Fork me on GitHub

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]
                       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, inclusive_start=False)
                       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]
                       Feature table containing training samples    [required]
  --o-test-table ARTIFACT FeatureTable[Frequency¹ | RelativeFrequency² |
    PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵]
                       Feature table containing 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).
  --citations          Show citations and exit.
  --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]
    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, inclusive_start=False), 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]
    Feature table containing training samples
test_table : FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵]
    Feature table containing test samples