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fit-regressor: Fit a supervised learning regressor.ΒΆ

Docstring:

Usage: qiime sample-classifier fit-regressor [OPTIONS]

  Fit a supervised learning regressor. Outputs the fit estimator (for
  prediction of test samples and/or unknown samples) and the relative
  importance of each feature for model accuracy. Optionally use k-fold
  cross-validation for automatic recursive feature elimination and
  hyperparameter tuning.

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-step PROPORTION Range(0.0, 1.0, inclusive_start=False)
                       If optimize-feature-selection is True, step is the
                       percentage of features to remove at each iteration.
                                                               [default: 0.05]
  --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-optimize-feature-selection / --p-no-optimize-feature-selection
                       Automatically optimize input feature selection using
                       recursive feature elimination.         [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-sample-estimator ARTIFACT SampleEstimator[Regressor]
                                                                    [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 fit_regressor

Docstring:

Fit a supervised learning regressor.

Fit a supervised learning regressor. Outputs the fit estimator (for
prediction of test samples and/or unknown samples) and the relative
importance of each feature for model accuracy. Optionally use k-fold cross-
validation for automatic recursive feature elimination and hyperparameter
tuning.

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.
step : Float % Range(0.0, 1.0, inclusive_start=False), optional
    If optimize_feature_selection is True, step is the percentage of
    features to remove at each iteration.
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.
optimize_feature_selection : Bool, optional
    Automatically optimize input feature selection using recursive feature
    elimination.
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
-------
sample_estimator : SampleEstimator[Regressor]
feature_importance : FeatureData[Importance]
    Importance of each input feature to model accuracy.