From fb53cec4c997568420e7ba85520cfce9fddf3db2 Mon Sep 17 00:00:00 2001 From: ktyacyfra Date: Sat, 4 Feb 2023 16:24:26 +0300 Subject: [PATCH 1/7] preliminary model for state prediction --- VSharp.ML.AIAgent/ml/data_loader.py | 226 +++++++++++++++++++ VSharp.ML.AIAgent/ml/game.py | 89 ++++++++ VSharp.ML.AIAgent/ml/main.py | 61 +++++ VSharp.ML.AIAgent/ml/models.py | 110 ++++++++- VSharp.ML.AIAgent/ml/predict_state_hetero.py | 106 +++++++++ 5 files changed, 590 insertions(+), 2 deletions(-) create mode 100644 VSharp.ML.AIAgent/ml/data_loader.py create mode 100644 VSharp.ML.AIAgent/ml/game.py create mode 100644 VSharp.ML.AIAgent/ml/main.py create mode 100644 VSharp.ML.AIAgent/ml/predict_state_hetero.py diff --git a/VSharp.ML.AIAgent/ml/data_loader.py b/VSharp.ML.AIAgent/ml/data_loader.py new file mode 100644 index 000000000..7c458cd00 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/data_loader.py @@ -0,0 +1,226 @@ +import argparse +import json +import os.path +from torch_geometric.data import Data, HeteroData +from os import walk +from typing import Dict +import torch +import numpy as np +from operator import itemgetter + +from game import GameState + +#NUM_NODE_FEATURES = 49 +NUM_NODE_FEATURES = 6 +EXPECTED_FILENAME = "expectedResults.txt" + + +class DataLoader: # TODO: inheritance and more ways to load (different predictions) + def __init__(self, data_dir): + self.data_dir = data_dir + self.graph_types_and_expected: Dict[str, Dict[int, float]] = {} # Dict[example name, Dict[graph №, expected]] + self.graph_types_and_data = {} + self.dataset = [] + self.process_directory(data_dir) + self.__process_files() + + def process_directory(self, data_dir): + example_dirs = next(walk(data_dir), (None, [], None))[1] + print(example_dirs) + for fldr in example_dirs: + fldr_path = os.path.join(data_dir, fldr) + graphs_to_convert = [] + for f in os.listdir(fldr_path): + if f != EXPECTED_FILENAME: + graphs_to_convert.append(f) + else: + self.graph_types_and_expected[fldr] = self.get_expected_values(fldr_path) + graphs_to_convert.sort(key=lambda x: int(x)) + self.graph_types_and_data[fldr] = graphs_to_convert + + def __process_files(self): + for (k, v) in self.graph_types_and_data.items(): + for file in v: + print(os.path.join(self.data_dir, k, file)) + graph = self.convert_file_to_graph_homo(os.path.join(self.data_dir, k, file), + self.graph_types_and_expected[k][int(file)]) + self.dataset.append(graph) + + @staticmethod + def get_expected_values(fldr_path: str) -> Dict[int, float]: + """Get TotalReachableRewardFromCurrentState for every graph + Headers: GraphID ExpectedStateNumber ExpectedRewardForStep TotalReachableRewardFromCurrentState""" + expected = {} + with open(os.path.join(fldr_path, EXPECTED_FILENAME)) as f: + next(f) + for line in f: + split = line.split() + expected[int(split[0])]= int(split[-1]) + return expected + + @staticmethod + def convert_file_to_graph_homo(filepath, expected) -> Data: + """ File headers: + Nodes: #VertexId InCoverageZone BasicBlockSize CoveredByTest + VisitedByState TouchedByState (State_i_ID State_i_Position)* + Edges: #VertexFrom VertexTo Terminal(0-CFG, 1-Call, 2-Return)""" + nodes = [] + edges = [] + edge_attr_ = [] + with open(filepath) as f: + parse_edges = False + for line in f: + if "#" not in line: # skip headers + if not parse_edges: # parse nodes + arr = np.zeros(NUM_NODE_FEATURES) + split = np.array(line.split()[1:]) + arr[0: split.size] = split + nodes.append(arr) + else: # parse edges + split = list(map(lambda x: int(x), line.split())) + edges.append(split[:-1]) + edge_attr_.append(split[2]) + else: + if "#Edges" in line: + parse_edges = True + x = torch.tensor(np.array(nodes), dtype=torch.float) + edge_index = torch.tensor(edges, dtype=torch.long) + edge_attr = torch.tensor(np.array(edge_attr_), dtype=torch.long) + data = Data(x=x, edge_index=edge_index.t().contiguous(), edge_attr = edge_attr, + y=expected) + return data + +class ServerDataloaderHetero(DataLoader): + def __init__(self, data_dir): + self.data_dir = data_dir + self.graph_types_and_expected: Dict[str, Dict[int, np.array]] = {} # Dict[example name, Dict[graph №, expected]] + self.graph_types_and_data = {} + self.dataset = [] + self.process_directory(data_dir) + self.__process_files() + + def __process_files(self): + for (k, v) in self.graph_types_and_data.items(): + for file in v: + with open(os.path.join(self.data_dir, k, file)) as f: + print(os.path.join(self.data_dir, k, file)) + data = json.load(f) + graph = self.convert_input_to_tensor(GameState.from_dict(data)) + #add_expected values + graph.y = self.graph_types_and_expected[k][int(file)] + self.dataset.append(graph) + + def get_expected_values(self, fldr_path: str) -> Dict[int, np.array]: + """Get TotalReachableRewardFromCurrentState for every graph + Headers: GraphID ExpectedStateNumber ExpectedRewardForCoveredInStep ExpectedRewardForVisitedInstructionsInStep + TotalReachableRewardFromCurrentState""" + expected = {} + with open(os.path.join(fldr_path, EXPECTED_FILENAME)) as f: + next(f) + for line in f: + split = line.split() + expected[int(split[0])] = np.array(split[1:], dtype=int) + return expected + + + @staticmethod + def convert_input_to_tensor(input: GameState): + """ + Converts game env to tensors + input later can be changed to + """ + mp = input.Map + data = HeteroData() + nodes_vertex_set = set() + nodes_state_set = set() + nodes_vertex = [] + nodes_state = [] + edges_index_v_v = [] + edges_index_s_s = [] + edges_index_s_v = [] + edges_index_v_s = [] + edges_attr_s_v = [] # 0 - history # 1 - state in + edges_attr_v_s = [] + edges_attr_v_v = [] + + for m in mp: + # process vertices + vertex_from, vertex_to = m.VertexFrom.__dict__, m.VertexTo.__dict__ + for v in [vertex_from, vertex_to]: + vertex_id = v['Id'] + if vertex_id not in nodes_vertex_set: + nodes_vertex_set.add(vertex_id) + nodes_vertex.append(np.array([vertex_id, int(v['InCoverageZone']), + v['BasicBlockSize'], int(v['CoveredByTest']), + int(v['VisitedByState']), int(v['TouchedByState']) + ])) + # proccess edge + edges_index_v_v.append(np.array([vertex_from['Id'], vertex_to['Id']])) + edges_attr_v_v.append(np.array([m.Label.Token])) + # dealing with states + nodes_number = 0 #unique id for every node! + for m in mp: + vertex_from, vertex_to = m.VertexFrom.__dict__, m.VertexTo.__dict__ + for v in [vertex_from, vertex_to]: + states = v['States'] + if states: #proccess states independently + for s in states: + dct = s.__dict__ + sid = dct['Id'] + nodes_number + if sid not in nodes_state_set: + nodes_state_set.add(sid) + nodes_state.append(np.array([sid, dct['Position'], + dct['PredictedUsefulness'], dct['PathConditionSize'], + dct['VisitedAgainVertices'], dct['VisitedNotCoveredVerticesInZone'], + dct['VisitedNotCoveredVerticesOutOfZone'] + ])) + # fix state position + edges_index_s_v.append(np.array([sid, vertex_id])) + edges_attr_s_v.append(np.array(1)) + edges_index_v_s.append(np.array([vertex_id, sid])) + edges_attr_v_s.append(np.array(1)) + #history edges + history = dct['History'] + if history: + for h in history: + edges_index_s_v.append(np.array([sid, h])) + edges_attr_s_v.append(np.array(0)) + #children edges + children = dct['Children'] + if children: + for c in children: + edges_index_s_s.append(np.array([sid, c + nodes_number])) + + nodes_vertex = sorted(nodes_vertex, key=itemgetter(0)) + nodes_state = sorted(nodes_state, key=itemgetter(0)) + 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 = torch.tensor(np.array(edges_index_v_v), + dtype=torch.long).t().contiguous() + data['state_vertex', 'to', 'game_vertex'].edge_index = torch.tensor(np.array(edges_index_s_v), + dtype=torch.long).t().contiguous() + data['game_vertex', 'to', 'state_vertex'].edge_index = torch.tensor(np.array(edges_index_v_s), + dtype=torch.long).t().contiguous() + if (edges_index_s_s): + data['state_vertex', 'parent_of', 'state_vertex'].edge_index = torch.tensor(np.array(edges_index_s_s), + dtype=torch.long).t().contiguous() + #print(data['state', 'parent_of', 'state'].edge_index) + data['game_vertex', 'to', 'game_vertex'].edge_attr = torch.tensor(np.array(edges_attr_v_v), dtype=torch.long) + data['state_vertex', 'to', 'game_vertex'].edge_attr = torch.tensor(np.array(edges_attr_s_v), dtype=torch.long) + #print(data) + return data + + +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(): + dl = DataLoader("../../GNN_V#/") + return dl.dataset + +def get_data_hetero(): + dl = ServerDataloaderHetero("../../GNN_V#/Serialized_test") + return dl.dataset \ No newline at end of file diff --git a/VSharp.ML.AIAgent/ml/game.py b/VSharp.ML.AIAgent/ml/game.py new file mode 100644 index 000000000..ade8d83ef --- /dev/null +++ b/VSharp.ML.AIAgent/ml/game.py @@ -0,0 +1,89 @@ +from dataclasses import dataclass +from dataclasses_json import dataclass_json +from typing import List + + +@dataclass_json +@dataclass +class State: + Id: int + Position: int + PredictedUsefulness: float + PathConditionSize: int + VisitedAgainVertices: int + VisitedNotCoveredVerticesInZone: int + VisitedNotCoveredVerticesOutOfZone: int + History: List[int] + Children: List[int] + + def __hash__(self) -> int: + return self.Id.