add the regression benchmark

benchmarks/regression/methods.py

@@ -0,0 +1,81 @@
+# Copyright (c) 2015, Zhenwen Dai
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import abc
+import numpy as np
+import GPy
+
+class RegressionMethod(object):
+    __metaclass__ = abc.ABCMeta
+    
+    def __init__(self):
+        self.preprocess = True
+        
+    def _preprocess(self, data,  train):
+        """Zero-mean, unit-variance normalization by default"""
+        if train:
+            inputs, labels = data
+            self.data_mean = inputs.mean(axis=0)
+            self.data_std = inputs.std(axis=0)
+            self.labels_mean = labels.mean(axis=0)
+            self.labels_std = labels.std(axis=0)
+            return ((inputs-self.data_mean)/self.data_std, (labels-self.labels_mean)/self.labels_std)
+        else:
+            return (data-self.data_mean)/self.data_std
+    
+    def _reverse_trans_labels(self, labels):
+        return labels*self.labels_std+self.labels_mean
+        
+    def fit(self, train_data):
+        if self.preprocess:
+            train_data = self._preprocess(train_data, True)
+        return self._fit(train_data)
+    
+    def predict(self, test_data):
+        if self.preprocess:
+            test_data = self._preprocess(test_data, False)
+        labels = self._predict(test_data)
+        if self.preprocess:
+            labels = self._reverse_trans_labels(labels)
+        return labels
+    
+    @abc.abstractmethod
+    def _fit(self, train_data):
+        """Fit the model. Return True if successful"""
+        return True
+    
+    @abc.abstractmethod
+    def _predict(self, test_data):
+        """Predict on test data"""
+        return None
+    
+class GP_RBF(RegressionMethod):
+    name = 'GP_RBF'
+    
+    def _fit(self, train_data):
+        inputs, labels = train_data
+        self.model = GPy.models.GPRegression(inputs, labels,kernel=GPy.kern.RBF(inputs.shape[-1],ARD=True) +GPy.kern.Linear(inputs.shape[1], ARD=True) + GPy.kern.White(inputs.shape[1],0.01)  )
+        self.model.likelihood.variance[:] = labels.var()*0.01
+        self.model.optimize()
+        return True
+    
+    def _predict(self, test_data):
+        return self.model.predict(test_data)[0]
+
+class SVIGP_RBF(RegressionMethod):
+    name = 'SVIGP_RBF'
+    
+    def _fit(self, train_data):
+        X, Y = train_data
+        
+        Z = X[np.random.permutation(X.shape[0])[:100]]
+        k = GPy.kern.RBF(X.shape[1], ARD=True) + GPy.kern.Linear(X.shape[1], ARD=True) + GPy.kern.White(X.shape[1],0.01) 
+
+        lik = GPy.likelihoods.StudentT(deg_free=3.)
+        self.model = GPy.core.SVGP(X, Y, Z=Z, kernel=k, likelihood=lik)
+        [self.model.optimize('scg', max_iters=40, gtol=0, messages=0, xtol=0, ftol=0) for i in range(10)]
+        self.model.optimize('bfgs', max_iters=1000, gtol=0, messages=0)
+        return True
+    
+    def _predict(self, test_data):
+        return self.model.predict(test_data)[0]