Rozwiazanie zadania bayes2.

rozwiazanie.py

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+#import numpy as np
+import gzip
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn import metrics
+
+#df = pd.read_csv('sport-text-classification-ball-ISI-public/train/train.tsv.gz', compression='gzip', header=None, sep='\t', error_bad_lines=False)
+train_X = [] 
+train_y = []
+with gzip.open('train/train.tsv.gz','r') as fin:        
+    for line in fin:
+        sline = line.decode('UTF-8').replace("\n", "").split("\t")
+        train_y.append(sline[0])
+        train_X.append(''.join(sline[1:]))
+
+test_X = []
+with open('dev-0/in.tsv','r') as test_in_file:
+    for line in test_in_file:
+        test_X.append(line.rstrip('\n'))
+
+test_y = []
+with open('dev-0/expected.tsv','r') as test_expected_file:
+    for line in test_expected_file:
+        test_y.append(line.rstrip('\n'))
+
+vectorizer = TfidfVectorizer(lowercase = True)
+X_train_tf = vectorizer.fit_transform(train_X)
+print("n_samples: %d, n_features: %d" % X_train_tf.shape)
+
+X_test_tf = vectorizer.transform(test_X)
+print("n_samples: %d, n_features: %d" % X_test_tf.shape)
+
+naive_bayes_classifier = MultinomialNB()
+naive_bayes_classifier.fit(X_train_tf, train_y)
+
+y_pred = naive_bayes_classifier.predict(X_test_tf)
+
+score1 = metrics.accuracy_score(test_y, y_pred)
+print("accuracy:   %0.3f" % score1)
+
+print(metrics.classification_report(test_y, y_pred,
+                                            target_names=['1', '0']))
+
+print("confusion matrix:")
+print(metrics.confusion_matrix(test_y, y_pred))
+
+print('------------------------------')
+
+file = open('dev-0/out.tsv',"w") 
+for i in y_pred:
+    file.writelines("{}\n".format(i))    
+file.close() 
+
+val_X = []
+with open('test-A/in.tsv','r') as test_in_file:
+    for line in test_in_file:
+        val_X.append(line.rstrip('\n'))
+        
+X_val_tf = vectorizer.transform(val_X)
+print("n_samples: %d, n_features: %d" % X_val_tf.shape)
+
+val_y_pred = naive_bayes_classifier.predict(X_val_tf)
+
+file = open('test-A/out.tsv',"w") 
+for i in val_y_pred:
+    file.writelines("{}\n".format(i))    
+file.close()