diff --git a/gptc/__init__.py b/gptc/__init__.py index 061c263..f92af62 100755 --- a/gptc/__init__.py +++ b/gptc/__init__.py @@ -9,68 +9,61 @@ def listify(text): def compile(raw_model): - model = {} + categories = {} for portion in raw_model: text = listify(portion['text']) category = portion['category'] + try: + categories[category] += text + except KeyError: + categories[category] = text + + categories_by_count = {} + + for category, text in categories.items(): + categories_by_count[category] = {} for word in text: try: - model[category].append(word) - except: - model[category] = [word] - model[category].sort() - all_models = [ { 'text': model, 'stopword': i/10} for i in range(0, 21) ] - for test_model in all_models: - correct = 0 - classifier = Classifier(test_model) - for text in raw_model: - if classifier.check(text['text']) == text['category']: - correct += 1 - test_model['correct'] = correct - print('tested a model') - best = all_models[0] - for test_model in all_models: - if test_model['correct'] > best['correct']: - best = test_model - del best['correct'] - return best - return {'text': model} + categories_by_count[category][word] += 1/len(categories[category]) + except KeyError: + categories_by_count[category][word] = 1/len(categories[category]) + word_weights = {} + for category, words in categories_by_count.items(): + for word, value in words.items(): + try: + word_weights[word][category] = value + except KeyError: + word_weights[word] = {category:value} + + return word_weights class Classifier: def __init__(self, model, supress_uncompiled_model_warning=False): - if type(model['text']) == dict: + if type(model) == dict: self.model = model else: self.model = compile(model) if not supress_uncompiled_model_warning: print('WARNING: model was not compiled', file=sys.stderr) - print('In development, this is OK, but precompiling the model is preferred for production use.', file=sys.stderr) + print('This makes everything slow, because compiling models takes far longer than using them.', file=sys.stderr) self.warn = supress_uncompiled_model_warning def check(self, text): model = self.model - stopword_value = 0.5 - try: - stopword_value = model['stopword'] - except: - pass - stopwords = spacy.lang.en.stop_words.STOP_WORDS - model = model['text'] text = listify(text) probs = {} for word in text: - for category in model.keys(): - for catword in model[category]: - if word == catword: - weight = ( stopword_value if word in stopwords else 1 ) / len(model[category]) - try: - probs[category] += weight - except: - probs[category] = weight - most_likely = ['unknown', 0] - for category in probs.keys(): - if probs[category] > most_likely[1]: - most_likely = [category, probs[category]] - return most_likely[0] + try: + for category, value in model[word].items(): + try: + probs[category] += value + except KeyError: + probs[category] = value + except KeyError: + pass + try: + return sorted(probs.items(), key=lambda x: x[1])[-1][0] + except IndexError: + return None diff --git a/gptc/__main__.py b/gptc/__main__.py index ac04aa7..806e1d2 100644 --- a/gptc/__main__.py +++ b/gptc/__main__.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 import argparse import json +import sys parser = argparse.ArgumentParser(description="General Purpose Text Classifier") parser.add_argument('model', help='model to use') diff --git a/twain_shakespeare.json b/twain_shakespeare.json index 05c51ca..2b245eb 100644 --- a/twain_shakespeare.json +++ b/twain_shakespeare.json @@ -1 +1 @@ -{"text": {"shakespeare": ["a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "abandon", "abundance", "achiever", "admiringly", "advantage", "again", "against", "age", "alas", "ale", "all", "all", "all", "all", "all", "all;to", "almost", "amendment", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "and", "anew;but", "arragon", "art", "as", "as", "as", "as", "as", 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