def _testTreeAccuracy(self): RF1 = RandomForest(ntrees=1, m=4, ndata=2, pRetrain=0.2) RF10 = RandomForest(ntrees=10, m=4, ndata=2, pRetrain=0.2) RF100 = RandomForest(ntrees=10, m=4, ndata=2, pRetrain=0.2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] RF1.addData(X, Y) RF10.addData(X, Y) RF100.addData(X, Y) rmse1 = 0.0 rmse10 = 0.0 rmse100 = 0.0 nsamp = 1000 for testx in lhcSample(tf.bounds, nsamp, seed=1): testy = tf.f(testx) rmse1 += (RF1.mu(testx) - testy) ** 2 rmse10 += (RF10.mu(testx) - testy) ** 2 rmse100 += (RF100.mu(testx) - testy) ** 2 # this isn't consistent, since random forests are, you know, random print "RMSE 1 = %.4f" % (rmse1 / nsamp) print "RMSE 10 = %.4f" % (rmse10 / nsamp) print "RMSE 100 = %.4f" % (rmse100 / nsamp) self.failUnless(rmse1 > rmse100)
def _testTreeAccuracy(self): RF1 = RandomForest(ntrees=1, m=4, ndata=2, pRetrain=.2) RF10 = RandomForest(ntrees=10, m=4, ndata=2, pRetrain=.2) RF100 = RandomForest(ntrees=10, m=4, ndata=2, pRetrain=.2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] RF1.addData(X, Y) RF10.addData(X, Y) RF100.addData(X, Y) rmse1 = 0.0 rmse10 = 0.0 rmse100 = 0.0 nsamp = 1000 for testx in lhcSample(tf.bounds, nsamp, seed=1): testy = tf.f(testx) rmse1 += (RF1.mu(testx) - testy)**2 rmse10 += (RF10.mu(testx) - testy)**2 rmse100 += (RF100.mu(testx) - testy)**2 # this isn't consistent, since random forests are, you know, random print 'RMSE 1 = %.4f' % (rmse1 / nsamp) print 'RMSE 10 = %.4f' % (rmse10 / nsamp) print 'RMSE 100 = %.4f' % (rmse100 / nsamp) self.failUnless(rmse1 > rmse100)
def testOneTree(self): forest = RandomForest(ntrees=1, m=4, ndata=2, pRetrain=.2) self.failUnlessEqual(forest.ntrees, 1) self.failUnlessEqual(forest.m, 4) self.failUnlessEqual(forest.ndata, 2) self.failUnlessEqual(forest.pRetrain, .2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] forest.addData(X, Y) self.failUnlessEqual(len(forest.forest), 1) # checkTree(forest.forest[0]) # maximizeEI(forest, tf.bounds) # print forest.forest[0] if False: figure(1, figsize=(5, 10)) c0 = [(i / 100.) * (tf.bounds[0][1] - tf.bounds[0][0]) + tf.bounds[0][0] for i in xrange(101)] c1 = [(i / 100.) * (tf.bounds[1][1] - tf.bounds[1][0]) + tf.bounds[1][0] for i in xrange(101)] ax = subplot(121) mu = array([[forest.mu(array([i, j])) for i in c0] for j in c1]) cs = ax.contourf(c0, c1, mu, 50) colorbar(cs) ax.plot([x[0] for x in X], [x[1] for x in X], 'ro', alpha=.2) ax.set_xbound(tf.bounds[0][0], tf.bounds[0][1]) ax.set_ybound(tf.bounds[1][0], tf.bounds[1][1]) ax.set_title(r'$\mu$') ax = subplot(122) mu = array([[forest.sigma2(array([i, j])) for i in c0] for j in c1]) cs = ax.contourf(c0, c1, mu, 50) colorbar(cs) ax.plot([x[0] for x in X], [x[1] for x in X], 'ro', alpha=.2) ax.set_xbound(tf.bounds[0][0], tf.bounds[0][1]) ax.set_ybound(tf.bounds[1][0], tf.bounds[1][1]) ax.set_title(r'$\sigma^2$') show()
def testOneTree(self): forest = RandomForest(ntrees=1, m=4, ndata=2, pRetrain=0.2) self.failUnlessEqual(forest.ntrees, 1) self.failUnlessEqual(forest.m, 4) self.failUnlessEqual(forest.ndata, 2) self.failUnlessEqual(forest.pRetrain, 0.2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] forest.addData(X, Y) self.failUnlessEqual(len(forest.forest), 1) # checkTree(forest.forest[0]) # maximizeEI(forest, tf.bounds) # print forest.forest[0] if False: figure(1, figsize=(5, 10)) c0 = [(i / 100.0) * (tf.bounds[0][1] - tf.bounds[0][0]) + tf.bounds[0][0] for i in xrange(101)] c1 = [(i / 100.0) * (tf.bounds[1][1] - tf.bounds[1][0]) + tf.bounds[1][0] for i in xrange(101)] ax = subplot(121) mu = array([[forest.mu(array([i, j])) for i in c0] for j in c1]) cs = ax.contourf(c0, c1, mu, 50) colorbar(cs) ax.plot([x[0] for x in X], [x[1] for x in X], "ro", alpha=0.2) ax.set_xbound(tf.bounds[0][0], tf.bounds[0][1]) ax.set_ybound(tf.bounds[1][0], tf.bounds[1][1]) ax.set_title(r"$\mu$") ax = subplot(122) mu = array([[forest.sigma2(array([i, j])) for i in c0] for j in c1]) cs = ax.contourf(c0, c1, mu, 50) colorbar(cs) ax.plot([x[0] for x in X], [x[1] for x in X], "ro", alpha=0.2) ax.set_xbound(tf.bounds[0][0], tf.bounds[0][1]) ax.set_ybound(tf.bounds[1][0], tf.bounds[1][1]) ax.set_title(r"$\sigma^2$") show()
