def sgf_properties(self,
save_comments_player=None,
save_comments_class=None,
eval_thresholds=None,
save_analysis=False):
properties = copy.copy(super().sgf_properties())
note = self.note.strip()
if save_analysis and self.analysis_complete:
try:
properties["KT"] = analysis_dumps(self.analysis)
except Exception as e:
print(f"Error in saving analysis: {e}")
if self.points_lost and save_comments_class is not None and eval_thresholds is not None:
show_class = save_comments_class[evaluation_class(
self.points_lost, eval_thresholds)]
else:
show_class = False
comments = properties.get("C", [])
if (self.parent and self.parent.analysis_exists
and self.analysis_exists and
(note or ((save_comments_player or {}).get(self.player, False)
and show_class))):
candidate_moves = self.parent.candidate_moves
top_x = Move.from_gtp(candidate_moves[0]["move"]).sgf(
self.board_size)
best_sq = [
Move.from_gtp(d["move"]).sgf(self.board_size)
for d in candidate_moves[1:] if d["pointsLost"] <= 0.5
]
if best_sq and "SQ" not in properties:
properties["SQ"] = best_sq
if top_x and "MA" not in properties:
properties["MA"] = [top_x]
comments.append(
self.comment(sgf=True, interactive=False) +
SGF_INTERNAL_COMMENTS_MARKER)
if self.is_root:
comments = [
i18n._("SGF start message") + SGF_INTERNAL_COMMENTS_MARKER +
"\n",
*comments,
f"\nSGF generated by {PROGRAM_NAME} {VERSION}{SGF_INTERNAL_COMMENTS_MARKER}\n",
]
properties["CA"] = ["UTF-8"]
properties["AP"] = [f"{PROGRAM_NAME}:{VERSION}"]
if self.shortcut_from:
properties["KTSF"] = [id(self.shortcut_from)]
elif "KTSF" in properties:
del properties["KTSF"]
if self.shortcuts_to:
properties["KTSID"] = [id(self)]
elif "KTSID" in properties:
del properties["KTSID"]
if note:
comments.append(f"{self.note}")
if comments:
properties["C"] = [SGF_SEPARATOR_MARKER.join(comments)]
elif "C" in properties:
del properties["C"]
return properties
def eval_color(
self,
points_lost,
show_dots_for_class: List[bool] = None) -> Optional[List[float]]:
i = evaluation_class(points_lost,
self.trainer_config["eval_thresholds"])
if show_dots_for_class is None or show_dots_for_class[i]:
return EVAL_COLORS[i]
def sgf_properties(self,
save_comments_player=None,
save_comments_class=None,
eval_thresholds=None):
properties = copy.copy(super().sgf_properties())
note = self.note.strip()
if self.points_lost and save_comments_class is not None and eval_thresholds is not None:
show_class = save_comments_class[evaluation_class(
self.points_lost, eval_thresholds)]
else:
show_class = False
if (self.parent and self.parent.analysis_ready and self.analysis_ready
and (note or
((save_comments_player or {}).get(self.player, False)
and show_class))):
candidate_moves = self.parent.candidate_moves
top_x = Move.from_gtp(candidate_moves[0]["move"]).sgf(
self.board_size)
best_sq = [
Move.from_gtp(d["move"]).sgf(self.board_size)
for d in candidate_moves[1:] if d["pointsLost"] <= 0.5
]
if best_sq and "SQ" not in properties:
properties["SQ"] = best_sq
if top_x and "MA" not in properties:
properties["MA"] = [top_x]
comment = self.comment(sgf=True, interactive=False)
if comment:
properties["C"] = [
"\n".join(properties.get("C", "")) + comment
]
if self.is_root:
properties["C"] = [
i18n._("SGF start message") + "\n" +
"\n".join(properties.get("C", "")) +
"\nSGF with review generated by KaTrain."
]
if note:
properties["C"] = [
"\n".join(properties.get("C", "")) + f"\nNote: {self.note}"
]
return properties
def game_report(game, thresholds, depth_filter=None):
cn = game.current_node
nodes = cn.nodes_from_root
while cn.children: # main branch
cn = cn.children[0]
nodes.append(cn)
x, y = game.board_size
depth_filter = [
math.ceil(board_frac * x * y)
for board_frac in depth_filter or (0, 1e9)
]
nodes = [
n for n in nodes if n.move and not n.is_root
and depth_filter[0] <= n.depth < depth_filter[1]
]
histogram = [{"B": 0, "W": 0} for _ in thresholds]
ai_top_move_count = {"B": 0, "W": 0}
ai_approved_move_count = {"B": 0, "W": 0}
player_ptloss = {"B": [], "W": []}
weights = {"B": [], "W": []}
for n in nodes:
points_lost = n.points_lost
if n.points_lost is None:
continue
else:
points_lost = max(0, points_lost)
bucket = len(thresholds) - 1 - evaluation_class(
points_lost, thresholds)
player_ptloss[n.player].append(points_lost)
histogram[bucket][n.player] += 1
cands = n.parent.candidate_moves
filtered_cands = [
d for d in cands
if d["order"] < ADDITIONAL_MOVE_ORDER and "prior" in d
]
weight = min(
1.0,
sum([max(d["pointsLost"], 0) * d["prior"]
for d in filtered_cands]) /
(sum(d["prior"] for d in filtered_cands) or 1e-6),
) # complexity capped at 1
# adj_weight between 0.05 - 1, dependent on difficulty and points lost
adj_weight = max(0.05, min(1.0, max(weight, points_lost / 4)))
weights[n.player].append((weight, adj_weight))
if n.parent.analysis_complete:
ai_top_move_count[n.player] += int(
cands[0]["move"] == n.move.gtp())
ai_approved_move_count[n.player] += int(n.move.gtp() in [
d["move"] for d in filtered_cands if d["order"] == 0 or (
d["pointsLost"] < 0.5 and d["order"] < 5)
])
wt_loss = {
bw: sum(s * aw for s, (w, aw) in zip(player_ptloss[bw], weights[bw])) /
(sum(aw for _, aw in weights[bw]) or 1e-6)
for bw in "BW"
}
sum_stats = {
bw: {
"accuracy": 100 * 0.75**wt_loss[bw],
"complexity": sum(w for w, aw in weights[bw]) /
len(player_ptloss[bw]),
"mean_ptloss": sum(player_ptloss[bw]) / len(player_ptloss[bw]),
"weighted_ptloss": wt_loss[bw],
"ai_top_move": ai_top_move_count[bw] / len(player_ptloss[bw]),
"ai_top5_move": ai_approved_move_count[bw] /
len(player_ptloss[bw]),
} if len(player_ptloss[bw]) > 0 else {}
for bw in "BW"
}
return sum_stats, histogram, player_ptloss