fin

skrypt-dev-0.py

@@ -0,0 +1,39 @@
+import numpy as np
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn import preprocessing
+from sklearn.pipeline import make_pipeline
+import csv
+
+prep = preprocessing.LabelEncoder()
+
+with open("train/train.tsv") as file_train:
+    csv_input = csv.reader(file_train, delimiter='\t')
+    X = []
+    Y = []
+    for line in csv_input:
+        Y.append(line[0])
+        X.append(line[1])
+    
+    Y = prep.fit_transform(Y)
+    with open("dev-0/in.tsv") as file_in:
+        work_file_lines = file_in.readlines()
+        MNB = make_pipeline(TfidfVectorizer(use_idf = True), MultinomialNB())
+      
+        model = MNB.fit(X,Y)
+
+        y_predict = model.predict(work_file_lines)
+        y_predict = np.array(y_predict)
+
+        np.set_printoptions(threshold=np.inf)
+        labels = np.array2string(y_predict.flatten(), separator='\n', suppress_small=True)
+
+        file_out = open("dev-0/out.tsv", 'w')
+        file_out.write(labels[1:-1])
+
+        with open("dev-0/out.tsv", 'r') as fix_space:
+            lines = fix_space.readlines()
+
+            lines = [line.replace(' ', '') for line in lines]
+        with open("dev-0/out.tsv", 'w') as fix_space:
+            fix_space.writelines(lines)

skrypt-test-a.py

@@ -0,0 +1,39 @@
+import numpy as np
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn import preprocessing
+from sklearn.pipeline import make_pipeline
+import csv
+
+prep = preprocessing.LabelEncoder()
+
+with open("train/train.tsv") as file_train:
+    csv_input = csv.reader(file_train, delimiter='\t')
+    X = []
+    Y = []
+    for line in csv_input:
+        Y.append(line[0])
+        X.append(line[1])
+    
+    Y = prep.fit_transform(Y)
+    with open("test-A/in.tsv") as file_in:
+        work_file_lines = file_in.readlines()
+        MNB = make_pipeline(TfidfVectorizer(use_idf = True), MultinomialNB())
+      
+        model = MNB.fit(X,Y)
+
+        y_predict = model.predict(work_file_lines)
+        y_predict = np.array(y_predict)
+
+        np.set_printoptions(threshold=np.inf)
+        labels = np.array2string(y_predict.flatten(), separator='\n', suppress_small=True)
+
+        file_out = open("test-A/out.tsv", 'w')
+        file_out.write(labels[1:-1])
+
+        with open("test-A/out.tsv", 'r') as fix_space:
+            lines = fix_space.readlines()
+
+            lines = [line.replace(' ', '') for line in lines]
+        with open("test-A/out.tsv", 'w') as fix_space:
+            fix_space.writelines(lines)