challenging-america-word-gap-prediction/run.py

import pandas as pd
import csv
from collections import Counter, defaultdict
from nltk.tokenize import RegexpTokenizer
from nltk import trigrams


class WordGapPrediction:

    def __init__(self):
        self.tokenizer = RegexpTokenizer(r"\w+")
        self.model = defaultdict(lambda: defaultdict(lambda: 0))
        self.vocab = set()
        self.alpha = 0.001

    def read_train_data(self, file):
        data = pd.read_csv(file, sep="\t", error_bad_lines=False, index_col=0, header=None)
        for index, row in data[:100000].iterrows():
            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
                    self.model[(w1, w2)][w3] += 1
                    self.vocab.add(w1)
                    self.vocab.add(w2)
                    self.vocab.add(w3)
        
        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] = (self.model[word_pair][word] + self.alpha) / (num_n_grams + self.alpha*len(self.vocab))

    def generate_outputs(self, input_file, output_file):
        data = pd.read_csv(input_file, sep='\t', error_bad_lines=False, index_col=0, header=None, quoting=csv.QUOTE_NONE)
        with open(output_file, 'w') as f:
            for index, 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')

    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 = ''

        for word, prob in most_common.items():
            total_prob += prob
            str_prediction += f'{word}:{prob} '

        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'

        if 1 - total_prob >= 0.01:
            str_prediction += f":{1-total_prob}"
        else:
            str_prediction += f":0.01"
        
        return str_prediction

word_gap_prediction = WordGapPrediction()
word_gap_prediction.read_train_data('./train/in.tsv')
word_gap_prediction.generate_outputs('dev-0/in.tsv', 'dev-0/out.tsv')
word_gap_prediction.generate_outputs('test-A/in.tsv', 'test-A/out.tsv')