updated test

test.py

@@ -28,27 +28,52 @@ def group_queries(data):
 		index += 1
 	return query_indexes
 
-training_data = get_data('/Users/madhavagrawal/Downloads/MQ2007/Fold1/train.txt')
-test_data = get_data('/Users/madhavagrawal/Downloads/MQ2007/Fold1/test.txt')
-# model = LambdaMART(training_data, 2, 10, 0.001)
-# model.fit()
-# model.save('lambdamart_model')
-model = LambdaMART()
-model.load('lambdamart_model.lmart')
-average_ndcg, predicted_scores = model.validate(test_data)
-
-print 'NDCG score: %f' % (average_ndcg)
-query_indexes = group_queries(test_data)
-index = query_indexes.keys()[0]
-testdata = [test_data[i][0] for i in query_indexes[index]]
-pred = [predicted_scores[i] for i in query_indexes[index]]
-output = pd.DataFrame({"True label": testdata, "prediction": pred})
-output = output.sort('prediction',ascending = False)
-output.to_csv("outdemo.csv", index =False)
-print output
-# for i in query_indexes[index]:
-# 	print test_data[i][0], predicted_scores[i]
 
+def main():
+	total_ndcg = 0.0
+	for i in [1,2,3,4,5]:
+		print 'start Fold ' + str(i)
+		training_data = get_data('Fold%d/train.txt' % (i))
+		test_data = get_data('Fold%d/test.txt' % (i))
+		model = LambdaMART(training_data, 300, 0.001, 'sklearn')
+		model.fit()
+		model.save('lambdamart_model_%d' % (i))
+		# model = LambdaMART()
+		# model.load('lambdamart_model.lmart')
+		average_ndcg, predicted_scores = model.validate(test_data, 10)
+		print average_ndcg
+		total_ndcg += average_ndcg
+	total_ndcg /= 5.0
+	print 'Original average ndcg at 10 is: ' + str(total_ndcg)
 
+	total_ndcg = 0.0
+	for i in [1,2,3,4,5]:
+		print 'start Fold ' + str(i)
+		training_data = get_data('Fold%d/train.txt' % (i))
+		test_data = get_data('Fold%d/test.txt' % (i))
+		model = LambdaMART(training_data, 300, 0.001, 'original')
+		model.fit()
+		model.save('lambdamart_model_sklearn_%d' % (i))
+		# model = LambdaMART()
+		# model.load('lambdamart_model.lmart')
+		average_ndcg, predicted_scores = model.validate(test_data, 10)
+		print average_ndcg
+		total_ndcg += average_ndcg
+	total_ndcg /= 5.0
+	print 'Sklearn average ndcg at 10 is: ' + str(total_ndcg)
 
+	# print 'NDCG score: %f' % (average_ndcg)
+	# query_indexes = group_queries(test_data)
+	# index = query_indexes.keys()[0]
+	# testdata = [test_data[i][0] for i in query_indexes[index]]
+	# pred = [predicted_scores[i] for i in query_indexes[index]]
+	# output = pd.DataFrame({"True label": testdata, "prediction": pred})
+	# output = output.sort('prediction',ascending = False)
+	# output.to_csv("outdemo.csv", index =False)
+	# print output
+	# # for i in query_indexes[index]:
+	# # 	print test_data[i][0], predicted_scores[i]
 
+
+if __name__ == '__main__':
+	main()