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fit-classifier: Fit a supervised learning classifier.ΒΆ

Docstring:

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

  Fit a supervised learning classifier. 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.

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[Categorical]
                                  Column from metadata file or artifact
                                  viewable as metadata. Numeric metadata
                                  column to use as prediction target.
                                  [required]
  --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 [KNeighborsClassifier|LinearSVC|RandomForestClassifier|ExtraTreesClassifier|AdaBoostClassifier|SVC|GradientBoostingClassifier]
                                  Estimator method to use for sample
                                  prediction.  [default:
                                  RandomForestClassifier]
  --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 [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[Classifier]
                                  Trained sample classifier.  [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]
  --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.methods import fit_classifier

Docstring:

Fit a supervised learning classifier.

Fit a supervised learning classifier. 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[Categorical]
    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({'AdaBoostClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'KNeighborsClassifier', 'LinearSVC', 'RandomForestClassifier', 'SVC'}), 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[Classifier]
    Trained sample classifier.
feature_importance : FeatureData[Importance]
    Importance of each input feature to model accuracy.