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Fixes failing import _joblib_parallel_args #106

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Merged
merged 3 commits into from
Feb 18, 2022
Merged

Fixes failing import _joblib_parallel_args #106

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75a8317
Fixes failing import _joblib_parallel_args
sdpython Feb 18, 2022
ec4d729
remove another instance
sdpython Feb 18, 2022
0e84267
remove one instance
sdpython Feb 18, 2022
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3 changes: 1 addition & 2 deletions mlinsights/mlmodel/interval_regressor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,6 @@
import numpy.random
from sklearn.base import RegressorMixin, clone, BaseEstimator
from sklearn.utils._joblib import Parallel, delayed
from sklearn.utils.fixes import _joblib_parallel_args
try:
from tqdm import tqdm
except ImportError: # pragma: no cover
Expand Down Expand Up @@ -93,7 +92,7 @@ def _fit_piecewise_estimator(i, est, X, y, sample_weight, alpha):

self.estimators_ = \
Parallel(n_jobs=self.n_jobs, verbose=verbose,
**_joblib_parallel_args(prefer='threads'))(
prefer='threads')(
delayed(_fit_piecewise_estimator)(
i, estimators[i], X, y, sample_weight, self.alpha)
for i in loop)
Expand Down
6 changes: 2 additions & 4 deletions mlinsights/mlmodel/piecewise_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.utils._joblib import Parallel, delayed
from sklearn.utils.fixes import _joblib_parallel_args
try:
from tqdm import tqdm
except ImportError: # pragma: no cover
Expand Down Expand Up @@ -260,8 +259,7 @@ def fit(self, X, y, sample_weight=None):
rnd = None

self.estimators_ = \
Parallel(n_jobs=self.n_jobs, verbose=verbose,
**_joblib_parallel_args(prefer='threads'))(
Parallel(n_jobs=self.n_jobs, verbose=verbose, prefer='threads')(
delayed(_fit_piecewise_estimator)(
i, estimators[i], X, y, sample_weight, association, nb_classes, rnd)
for i in loop)
Expand All @@ -288,7 +286,7 @@ def _apply_predict_method(self, X, method, parallelized, dimout):

association = self.transform_bins(X)

indpred = Parallel(n_jobs=self.n_jobs, **_joblib_parallel_args(prefer='threads'))(
indpred = Parallel(n_jobs=self.n_jobs, prefer='threads')(
delayed(parallelized)(i, model, X, association)
for i, model in enumerate(self.estimators_))

Expand Down