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feat: C engine

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This commit is contained in:
Krzysztof Rudnicki 2025-09-06 18:26:31 +02:00
parent dd8763f30f
commit 03f8a2a5ac
9 changed files with 416 additions and 1611 deletions
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6
.vscode/tasks.json vendored
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@ -23,6 +23,12 @@
"command": "python -m pip install -r requirements.txt && pytest -q",
"isBackground": false,
"group": "build"
},
{
"label": "pytest quick",
"type": "shell",
"command": "python -m pip install -r requirements.txt && pytest -q",
"group": "build"
}
]
}

18
C/lichess_random_engine/Makefile Normal file
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@ -0,0 +1,18 @@
CC := gcc
CFLAGS := -O2 -std=c11 -Wall -Wextra -Wno-unused-parameter
LDFLAGS :=
SRC := main.c
BIN := random_engine
.PHONY: all clean rebuild
all: $(BIN)
$(BIN): $(SRC)
$(CC) $(CFLAGS) -o $@ $^ $(LDFLAGS)
clean:
rm -f $(BIN)
rebuild: clean all

217
C/lichess_random_engine/main.c Normal file
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@ -0,0 +1,217 @@
// Heuristic engine with optional explanation and analysis output
// Usage:
// random_engine [--seed N] [--explain] [--analyze UCI] <move1> <move2> ...
// Behavior:
// - If a move is annotated as 'uci;key=value;...' the engine parses features and
// computes a heuristic score. Recognized keys: chk (0/1), c (capture cp), prom (cp gain),
// mat (cp delta), mate (0/1).
// - Otherwise, assigns a pseudo-random score using the seed.
// - Picks the highest-scoring move
// - Default output: prints chosen move
// - With --explain: prints JSON including scores, chosen index, seed, and optional analysis of a provided candidate
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <unistd.h>
static unsigned int parse_seed_or_default(int *pargc, char ***pargv) {
unsigned int seed = (unsigned int)time(NULL) ^ (unsigned int)getpid();
int argc = *pargc;
char **argv = *pargv;
for (int i = 1; i < argc; ++i) {
if (strcmp(argv[i], "--seed") == 0 && i + 1 < argc) {
seed = (unsigned int)strtoul(argv[i + 1], NULL, 10);
// remove the two args
for (int j = i; j + 2 < argc; ++j) argv[j] = argv[j + 2];
*pargc -= 2;
return seed;
}
}
return seed;
}
typedef struct {
const char *arg_raw; // original argument string
char uci[16]; // extracted UCI (up to 7-8 chars normally)
int has_anno; // whether annotations were present
// parsed features
int chk; // 0/1
int mate; // 0/1
double cap_cp; // capture centipawns
double prom_cp; // promotion centipawns
double mat_cp; // material delta centipawns
double score; // computed score
} MoveInfo;
static void parse_move_spec(const char *spec, MoveInfo *mi) {
// Copy UCI up to ';' or end
mi->arg_raw = spec;
mi->uci[0] = '\0';
mi->has_anno = 0;
mi->chk = 0;
mi->mate = 0;
mi->cap_cp = 0.0;
mi->prom_cp = 0.0;
mi->mat_cp = 0.0;
mi->score = 0.0;
const char *semi = strchr(spec, ';');
size_t uci_len = semi ? (size_t)(semi - spec) : strlen(spec);
if (uci_len >= sizeof(mi->uci)) uci_len = sizeof(mi->uci) - 1;
memcpy(mi->uci, spec, uci_len);
mi->uci[uci_len] = '\0';
if (!semi) return;
mi->has_anno = 1;
const char *p = semi + 1;
while (*p) {
// key=value; segments
const char *kv_end = strchr(p, ';');
size_t len = kv_end ? (size_t)(kv_end - p) : strlen(p);
if (len > 0) {
// Parse known keys: chk, mate, c, prom, mat
if (strncmp(p, "chk=", 4) == 0) {
mi->chk = atoi(p + 4);
