Ml searcher

.gitignore

@@ -244,12 +244,12 @@ VSharp.Test/GeneratedTests/**
 
 #Pytjon environments
 **/.env/**
+/torch_venv/**
 
 #Python caches
 **/__pycache__/**
 
 #logs
 **/logs_full*/**
-
 /output/**
 /References/*.dll

VSharp.ML.AIAgent/.gitignore

@@ -1,6 +1,7 @@
 # python cache and venv
 .env
 __pycache__/
-report/
+report**/
+ml/pretrained_models/
 *.pkl
 *.onnx

VSharp.ML.AIAgent/learning/play_game.py

@@ -2,6 +2,7 @@
 from statistics import StatisticsError
 from time import perf_counter
 from typing import TypeAlias
+import random
 
 import tqdm
 from func_timeout import FunctionTimedOut, func_set_timeout
@@ -83,6 +84,7 @@ def play_map(
         actual_coverage_percent=actual_coverage,
     )
 
+    with_predictor.update(with_connector.map.MapName, model_result)
     return model_result, end_time - start_time
 
 
@@ -116,6 +118,7 @@ def play_map_with_timeout(
 def play_game(with_predictor: Predictor, max_steps: int, maps_type: MapsType):
     with game_server_socket_manager() as ws:
         maps = get_maps(websocket=ws, type=maps_type)
+    random.shuffle(maps)
     with tqdm.tqdm(
         total=len(maps),
         desc=f"{with_predictor.name():20}: {maps_type.value}",

VSharp.ML.AIAgent/ml/common_model/models.py

@@ -0,0 +1,89 @@
+import torch
+from torch import nn
+from torch_geometric.nn import (
+    GATConv,
+    SAGEConv,
+    TAGConv,
+)
+from torchvision.ops import MLP
+
+
+class CommonModel(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        num_gv_layers=2,
+        num_sv_layers=2,
+    ):
+        super().__init__()
+        self.tag_conv1 = TAGConv(5, hidden_channels, 2)
+        self.tag_conv2 = TAGConv(6, hidden_channels, 3)
+        self.gv_layers = nn.ModuleList()
+        self.gv_layers.append(self.tag_conv1)
+        self.gv_layers.append(SAGEConv(-1, hidden_channels))
+        for i in range(num_gv_layers - 1):
+            sage_gv = SAGEConv(-1, hidden_channels)
+            self.gv_layers.append(sage_gv)
+
+        self.sv_layers = nn.ModuleList()
+        self.sv_layers.append(self.tag_conv2)
+        self.sv_layers.append(SAGEConv(-1, hidden_channels))
+        for i in range(num_sv_layers - 1):
+            sage_sv = SAGEConv(-1, hidden_channels)
+            self.sv_layers.append(sage_sv)
+
+        self.history1 = GATConv((-1, -1), hidden_channels, add_self_loops=False)
+
+        self.in1 = SAGEConv((-1, -1), hidden_channels)
+
+        self.sv_layers2 = nn.ModuleList()
+        self.sv_layers2.append(SAGEConv(-1, hidden_channels))
+        for i in range(num_sv_layers - 1):
+            sage_sv = SAGEConv(-1, hidden_channels)
+            self.sv_layers2.append(sage_sv)
+
+        self.mlp = MLP(hidden_channels, [1])
+
+    def forward(self, x_dict, edge_index_dict, edge_attr_dict):
+        game_x = self.gv_layers[0](
+            x_dict["game_vertex"],
+            edge_index_dict[("game_vertex", "to", "game_vertex")],
+        ).relu()
+        for layer in self.gv_layers[1:]:
+            game_x = layer(
+                game_x,
+                edge_index_dict[("game_vertex", "to", "game_vertex")],
+            ).relu()
+
+        state_x = self.sv_layers[0](
+            x_dict["state_vertex"],
+            edge_index_dict[("state_vertex", "parent_of", "state_vertex")],
+        ).relu()
+        for layer in self.sv_layers[1:]:
+            state_x = layer(
+                state_x,
+                edge_index_dict[("state_vertex", "parent_of", "state_vertex")],
+            ).relu()
+
+        history_x = self.history1(
+            (game_x, state_x),
+            edge_index_dict[("game_vertex", "history", "state_vertex")],
+            edge_attr_dict,
+            size=(game_x.size(0), state_x.size(0)),
+        ).relu()
+
+        in_x = self.in1(
+            (game_x, history_x), edge_index_dict[("game_vertex", "in", "state_vertex")]
+        ).relu()
+
+        state_x = self.sv_layers2[0](
+            in_x,
+            edge_index_dict[("state_vertex", "parent_of", "state_vertex")],
+        ).relu()
+        for layer in self.sv_layers2[1:]:
+            state_x = layer(
+                state_x,
+                edge_index_dict[("state_vertex", "parent_of", "state_vertex")],
+            ).relu()
+        x = self.mlp(in_x)
+        return x

VSharp.ML.AIAgent/ml/common_model/paths.py

@@ -0,0 +1,6 @@
+import os
+
+csv_path = os.path.join("report", "epochs_tables")
+models_path = os.path.join("report", "epochs_best")
+common_models_path = os.path.join("report", "common_models")
+best_models_dict_path = os.path.join("report", "updated_best_models_dicts")