def run(cross=True, verbose=False, xml="../xml/RTE2_dev.xml", pre_processec_xml="../xml/RTE2_dev.preprocessed.xml"):
learning_data="learningdata.tab" # the data features. extracted from an earlier run of features.
filename = "results_part3.txt"
clean_file(filename)
if cross: features.run(xml, pre_processec_xml) # extracts the features
data = orange.ExampleTable(learning_data)
l = orange.BayesLearner(data)
if cross:
if verbose:
print "result: ", validation(data)
for item in data:
if item.getclass() != l(item):
print '\033[1;41m'
print item, l(item),
print '\033[1;m'
print
else:
print item, l(item)
else:
print "result: ", validation(data)
else:
file = open(filename, "a")
file.write("ranked: no\n")
if file:
for item in data:
s = str(item['id']) +" "+ str(l(item))
file.write(s+"\n")
else:
print "Error opening file"
file.close()
print "finished writing to results_part3"
#run(True, False, "../xml/blind-test-data.xml") # runs the learning
#run(False) # runs the writing to results file.
if cfg.quick == True:
g.run(cfg.name, quick = True)
else:
g.run(cfg.name)
if cfg.roc == True:
import roccurves as rc
if cfg.quick == True:
rc.run(cfg.name + str(cfg.maxdepth), quick = True)
else:
rc.run(cfg.name + str(cfg.maxdepth))
if cfg.features == True:
import features as f
if cfg.quick == True:
f.run(cfg.name + str(cfg.maxdepth), quick = True)
else:
f.run(cfg.name + str(cfg.maxdepth))
if cfg.checksignal == True:
import checksignal as cs
if cfg.quick == True:
cs.run(cfg.name + str(cfg.maxdepth), quick = True)
else:
cs.run(cfg.name + str(cfg.maxdepth))
if cfg.crossvalidation == True:
import crossvalidation as cv
if cfg.quick == True:
cv.run(cfg.name + str(cfg.maxdepth), quick = True)
else:
