data as number

predict_rm.py

@@ -1,30 +1,24 @@
-#!/usr/bin/python3
-
-import pandas as pd
-import csv
-import pickle
-
-def predict():
-    dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["document","date"], quoting=csv.QUOTE_NONE)
-    testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["document","date"], quoting=csv.QUOTE_NONE)
-    devdoc = dev["document"]
-    testdoc = testA["document"]
-
-    clf = pickle.load(open("clf.model", "rb"))
-    vectorizer = pickle.load(open("vectorizer.model", "rb"))
-    
-    dev0_vectorizer = vectorizer.transform(devdoc)
-    testA_vectorizer = vectorizer.transform(testdoc)
-
-    y_dev = clf.predict_proba(dev0_vectorizer)
-    y_test = clf.predict_proba(testA_vectorizer)
-
-    with open("dev-0/out.tsv", "w") as devout:
-        for line in y_dev:
-            devout.write(str(line[1])+"\n")
-
-    with open("test-A/out.tsv", "w") as testaout:
-        for line in y_test:
-            testaout.write(str(line[1])+"\n")
-
-predict()
+#!/usr/bin/python3
+
+import pandas as pd
+import csv
+import pickle
+import numpy as np
+
+def predict():
+    dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["document","date"], quoting=csv.QUOTE_NONE)["document"]
+    testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["document","date"], quoting=csv.QUOTE_NONE)["document"]
+
+    clf = pickle.load(open("clf.model", "rb"))
+    vectorizer = pickle.load(open("vectorizer.model", "rb"))
+    
+    dev0_vectorizer = vectorizer.transform(dev0)
+    testA_vectorizer = vectorizer.transform(testA)
+
+    y_dev = clf.predict_proba(dev0_vectorizer)[:, 1]
+    y_test = clf.predict_proba(testA_vectorizer)[:, 1]
+
+    np.savetxt('test-A/out.tsv', y_dev, '%f')
+    np.savetxt('dev-0/out.tsv', y_test, '%f')
+
+predict()