bayes.py

@@ -1,61 +0,0 @@
-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()