Hello, I am receiving a similar error but for a different reason that I cannot interpret. I am using Greengenes 99_otus following the train your classifier tutorial. This is what I’ve entered and the error I got with --verbose
(qiime2-2020.8) qiime2@qiime2core2020-8:/media/sf_Desktop/Candice/BabyPoohStudy/breastmilk-Microbiome$ qiime feature-classifier fit-classifier-naive-bayes --i-reference-reads ref-seqs_210.qza --i-reference-taxonomy ref-taxoomy.qza --o-classifier classifier.qza --verbose
/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/q2_feature_classifier/classifier.py:102: UserWarning: The TaxonomicClassifier artifact that results from this method was trained using scikit-learn version 0.23.1. It cannot be used with other versions of scikit-learn. (While the classifier may complete successfully, the results will be unreliable.)
warnings.warn(warning, UserWarning)
Traceback (most recent call last):
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/q2cli/commands.py”, line 329, in call
results = action(**arguments)
File “”, line 2, in fit_classifier_naive_bayes
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/qiime2/sdk/action.py”, line 245, in bound_callable
output_types, provenance)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/qiime2/sdk/action.py”, line 390, in callable_executor
output_views = self._callable(**view_args)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/q2_feature_classifier/classifier.py”, line 331, in generic_fitter
pipeline)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/q2_feature_classifier/_skl.py”, line 32, in fit_pipeline
pipeline.fit(X, y)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/sklearn/pipeline.py”, line 335, in fit
self.final_estimator.fit(Xt, y, **fit_params_last_step)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/q2_feature_classifier/custom.py”, line 41, in fit
classes=classes)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/sklearn/naive_bayes.py”, line 565, in partial_fit
Y = label_binarize(y, classes=self.classes)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/sklearn/utils/validation.py”, line 73, in inner_f
return f(**kwargs)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/sklearn/preprocessing/_label.py”, line 683, in label_binarize
Y = Y.toarray()
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/scipy/sparse/compressed.py”, line 1025, in toarray
out = self._process_toarray_args(order, out)
File “/home/qiime2/miniconda/envs/qiime2-2020.8/lib/python3.6/site-packages/scipy/sparse/base.py”, line 1185, in _process_toarray_args
return np.zeros(self.shape, dtype=self.dtype, order=order)
MemoryError: Unable to allocate 820. MiB for an array with shape (20000, 5377) and data type int64
Plugin error from feature-classifier:
Unable to allocate 820. MiB for an array with shape (20000, 5377) and data type int64
See above for debug info.