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split-table: Split a feature table into training and testing sets.

Docstring:

Usage: qiime sample-classifier split-table [OPTIONS]

  Split a feature table into training and testing sets. By default stratifies
  training and test sets on a metadata column, such that values in that column
  are evenly represented across training and test sets.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency¹ | RelativeFrequency² |
    PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶ |
    Composition⁷]        Feature table containing all features that should be
                         used for target prediction.                [required]
Parameters:
  --m-metadata-file METADATA
  --m-metadata-column COLUMN  MetadataColumn[Numeric | Categorical]
                         Numeric metadata column to use as prediction target.
                                                                    [required]
  --p-test-size PROPORTION
    Range(0.0, 1.0)      Fraction of input samples to exclude from training
                         set and use for classifier testing.    [default: 0.2]
  --p-random-state INTEGER
                         Seed used by random number generator.      [optional]
  --p-stratify / --p-no-stratify
                         Evenly stratify training and test data among
                         metadata categories. If True, all values in column
                         must match at least two samples.      [default: True]
  --p-missing-samples TEXT Choices('error', 'ignore')
                         How to handle missing samples in metadata. "error"
                         will fail if missing samples are detected. "ignore"
                         will cause the feature table and metadata to be
                         filtered, so that only samples found in both files
                         are retained.                      [default: 'error']
Outputs:
  --o-training-table ARTIFACT FeatureTable[Frequency¹ | RelativeFrequency²
    | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶ |
    Composition⁷]        Feature table containing training samples  [required]
  --o-test-table ARTIFACT FeatureTable[Frequency¹ | RelativeFrequency² |
    PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶ |
    Composition⁷]        Feature table containing test samples      [required]
  --o-training-targets ARTIFACT SampleData[TrueTargets]
                         Series containing true target values of train
                         samples                                    [required]
  --o-test-targets ARTIFACT SampleData[TrueTargets]
                         Series containing true target values of test samples
                                                                    [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 split_table

Docstring:

Split a feature table into training and testing sets.

Split a feature table into training and testing sets. By default stratifies
training and test sets on a metadata column, such that values in that
column are evenly represented across training and test sets.

Parameters
----------
table : FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶ | Composition⁷]
    Feature table containing all features that should be used for target
    prediction.
metadata : MetadataColumn[Numeric | Categorical]
    Numeric metadata column to use as prediction target.
test_size : Float % Range(0.0, 1.0), optional
    Fraction of input samples to exclude from training set and use for
    classifier testing.
random_state : Int, optional
    Seed used by random number generator.
stratify : Bool, optional
    Evenly stratify training and test data among metadata categories. If
    True, all values in column must match at least two samples.
missing_samples : Str % Choices('error', 'ignore'), optional
    How to handle missing samples in metadata. "error" will fail if missing
    samples are detected. "ignore" will cause the feature table and
    metadata to be filtered, so that only samples found in both files are
    retained.

Returns
-------
training_table : FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶ | Composition⁷]
    Feature table containing training samples
test_table : FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶ | Composition⁷]
    Feature table containing test samples
training_targets : SampleData[TrueTargets]
    Series containing true target values of train samples
test_targets : SampleData[TrueTargets]
    Series containing true target values of test samples