57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
from collections import Counter
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from nltk import bigrams, word_tokenize
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from utils import read_csv, ENCODING, clean_text
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DEFAULT_PREDICTION = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
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def train_model(data, model,vocab,alpha):
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for _, row in data.iterrows():
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words = word_tokenize(clean_text(row["607"]))
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for w1, w2 in bigrams(words, pad_left=True, pad_right=True):
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if w1 and w2:
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model[w2][w1] += 1
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vocab.add(w2)
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vocab.add(w1)
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for w2 in model:
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total_count = float(sum(model[w2].values()))
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denominator = total_count + alpha * len(vocab)
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for w1 in model[w2]:
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nominator = model[w2][w1] + alpha
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model[w2][w1] /= nominator / denominator
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def predict_data(read_path, save_path, model):
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data = read_csv(read_path)
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with open(save_path, "w", encoding=ENCODING) as f:
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for _, row in data.iterrows():
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words = word_tokenize(clean_text(row[7]))
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if len(words) < 3:
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prediction = DEFAULT_PREDICTION
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else:
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prediction = predict(words[0], model)
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f.write(prediction + "\n")
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def predict(word, model):
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predictions = dict(model[word])
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most_common = dict(Counter(predictions).most_common(6))
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total_prob = 0.0
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str_prediction = ""
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for word, prob in most_common.items():
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total_prob += prob
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str_prediction += f"{word}:{prob} "
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if total_prob == 0.0:
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return DEFAULT_PREDICTION
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rem_prob = 1 - total_prob
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if rem_prob < 0.01:
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rem_prob = 0.01
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str_prediction += f":{rem_prob}"
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return str_prediction |