} else if (strncmp(p, "mate=", 5) == 0) {
mi->mate = atoi(p + 5);
} else if (strncmp(p, "c=", 2) == 0) {
mi->cap_cp = atof(p + 2);
} else if (strncmp(p, "prom=", 6) == 0) {
mi->prom_cp = atof(p + 6);
} else if (strncmp(p, "mat=", 4) == 0) {
mi->mat_cp = atof(p + 4);
}
}
if (!kv_end) break;
p = kv_end + 1;
}
}
static double heuristic_score(const MoveInfo *mi, unsigned int seed_state) {
// Weighted score from features; add tiny noise from seed to break ties
double s = 0.0;
if (mi->mate) s += 100000.0; // winning immediately trumps all
if (mi->chk) s += 50.0; // modest bonus for checks
s += 1.0 * mi->cap_cp; // prioritize raw capture value
s += 1.2 * mi->prom_cp; // promotions are very strong
s += 2.0 * mi->mat_cp; // overall material delta dominates
// tiny deterministic jitter from seed
double jitter = (double)(seed_state % 1000) / 1000000.0; // up to 0.001
return s + jitter;
}
int main(int argc, char **argv) {
if (argc <= 1) {
fprintf(stderr, "usage: %s [--seed N] [--explain] [--analyze UCI] <move1> <move2> ...\n", argv[0]);
return 1;
}
// Extract seed first (if any)
unsigned int seed = parse_seed_or_default(&argc, &argv);
srand(seed);
// Parse flags --explain and --analyze UCI
int explain = 0;
const char *analyze_uci = NULL;
for (int i = 1; i < argc; ++i) {
if (strcmp(argv[i], "--explain") == 0) {
explain = 1;
for (int j = i; j + 1 < argc; ++j) argv[j] = argv[j + 1];
argc -= 1;
i -= 1;
} else if (strcmp(argv[i], "--analyze") == 0 && i + 1 < argc) {
analyze_uci = argv[i + 1];
for (int j = i; j + 2 < argc; ++j) argv[j] = argv[j + 2];
argc -= 2;
i -= 1;
}
}
if (argc <= 1) {
fprintf(stderr, "no moves provided\n");
return 1;
}
// Remaining args are moves
int n = argc - 1;
char **moves = &argv[1];
// Parse move specs
MoveInfo *info = (MoveInfo *)malloc(sizeof(MoveInfo) * (size_t)n);
if (!info) {
fprintf(stderr, "alloc failed\n");
return 1;
}
for (int i = 0; i < n; ++i) {
parse_move_spec(moves[i], &info[i]);
if (info[i].has_anno) {
// compute heuristic score
unsigned int local = seed ^ (unsigned int)i * 2654435761u;
info[i].score = heuristic_score(&info[i], local);
} else {
// fallback: random score
info[i].score = (double)rand() / (double)RAND_MAX;
}
}
double best_score = -1e300;
int best_idx = -1;
for (int i = 0; i < n; ++i) {
if (info[i].score > best_score) {
best_score = info[i].score;
best_idx = i;
}
}
if (best_idx < 0) {
free(info);
fprintf(stderr, "no moves\n");
return 1;
}
if (!explain) {
printf("%s\n", info[best_idx].uci);
free(info);
return 0;
}
// JSON explanation output
printf("{\n");
printf(" \"seed\": %u,\n", seed);
printf(" \"n\": %d,\n", n);
printf(" \"moves\": [");
for (int i = 0; i < n; ++i) {
printf("\"%s\"%s", info[i].uci, (i + 1 < n ? ", " : ""));
}
printf("],\n");
printf(" \"scores\": [");
for (int i = 0; i < n; ++i) {
printf("%.6f%s", info[i].score, (i + 1 < n ? ", " : ""));
}
printf("],\n");
printf(" \"chosen_index\": %d,\n", best_idx);
printf(" \"chosen_move\": \"%s\"", info[best_idx].uci);
if (analyze_uci) {
int cand_idx = -1;
for (int i = 0; i < n; ++i) {
if (strcmp(info[i].uci, analyze_uci) == 0) { cand_idx = i; break; }
}
double cand_score = (cand_idx >= 0 ? info[cand_idx].score : -1.0);
const char *cmp = "unknown";
if (cand_idx >= 0) {
cmp = (cand_score > best_score ? "higher" : (cand_score < best_score ? "lower" : "equal"));
}
printf(
",\n \"analyze\": { \"candidate\": \"%s\", \"candidate_index\": %d, \"candidate_score\": %.6f, \"compare_to_chosen\": \"%s\" }\n",
