81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
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#%%
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import pandas as pd
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from collections import defaultdict, Counter
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from sqlalchemy import true
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from nltk import trigrams, word_tokenize, bigrams
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import csv
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#%%
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class Model:
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def __init__(self):
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self.model = defaultdict(lambda: defaultdict(lambda: 0))
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self.model_bi = defaultdict(lambda: defaultdict(lambda: 0))
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train_in = pd.read_csv("train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE)[[6, 7]]
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train_expected = pd.read_csv("train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE)
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data = pd.concat([train_in, train_expected], axis=1)
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self.data = data[6] + data[0] + data[7]
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self.data = self.data.apply(self.clean)
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def clean(self, text):
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text = str(text).lower().strip().replace("’", "'").replace('\\n', " ").replace("'t", " not").replace("'s", " is").replace("'ll", " will").replace("'m", " am").replace("'ve", " have").replace(",", "").replace("-", "")
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return text
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def train(self):
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alpha = 0.7
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vocab = set()
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for text in model.data:
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words = word_tokenize(text)
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for w1, w2, w3 in trigrams(words, pad_left=True, pad_right=True):
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self.model[w1, w2][w3] += 1
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vocab.add(w1)
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vocab.add(w2)
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vocab.add(w3)
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for w1, w2 in bigrams(words, pad_left=True, pad_right=True):
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self.model_bi[w1][w2] +=1
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for w1, w2 in self.model:
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total_count = float(sum(self.model[w1, w2].values()))
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denominator = total_count * len(vocab)
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for w in self.model[w1, w2]:
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self.model[w1, w2][w] = self.model[w1, w2][w] / denominator * alpha
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for w1 in self.model_bi:
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total_count = float(sum(self.model_bi[w1].values()))
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denominator = total_count * len(vocab)
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for w in self.model_bi[w1]:
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self.model_bi[w1][w] = self.model_bi[w1][w] / denominator * (1-alpha)
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def predict(self, words):
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trigrams = Counter(dict(self.model[words]))
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bigrams = Counter(dict(self.model_bi[words[-1]]))
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predictions = dict((trigrams + bigrams).most_common(6))
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total_prob = 0
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result = ""
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for word, prob in predictions.items():
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total_prob += prob
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result += f"{word}:{prob} "
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if len(result) == 0:
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return "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1"
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return result + f":{max(1-total_prob, 0.01)}"
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model = Model()
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#%%
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model.data
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model.train()
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#%%
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def predict(model, path, result_path):
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data = pd.read_csv(path, sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE)[7]
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with open(result_path, "w+", encoding="UTF-8") as f:
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for text in data:
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words = word_tokenize(model.clean(text))
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if len(words) < 2:
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prediction = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1"
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else:
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prediction = model.predict((words[-2], words[-1]))
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f.write(prediction + "\n")
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predict(model, "dev-0/in.tsv.xz", "dev-0/out.tsv")
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predict(model, "test-A/in.tsv.xz", "test-A/out.tsv")
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