==========================
unclustered_recruitment.py
==========================

.. code-block:: bash

	usage: unclustered_recruitment.py

	Recruit unclustered contigs given metagenome annotations and Autometa binning
	results. Note: All tables must contain a 'contig' column to be used as the
	unique table index

	optional arguments:
	  -h, --help            show this help message and exit
	  --kmers KMERS         Path to normalized kmer frequencies table. (default:
	                        None)
	  --coverage COVERAGE   Path to coverage table. (default: None)
	  --binning BINNING     Path to autometa binning output [will look for
	                        col='cluster'] (default: None)
	  --markers MARKERS     Path to domain-specific markers table. (default: None)
	  --output-binning OUTPUT_BINNING
	                        Path to output unclustered recruitment table.
	                        (default: None)
	  --output-main OUTPUT_MAIN
	                        Path to write Autometa main table used during/after
	                        unclustered recruitment. (default: None)
	  --output-features OUTPUT_FEATURES
	                        Path to write Autometa features table used during
	                        unclustered recruitment. (default: None)
	  --taxonomy TAXONOMY   Path to taxonomy table. (default: None)
	  --taxa-dimensions TAXA_DIMENSIONS
	                        Num of dimensions to reduce taxonomy encodings
	                        (default: None)
	  --additional-features [ADDITIONAL_FEATURES ...]
	                        Path to additional features with which to add to
	                        classifier training data. (default: [])
	  --confidence CONFIDENCE
	                        Percent confidence to allow classification (confidence
	                        = num. consistent predictions/num. classifications)
	                        (default: 1.0)
	  --num-classifications NUM_CLASSIFICATIONS
	                        Num classifications for predicting/validating contig
	                        cluster recruitment (default: 10)
	  --classifier {decision_tree,random_forest}
	                        classifier to use for recruitment of contigs (default:
	                        decision_tree)
	  --kmer-dimensions KMER_DIMENSIONS
	                        Num of dimensions to reduce normalized k-mer
	                        frequencies (default: 50)
	  --seed SEED           Seed to use for RandomState when initializing
	                        classifiers. (default: 42)