analyze_uci, cand_idx, cand_score, cmp
);
} else {
printf("\n");
}
printf("}\n");
free(info);
return 0;
}

2
PYTHON/lichess_bot/.bot_version
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@ -1 +1 @@
32
34

1338
PYTHON/lichess_bot/engine.py
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372
PYTHON/lichess_bot/tests/test_blunders_all.py
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@ -1,372 +0,0 @@
import os
import sys
import chess
import pytest
import re
# Ensure repo root is importable when running pytest directly
# Go up to the workspace root (tests -> lichess_bot -> PYTHON -> repo root)
REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
if REPO_ROOT not in sys.path:
sys.path.insert(0, REPO_ROOT)
from PYTHON.lichess_bot.engine import RandomEngine # noqa: E402
# Consolidated blunder cases from all per-game test files
BLUNDER_CASES = [
("r2q1rk1/pp2ppbp/2np2p1/2p3P1/2P5/5b1P/P4P2/R1B1KB1R w KQ - 1 15", "e1d2", "ply29_W_e1d2_best_c1d2"),
("r2q1rk1/pp2ppbp/2np2p1/2p3P1/2P5/7P/P2K1P2/R1B2B1b w - - 0 16", "f2f3", "ply31_W_f2f3_best_d2e1"),
("r2q1rk1/pp2ppbp/2np2p1/2p3P1/2P5/4Kb1P/P7/R1B2B2 b - - 1 17", "c6d4", "ply34_B_c6d4_best_d8a5"),
("r2q1rk1/pp2ppbp/3p2p1/2p3P1/2Pn4/4Kb1P/P7/R1B2B2 w - - 2 18", "e3d3", "ply35_W_e3d3_best_c1b2"),
("r2q1rk1/pp3pbp/3p2p1/2p1p1P1/2Pn4/3K1b1P/P7/R1B2B2 w - - 0 19", "d3e3", "ply37_W_d3e3_best_h3h4"),
("r2q1rk1/pp3pbp/3p2p1/2p3P1/2Pnp3/4Kb1P/P7/R1B2B2 w - - 0 20", "e3f4", "ply39_W_e3f4_best_e3f2"),
("r4rk1/pp3p1p/3p2p1/2p1b1q1/2Pn4/4pb1P/P7/R1B1KB2 b - - 1 23", "e5g3", "ply46_B_e5g3_best_g5g3"),
("r4qk1/p1p4p/np4p1/3p1p2/3P4/3K1N2/PP3nPP/RN5R w - - 2 18", "d3c3", "ply35_W_d3c3_best_d3e2"),
("r4qk1/p1p4p/np4p1/3p1p2/3P4/2K2N2/PP3nPP/RN5R b - - 3 18", "f2h1", "ply36_B_f2h1_best_f8b4"),
("r4qk1/p1p4p/1p4p1/3p1p2/1n1P4/3K1N2/PP4PP/RN5n w - - 2 20", "d3e3", "ply39_W_d3e3_best_d3c3"),
("r5k1/p1p1q2p/1p4p1/3p1p2/1n1P4/4KN2/PP4PP/RN5n w - - 4 21", "e3f4", "ply41_W_e3f4_best_f3e5"),
("r5k1/p1p1q2p/1p4p1/3p1p2/1n1P1K2/5N2/PP4PP/RN5n b - - 5 21", "e7e4", "ply42_B_e7e4_best_b4d3"),
("r5k1/p1p4p/1p4p1/3p1pK1/1n1P2q1/5N2/PP4PP/RN5n w - - 8 23", "g5h6", "ply45_W_g5h6_best_g5f6"),
("r5k1/p1p4p/1p4pK/3p1p2/1n1P2q1/5N2/PP4PP/RN5n b - - 9 23", "g4h5", "ply46_B_g4h5_best_g4h5"),
("r1bqk2r/ppp2ppp/2np1n2/2b1p3/2BPP3/2P2N2/PP3PPP/RNBQ1RK1 b kq - 0 6", "h7h5", "ply12_B_h7h5_best_c5b6"),
("r1br3k/5B2/2n2n2/pp2p1Bp/4P3/1QP2N2/PP3PPP/RN3RK1 b - - 0 13", "h5h4", "ply26_B_h5h4_best_d8d6"),
("r1br4/5B1k/2n2B2/pp2p3/4P2p/1QP2N2/PP3PPP/RN3RK1 w - - 1 15", "f6d8", "ply29_W_f6d8_best_f7g8"),
("r1bB4/5B1k/2n5/pp2p3/4P2p/1QP2N2/PP3PPP/RN3RK1 b - - 0 15", "b5b4", "ply30_B_b5b4_best_a8a7"),
("r1bB4/5B1k/2n5/p3p3/1p2P2p/1QP2N2/PP3PPP/RN3RK1 w - - 0 16", "f7d5", "ply31_W_f7d5_best_f7g8"),
("r1bB4/7k/2n5/p2Bp3/1p2P2p/1QP2N2/PP3PPP/RN3RK1 b - - 1 16", "b4c3", "ply32_B_b4c3_best_c6d8"),
("r1bB4/7k/2B5/p3p3/4P2p/1Qp2N2/PP3PPP/RN3RK1 b - - 0 17", "c3c2", "ply34_B_c3c2_best_a8a7"),
("B1bB4/7k/8/p3p3/4P2p/1Q3N2/PPp2PPP/RN3RK1 b - - 0 18", "c2b1q", "ply36_B_c2b1q_best_h7g7"),
("B1bB4/7k/8/p3p3/4P2p/1Q3N2/PP3PPP/1R3RK1 b - - 0 19", "a5a4", "ply38_B_a5a4_best_h7g7"),
("B1bB4/5Q2/7k/4p3/p3P2p/5N2/PP3PPP/1R3RK1 w - - 2 21", "d8g5", "ply41_W_d8g5_best_d8g5"),
("r2q1rk1/ppp1pp1p/6p1/2Pp3P/PP1nnPb1/8/8/RNB1KB1R w KQ - 0 13", "h5g6", "ply25_W_h5g6_best_a1a2"),
("r2q1rk1/ppp1pp1p/6P1/2Pp4/PP1nnPb1/8/8/RNB1KB1R b KQ - 0 13", "f7g6", "ply26_B_f7g6_best_d4c2"),
