challenging-america-word-ga.../run.py

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import pandas as pd
import csv
from collections import Counter, defaultdict
from nltk.tokenize import RegexpTokenizer
from nltk import trigrams
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class WordPred:
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def __init__(self):
self.tokenizer = RegexpTokenizer(r"\w+")
self.model = defaultdict(lambda: defaultdict(lambda: 0))
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def read_train_data(self, file):
data = pd.read_csv(file, compression='xz', sep="\t", error_bad_lines=False, index_col=0, header=None)
for row in data[:140000].itertuples():
if len(row)<8:
continue
text = str(row[6]) + ' ' + str(row[7])
tokens = self.tokenizer.tokenize(text)
for w1, w2, w3 in trigrams(tokens, pad_right=True, pad_left=True):
if w1 and w2 and w3:
self.model[(w2, w3)][w1] += 1
for word_pair in self.model:
num_n_grams = float(sum(self.model[word_pair].values()))
for word in self.model[word_pair]:
self.model[word_pair][word] /= num_n_grams
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def generate_outputs(self, input_file, output_file):
data = pd.read_csv(input_file, compression='xz', sep='\t', error_bad_lines=False, index_col=0, header=None, quoting=csv.QUOTE_NONE)
with open(output_file, 'w') as f:
for row in data.iterrows():
text = str(row[7])
tokens = self.tokenizer.tokenize(text)
if len(tokens) < 4:
prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
else:
prediction = word_gap_prediction.predict_probs(tokens[0], tokens[1])
f.write(prediction + '\n')
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def predict_probs(self, word1, word2):
predictions = dict(self.model[word1, word2])
most_common = dict(Counter(predictions).most_common(6))
total_prob = 0.0
str_prediction = ''
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for word, prob in most_common.items():
total_prob += prob
str_prediction += f'{word}:{prob} '
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if total_prob == 0.0:
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:
str_prediction += f":{1-total_prob}"
else:
str_prediction += f":0.01"
return str_prediction
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word_gap_prediction = WordPred()
word_gap_prediction.read_train_data('./train/in.tsv.xz')
# word_gap_prediction.generate_outputs('dev-0/in.tsv.xz', 'dev-0/out.tsv')
# word_gap_prediction.generate_outputs('test-A/in.tsv.xz', 'test-A/out.tsv')