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.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency | RelativeFrequency |
PresenceAbsence | Composition]
Feature table containing all features that should be
used for target prediction. [required]
Parameters:
--m-metadata-file METADATA
--m-metadata-column COLUMN MetadataColumn[Categorical]
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 NTHREADS 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('RandomForestClassifier',
'ExtraTreesClassifier', 'GradientBoostingClassifier',
'AdaBoostClassifier[DecisionTree]', 'AdaBoostClassifier[ExtraTrees]',
'KNeighborsClassifier', 'LinearSVC', 'SVC')
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 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[Classifier]
Trained sample classifier. [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).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--use-cache DIRECTORY Specify the cache to be used for the intermediate
work of this action. If not provided, the default
cache under $TMP/qiime2/ will be used.
IMPORTANT FOR HPC USERS: If you are on an HPC system
and are using parallel execution it is important to
set this to a location that is globally accessible to
all nodes in the cluster.
--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 | RelativeFrequency | PresenceAbsence | Composition]
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 : Threads, 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('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier[DecisionTree]', 'AdaBoostClassifier[ExtraTrees]', 'KNeighborsClassifier', 'LinearSVC', '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.