("r2q1rk1/ppp1p2p/6p1/2Pp4/PP1nnPb1/8/8/RNB1KB1R w KQ - 0 14", "c5c6", "ply27_W_c5c6_best_b1a3"),
("r2q1rk1/ppp1p2p/2P3p1/3p4/PP1nnPb1/8/8/RNB1KB1R b KQ - 0 14", "g4f3", "ply28_B_g4f3_best_d4c2"),
("r2q1rk1/ppp1p2p/2P3p1/3p4/PP1nnP2/5b2/8/RNB1KB1R w KQ - 1 15", "c6b7", "ply29_W_c6b7_best_a1a2"),
("r2q1rk1/pPp1p2p/6p1/3p4/PP1nnP2/5b2/8/RNB1KB1R b KQ - 0 15", "a8b8", "ply30_B_a8b8_best_d4c2"),
("1r1q1rk1/pPp1p2p/6p1/3p4/PP1nnP2/5b2/8/RNB1KB1R w KQ - 1 16", "f4f5", "ply31_W_f4f5_best_f1g2"),
("1r1q1rk1/pPp1p2p/6p1/3p1P2/PP1nn3/5b2/8/RNB1KB1R b KQ - 0 16", "f3h1", "ply32_B_f3h1_best_d4c2"),
("1r1q1rk1/pPp1p2p/6p1/3p1P2/PP1nn3/8/8/RNB1KB1b w Q - 0 17", "f5g6", "ply33_W_f5g6_best_c1f4"),
("1r1q1rk1/pPp1p3/6p1/3p4/PP1nn3/8/8/RNB1KB1b w Q - 0 18", "b4b5", "ply35_W_b4b5_best_c1f4"),
("1r1q1rk1/pPp1p3/6p1/1P1p4/P3n3/5n2/8/RNB1KB1b w Q - 1 19", "e1e2", "ply37_W_e1e2_best_e1d1"),
("1r1q1rk1/pPp1p3/6p1/1P1p4/P3n3/5n2/4K3/RNB2B1b b - - 2 19", "e4g3", "ply38_B_e4g3_best_f3d4"),
("1r1q1rk1/pPp1p3/6p1/1P1p4/P7/5nn1/4K3/RNB2B1b w - - 3 20", "e2e3", "ply39_W_e2e3_best_e2d1"),
("1r1q1rk1/pPp1p3/6p1/1P1p4/P7/3K4/8/RNB1nn1b w - - 2 22", "d3d4", "ply43_W_d3d4_best_d3c3"),
("1r1q2k1/pPp1p3/6p1/1P1p4/P2K1r2/8/8/RNB1nn1b w - - 4 23", "d4e5", "ply45_W_d4e5_best_d4c3"),
("1r1q2k1/pPp1p3/6p1/1P1pK3/P4r2/8/8/RNB1nn1b b - - 5 23", "d8d6", "ply46_B_d8d6_best_f4e4"),
("rnbqkb1r/pppppppp/8/4P3/5n2/2NP4/PPP2PPP/R1BQKBNR b KQkq - 0 4", "g7g6", "ply8_B_g7g6_best_f4g6"),
("rnbqkb1r/pppppp1p/6p1/4P3/5n2/2NP4/PPP2PPP/R1BQKBNR w KQkq - 0 5", "g1f3", "ply9_W_g1f3_best_c1f4"),
("rnbqkb1r/pppppp1p/6p1/4P3/5n2/2NP1N2/PPP2PPP/R1BQKB1R b KQkq - 1 5", "f8g7", "ply10_B_f8g7_best_f4e6"),
("rnbq1rk1/3p1pbp/p1p3p1/3pP3/Pp3BPP/2N2N2/1PP2P2/R2QKB1R w KQ - 0 13", "b2b3", "ply25_W_b2b3_best_c3e2"),
("1r1q1r2/pPp1pp1k/5P2/3p4/P7/1b6/8/bNB1KB1n b - - 0 18", "a1c3", "ply36_B_a1c3_best_e7f6"),
("1r1q1r2/pPp1pp1k/5P2/3p4/P7/1bb5/8/1NB1KB1n w - - 1 19", "b1d2", "ply37_W_b1d2_best_b1c3"),
("1r1q1r2/pPp1pp1k/5P2/3p4/P7/1bb5/3N4/2B1KB1n b - - 2 19", "c3d2", "ply38_B_c3d2_best_e7f6"),
("1r1q1r2/pPp1pp1k/5P2/3p4/P7/1b6/3b4/2B1KB1n w - - 0 20", "c1d2", "ply39_W_c1d2_best_e1d2"),
("1r1q1r2/pPp1pp1k/5P2/3p4/P7/1b6/3B4/4KB1n b - - 0 20", "h1g3", "ply40_B_h1g3_best_e7f6"),
("1r1q1r2/pPp1pp1k/5P2/3p4/P7/1b4n1/3B4/4KB2 w - - 1 21", "f6e7", "ply41_W_f6e7_best_f1d3"),
("1r3r2/pPp1qp1k/8/3p4/P7/1b4n1/3B4/4KB2 w - - 0 22", "f1e2", "ply43_W_f1e2_best_e1f2"),
("1r3r2/pPp1qp1k/8/3p4/P7/1b4n1/3BB3/4K3 b - - 1 22", "e7e3", "ply44_B_e7e3_best_e7e2"),
("1r3r2/pPp2p1k/8/3p4/P7/1b2q1n1/3BB3/4K3 w - - 2 23", "a4a5", "ply45_W_a4a5_best_d2e3"),
("1r3r2/pPp2p1k/8/P2p4/8/1b2q1n1/3BB3/4K3 b - - 0 23", "e3e2", "ply46_B_e3e2_best_e3e2"),
("r1bqk2r/ppp2ppp/2np1n2/2b1p3/2BPP3/2P2N2/PP3PPP/RNBQ1RK1 b kq - 0 6", "d6d5", "ply12_B_d6d5_best_c5b6"),
("r1bqk2r/ppp2ppp/2n2n2/2bpp3/2BPP3/2P2N2/PP3PPP/RNBQ1RK1 w kq - 0 7", "d4c5", "ply13_W_d4c5_best_e4d5"),
("r1bqk2r/ppp2ppp/2n2n2/2Ppp3/2B1P3/2P2N2/PP3PPP/RNBQ1RK1 b kq - 0 7", "d5e4", "ply14_B_d5e4_best_d5c4"),
("r1bB2kr/2p2p2/p1B5/2P3pp/1Pp1p3/4P3/P2N2PP/RN1Q1RK1 b - - 0 17", "g8h7", "ply34_B_g8h7_best_c8g4"),
("B1bB3r/2p2p1k/p7/2P3pp/1Pp1p3/4P3/P2N2PP/RN1Q1RK1 b - - 0 18", "h7g7", "ply36_B_h7g7_best_h7g6"),
("B1b4r/2p2pk1/p4B2/2P3pp/1Pp1p3/4P3/P2N2PP/RN1Q1RK1 b - - 2 19", "g7g8", "ply38_B_g7g8_best_g7h6"),
("B1b3kB/2p2p2/p7/2P3pp/1Pp1p3/4P3/P2N2PP/RN1Q1RK1 b - - 0 20", "g8f8", "ply40_B_g8f8_best_c8g4"),
("B1b2k1B/2p2p2/p7/2P3pQ/1Pp1p3/4P3/P2N2PP/RN3RK1 b - - 0 21", "f8e8", "ply42_B_f8e8_best_f7f6"),
