from classifier_tree import TreeSingleTagWordsClassifier
from mainleave1out_biggestcategory_svm import extract_most_common_categ
if __name__ == '__main__':
#read words that are most important:
extr_fromfname = sys.argv[1]
basefname = sys.argv[2]
words_count = int(sys.argv[3])
thresh_div = float(sys.argv[4])
records_file = sys.argv[5]
test_samples = int(sys.argv[6])
print "Arguments read:"
print "extr_fromfname =", extr_fromfname
print "basefname =", basefname
print "words_count =", words_count
print "thresh_div =", thresh_div
print "records_file =", records_file
print "test_samples =", test_samples
words = select_descriptive_words.select_descriptive_words_quotientmethod(extr_fromfname, basefname, words_count, thresh_div)
#read records and convert them into feature-vectors:
frecords = list(records_to_words_weights_converter.convert_records_to_words(record_read.read_list_records(records_file), words))
#create frecors with numerical etiquettes:
#build multi-label-SVM based on this data:
most_common_categ, max_cnt = extract_most_common_categ(frecords)
print "Most common category is:", most_common_categ, " with ", max_cnt, " occurences."
loo = LeaveOneOut(lambda samples: TreeSingleTagWordsClassifier(most_common_categ, samples, featurenames=words), frecords, lambda x: [int(most_common_categ in x[1])])
corr = loo.test(test_samples)
print "Correctness:", corr
from wordsfreq import select_descriptive_words
from features import records_to_words_weights_converter
from zbl2py import record_read
from classifier_tester import LeaveOneOutAllCategories
from classifier_svm import SvmWordsClassifier
if __name__ == '__main__':
#read words that are most important:
extr_fromfname = sys.argv[1]
basefname = sys.argv[2]
words_count = int(sys.argv[3])
thresh_div = float(sys.argv[4])
records_file = sys.argv[5]
test_samples = int(sys.argv[6])
print "Arguments read:"
print "extr_fromfname =", extr_fromfname
print "basefname =", basefname
print "words_count =", words_count
print "thresh_div =", thresh_div
print "records_file =", records_file
print "test_samples =", test_samples
words = select_descriptive_words.select_descriptive_words_quotientmethod(extr_fromfname, basefname, words_count, thresh_div)
#read records and convert them into feature-vectors:
frecords = list(records_to_words_weights_converter.convert_records_to_words(record_read.read_list_records(records_file), words))
#create frecors with numerical etiquettes:
#build multi-label-SVM based on this data:
loo = LeaveOneOutAllCategories(SvmWordsClassifier, frecords)
corr = loo.test(test_samples)
print "Correctness:", corr
if __name__ == '__main__':
#read words that are most important:
extr_fromfname = sys.argv[1]
basefname = sys.argv[2]
words_count = int(sys.argv[3])
thresh_div = float(sys.argv[4])
records_file = sys.argv[5]
k = int(sys.argv[6])
print "Arguments read:"
print "extr_fromfname =", extr_fromfname
print "basefname =", basefname
print "words_count =", words_count
print "thresh_div =", thresh_div
print "records_file =", records_file
print "k =", k
records = record_read.read_list_records(records_file)
words = select_descriptive_words.select_descriptive_words_keywords(records)
#words = select_descriptive_words.select_descriptive_words_quotientmethod(extr_fromfname, basefname, words_count, thresh_div)
#read records and convert them into feature-vectors:
frecords = list(records_to_words_weights_converter.convert_records_to_words(records, words))
#create frecors with numerical etiquettes:
#build multi-label-SVM based on this data:
most_common_categ, max_cnt = extract_most_common_categ(frecords)
print "Most common category is:", most_common_categ, " with ", max_cnt, " occurences."
kf = KFold(lambda samples: TreeSingleTagWordsClassifier(most_common_categ, samples, featurenames=words), frecords, lambda x: [int(most_common_categ in x[1])], k)
corr = kf.test()
print "Correctness:", corr
