Difference between revisions of "PICA"

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Phenotype Investigation with Classification Algorithms (PICA) is a Python framework for testing genotype-phenotype association algorithms.
 
Phenotype Investigation with Classification Algorithms (PICA) is a Python framework for testing genotype-phenotype association algorithms.
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== Command-line ==
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Use the option -h for help with any command.
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* train: Train a given data mining algorithm and output model to file.
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Example usage:
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train.py --algorithm cpar.CPARTrainer --samples examples/genotype_prokaryote.profile --classes examples/phenotype.profile --targetclass THERM --output output.rules
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* test
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Test a model with a classification algorithm and given model.
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Example usage:
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test.py --algorithm cpar.CPARClassifier --samples examples/genotype_prokaryote.profile --classes examples/phenotype.profile --targetclass THERM --model_filename output.rules --model_accuracy mi
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* crossvalidate
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Train and test various replicates with the given training and testing algorithms.
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Example usage:
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crossvalidate.py --training_algorithm cpar.CPARTrainer --classification_algorithm cpar.CPARClassifier --accuracy_measure mi --replicates 10 --folds 5 --samples examples/genotype_prokaryote.profile --classes examples/phenotype.profile --targetclass THERM --output_filename resutls.txt --metadata examples/taxonomic_confounders_propagated.txt
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== Python API ==

Revision as of 17:01, 12 April 2010

Phenotype Investigation with Classification Algorithms (PICA) is a Python framework for testing genotype-phenotype association algorithms.


Command-line

Use the option -h for help with any command.

  • train: Train a given data mining algorithm and output model to file.

Example usage: train.py --algorithm cpar.CPARTrainer --samples examples/genotype_prokaryote.profile --classes examples/phenotype.profile --targetclass THERM --output output.rules

  • test

Test a model with a classification algorithm and given model. Example usage: test.py --algorithm cpar.CPARClassifier --samples examples/genotype_prokaryote.profile --classes examples/phenotype.profile --targetclass THERM --model_filename output.rules --model_accuracy mi

  • crossvalidate

Train and test various replicates with the given training and testing algorithms. Example usage: crossvalidate.py --training_algorithm cpar.CPARTrainer --classification_algorithm cpar.CPARClassifier --accuracy_measure mi --replicates 10 --folds 5 --samples examples/genotype_prokaryote.profile --classes examples/phenotype.profile --targetclass THERM --output_filename resutls.txt --metadata examples/taxonomic_confounders_propagated.txt


Python API