__hash__() + + +@dataclass_json +@dataclass +class GameMapVertex: + Uid: int + Id: int + InCoverageZone: bool + BasicBlockSize: int + CoveredByTest: bool + VisitedByState: bool + TouchedByState: bool + States: List[State] + + +@dataclass_json +@dataclass +class GameEdgeLabel: + Token: int + + +@dataclass_json +@dataclass +class GameMapEdge: + VertexFrom: GameMapVertex + VertexTo: GameMapVertex + Label: GameEdgeLabel + + +@dataclass_json +@dataclass +class GameState: + Map: List[GameMapEdge] + + +@dataclass_json +@dataclass +class GameMap: + Id: int + CoverageToStart: int + AssemblyFullName: str + CoverageZone: bool + NameOfObjectToCover: str + + +@dataclass_json +@dataclass +class MoveReward: + ForCoverage: int + ForVisitedInstructions: int + + def __eq__(self, __o: "MoveReward") -> bool: + if type(self) != type(__o): + return RuntimeError(f"Can't compare {type(self)} with {type(__o)}") + + def __add__(self, __o: "MoveReward") -> "MoveReward": + if type(self) != type(__o): + return RuntimeError(f"Can't add {type(__o)} to {type(self)}") + return MoveReward( + self.ForCoverage + __o.ForCoverage, + self.ForVisitedInstructions + __o.ForVisitedInstructions, + ) + + +@dataclass_json +@dataclass +class Reward: + ForMove: MoveReward + MaxPossibleReward: int diff --git a/VSharp.ML.AIAgent/ml/main.py b/VSharp.ML.AIAgent/ml/main.py new file mode 100644 index 000000000..ad509661c --- /dev/null +++ b/VSharp.ML.AIAgent/ml/main.py @@ -0,0 +1,61 @@ +import torch +from torch.autograd import Variable +from torch_geometric.nn import to_hetero, to_hetero_with_bases + +import data_loader +from models import GCN, GCN_SimpleMultipleOutput, GNN_MultipleOutput, GAT, GNN_Het +from random import shuffle +from torch_geometric.loader import DataLoader +import torch.nn.functional as F + + +def main(): + dataset = data_loader.get_data() + torch.manual_seed(12345) + #shuffle(dataset) + + split_at = round(len(dataset)*0.85) + + train_dataset = dataset[:split_at] + test_dataset = dataset[split_at:] + + print(f'Number of training graphs: {len(train_dataset)}') + print(f'Number of test graphs: {len(test_dataset)}') + + train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) + test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) + + #device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model = GCN(hidden_channels=64) + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + criterion = torch.nn.CrossEntropyLoss() + + for epoch in range(1, 201): + train(model, train_loader, criterion, optimizer) + train_acc = tst(model, train_loader) + test_acc = tst(model, test_loader) + print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}') + +def train(model, train_loader, criterion, optimizer): + model.train() + + for data in train_loader: # Iterate in batches over the training dataset. + out = model(data.x, data.edge_index, data.batch) # Perform a single forward pass. + loss = criterion(out, data.y) # Compute the loss. + loss.backward() # Derive gradients. + optimizer.step() # Update parameters based on gradients. + optimizer.zero_grad() # Clear gradients. + +def tst(model, loader): + model.eval() + correct = 0 + for data in loader: + out = model(data.x, data.edge_index, data.batch) + pred = out.argmax(dim=1) + #for (p, e) in zip(pred, data.y): + #print(p, e) + correct += int((pred == data.y).sum()) + return correct / len(loader.dataset) + + + diff --git a/VSharp.ML.AIAgent/ml/models.py b/VSharp.ML.AIAgent/ml/models.py index ecef1a7b1..87b65fc2a 100644 --- a/VSharp.ML.AIAgent/ml/models.py +++ b/VSharp.ML.AIAgent/ml/models.py @@ -1,11 +1,35 @@ import torch + + +from torch_geometric.nn import GATConv, Linear + +from data_loader import NUM_NODE_FEATURES from torch.nn import Linear import torch.nn.functional as F -from torch_geometric.nn import GCNConv +from torch_geometric.nn import GCNConv, SAGEConv, HeteroConv from torch_geometric.nn import global_mean_pool -NUM_NODE_FEATURES = 49 +NUM_PREDICTED_VALUES = 4 + +class HeteroGNN(torch.nn.Module): + def __init__(self, metadata, hidden_channels, out_channels, num_layers): + super().__init__() + self.convs = torch.nn.ModuleList() + for _ in range(num_layers): + conv = HeteroConv({ + edge_type: SAGEConv((-1, -1), hidden_channels) + for edge_type in metadata[1] + }) + self.convs.append(conv) + + self.lin = Linear(hidden_channels, out_channels) + + def forward(self, x_dict, edge_index_dict): + for conv in self.convs: + x_dict = conv(x_dict, edge_index_dict) + x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()} + return self.lin(x_dict['author']) class GCN_SimpleNoEdgeLabels(torch.nn.Module): def __init__(self): @@ -23,6 +47,74 @@ def forward(self, data): return F.log_softmax(x, dim=1) +class GNN_MultipleOutput(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = SAGEConv((-1, -1), hidden_channels) + self.conv2 = SAGEConv((-1, -1), out_channels) + + def forward(self, x, edge_index): + x1 = self.conv1(x, edge_index).relu() + x1 = self.conv2(x1, edge_index) + + x2 = self.conv1(x, edge_index).relu() + x2 = self.conv2(x2, edge_index) + + x3 = self.conv1(x, edge_index).relu() + x3 = self.conv2(x3, edge_index) + + x4 = self.conv1(x, edge_index).relu() + x4 = self.conv2(x4, edge_index) + return x1, x2, x3, x4 + + +class GCN_SimpleMultipleOutput(torch.nn.Module): + #https://discuss.pytorch.org/t/a-model-with-multiple-outputs/10440 + def __init__(self, hidden_channels): + + super(GCN_SimpleMultipleOutput, self).__init__() + torch.manual_seed(12345) + self.conv1 = GCNConv(NUM_NODE_FEATURES, hidden_channels) + self.conv1.add_self_loops = False + self.conv2 = GCNConv(hidden_channels, NUM_PREDICTED_VALUES) + self.conv2.add_self_loops = False + + def forward(self, x, edge_index): + + x1 = self.conv1(x, edge_index) + x1 = F.relu(x1) + x1 = F.dropout(x1, training=self.training) + x1 = self.conv2(x1, edge_index) + + x2 = self.conv1(x, edge_index) + x2 = F.relu(x2) + x2 = F.dropout(x2, training=self.training) + x2 = self.conv2(x2, edge_index) + + x3 = self.conv1(x, edge_index) + x3 = F.relu(x3) + x3 = F.dropout(x3, training=self.training) + x3 = self.conv2(x3, edge_index) + + x4 = self.conv1(x, edge_index) + x4 = F.relu(x4) + x4 = F.dropout(x4, training=self.training) + x4 = self.conv2(x4, edge_index) + + return F.log_softmax(x1, dim=1), F.log_softmax(x2, dim=1), \ + F.log_softmax(x3, dim=1), F.log_softmax(x4, dim=1) + + +class GNN_Het(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = SAGEConv((-1, -1), hidden_channels) + self.conv2 = SAGEConv((-1, -1), out_channels) + + def forward(self, x, edge_index): + x = self.conv1(x, edge_index).relu() + x = self.conv2(x, edge_index) + return x class GCN(torch.nn.Module): def __init__(self, hidden_channels): @@ -46,3 +138,17 @@ def forward(self, x, edge_index, batch): x = self.lin(x) return x + +class GAT(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = GATConv((-1, -1), hidden_channels, add_self_loops=False) + self.lin1 = Linear(-1, hidden_channels) + self.conv2 = GATConv((-1, -1), out_channels, add_self_loops=False) + self.lin2 = Linear(-1, out_channels) + + def forward(self, x, edge_index): + x = self.conv1(x, edge_index) + self.lin1(x) + x = x.relu() + x = self.conv2(x, edge_index) + self.lin2(x) + return x \ No newline at end of file diff --git a/VSharp.ML.AIAgent/ml/predict_state_hetero.py b/VSharp.ML.AIAgent/ml/predict_state_hetero.py new file mode 100644 index 000000000..92ae5bff0 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/predict_state_hetero.py @@ -0,0 +1,106 @@ +import torch +from torch.autograd import Variable +from torch_geometric.nn import to_hetero, to_hetero_with_bases +from torch_geometric.data import HeteroData + +import data_loader +from models import GCN, GCN_SimpleMultipleOutput, GNN_MultipleOutput, GAT, GNN_Het +from random import shuffle +from torch_geometric.loader import DataLoader +import torch.nn.functional as F + + +class PredictStateHetGNN: + """predicts ExpectedStateNumber using Heterogeneous GNN""" + + def __init__(self): + self.start() + + def start(self): + dataset = data_loader.get_data_hetero() + torch.manual_seed(12345) + # shuffle(dataset) + + split_at = round(len(dataset) * 0.85) + + train_dataset = dataset[:split_at] + test_dataset = dataset[split_at:] + + print(f'Number of training graphs: {len(train_dataset)}') + print(f'Number of test graphs: {len(test_dataset)}') + + train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True) # TODO: add learning by batches! + test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False) + + # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model = GNN_Het(hidden_channels=64, out_channels=1) + model = to_hetero(model, dataset[0].metadata(), aggr='sum') + # model = to_hetero_with_bases(model, dataset[0].metadata(), num_bases=3) + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + # criterion = torch.nn.CrossEntropyLoss() + + for epoch in range(1, 201): + self.train(model, train_loader, optimizer) + train_acc = self.tst(model, train_loader) + test_acc = self.tst(model, test_loader) + print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}') + + # loss function from link prediction example + def weighted_mse_loss(self, pred, target, weight=None): + weight = 1. if weight is None else weight[target].to(pred.dtype) + return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() + + def train(self, model, train_loader, optimizer): + model.train() + + for data in train_loader: # Iterate in batches over the training dataset. + out = model(data.x_dict, data.edge_index_dict) + pred = out['state_vertex'] + target = torch.zeros(pred.size()) + state_to_move = data.y[0][0] + # TODO: more intellectual + target[state_to_move][0] = 5000. # should be substantially greater than the total number of states! + loss = F.mse_loss(pred, target) + loss.backward() # Derive gradients. + optimizer.step() # Update parameters based on gradients. + optimizer.zero_grad() # Clear gradients. + + def train_single_value(self, model, train_loader, optimizer): + # It does not work properly now --- no actual learning!!!''' + model.train() + + for data in train_loader: # Iterate in batches over the training dataset. + out = model(data.x_dict, data.edge_index_dict) + pred = out['state_vertex'].argmax(dim=0)[0].float() + target = torch.tensor(data.y[0][0]).float() + # loss = self.weighted_mse_loss(torch.tensor(pred, dtype=float), + # torch.tensor(pred, dtype=float)).sqrt() + loss = self.weighted_mse_loss(pred, target) + # print("loss", loss) + # loss = Variable(loss, requires_grad=True) #TODO: dirty hack, fix it later + loss.requires_grad = True + # print(loss) + # loss = criterion(torch.tensor(pred, dtype=float), torch.tensor(data.y[0][0])) # Compute the loss. + loss.backward() # Derive gradients. + optimizer.step() # Update parameters based on gradients. + optimizer.zero_grad() # Clear gradients. + + def tst(self, model, loader): + model.eval() + correct = 0 + for data in loader: + pred = self.predict_state(model, data) + target = data.y[0][0] + #print(pred, target) + correct += int((pred == target)) + return correct / len(loader.dataset) + + @staticmethod + def predict_state(model, data: HeteroData) -> int: + '''Gets state id from model and heterogeneous graph''' + out = model(data.x_dict, data.edge_index_dict) + return int(out['state_vertex'].argmax(dim=0)[0]) + + +if __name__ == '__main__': + PredictStateHetGNN() From 598b3fcaa0361ecac904935f5c09ccae270f051c Mon Sep 17 00:00:00 2001 From: Ekaterina Shemetova Date: Mon, 13 Feb 2023 23:00:52 +0300 Subject: [PATCH 2/7] fixes; state mapping from index to id --- VSharp.ML.AIAgent/ml/data_loader.py | 223 ++++++++++++------ VSharp.ML.AIAgent/ml/main.py | 61 ----- VSharp.ML.AIAgent/ml/models.py | 102 +------- .../{ml => }/predict_state_hetero.py | 73 ++++-- 4 files changed, 203 insertions(+), 256 deletions(-) delete mode 100644 VSharp.ML.AIAgent/ml/main.py rename VSharp.ML.AIAgent/{ml => }/predict_state_hetero.py (60%) diff --git a/VSharp.ML.AIAgent/ml/data_loader.py b/VSharp.ML.AIAgent/ml/data_loader.py index 7c458cd00..b4436b567 100644 --- a/VSharp.ML.AIAgent/ml/data_loader.py +++ b/VSharp.ML.AIAgent/ml/data_loader.py @@ -3,14 +3,13 @@ import os.path from torch_geometric.data import Data, HeteroData from os import walk -from typing import Dict +from typing import Dict, Tuple import torch import numpy as np -from operator import itemgetter -from game import GameState +from common.game import GameState -#NUM_NODE_FEATURES = 49 +# NUM_NODE_FEATURES = 49 NUM_NODE_FEATURES = 6 EXPECTED_FILENAME = "expectedResults.txt" @@ -18,7 +17,9 @@ class DataLoader: # TODO: inheritance and more ways to load (different predictions) def __init__(self, data_dir): self.data_dir = data_dir - self.graph_types_and_expected: Dict[str, Dict[int, float]] = {} # Dict[example name, Dict[graph №, expected]] + self.graph_types_and_expected: Dict[ + str, Dict[int, float] + ] = {} # Dict[example name, Dict[graph №, expected]] self.graph_types_and_data = {} self.dataset = [] self.process_directory(data_dir) @@ -34,36 +35,41 @@ def process_directory(self, data_dir): if f != EXPECTED_FILENAME: graphs_to_convert.append(f) else: - self.graph_types_and_expected[fldr] = self.get_expected_values(fldr_path) + self.graph_types_and_expected[fldr] = self.get_expected_values( + fldr_path + ) graphs_to_convert.sort(key=lambda x: int(x)) self.graph_types_and_data[fldr] = graphs_to_convert def __process_files(self): - for (k, v) in self.graph_types_and_data.items(): + for k, v in self.graph_types_and_data.items(): for file in v: print(os.path.join(self.data_dir, k, file)) - graph = self.convert_file_to_graph_homo(os.path.join(self.data_dir, k, file), - self.graph_types_and_expected[k][int(file)]) + graph = self.convert_file_to_graph_homo( + os.path.join(self.data_dir, k, file), + self.graph_types_and_expected[k][int(file)], + ) self.dataset.append(graph) @staticmethod def get_expected_values(fldr_path: str) -> Dict[int, float]: """Get TotalReachableRewardFromCurrentState for every graph - Headers: GraphID ExpectedStateNumber ExpectedRewardForStep TotalReachableRewardFromCurrentState""" + Headers: GraphID ExpectedStateNumber ExpectedRewardForStep TotalReachableRewardFromCurrentState + """ expected = {} with open(os.path.join(fldr_path, EXPECTED_FILENAME)) as f: next(f) for line in f: split = line.split() - expected[int(split[0])]= int(split[-1]) + expected[int(split[0])] = int(split[-1]) return expected @staticmethod def convert_file_to_graph_homo(filepath, expected) -> Data: - """ File headers: - Nodes: #VertexId InCoverageZone BasicBlockSize CoveredByTest - VisitedByState TouchedByState (State_i_ID State_i_Position)* - Edges: #VertexFrom VertexTo Terminal(0-CFG, 1-Call, 2-Return)""" + """File headers: + Nodes: #VertexId InCoverageZone BasicBlockSize CoveredByTest + VisitedByState TouchedByState (State_i_ID State_i_Position)* + Edges: #VertexFrom VertexTo Terminal(0-CFG, 1-Call, 2-Return)""" nodes = [] edges = [] edge_attr_ = [] @@ -74,7 +80,7 @@ def convert_file_to_graph_homo(filepath, expected) -> Data: if not parse_edges: # parse nodes arr = np.zeros(NUM_NODE_FEATURES) split = np.array(line.split()[1:]) - arr[0: split.size] = split + arr[0 : split.size] = split nodes.append(arr) else: # parse edges split = list(map(lambda x: int(x), line.split())) @@ -86,28 +92,35 @@ def convert_file_to_graph_homo(filepath, expected) -> Data: x = torch.tensor(np.array(nodes), dtype=torch.float) edge_index = torch.tensor(edges, dtype=torch.long) edge_attr = torch.tensor(np.array(edge_attr_), dtype=torch.long) - data = Data(x=x, edge_index=edge_index.t().contiguous(), edge_attr = edge_attr, - y=expected) + data = Data( + x=x, edge_index=edge_index.t().contiguous(), edge_attr=edge_attr, y=expected + ) return data + class ServerDataloaderHetero(DataLoader): def __init__(self, data_dir): self.data_dir = data_dir - self.graph_types_and_expected: Dict[str, Dict[int, np.array]] = {} # Dict[example name, Dict[graph №, expected]] + self.graph_types_and_expected: Dict[ + str, Dict[int, np.array] + ] = {} # Dict[example name, Dict[graph №, expected]] self.graph_types_and_data = {} self.dataset = [] self.process_directory(data_dir) self.