Error from feature-classifier RDP-database

Hi.
I try to create a classifier for the complete RDP-database.
(RDP database - Unaligned Bacteria 16S fasta file)

To start with workflow, I need the database file separated into taxonomy and otu file. It separate the one RDP-database into files.
(RDP-ref-seq.fna.gz, RDP-ref-taxonomy.txt)

[RDP-ref-seq.fna.gz]

>000494589
GCGGCGTGCTACACATGCAGTCGTACGCGGTGGCAC...

[RDP-ref-taxonomy.txt]

000494589 k__Bacteria; p__Actinobacteria; c__Actinobacteria; Acidimicrobidae; o__Acidimicrobiales Acidimicrobineae; f__Acidimicrobiaceae;
...

Importing these files inrto qiime2(2018.11) artifacts.
So, I am able to start

qiime feature-classifier fit-classifier-naive-bayes
--i-reference-reads RDP-ref-seq.qza
--i-reference-taxonomy RDP-ref-taxonomy.qza
--o-classifier RDP-classifier.qza

After a while I get the following error.
Plugin error from feature-classifier:
indices and data should have the same size

This is full error message from the log file.

UserWarning: The TaxonomicClassifier artifact that results from this method was trained using scikit-learn version 0.19.1. 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 "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/q2cli/commands.py", line 274, in call
results = action(**arguments)
File "", line 2, in fit_classifier_naive_bayes
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/qiime2/sdk/action.py", line 231, in bound_callable
output_types, provenance)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/qiime2/sdk/action.py", line 362, in callable_executor
output_views = self._callable(**view_args)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/q2_feature_classifier/classifier.py", line 316, in generic_fitter
pipeline)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/q2_feature_classifier/_skl.py", line 32, in fit_pipeline
pipeline.fit(X, y)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/pipeline.py", line 248, in fit
Xt, fit_params = self._fit(X, y, **fit_params)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/pipeline.py", line 213, in _fit
**fit_params_steps[name])
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 362, in call
return self.func(*args, **kwargs)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/pipeline.py", line 581, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/base.py", line 520, in fit_transform
return self.fit(X, y, **fit_params).transform(X)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/feature_extraction/text.py", line 519, in transform
X = self._get_hasher().transform(analyzer(doc) for doc in X)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/sklearn/feature_extraction/hashing.py", line 167, in transform
shape=(n_samples, self.n_features))
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/scipy/sparse/compressed.py", line 98, in init
self.check_format(full_check=False)
File "/TBI/People/tbi/keyun/miniconda2/envs/qiime2/lib/python3.5/site-packages/scipy/sparse/compressed.py", line 167, in check_format
raise ValueError("indices and data should have the same size")

I don't understand the meaning of error message.
Help me... Please..

1 Like

See the explanation here: Error from feature-classifier: indices and data should have the same size - #4 by Nicholas_Bokulich

2 Likes

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