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regress-samples-ncv: Nested cross-validated supervised learning regressor.ΒΆ

Docstring:

Usage: qiime sample-classifier regress-samples-ncv [OPTIONS]

  Predicts a continuous sample metadata column using a supervised learning
  regressor. Uses nested stratified k-fold cross validation for automated
  hyperparameter optimization and sample prediction. Outputs predicted
  values for each input sample, and relative importance of each feature for
  model accuracy.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                       Feature table containing all features that should be
                       used for target prediction.                  [required]
Parameters:
  --m-metadata-file METADATA
  --m-metadata-column COLUMN  MetadataColumn[Numeric]
                       Numeric metadata column to use as prediction target.
                                                                    [required]
  --p-cv INTEGER       Number of k-fold cross-validations to perform.
    Range(1, None)                                                [default: 5]
  --p-random-state INTEGER
                       Seed used by random number generator.        [optional]
  --p-n-jobs INTEGER   Number of jobs to run in parallel.         [default: 1]
  --p-n-estimators INTEGER
    Range(1, None)     Number of trees to grow for estimation. More trees
                       will improve predictive accuracy up to a threshold
                       level, but will also increase time and memory
                       requirements. This parameter only affects ensemble
                       estimators, such as Random Forest, AdaBoost,
                       ExtraTrees, and GradientBoosting.        [default: 100]
  --p-estimator TEXT Choices('RandomForestRegressor',
    'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor',
    'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')
                       Estimator method to use for sample prediction.
                                            [default: 'RandomForestRegressor']
  --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: False]
  --p-parameter-tuning / --p-no-parameter-tuning
                       Automatically tune hyperparameters using random grid
                       search.                                [default: False]
  --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-predictions ARTIFACT SampleData[RegressorPredictions]
                       Predicted target values for each input sample.
                                                                    [required]
  --o-feature-importance ARTIFACT FeatureData[Importance]
                       Importance of each input feature to model accuracy.
                                                                    [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 regress_samples_ncv

Docstring:

Nested cross-validated supervised learning regressor.

Predicts a continuous sample metadata column using a supervised learning
regressor. Uses nested stratified k-fold cross validation for automated
hyperparameter optimization and sample prediction. Outputs predicted values
for each input sample, and relative importance of each feature for model
accuracy.

Parameters
----------
table : FeatureTable[Frequency]
    Feature table containing all features that should be used for target
    prediction.
metadata : MetadataColumn[Numeric]
    Numeric metadata column to use as prediction target.
cv : Int % Range(1, None), optional
    Number of k-fold cross-validations to perform.
random_state : Int, optional
    Seed used by random number generator.
n_jobs : Int, optional
    Number of jobs to run in parallel.
n_estimators : Int % Range(1, None), optional
    Number of trees to grow for estimation. More trees will improve
    predictive accuracy up to a threshold level, but will also increase
    time and memory requirements. This parameter only affects ensemble
    estimators, such as Random Forest, AdaBoost, ExtraTrees, and
    GradientBoosting.
estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
    Estimator method to use for sample prediction.
stratify : Bool, optional
    Evenly stratify training and test data among metadata categories. If
    True, all values in column must match at least two samples.
parameter_tuning : Bool, optional
    Automatically tune hyperparameters using random grid search.
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
-------
predictions : SampleData[RegressorPredictions]
    Predicted target values for each input sample.
feature_importance : FeatureData[Importance]
    Importance of each input feature to model accuracy.