Docstring:
Usage: qiime sample-classifier predict-classification [OPTIONS]
Use trained estimator to predict target values for new samples. These will
typically be unseen samples, e.g., test data (derived manually or from
split_table) or samples with unknown values, but can theoretically be any
samples present in a feature table that contain overlapping features with
the feature table used to train the estimator.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency | RelativeFrequency |
PresenceAbsence | Composition]
Feature table containing all features that should be
used for target prediction. [required]
--i-sample-estimator ARTIFACT SampleEstimator[Classifier]
Sample classifier trained with fit_classifier.
[required]
Parameters:
--p-n-jobs NTHREADS Number of jobs to run in parallel. [default: 1]
Outputs:
--o-predictions ARTIFACT SampleData[ClassifierPredictions]
Predicted target values for each input sample.
[required]
--o-probabilities ARTIFACT SampleData[Probabilities]
Predicted class probabilities for each input sample.
[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.sample_classifier.methods import predict_classification
Docstring:
Use trained classifier to predict target values for new samples.
Use trained estimator to predict target values for new samples. These will
typically be unseen samples, e.g., test data (derived manually or from
split_table) or samples with unknown values, but can theoretically be any
samples present in a feature table that contain overlapping features with
the feature table used to train the estimator.
Parameters
----------
table : FeatureTable[Frequency | RelativeFrequency | PresenceAbsence | Composition]
Feature table containing all features that should be used for target
prediction.
sample_estimator : SampleEstimator[Classifier]
Sample classifier trained with fit_classifier.
n_jobs : Threads, optional
Number of jobs to run in parallel.
Returns
-------
predictions : SampleData[ClassifierPredictions]
Predicted target values for each input sample.
probabilities : SampleData[Probabilities]
Predicted class probabilities for each input sample.
