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Stop using Parallel for SparkFeatureUnion #69

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Stop using Parallel for SparkFeatureUnion #69

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12 changes: 7 additions & 5 deletions splearn/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,8 @@
import numpy as np
import scipy.sparse as sp
from sklearn.externals import six
from sklearn.externals.joblib import Parallel, delayed
from sklearn.pipeline import FeatureUnion, Pipeline, _name_estimators
from splearn.rdd import ArrayRDD, DictRDD
from splearn.rdd import DictRDD


class SparkPipeline(Pipeline):
Expand Down Expand Up @@ -218,6 +217,7 @@ class SparkFeatureUnion(FeatureUnion):
List of transformer objects to be applied to the data. The first
half of each tuple is the name of the transformer.
n_jobs: int, optional
Ignored on spark, useful after conversion to scikit object
Number of jobs to run in parallel (default 1).
transformer_weights: dict, optional
Multiplicative weights for features per transformer.
Expand All @@ -239,9 +239,11 @@ def fit(self, Z, **fit_params):
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval

transformers = Parallel(n_jobs=self.n_jobs, backend="threading")(
delayed(_fit_one_transformer)(trans, Z, **fit_params_steps[name])
for name, trans in self.transformer_list)
transformers = [_fit_one_transformer(trans,
Z,
**fit_params_steps[name])
for name, trans in self.transformer_list]

self._update_transformer_list(transformers)
return self

Expand Down