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,