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

from ast import Mod
import pandas as pd
import csv
import regex as re
from nltk import bigrams, word_tokenize
from collections import Counter, defaultdict


def clean(text):
    text = str(text).lower().replace("-\\n", "").replace("\\n", " ")
    return re.sub(r"\p{P}", "", text)

class Model:
    def __init__(self, alpha):
        self.alpha = alpha
        self.model = defaultdict(lambda: defaultdict(lambda: 0))
        self.vocab = set()

    def train(self, data):
        for _, row in data.iterrows():
            words = word_tokenize(clean(row["final"]))
            for w1, w2 in bigrams(words, pad_left=True, pad_right=True):
                if w1 and w2:
                    self.model[w1][w2] += 1
                    self.vocab.add(w1)
                    self.vocab.add(w2)
                    
        for w1 in self.model:
            total_count = float(sum(self.model[w1].values()))
            denominator = total_count + self.alpha * len(self.vocab)
            for w2 in self.model[w1]:
                nominator = self.model[w1][w2] + self.alpha
                self.model[w1][w2] = nominator / denominator


    def _predict(self, word):
        predictions = dict(self.model[word])
        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 not total_prob:
            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


    def predict(self, read_path, save_path):
        data = pd.read_csv(
            read_path, sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE
        )
        with open(save_path, "w") as file:
            for _, row in data.iterrows():
                words = word_tokenize(clean(row[6]))
                if len(words) < 3:
                    prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
                else:
                    prediction = self._predict(words[-1])
                file.write(prediction + "\n")


if __name__ == '__main__':

    data = pd.read_csv(
        "train/in.tsv.xz",
        sep="\t",
        error_bad_lines=False,
        header=None,
        quoting=csv.QUOTE_NONE,
    )
    train_labels = pd.read_csv(
        "train/expected.tsv",
        sep="\t",
        error_bad_lines=False,
        header=None,
        quoting=csv.QUOTE_NONE,
    )

    train_data = data[[6, 7]]
    train_data = pd.concat([train_data, train_labels], axis=1)

    train_data["final"] = train_data[6] + train_data[0] + train_data[7]

    model = Model(0.0001)
    model.train(train_data)
    model.predict("dev-0/in.tsv.xz", "dev-0/out.tsv")
    model.predict("test-A/in.tsv.xz", "test-A/out.tsv")