added a cross-validation example

examples/2D_leave_n_out.py

@@ -0,0 +1,59 @@
+__author__ = 'cpaulson'
+import pyKriging
+from pyKriging.krige import kriging
+from pyKriging.samplingplan import samplingplan
+from pyKriging.CrossValidation import Cross_Validation
+from pyKriging.utilities import saveModel
+
+# The Kriging model starts by defining a sampling plan, we use an optimal Latin Hypercube here
+sp = samplingplan(2)
+X = sp.optimallhc(5)
+
+# Next, we define the problem we would like to solve
+testfun = pyKriging.testfunctions().branin
+
+# We generate our observed values based on our sampling plan and the test function
+y = testfun(X)
+
+print 'Setting up the Kriging Model'
+cvMSE = []
+# Now that we have our initial data, we can create an instance of a kriging model
+k = kriging(X, y, testfunction=testfun, name='simple', testPoints=300)
+k.train(optimizer='ga')
+k.snapshot()
+# cv = Cross_Validation(k)
+# cvMSE.append( cv.leave_n_out(q=5)[0] )
+
+k.plot()
+for i in range(15):
+    print i
+    newpoints = k.infill(1)
+    for point in newpoints:
+        # print 'Adding point {}'.format(point)
+        k.addPoint(point, testfun(point)[0])
+    k.train(optimizer='pso')
+    k.snapshot()
+    # cv = Cross_Validation(k)
+    # cvMSE.append( cv.leave_n_out(q=5)[0] )
+k.plot()
+
+
+
+# saveModel(k, 'crossValidation.plk')
+
+# #And plot the model
+
+print 'Now plotting final results...'
+# k.plot()
+
+
+print k.testPoints
+print k.history['points']
+print k.history['rsquared']
+print k.history['avgMSE']
+print cvMSE
+from matplotlib import pylab as plt
+plt.plot(range(len(k.history['rsquared'])), k.history['rsquared'])
+plt.plot(range(len(cvMSE)), cvMSE)
+plt.show()
+

examples/2d_regression_Kriging.py

@@ -5,9 +5,11 @@
 from pyKriging.regressionkrige import regression_kriging
 from pyKriging.samplingplan import samplingplan
 
+
+from pyKriging.krige import kriging
 # The Kriging model starts by defining a sampling plan, we use an optimal Latin Hypercube here
 sp = samplingplan(2)
-X = sp.optimallhc(25)
+X = sp.optimallhc(30)
 
 # Next, we define the problem we would like to solve
 testfun = pyKriging.testfunctions().branin_noise
@@ -16,25 +18,36 @@
 y = testfun(X)
 print X, y
 
+testfun = pyKriging.testfunctions().branin
+
+
 print 'Setting up the Kriging Model'
 
 # Now that we have our initial data, we can create an instance of a kriging model
 k = regression_kriging(X, y, testfunction=testfun, name='simple', testPoints=250)
-k.train(optimizer='ga')
+k.train(optimizer='pso')
+k1 = kriging(X, y, testfunction=testfun, name='simple', testPoints=250)
+k1.train(optimizer='pso')
 print k.Lambda
-# k.snapshot()
-#
-for i in range(5):
+k.snapshot()
+
+
+for i in range(1):
     newpoints = k.infill(5)
     for point in newpoints:
         print 'Adding point {}'.format(point)
-        k.addPoint(point, testfun(point)[0])
+        newValue = testfun(point)[0]
+        k.addPoint(point, newValue)
+        k1.addPoint(point, newValue)
     k.train()
+    k1.train()
     # k.snapshot()
 #
 # # #And plot the model
 
 print 'Now plotting final results...'
-k.plot()
+print k.Lambda
+k.plot(show=False)
+k1.plot()
 
 

pyKriging/CrossValidation.py

@@ -3,21 +3,24 @@
 """
 import numpy as np
 from matplotlib import pyplot as plt
-from pyKrige.krige import kriging
+import pyKriging
+from pyKriging.krige import kriging
+from pyKriging.utilities import *
 import random
 import scipy.stats as stats
 
 
 class Cross_Validation():
 
