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