Exemplo n.º 1
0
    tokens = labelselect.__call__(tokens, args.labels) # Select only the labels

    print "Normalizing dataset..."
    #tokens = normalize.__call__(tokens) # Normalize the tokens
    if args.normalize and args.normalize_words:
        normalize.normalize_words(tokens)
    if args.normalize and args.normalize_punct:
        normalize.normalize_punct(tokens)
    if args.normalize and args.normalize_emoticons:
        normalize.normalize_emoticons(tokens)
    if args.normalize and args.normalize_users:
        normalize.normalize_users(tokens)
    if args.normalize and args.normalize_hashtags:
        normalize.normalize_hashtags(tokens)
    if args.normalize and args.normalize_nums:
        normalize.normalize_nums(tokens)
    if args.normalize and args.normalize_urls:
        normalize.normalize_urls(tokens)

    print "Transforming dataset..."
    feature_list = dictizer.__call__(tokens)

    docfreq = wordselection.calculate_docfreq(feature_list)

    if args.stopword_removal:
        print "Removing stopwords from the dataset..."
        feature_list = wordselection.remove_stopwords(feature_list)

    if args.uncommon_selection:
        print "Removing uncommon words from the dataset..."
        feature_list = wordselection.remove_uncommon(feature_list, docfreq, args.df_cutoff)
Exemplo n.º 2
0
def main(args):
    print "Opening dataset..."
    tokens = tokenize.open_tweets_file("../data/b.tsv", 0, args.items)

    print "Selecting labels..."
    tokens = labelselect.__call__(tokens, args.labels) # Select only the labels

    print "Normalizing dataset..."
    #tokens = normalize.__call__(tokens) # Normalize the tokens
    if args.normalize and args.normalize_words:
        normalize.normalize_words(tokens)
    if args.normalize and args.normalize_punct:
        normalize.normalize_punct(tokens)
    if args.normalize and args.normalize_emoticons:
        normalize.normalize_emoticons(tokens)
    if args.normalize and args.normalize_users:
        normalize.normalize_users(tokens)
    if args.normalize and args.normalize_hashtags:
        normalize.normalize_hashtags(tokens)
    if args.normalize and args.normalize_nums:
        normalize.normalize_nums(tokens)
    if args.normalize and args.normalize_urls:
        normalize.normalize_urls(tokens)

    print "Transforming dataset..."
    feature_list = dictizer.__call__(tokens)

    docfreq = wordselection.calculate_docfreq(feature_list)

    if args.stopword_removal:
        print "Removing stopwords from the dataset..."
        feature_list = wordselection.remove_stopwords(feature_list)

    if args.uncommon_selection:
        print "Removing uncommon words from the dataset..."
        feature_list = wordselection.remove_uncommon(feature_list, docfreq, args.df_cutoff)

    wordselection.print_reatined_features(docfreq, args.df_cutoff)

    # Write the features out to a file
    with open("filtered_docs.txt", "w") as w:
        for row in feature_list:
            w.write(str(row[0]) + "\n")

    print "Generating feature set statistics..."
    statsify.__call__(feature_list, args.labels)

    print "Splitting the dataset..."
    partitions = split_dataset.partition(feature_list, args.num_folds)

    #print len(partitions), "Partitions"
    #for p in partitions:
        #print
        #for t in p:
            #print t
    #return

    accumulation_dict = {}
    if args.classifier_type == "max_ent":
        validation = True
    else:
        validation = False

    for i, fold in enumerate(generate_folds(partitions, validation)):
        print "Fold number: {} looks like: {}".format(i, "".join(fold))
        #print fold
        print "Training fold", i
        train_set = select_set("t", fold, partitions)
        validation_set = select_set("v", fold, partitions)
        test_set = select_set("T", fold, partitions)

        if args.validation_metric != "none":
            classifier = maxent_classifier_with_validation(train_set, validation_set,
                    args.validation_metric, 3)
        elif args.numIterations:
            classifier = maxent_classifier(train_set, iterations=args.numIterations)
        else:
            classifier = naive_bayes_classifier(train_set)

        print "Testing fold {}...".format(i),
        results_dict = classifier.test(test_set, args.labels, trace=False)
        #Add results to the accumulation dict
        for key in results_dict.keys():
         try:
             accumulation_dict[key].append(results_dict[key])
         except KeyError:
             accumulation_dict[key] = [results_dict[key]]
        print "done.\n"
        #classifier.show_informative_features(30)
        #classifier.inspect_errors(test_set)

    print "\n\nAccumulating Results"
    for key in sorted(accumulation_dict.keys(), reverse=True):
        print key, ":\t", accumulation_dict[key]
        print "{}-avg:\t".format(key), numpy.mean(accumulation_dict[key])
        print "{}-std:\t".format(key), numpy.std(accumulation_dict[key])
Exemplo n.º 3
0
def main(args):
    print "Opening dataset..."
    tokens = tokenize.open_tweets_file("../data/b.tsv", 0, args.items)

