Run autopep8

naivebayes.py

@@ -0,0 +1,54 @@
+import numpy as np
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.naive_bayes import GaussianNB, MultinomialNB
+from sklearn.pipeline import make_pipeline, Pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+from nltk.corpus import stopwords
+
+
+# pogarsza wynik z 0.73 na 0.7
+def preprocess(line, stop_words):
+    return " ".join([word for word in line.split() if word not in stop_words])
+
+
+def train_model(train_in, train_expected):
+    with open(train_expected, 'r') as f:
+        exp = f.readlines()
+
+    with open(train_in, 'r') as f:
+        train_data = f.readlines()
+
+    exp_encoded = LabelEncoder().fit_transform(exp)
+
+    # vectors = TfidfVectorizer().fit_transform(train_data)
+    # vectors = vectors.reshape(-1, 1)
+    # model = MultinomialNB()
+    # return model.fit(vectors, exp_encoded)
+    # MemoryError
+
+    pipeline = Pipeline(steps=[
+        ('tfidf', TfidfVectorizer()),
+        ('naive-bayes', MultinomialNB())
+    ])
+
+    return pipeline.fit(train_data, exp_encoded)
+
+
+def predict(model, in_file, out_file):
+    with open(in_file, 'r') as f:
+        lines = f.readlines()
+    prediction = model.predict(lines)
+    np.savetxt(out_file, prediction, fmt='%d')
+
+
+def main():
+    #stop_words = set(stopwords.words('english'))
+    model = train_model("train/in.tsv", "train/expected.tsv")
+    predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
+    predict(model, "test-A/in.tsv", "test-A/out.tsv")
+
+
+if __name__ == '__main__':
+    main()