("B1b1k2B/2p2R2/p7/2P3pQ/1Pp1p3/4P3/P2N2PP/RN4K1 b - - 0 22", "c7c6", "ply44_B_c7c6_best_c8d7"),
("5k1B/3R4/p1B5/2P3pQ/1Pp1p3/4P3/P2N2PP/RN4K1 w - - 1 25", "d7d8", "ply49_W_d7d8_best_h5f7"),
("3R3B/4k3/p1B5/2P3pQ/1Pp1p3/4P3/P2N2PP/RN4K1 w - - 3 26", "h5e8", "ply51_W_h5e8_best_h5e8"),
("r1bqk2r/ppp2ppp/2np1n2/2b1p3/2BPP3/2P2N2/PP3PPP/RNBQ1RK1 b kq - 0 6", "e8g8", "ply12_B_e8g8_best_c5b6"),
("r1bq1r1k/ppP2ppp/2n2n2/4p1B1/2B1P3/1NP2N2/PP3PPP/R2Q1RK1 b - - 0 12", "h8g8", "ply24_B_h8g8_best_d8c7"),
("r1bR2k1/pp3ppp/2n2n2/4p1B1/2B1P3/1NP2N2/PPQ2PPP/5RK1 b - - 0 16", "f6e8", "ply32_B_f6e8_best_c6d8"),
("r1bRn1k1/pp3ppp/2n5/4p1B1/2B1P3/1NP2N2/PPQ2PPP/5RK1 w - - 1 17", "d8e8", "ply33_W_d8e8_best_d8e8"),
("r4r2/p2p3k/n2Np1p1/q1p5/P5Q1/8/3NKP2/8 b - - 2 24", "a5c7", "ply48_B_a5c7_best_f8f2"),
("r4N2/p6k/n5pq/P1pp4/6Q1/5N2/4KP2/8 b - - 0 30", "h6f8", "ply60_B_h6f8_best_a8f8"),
("r4q2/p6k/n5p1/P1pp4/6Q1/5N2/4KP2/8 w - - 0 31", "g4h3", "ply61_W_g4h3_best_f3g5"),
("r4qk1/p2Q4/n5p1/P1pp4/8/5N2/4KP2/8 w - - 4 33", "e2e3", "ply65_W_e2e3_best_d7d5"),
("4rqk1/p2Q4/n5p1/P1pp4/8/4KN2/5P2/8 w - - 6 34", "e3d2", "ply67_W_e3d2_best_d7e8"),
("4rqk1/p2Q4/n5p1/P1pp4/8/5N2/3K1P2/8 b - - 7 34", "d5d4", "ply68_B_d5d4_best_f8f4"),
("4rqk1/p2Q4/n5p1/P1p5/3p4/5N2/3K1P2/8 w - - 0 35", "d7a7", "ply69_W_d7a7_best_d2c1"),
("4r1k1/Q7/n5p1/P1p5/3p4/5q2/3K1P2/8 w - - 0 36", "d2c2", "ply71_W_d2c2_best_a7a8"),
("6k1/Q7/n5p1/P1p5/3p4/8/4rq2/3K4 w - - 0 38", "d1c1", "ply75_W_d1c1_best_a7a8"),
("6k1/Q7/n5p1/P1p5/3p4/8/4rq2/2K5 b - - 1 38", "f2e1", "ply76_B_f2e1_best_e2e1"),
("r1bqk2r/ppp2ppp/2np1n2/2b5/2BPP3/5N2/PP3PPP/RNBQ1RK1 b kq - 0 7", "f6e4", "ply14_B_f6e4_best_c5b6"),
("r2qk2r/pppb2pp/2n5/2p3B1/Q1B1p3/5N2/PP3PPP/R4RK1 b kq - 1 12", "e4f3", "ply24_B_e4f3_best_c6d4"),
("r2Bk2r/pppb2pp/2n5/2p5/Q1B5/8/PP3PpP/R4RK1 w kq - 0 14", "g1g2", "ply27_W_g1g2_best_f1d1"),
("r1k4r/pppb2pp/2n5/2p5/2B5/1Q6/PP3PKP/3R1R2 b - - 3 16", "g7g6", "ply32_B_g7g6_best_c6d4"),
("rk5r/ppp4p/2n2Qp1/2p5/8/8/PP3PKP/3R1R2 b - - 2 19", "b7b5", "ply38_B_b7b5_best_h8c8"),
("rk5r/p1p4p/2n2Qp1/1pp5/8/8/PP3PKP/3R1R2 w - - 0 20", "f6h8", "ply39_W_f6h8_best_f6c6"),
("r7/1kp4p/2n2Qp1/ppp5/8/8/PP3PKP/3R1R2 w - - 0 22", "d1d6", "ply43_W_d1d6_best_f1e1"),
("8/8/2k3pR/1p6/p1p5/8/PP3PKP/8 b - - 1 29", "c6d7", "ply58_B_c6d7_best_b5b4"),
("4k3/8/6R1/1p6/p1p5/8/PP3PKP/8 w - - 1 31", "f2f4", "ply61_W_f2f4_best_g2f3"),
("4k3/8/6R1/1p3P2/p1p5/8/PP4KP/8 w - - 1 33", "f5f6", "ply65_W_f5f6_best_g2f3"),
("4k3/8/5PR1/1p6/p1p5/8/PP4KP/8 b - - 0 33", "e8d8", "ply66_B_e8d8_best_b5b4"),
("5k2/5P1R/8/1p6/p1p4P/8/PP4K1/8 w - - 1 38", "h4h5", "ply75_W_h4h5_best_g2f3"),
("5k2/5P1R/8/1p5P/p1p5/8/PP4K1/8 b - - 0 38", "f8e7", "ply76_B_f8e7_best_b5b4"),
("5k2/5PR1/7P/1p6/p1p5/8/PP4K1/8 b - - 2 40", "c4c3", "ply80_B_c4c3_best_a4a3"),
("5k2/5PR1/7P/1p6/p7/2P5/P5K1/8 b - - 0 41", "f8e7", "ply82_B_f8e7_best_a4a3"),
("5Q2/6R1/8/1p1k3Q/p7/2P5/P5K1/8 b - - 2 45", "d5e6", "ply90_B_d5e6_best_d5c4"),
("5Q2/6R1/4k3/1p5Q/p7/2P5/P5K1/8 w - - 3 46", "g7g6", "ply91_W_g7g6_best_h5f5"),
("5Q2/3k4/6R1/1p5Q/p7/2P5/P5K1/8 w - - 5 47", "f8f7", "ply93_W_f8f7_best_h5h7"),
("3k4/5Q2/6R1/1p5Q/p7/2P5/P5K1/8 w - - 7 48", "h5h8", "ply95_W_h5h8_best_h5h8"),
("r1bqk2r/ppp2ppp/2np1n2/2b1p1B1/2B1P3/3P1N2/PPP2PPP/RN1Q1RK1 b kq - 1 6", "c5f2", "ply12_B_c5f2_best_h7h6"),
("r1bq1rk1/pp3ppp/3p1n2/2p1p1B1/2P1P3/2PP1N2/P5PP/RN1Q1RK1 b - - 0 11", "f6e4", "ply22_B_f6e4_best_h7h6"),
("3r1r2/pp3ppk/3N3p/2p1N3/4P3/2P5/P5PP/R2Q1RK1 b - - 0 17", "h7g8", "ply34_B_h7g8_best_f7f6"),
