add geval

bayes.py

@@ -0,0 +1,61 @@
+from sklearn.naive_bayes import GaussianNB
+import pandas as pd
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+PATHS = ['./train/train.tsv', './dev-0/in.tsv', './test-A/in.tsv']
+PATHS_OUTPUT = ['./dev-0/out.tsv', './test-A/out.tsv']
+
+def get_data(path):
+  return pd.read_table(path, error_bad_lines=False, sep='\t', header=None)
+
+def get_X_y_train(data):
+  X_train = data[1].values
+  y_train = data[0].values
+
+  return X_train, y_train
+
+def training(x, y):
+  vectorizer = TfidfVectorizer()
+  result = vectorizer.fit_transform(x)
+  classifier = MultinomialNB()
+  classifier.fit(result, y)
+
+  return classifier, vectorizer
+
+
+def predict(vectorizer, classifier, x):
+  result = vectorizer.transform(x)
+  pred = classifier.predict(result)
+
+  return pred
+
+def generate_output(pred, path):
+  pred.tofile(path, sep = '\n')
+
+def main():
+  #prepare train
+  train = get_data(PATHS[0])
+  X_train, y_train = get_X_y_train(train)
+
+  #train
+  classifier, vectorizer = training(X_train, y_train)
+
+  #dev
+  X_dev = get_data(PATHS[1])
+  X_dev = X_dev[0].values
+  pred_dev = predict(vectorizer, classifier, X_dev)
+
+  #test
+  X_test = get_data(PATHS[2])
+  X_test = X_test[0].values
+  pred_test = predict(vectorizer, classifier, X_test)
+
+  #generate output
+  generate_output(pred_dev, PATHS_OUTPUT[0])
+  generate_output(pred_test, PATHS_OUTPUT[1])
+
+
+
+if __name__ == '__main__':
+  main()