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Unread 28-03-2015, 14:52
dakaufma dakaufma is offline
Registered User
AKA: David Kaufman
FRC #0449 (Blair Robot Project)
Team Role: Programmer
 
Join Date: Jan 2012
Rookie Year: 2009
Location: Silver Spring
Posts: 28
dakaufma is an unknown quantity at this point
Re: Analysis of team ranking

It runs out of memory after a few seconds computing least squares. I guess my script was too quick and dirty to handle that much data

Ok, closed out of my memory-hog browser and reran it. It finished in 68 seconds. It's single threaded --> only using 1 core. I don't know the exact specs but I'm using an old laptop. If you want it to run faster you could get a reasonable estimate running the script for each regional and averaging the results for teams that competed in multiple regionals. My script basically ran instantly on the DC regional alone, it just didn't scale well.

Results:

1 (1114, 119.53710628381191)
2 (2056, 107.52392526361433)
3 (1730, 98.937520154912633)
4 (2481, 85.411141027422389)
5 (1678, 83.604692998775761)
6 (987, 81.614933990522147)
7 (118, 80.969024034928296)
8 (4488, 80.539400149979258)
9 (254, 79.885031034931174)
10 (148, 79.432991922526838)
11 (5406, 78.607729049050903)
12 (1519, 78.606114913994432)
13 (1619, 77.525461715196158)
14 (1658, 72.769643357190205)
15 (3130, 71.271318091324432)
16 (1671, 70.02262062115436)
17 (2338, 68.377599976884255)
18 (1280, 68.31086262054076)
19 (3683, 68.046518730572274)
20 (314, 67.063058451290729)
21 (4143, 66.197146406242695)
22 (2085, 65.826088138212199)
23 (1756, 65.300090499752486)
24 (225, 64.935706448058212)
25 (1538, 64.559840785724475)
26 (330, 64.541264057923442)
27 (2852, 64.483030815000632)
28 (1986, 62.974425411332938)
29 (2451, 62.637597604073804)
30 (2974, 60.564349115146001)
31 (1023, 60.560211511217332)
32 (1983, 59.916674128430934)
33 (744, 59.594728708265407)
34 (1657, 59.279400181300687)
35 (67, 58.964359968912603)
36 (2122, 58.817777660610751)
37 (2342, 57.489327516287716)
38 (1640, 57.464812178955)
39 (2826, 56.916928984985773)
40 (234, 56.858881704686439)
41 (3339, 56.533409767618949)
42 (4655, 56.39313720643888)
43 (3230, 56.122255030023631)
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45 (624, 55.340768974856552)
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47 (525, 55.123176093827624)
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49 (303, 54.677252325474406)
50 (2512, 54.663228166283453)
51 (1156, 53.95917064433668)
52 (701, 53.489926552157726)
53 (135, 53.310632416341505)
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55 (4678, 52.90258841628895)
56 (2137, 52.82827564039772)
57 (1806, 52.545135953520244)
58 (4201, 52.100455606412289)
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60 (58, 51.708557689346009)
61 (1569, 50.999721751403754)
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65 (70, 50.212423608681007)
66 (456, 49.514397347218271)
67 (226, 49.421619066877071)
68 (3419, 49.291886530691805)
69 (4613, 49.182337568239383)
70 (2383, 48.952668799007341)
71 (4451, 48.461565771037783)
72 (4039, 48.428887067643302)
73 (60, 48.304027027456911)
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76 (2643, 47.617699872486028)
77 (85, 47.524384916070105)
78 (1403, 47.469950277471035)
79 (494, 47.374147160288466)
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81 (287, 47.257974979802917)
82 (2386, 46.953430407180903)
83 (203, 46.861955923046182)
84 (1024, 46.697459369030227)
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86 (3646, 46.13325578237896)
87 (340, 46.084590782880028)
88 (3132, 45.713812619738299)
89 (4564, 45.704005687502573)
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92 (3200, 45.067571065424303)
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101 (5188, 44.006924999206518)
102 (33, 43.991463573919681)
103 (662, 43.937410242633831)
104 (107, 43.844215107033079)
105 (2883, 43.838433292634448)
106 (126, 43.680710097846031)
107 (4522, 43.658797513513953)
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109 (4539, 43.541714417941485)
110 (2457, 43.313197516131233)
111 (1768, 43.202978983685014)
112 (2062, 42.91184275827608)
113 (3996, 42.878462653349217)
114 (1218, 42.221423588184592)
115 (4947, 42.21385679919382)
116 (4028, 42.184039856147834)
117 (4003, 42.058582095894693)
118 (230, 41.974917059019731)
119 (610, 41.906402824077851)
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121 (4917, 41.384462943129392)
122 (3688, 41.147751248112812)
123 (3930, 41.033422854244797)
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127 (971, 40.523141542947506)
128 (3238, 40.466262103026601)
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130 (61, 40.330053037398599)
131 (3418, 40.150604856512359)
132 (48, 40.137305510265087)
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153 (973, 38.463844062433054)
154 (51, 38.334314781676767)
155 (384, 38.201138768628411)
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157 (4096, 38.116308218237869)
158 (4623, 38.092545615566564)
159 (4952, 37.86114845595975)
160 (133, 37.828067879761136)
161 (1718, 37.787600674391747)
162 (2474, 37.692730385543939)
163 (3743, 37.59312775479782)
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67.87user 0.10system 1:08.05elapsed 99%CPU (0avgtext+0avgdata 399012maxresident)k
0inputs+0outputs (0major+53269minor)pagefaults 0swaps
  #2   Spotlight this post!  
Unread 28-03-2015, 15:12
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MikLast MikLast is offline
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Re: Analysis of team ranking

