finalise sparsity results

GiovanniPasserello · May 30, 2021 · 5158c67 · 5158c67
1 parent 163ca46
commit 5158c67
Showing 1 changed file with 48 additions and 44 deletions.
diff --git a/shgp/data/metadata_sparsity.py b/shgp/data/metadata_sparsity.py
@@ -42,7 +42,7 @@ class SparsityMetaDataset:
 """ Banana with Exp kernel - np.arange(5, 51, 5):
 results_hgv = [-265.21812865, -199.13950339, -146.41359466, -132.61964685, -124.94452836, 
  -122.44237289, -120.67969943, -120.04857654, -119.89991836, -119.80383267]
-results_hgv_then_optimise = [-187.90945912, -143.35572839, -129.54256206, -122.85893017, -120.83210659
+results_hgv_then_optimise = [-187.90945912, -143.35572839, -129.54256206, -122.85893017, -120.83210659,
  -120.07338803, -119.83879177, -119.76479951, -119.74784784, -119.74348298]
 optimal = -119.74156303725914
 """
@@ -74,10 +74,10 @@ def __init__(self):
 results_hgv = [-77.020314, -33.02012411, -30.70187662, -30.26146468, -30.19302943,
  -30.16403339, -30.13561464, -30.13122342, -30.12628466, -30.12465734,
  -30.12163524, -30.12093994, -30.12056074, -30.12043881, -30.12021612]
-results_hgv_then_optimise = [-36.00822127 -31.25506725 -30.33460073 -30.14617903 -30.12271089
- -30.12136622 -30.12073425 -30.12034384 -30.12022219 -30.12011015
- -30.12005339 -30.1200338 -30.12003275 -30.1200168 -30.11999865]
-optimal = -30.17694684298644
+results_hgv_then_optimise = [-36.00822127, -31.25506725, -30.33460073, -30.14617903, -30.12271089,
+  -30.12136622, -30.12073425, -30.12034384, -30.12022219, -30.12011015,
+  -30.12005339, -30.1200338, -30.12003275, -30.1200168, -30.11999865]
+optimal = -30.12021581
 """
 
 
@@ -101,10 +101,10 @@ def __init__(self):
 """
 
 """ Heart with Exp kernel - np.arange(5, 51, 5):
-results_hgv = [-149.12100835 -126.93577783 -117.56235171 -117.23099965 -117.01637655
- -116.87625325 -116.79155455 -116.7388337 -116.68191999 -116.61718728]
-results_hgv_then_optimise = [-143.75599 -120.08909116 -116.57998049 -116.46777726 -116.4453033
- -123.50099838 -116.41999216 -116.41201695 -116.40704368 -116.40335913]
+results_hgv = [-149.12100835, -126.93577783, -117.56235171, -117.23099965, -117.01637655,
+  -116.87625325, -116.79155455, -116.7388337, -116.68191999, -116.61718728]
+results_hgv_then_optimise = [-143.75599, -120.08909116, -116.57998049, -116.46777726, -116.4453033,
+  -116.43211438, -116.41999216, -116.41201695, -116.40704368, -116.40335913]
 optimal = -116.39046838357692
 """
 
@@ -140,11 +140,11 @@ def __init__(self):
  -136.90363315, -131.61687268, -131.34020317, -131.13195079, -130.96671821,
  -130.79279399, -130.69150494, -130.52185487, -130.41177432, -130.27371917,
  -130.21010736, -130.11656349, -129.99983508, -129.78540044, -129.79948968]
-results_hgv_then_optimise = [-153.73386594882237, -143.87917141067973, -139.5352554664604, -136.33786916900803, 
--133.79081164982009, -131.2309264156396, -129.59001708784606, -129.276073055637, -129.0732634622664, 
--128.88357852294703, -128.72630150779162, -128.50254430015048, -128.3291856770311, -128.13987618189543, 
--127.96852417887922, -127.80981361806997, -127.68314589407956, -127.58164336658196, -127.45071692472926, 
--127.35130477028518]
+results_hgv_then_optimise = [-153.73386594882237, -143.87917141067973, -139.5352554664604,  -136.33786916900803, 
+ -133.79081164982009, -131.2309264156396,  -129.59001708784606, -129.276073055637, 
+ -129.0732634622664, -128.88357852294703, -128.72630150779162, -128.50254430015048, 
+ -128.3291856770311, -128.13987618189543, -127.96852417887922, -127.80981361806997, 
+ -127.68314589407956, -127.58164336658196, -127.45071692472926, -127.35130477028518]
 optimal = -127.00553397076634
 """
 
