skrypt-dev-0.py

@@ -1,39 +0,0 @@
-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

@@ -1,39 +0,0 @@
-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)