linear regresion solution

run.py

@@ -0,0 +1,69 @@
+import lzma
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import recall_score
+from sklearn.metrics import precision_score
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import f1_score
+
+X_train = []
+Y_train = []
+
+stop = 0
+
+with lzma.open('train/in.tsv.xz', 'rt', encoding="utf-8") as f:
+    for line in f:
+        if(stop > 5000):
+            break
+        else:
+            text = line.strip()
+            X_train.append(text)
+            #stop = stop + 1
+
+stop = 0
+
+with open('train/expected.tsv', 'rt') as f2:
+    for line in f2:
+        if(stop > 5000):
+                break
+        else:
+            text = line.strip()
+            Y_train.append(int(text))
+            #stop = stop + 1
+
+vectorizer = TfidfVectorizer()
+document_vectors = vectorizer.fit_transform(X_train)
+
+model = LogisticRegression()
+model.fit(document_vectors, Y_train)
+
+
+def readFile(filename):
+    X_dev = []
+    with open(filename, 'r', encoding="utf-8") as dev_in:
+        for line in dev_in:
+            text = line.split("\t")[0].strip()
+            X_dev.append(text)
+    return X_dev
+
+def writePred(filename, predictions):
+    with open(filename, "w") as out_file:
+        for pred in predictions:
+            out_file.write(str(pred) + "\n")
+
+X_dev = readFile('dev-0/in.tsv')
+X_dev = vectorizer.transform(X_dev)
+predictions = model.predict(X_dev)
+writePred('dev-0/out.tsv',predictions)
+
+X_dev = readFile('dev-1/in.tsv')
+X_dev = vectorizer.transform(X_dev)
+predictions = model.predict(X_dev)
+writePred('dev-1/out.tsv',predictions)
+
+X_dev = readFile('test-A/in.tsv')
+X_dev = vectorizer.transform(X_dev)
+predictions = model.predict(X_dev)
+writePred('test-A/out.tsv',predictions)
+
+