label_mappings,
record_mappings)
from tools.pickle_tools import save_pickle
save_pickle(hierarhical_mlknn.mltree.content,
save_hierarchical_path + "mlknn")
save_pickle(hierarhical_mlknn, save_hierarchical_path)
save_pickle(list(train_generator()), save_train_generator_path)
save_pickle(len(labels), save_lenlabels_path)
classify_oracle = lambda x: mc2lmc_tomka_blad(x)
print "----------------------------------------------------"
print "MLKNN:"
print "PRINTING TEST SAMPLES:"
for i in test_generator():
print classify_oracle(i)
multilabel_evaluate_printresults(
test_generator, classify_oracle, hierarhical_mlknn.classify,
len(labels), {
'full label': lambda x: x,
'half label': lambda x: x[:3],
'low label': lambda x: x[:2]
})
#print "----------------------------------------------------"
#print "STUPID KNN:"
#multilabel_evaluate_printresults(test_generator, classify_oracle, hierarhical_mlknn.classify_stupid, len(labels),
# #{'full label': lambda x: x, 'short label': lambda x: x[:1]})
# {'full label': lambda x: x, 'half label': lambda x: x[:3], 'low label': lambda x: x[:2]})
mlknn_callable = lambda train_gen: mlknn.MlKnn(train_gen, zbldistance, find_closest_points.find_closest_points,
k, smoothingparam)
label_mappings = (lambda x: x[:2], lambda x: x[:3], lambda x: x)
record_mappings = (lambda x: gen_1record_prefixed(x, 2), lambda x: gen_1record_prefixed(x, 3), lambda x: x)
hierarhical_mlknn = ml_hierarchical.MlHierarchical(train_generator, mlknn_callable, label_mappings, record_mappings)
from tools.pickle_tools import save_pickle
save_pickle(hierarhical_mlknn.mltree.content, save_hierarchical_path+"mlknn")
save_pickle(hierarhical_mlknn, save_hierarchical_path)
save_pickle(list(train_generator()), save_train_generator_path)
save_pickle(len(labels), save_lenlabels_path)
classify_oracle = lambda x: mc2lmc_tomka_blad(x)
print "----------------------------------------------------"
print "MLKNN:"
print "PRINTING TEST SAMPLES:"
for i in test_generator():
print classify_oracle(i)
multilabel_evaluate_printresults(test_generator, classify_oracle, hierarhical_mlknn.classify, len(labels),
{'full label': lambda x: x, 'half label': lambda x: x[:3], 'low label': lambda x: x[:2]})
#print "----------------------------------------------------"
#print "STUPID KNN:"
#multilabel_evaluate_printresults(test_generator, classify_oracle, hierarhical_mlknn.classify_stupid, len(labels),
# #{'full label': lambda x: x, 'short label': lambda x: x[:1]})
# {'full label': lambda x: x, 'half label': lambda x: x[:3], 'low label': lambda x: x[:2]})
prefix_code_generator = lambda: gen_record_filteredbylabels(prefixed_rec_generator, labelsset)
print "counting elements..."
elements_count = len(list(prefix_code_generator()))
print "number of elements:", elements_count
#split into training and testing samples
print "splitting into training and testing..."
train_inds, test_inds = randomly_divide(elements_count, int(elements_count/10))
train_generator = lambda: gen_record_fromshifts(prefix_code_generator, train_inds)
test_generator = lambda: gen_record_fromshifts(prefix_code_generator, test_inds)
print "splitted."
#train mlknn:
print "training distance..."
zbldistance = jaccard_distance.JaccardDistance(train_generator, elements_count-int(elements_count/10), distancetrainingsteps)
#zbldistance = lsi_distance.LsiDistance(train_generator, elements_count-int(elements_count/10), distancetrainingsteps, number_of_topics = 100)
print "training mlknn..."
mk = mlknn.MlKnn(train_generator, zbldistance, find_closest_points.find_closest_points,
k, smoothingparam)
classify_oracle = lambda x: mc2lmc_tomka_blad(x)
print "----------------------------------------------------"
print "MLKNN:"
multilabel_evaluate_printresults(test_generator, classify_oracle, mk.classify, len(labels),
{'full label': lambda x: x, 'half label': lambda x: x[:3], 'low label': lambda x: x[:2]})
print "----------------------------------------------------"
print "STUPID KNN:"
multilabel_evaluate_printresults(test_generator, classify_oracle, mk.classify_stupid, len(labels),
#{'full label': lambda x: x, 'short label': lambda x: x[:1]})
{'full label': lambda x: x, 'half label': lambda x: x[:3], 'low label': lambda x: x[:2]})