    print "Selecting labels..."
    tokens = labelselect.__call__(tokens,
                                  args.labels)  # Select only the labels

    print "Normalizing dataset..."
    #tokens = normalize.__call__(tokens) # Normalize the tokens
    if args.normalize and args.normalize_words:
        normalize.normalize_words(tokens)
    if args.normalize and args.normalize_punct:
        normalize.normalize_punct(tokens)
    if args.normalize and args.normalize_emoticons:
        normalize.normalize_emoticons(tokens)
    if args.normalize and args.normalize_users:
        normalize.normalize_users(tokens)
    if args.normalize and args.normalize_hashtags:
        normalize.normalize_hashtags(tokens)
    if args.normalize and args.normalize_nums:
        normalize.normalize_nums(tokens)
    if args.normalize and args.normalize_urls:
        normalize.normalize_urls(tokens)

    print "Transforming dataset..."
    feature_list = dictizer.__call__(tokens)

    docfreq = wordselection.calculate_docfreq(feature_list)

    if args.stopword_removal:
        print "Removing stopwords from the dataset..."
        feature_list = wordselection.remove_stopwords(feature_list)

    if args.uncommon_selection:
        print "Removing uncommon words from the dataset..."
        feature_list = wordselection.remove_uncommon(feature_list, docfreq,
                                                     args.df_cutoff)

    wordselection.print_reatined_features(docfreq, args.df_cutoff)

    # Write the features out to a file
    with open("filtered_docs.txt", "w") as w:
        for row in feature_list:
            w.write(str(row[0]) + "\n")

    print "Generating feature set statistics..."
    statsify.__call__(feature_list, args.labels)

    print "Splitting the dataset..."
    partitions = split_dataset.partition(feature_list, args.num_folds)

    #print len(partitions), "Partitions"
    #for p in partitions:
    #print
    #for t in p:
    #print t
    #return

    accumulation_dict = {}
    if args.classifier_type == "max_ent":
        validation = True
    else:
        validation = False

    for i, fold in enumerate(generate_folds(partitions, validation)):
        print "Fold number: {} looks like: {}".format(i, "".join(fold))
        #print fold
        print "Training fold", i
        train_set = select_set("t", fold, partitions)
        validation_set = select_set("v", fold, partitions)
        test_set = select_set("T", fold, partitions)

        if args.validation_metric != "none":
            classifier = maxent_classifier_with_validation(
                train_set, validation_set, args.validation_metric, 3)
        elif args.numIterations:
            classifier = maxent_classifier(train_set,
                                           iterations=args.numIterations)
        else:
            classifier = naive_bayes_classifier(train_set)

        print "Testing fold {}...".format(i),
        results_dict = classifier.test(test_set, args.labels, trace=False)
        #Add results to the accumulation dict
        for key in results_dict.keys():
            try:
                accumulation_dict[key].append(results_dict[key])
            except KeyError:
                accumulation_dict[key] = [results_dict[key]]
        print "done.\n"
        #classifier.show_informative_features(30)
        #classifier.inspect_errors(test_set)

    print "\n\nAccumulating Results"
    for key in sorted(accumulation_dict.keys(), reverse=True):
        print key, ":\t", accumulation_dict[key]
        print "{}-avg:\t".format(key), numpy.mean(accumulation_dict[key])
        print "{}-std:\t".format(key), numpy.std(accumulation_dict[key])
Exemplo n.º 4
0
                                  args.labels)  # Select only the labels

    print "Normalizing dataset..."
    #tokens = normalize.__call__(tokens) # Normalize the tokens
    if args.normalize and args.normalize_words:
        normalize.normalize_words(tokens)
    if args.normalize and args.normalize_punct:
        normalize.normalize_punct(tokens)
    if args.normalize and args.normalize_emoticons:
        normalize.normalize_emoticons(tokens)
    if args.normalize and args.normalize_users:
        normalize.normalize_users(tokens)
    if args.normalize and args.normalize_hashtags:
        normalize.normalize_hashtags(tokens)
    if args.normalize and args.normalize_nums:
        normalize.normalize_nums(tokens)
    if args.normalize and args.normalize_urls:
        normalize.normalize_urls(tokens)

    print "Transforming dataset..."
    feature_list = dictizer.__call__(tokens)

    docfreq = wordselection.calculate_docfreq(feature_list)

    if args.stopword_removal:
        print "Removing stopwords from the dataset..."
        feature_list = wordselection.remove_stopwords(feature_list)

    if args.uncommon_selection:
        print "Removing uncommon words from the dataset..."
        feature_list = wordselection.remove_uncommon(feature_list, docfreq,