stockfisher
stockfisher is an R interface for UCI based chess engines. Any chess engine can be used but it comes with and tested with stockfish.
Using stockfisher
The first thing that should be done is to spawn an engine process. If an engine is not spawned, stockStep and pgnAnalysis functions will spawn their own processes and terminate at function termination which might add some overhead.
To start the stockfish engine that is included in the package, use
stockfish = startStockfish()If you want to start a different engine or your own stockfish installation just do
engine = subprocess::spawn_process('path/to/engine')Spawning a process beforehand is also good if you want to set some engine parameters.
# see available options
getOptions(stockfish)## name
## 1 Debug Log File
## 2 Contempt
## 3 Analysis Contempt
## 4 Threads
## 5 Hash
## 6 Clear Hash
## 7 Ponder
## 8 MultiPV
## 9 Skill Level
## 10 Move Overhead
## 11 Minimum Thinking Time
## 12 Slow Mover
## 13 nodestime
## 14 UCI_Chess960
## 15 UCI_AnalyseMode
## 16 SyzygyPath
## 17 SyzygyProbeDepth
## 18 Syzygy50MoveRule
## 19 SyzygyProbeLimit
## info
## 1 type string default
## 2 type spin default 24 min -100 max 100
## 3 type combo default Both var Off var White var Black var Both
## 4 type spin default 1 min 1 max 512
## 5 type spin default 16 min 1 max 131072
## 6 type button
## 7 type check default false
## 8 type spin default 1 min 1 max 500
## 9 type spin default 20 min 0 max 20
## 10 type spin default 30 min 0 max 5000
## 11 type spin default 20 min 0 max 5000
## 12 type spin default 84 min 10 max 1000
## 13 type spin default 0 min 0 max 10000
## 14 type check default false
## 15 type check default false
## 16 type string default <empty>
## 17 type spin default 1 min 1 max 100
## 18 type check default true
## 19 type spin default 7 min 0 max 7
# set the move overhead to 30
setOptions(stockfish,optionList = list(`Move Overhead` = 30))Next best move
stockStep function will analyze the current position of the board and return you the next best move the engine can come up with. It also gives you the best opposing move (if it can), and the score for the current position from it's perspective (positive integers if the engine is winning, negative if its losing).
As an input it accepts a Chess object from the rchess package or a string that could be read by UCI (FEN notation or something like startpos moves e2e4).
library(rchess)
board = Chess$new()
# use the position of the board as input
# think for 2 seconds and return the bestmove it could think of
stockStep(board,movetime= 2000, stockfish = stockfish)## $bestmove
## [1] "e2e4"
##
## $ponder
## [1] "e7e5"
##
## $score
## [1] 62
##
## $scoreType
## [1] "cp"
##
## $time
## [1] 2001
# use the string as input
stockStep(posString = 'startpos moves e2e4',movetime= 1000, stockfish = stockfish)## $bestmove
## [1] "c7c5"
##
## $ponder
## [1] "g1f3"
##
## $score
## [1] -17
##
## $scoreType
## [1] "cp"
##
## $time
## [1] 1001
If you want to pass a move back to the rchess board, the short algebraic notation should be used. You can have stockStep to return in this format using the translate argument.
stockStep(board,movetime= 2000, stockfish = stockfish,translate = TRUE)## $bestmove
## [1] "e4"
##
## $ponder
## [1] "c5"
##
## $score
## [1] 68
##
## $scoreType
## [1] "cp"
##
## $time
## [1] 2001
Note that the input must always be an rchess board rather than the startPos argument for this to work.
