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Ml searcher add pretrained #76

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merged 8 commits into from
Nov 21, 2023
Merged

Ml searcher add pretrained #76

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fb53cec
preliminary model for state prediction
katyacyfra Feb 4, 2023
598b3fc
fixes; state mapping from index to id
katyacyfra Feb 13, 2023
45ee79d
remove extra game.py
katyacyfra Feb 17, 2023
2a18d21
added models;fix data loader
katyacyfra Nov 3, 2023
8a8e168
merge
katyacyfra Nov 3, 2023
ba188c0
delete old
katyacyfra Nov 3, 2023
d4f9bad
fix codestyle
katyacyfra Nov 8, 2023
a31cb41
delete hardcoded path
katyacyfra Nov 8, 2023
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47 changes: 18 additions & 29 deletions VSharp.ML.AIAgent/ml/data_loader_compact.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
import argparse
import json
import os.path
import pickle
Expand Down Expand Up @@ -46,6 +45,7 @@ def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int
edges_index_s_v_history = []
edges_index_v_s_history = []
edges_attr_v_v = []
edges_types_v_v = []

edges_attr_s_v = []
edges_attr_v_s = []
Expand Down Expand Up @@ -77,9 +77,8 @@ def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int
edges_index_v_v.append(
np.array([vertex_map[e.VertexFrom], vertex_map[e.VertexTo]])
)
edges_attr_v_v.append(
np.array([e.Label.Token])
) # TODO: consider token in a model
edges_attr_v_v.append(np.array([e.Label.Token]))
edges_types_v_v.append(e.Label.Token)

state_doubles = 0

Expand All @@ -101,7 +100,7 @@ def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int
)
)
# history edges: state -> vertex and back
for h in s.History: # TODO: process NumOfVisits as edge label
for h in s.History:
v_to = vertex_map[h.GraphVertexId]
edges_index_s_v_history.append(np.array([state_index, v_to]))
edges_index_v_s_history.append(np.array([v_to, state_index]))
Expand All @@ -124,15 +123,21 @@ def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int

data["game_vertex"].x = torch.tensor(np.array(nodes_vertex), dtype=torch.float)
data["state_vertex"].x = torch.tensor(np.array(nodes_state), dtype=torch.float)
data["game_vertex", "to", "game_vertex"].edge_index = (
data["game_vertex_to_game_vertex"].edge_index = (
torch.tensor(np.array(edges_index_v_v), dtype=torch.long).t().contiguous()
)
data["state_vertex", "in", "game_vertex"].edge_index = (
data["game_vertex_to_game_vertex"].edge_attr = torch.tensor(
np.array(edges_attr_v_v), dtype=torch.long
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тут если edges_attr_v_v, edges_types_v_v могут быть пустыми, то нужно соответствующие тензоры оборачивать в null_if_empty. но это ладно, сделаю сам

)
data["game_vertex_to_game_vertex"].edge_type = torch.tensor(
np.array(edges_types_v_v), dtype=torch.long
)
data["state_vertex_in_game_vertex"].edge_index = (
torch.tensor(np.array(edges_index_s_v_in), dtype=torch.long)
.t()
.contiguous()
)
data["game_vertex", "in", "state_vertex"].edge_index = (
data["game_vertex_in_state_vertex"].edge_index = (
torch.tensor(np.array(edges_index_v_s_in), dtype=torch.long)
.t()
.contiguous()
Expand All @@ -149,24 +154,24 @@ def null_if_empty(tensor):
else torch.empty((2, 0), dtype=torch.int64)
)

data["state_vertex", "history", "game_vertex"].edge_index = null_if_empty(
data["state_vertex_history_game_vertex"].edge_index = null_if_empty(
torch.tensor(np.array(edges_index_s_v_history), dtype=torch.long)
.t()
.contiguous()
)
data["game_vertex", "history", "state_vertex"].edge_index = null_if_empty(
data["game_vertex_history_state_vertex"].edge_index = null_if_empty(
torch.tensor(np.array(edges_index_v_s_history), dtype=torch.long)
.t()
.contiguous()
)
data["state_vertex", "history", "game_vertex"].edge_attr = torch.tensor(
data["state_vertex_history_game_vertex"].edge_attr = torch.tensor(
np.array(edges_attr_s_v), dtype=torch.long
)
data["game_vertex", "history", "state_vertex"].edge_attr = torch.tensor(
data["game_vertex_history_state_vertex"].edge_attr = torch.tensor(
np.array(edges_attr_v_s), dtype=torch.long
)
# if (edges_index_s_s): #TODO: empty?
data["state_vertex", "parent_of", "state_vertex"].edge_index = null_if_empty(
data["state_vertex_parent_of_state_vertex"].edge_index = null_if_empty(
torch.tensor(np.array(edges_index_s_s), dtype=torch.long).t().contiguous()
)
# print(data['state', 'parent_of', 'state'].edge_index)
Expand Down Expand Up @@ -242,19 +247,3 @@ def __process_files(self):
with open(PIK, "wb") as f:
pickle.dump(self.dataset, f)
self.dataset = []


def parse_cmd_line_args():
parser = argparse.ArgumentParser(
prog="V# pytorch-geometric data conversion", description="Symbolic execution"
)
parser.add_argument("--dataset", required=True, help="Dataset folder")
parser.add_argument(
"--mode", help="heterogeneous or homogeneous graph model (het|hom)"
)


def get_data_hetero_vector():
dl = ServerDataloaderHeteroVector("../../GNN_V#/all")
# dl = ServerDataloaderHetero("../../GNN_V#/Serialized_test")
return dl.dataset
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