Docstring:
Usage: qiime rescript evaluate-classifications [OPTIONS]
Evaluate taxonomic classification accuracy by comparing one or more sets of
true taxonomic labels to the predicted taxonomies for the same set(s) of
features. Output an interactive line plot of classification accuracy for
each pair of expected/observed taxonomies. The x-axis in these plots
represents the taxonomic levels present in the input taxonomies so are
labeled numerically instead of by rank, but typically for 7-level taxonomies
these will represent: 1 = domain/kingdom, 2 = phylum, 3 = class, 4 = order,
5 = family, 6 = genus, 7 = species.
Inputs:
--i-expected-taxonomies ARTIFACTS... List[FeatureData[Taxonomy]]
True taxonomic labels for one more more sets of
features. [required]
--i-observed-taxonomies ARTIFACTS... List[FeatureData[Taxonomy]]
Predicted classifications of same sets of features,
input in same order as expected-taxonomies.
[required]
Parameters:
--p-labels TEXT... List of labels to use for labeling evaluation
List[Str] results in the resulting visualization. Inputs are
labeled with labels in the order that each is input.
If there are fewer labels than inputs (or no
labels), unnamed inputs are labeled numerically in
sequential order. Extra labels are ignored.
[optional]
Outputs:
--o-evaluation VISUALIZATION
Visualization of classification accuracy results.
[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).
--recycle-pool TEXT Use a cache pool for pipeline resumption. QIIME 2
will cache your results in this pool for reuse by
future invocations. These pool are retained until
deleted by the user. If not provided, QIIME 2 will
create a pool which is automatically reused by
invocations of the same action and removed if the
action is successful. Note: these pools are local to
the cache you are using.
--no-recycle Do not recycle results from a previous failed
pipeline run or save the results from this run for
future recycling.
--parallel Execute your action in parallel. This flag will use
your default parallel config.
--parallel-config FILE Execute your action in parallel using a config at
the indicated path.
--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.rescript.pipelines import evaluate_classifications
Docstring:
Interactively evaluate taxonomic classification accuracy.
Evaluate taxonomic classification accuracy by comparing one or more sets of
true taxonomic labels to the predicted taxonomies for the same set(s) of
features. Output an interactive line plot of classification accuracy for
each pair of expected/observed taxonomies. The x-axis in these plots
represents the taxonomic levels present in the input taxonomies so are
labeled numerically instead of by rank, but typically for 7-level
taxonomies these will represent: 1 = domain/kingdom, 2 = phylum, 3 = class,
4 = order, 5 = family, 6 = genus, 7 = species.
Parameters
----------
expected_taxonomies : List[FeatureData[Taxonomy]]
True taxonomic labels for one more more sets of features.
observed_taxonomies : List[FeatureData[Taxonomy]]
Predicted classifications of same sets of features, input in same order
as expected_taxonomies.
labels : List[Str], optional
List of labels to use for labeling evaluation results in the resulting
visualization. Inputs are labeled with labels in the order that each is
input. If there are fewer labels than inputs (or no labels), unnamed
inputs are labeled numerically in sequential order. Extra labels are
ignored.
Returns
-------
evaluation : Visualization
Visualization of classification accuracy results.