("3r1rk1/pp3pp1/3N3p/2p1N3/4P3/2P5/P5PP/R2Q1RK1 w - - 1 18", "e5f7", "ply35_W_e5f7_best_f1f7"),
("3r1rk1/pp3Np1/3N3p/2p5/4P3/2P5/P5PP/R2Q1RK1 b - - 0 18", "f8f7", "ply36_B_f8f7_best_d8d7"),
("5Q2/p5pk/4P2p/1pp5/8/2P5/P5PP/5RK1 b - - 0 24", "h7g6", "ply48_B_h7g6_best_b5b4"),
("5Q2/p5p1/4P1kp/1pp5/8/2P5/P5PP/5RK1 w - - 1 25", "e6e7", "ply49_W_e6e7_best_f8f5"),
("5Q2/p3P1p1/6kp/1pp5/8/2P5/P5PP/5RK1 b - - 0 25", "g6h7", "ply50_B_g6h7_best_g6h5"),
("4QQ2/p6k/6pp/1pp5/8/2P5/P5PP/5RK1 w - - 0 27", "f8h8", "ply53_W_f8h8_best_f8h8"),
("rnb1kb1r/pp3ppp/4q3/2p3N1/4p3/8/PPPPNPPP/R1BQ1RK1 b kq - 1 9", "e6a2", "ply18_B_e6a2_best_e6g6"),
("2kr3r/1p4p1/4bp2/7p/Q2b1B2/2P5/1P3PPP/5RK1 w - - 0 20", "c3d4", "ply39_W_c3d4_best_c3d4"),
("2kr3r/1p4p1/4bp2/7p/Q2P1B2/8/1P3PPP/5RK1 b - - 0 20", "e6d5", "ply40_B_e6d5_best_d8d5"),
("2kr3r/1p4p1/5p2/3b3p/Q2P1B2/8/1P3PPP/5RK1 w - - 1 21", "a4a8", "ply41_W_a4a8_best_f1e1"),
("3r3r/1p1k2p1/5p2/3b3p/Q2P1B2/8/1P3PPP/5RK1 b - - 4 22", "d7c8", "ply44_B_d7c8_best_d7e6"),
("2kr3r/1p4p1/5p2/Q2b3p/3P1B2/8/1P3PPP/5RK1 b - - 6 23", "b7b6", "ply46_B_b7b6_best_d8d6"),
("2kr3r/6p1/1Q3p2/3b3p/3P1B2/8/1P3PPP/5RK1 b - - 0 24", "c8d7", "ply48_B_c8d7_best_d8d6"),
("3r3r/3k2p1/1Q3p2/3b3p/3P1B2/8/1P3PPP/5RK1 w - - 1 25", "f4c7", "ply49_W_f4c7_best_f1e1"),
("3r3r/2Bk2p1/1Q3p2/3b3p/3P4/8/1P3PPP/5RK1 b - - 2 25", "d8c8", "ply50_B_d8c8_best_d7e8"),
("2r4r/2Bk2p1/1Q3p2/3b3p/3P4/8/1P3PPP/5RK1 w - - 3 26", "c7b8", "ply51_W_c7b8_best_b6d6"),
("1r5r/Q5p1/4kp2/3b3p/3P4/8/1P3PPP/4R1K1 b - - 3 28", "e6d6", "ply56_B_e6d6_best_e6f5"),
("1r5r/2k1R1p1/5p2/3Q3p/3P4/8/1P3PPP/6K1 b - - 2 31", "c7c8", "ply62_B_c7c8_best_c7b6"),
("1rk4r/4R1p1/5p2/3Q3p/3P4/8/1P3PPP/6K1 w - - 3 32", "d5c5", "ply63_W_d5c5_best_d5d7"),
("1r1k3r/4R1p1/5p2/2Q4p/3P4/8/1P3PPP/6K1 w - - 5 33", "d4d5", "ply65_W_d4d5_best_c5c7"),
("3k3r/1Q2R3/5pp1/3P3p/8/8/1P3PPP/6K1 w - - 0 37", "d5d6", "ply73_W_d5d6_best_b7c7"),
("3k3r/1Q2R3/3P1p2/6pp/8/8/1P3PPP/6K1 w - - 0 38", "b7b8", "ply75_W_b7b8_best_b7c7"),
("rnb2rk1/pp2bppp/8/8/2qN4/2N5/PPP2PPP/R1BQK2R w - - 3 13", "b2b3", "ply25_W_b2b3_best_d1d3"),
("rn3rk1/pp2bppp/8/3Q1b2/8/1P6/2q2PPP/2B2K1R w - - 0 18", "d5b7", "ply35_W_d5b7_best_d5d2"),
("rn3rk1/pQ2bppp/8/5b2/8/1P6/2q2PPP/2B2K1R b - - 0 18", "c2f2", "ply36_B_c2f2_best_c2d1"),
("6k1/p2n1ppp/8/2b5/6b1/1P6/3K3P/4R3 b - - 0 27", "c5b4", "ply54_B_c5b4_best_c5b4"),
("6k1/p2n1ppp/8/8/5Kb1/1P6/7P/4b3 b - - 1 29", "e1c3", "ply58_B_e1c3_best_g4e6"),
("6k1/p2n1ppp/8/8/6K1/1Pb5/7P/8 b - - 0 30", "d7f6", "ply60_B_d7f6_best_g7g6"),
("6k1/p4ppp/5n2/5K2/8/1Pb5/7P/8 b - - 2 31", "f6d7", "ply62_B_f6d7_best_g7g6"),
("6k1/p2n1ppp/8/5K2/8/1Pb5/7P/8 w - - 3 32", "f5e4", "ply63_W_f5e4_best_f5e4"),
("6k1/p2n1ppp/8/8/4K3/1Pb5/7P/8 b - - 4 32", "d7f6", "ply64_B_d7f6_best_d7b6"),
("6k1/p4ppp/8/4b2n/4K3/1P5P/8/8 b - - 2 35", "e5b8", "ply70_B_e5b8_best_e5c7"),
("1b4k1/p4ppp/8/7n/4K3/1P5P/8/8 w - - 3 36", "e4f5", "ply71_W_e4f5_best_e4d3"),
("1b4k1/p4ppp/8/5K1n/8/1P5P/8/8 b - - 4 36", "b8d6", "ply72_B_b8d6_best_g7g6"),
("6k1/p4ppp/3b4/5K1n/8/1P5P/8/8 w - - 5 37", "f5g5", "ply73_W_f5g5_best_f5e4"),
("6k1/p4ppp/3b4/6Kn/8/1P5P/8/8 b - - 6 37", "h5f4", "ply74_B_h5f4_best_g7g6"),
("6k1/p4ppp/3b4/8/5nK1/1P5P/8/8 b - - 8 38", "h7h5", "ply76_B_h7h5_best_g7g6"),
("6k1/p4pp1/3b4/7p/5nK1/1P5P/8/8 w - - 0 39", "g4g3", "ply77_W_g4g3_best_g4f3"),
("6k1/p4pp1/3b4/7p/5n2/1P4KP/8/8 b - - 1 39", "f4e6", "ply78_B_f4e6_best_f4d5"),
