77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import pandas as pd
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import nltk
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from collections import Counter, defaultdict
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from utils import get_csv, check_prerequisites, ENCODING, clean_text
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def main():
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check_prerequisites()
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data = get_csv("train/in.tsv.xz")
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train_words = get_csv("train/expected.tsv")
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train_data = data[[6, 7]]
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train_data = pd.concat([train_data, train_words], axis=1)
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train_data[607] = train_data[6] + train_data[0] + train_data[7]
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model = defaultdict(lambda: defaultdict(lambda: 0))
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train_model(train_data, model)
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predict_data("dev-0/in.tsv.xz", "dev-0/out.tsv", model)
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predict_data("test-A/in.tsv.xz", "test-A/out.tsv", model)
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def train_model(data, model):
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for _, row in data.iterrows():
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words = nltk.word_tokenize(clean_text(row[607]))
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for w1, w2 in nltk.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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for w2 in model:
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total_count = float(sum(model[w2].values()))
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for w1 in model[w2]:
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model[w2][w1] /= total_count
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def predict_data(read_path, save_path, model):
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data = get_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 = nltk.word_tokenize(clean_text(row[7]))
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if len(words) < 3:
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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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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 "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
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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
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if __name__ == "__main__":
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main()
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