for sample in samples: if (sample.id == target_sample_id): target_samples.append(sample) else: print "You must specify a target sample" sys.exit(1) if len(target_samples) ==0: print "Could not find samples!" sys.exit() samples_time = pt.stop() print "Loaded samples (%0.2fs)"%(samples_time) pt.start() rules = load_rules(options.model_filename) rules = rules.remap_feature_to_index(samples) training_time = pt.stop() newrules = [] for rule in rules: keep_rule = False for target_sample in target_samples: if target_sample.satisfies(rule.ls): keep_rule = True if keep_rule: newrules.append(rule) newruleset = AssociationRuleSet() newruleset.extend(newrules) newruleset = newruleset.remap_index_to_feature(samples) newruleset.write(filename=options.output_filename)
errorCount += 1
if errorCount > 0:
error("For help on usage, try calling:\n\tpython %s -h" % os.path.basename(sys.argv[0]))
exit(1)
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)
(options, args) = parser.parse_args()
pt.start()
fileio = FileIO()
samples = fileio.load_samples(options.samples_filename)
classes = fileio.load_classes(options.classes_filename)
samples.load_class_labels(classes)
samples.set_current_class(options.target_class)
target_samples = []
samples_time = pt.stop()
print "Loaded samples (%0.2fs)" % (samples_time)
pt.start()
rules = load_rules(options.model_filename)
indexed_rules = rules.remap_feature_to_index(samples)
training_time = pt.stop()
newsamples = {}
for sample in samples:
keep_sample = False
for rule in indexed_rules:
if sample.satisfies(rule.ls):
if not newsamples.has_key(sample.id):
newsamples[sample.id] = []
newsamples[sample.id].append(rule)
sets = get_same_rulesets(newsamples)
finished = {}
f = open(options.output_filename, "w")
error("Please provide the phenotype target to be predicted with -t \"TRAITNAME\"")
errorCount += 1
if not options.output_filename:
error("Please specify a file for the output with -o /path/to/result.file")
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)
def load_model(self,model_filename): return load_rules(model)
