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predict-classification: Use trained classifier to predict target values for new samples.

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.