Attempting to cluster correlation outputs using OPTICS

python/play.py

@@ -11,6 +11,9 @@
 import colorsys
 import json
 import numpy as np
+from sklearn.cluster import OPTICS as optics
+from sklearn.metrics.pairwise import pairwise_distances
+from scipy.spatial.distance import cosine as cosine_similarity
 
 from board import Board
 from features import Features
@@ -40,6 +43,9 @@
 # Hardcoded max board size
 pos_len = 19
 
+# Hardcoded correlation feature length size
+corr_feature_len = 32
+
 # Model ----------------------------------------------------------------
 
 logging.root.handlers = []
@@ -138,6 +144,8 @@ def get_outputs(gs, rules):
         seki = seki_probs[1] - seki_probs[2]
         seki2 = torch.sigmoid(seki_logits[3,:,:]).cpu().numpy()
         scorebelief = torch.nn.functional.softmax(scorebelief_logits,dim=0).cpu().numpy()
+        ownership_corr = torch.tanh(out_ownership_corr).cpu().numpy()
+        futurepos_corr = torch.tanh(out_futurepos_corr).cpu().numpy()
 
     board = gs.board
 
@@ -271,7 +279,9 @@ def get_outputs(gs, rules):
         "seki2": seki2,
         "seki_by_loc2": seki_by_loc2,
         "scorebelief": scorebelief,
-        "genmove_result": genmove_result
+        "genmove_result": genmove_result,
+        "ownership_corr": ownership_corr,
+        "futurepos_corr": futurepos_corr
     }
 
 def get_input_feature(gs, rules, feature_idx):
@@ -531,6 +541,19 @@ def print_scorebelief(gs,outputs):
     return ret
 
 
+def abs_cosine_metric(x, y):
+    return 1 - abs(cosine_similarity(x, y))
+
+
+def corr_distances_by_cosine_metric(corr_input):
+    clustering = optics(metric=abs_cosine_metric).fit(corr_input)
+    if max(clustering.labels_ == -1):
+        raise RuntimeError("No clusters found")
+    centers = np.vstack([np.average(corr_input[clustering.labels_ == i], axis=0) for i in range(max(clustering.labels_) + 1)])
+    results = pairwise_distances(corr_input, centers, metric='cosine')
+    return np.transpose(results)
+
+
 # Basic parsing --------------------------------------------------------
 colstr = 'ABCDEFGHJKLMNOPQRST'
 def parse_coord(s,board):
@@ -578,6 +601,7 @@ def str_coord(loc,board):
     'scorebelief',
     'passalive',
 ]
+
 known_analyze_commands = [
     'gfx/Policy/policy',
     'gfx/Policy1/policy1',
@@ -675,6 +699,24 @@ def get_board_matrix_str(matrix, scale, formatstr):
         gs.boards.append(gs.board.copy())
         ret = str_coord(loc,gs.board)
 
+    elif command[0] == "genmoves":
+        count = 10
+        if len(command) > 1:
+            count = int(command[1])
+            if count < 0 or count > pos_len**2:
+                count = 10
+
+        for i in range(count):
+            outputs = get_outputs(gs, rules)
+            loc = outputs["genmove_result"]
+            pla = gs.board.pla
+
+            gs.board.play(pla,loc)
+            gs.moves.append((pla,loc))
+            gs.boards.append(gs.board.copy())
+            ret += str_coord(loc,gs.board)
+
+
     elif command[0] == "name":
         ret = 'KataGo Raw Neural Net Debug/Test Script'
     elif command[0] == "version":
@@ -804,6 +846,35 @@ def get_board_matrix_str(matrix, scale, formatstr):
     elif command[0] == "futurepos1_raw":
         outputs = get_outputs(gs, rules)
         ret = get_board_matrix_str(outputs["futurepos"][1], 100.0, "%+7.3f")
+
+    elif command[0] == "ownership_corr":
+        outputs = get_outputs(gs, rules)
+        corr = np.reshape(outputs["ownership_corr"], (corr_feature_len, features.pos_len ** 2))
+        corr = np.transpose(corr)
+        try:
+            distances = corr_distances_by_cosine_metric(corr)
+            ret = '\n\n'.join(list(get_board_matrix_str(i, 100.0, "%+7.3f") for i in distances))
+        except RuntimeError:
+            ret = "No clusters found returning raw output instead\n\n"
+            corr = np.transpose(corr)
+            for i in range(corr_feature_len):
+                ret += get_board_matrix_str(corr[i], 100.0, "%+7.3f")
+                ret += '\n'
+
+    elif command[0] == "futurepos_corr":
+        outputs = get_outputs(gs, rules)
+        corr = np.reshape(outputs["futurepos_corr"], (corr_feature_len, features.pos_len ** 2))
+        corr = np.transpose(corr)
+        try:
+            distances = corr_distances_by_cosine_metric(corr)
+            ret = '\n\n'.join(list(get_board_matrix_str(i, 100.0, "%+7.3f") for i in distances))
+        except RuntimeError:
+            ret = "No clusters found returning raw output instead\n\n"
+            corr = np.transpose(corr)
+            for i in range(corr_feature_len):
+                ret += get_board_matrix_str(corr[i], 100.0, "%+7.3f")
+                ret += '\n'
+
     elif command[0] == "seki_raw":
         outputs = get_outputs(gs, rules)
         ret = get_board_matrix_str(outputs["seki"], 100.0, "%+7.3f")