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
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-test-size PROPORTION
Range(0.0, 1.0) Fraction of input samples to exclude from training set
and use for classifier testing. [default: 0.2]
--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-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-sample-estimator ARTIFACT SampleEstimator[Regressor]
Trained sample estimator. [required]
--o-feature-importance ARTIFACT FeatureData[Importance]
Importance of each input feature to model accuracy.
[required]
--o-predictions ARTIFACT SampleData[RegressorPredictions]
Predicted target values for each input sample.
[required]
--o-model-summary VISUALIZATION
Summarized parameter and (if enabled) feature
selection information for the trained estimator.
[required]
--o-accuracy-results VISUALIZATION
Accuracy results visualization. [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).
--example-data PATH Write example data and exit.
--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), 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('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.
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.