-    def __init__(self, X, y, name):
+    def __init__(self, model, name=None):
         """
         X- sampling plane
         y- Objective function evaluations
         name- the name of the model
         """
-        self.X = X
-        self.y = y
+        self.model = model
+        self.X = self.model.X
+        self.y = self.model.y
         self.n, self.k = np.shape(self.X)
         self.predict_list, self.predict_varr, self.scvr = [], [], []
         self.name = name
@@ -209,4 +212,19 @@ def QQ_plot(self):
         plt.ylabel('Standard quantile')
         plt.show()
 
+    def leave_n_out(self, q=5):
+        '''
+        :param q: the numer of groups to split the model data inot
+        :return:
+        '''
+        mseArray = []
+        for i in splitArrays(self.model,5):
+            testk = kriging( i[0], i[1] )
+            testk.train()
+            for j in range(len(i[2])):
+                mseArray.append(mse(i[3][j], testk.predict( i[2][j] )))
+            del(testk)
+        return np.average(mseArray), np.std(mseArray)
+
+
 ## Example Use Case:

pyKriging/krige.py

@@ -18,6 +18,7 @@
 import math as m
 
 
+
 class kriging(matrixops):
     def __init__(self, X, y, testfunction=None, name='', testPoints=None, **kwargs):
         self.X = copy.deepcopy(X)
@@ -36,7 +37,7 @@ def __init__(self, X, y, testfunction=None, name='', testPoints=None, **kwargs):
         self.updateData()
         self.updateModel()
 
-        self.thetamin = 1e-4
+        self.thetamin = 1e-5
         self.thetamax = 100
         self.pmin = 1
         self.pmax = 2
@@ -51,6 +52,7 @@ def __init__(self, X, y, testfunction=None, name='', testPoints=None, **kwargs):
         self.history['adjrsquared'] = [0]
         self.history['chisquared'] = [1000]
         self.history['lastPredictedPoints'] = []
+        self.history['avgMSE'] = []
         if testPoints:
             self.history['pointData'] = []
             self.testPoints = self.sp.rlh(testPoints)
@@ -116,10 +118,10 @@ def normalizeData(self):
         for i in range(self.k):
             self.normRange.append([min(self.X[:, i]), max(self.X[:, i])])
 
-        print self.X
+        # print self.X
         for i in range(self.n):
             self.X[i] = self.normX(self.X[i])
-        print self.X
+        #print self.X
 
         self.ynormRange.append(min(self.y))
         self.ynormRange.append(max(self.y))
@@ -688,6 +690,8 @@ def snapshot(self):
         self.history['theta'].append(copy.deepcopy(self.theta))
         self.history['p'].append(copy.deepcopy(self.pl))
 
+        self.history['avgMSE'].append(self.calcuatemeanMSE(points=self.testPoints)[0])
+
         currentPredictions = []
         if self.history['pointData']!=None:
             for pointprim in self.history['pointData']:

pyKriging/regressionkrige.py

@@ -167,9 +167,9 @@ def updateModel(self):
         try:
             self.regupdatePsi()
         except Exception, err:
-            # pass
+            pass
             # print Exception, err
-            raise Exception("bad params")
+            # raise Exception("bad params")
 
     def predict(self, X, norm=True):
         '''

pyKriging/testfunctions.py

@@ -84,7 +84,7 @@ def branin_noise(self, X):
         noiseFree =  ((a*( X2 - b*X1**2 + c*X1 - d )**2 + e*(1-ff)*np.cos(X1) + e)+5*x)
         withNoise=[]
         for i in noiseFree:
-            withNoise.append(i + np.random.standard_normal()*7)
+            withNoise.append(i + np.random.standard_normal()*15)
         return np.array(withNoise)
 
 

pyKriging/utilities.py

@@ -9,7 +9,7 @@ def norm(x):
     return x-min(x)
 
 def saveModel(model, filePath):
-    pickle.dump(model, open(filePath, 'w'))
+    pickle.dump(model, open(filePath, 'w'), byref=True)
 
 def loadModel(filePath):
     return pickle.load(open(filePath,'r'))