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feature-volatility: Feature volatility analysisΒΆ

Docstring:

Usage: qiime longitudinal feature-volatility [OPTIONS]

  Identify features that are predictive of a numeric metadata column,
  state_column (e.g., time), and plot their relative frequencies across
  states using interactive feature volatility plots. A supervised learning
  regressor is used to identify important features and assess their ability
  to predict sample states. state_column will typically be a measure of
  time, but any numeric metadata column can be used.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                         Feature table containing all features that should be
                         used for target prediction.                [required]
Parameters:
  --m-metadata-file METADATA...
    (multiple            Sample metadata file containing
     arguments will be   individual-id-column.
     merged)                                                        [required]
  --p-state-column TEXT  Metadata containing collection time (state) values
                         for each sample. Must contain exclusively numeric
                         values.                                    [required]
  --p-individual-id-column TEXT
                         Metadata column containing IDs for individual
                         subjects.                                  [optional]
  --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-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-filtered-table ARTIFACT FeatureTable[RelativeFrequency]
                         Feature table containing only important features.
                                                                    [required]
  --o-feature-importance ARTIFACT FeatureData[Importance]
                         Importance of each input feature to model accuracy.
                                                                    [required]
  --o-volatility-plot VISUALIZATION
                         Interactive volatility plot visualization. [required]
  --o-accuracy-results VISUALIZATION
                         Accuracy results visualization.            [required]
  --o-sample-estimator ARTIFACT SampleEstimator[Regressor]
                         Trained sample regressor.                  [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.longitudinal.pipelines import feature_volatility

Docstring:

Feature volatility analysis

Identify features that are predictive of a numeric metadata column,
state_column (e.g., time), and plot their relative frequencies across
states using interactive feature volatility plots. A supervised learning
regressor is used to identify important features and assess their ability
to predict sample states. state_column will typically be a measure of time,
but any numeric metadata column can be used.

Parameters
----------
table : FeatureTable[Frequency]
    Feature table containing all features that should be used for target
    prediction.
metadata : Metadata
    Sample metadata file containing individual_id_column.
state_column : Str
    Metadata containing collection time (state) values for each sample.
    Must contain exclusively numeric values.
individual_id_column : Str, optional
    Metadata column containing IDs for individual subjects.
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.
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
-------
filtered_table : FeatureTable[RelativeFrequency]
    Feature table containing only important features.
feature_importance : FeatureData[Importance]
    Importance of each input feature to model accuracy.
volatility_plot : Visualization
    Interactive volatility plot visualization.
accuracy_results : Visualization
    Accuracy results visualization.
sample_estimator : SampleEstimator[Regressor]
    Trained sample regressor.