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

#!/usr/bin/env python
# coding: utf-8

# In[136]:

import nltk
from nltk import trigrams
from nltk.tokenize import word_tokenize
from collections import defaultdict, Counter
from nltk import ngrams
import pandas as pd
import csv
import re



model = defaultdict(lambda: defaultdict(lambda: 0))


# In[173]:


train_file_in = pd.read_csv("train/in.tsv.xz", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=200000)
train_file_out = pd.read_csv("train/expected.tsv", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=200000)

print("read train file")
# In[174]:


stop_words= nltk.corpus.stopwords.words('english')

def get_20common_2grams(text, n):
    outputTrigrams = []
    n_grams = ngrams(nltk.tokenize.word_tokenize(text), n)
    for grams in n_grams:
        outputTrigrams.append(grams)
    return outputTrigrams

def get_20common_2grams_no_stop(text, n):
    tokenized_world = nltk.tokenize.word_tokenize(text)
    stop_words= nltk.corpus.stopwords.words('english') 
    tokenized_no_stop = [i for i in tokenized_world if i not in stop_words]
    n_grams = ngrams(tokenized_no_stop, n)
    return n_grams

def predict(word_before, word_after):
    prob_list = dict(Counter(model[(word_before, word_after)]).most_common(6)).items()
    predictions = []
    prob_sum = 0.0
    for key, value in prob_list:
        prob_sum += value
        predictions.append(f'{key}:{value}')
    if prob_sum == 0.0:
        return 'the:0:2 be:0.2 to:0.2 of:0.15 and:0.15 :0.1'
    remaining_prob = 1 - prob_sum
    if remaining_prob < 0.01:
        predictions.append(f':{0.01}')
    return ' '.join(predictions)


# In[175]:


train = train_file_in[[6, 7]]
train = pd.concat([train, train_file_out], axis=1)

train["result"] = train[6] + train[0] + train[7]


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for index, row in train.iterrows():
    text = str(row["result"]).lower().replace('-\\n', '').replace('\\n', ' ')
    words = word_tokenize(text)
    for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
        if w1 and w2 and w3:
            model[(w2, w3)][w1] += 1


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print("train model")

for key in model:
    total_count = float(sum(model[key].values()))
    for value in model[key]:
        model[key][value] /= total_count


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dev_data = pd.read_csv('dev-0/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
test_a_data = pd.read_csv('test-A/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)


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print("dev_0");

with open('dev-0/out.tsv', 'w', encoding="utf-8") as file:
    for index, row in dev_data.iterrows():
        text = str(row[7]).lower().replace('-\\n', '').replace('\\n', ' ')
        words = word_tokenize(text)
        if len(words) < 4:
            print(words)
            prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
        else:
            prediction = predict(words[0], words[1])
        file.write(prediction + '\n')

print("test_A");
with open('test-A/out.tsv', 'w', encoding="utf-8") as file:
    for index, row in test_a_data.iterrows():
        text = str(row[7]).lower().replace('-\\n', '').replace('\\n', ' ')
        words = word_tokenize(text)
        if len(words) < 4:
            print(words)
            prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
        else:
            prediction = predict(words[0], words[1])
        file.write(prediction + '\n')

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