__process_files() def __process_files(self): - for (k, v) in self.graph_types_and_data.items(): + for k, v in self.graph_types_and_data.items(): for file in v: with open(os.path.join(self.data_dir, k, file)) as f: print(os.path.join(self.data_dir, k, file)) data = json.load(f) - graph = self.convert_input_to_tensor(GameState.from_dict(data)) - #add_expected values - graph.y = self.graph_types_and_expected[k][int(file)] + graph, state_map = self.convert_input_to_tensor( + GameState.from_dict(data) + ) + # add_expected values + expected = self.graph_types_and_expected[k][int(file)][0] + graph.y = state_map[expected] self.dataset.append(graph) def get_expected_values(self, fldr_path: str) -> Dict[int, np.array]: @@ -122,11 +135,10 @@ def get_expected_values(self, fldr_path: str) -> Dict[int, np.array]: expected[int(split[0])] = np.array(split[1:], dtype=int) return expected - @staticmethod - def convert_input_to_tensor(input: GameState): + def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int]]: """ - Converts game env to tensors + Converts game env to tensors and state_map input later can be changed to """ mp = input.Map @@ -139,88 +151,147 @@ def convert_input_to_tensor(input: GameState): edges_index_s_s = [] edges_index_s_v = [] edges_index_v_s = [] - edges_attr_s_v = [] # 0 - history # 1 - state in + edges_attr_s_v = [] # 0 - history # 1 - state in edges_attr_v_s = [] edges_attr_v_v = [] + state_map: Dict[int, int] = {} # Maps real state id to its index + vertex_map: Dict[int, int] = {} # Maps real vertex id to its index + vertex_index = 0 + state_index = 0 for m in mp: # process vertices vertex_from, vertex_to = m.VertexFrom.__dict__, m.VertexTo.__dict__ for v in [vertex_from, vertex_to]: - vertex_id = v['Id'] + vertex_id = int(v["Id"]) if vertex_id not in nodes_vertex_set: nodes_vertex_set.add(vertex_id) - nodes_vertex.append(np.array([vertex_id, int(v['InCoverageZone']), - v['BasicBlockSize'], int(v['CoveredByTest']), - int(v['VisitedByState']), int(v['TouchedByState']) - ])) + vertex_map[vertex_id] = vertex_index + vertex_index = vertex_index + 1 + nodes_vertex.append( + np.array( + [ + int(v["InCoverageZone"]), + v["BasicBlockSize"], + int(v["CoveredByTest"]), + int(v["VisitedByState"]), + int(v["TouchedByState"]), + ] + ) + ) # proccess edge - edges_index_v_v.append(np.array([vertex_from['Id'], vertex_to['Id']])) + edges_index_v_v.append( + np.array( + [ + vertex_map[int(vertex_from["Id"])], + vertex_map[int(vertex_to["Id"])], + ] + ) + ) edges_attr_v_v.append(np.array([m.Label.Token])) # dealing with states - nodes_number = 0 #unique id for every node! + # nodes_number = 0 #unique id for every node! + states_info = {} for m in mp: vertex_from, vertex_to = m.VertexFrom.__dict__, m.VertexTo.__dict__ for v in [vertex_from, vertex_to]: - states = v['States'] - if states: #proccess states independently + vertex_id = v["Id"] + states = v["States"] + if states: # proccess states independently for s in states: dct = s.__dict__ - sid = dct['Id'] + nodes_number + sid = int(dct["Id"]) + states_info[sid] = dct if sid not in nodes_state_set: nodes_state_set.add(sid) - nodes_state.append(np.array([sid, dct['Position'], - dct['PredictedUsefulness'], dct['PathConditionSize'], - dct['VisitedAgainVertices'], dct['VisitedNotCoveredVerticesInZone'], - dct['VisitedNotCoveredVerticesOutOfZone'] - ])) + state_map[sid] = state_index + state_index = state_index + 1 + nodes_state.append( + np.array( + [ + dct["Position"], + dct["PredictedUsefulness"], + dct["PathConditionSize"], + dct["VisitedAgainVertices"], + dct["VisitedNotCoveredVerticesInZone"], + dct["VisitedNotCoveredVerticesOutOfZone"], + ] + ) + ) # fix state position - edges_index_s_v.append(np.array([sid, vertex_id])) + edges_index_s_v.append( + np.array([state_map[sid], vertex_map[vertex_id]]) + ) edges_attr_s_v.append(np.array(1)) - edges_index_v_s.append(np.array([vertex_id, sid])) + edges_index_v_s.append( + np.array([vertex_map[vertex_id], state_map[sid]]) + ) edges_attr_v_s.append(np.array(1)) - #history edges - history = dct['History'] + # children edges + history = dct["History"] if history: for h in history: - edges_index_s_v.append(np.array([sid, h])) + edges_index_s_v.append( + np.array([state_map[sid], vertex_map[int(h)]]) + ) edges_attr_s_v.append(np.array(0)) - #children edges - children = dct['Children'] - if children: - for c in children: - edges_index_s_s.append(np.array([sid, c + nodes_number])) - - nodes_vertex = sorted(nodes_vertex, key=itemgetter(0)) - nodes_state = sorted(nodes_state, key=itemgetter(0)) - 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 = torch.tensor(np.array(edges_index_v_v), - dtype=torch.long).t().contiguous() - data['state_vertex', 'to', 'game_vertex'].edge_index = torch.tensor(np.array(edges_index_s_v), - dtype=torch.long).t().contiguous() - data['game_vertex', 'to', 'state_vertex'].edge_index = torch.tensor(np.array(edges_index_v_s), - dtype=torch.long).t().contiguous() - if (edges_index_s_s): - data['state_vertex', 'parent_of', 'state_vertex'].edge_index = torch.tensor(np.array(edges_index_s_s), - dtype=torch.long).t().contiguous() - #print(data['state', 'parent_of', 'state'].edge_index) - data['game_vertex', 'to', 'game_vertex'].edge_attr = torch.tensor(np.array(edges_attr_v_v), dtype=torch.long) - data['state_vertex', 'to', 'game_vertex'].edge_attr = torch.tensor(np.array(edges_attr_s_v), dtype=torch.long) - #print(data) - return data + # children edges --- add after all states + for k, v in states_info.items(): + children = v["Children"] + if children: + for c in children: + edges_index_s_s.append(np.array([state_map[k], state_map[int(c)]])) + + # nodes_vertex = sorted(nodes_vertex, key=itemgetter(0)) + # nodes_state = sorted(nodes_state, key=itemgetter(0)) + 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 = ( + torch.tensor(np.array(edges_index_v_v), dtype=torch.long).t().contiguous() + ) + data["state_vertex", "to", "game_vertex"].edge_index = ( + torch.tensor(np.array(edges_index_s_v), dtype=torch.long).t().contiguous() + ) + data["game_vertex", "to", "state_vertex"].edge_index = ( + torch.tensor(np.array(edges_index_v_s), dtype=torch.long).t().contiguous() + ) + if edges_index_s_s: + data["state_vertex", "parent_of", "state_vertex"].edge_index = ( + torch.tensor(np.array(edges_index_s_s), dtype=torch.long) + .t() + .contiguous() + ) + # print(data['state', 'parent_of', 'state'].edge_index) + data["game_vertex", "to", "game_vertex"].edge_attr = torch.tensor( + np.array(edges_attr_v_v), dtype=torch.long + ) + data["state_vertex", "to", "game_vertex"].edge_attr = torch.tensor( + np.array(edges_attr_s_v), dtype=torch.long + ) + + # data.state_map = state_map + return data, state_map 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)") + 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(): dl = DataLoader("../../GNN_V#/") return dl.dataset + def get_data_hetero(): - dl = ServerDataloaderHetero("../../GNN_V#/Serialized_test") - return dl.dataset \ No newline at end of file + dl = ServerDataloaderHetero("../../../GNN_V#/Serialized_test_simple") + return dl.dataset + + +if __name__ == "__main__": + get_data_hetero() diff --git a/VSharp.ML.AIAgent/ml/main.py b/VSharp.ML.AIAgent/ml/main.py deleted file mode 100644 index ad509661c..000000000 --- a/VSharp.ML.AIAgent/ml/main.