Dockerfile

@@ -1,5 +0,0 @@
-FROM ubuntu:latest
-WORKDIR /isi
-COPY . /isi
-RUN pip install -r /isi/requirements.txt
-CMD ["program.py"]

program.py

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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()

requirements.txt

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-pandas
-sklearn
-xgboost
-numpy