103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
from ast import Mod
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import pandas as pd
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import csv
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import regex as re
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from nltk import bigrams, word_tokenize
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from collections import Counter, defaultdict
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def read(self):
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self.data = pd.read_csv(
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self._path,
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sep="\t",
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error_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE
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)
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class Model:
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def __init__(self, alpha):
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self.alpha = alpha
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self.model = defaultdict(lambda: defaultdict(lambda: 0))
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self.vocab = set()
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def train(self, data):
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for _, row in data.iterrows():
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words = word_tokenize(clean(row["final"]))
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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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self.model[w1][w2] += 1
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self.vocab.add(w1)
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self.vocab.add(w2)
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for w1 in self.model:
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total_count = float(sum(self.model[w1].values()))
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denominator = total_count + self.alpha * len(self.vocab)
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for w2 in self.model[w1]:
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nominator = self.model[w1][w2] + self.alpha
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self.model[w1][w2] = nominator / denominator
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def _predict(self, word):
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predictions = dict(self.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 not total_prob:
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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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if 1 - total_prob >= 0.01:
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str_prediction += f":{1-total_prob}"
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else:
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str_prediction += f":0.01"
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return str_prediction
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def predict(self, read_path, save_path):
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data = pd.read_csv(
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read_path, sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE
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)
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with open(save_path, "w") as file:
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for _, row in data.iterrows():
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words = word_tokenize(clean(row[6]))
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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 = self._predict(words[-1])
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file.write(prediction + "\n")
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if __name__ == '__main__':
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data = pd.read_csv(
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"train/in.tsv.xz",
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sep="\t",
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error_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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)
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train_labels = pd.read_csv(
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"train/expected.tsv",
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sep="\t",
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error_bad_lines=False,
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header=None,
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quoting=csv.QUOTE_NONE,
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)
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train_data = data[[6, 7]]
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train_data = pd.concat([train_data, train_labels], axis=1)
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train_data["final"] = train_data[6] + train_data[0] + train_data[7]
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model = Model(0.0001)
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model.train(train_data)
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model.predict("dev-0/in.tsv.xz", "dev-0/out.tsv")
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model.predict("test-A/in.tsv.xz", "test-A/out.tsv")
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