bayes

dev0_accuracy.txt

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+0.9889948642699926

main.py

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+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics import accuracy_score
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+
+df = pd.read_csv("train/train.tsv.gz", header=None, sep="\t", error_bad_lines=False)
+dev0 = pd.read_csv("dev-0/in.tsv", header=None, sep="\t", error_bad_lines=False)
+testA = pd.read_csv("test-A/in.tsv", header=None, sep="\t", error_bad_lines=False)
+expected = pd.read_csv("dev-0/expected.tsv", header=None, sep="\t", error_bad_lines=False)
+
+dev0_X = dev0.iloc[:, 0].tolist()
+testA_X = testA.iloc[:, 0].tolist()
+Y = df.iloc[:, 0].tolist()
+X = df.iloc[:, 1].tolist()
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+model.fit(X, Y)
+
+predicted_dev0 = model.predict(dev0_X)
+predicted_testA = model.predict(testA_X)
+
+pd.Series(predicted_dev0).to_csv("dev-0/out.tsv", header=False, sep="\t", index=False)
+pd.Series(predicted_testA).to_csv("test-A/out.tsv", header=False, sep="\t", index=False)
+dev0_accuracy = accuracy_score(expected, predicted_dev0)
+f = open("dev0_accuracy.txt", "w")
+f.write(str(dev0_accuracy))
+f.close()