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Description
Thanks for publishing such a useful tool!
A few days ago, LightGBM's new version 4.0.0 has been released.
In this release, early_stopping_rounds argument in fit() was removed.
So, functions that use cross_validate() such as run_experiment don't work.
(There may be other functions that don't work, I haven't investigated yet.)
Of cource, there is no probrem with versions before 3.3.5.
pytest log
(nyaggle) yuta100101:~/nyaggle(master =)$ pytest tests/validation/test_cross_validate.py::test_cv_lgbm
========================================================================================== test session starts ===========================================================================================
platform linux -- Python 3.9.17, pytest-7.4.0, pluggy-1.2.0
rootdir: /home/yuta100101/practice/nyaggle
collected 1 item
tests/validation/test_cross_validate.py F [100%]
================================================================================================ FAILURES ================================================================================================
______________________________________________________________________________________________ test_cv_lgbm ______________________________________________________________________________________________
def test_cv_lgbm():
X, y = make_classification(n_samples=1024, n_features=20, class_sep=0.98, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
models = [LGBMClassifier(n_estimators=300) for _ in range(5)]
> pred_oof, pred_test, scores, importance = cross_validate(models, X_train, y_train, X_test, cv=5,
eval_func=roc_auc_score,
fit_params={'early_stopping_rounds': 200})
tests/validation/test_cross_validate.py:52:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
estimator = [LGBMClassifier(n_estimators=300), LGBMClassifier(n_estimators=300), LGBMClassifier(n_estimators=300), LGBMClassifier(n_estimators=300), LGBMClassifier(n_estimators=300)]
X_train = 0 1 2 3 4 5 6 7 8 ... 11 12... ... -0.109782 -0.412230 1.707714 -0.240937 -0.276747 0.481276 -0.278111 1.304773 -0.139538
[512 rows x 20 columns]
y = 0 0
1 0
2 0
3 1
4 0
..
507 0
508 1
509 0
510 1
511 0
Name: target, Length: 512, dtype: int64
X_test = 0 1 2 3 4 5 6 7 8 ... 11 12... ... -2.598922 -0.351561 0.233836 -1.873634 -1.089221 0.373956 -0.520939 -0.489945 2.452996
[512 rows x 20 columns]
cv = KFold(n_splits=5, random_state=0, shuffle=True), groups = None, eval_func = <function roc_auc_score at 0x7fe910196ee0>, logger = <Logger nyaggle.validation.cross_validate (WARNING)>
on_each_fold = None, fit_params = {'early_stopping_rounds': 200}, importance_type = 'gain', early_stopping = True, type_of_target = 'binary'
def cross_validate(estimator: Union[BaseEstimator, List[BaseEstimator]],
X_train: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray],
X_test: Union[pd.DataFrame, np.ndarray] = None,
cv: Optional[Union[int, Iterable, BaseCrossValidator]] = None,
groups: Optional[pd.Series] = None,
eval_func: Optional[Callable] = None, logger: Optional[Logger] = None,
on_each_fold: Optional[Callable[[int, BaseEstimator, pd.DataFrame, pd.Series], None]] = None,
fit_params: Optional[Union[Dict[str, Any], Callable]] = None,
importance_type: str = 'gain',
early_stopping: bool = True,
type_of_target: str = 'auto') -> CVResult:
"""
Evaluate metrics by cross-validation. It also records out-of-fold prediction and test prediction.
Args:
estimator:
The object to be used in cross-validation. For list inputs, ``estimator[i]`` is trained on i-th fold.
X_train:
Training data
y:
Target
X_test:
Test data (Optional). If specified, prediction on the test data is performed using ensemble of models.
cv:
int, cross-validation generator or an iterable which determines the cross-validation splitting strategy.
- None, to use the default ``KFold(5, random_state=0, shuffle=True)``,
- integer, to specify the number of folds in a ``(Stratified)KFold``,
- CV splitter (the instance of ``BaseCrossValidator``),
- An iterable yielding (train, test) splits as arrays of indices.
groups:
Group labels for the samples. Only used in conjunction with a “Group” cv instance (e.g., ``GroupKFold``).
eval_func:
Function used for logging and returning scores
logger:
logger
on_each_fold:
called for each fold with (idx_fold, model, X_fold, y_fold)
fit_params:
Parameters passed to the fit method of the estimator
importance_type:
The type of feature importance to be used to calculate result.
Used only in ``LGBMClassifier`` and ``LGBMRegressor``.
early_stopping:
If ``True``, ``eval_set`` will be added to ``fit_params`` for each fold.
``early_stopping_rounds = 100`` will also be appended to fit_params if it does not already have one.
type_of_target:
The type of target variable. If ``auto``, type is inferred by ``sklearn.utils.multiclass.type_of_target``.
