bayes lib

bayes2.py

@@ -0,0 +1,40 @@
+import gzip
+import io
+import pandas as pd 
+import numpy as np 
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import accuracy_score
+
+def read_data_gz(baseUrl):
+    f = gzip.open(baseUrl,'r')
+    data_unzip = f.read()
+    data = pd.read_table(io.StringIO(data_unzip.decode('utf-8')), error_bad_lines=False, header= None)
+    return data
+
+baseUrl = '/home/przemek/ekstrakcja/sport-text-classification-ball-ISI-public/'
+data = read_data_gz(baseUrl + 'train/train.tsv.gz')
+
+y_train = data[0].values
+x_train = data[1].values
+
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+model.fit(x_train, y_train)
+
+# dev-0
+x_dev = pd.read_table(baseUrl + 'dev-0/in.tsv', error_bad_lines=False, header= None)
+x_dev = x_dev[0].values
+
+y_pred = model.predict(x_dev)
+y_pred.tofile(baseUrl + 'dev-0/out.tsv', sep='\n')
+# --------------
+
+# test-A
+x_testA =  pd.read_table(baseUrl + '/test-A/in.tsv', error_bad_lines=False, header= None)
+x_testA= x_testA[0].values
+
+y_predA = model.predict(x_testA)
+y_predA.tofile(baseUrl + 'test-A/out.tsv', sep='\n')
+# --------------