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch.autograd import Variable -from torch_geometric.nn import to_hetero, to_hetero_with_bases - -import data_loader -from models import GCN, GCN_SimpleMultipleOutput, GNN_MultipleOutput, GAT, GNN_Het -from random import shuffle -from torch_geometric.loader import DataLoader -import torch.nn.functional as F - - -def main(): - dataset = data_loader.get_data() - torch.manual_seed(12345) - #shuffle(dataset) - - split_at = round(len(dataset)*0.85) - - train_dataset = dataset[:split_at] - test_dataset = dataset[split_at:] - - print(f'Number of training graphs: {len(train_dataset)}') - print(f'Number of test graphs: {len(test_dataset)}') - - train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) - test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) - - #device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - model = GCN(hidden_channels=64) - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - criterion = torch.nn.CrossEntropyLoss() - - for epoch in range(1, 201): - train(model, train_loader, criterion, optimizer) - train_acc = tst(model, train_loader) - test_acc = tst(model, test_loader) - print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}') - -def train(model, train_loader, criterion, optimizer): - model.train() - - for data in train_loader: # Iterate in batches over the training dataset. - out = model(data.x, data.edge_index, data.batch) # Perform a single forward pass. - loss = criterion(out, data.y) # Compute the loss. - loss.backward() # Derive gradients. - optimizer.step() # Update parameters based on gradients. - optimizer.zero_grad() # Clear gradients. - -def tst(model, loader): - model.eval() - correct = 0 - for data in loader: - out = model(data.x, data.edge_index, data.batch) - pred = out.argmax(dim=1) - #for (p, e) in zip(pred, data.y): - #print(p, e) - correct += int((pred == data.y).sum()) - return correct / len(loader.dataset) - - - diff --git a/VSharp.ML.AIAgent/ml/models.py b/VSharp.ML.AIAgent/ml/models.py index 87b65fc2a..d68bbe778 100644 --- a/VSharp.ML.AIAgent/ml/models.py +++ b/VSharp.ML.AIAgent/ml/models.py @@ -1,109 +1,15 @@ import torch - from torch_geometric.nn import GATConv, Linear -from data_loader import NUM_NODE_FEATURES +from .data_loader import NUM_NODE_FEATURES from torch.nn import Linear import torch.nn.functional as F -from torch_geometric.nn import GCNConv, SAGEConv, HeteroConv +from torch_geometric.nn import GCNConv, SAGEConv from torch_geometric.nn import global_mean_pool NUM_PREDICTED_VALUES = 4 -class HeteroGNN(torch.nn.Module): - def __init__(self, metadata, hidden_channels, out_channels, num_layers): - super().__init__() - - self.convs = torch.nn.ModuleList() - for _ in range(num_layers): - conv = HeteroConv({ - edge_type: SAGEConv((-1, -1), hidden_channels) - for edge_type in metadata[1] - }) - self.convs.append(conv) - - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - for conv in self.convs: - x_dict = conv(x_dict, edge_index_dict) - x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()} - return self.lin(x_dict['author']) - -class GCN_SimpleNoEdgeLabels(torch.nn.Module): - def __init__(self): - super().__init__() - self.conv1 = GCNConv(NUM_NODE_FEATURES, 16) - self.conv2 = GCNConv(16, 1) - - def forward(self, data): - x, edge_index = data.x, data.edge_index - - x = self.conv1(x, edge_index) - x = F.relu(x) - x = F.dropout(x, training=self.training) - x = self.conv2(x, edge_index) - - return F.log_softmax(x, dim=1) - -class GNN_MultipleOutput(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) - - def forward(self, x, edge_index): - x1 = self.conv1(x, edge_index).relu() - x1 = self.conv2(x1, edge_index) - - x2 = self.conv1(x, edge_index).relu() - x2 = self.conv2(x2, edge_index) - - x3 = self.conv1(x, edge_index).relu() - x3 = self.conv2(x3, edge_index) - - x4 = self.conv1(x, edge_index).relu() - x4 = self.conv2(x4, edge_index) - return x1, x2, x3, x4 - - -class GCN_SimpleMultipleOutput(torch.nn.Module): - #https://discuss.pytorch.org/t/a-model-with-multiple-outputs/10440 - def __init__(self, hidden_channels): - - super(GCN_SimpleMultipleOutput, self).__init__() - torch.manual_seed(12345) - self.conv1 = GCNConv(NUM_NODE_FEATURES, hidden_channels) - self.conv1.add_self_loops = False - self.conv2 = GCNConv(hidden_channels, NUM_PREDICTED_VALUES) - self.conv2.add_self_loops = False - - def forward(self, x, edge_index): - - x1 = self.conv1(x, edge_index) - x1 = F.relu(x1) - x1 = F.dropout(x1, training=self.training) - x1 = self.conv2(x1, edge_index) - - x2 = self.conv1(x, edge_index) - x2 = F.relu(x2) - x2 = F.dropout(x2, training=self.training) - x2 = self.conv2(x2, edge_index) - - x3 = self.conv1(x, edge_index) - x3 = F.relu(x3) - x3 = F.dropout(x3, training=self.training) - x3 = self.conv2(x3, edge_index) - - x4 = self.conv1(x, edge_index) - x4 = F.relu(x4) - x4 = F.dropout(x4, training=self.training) - x4 = self.conv2(x4, edge_index) - - return F.log_softmax(x1, dim=1), F.log_softmax(x2, dim=1), \ - F.log_softmax(x3, dim=1), F.log_softmax(x4, dim=1) - class GNN_Het(torch.nn.Module): def __init__(self, hidden_channels, out_channels): @@ -116,6 +22,7 @@ def forward(self, x, edge_index): x = self.conv2(x, edge_index) return x + class GCN(torch.nn.Module): def __init__(self, hidden_channels): super(GCN, self).__init__() @@ -139,6 +46,7 @@ def forward(self, x, edge_index, batch): return x + class GAT(torch.nn.Module): def __init__(self, hidden_channels, out_channels): super().__init__() @@ -151,4 +59,4 @@ def forward(self, x, edge_index): x = self.conv1(x, edge_index) + self.lin1(x) x = x.relu() x = self.conv2(x, edge_index) + self.lin2(x) - return x \ No newline at end of file + return x diff --git a/VSharp.ML.AIAgent/ml/predict_state_hetero.py b/VSharp.ML.AIAgent/predict_state_hetero.py similarity index 60% rename from VSharp.ML.AIAgent/ml/predict_state_hetero.py rename to VSharp.ML.AIAgent/predict_state_hetero.py index 92ae5bff0..05556d0b1 100644 --- a/VSharp.ML.AIAgent/ml/predict_state_hetero.py +++ b/VSharp.ML.AIAgent/predict_state_hetero.py @@ -1,10 +1,14 @@ +import os.path + +from typing import Dict + import torch from torch.autograd import Variable from torch_geometric.nn import to_hetero, to_hetero_with_bases from torch_geometric.data import HeteroData -import data_loader -from models import GCN, GCN_SimpleMultipleOutput, GNN_MultipleOutput, GAT, GNN_Het +from ml.data_loader import get_data_hetero +from ml.models import GNN_Het from random import shuffle from torch_geometric.loader import DataLoader import torch.nn.functional as F @@ -14,11 +18,13 @@ class PredictStateHetGNN: """predicts ExpectedStateNumber using Heterogeneous GNN""" def __init__(self): + self.state_maps = {} self.start() def start(self): - dataset = data_loader.get_data_hetero() + dataset = get_data_hetero() torch.manual_seed(12345) + # shuffle(dataset) split_at = round(len(dataset) * 0.85) @@ -26,28 +32,38 @@ def start(self): train_dataset = dataset[:split_at] test_dataset = dataset[split_at:] - print(f'Number of training graphs: {len(train_dataset)}') - print(f'Number of test graphs: {len(test_dataset)}') + print(f"Number of training graphs: {len(train_dataset)}") + print(f"Number of test graphs: {len(test_dataset)}") - train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True) # TODO: add learning by batches! + train_loader = DataLoader( + train_dataset, batch_size=1, shuffle=True + ) # TODO: add learning by batches! test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False) # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = GNN_Het(hidden_channels=64, out_channels=1) - model = to_hetero(model, dataset[0].metadata(), aggr='sum') + model = to_hetero(model, dataset[0].metadata(), aggr="sum") # model = to_hetero_with_bases(model, dataset[0].metadata(), num_bases=3) optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # criterion = torch.nn.CrossEntropyLoss() - for epoch in range(1, 201): + for epoch in range(1, 