@@ -180,11 +180,11 @@ def __init__(self):
  -75.41824255, -75.11827172, -74.95152676, -74.86525487, -74.79757264,
  -74.77744846, -74.74552347, -74.73493794, -74.71050664, -74.69445384,
  -74.68535608, -74.67809315, -74.67292411, -74.66851115, -74.66450929]
-results_hgv_then_optimise = [-97.32619739 -82.72792927 -76.78488875 -75.19711647 -74.78423776
- -74.69878274 -74.67597054 -74.66463683 -74.65998954 -74.65666712
- -74.65461688 -74.65336598 -74.65256175 -74.65220575 -74.65171341
- -74.65135656 -74.65113877 -74.65078521 -74.65077963 -74.65079426]
-optimal = -75.02901627637823
+results_hgv_then_optimise = [-97.32619739, -82.72792927, -76.78488875, -75.19711647, -74.78423776,
+  -74.69878274, -74.67597054, -74.66463683, -74.65998954, -74.65666712,
+  -74.65461688, -74.65336598, -74.65256175, -74.65220575, -74.65171341,
+  -74.65135656, -74.65113877, -74.65078521, -74.65077963, -74.65079426]
+optimal = -74.65003242
 """
 
 
@@ -195,7 +195,7 @@ def __init__(self):
  SparsityMetaDataset.__init__(self, 5, 10, 100, 10, np.arange(5, 101, 5))
 
 
-""" Pima Diabetes with Exp kernel - np.arange(5, 101, 5):
+""" Pima Diabetes with Exp kernel - np.arange(5, 151, 5):
 results_uniform = [-444.77996138, -386.91316907, -386.02783327, -385.31543353, -384.54316826,
  -383.67183261, -382.85791003, -382.46500407, -381.84566833, -380.99304483,
  -380.63460644, -380.55328098, -380.27110464, -379.96738077, -379.8475552,
@@ -223,15 +223,19 @@ def __init__(self):
 optimal = -377.60474770202654
 """
 
-""" Pima Diabetes with Exp kernel - np.arange(5, 101, 5):
+""" Pima Diabetes with Exp kernel - np.arange(5, 151, 5):
 results_hgv = [-428.40331381, -386.66910472, -386.26175908, -385.68283928, -385.15406953,
  -384.15858353, -382.72303009, -381.98085024, -381.21443913, -380.69221024,
  -380.28591426, -379.9466808, -379.46890005, -379.23909147, -379.00873003,
- -378.79243527, -378.63612655, -378.57201862, -378.42718324, -378.30245032]
-results_hgv_then_optimise = [-416.0128765 -383.12674192 -381.24907292 -380.23462236 -379.5407066
- -379.01157183 -378.63276174 -378.38281912 -378.18601216 -378.05279903
- -377.94867806 -377.87153446 -377.81553453 -377.77530287 -377.73936246
- -377.71547247 -377.69614057 -377.67934838 -377.66691497 -377.65551202]
+ -378.79243527, -378.63612655, -378.57201862, -378.42718324, -378.30245032,
+ -378.24541369, -378.12956747, -378.07115572, -378.03255121, -377.96465316,
+ -377.94687693, -377.91596828, -377.85527252, -377.82121455, -377.80279939]
+results_hgv_then_optimise = [-416.0128765, -383.12674192, -381.24907292, -380.23462236, -379.5407066,
+ -379.01157183, -378.63276174, -378.38281912, -378.18601216, -378.05279903,
+ -377.94867806, -377.87153446, -377.81553453, -377.77530287, -377.73936246,
+ -377.71547247, -377.69614057, -377.67934838, -377.66691497, -377.65551202,
+ -377.64811292, -377.64112501, -377.63499536, -377.63138791, -377.62710808,
+ -377.62456957, -377.62115252, -377.61966475, -377.61779005, -377.61627136]
 optimal = -377.6047477020269
 """
 
