, None, results)
assert best_index == 8
def test_halving_random_search_list_of_dicts():
"""Check the behaviour of the `HalvingRandomSearchCV` with `param_distribution`
being a list of dictionary.
"""
X, y = make_classification(n_samples=150, n_features=4, random_state=42)
params = [
{"kernel": ["rbf"], "C": expon(scale=10), "gamma": expon(scale=0.1)},
{"kernel": ["poly"], "degree": [2, 3]},
]
param_keys = (
"param_C",
"param_degree",
"param_gamma",
"param_kernel",
)
score_keys = (
"mean_test_score",
"mean_train_score",
"rank_test_score",
"split0_test_score",
"split1_test_score",
"split2_test_score",
"split0_train_score",
"split1_train_score",
"split2_train_score",
"std_test_score",
"std_train_score",
"mean_fit_time",
"std_fit_time",
"mean_score_time",
"std_score_time",
)
extra_keys = ("n_resources", "iter")
search = HalvingRandomSearchCV(
SVC(), cv=3, param_distributions=params, return_train_score=True, random_state=0
)
search.fit(X, y)
n_candidates = sum(search.n_candidates_)
cv_results = search.cv_results_
# Check results structure
check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates, extra_keys)
check_cv_results_array_types(search, param_keys, score_keys)
assert all(
(
cv_results["param_C"].mask[i]
and cv_results["param_gamma"].mask[i]
and not cv_results["param_degree"].mask[i]
)
for i in range(n_candidates)
if cv_results["param_kernel"][i] == "poly"
)
assert all(
(
not cv_results["param_C"].mask[i]
and not cv_results["param_gamma"].mask[i]
and cv_results["param_degree"].mask[i]
)
for i in range(n_candidates)
if cv_results["param_kernel"][i] == "rbf"
)
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