bayes.py

@@ -1,30 +0,0 @@
-import numpy as np
-import pandas as pd
-import gzip
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import CountVectorizer
-
-bayes = MultinomialNB()
-vec_q = CountVectorizer()
-
-with gzip.open('./train/train.tsv.gz', 'rb') as f:
-    train = pd.read_csv(f, error_bad_lines=False, header=None, sep="\t")
-dev = pd.read_csv("./dev-0/in.tsv", error_bad_lines=False, header=None, sep="\t")
-test = pd.read_csv("./test-A/in.tsv", error_bad_lines=False, header=None, sep="\t")
-
-# model
-X_train, y_train = train[0].astype(str).tolist(), train[1].astype(str).tolist()
-y_train=vec_q.fit_transform(y_train)
-bayes.fit(y_train, X_train)
-
-# dev 
-X_dev = dev[0].astype(str).tolist()
-y_dev = vec_q.transform(X_dev)
-dev_pred = bayes.predict(y_dev)
-pd.DataFrame(dev_pred).to_csv('./dev-0/out.tsv', sep='\t', index=False, header=False)
-
-# test
-X_test = train[0].astype(str).tolist()
-y_test = vec_q.transform(X_test)
-test_pred = bayes.predict(y_test)
-pd.DataFrame(test_pred).to_csv('./test-A/out.tsv', sep='\t', index=False, header=False)