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FROM ubuntu:latest
WORKDIR /isi
COPY . /isi
RUN pip install -r /isi/requirements.txt
CMD ["program.py"]

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dev-0/out.tsv

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dev-1/out.tsv

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import pandas as pd
import csv
from sklearn.feature_extraction.text import CountVectorizer
from xgboost import XGBClassifier
import numpy as np
def load_data(path):
with open(path, encoding='utf-8') as f:
data = f.readlines()
data = [x.strip() for x in data]
return data
def save_data(path, data):
with open(path, "w") as out:
for line in data:
out.write(str(line[1]))
out.write("\n")
def program():
train = load_data("train/in.tsv")
y = load_data("train/expected.tsv")
vectorizer = CountVectorizer()
x_vectorizer = vectorizer.fit_transform(train)
clf = XGBClassifier().fit(x_vectorizer, y)
dev0 = load_data("dev-0/in.tsv")
dev0_vectorizer = vectorizer.transform(dev0)
y_dev0 = clf.predict_proba(dev0_vectorizer)
save_data("dev-0/out.tsv", y_dev0)
dev1 = load_data("dev-1/in.tsv")
dev1_vectorizer = vectorizer.transform(dev1)
y_dev1 = clf.predict_proba(dev1_vectorizer)
save_data("dev-1/out.tsv", y_dev1)
testA = load_data("test-A/in.tsv")
testA_vectorizer = vectorizer.transform(testA)
y_testA = clf.predict_proba(testA_vectorizer)
save_data("test-A/out.tsv", y_testA)
program()

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pandas
sklearn
xgboost
numpy

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