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regress-samples: Train and test a cross-validated supervised learning regressor.ΒΆ

Docstring:

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

  Predicts a continuous sample metadata column using a supervised learning
  regressor. Splits input data into training and test sets. The training set
  is used to train and test the estimator using a stratified k-fold cross-
  validation scheme. This includes optional steps for automated feature
  extraction and hyperparameter optimization. The test set validates
  classification accuracy of the optimized estimator. Outputs classification
  results for test set. For more details on the learning algorithm, see
  http://scikit-learn.org/stable/supervised_learning.html

Options:
  --i-table ARTIFACT PATH FeatureTable[Frequency]
                                  Feature table containing all features that
                                  should be used for target prediction.
                                  [required]
  --m-metadata-file MULTIPLE FILE
                                  Metadata file or artifact viewable as
                                  metadata. This option may be supplied
                                  multiple times to merge metadata.
                                  [required]
  --m-metadata-column MetadataColumn[Numeric]
                                  Column from metadata file or artifact
                                  viewable as metadata. Numeric metadata
                                  column to use as prediction target.
                                  [required]
  --p-test-size FLOAT             Fraction of input samples to exclude from
                                  training set and use for classifier testing.
                                  [default: 0.2]
  --p-step FLOAT                  If optimize_feature_selection is True, step
                                  is the percentage of features to remove at
                                  each iteration.  [default: 0.05]
  --p-cv INTEGER RANGE            Number of k-fold cross-validations to
                                  perform.  [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  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 [SVR|ElasticNet|ExtraTreesRegressor|RandomForestRegressor|LinearSVR|Ridge|AdaBoostRegressor|Lasso|KNeighborsRegressor|GradientBoostingRegressor]
                                  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-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 [ignore|error]
                                  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]
  --o-sample-estimator ARTIFACT PATH SampleEstimator[Regressor]
                                  Trained sample estimator.  [required if not
                                  passing --output-dir]
  --o-feature-importance ARTIFACT PATH FeatureData[Importance]
                                  Importance of each input feature to model
                                  accuracy.  [required if not passing
                                  --output-dir]
  --o-predictions ARTIFACT PATH SampleData[RegressorPredictions]
                                  Predicted target values for each input
                                  sample.  [required if not passing --output-
                                  dir]
  --o-model-summary VISUALIZATION PATH
                                  Summarized parameter and (if enabled)
                                  feature selection information for the
                                  trained estimator.  [required if not passing
                                  --output-dir]
  --o-accuracy-results VISUALIZATION PATH
                                  Accuracy results visualization.  [required
                                  if not passing --output-dir]
  --output-dir DIRECTORY          Output unspecified results to a directory
  --cmd-config FILE               Use config file for command options
  --verbose                       Display verbose output to stdout and/or
                                  stderr during execution of this action.
                                  [default: False]
  --quiet                         Silence output if execution is successful
                                  (silence is golden).  [default: False]
  --citations                     Show citations and exit.
  --help                          Show this message and exit.

Import:

from qiime2.plugins.sample_classifier.pipelines import regress_samples

Docstring:

Train and test a cross-validated supervised learning regressor.

Predicts a continuous sample metadata column using a supervised learning
regressor. Splits input data into training and test sets. The training set
is used to train and test the estimator using a stratified k-fold cross-
validation scheme. This includes optional steps for automated feature
extraction and hyperparameter optimization. The test set validates
classification accuracy of the optimized estimator. Outputs classification
results for test set. For more details on the learning algorithm, see
http://scikit-learn.org/stable/supervised_learning.html

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.
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.
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({'AdaBoostRegressor', 'ElasticNet', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'KNeighborsRegressor', 'Lasso', 'LinearSVR', 'RandomForestRegressor', 'Ridge', 'SVR'}), optional
    Estimator method to use for sample prediction.
optimize_feature_selection : Bool, optional
    Automatically optimize input feature selection using recursive feature
    elimination.
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
-------
sample_estimator : SampleEstimator[Regressor]
    Trained sample estimator.
feature_importance : FeatureData[Importance]
    Importance of each input feature to model accuracy.
predictions : SampleData[RegressorPredictions]
    Predicted target values for each input sample.
model_summary : Visualization
    Summarized parameter and (if enabled) feature selection information for
    the trained estimator.
accuracy_results : Visualization
    Accuracy results visualization.