Initial commit of V2
V2 is a major upgrade that (hopefully) makes it more scalable by moving the majority of the work to compile time instead of runtime.
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@ -9,68 +9,61 @@ def listify(text):
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def compile(raw_model):
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model = {}
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categories = {}
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for portion in raw_model:
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text = listify(portion['text'])
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category = portion['category']
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try:
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categories[category] += text
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except KeyError:
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categories[category] = text
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categories_by_count = {}
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for category, text in categories.items():
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categories_by_count[category] = {}
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for word in text:
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try:
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model[category].append(word)
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except:
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model[category] = [word]
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model[category].sort()
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all_models = [ { 'text': model, 'stopword': i/10} for i in range(0, 21) ]
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for test_model in all_models:
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correct = 0
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classifier = Classifier(test_model)
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for text in raw_model:
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if classifier.check(text['text']) == text['category']:
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correct += 1
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test_model['correct'] = correct
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print('tested a model')
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best = all_models[0]
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for test_model in all_models:
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if test_model['correct'] > best['correct']:
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best = test_model
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del best['correct']
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return best
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return {'text': model}
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categories_by_count[category][word] += 1/len(categories[category])
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except KeyError:
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categories_by_count[category][word] = 1/len(categories[category])
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word_weights = {}
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for category, words in categories_by_count.items():
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for word, value in words.items():
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try:
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word_weights[word][category] = value
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except KeyError:
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word_weights[word] = {category:value}
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return word_weights
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class Classifier:
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def __init__(self, model, supress_uncompiled_model_warning=False):
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if type(model['text']) == dict:
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if type(model) == dict:
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self.model = model
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else:
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self.model = compile(model)
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if not supress_uncompiled_model_warning:
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print('WARNING: model was not compiled', file=sys.stderr)
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print('In development, this is OK, but precompiling the model is preferred for production use.', file=sys.stderr)
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print('This makes everything slow, because compiling models takes far longer than using them.', file=sys.stderr)
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self.warn = supress_uncompiled_model_warning
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def check(self, text):
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model = self.model
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stopword_value = 0.5
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try:
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stopword_value = model['stopword']
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except:
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pass
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stopwords = spacy.lang.en.stop_words.STOP_WORDS
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model = model['text']
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text = listify(text)
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probs = {}
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for word in text:
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for category in model.keys():
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for catword in model[category]:
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if word == catword:
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weight = ( stopword_value if word in stopwords else 1 ) / len(model[category])
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try:
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probs[category] += weight
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except:
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probs[category] = weight
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most_likely = ['unknown', 0]
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for category in probs.keys():
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if probs[category] > most_likely[1]:
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most_likely = [category, probs[category]]
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return most_likely[0]
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for category, value in model[word].items():
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try:
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probs[category] += value
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except KeyError:
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probs[category] = value
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except KeyError:
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pass
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try:
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return sorted(probs.items(), key=lambda x: x[1])[-1][0]
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except IndexError:
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return None
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@ -1,6 +1,7 @@
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#!/usr/bin/env python3
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import argparse
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import json
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import sys
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parser = argparse.ArgumentParser(description="General Purpose Text Classifier")
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parser.add_argument('model', help='model to use')
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