Error from evaluate-fit-classifier: "This classifier does not support confidence values"

[alexkrohn]

I got this error using rescript evaluate-fit-classifier. I am using qiime 2022.2 and scikit-learn 0.24.1 on a Linux server. I am attempting to create a reference database of the Sylvilagus bachmani genome so I can blast sequences to it to determine if they are from S. bachmani or another organism. I can recreate this error by doing this:

wget https://data.qiime2.org/distro/core/qiime2-2022.2-py38-linux-conda.yml
conda env create -n qiime2-2022.2 --file qiime2-2022.2-py38-linux-conda.yml
conda activate qiime2-2022.2

pip install git+https://github.com/bokulich-lab/RESCRIPt.git

qiime rescript get-ncbi-data --p-query '(512907[BioProject]) AND "Sylvilagus bachmani"[porgn:__txid365149] ' \
--o-sequences bachmani-refseqs-unfiltered.qza \
--o-taxonomy bachmani-refseqs-taxonomy-unfiltered.qza \
--p-n-jobs 5

qiime rescript evaluate-fit-classifier  --i-sequences bachmani-refseqs-unfiltered.qza \
 --i-taxonomy bachmani-refseqs-taxonomy-unfiltered.qza  \
 --o-classifier bachmani-classifier.qza \
--o-observed-taxonomy bachmani-classifier-predicted-taxonomy.qza \
--verbose

Error info:

Validation: 134.04s
/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/q2_feature_classifier/classifier.py:102: UserWarning: The TaxonomicClassifier artifact that results from this method was trained using scikit-learn version 0.24.1. 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)
Training: 2663.73s
Traceback (most recent call last):
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/q2cli/commands.py", line 339, in __call__
    results = action(**arguments)
  File "<decorator-gen-457>", line 2, in evaluate_fit_classifier
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/qiime2/sdk/action.py", line 245, in bound_callable
    outputs = self._callable_executor_(scope, callable_args,
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/qiime2/sdk/action.py", line 485, in _callable_executor_
    outputs = self._callable(scope.ctx, **view_args)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/rescript/cross_validate.py", line 48, in evaluate_fit_classifie
r
    observed_taxonomy, = classify(reads=sequences,
  File "<decorator-gen-513>", line 2, in classify_sklearn
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/qiime2/sdk/action.py", line 245, in bound_callable
    outputs = self._callable_executor_(scope, callable_args,
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/qiime2/sdk/action.py", line 391, in _callable_executor_
    output_views = self._callable(**view_args)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/q2_feature_classifier/classifier.py", line 220, in classify_skl
earn
    seq_ids, taxonomy, confidence = list(zip(*predictions))
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/q2_feature_classifier/_skl.py", line 46, in predict
    for calculated in workers(jobs):
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/joblib/parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/q2_feature_classifier/_skl.py", line 54, in _predict_chunk
    return _predict_chunk_with_conf(pipeline, separator, confidence, chunk)
  File "/home/tangled/miniconda3/envs/qiime/lib/python3.8/site-packages/q2_feature_classifier/_skl.py", line 70, in _predict_chunk_with
_conf
    raise ValueError('this classifier does not support confidence values')
ValueError: this classifier does not support confidence values

Plugin error from rescript:

  this classifier does not support confidence values

See above for debug info

Any idea why this error was created and what I'm doing wrong here? Is this a problem with the new 2022.2 qiime version?

[nbokulich]

Hi @alexkrohn can you please post your question to the QIIME 2 forum in the "community plugin" category? we can provide user support over there. Thanks!

[alexkrohn]

Sounds good @nbokulich. I didn't think to go to qiime first for a rescript issue. Looks like there are some other folks on that forum with the same problems. I'll see what I can dig up before reposting.