def load_confounders(self,confounder_filename): nfileio = FileIO() return nfileio.load_metadata(confounder_filename)
import sys
samples_filename = sys.argv[1]
class_labels_filename = sys.argv[2]
metadata_filename = sys.argv[3]
output_filename = sys.argv[4]
from pica.Sample import SampleSet, ClassLabelSet
from pica.io.FileIO import FileIO
from pica.IntegerMapping import IntegerMapping
from pica.trainers.cwmi.CWMILibrary import CWMILibrary
fileio = FileIO()
cwmilibrary = CWMILibrary()
metadata = fileio.load_metadata(metadata_filename)
samples = fileio.load_samples(samples_filename)
classes = fileio.load_classes(class_labels_filename)
samples.load_class_labels(classes)
confounders = metadata.get_key_list()[1:]
outlines = []
header_line = ["phenotype"]
header_line.extend(confounders)
header_line.append("total")
outlines.append("\t".join(header_line))
for class_name in classes.get_classes():
"generate phenotype map"
ClassifierClass = __import__(modulepath, fromlist=(classname,))
classifier = ClassifierClass.__dict__[classname](options.parameters)
classifier.set_null_flag("NULL")
test_configurations = [TestConfiguration("A",None,trainer,classifier)]
#RVF changed (added the last 3 parameters)
if ( options.crossval_files ):
crossvalidator = CrossValidation(samples,options.parameters,options.folds,options.replicates,test_configurations,False,None,options.target_class,options.output_filename)
else:
crossvalidator = CrossValidation(samples,options.parameters,options.folds,options.replicates,test_configurations)
crossvalidator.crossvalidate()
classifications,misclassifications = crossvalidator.get_classification_vector()
metadata = None
if options.metadata:
metadata = fileio.load_metadata(options.metadata)
fout = open(options.output_filename,"w")
fout.write("who\t%s\tFalse Classifications\tTrue Classifications\tTotal"%(options.target_class))
if metadata:
for key in metadata.get_key_list():
fout.write("\t%s"%(key))
fout.write("\n")
for who in misclassifications.keys():
fout.write("%s\t%s\t%d\t%d\t%d"%(who,classes[who][options.target_class],misclassifications[who][0],misclassifications[who][1],misclassifications[who][0]+misclassifications[who][1]))
if metadata:
m = metadata.get(who,{})
for key in metadata.get_key_list():
fout.write("\t%s"%(m.get(key,"")))
fout.write("\n")
stats = crossvalidator.get_summary_statistics(0)
import sys
samples_filename = sys.argv[1]
class_labels_filename = sys.argv[2]
metadata_filename = sys.argv[3]
output_filename = sys.argv[4]
from pica.Sample import SampleSet, ClassLabelSet
from pica.io.FileIO import FileIO
from pica.IntegerMapping import IntegerMapping
from pica.trainers.cwmi.CWMILibrary import CWMILibrary
fileio = FileIO()
cwmilibrary = CWMILibrary()
metadata = fileio.load_metadata(metadata_filename)
samples = fileio.load_samples(samples_filename)
classes = fileio.load_classes(class_labels_filename)
samples.load_class_labels(classes)
confounders = metadata.get_key_list()[1:]
outlines = []
header_line = ["phenotype"]
header_line.extend(confounders)
header_line.append("total")
outlines.append("\t".join(header_line))
for class_name in classes.get_classes():
"generate phenotype map"
