sport

run.py

@@ -0,0 +1,36 @@
+import pandas as pd
+import numpy as np
+import gzip
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import accuracy_score
+
+
+with gzip.open('train/train.tsv.gz', 'rb') as file:
+    data = pd.read_csv(file, sep='\t', header=None, error_bad_lines=False)
+
+# dane do trenowania
+y_train = data[0]
+x_train = data[1]
+
+# dev
+x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')
+x_dev = x_dev[0]
+
+#test
+x_test_A = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')
+x_test_A = x_test_A[0]
+
+# model 
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+model.fit(x_train, y_train)
+
+def predictions(zb, path_out, model):
+    res = model.predict(zb)
+    with open(path_out, 'wt') as file:
+        for i in res:
+            file.write(str(i) + '\n')
+
+predictions(x_dev,'dev-0/out.tsv', model)
+predictions(x_test_A,'test-A/out.tsv', model)