PICA

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

Command-line Interface

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: Replicated cross-validation 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 results.txt 
                  --metadata examples/taxonomic_confounders_propagated.txt

Python API

Shortened example of a paired test between mutual information and conditionally weighted mutual information using the CWMIRankFeatureSelector class and the LIBSVM interface for testing each set of features.

See util/batch_validate.py for more details on an example of setting up a programmatic comparison.

 test_configurations = []
 trainer = libSVMTrainer(kernel_type="LINEAR",C=5)
 classifier = libSVMClassifier()
 
 for score in ("mi","cwmi"):
     feature_selector = CWMIRankFeatureSelector(confounders_filename=confounders_filename,
                                                scores=(score,),
                                                features_per_class=10,
                                                confounder="order")
     
     tc = TestConfiguration(score,feature_selector,trainer,classifier)
     test_configurations.append(tc)
 
 crossvalidator = CrossValidation(samples=samples,
                                  parameters=None,
                                  replicates=10,
                                  folds=5,
                                  test_configurations=test_configurations,
                                  root_output=root_output)
 crossvalidator.crossvalidate()