def testForestUCB(self): RF = RandomForest(ntrees=2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] RF.addData(X, Y) ucbf = UCB(RF, len(tf.bounds)) dopt, doptx = direct(ucbf.negf, tf.bounds, maxiter=10) copt, coptx = cdirect(ucbf.negf, tf.bounds, maxiter=10) mopt, moptx = maximizeUCB(RF, tf.bounds, maxiter=10) self.failUnlessAlmostEqual(dopt, copt, 4) self.failUnlessAlmostEqual(-dopt, mopt, 4) self.failUnlessAlmostEqual(-copt, mopt, 4) self.failUnless(sum(abs(doptx - coptx)) < 0.01) self.failUnless(sum(abs(moptx - coptx)) < 0.01) self.failUnless(sum(abs(moptx - doptx)) < 0.01)
def testForestUCB(self): RF = RandomForest(ntrees=2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] RF.addData(X, Y) ucbf = UCB(RF, len(tf.bounds)) dopt, doptx = direct(ucbf.negf, tf.bounds, maxiter=10) copt, coptx = cdirect(ucbf.negf, tf.bounds, maxiter=10) mopt, moptx = maximizeUCB(RF, tf.bounds, maxiter=10) self.failUnlessAlmostEqual(dopt, copt, 4) self.failUnlessAlmostEqual(-dopt, mopt, 4) self.failUnlessAlmostEqual(-copt, mopt, 4) self.failUnless(sum(abs(doptx - coptx)) < .01) self.failUnless(sum(abs(moptx - coptx)) < .01) self.failUnless(sum(abs(moptx - doptx)) < .01)
def testForestPI(self): RF = RandomForest(ntrees=2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] RF.addData(X, Y) mu, sigma = RF.posterior(ones(len(tf.bounds)) * 0.4) print "[python] = 0.4 x 2, mu =", mu, " sigma =", sigma pif1 = PI(RF) dopt1, doptx1 = direct(pif1.negf, tf.bounds, maxiter=10) copt1, coptx1 = cdirect(pif1.negf, tf.bounds, maxiter=10) mopt1, moptx1 = maximizePI(RF, tf.bounds, maxiter=10) self.failUnlessAlmostEqual(dopt1, copt1, 4) self.failUnlessAlmostEqual(-dopt1, mopt1, 4) self.failUnlessAlmostEqual(-copt1, mopt1, 4) self.failUnless(sum(abs(doptx1 - coptx1)) < 0.01) self.failUnless(sum(abs(moptx1 - coptx1)) < 0.01) self.failUnless(sum(abs(moptx1 - doptx1)) < 0.01) pif2 = PI(RF, xi=0.5) dopt2, doptx2 = direct(pif2.negf, tf.bounds, maxiter=10) copt2, coptx2 = cdirect(pif2.negf, tf.bounds, maxiter=10) mopt2, moptx2 = maximizePI(RF, tf.bounds, xi=0.5, maxiter=10) self.failUnlessAlmostEqual(dopt2, copt2, 4) self.failUnlessAlmostEqual(-dopt2, mopt2, 4) self.failUnlessAlmostEqual(-copt2, mopt2, 4) self.failUnless(sum(abs(doptx2 - coptx2)) < 0.01) self.failUnless(sum(abs(moptx2 - coptx2)) < 0.01) self.failUnless(sum(abs(moptx2 - doptx2)) < 0.01) self.failIfAlmostEqual(dopt1, dopt2, 4) self.failIfAlmostEqual(copt1, copt2, 4) self.failIfAlmostEqual(mopt1, mopt2, 4)
def testForestPI(self): RF = RandomForest(ntrees=2) tf = Branin() X = lhcSample(tf.bounds, 20, seed=0) Y = [tf.f(x) for x in X] RF.addData(X, Y) mu, sigma = RF.posterior(ones(len(tf.bounds)) * .4) print '[python] = 0.4 x 2, mu =', mu, ' sigma =', sigma pif1 = PI(RF) dopt1, doptx1 = direct(pif1.negf, tf.bounds, maxiter=10) copt1, coptx1 = cdirect(pif1.negf, tf.bounds, maxiter=10) mopt1, moptx1 = maximizePI(RF, tf.bounds, maxiter=10) self.failUnlessAlmostEqual(dopt1, copt1, 4) self.failUnlessAlmostEqual(-dopt1, mopt1, 4) self.failUnlessAlmostEqual(-copt1, mopt1, 4) self.failUnless(sum(abs(doptx1 - coptx1)) < .01) self.failUnless(sum(abs(moptx1 - coptx1)) < .01) self.failUnless(sum(abs(moptx1 - doptx1)) < .01) pif2 = PI(RF, xi=0.5) dopt2, doptx2 = direct(pif2.negf, tf.bounds, maxiter=10) copt2, coptx2 = cdirect(pif2.negf, tf.bounds, maxiter=10) mopt2, moptx2 = maximizePI(RF, tf.bounds, xi=0.5, maxiter=10) self.failUnlessAlmostEqual(dopt2, copt2, 4) self.failUnlessAlmostEqual(-dopt2, mopt2, 4) self.failUnlessAlmostEqual(-copt2, mopt2, 4) self.failUnless(sum(abs(doptx2 - coptx2)) < .01) self.failUnless(sum(abs(moptx2 - coptx2)) < .01) self.failUnless(sum(abs(moptx2 - doptx2)) < .01) self.failIfAlmostEqual(dopt1, dopt2, 4) self.failIfAlmostEqual(copt1, copt2, 4) self.failIfAlmostEqual(mopt1, mopt2, 4)