("6k1/p4pp1/3bn3/7p/8/1P4KP/8/8 w - - 2 40", "g3h4", "ply79_W_g3h4_best_g3f3"),
("6k1/p4pp1/3bn3/7p/7K/1P5P/8/8 b - - 3 40", "e6c5", "ply80_B_e6c5_best_g7g6"),
("6k1/p4pp1/3b4/2n4p/7K/1P5P/8/8 w - - 4 41", "h4h5", "ply81_W_h4h5_best_h4h5"),
("6k1/p4pp1/3b4/2n4K/8/1P5P/8/8 b - - 0 41", "c5b3", "ply82_B_c5b3_best_g7g6"),
("6k1/p4pp1/3b4/6K1/8/1n5P/8/8 b - - 1 42", "d6e7", "ply84_B_d6e7_best_b3d4"),
("6k1/p3bpp1/8/5K2/8/1n5P/8/8 b - - 3 43", "b3d4", "ply86_B_b3d4_best_a7a5"),
("8/p3kpp1/5b2/8/3nK3/7P/8/8 w - - 10 47", "e4f4", "ply93_W_e4f4_best_e4d3"),
("8/p3kpp1/4nb2/8/5K2/7P/8/8 w - - 12 48", "f4f5", "ply95_W_f4f5_best_f4e3"),
("6k1/p4pp1/5b2/3K4/3n4/7P/8/8 w - - 18 51", "d5d6", "ply101_W_d5d6_best_d5c4"),
("6k1/p4pp1/4nb2/2K5/8/7P/8/8 w - - 24 54", "c5d6", "ply107_W_c5d6_best_c5b4"),
("6k1/p4pp1/3K1b2/8/5n2/7P/8/8 w - - 26 55", "d6d7", "ply109_W_d6d7_best_h3h4"),
("6k1/p2K1pp1/5b2/8/8/7n/8/8 w - - 0 56", "d7e8", "ply111_W_d7e8_best_d7c6"),
]
# --- Helpers to resolve optimal move (UCI) from past game logs by label ---
_PLY_LABEL_RE = re.compile(
r"^ply(?P<num>\d+)_([WB])_([a-h][1-8][a-h][1-8](?:[qrbn])?)(?:_best_([a-h][1-8][a-h][1-8](?:[qrbn])?))?$"
)
def _parse_label(label: str) -> tuple[int | None, str | None, str | None, str | None]:
m = _PLY_LABEL_RE.match(label)
if not m:
return None, None, None, None
num = int(m.group("num"))
# Extract side and the trailing UCI from label for context if needed
parts = label.split("_")
side = parts[1] if len(parts) > 1 else None
uci = parts[2] if len(parts) > 2 else None
best_uci = None
if len(parts) >= 5 and parts[3] == 'best':
best_uci = parts[4]
return num, side, uci, best_uci
def _iter_past_game_logs(repo_root: str):
# Search both the canonical tools/past_games folder and repo root, since logs may be kept in either
candidate_dirs = [
os.path.join(repo_root, "PYTHON", "lichess_bot", "tools", "past_games"),
repo_root,
]
seen = set()
for logs_dir in candidate_dirs:
if not os.path.isdir(logs_dir):
continue
for name in os.listdir(logs_dir):
if not name.startswith("lichess_bot_game_") or not name.endswith(".log"):
continue
if (logs_dir, name) in seen:
continue
seen.add((logs_dir, name))
path = os.path.join(logs_dir, name)
try:
with open(path, "r", encoding="utf-8", errors="ignore") as f:
yield name, f.read()
except Exception:
continue
def _bot_side_from_log(txt: str) -> str | None:
# Try to infer whether VibeBot was White (W) or Black (B)
# Look for line: Players: VibeBot vs Reduktor OR Reduktor vs VibeBot
for line in txt.splitlines():
if line.startswith("Players:"):
low = line.lower()
if "vibebot vs" in low:
return "W"
if "vs vibebot" in low:
return "B"
return None
def _resolve_optimal_uci_from_logs(repo_root: str, label: str, fen: str) -> str | None:
ply_num, side, _, _ = _parse_label(label)
if ply_num is None or side is None:
return None
target_prefix = f"ply {ply_num}:"
# We'll only return a move that's legal in the provided FEN
try:
position = chess.Board(fen)
except Exception:
position = None
for fname, txt in _iter_past_game_logs(repo_root):
bot_side = _bot_side_from_log(txt)
# Prefer logs where the bot side matches the label side (heuristic)
if bot_side is not None and bot_side != side:
continue
lines = txt.splitlines()
for i, line in enumerate(lines):
if line.strip().startswith(target_prefix):
# Look for a following line starting with 'best '
# Usually immediate or within a couple of lines
for j in range(i + 1, min(i + 6, len(lines))):
s = lines[j].strip()
if s.startswith("best "):