21): self.train(model, train_loader, optimizer) train_acc = self.tst(model, train_loader) test_acc = self.tst(model, test_loader) - print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}') + print( + f"Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}" + ) + + path = "./saved_models" + if not os.path.exists(path): + os.makedirs(path) + + self.save(model, path) # loss function from link prediction example def weighted_mse_loss(self, pred, target, weight=None): - weight = 1. if weight is None else weight[target].to(pred.dtype) + weight = 1.0 if weight is None else weight[target].to(pred.dtype) return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() def train(self, model, train_loader, optimizer): @@ -55,11 +71,13 @@ def train(self, model, train_loader, optimizer): for data in train_loader: # Iterate in batches over the training dataset. out = model(data.x_dict, data.edge_index_dict) - pred = out['state_vertex'] + pred = out["state_vertex"] target = torch.zeros(pred.size()) - state_to_move = data.y[0][0] + state_to_move = data.y.item() # TODO: more intellectual - target[state_to_move][0] = 5000. # should be substantially greater than the total number of states! + target[state_to_move][ + 0 + ] = 5000.0 # pred.size()[0]*100 # should be substantially greater than the total number of states! loss = F.mse_loss(pred, target) loss.backward() # Derive gradients. optimizer.step() # Update parameters based on gradients. @@ -71,7 +89,7 @@ def train_single_value(self, model, train_loader, optimizer): for data in train_loader: # Iterate in batches over the training dataset. out = model(data.x_dict, data.edge_index_dict) - pred = out['state_vertex'].argmax(dim=0)[0].float() + pred = out["state_vertex"].argmax(dim=0)[0].float() target = torch.tensor(data.y[0][0]).float() # loss = self.weighted_mse_loss(torch.tensor(pred, dtype=float), # torch.tensor(pred, dtype=float)).sqrt() @@ -89,18 +107,29 @@ def tst(self, model, loader): model.eval() correct = 0 for data in loader: - pred = self.predict_state(model, data) - target = data.y[0][0] - #print(pred, target) - correct += int((pred == target)) + out = model(data.x_dict, data.edge_index_dict) + pred = int(out["state_vertex"].argmax(dim=0)[0]) + target = data.y.item() + # print(pred, target) + correct += int(pred == target) return correct / len(loader.dataset) @staticmethod - def predict_state(model, data: HeteroData) -> int: - '''Gets state id from model and heterogeneous graph''' + def predict_state(model, data: HeteroData, state_map: Dict[int, int]) -> int: + """Gets state id from model and heterogeneous graph + data.state_map - maps real state id to state index""" + state_map = {v: k for k, v in state_map.items()} # inversion for prediction out = model(data.x_dict, data.edge_index_dict) - return int(out['state_vertex'].argmax(dim=0)[0]) + return state_map[int(out["state_vertex"].argmax(dim=0)[0])] + + def save(self, model, dir): + filepath = os.path.join(dir, "GNN_state_pred_het_dict") + # case 1 + torch.save(model.state_dict(), filepath) + # case 2 + filepath = os.path.join(dir, "GNN_state_pred_het_full") + torch.save(model, filepath) -if __name__ == '__main__': +if __name__ == "__main__": PredictStateHetGNN() From 45ee79dea533f9bff47007608c2d6e33aa9a78ff Mon Sep 17 00:00:00 2001 From: Ekaterina Shemetova Date: Fri, 17 Feb 2023 08:26:21 +0300 Subject: [PATCH 3/7] remove extra game.py --- VSharp.ML.AIAgent/ml/game.py | 89 ------------------------------------ 1 file changed, 89 deletions(-) delete mode 100644 VSharp.ML.AIAgent/ml/game.py diff --git a/VSharp.ML.AIAgent/ml/game.py b/VSharp.ML.AIAgent/ml/game.py deleted file mode 100644 index ade8d83ef..000000000 --- a/VSharp.ML.AIAgent/ml/game.py +++ /dev/null @@ -1,89 +0,0 @@ -from dataclasses import dataclass -from dataclasses_json import dataclass_json -from typing import List - - -@dataclass_json -@dataclass -class State: - Id: int - Position: int - PredictedUsefulness: float - PathConditionSize: int - VisitedAgainVertices: int - VisitedNotCoveredVerticesInZone: int - VisitedNotCoveredVerticesOutOfZone: int - History: List[int] - Children: List[int] - - def __hash__(self) -> int: - return self.Id.__hash__() - - -@dataclass_json -@dataclass -class GameMapVertex: - Uid: int - Id: int - InCoverageZone: bool - BasicBlockSize: int - CoveredByTest: bool - VisitedByState: bool - TouchedByState: bool - States: List[State] - - -@dataclass_json -@dataclass -class GameEdgeLabel: - Token: int - - -@dataclass_json -@dataclass -class GameMapEdge: - VertexFrom: GameMapVertex - VertexTo: GameMapVertex - Label: GameEdgeLabel - - -@dataclass_json -@dataclass -class GameState: - Map: List[GameMapEdge] - - -@dataclass_json -@dataclass -class GameMap: - Id: int - CoverageToStart: int - AssemblyFullName: str - CoverageZone: bool - NameOfObjectToCover: str - - -@dataclass_json -@dataclass -class MoveReward: - ForCoverage: int - ForVisitedInstructions: int - - def __eq__(self, __o: "MoveReward") -> bool: - if type(self) != type(__o): - return RuntimeError(f"Can't compare {type(self)} with {type(__o)}") - - def __add__(self, __o: "MoveReward") -> "MoveReward": - if type(self) != type(__o): - return RuntimeError(f"Can't add {type(__o)} to {type(self)}") - return MoveReward( - self.ForCoverage + __o.ForCoverage, - self.ForVisitedInstructions + __o.ForVisitedInstructions, - ) - - -@dataclass_json -@dataclass -class Reward: - ForMove: MoveReward - MaxPossibleReward: int From 2a18d21aa8e61825bd20355583cc60a878c2d724 Mon Sep 17 00:00:00 2001 From: katyacyfra Date: Fri, 3 Nov 2023 19:01:54 +0300 Subject: [PATCH 4/7] added models;fix data loader --- VSharp.ML.AIAgent/ml/data_loader_compact.py | 8 +- .../GNN_state_pred_het_dict_RCGN_Sage_64_100e | Bin 0 -> 97083 bytes .../GNN_state_pred_het_full_RCGN_Sage_64_100e | Bin 0 -> 104699 bytes .../GNN_state_pred_het_dict_RCGN_Sage_64_20e | Bin 0 -> 97083 bytes .../GNN_state_pred_het_full_RCGN_Sage_64_20e | Bin 0 -> 104699 bytes .../GNN_state_pred_het_dict_RCGN_Sage_64_50e | Bin 0 -> 97083 bytes .../GNN_state_pred_het_full_RCGN_Sage_64_50e | Bin 0 -> 104699 bytes .../EdgeTypeRGCNSageSubgraphs64ch/description | 210 ++++++++++++++++++ .../EdgeTypeRGCNSageSubgraphs64ch/model.py | 49 ++++ VSharp.ML.AIAgent/ml/models/README.md | 32 +++ .../GNN_state_pred_het_dict_compact | Bin 0 -> 153392 bytes .../GNN_state_pred_het_full_compact | Bin 0 -> 166256 bytes .../description | 56 +++++ .../StateGNNEncoderConvEdgeAttrBasic/model.py | 64 ++++++ ..._het_dict_StateGNNEncoderConvEdgeAttr_32ch | Bin 0 -> 48304 bytes ..._het_full_StateGNNEncoderConvEdgeAttr_32ch | Bin 0 -> 61168 bytes .../description | 56 +++++ .../model.py | 64 ++++++ ...t__StateGNNEncoderConvEdgeAttr_state_after | Bin 0 -> 58288 bytes ...ll_StateGNNEncoderConvEdgeAttr_state_after | Bin 0 -> 71152 bytes .../description | 56 +++++ .../StateGNNEncoderConvEdgeAttrSPA/model.py | 62 ++++++ .../20e/GNN_state_pred_het_dict | Bin 0 -> 40892 bytes .../20e/GNN_state_pred_het_full | Bin 0 -> 53948 bytes .../50e/GNN_state_pred_het_dict | Bin 0 -> 40892 bytes .../50e/GNN_state_pred_het_full | Bin 0 -> 53948 bytes .../StatesAfterAllCompact32ch/description | 113 ++++++++++ .../models/StatesAfterAllCompact32ch/model.py | 50 +++++ 28 files changed, 818 insertions(+), 2 deletions(-) create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/100e/GNN_state_pred_het_dict_RCGN_Sage_64_100e create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/100e/GNN_state_pred_het_full_RCGN_Sage_64_100e create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/20e/GNN_state_pred_het_dict_RCGN_Sage_64_20e create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/20e/GNN_state_pred_het_full_RCGN_Sage_64_20e create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/50e/GNN_state_pred_het_dict_RCGN_Sage_64_50e create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/50e/GNN_state_pred_het_full_RCGN_Sage_64_50e create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description create mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py create mode 100644 VSharp.ML.AIAgent/ml/models/README.md