Docstring:
Usage: qiime feature-classifier classify-sklearn [OPTIONS]
Classify reads by taxon using a fitted classifier.
Inputs:
--i-reads ARTIFACT FeatureData[Sequence]
The feature data to be classified. [required]
--i-classifier ARTIFACT
TaxonomicClassifier The taxonomic classifier for classifying the reads.
[required]
Parameters:
--p-reads-per-batch VALUE Int % Range(1, None) | Str % Choices('auto')
Number of reads to process in each batch. If "auto",
this parameter is autoscaled to min( number of query
sequences / n-jobs, 20000). [default: 'auto']
--p-n-jobs NTHREADS The maximum number of concurrent worker processes.
If 0 all CPUs are used. If 1 is given, no parallel
computing code is used at all, which is useful for
debugging. [default: 1]
--p-pre-dispatch TEXT "all" or expression, as in "3*n_jobs". The number of
batches (of tasks) to be pre-dispatched.
[default: '2*n_jobs']
--p-confidence VALUE Float % Range(0, 1, inclusive_end=True) | Str %
Choices('disable') Confidence threshold for limiting taxonomic depth.
Set to "disable" to disable confidence calculation,
or 0 to calculate confidence but not apply it to
limit the taxonomic depth of the assignments.
[default: 0.7]
--p-read-orientation TEXT Choices('same', 'reverse-complement', 'auto')
Direction of reads with respect to reference
sequences. same will cause reads to be classified
unchanged; reverse-complement will cause reads to be
reversed and complemented prior to classification.
"auto" will autodetect orientation based on the
confidence estimates for the first 100 reads.
[default: 'auto']
Outputs:
--o-classification ARTIFACT FeatureData[Taxonomy]
[required]
Miscellaneous:
--output-dir PATH Output unspecified results to a directory
--verbose / --quiet Display verbose output to stdout and/or stderr
during execution of this action. Or silence output if
execution is successful (silence is golden).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--use-cache DIRECTORY Specify the cache to be used for the intermediate
work of this action. If not provided, the default
cache under $TMP/qiime2/ will be used.
IMPORTANT FOR HPC USERS: If you are on an HPC system
and are using parallel execution it is important to
set this to a location that is globally accessible to
all nodes in the cluster.
--help Show this message and exit.
Import:
from qiime2.plugins.feature_classifier.methods import classify_sklearn
Docstring:
Pre-fitted sklearn-based taxonomy classifier
Classify reads by taxon using a fitted classifier.
Parameters
----------
reads : FeatureData[Sequence]
The feature data to be classified.
classifier : TaxonomicClassifier
The taxonomic classifier for classifying the reads.
reads_per_batch : Int % Range(1, None) | Str % Choices('auto'), optional
Number of reads to process in each batch. If "auto", this parameter is
autoscaled to min( number of query sequences / n_jobs, 20000).
n_jobs : Threads, optional
The maximum number of concurrent worker processes. If 0 all CPUs are
used. If 1 is given, no parallel computing code is used at all, which
is useful for debugging.
pre_dispatch : Str, optional
"all" or expression, as in "3*n_jobs". The number of batches (of tasks)
to be pre-dispatched.
confidence : Float % Range(0, 1, inclusive_end=True) | Str % Choices('disable'), optional
Confidence threshold for limiting taxonomic depth. Set to "disable" to
disable confidence calculation, or 0 to calculate confidence but not
apply it to limit the taxonomic depth of the assignments.
read_orientation : Str % Choices('same', 'reverse-complement', 'auto'), optional
Direction of reads with respect to reference sequences. same will cause
reads to be classified unchanged; reverse-complement will cause reads
to be reversed and complemented prior to classification. "auto" will
autodetect orientation based on the confidence estimates for the first
100 reads.
Returns
-------
classification : FeatureData[Taxonomy]
