.gitignore

@@ -1,4 +0,0 @@
-.idea
-train.tsv
-*.sav
-*.pickle

solution.py

@@ -1,55 +0,0 @@
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LinearRegression
-import pickle
-
-filename = 'regressor.sav'
-vec_file = 'vectorizer.pickle'
-regressor = LinearRegression()
-# regressor = pickle.load(open(filename, 'rb'))
-vectorizer = TfidfVectorizer()
-# vectorizer = pickle.load(open(vec_file, 'rb'))
-
-
-def train():
-    with open('train/train.tsv') as f:
-        docs = [line.rstrip() for line in f]
-    docs_preprocessed = []
-    y = []
-    for doc in docs:
-        row = doc.split('\t')
-        start_date = row[0]
-        end_date = row[1]
-        end_date = end_date.split(' ')
-        if len(end_date) > 1:
-            row.insert(4, end_date[1])
-        end_date = end_date[0]
-        doc = row[4:5][0]
-        docs_preprocessed.append(doc)
-        y.append((float(start_date) + float(end_date))/2)
-    y = [float(value) for value in y]
-    print('Fitting vectorizer...')
-    x = vectorizer.fit_transform(docs_preprocessed)
-    pickle.dump(vectorizer, open(vec_file, 'wb'))
-    print('DONE!')
-    print('Fitting regressor...')
-    regressor.fit(x, y)
-    pickle.dump(regressor, open(filename, 'wb'))
-    print('DONE!')
-
-
-def classify(path):
-    print("Predicting for", path)
-    with open(path + 'in.tsv') as f:
-        docs = [line.rstrip() for line in f]
-    test_x = vectorizer.transform(docs)
-    predictions = regressor.predict(test_x)
-    with open(path + 'out.tsv', 'w') as file:
-        for prediction in predictions:
-            file.write("%f\n" % prediction)
-
-
-train()
-classify('dev-0/')
-classify('dev-1/')
-classify('test-A/')
-