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.
--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