so is this just if all teams were at the DC regional based on their score?

It would put us in the top 30% of teams...
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Last edited by MikLast : 28-03-2015 at 15:15.
  #3   Spotlight this post!  
Unread 28-03-2015, 16:09
dakaufma dakaufma is offline
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Re: Analysis of team ranking

Quote:
Originally Posted by MikLast View Post
so is this just if all teams were at the DC regional based on their score?
No, not quite, this is a slightly different computation. I'm trying to answer the question "how much value does each robot contribute to its alliance?"

In most (qualification) matches robots operate reasonably independently from one another. This makes my question a lot easier to answer -- if robots don't influence each other's performance, then each robot should contribute (on average) the same score to any alliance it plays with. So I've got one variable per team -- how many points it contributes to its alliance -- and I'm trying to solve for the set of values for these variables that comes closest to predicting the actual match scores.

The listing above is my estimate of how many points each robot contributes to its alliance (given no interaction with other robots)
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Unread 28-03-2015, 16:13
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Re: Analysis of team ranking

Quote:
Originally Posted by dakaufma View Post
No, not quite, this is a slightly different computation. I'm trying to answer the question "how much value does each robot contribute to its alliance?"

In most (qualification) matches robots operate reasonably independently from one another. This makes my question a lot easier to answer -- if robots don't influence each other's performance, then each robot should contribute (on average) the same score to any alliance it plays with. So I've got one variable per team -- how many points it contributes to its alliance -- and I'm trying to solve for the set of values for these variables that comes closest to predicting the actual match scores.

The listing above is my estimate of how many points each robot contributes to its alliance (given no interaction with other robots)
I understand that, then this just means that the teams at the DC regional were skewed, or something else?
Quote:
My script basically ran instantly on the DC regional alone, it just didn't scale well.
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  #5   Spotlight this post!  
Unread 28-03-2015, 16:22
dakaufma dakaufma is offline
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Re: Analysis of team ranking

I haven't done any in-depth analysis, but DC doesn't look too far out of line from the set of all regionals.

@MikLast I think I misinterpreted what you were saying. This would be a reasonable estimate of how well teams would do if each team played this game without an alliance. So congrats if you team is in the top 30%, you have a good robot
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Unread 28-03-2015, 16:26
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Re: Analysis of team ranking

Quote:
Originally Posted by dakaufma View Post
@MikLast I think I misinterpreted what you were saying. This would be a reasonable estimate of how well teams would do if each team played this game without an alliance. So congrats if you team is in the top 30%, you have a good robot
Ah, i misunderstood you also then. Thank you for clarifying, and i cant wait to show my team this next week.
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  #7   Spotlight this post!  
Unread 28-03-2015, 16:01
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Ether Ether is offline
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Re: Analysis of team ranking

Quote:
Originally Posted by dakaufma View Post
It finished in 68 seconds.
Try this instead:

1) compute N = AT∙A

2) compute d = AT∙b

3) compute teamscores = scipy.linalg.cho_solve(scipy.linalg.cho_factor(N), d)

... where A is np.array(matchdata) and b is np.array(matchscores)

The computation time should be reduced from 68 seconds to about 2 seconds or less.



Last edited by Ether : 28-03-2015 at 20:16.
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Unread 28-03-2015, 20:42
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Ether Ether is offline
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Re: Analysis of team ranking

Quote:
Originally Posted by Ether View Post

1) compute N = AT∙A

2) compute d = AT∙b

3) compute teamscores = scipy.linalg.cho_solve(scipy.linalg.cho_factor(N), d)

... where A is np.array(matchdata) and b is np.array(matchscores)

The computation time should be reduced from 68 seconds to about 2 seconds or less.
I'd run this test myself but I have Python2.7.5 installed and your Python3 code crashes when I try to run it. Not being very fluent in Python, I'm not in a good position to try to port it.

Based on some testing I did here, I'm fairly confident that your computation time can be dramatically reduced by making the small changes shown above.



Last edited by Ether : 28-03-2015 at 22:13.
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