sys.exit(1)
try:
distancetrainingsteps = int(sys.argv[6])
except:
print '6th argument expected: number of steps in training to be performed'
sys.exit(1)
try:
distancetype = sys.argv[7]
except:
print '7th argument expected: type of distance. Available: jac, g0, g1, g2'
sys.exit(1)
PRINTER('Loading training list...')
from tools.pickle_tools import read_pickle
train_generator_list = read_pickle(load_train_generator_path)
PRINTER('Loading labels path and elements count...')
lenlabels = len(read_pickle(load_labels_path))
elements_count = read_pickle(load_elements_count_path)
PRINTER("training distance...")
train_generator = lambda: train_generator_list
if distancetype=='jac':
from mlknn.jaccard_distance import JaccardDistance
zbldistance = JaccardDistance(train_generator, elements_count-int(elements_count/10), distancetrainingsteps)
else:
from mlknn.txt_cosine_distance import TxtCosineDistance
zbldistance = TxtCosineDistance(distancetype)
PRINTER("Finding label list...")
print '6th argument expected: smoothing parameter'
sys.exit(1)
try:
distancematrix = sys.argv[7]
except:
print '7th argument expected: path to distance matrix.'
sys.exit(1)
try:
load_test_generator = sys.argv[8]
except:
print '8th argument expected: load_test_generator parameter'
sys.exit(1)
PRINTER('Loading training list...')
from tools.pickle_tools import read_pickle
train_generator_list = read_pickle(load_train_generator_path)
PRINTER('Loading labels path and elements count...')
lenlabels = len(read_pickle(load_labels_path))
elements_count = read_pickle(load_elements_count_path)
PRINTER("Finding label list...")
get_labels_of_record = mc2lmc_tomka_blad
find_all_labels = lambda frecords: get_labels_min_occurence(
lambda: gen_lmc(frecords), 1)
PRINTER("Loading distance matrix...")
import sys
sys.path.append(r'../')
from data_io.matrix_io import fread_smatrix
(rows, cols, data) = fread_smatrix(distancematrix)
if __name__ == '__main__':
load_hierarchical_path = sys.argv[1]
load_train_generator = sys.argv[2]
lenlabels_path = sys.argv[3]
PRINTER("Input arguments:")
PRINTER("load_hierarchical_path: "+str(load_hierarchical_path))
PRINTER("load_train_generator: "+str(load_train_generator))
PRINTER("lenlabels_path: "+str(lenlabels_path))
log_level = logging.INFO
logging.basicConfig(level=log_level)
from tools.pickle_tools import read_pickle
hierarhical_mlknn = read_pickle(load_hierarchical_path)
test_generator = read_pickle(load_train_generator)
lenlabels = read_pickle(lenlabels_path)
#print "Finding out if the ML-hierarchical has internal data..."
#check_internal_data(hierarhical_mlknn)
print "----------------------------------------------------"
#print "MLKNN:"
#print "PRINTING TEST SAMPLES:"
#for i in test_generator:
# print classify_oracle(i)
classify_oracle = lambda x: mc2lmc_tomka_blad(x)
multilabel_evaluate_printresults(lambda: test_generator, classify_oracle, hierarhical_mlknn.classify, lenlabels,
print consecutive counts in c and c_prim.
'''
for code, value in c.iteritems():
PRINTER('[inspect_counts]: code: '+str(code))
for no, cnts in value.iteritems():
PRINTER('[inspect_counts]: no: '+str(no))
PRINTER('[inspect_counts]: c, c_prim: '+str(cnts)+" "+str(c_prim[code][no]))
PRINTER('[inspect_counts]:-------------------------')
if __name__ == '__main__':
import sys
try:
load_classifier_path = sys.argv[1]
except:
print 'Argument expected: path to a classifier'
sys.exit(1)
#PRINTER("Input arguments:")
#PRINTER("load_classifier_path: "+str(load_classifier_path))
sys.path.append(r'../')
from tools.pickle_tools import read_pickle
classifier = read_pickle(load_classifier_path)
#print "Finding out about the counts c and c_prim
#PRINTER("-----------C-----------")
#inspect_counts(classifier.c)
#PRINTER("-----------C_PRIM-----------")
#inspect_counts(classifier.c_prim)
PRINTER("-----------COMPARE COUNTS-----------")
compare_counts(classifier.c, classifier.c_prim)
labels_path = sys.argv[3]
except:
print '3d argument expected: path to a pickled labels list.'
sys.exit(1)
try:
classify_method_name = sys.argv[4]
except:
print '4th argument expected: classify method name.'
sys.exit(1)
#PRINTER("Input arguments:")
#PRINTER("load_classifier_path: "+str(load_classifier_path))
#PRINTER("load_test_generator: "+str(load_test_generator))
#PRINTER("labels_path: "+str(labels_path))
#PRINTER("classify_method_name: "+str(classify_method_name))
from tools.pickle_tools import read_pickle
classifier = read_pickle(load_classifier_path)
test_generator = read_pickle(load_test_generator)
labels = read_pickle(labels_path)
#print "Finding out if the ML-hierarchical has internal data..."
#check_internal_data(hierarhical_mlknn)
classify_oracle = mc2lmc_tomka_blad
#print "----------------------------------------------------"
#print "Hierachical MLKNN:"
PRINTER("-----------RESULTS-----------")
multilabel_evaluate_printresults(lambda: test_generator, classify_oracle, classifier.__getattribute__(classify_method_name), len(labels),
{'full label': lambda x: x, 'half label': lambda x: x[:3], 'low label': lambda x: x[:2]}, labels)
if __name__ == '__main__':
load_hierarchical_path = sys.argv[1]
load_train_generator = sys.argv[2]
lenlabels_path = sys.argv[3]
PRINTER("Input arguments:")
PRINTER("load_hierarchical_path: " + str(load_hierarchical_path))
PRINTER("load_train_generator: " + str(load_train_generator))
PRINTER("lenlabels_path: " + str(lenlabels_path))
log_level = logging.INFO
logging.basicConfig(level=log_level)
from tools.pickle_tools import read_pickle
hierarhical_mlknn = read_pickle(load_hierarchical_path)
test_generator = read_pickle(load_train_generator)
lenlabels = read_pickle(lenlabels_path)
#print "Finding out if the ML-hierarchical has internal data..."
#check_internal_data(hierarhical_mlknn)
print "----------------------------------------------------"
#print "MLKNN:"
#print "PRINTING TEST SAMPLES:"
#for i in test_generator:
# print classify_oracle(i)
classify_oracle = lambda x: mc2lmc_tomka_blad(x)
multilabel_evaluate_printresults(
lambda: test_generator, classify_oracle, hierarhical_mlknn.classify,