create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/GNN_state_pred_het_dict_compact create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/GNN_state_pred_het_full_compact create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/description create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/GNN_state_pred_het_dict_StateGNNEncoderConvEdgeAttr_32ch create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/GNN_state_pred_het_full_StateGNNEncoderConvEdgeAttr_32ch create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/description create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/model.py create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrSPA/GNN_state_pred_het_dict__StateGNNEncoderConvEdgeAttr_state_after create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrSPA/GNN_state_pred_het_full_StateGNNEncoderConvEdgeAttr_state_after create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrSPA/description create mode 100644 VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrSPA/model.py create mode 100644 VSharp.ML.AIAgent/ml/models/StatesAfterAllCompact32ch/20e/GNN_state_pred_het_dict create mode 100644 VSharp.ML.AIAgent/ml/models/StatesAfterAllCompact32ch/20e/GNN_state_pred_het_full create mode 100644 VSharp.ML.AIAgent/ml/models/StatesAfterAllCompact32ch/50e/GNN_state_pred_het_dict create mode 100644 VSharp.ML.AIAgent/ml/models/StatesAfterAllCompact32ch/50e/GNN_state_pred_het_full create mode 100644 VSharp.ML.AIAgent/ml/models/StatesAfterAllCompact32ch/description create mode 100644 VSharp.ML.AIAgent/ml/models/StatesAfterAllCompact32ch/model.py diff --git a/VSharp.ML.AIAgent/ml/data_loader_compact.py b/VSharp.ML.AIAgent/ml/data_loader_compact.py index 778e90049..95e5f0610 100644 --- a/VSharp.ML.AIAgent/ml/data_loader_compact.py +++ b/VSharp.ML.AIAgent/ml/data_loader_compact.py @@ -46,6 +46,7 @@ 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 = [] @@ -79,7 +80,8 @@ def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int ) edges_attr_v_v.append( np.array([e.Label.Token]) - ) # TODO: consider token in a model + ) + edges_types_v_v.append(e.Label.Token) state_doubles = 0 @@ -101,7 +103,7 @@ def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int ) ) # history edges: 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a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description new file mode 100644 index 000000000..54c02ebba --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description @@ -0,0 +1,210 @@ +=========================================================================== +Layer (type:depth-idx) Param # +=========================================================================== +StateModelEncoder -- +├─StateGNNEncoderConvEdgeAttrGraphConvAll: 1-1 -- +│ └─RGCNConv: 2-1 1,344 +│ │ └─MeanAggregation: 3-1 -- +│ └─SAGEConv: 2-2 -- +│ │ └─MeanAggregation: 3-2 -- +│ │ └─Linear: 3-3 4,160 +│ │ └─Linear: 3-4 4,096 +│ └─GraphConv: 2-3 -- +│ │ └─SumAggregation: 3-5 -- +│ │ └─Linear: 3-6 4,160 +│ │ └─Linear: 3-7 384 +│ └─SAGEConv: 2-4 -- +│ │ └─MeanAggregation: 3-8 -- +│ │ └─Linear: 3-9 4,160 +│ │ └─Linear: 3-10 4,096 +│ └─Linear: 2-5 520 +=========================================================================== +Total params: 22,920 +Trainable params: 22,920 +Non-trainable params: 0 +=========================================================================== +20 epochs +====================================================================== +Number of training graphs: 11159 +Number of test graphs: 1969 +Epoch: 001, Train Loss: 0.090079, Test Loss: 0.093262 +Epoch: 002, Train Loss: 0.045897, Test Loss: 0.048197 +Epoch: 003, Train Loss: 0.031259, Test Loss: 0.032865 +Epoch: 004, Train Loss: 0.026366, Test Loss: 0.027732 +Epoch: 005, Train Loss: 0.023653, Test Loss: 0.024858 +Epoch: 006, Train Loss: 0.019886, Test Loss: 0.021016 +Epoch: 007, Train Loss: 0.017092, Test Loss: 0.018127 +Epoch: 008, Train Loss: 0.016015, Test Loss: 0.016991 +Epoch: 009, Train Loss: 0.014284, Test Loss: 0.015263 +Epoch: 010, Train Loss: 0.013691, Test Loss: 0.014667 +Epoch: 011, Train Loss: 0.013650, Test Loss: 0.014604 +Epoch: 012, Train Loss: 0.012289, Test Loss: 0.013232 +Epoch: 013, Train Loss: 0.012020, Test Loss: 0.012884 +Epoch: 014, Train Loss: 0.011039, Test Loss: 0.011909 +Epoch: 015, Train Loss: 0.010677, Test Loss: 0.011484 +Epoch: 016, Train Loss: 0.010178, Test Loss: 0.011045 +Epoch: 017, Train Loss: 0.010390, Test Loss: 0.011162 +Epoch: 018, Train Loss: 0.009788, Test Loss: 0.010556 +Epoch: 019, Train Loss: 0.009483, Test Loss: 0.010253 +Epoch: 020, Train Loss: 0.009276, Test Loss: 0.009997 +====================================================================== +50 epochs +====================================================================== +Number of training graphs: 11159 +Number of test graphs: 1969 +Epoch: 001, Train Loss: 0.106262, Test Loss: 0.101664 +Epoch: 002, Train Loss: 0.045628, Test Loss: 0.045041 +Epoch: 003, Train Loss: 0.029963, Test Loss: 0.029063 +Epoch: 004, Train Loss: 0.025636, Test Loss: 0.024923 +Epoch: 005, Train Loss: 0.022738, Test Loss: 0.022126 +Epoch: 006, Train Loss: 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Test Loss: 0.008158 +Epoch: 025, Train Loss: 0.007669, Test Loss: 0.007761 +Epoch: 026, Train Loss: 0.007696, Test Loss: 0.007773 +Epoch: 027, Train Loss: 0.007788, Test Loss: 0.007853 +Epoch: 028, Train Loss: 0.007624, Test Loss: 0.007743 +Epoch: 029, Train Loss: 0.007382, Test Loss: 0.007479 +Epoch: 030, Train Loss: 0.007387, Test Loss: 0.007493 +Epoch: 031, Train Loss: 0.007157, Test Loss: 0.007255 +Epoch: 032, Train Loss: 0.007329, Test Loss: 0.007456 +Epoch: 033, Train Loss: 0.007319, Test Loss: 0.007407 +Epoch: 034, Train Loss: 0.007313, Test Loss: 0.007414 +Epoch: 035, Train Loss: 0.007181, Test Loss: 0.007287 +Epoch: 036, Train Loss: 0.006902, Test Loss: 0.006980 +Epoch: 037, Train Loss: 0.006935, Test Loss: 0.007019 +Epoch: 038, Train Loss: 0.006700, Test Loss: 0.006774 +Epoch: 039, Train Loss: 0.006823, Test Loss: 0.006906 +Epoch: 040, Train Loss: 0.006726, Test Loss: 0.006817 +Epoch: 041, Train Loss: 0.006584, Test Loss: 0.006677 +Epoch: 042, Train Loss: 0.006661, Test Loss: 0.006756 +Epoch: 043, Train Loss: 0.006629, Test Loss: 0.006735 +Epoch: 044, Train Loss: 0.006494, Test Loss: 0.006589 +Epoch: 045, Train Loss: 0.006597, Test Loss: 0.006696 +Epoch: 046, Train Loss: 0.006693, Test Loss: 0.006806 +Epoch: 047, Train Loss: 0.006161, Test Loss: 0.006259 +Epoch: 048, Train Loss: 0.006176, Test Loss: 0.006275 +Epoch: 049, Train Loss: 0.006586, Test Loss: 0.006698 +Epoch: 050, Train Loss: 0.006144, Test Loss: 0.006247 +====================================================================== +100 epochs +====================================================================== +Number of training graphs: 11159 +Number of test graphs: 1969 +Epoch: 001, Train Loss: 0.088840, Test Loss: 0.090856 +Epoch: 002, Train Loss: 0.048259, Test Loss: 0.049335 +Epoch: 003, Train Loss: 0.033102, Test Loss: 0.033849 +Epoch: 004, Train Loss: 0.027116, Test Loss: 0.027763 +Epoch: 005, Train Loss: 0.022399, Test Loss: 0.022726 +Epoch: 006, Train Loss: 0.020036, Test Loss: 0.020331 +Epoch: 007, Train Loss: 0.017806, Test Loss: 0.018003 +Epoch: 008, Train Loss: 0.016133, Test Loss: 0.016251 +Epoch: 009, Train Loss: 0.015785, Test Loss: 0.015922 +Epoch: 010, Train Loss: 0.014697, Test Loss: 0.014795 +Epoch: 011, Train Loss: 0.013341, Test Loss: 0.013371 +Epoch: 012, Train Loss: 0.012576, Test Loss: 0.012609 +Epoch: 013, Train Loss: 0.012214, Test Loss: 0.012219 +Epoch: 014, Train Loss: 0.011537, Test Loss: 0.011597 +Epoch: 015, Train Loss: 0.011197, Test Loss: 0.011236 +Epoch: 016, Train Loss: 0.010738, Test Loss: 0.010762 +Epoch: 017, Train Loss: 0.010890, Test Loss: 0.010922 +Epoch: 018, Train Loss: 0.010255, Test Loss: 0.010338 +Epoch: 019, Train Loss: 0.009713, Test Loss: 0.009780 +Epoch: 020, Train Loss: 0.009852, Test Loss: 0.009901 +Epoch: 021, Train Loss: 0.009622, Test Loss: 0.009652 +Epoch: 022, Train Loss: 0.009537, Test Loss: 0.009565 +Epoch: 023, Train Loss: 0.008998, Test Loss: 0.009045 +Epoch: 024, Train Loss: 