main.py

@@ -1,47 +0,0 @@
-import pandas as pd
-import numpy as np
-import gzip
-import os
-import sys
-from sklearn.pipeline import make_pipeline
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-IN_FILE_NAME = "in.tsv"
-OUT_FILE_NAME = "out.tsv"
-
-
-def main(dirname):
-    in_path = os.path.join(dirname, IN_FILE_NAME)
-    if not os.path.exists(in_path):
-        raise Exception(f"Path {in_path} does not exist!")
-
-    input = pd.read_table(in_path,
-                          error_bad_lines=False, header=None)
-
-    X_train = []
-    y_train = []
-
-    with gzip.open('train/train.tsv.gz', 'r') as f:
-        for l in f:
-            line = l.decode('UTF-8').replace("\n", "").split("\t")
-            y_train.append(int(line[0]))
-            X_train.append(str(line[1:]))
-
-    X_train = np.asarray(X_train)
-    y_train = np.asarray(y_train)
-
-    X = input[0].values
-
-    model = make_pipeline(TfidfVectorizer(), MultinomialNB())
-    model.fit(X_train, y_train)
-
-    pred = model.predict(X)
-
-    pred.tofile(os.path.join(dirname, OUT_FILE_NAME), sep='\n')
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        raise Exception("Name of working dir not specified!")
-    main(sys.argv[1])