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classify-samples: Train and test a cross-validated supervised learning classifier.¶
Docstring:
Usage: qiime sample-classifier classify-samples [OPTIONS] Predicts a categorical sample metadata column using a supervised learning classifier. 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 | 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] Categorical 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 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-palette TEXT Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'cividis', 'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy', 'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream', 'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale') The color palette to use for plotting. [default: 'sirocco'] --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 estimator. [required] --o-feature-importance ARTIFACT FeatureData[Importance] Importance of each input feature to model accuracy. [required] --o-predictions ARTIFACT SampleData[ClassifierPredictions] 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] --o-probabilities ARTIFACT SampleData[Probabilities] Predicted class probabilities for each input sample. [required] --o-heatmap VISUALIZATION A heatmap of the top 50 most important features from the table. [required] --o-training-targets ARTIFACT SampleData[TrueTargets] Series containing true target values of train samples [required] --o-test-targets ARTIFACT SampleData[TrueTargets] Series containing true target values of test samples [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). --recycle-pool TEXT Use a cache pool for pipeline resumption. QIIME 2 will cache your results in this pool for reuse by future invocations. These pool are retained until deleted by the user. If not provided, QIIME 2 will create a pool which is automatically reused by invocations of the same action and removed if the action is successful. Note: these pools are local to the cache you are using. --no-recycle Do not recycle results from a previous failed pipeline run or save the results from this run for future recycling. --parallel Execute your action in parallel. This flag will use your default parallel config. --parallel-config FILE Execute your action in parallel using a config at the indicated path. --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.pipelines import classify_samples
Docstring:
Train and test a cross-validated supervised learning classifier. Predicts a categorical sample metadata column using a supervised learning classifier. 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 | RelativeFrequency | PresenceAbsence | Composition] Feature table containing all features that should be used for target prediction. metadata : MetadataColumn[Categorical] Categorical 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 : 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. palette : Str % Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'cividis', 'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy', 'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream', 'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale'), optional The color palette to use for plotting. 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 estimator. feature_importance : FeatureData[Importance] Importance of each input feature to model accuracy. predictions : SampleData[ClassifierPredictions] 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. probabilities : SampleData[Probabilities] Predicted class probabilities for each input sample. heatmap : Visualization A heatmap of the top 50 most important features from the table. training_targets : SampleData[TrueTargets] Series containing true target values of train samples test_targets : SampleData[TrueTargets] Series containing true target values of test samples