Game analysis
If you have a pgn file or an rchess board with a history, gameAnalysis function can be used to analyze all moves of the game and return best moves and scores. The scores returned are always from the point of view of the white player. The processing time is controlled by the movetime or depth arguments. Below I analyze a Kasparov vs Topalov game whose pgn is included in the rchess package.
pgn = system.file("extdata/pgn/kasparov_vs_topalov.pgn", package = "rchess")
pgn = readLines(pgn, warn = FALSE)
pgn <- paste(pgn, collapse = "\n")
board = rchess::Chess$new()
board$load_pgn(pgn)## [1] TRUE
# either pgn or board can be used as an input for gameAnalysis
evaluations = gameAnalysis(pgn,movetime = 500,stockfish = stockfish,progress = FALSE)
scores = evaluations$score
# if a score is of type mate, it no longer counts in centipawns but turns
# left for an expected mate. Here I process these to be one above the maximum cp value.
# this game does not include a mate evaluation so this line does nothing
scores[evaluations$scoreType=='mate'] = (evaluations$score %>% max)+1
plot(scores)
In this plot positive scores indicate a white advantage while negative indicates black advantage. This game is ultimately won by Kasparow, the white player. If you allow the engine to think longer, the evaluations will be more accurate.
Saving game animations
You can save and create animations of rchess boards with history using animateGame. animateGame invisibly returns a magick-image object.
dir.create('README_files',showWarnings = FALSE)
animateGame(board,file = 'README_files/kasparov_vs_topalov.gif',
width = 4,
height = 4,
fps = 1,
piecesize = 12)
Running games
Here I'll use stockfisher to run a timed game between 2 AI opponents. 2 stockfish sessions are used here to demonstrate pondering (setting ponder=TRUE allows the engine to continue processing assuming the opponent will move as predicted in ponder). In this context one of the players can be replaced with a different engine. A simpler implementation with a single session is also possible if you just want to see how the engine functions under different parameters without pondering.
library(tictoc)
players = list(
w = startStockfish(),
b = startStockfish()
)
# an estimate of the move overhead
setOptions(players$w,optionList = list(`Move Overhead` = 400))
setOptions(players$b,optionList = list(`Move Overhead` = 400))
ponder = list(
w = '',
b = ''
)
# 2 minute timer for each player
timer = list(
w = 120000,
b = 120000
)
board = Chess$new()
while(!board$game_over() & timer[[board$turn()]] > 0){
turn = board$turn()
history = board$history()
tic()
if(!is.na(ponder[[turn]]) && length(history)>0 && history[length(history)] == ponder[[turn]]){
# if the opponent moves as the engine predicted, send a ponderhit
# print('ponderhit')
move = ponderhit(board,
wtime = timer$w,
btime = timer$b,
translate = TRUE,
ponder = TRUE,
stockfish = players[[turn]])
} else{
# if the engine couldn't predict how the opponent will move in the previous turn, get the best move as normal
# print('normal move')
move = stockStep(board,
wtime = timer$w,
btime = timer$b,
translate = TRUE,
stockfish = players[[turn]],
ponder = TRUE)
}
board$move(move$bestmove)
ponder[[turn]] = move$ponder
# update timers
time = toc(quiet = TRUE)
timePassed = 1000*(time$toc - time$tic)
timer[[turn]] = unname(timer[[turn]] - timePassed)
# overhead calculation. negative returns are due to added ponder time.
# tend to fluctuate between 350-250 on my machine
# overhead = timePassed-(move$time)
# print(overhead)
}
# shutdown both player processes
players %>% sapply(stopStockfish) %>% invisible()
# save game for a later look
writeLines(board$pgn(),'README_files/stockfish_vs_stockfish.pgn')
# animate the game board
animateGame(board,file = 'README_files/stockfish_vs_stockfish.gif',
width = 4,
height = 4,
fps = 1,
piecesize = 12)
The resulting game can be analyzed using gameAnalysis. These games tend to end in a stalemate as both engines have access to the same resources. One could try giving less time to one of the players but with pondering on, any time one player spends thinking gives other player the time to think, especially since they are more likely to ponderhit each other since they are the same engine.
evaluations = gameAnalysis(board,movetime = 500,stockfish = stockfish,progress = FALSE)
scores = evaluations$score
scores[evaluations$scoreType=='mate'] = (evaluations$score %>% max)+1
plot(scores)
How did the game end? rchess doesn't include a single function to get the game state to we have
gameState(board)## [1] "draw-threefold repetition"
Finally use stopStockfish to stop the engine process
stopStockfish(stockfish)## [1] TRUE