s434804 added solution

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Dawid 2022-04-03 19:15:50 +02:00
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# challenging-america-word-gap-prediction
Challenging America word-gap prediction
===================================
Guess a word in a gap.
Evaluation metric
-----------------
LikelihoodHashed is the metric

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--metric PerplexityHashed --precision 2 --in-header in-header.tsv --out-header out-header.tsv

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FileId Year LeftContext RightContext
1 FileId Year LeftContext RightContext

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Word
1 Word

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import csv
import pandas as pd
import regex as re
import nltk
from nltk import trigrams, word_tokenize
from collections import Counter, defaultdict
# nltk.download("punkt")
# train set
train_data = pd.read_csv(
"train/in.tsv.xz",
sep="\t",
error_bad_lines=False,
warn_bad_lines=False,
header=None,
quoting=csv.QUOTE_NONE,
nrows=100_000
)
# training labels
train_labels = pd.read_csv(
"train/expected.tsv",
sep="\t",
error_bad_lines=False,
warn_bad_lines=False,
header=None,
quoting=csv.QUOTE_NONE,
nrows=100_000
)
# dev set
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 set
test_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,
)
def prepare_text(text):
text = text.lower().replace("-\\n", "").replace("\\n", " ")
text = re.sub(r"\p{P}", "", text)
return text
def train_trigrams():
for _, row in train_data.iterrows():
text = prepare_text(str(row["final"]))
words = word_tokenize(text)
for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
if all([w1, w2, w3]):
model[(w2, w3)][w1] += 1
model[(w1, w2)][w3] += 1
for w_pair in model:
ngram_count = float(sum(model[w_pair].values()))
for w3 in model[w_pair]:
model[w_pair][w3] /= ngram_count
def predict_probs(word1, word2):
raw_prediction = dict(model[word1, word2])
prediction = dict(Counter(raw_prediction).most_common(6))
total_prob = 0.0
str_prediction = ""
for word, prob in prediction.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"
remaining_prob = 1 - total_prob
if remaining_prob < 0.01:
remaining_prob = 0.01
str_prediction += f":{remaining_prob}"
return str_prediction
def write_output():
with open("dev-0/out.tsv", "w") as file:
for _, row in dev_data.iterrows():
text = prepare_text(str(row[7]))
words = word_tokenize(text)
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 = predict_probs(words[0], words[1])
file.write(prediction + "\n")
with open("test-A/out.tsv", "w") as file:
for _, row in test_data.iterrows():
text = prepare_text(str(row[7]))
words = word_tokenize(text)
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 = predict_probs(words[0], words[1])
file.write(prediction + "\n")
if __name__ == "__main__":
# Preapare train data
print("Preparing data...")
train_data = train_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]
# declare model
print("Preparing model...")
model = defaultdict(lambda: defaultdict(lambda: 0))
# train model
print("Model training...")
train_trigrams()
# write outputs
print("Writing outputs...")
write_output()

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