@@ -240,7 +244,7 @@ def __init__(self):
 class PimaSparsityMetaDataset(PimaDataset, SparsityMetaDataset):
  def __init__(self):
  PimaDataset.__init__(self)
- SparsityMetaDataset.__init__(self, 5, 10, 100, 10, np.arange(5, 151, 5))
+ SparsityMetaDataset.__init__(self, 5, 10, 100, 10, np.arange(105, 151, 5))
 
 
 """ Twonorm with Exp kernel - np.arange(5, 51, 5):
@@ -362,23 +366,23 @@ def __init__(self):
 """
 
 """ MAGIC with Exp kernel - np.arange(5, 306, 10):
-results_hgv = [-2622.88920078 -2165.29989543 -2030.89551171 -1935.82762473
- -1845.58860804 -1778.75346152 -1761.10452395 -1756.82255539
- -1753.64038911 -1751.31769235 -1748.77866817 -1745.81058575
- -1743.88910324 -1741.05621014 -1737.86539163 -1736.05263271
- -1732.48842931 -1730.87240599 -1728.49708456 -1726.61598566
- -1724.54669587 -1722.89507483 -1721.96259604 -1719.88484892
- -1718.15797201 -1716.33362112 -1715.11453179 -1714.16480832
- -1712.64782855 -1711.71690642 -1711.33342417]
-results_hgv_then_optimise = [-2012.34005544 -1870.35952549 -1814.63618322 -1781.81814063
- -1759.79239295 -1744.64846209 -1735.52586658 -1729.87674608
- -1725.10174498 -1721.70709836 -1718.36769472 -1715.75304107
- -1713.44155478 -1711.49321582 -1709.97927022 -1708.11360013
- -1707.04489305 -1705.98416789 -1705.01974408 -1704.47648932
- -1703.74840548 -1703.19588689 -1702.76273437 -1702.36497604
- -1701.93507812 -1701.61450529 -1701.30799658 -1701.01032498
- -1700.7762311 -1700.50261561 -1700.36405207]
-optimal = -1702.8659218764612
+results_hgv = [-2622.88920078, -2165.29989543, -2030.89551171, -1935.82762473,
+  -1845.58860804, -1778.75346152, -1761.10452395, -1756.82255539,
+  -1753.64038911, -1751.31769235, -1748.77866817, -1745.81058575,
+  -1743.88910324, -1741.05621014, -1737.86539163, -1736.05263271,
+  -1732.48842931, -1730.87240599, -1728.49708456, -1726.61598566,
+  -1724.54669587, -1722.89507483, -1721.96259604, -1719.88484892,
+  -1718.15797201, -1716.33362112, -1715.11453179, -1714.16480832,
+  -1712.64782855, -1711.71690642, -1711.33342417]
+results_hgv_then_optimise = [-2012.34005544, -1870.35952549, -1814.63618322, -1781.81814063,
+  -1759.79239295, -1744.64846209, -1735.52586658, -1729.87674608,
+  -1725.10174498, -1721.70709836, -1718.36769472, -1715.75304107,
+  -1713.44155478, -1711.49321582, -1709.97927022, -1708.11360013,
+  -1707.04489305, -1705.98416789, -1705.01974408, -1704.47648932,
+  -1703.74840548, -1703.19588689, -1702.76273437, -1702.36497604,
+  -1701.93507812, -1701.61450529, -1701.30799658, -1701.01032498,
+  -1700.7762311, -1700.50261561, -1700.36405207]
+optimal = -1700.3215487232228
 """