Fork me on GitHub

fit-classifier-naive-bayes: Train the naive_bayes classifier

Citations
  • Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. Scikit-learn: machine learning in python. Journal of machine learning research, 12(Oct):2825–2830, 2011.

Docstring:

Usage: qiime feature-classifier fit-classifier-naive-bayes
           [OPTIONS]

  Create a scikit-learn naive_bayes classifier for reads

Inputs:
  --i-reference-reads ARTIFACT FeatureData[Sequence]
                                                                    [required]
  --i-reference-taxonomy ARTIFACT FeatureData[Taxonomy]
                                                                    [required]
  --i-class-weight ARTIFACT FeatureTable[RelativeFrequency]
                                                                    [optional]
Parameters:
  --p-classify--alpha NUMBER
                                                              [default: 0.001]
  --p-classify--chunk-size INTEGER
                                                              [default: 20000]
  --p-classify--class-prior TEXT
                                                             [default: 'null']
  --p-classify--fit-prior / --p-no-classify--fit-prior
                                                              [default: False]
  --p-feat-ext--alternate-sign / --p-no-feat-ext--alternate-sign
                                                              [default: False]
  --p-feat-ext--analyzer TEXT
                                                          [default: 'char_wb']
  --p-feat-ext--binary / --p-no-feat-ext--binary
                                                              [default: False]
  --p-feat-ext--decode-error TEXT
                                                           [default: 'strict']
  --p-feat-ext--encoding TEXT
                                                            [default: 'utf-8']
  --p-feat-ext--input TEXT
                                                          [default: 'content']
  --p-feat-ext--lowercase / --p-no-feat-ext--lowercase
                                                               [default: True]
  --p-feat-ext--n-features INTEGER
                                                               [default: 8192]
  --p-feat-ext--ngram-range TEXT
                                                           [default: '[7, 7]']
  --p-feat-ext--norm TEXT
                                                               [default: 'l2']
  --p-feat-ext--preprocessor TEXT
                                                             [default: 'null']
  --p-feat-ext--stop-words TEXT
                                                             [default: 'null']
  --p-feat-ext--strip-accents TEXT
                                                             [default: 'null']
  --p-feat-ext--token-pattern TEXT
                                                [default: '(?u)\\b\\w\\w+\\b']
  --p-feat-ext--tokenizer TEXT
                                                             [default: 'null']
  --p-verbose / --p-no-verbose
                                                              [default: False]
Outputs:
  --o-classifier ARTIFACT
    TaxonomicClassifier                                             [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.feature_classifier.methods import fit_classifier_naive_bayes

Docstring:

Train the naive_bayes classifier

Create a scikit-learn naive_bayes classifier for reads

Parameters
----------
reference_reads : FeatureData[Sequence]
reference_taxonomy : FeatureData[Taxonomy]
class_weight : FeatureTable[RelativeFrequency], optional
classify__alpha : Float, optional
classify__chunk_size : Int, optional
classify__class_prior : Str, optional
classify__fit_prior : Bool, optional
feat_ext__alternate_sign : Bool, optional
feat_ext__analyzer : Str, optional
feat_ext__binary : Bool, optional
feat_ext__decode_error : Str, optional
feat_ext__encoding : Str, optional
feat_ext__input : Str, optional
feat_ext__lowercase : Bool, optional
feat_ext__n_features : Int, optional
feat_ext__ngram_range : Str, optional
feat_ext__norm : Str, optional
feat_ext__preprocessor : Str, optional
feat_ext__stop_words : Str, optional
feat_ext__strip_accents : Str, optional
feat_ext__token_pattern : Str, optional
feat_ext__tokenizer : Str, optional
verbose : Bool, optional

Returns
-------
classifier : TaxonomicClassifier