add the regression benchmark

benchmarks/regression/tasks.py

@@ -0,0 +1,86 @@
+# Copyright (c) 2015, Zhenwen Dai
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import abc
+import os
+import numpy as np
+
+class RegressionTask(object):
+    __metaclass__ = abc.ABCMeta
+    
+    def __init__(self, datapath='./'):
+        self.datapath = datapath
+    
+    @abc.abstractmethod
+    def load_data(self):
+        """Download the dataset if not exist. Return True if successful"""
+        return True
+    
+    @abc.abstractmethod
+    def get_training_data(self):
+        """Return the training data: training data and labels"""
+        return None
+    
+    @abc.abstractmethod
+    def get_test_data(self):
+        """Return the test data: training data and labels"""
+        return None
+    
+class Housing(RegressionTask):
+    
+    name='Housing'
+    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
+    filename = 'housing.data'
+    
+    def load_data(self):
+        from GPy.util.datasets import download_url, data_path
+        if not os.path.exists(os.path.join(data_path,self.datapath, self.filename)):
+            download_url(Housing.url, self.datapath, messages=True)
+            if not os.path.exists(os.path.join(data_path, self.datapath, self.filename)):
+                return False
+        
+        data = np.loadtxt(os.path.join(data_path, self.datapath, self.filename))
+        self.data = data
+        data_train = data[:250,:-1]
+        label_train = data[:250, -1:]
+        self.train = (data_train, label_train)
+        data_test = data[250:,:-1]
+        label_test = data[250:,-1:]
+        self.test = (data_test, label_test)
+        return True
+    
+    def get_training_data(self):
+        return self.train
+    
+    def get_test_data(self):
+        return self.test
+    
+class WineQuality(RegressionTask):
+    
+    name='WineQuality'
+    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
+    filename = 'winequality-red.csv'
+    
+    def load_data(self):
+        from GPy.util.datasets import download_url, data_path
+        if not os.path.exists(os.path.join(data_path,self.datapath, self.filename)):
+            download_url(self.url, self.datapath, messages=True)
+            if not os.path.exists(os.path.join(data_path, self.datapath, self.filename)):
+                return False
+        
+        data = np.loadtxt(os.path.join(data_path, self.datapath, self.filename),skiprows=1,delimiter=';')
+        self.data = data
+        data_train = data[:1000,:-1]
+        label_train = data[:1000, -1:]
+        self.train = (data_train, label_train)
+        data_test = data[1000:,:-1]
+        label_test = data[1000:,-1:]
+        self.test = (data_test, label_test)
+        return True
+    
+    def get_training_data(self):
+        return self.train
+    
+    def get_test_data(self):
+        return self.test
+