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 == | ||
| + | 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 | ||
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| + | * 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 | ||
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| + | == Python API == | ||
Revision as of 20:01, 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
