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

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]
                       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 INTEGER   Number of jobs to run in parallel.         [default: 1]
Outputs:
  --o-predictions ARTIFACT SampleData[ClassifierPredictions]
                       Predicted target values 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).
  --citations          Show citations and exit.
  --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]
    Feature table containing all features that should be used for target
    prediction.
sample_estimator : SampleEstimator[Classifier]
    Sample classifier trained with fit_classifier.
n_jobs : Int, optional
    Number of jobs to run in parallel.

Returns
-------
predictions : SampleData[ClassifierPredictions]
    Predicted target values for each input sample.