pt.start() fileio = FileIO() samples = fileio.load_samples(options.input_samples_filename) samples_time = pt.stop() print "Loaded samples (%0.2fs)"%(samples_time) if options.feature_select: print "Selecting top %d features from %s, ordered by %s"%(options.feature_select_top_n,options.feature_select,options.feature_select_score) pt.start() from pica.AssociationRule import load_rules,AssociationRuleSet selected_rules = AssociationRuleSet() rules = load_rules(options.feature_select) rules.set_target_accuracy(options.feature_select_score) selected_rules.extend(rules[:options.feature_select_top_n]) samples = samples.feature_select(selected_rules) print "Finished feature selection (%0.2fs)"%(pt.stop()) classes = fileio.load_classes(options.input_classes_filename) samples.load_class_labels(classes) print samples.get_number_of_features() samples.set_current_class(options.target_class) pt.start() print "Compressing features...", samples = samples.compress_features() compression_time = pt.stop() print "\bfinished compression.(%0.2fs)"%(compression_time) samples.set_current_class(options.target_class) samples.hide_nulls(options.target_class) modulepath = "pica.trainers.%s"%(options.algorithm)
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"
samples.set_current_class(class_name)
samples.hide_nulls(class_name)
errorCount += 1
if errorCount > 0:
error("For help on usage, try calling:\n\tpython %s -h" % os.path.basename(sys.argv[0]))
exit(1)
fileio = FileIO()
samples = fileio.load_samples(options.input_samples_filename)
if options.feature_select:
print "Selecting top %d features from %s, ordered by %s"%(options.feature_select_top_n,options.feature_select,options.feature_select_score)
from pica.AssociationRule import load_rules,AssociationRuleSet
selected_rules = AssociationRuleSet()
rules = load_rules(options.feature_select)
rules.set_target_accuracy(options.feature_select_score)
selected_rules.extend(rules[:options.feature_select_top_n])
samples = samples.feature_select(selected_rules)
classes = fileio.load_classes(options.input_classes_filename)
samples.load_class_labels(classes)
print "Sample set has %d features."%(samples.get_number_of_features())
samples.set_current_class(options.target_class)
print "Parameters from %s"%(options.parameters)
print "Compressing features...",
samples = samples.compress_features()
print "compressed to %d distinct features."%(samples.get_number_of_features())
samples.set_current_class(options.target_class)
samples.hide_nulls(options.target_class)
modulepath = "pica.trainers.%s"%(options.training_algorithm)
classname = options.training_algorithm.split(".")[-1]
TrainerClass = __import__(modulepath, fromlist=(classname,))
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"
samples.set_current_class(class_name)
samples.hide_nulls(class_name)