Otherwise, ``binary``, ``continuous``, or ``multiclass`` are supported.
Returns:
Namedtuple with following members
* oof_prediction (numpy array, shape (len(X_train),)):
The predicted value on put-of-Fold validation data.
* test_prediction (numpy array, hape (len(X_test),)):
The predicted value on test data. ``None`` if X_test is ``None``.
* scores (list of float, shape (nfolds+1,)):
``scores[i]`` denotes validation score in i-th fold.
``scores[-1]`` is the overall score. `None` if eval is not specified.
* importance (list of pandas DataFrame, shape (nfolds,)):
``importance[i]`` denotes feature importance in i-th fold model.
If the estimator is not GBDT, empty array is returned.
Example:
>>> from sklearn.datasets import make_regression
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import mean_squared_error
>>> from nyaggle.validation import cross_validate
>>> X, y = make_regression(n_samples=8)
>>> model = Ridge(alpha=1.0)
>>> pred_oof, pred_test, scores, _ = \
>>> cross_validate(model,
>>> X_train=X[:3, :],
>>> y=y[:3],
>>> X_test=X[3:, :],
>>> cv=3,
>>> eval_func=mean_squared_error)
>>> print(pred_oof)
[-101.1123267 , 26.79300693, 17.72635528]
>>> print(pred_test)
[-10.65095894 -12.18909059 -23.09906427 -17.68360714 -20.08218267]
>>> print(scores)
[71912.80290003832, 15236.680239881942, 15472.822033121925, 34207.43505768073]
"""
cv = check_cv(cv, y)
n_output_cols = 1
if type_of_target == 'auto':
type_of_target = multiclass.type_of_target(y)
if type_of_target == 'multiclass':
n_output_cols = y.nunique(dropna=True)
if isinstance(estimator, list):
assert len(estimator) == cv.get_n_splits(), "Number of estimators should be same to nfolds."
X_train = convert_input(X_train)
y = convert_input_vector(y, X_train.index)
if X_test is not None:
X_test = convert_input(X_test)
if not isinstance(estimator, list):
estimator = [estimator] * cv.get_n_splits()
assert len(estimator) == cv.get_n_splits()
if logger is None:
logger = getLogger(__name__)
def _predict(model: BaseEstimator, x: pd.DataFrame, _type_of_target: str):
if _type_of_target in ('binary', 'multiclass'):
if hasattr(model, "predict_proba"):
proba = model.predict_proba(x)
elif hasattr(model, "decision_function"):
warnings.warn('Since {} does not have predict_proba method, '
'decision_function is used for the prediction instead.'.format(type(model)))
proba = model.decision_function(x)
else:
raise RuntimeError('Estimator in classification problem should have '
'either predict_proba or decision_function')
if proba.ndim == 1:
return proba
else:
return proba[:, 1] if proba.shape[1] == 2 else proba
else:
return model.predict(x)
oof = np.zeros((len(X_train), n_output_cols)) if n_output_cols > 1 else np.zeros(len(X_train))
evaluated = np.full(len(X_train), False)
test = None
if X_test is not None:
test = np.zeros((len(X_test), n_output_cols)) if n_output_cols > 1 else np.zeros(len(X_test))
scores = []
eta_all = []
importance = []
for n, (train_idx, valid_idx) in enumerate(cv.split(X_train, y, groups)):
start_time = time.time()
train_x, train_y = X_train.iloc[train_idx], y.iloc[train_idx]
valid_x, valid_y = X_train.iloc[valid_idx], y.iloc[valid_idx]
if fit_params is None:
fit_params_fold = {}
elif callable(fit_params):
fit_params_fold = fit_params(n, train_idx, valid_idx)
else:
fit_params_fold = copy.copy(fit_params)
if is_gbdt_instance(estimator[n], ('lgbm', 'cat', 'xgb')):
if early_stopping:
if 'eval_set' not in fit_params_fold:
fit_params_fold['eval_set'] = [(valid_x, valid_y)]
if 'early_stopping_rounds' not in fit_params_fold:
fit_params_fold['early_stopping_rounds'] = 100
> estimator[n].fit(train_x, train_y, **fit_params_fold)
E TypeError: fit() got an unexpected keyword argument 'early_stopping_rounds'
nyaggle/validation/cross_validate.py:177: TypeError
======================================================================================== short test summary info =========================================================================================
FAILED tests/validation/test_cross_validate.py::test_cv_lgbm - TypeError: fit() got an unexpected keyword argument 'early_stopping_rounds'
=========================================================================================== 1 failed in 1.90s ============================================================================================
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