# Expect format: best <SAN> (<uci>) ...
m = re.search(r"\(([a-h][1-8][a-h][1-8](?:[qrbn])?)\)", s)
if m:
uci = m.group(1)
if position is not None:
try:
mv = chess.Move.from_uci(uci)
if mv in position.legal_moves:
return uci
else:
# Skip illegal candidates and keep searching
continue
except Exception:
continue
return uci
# Fallback: sometimes lines like 'avoided risky ...' appear first, keep scanning
continue
# If we hit here, this ply in this log lacks a 'best' line nearby
# Try a little farther just in case
for j in range(i + 1, min(i + 12, len(lines))):
s = lines[j].strip()
if s.startswith("best "):
m = re.search(r"\(([a-h][1-8][a-h][1-8](?:[qrbn])?)\)", s)
if m:
uci = m.group(1)
if position is not None:
try:
mv = chess.Move.from_uci(uci)
if mv in position.legal_moves:
return uci
else:
continue
except Exception:
continue
return uci
# Otherwise move to next log
return None
@pytest.mark.parametrize('fen,blunder_uci,label', BLUNDER_CASES, ids=[c[2] for c in BLUNDER_CASES])
def test_engine_avoids_logged_blunder(fen, blunder_uci, label):
board = chess.Board(fen)
eng = RandomEngine(depth=4, max_time_sec=1.2)
# Prefer explanation variant if available for better failure messages
move = None
explanation = ''
if hasattr(eng, 'choose_move_with_explanation'):
try:
mv, expl = eng.choose_move_with_explanation(board, time_budget_sec=1.2)
move, explanation = mv, expl or ''
except Exception:
move = eng.choose_move(board)
else:
move = eng.choose_move(board)
assert move is not None, 'Engine returned no move'
assert move in board.legal_moves, 'Engine move is illegal'
# If the engine repeats the blunder, gather deeper diagnostics: compare eval of blunder vs engine's best
if move.uci() == blunder_uci:
# Pick optimal move from parametrized data, with fallback to resolving from logs
# First, try to extract an explicit optimal UCI encoded in the test label
_, _, _, optimal_from_label = _parse_label(label)
optimal_from_logs = optimal_from_label or _resolve_optimal_uci_from_logs(REPO_ROOT, label, fen)
details = [
f'Engine repeated blunder {blunder_uci} at {label}.',
f'engine_move_explanation: {explanation}'.strip(),
]
# Try to request a side-by-side evaluation (blunder vs optimal) from the engine if available
try:
if hasattr(eng, 'evaluate_proposed_move_with_suggestion'):
try:
proposed_score_cp, proposed_expl, best_move, best_expl = eng.evaluate_proposed_move_with_suggestion(
board, blunder_uci, time_budget_sec=1.0
)
details.append('--- comparative analysis (engine provided) ---')
details.append(f'blunder {blunder_uci}: score={proposed_score_cp}cp explanation: {proposed_expl}')
# If we found an optimal move from logs, evaluate that exact move as well
if optimal_from_logs:
try:
opt_score_cp, opt_expl, _, _ = eng.evaluate_proposed_move_with_suggestion(
board, optimal_from_logs, time_budget_sec=1.0
)
details.append(f'optimal_from_logs {optimal_from_logs}: score={opt_score_cp}cp explanation: {opt_expl}')
except Exception as e:
details.append(f'optimal_from_logs {optimal_from_logs}: <error evaluating: {e}>')
elif optimal_from_logs is None:
details.append('optimal_from_logs: <not found>')
if best_move is not None:
details.append(
f'engine_optimal {best_move.uci()}: explanation: {best_expl}'
)
else:
details.append('engine_optimal: <none>')
except Exception as e: # fall back if the evaluation API fails
# Fallback 1: at least get engine optimal move + explanation
if hasattr(eng, 'choose_move_with_explanation'):
try:
best_mv, best_expl = eng.choose_move_with_explanation(board, time_budget_sec=1.0)
details.append('--- fallback analysis ---')
details.append(f'engine_optimal {best_mv.uci() if best_mv else "<none>"}: explanation: {best_expl}')
except Exception:
details.append(f'engine_optimal: <error obtaining explanation: {e}>')
else:
# Fallback 2: engine lacks evaluation API; get its best move explanation as context
if hasattr(eng, 'choose_move_with_explanation'):
try:
best_mv, best_expl = eng.choose_move_with_explanation(board, time_budget_sec=1.0)
details.append('--- fallback analysis ---')
details.append(f'engine_optimal {best_mv.uci() if best_mv else "<none>"}: explanation: {best_expl}')
if optimal_from_logs:
details.append(f'optimal_from_logs {optimal_from_logs} (no eval available)')
except Exception as e:
details.append(f'engine_optimal: <error obtaining explanation: {e}>')
except Exception as outer:
details.append(f'<error during diagnostic analysis: {outer}>')
pytest.fail("\n".join(d for d in details if d))
assert move.uci() != blunder_uci, f'Engine repeated blunder {blunder_uci} at {label}. Explanation: {explanation}'

10
PYTHON/lichess_bot/tests/test_engine.py
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@ -1,10 +0,0 @@
import chess
from PYTHON.lichess_bot.engine import RandomEngine
def test_random_engine_returns_move_on_start_position():
board = chess.Board()
eng = RandomEngine()
move = eng.choose_move(board)
assert move is not None
assert move in board.legal_moves

64
PYTHON/lichess_bot/tests/test_puzzles_top10.py Normal file
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@ -0,0 +1,64 @@
import csv
import os
from typing import List, Tuple
import chess
import pytest
from PYTHON.lichess_bot.engine import RandomEngine
def _load_top_puzzles(csv_path: str, limit: int = 10) -> List[Tuple[str, str]]:
"""
Return a list of (FEN, solution_moves_str) for the first `limit` rows in the CSV.
CSV columns: PuzzleId,FEN,Moves,...
"""
puzzles: List[Tuple[str, str]] = []
with open(csv_path, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
fen = row["FEN"].strip()
moves = row["Moves"].strip()
if fen and moves:
puzzles.append((fen, moves))
if len(puzzles) >= limit:
break
return puzzles
@pytest.mark.parametrize("fen,moves_str", _load_top_puzzles(os.path.join(os.path.dirname(__file__), "lichess_db_puzzle.csv")))
def test_puzzle_engine_follow_solution(fen: str, moves_str: str):
board = chess.Board(fen)
eng = RandomEngine(max_time_sec=1.0)
# Moves are space-separated UCIs alternating sides starting from side-to-move in the FEN
solution_moves = moves_str.split()
step = 0
for uci in solution_moves:
step += 1
# Engine move on this ply
mv, expl = eng.choose_move_with_explanation(board, time_budget_sec=0.5)
assert mv is not None, f"No move returned at step {step}.\nExplanation: {expl}"
# If engine move differs from solution, fail immediately but provide analysis of the correct move
if mv.uci() != uci:
# Ask the engine to analyze the correct move for debug
score_cp, proposed_expl, best_mv, best_expl = eng.evaluate_proposed_move_with_suggestion(board, uci, time_budget_sec=0.5)
details = [
f"Puzzle failed at step {step}.",
f"FEN: {fen}",
f"Expected: {uci}",
f"Engine played: {mv.uci()}",
"--- engine explanation ---",
expl,
"--- analysis of expected move ---",
f"score_cp: {score_cp}",
proposed_expl,
]
if best_mv is not None:
details.append("--- engine best move analysis ---")
details.append(best_expl)
pytest.fail("\n".join(details))
# Apply the move and continue
board.push(mv)

0
pytest.ini Normal file
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