add the regression benchmark

benchmarks/regression/run.py

@@ -0,0 +1,53 @@
+# Copyright (c) 2015, Zhenwen Dai
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from __future__ import print_function
+from evaluation import RMSE
+from methods import GP_RBF, SVIGP_RBF
+from tasks import Housing, WineQuality
+from outputs import ScreenOutput, CSVOutput, H5Output
+import numpy as np
+import time
+
+outpath = '.'
+prjname = 'regression'
+config = {
+          'evaluations':[RMSE],
+          'methods':[GP_RBF, SVIGP_RBF],
+          'tasks':[WineQuality,Housing],
+          'repeats':2,
+          'outputs': [ScreenOutput(), CSVOutput(outpath, prjname), H5Output(outpath, prjname)]
+          }
+
+if __name__=='__main__':
+    results = np.zeros((len(config['tasks']), len(config['methods']), len(config['evaluations'])+1, config['repeats']))
+    
+    for task_i in range(len(config['tasks'])):
+        dataset = config['tasks'][task_i]()
+        print('Benchmarking on '+dataset.name)
+        res = dataset.load_data()
+        if not res: print('Fail to load '+config['tasks'][task_i].name); continue
+        train = dataset.get_training_data()
+        test = dataset.get_test_data()
+        
+        for method_i in range(len(config['methods'])):
+            method = config['methods'][method_i]
+            print('With the method '+method.name, end='')
+            for ri in range(config['repeats']):
+                m = method()
+                t_st = time.time()
+                m.fit(train)
+                pred = m.predict(test[0])
+                t_pd = time.time() - t_st
+                for ei in range(len(config['evaluations'])):
+                    evalu = config['evaluations'][ei]()
+                    results[task_i, method_i, ei, ri] = evalu.evaluate(test[1], pred)
+                results[task_i, method_i, -1, ri] = t_pd
+                print('.',end='')
+            print()
+                    
+    [out.output(config, results) for out in config['outputs']]
+
+
+            
+