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

  • 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.


Usage: qiime feature-classifier fit-classifier-naive-bayes

  Create a scikit-learn naive_bayes classifier for reads

  --i-reference-reads ARTIFACT FeatureData[Sequence]
  --i-reference-taxonomy ARTIFACT FeatureData[Taxonomy]
  --i-class-weight ARTIFACT FeatureTable[RelativeFrequency]
  --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]
  --o-classifier ARTIFACT TaxonomicClassifier
  --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.


from qiime2.plugins.feature_classifier.methods import fit_classifier_naive_bayes


Train the naive_bayes classifier

Create a scikit-learn naive_bayes classifier for reads

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

classifier : TaxonomicClassifier