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[英]Nuitka Error, implicit module 'sklearn.utils._unittest_backport' expected by 'sklearn.utils' not found
[英]AttributeError: module “sklearn.utils” has no attribute “_joblib” when inheriting class `sklearn.ensemble.BaggingClassifier.`
我需要提取在sklearn.ensemble.BaggingClassifier
訓練的每個模型的概率。 這樣做的原因是為了估計XGBoostClassifier模型周圍的不確定性。
為此,我創建了一個繼承自sklearn.ensemble.BaggingClassifier
的擴展類,並添加了一個新方法來獲取這些概率。 請注意,此問題與ModuleNotFoundError不同:沒有名為“ sklearn.utils._joblib”的模塊
我在下面顯示了到目前為止已經實現的代碼:
必要的模塊
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble.base import _partition_estimators
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
import sklearn.utils as su
從BaggingClassifier
繼承的子類
class EBaggingClassifier(BaggingClassifier):
"""
Extends the class BaggingClassifier fromsklearn
"""
def __init__(self,
base_estimator=None,
n_estimators=10,
max_samples=1.0,
max_features=1.0,
bootstrap=True,
bootstrap_features=False,
oob_score=False,
warm_start=False,
n_jobs=1,
random_state=None,
verbose=0):
super().__init__(
base_estimator,
n_estimators,
max_samples,
max_features,
bootstrap,
bootstrap_features,
oob_score,
warm_start,
n_jobs,
random_state,
verbose)
下面定義了可以計算每個估計量概率的新方法。
def predict_proball(self, X):
"""
Computes the probability of each individual estimator
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
Returns
-------
p : array of shape = [n_samples, n_classes]
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
check_is_fitted(self, "classes_")
# Check data
X = check_array(
X, accept_sparse=['csr', 'csc'], dtype=None,
force_all_finite=False
)
if self.n_features_ != X.shape[1]:
raise ValueError("Number of features of the model must "
"match the input. Model n_features is {0} and "
"input n_features is {1}."
"".format(self.n_features_, X.shape[1]))
# Parallel loop
n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
self.n_jobs)
all_proba = su._joblib.Parallel(n_jobs=n_jobs, verbose=self.verbose,
**self._parallel_args())(
su._joblib.delayed(BaggingClassifier._parallel_predict_proba)(
self.estimators_[starts[i]:starts[i + 1]],
self.estimators_features_[starts[i]:starts[i + 1]],
X,
self.n_classes_)
for i in range(n_jobs))
return all_proba
我使用XGBoostClassifier
作為基本估計器來實例化此類:
base_estimator = XGBoostClassifier(**params)
estimator = EBaggingClassifier(base_estimator=base_estimator, max_samples=0.8, n_estimators=10)
然后estimator
使用estimator.fit(X, y)
其中X
和y
是pandas.DataFrame
對象。 當我嘗試運行estimator.predict_proball(X)
我得到了
>>> estimator.predict_proball(X)
AttributeError: module 'sklearn.utils' has no attribute '_joblib'
有人知道為什么會這樣嗎? 查看BaggingClassifier
腳本 ,函數“ sklearn.utils._joblib”應該可用。
僅供參考:
>>> sklearn.__version__
'0.19.2'
問題出在您的scikit-learn
版本。 版本'0.19.2'
沒有_joblib
,您可以在這里參考。 或者您可以使用以下方法進行檢查:
dir(su)
您需要更新scikit-learn
並且最新版本具有_joblib
,您可以在這里參考。
您在版本'0.20.2'
得到以下內容:
>>> dir(su)
['Bunch', 'DataConversionWarning', 'IS_PYPY', 'Memory', 'Parallel', 'Sequence',
'_IS_32BIT', '__all__', '__builtins__', '__cached__', '__doc__', '__file__',
'__loader__', '__name__', '__package__', '__path__', '__spec__', '_joblib',
'_show_versions', 'as_float_array', 'assert_all_finite', 'axis0_safe_slice',
'check_X_y', 'check_array', 'check_consistent_length', 'check_random_state',
'check_symmetric', 'class_weight', 'column_or_1d', 'compute_class_weight',
'compute_sample_weight', 'cpu_count', 'delayed', 'deprecate', 'deprecated',
'deprecation', 'effective_n_jobs', 'fixes', 'gen_batches', 'gen_even_slices',
'get_chunk_n_rows', 'get_config', 'hash', 'indexable', 'indices_to_mask',
'is_scalar_nan', 'issparse', 'msg', 'murmurhash', 'murmurhash3_32', 'np',
'numbers', 'parallel_backend', 'platform', 'register_parallel_backend',
'resample', 'safe_indexing', 'safe_mask', 'safe_sqr', 'shuffle', 'struct',
'tosequence', 'validation', 'warnings']
您可以按以下方式更新scikit-learn
:
pip install -U scikit-learn
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