0.008879, Test Loss: 0.008931 +Epoch: 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Test Loss: 0.005166 +Epoch: 080, Train Loss: 0.005217, Test Loss: 0.005319 +Epoch: 081, Train Loss: 0.004961, Test Loss: 0.005047 +Epoch: 082, Train Loss: 0.005292, Test Loss: 0.005405 +Epoch: 083, Train Loss: 0.005039, Test Loss: 0.005148 +Epoch: 084, Train Loss: 0.004908, Test Loss: 0.005010 +Epoch: 085, Train Loss: 0.005187, Test Loss: 0.005285 +Epoch: 086, Train Loss: 0.004868, Test Loss: 0.004969 +Epoch: 087, Train Loss: 0.005173, Test Loss: 0.005261 +Epoch: 088, Train Loss: 0.004844, Test Loss: 0.004941 +Epoch: 089, Train Loss: 0.005036, Test Loss: 0.005125 +Epoch: 090, Train Loss: 0.004846, Test Loss: 0.004941 +Epoch: 091, Train Loss: 0.004800, Test Loss: 0.004897 +Epoch: 092, Train Loss: 0.005034, Test Loss: 0.005135 +Epoch: 093, Train Loss: 0.004912, Test Loss: 0.005009 +Epoch: 094, Train Loss: 0.004924, Test Loss: 0.005011 +Epoch: 095, Train Loss: 0.004784, Test Loss: 0.004886 +Epoch: 096, Train Loss: 0.004772, Test Loss: 0.004874 +Epoch: 097, Train Loss: 0.004694, Test Loss: 0.004792 +Epoch: 098, Train Loss: 0.004661, Test Loss: 0.004745 +Epoch: 099, Train Loss: 0.004699, Test Loss: 0.004792 +Epoch: 100, Train Loss: 0.004633, Test Loss: 0.004714 + diff --git a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py new file mode 100644 index 000000000..b67c4797a --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py @@ -0,0 +1,49 @@ +import torch +from torch.nn import Linear +from torch_geometric.nn import TAGConv, GraphConv, SAGEConv + + +class StateGNNEncoderConvEdgeAttrGraphConvAll(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = RGCNConv(5, hidden_channels, 3) + self.conv2 = SAGEConv(hidden_channels, hidden_channels) + self.conv3 = GraphConv((-1, -1), hidden_channels) + self.conv4 = SAGEConv((-1, -1), hidden_channels) + self.lin = Linear(hidden_channels, out_channels) + + def forward(self, x_dict, edge_index_dict, edge_attr=None, edge_type=None): + game_x = self.conv1( + x_dict['game_vertex'], + edge_index_dict[('game_vertex', 'to', 'game_vertex')], + edge_type + ).relu() + + state_x = self.conv3( + (game_x, x_dict['state_vertex']), + edge_index_dict[('game_vertex', 'history', 'state_vertex')], + edge_attr[('game_vertex', 'history', 'state_vertex')], + ).relu() + + state_x = self.conv4( + (game_x, state_x), + edge_index_dict[('game_vertex', 'in', 'state_vertex')], + ).relu() + + state_x = self.conv2( + state_x, + edge_index_dict[('state_vertex', 'parent_of', 'state_vertex')], + ).relu() + + return self.lin(state_x) + +class StateModelEncoder(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.state_encoder = StateGNNEncoderConvEdgeAttrGraphConvAll(hidden_channels, out_channels) + + def forward(self, x_dict, edge_index_dict, edge_attr=None): + z_dict = {} + z_dict['state_vertex'] = self.state_encoder(x_dict, edge_index_dict, edge_attr, edge_type) + z_dict['game_vertex'] = x_dict['game_vertex'] + return z_dict diff --git a/VSharp.ML.AIAgent/ml/models/README.md b/VSharp.ML.AIAgent/ml/models/README.md new file mode 100644 index 000000000..a515da2cc --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/README.md @@ -0,0 +1,32 @@ + + +## Pretrained Models Summary Balanced Dataset + +| Name | Train Loss | Test Loss |#parameters|#epochs|#hidden|lr|Comments| +|--|--|--|--|--|--|--|--| +|[EdgeTypeRGCNSageSubgraphs64ch](./models/EdgeTypeRGCNSageSubgraphs64ch/) |0.009276 |0.009997 |22,920|20|64|0.0001|| +|[EdgeTypeRGCNSageSubgraphs64ch](./models/EdgeTypeRGCNSageSubgraphs64ch/) |0.006144 |0.006247 |22,920|50|64|0.0001|| +|[EdgeTypeRGCNSageSubgraphs64ch](./models/EdgeTypeRGCNSageSubgraphs64ch/) |0.004633 |0.004714 |22,920|100|64|0.0001|| + + +## Inference +```python +model = StateModelEncoder(hidden_channels=#hidden, out_channels=8) +``` + + +## Pretrained Models Summary Old + +| Name | Train Loss | Test Loss |#parameters|#epochs|#hidden|lr|Comments| +|--|--|--|--|--|--|--|--| +|[StateGNNEncoderConvEdgeAttrSPA](./models/StateGNNEncoderConvEdgeAttrSPA/) |0.006074 |0.006214 |12,392|20|32|0.0001|| +|[StateGNNEncoderConvEdgeAttrBasic32Ch](./models/StateGNNEncoderConvEdgeAttrBasic32Ch/) |0.007862 |0.007962 |9,896|20|32|0.0001|| +|[StateGNNEncoderConvEdgeAttrBasic](./models/StateGNNEncoderConvEdgeAttrBasic/) |0.007512 |0.007499 |36,168|20|64|0.0001|| +|[StatesAfterAllCompact32ch](./models/StatesAfterAllCompact32ch/) |0.006934 |0.006977 |8,296|20|32|0.0001|| +|[StatesAfterAllCompact32ch](./models/StatesAfterAllCompact32ch/) |0.006135 |0.006215 |8,296|50|32|0.0001|| + +## Inference +```python +model = StateModelEncoder(hidden_channels=#hidden, out_channels=8) +``` + diff --git a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/GNN_state_pred_het_dict_compact b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/GNN_state_pred_het_dict_compact new file mode 100644 index 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+================================================================= +StateModelEncoder -- +├─StateGNNEncoderConvEdgeAttr: 1-1 -- +│ └─TAGConv: 2-1 64 +│ │ └─SumAggregation: 3-1 -- +│ │ └─ModuleList: 3-2 960 +│ └─TAGConv: 2-2 64 +│ │ └─SumAggregation: 3-3 -- +│ │ └─ModuleList: 3-4 1,536 +│ └─GraphConv: 2-3 -- +│ │ └─SumAggregation: 3-5 -- +│ │ └─Linear: 3-6 4,160 +│ │ └─Linear: 3-7 4,096 +│ └─GraphConv: 2-4 -- +│ │ └─SumAggregation: 3-8 -- +│ │ └─Linear: 3-9 4,160 +│ │ └─Linear: 3-10 4,096 +│ └─SAGEConv: 2-5 -- +│ │ └─MeanAggregation: 3-11 -- +│ │ └─Linear: 3-12 4,160 +│ │ └─Linear: 3-13 4,096 +│ └─SAGEConv: 2-6 -- +│ │ └─MeanAggregation: 3-14 -- +│ │ └─Linear: 3-15 4,160 +│ │ └─Linear: 3-16 4,096 +│ └─Linear: 2-7 520 +================================================================= +Total params: 36,168 +Trainable params: 36,168 +Non-trainable params: 0 +================================================================= +================================================================= +Number of training graphs: 170186 +Number of test graphs: 30033 +Epoch: 001, Train Loss: 0.021431, Test Loss: 0.021463 +Epoch: 002, Train Loss: 0.014803, Test Loss: 0.014820 +Epoch: 003, Train Loss: 0.012827, Test Loss: 0.012861 +Epoch: 004, Train Loss: 0.011392, Test Loss: 0.011396 +Epoch: 005, Train Loss: 0.011159, Test Loss: 0.011139 +Epoch: 006, Train Loss: 0.009657, Test Loss: 0.009621 +Epoch: 007, Train Loss: 0.010212, Test Loss: 0.010188 +Epoch: 008, Train Loss: 0.009641, Test Loss: 0.009652 +Epoch: 009, Train Loss: 0.008862, Test Loss: 0.008845 +Epoch: 010, Train Loss: 0.008803, Test Loss: 0.008784 +Epoch: 011, Train Loss: 0.008282, Test Loss: 0.008267 +Epoch: 012, Train Loss: 0.008095, Test Loss: 0.008067 +Epoch: 013, Train Loss: 0.007825, Test Loss: 0.007809 +Epoch: 014, Train Loss: 0.007740, Test Loss: 0.007749 +Epoch: 015, Train Loss: 0.007546, Test Loss: 0.007539 +Epoch: 016, Train Loss: 0.007591, Test Loss: 0.007591 +Epoch: 017, Train Loss: 0.007772, Test Loss: 0.007759 +Epoch: 018, Train Loss: 0.007730, Test Loss: 0.007716 +Epoch: 019, Train Loss: 0.007357, Test Loss: 0.007346 +Epoch: 020, Train Loss: 0.007512, Test Loss: 0.007499 diff --git a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py new file mode 100644 index 000000000..e26f9dcf9 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py @@ -0,0 +1,64 @@ +import torch +from torch.nn import Linear +from torch_geometric.nn import TAGConv, GraphConv, SAGEConv + + +class StateGNNEncoderConvEdgeAttr(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = TAGConv(5, hidden_channels, 2) + self.conv2 = TAGConv(6, hidden_channels, 3) #TAGConv + self.conv3 = GraphConv((-1, -1), hidden_channels) # SAGEConv + self.conv32 = GraphConv((-1, -1), hidden_channels) + self.conv4 = SAGEConv((-1, -1), hidden_channels) + self.conv42 = SAGEConv((-1, -1), hidden_channels) + self.lin = Linear(hidden_channels, out_channels) + + def forward(self, x_dict, edge_index_dict, edge_attr=None): + game_x = self.conv1( + x_dict['game_vertex'], + edge_index_dict[('game_vertex', 'to', 'game_vertex')], + ).relu() + + + state_x = self.conv2( + x_dict['state_vertex'], + edge_index_dict[('state_vertex', 'parent_of', 'state_vertex')], + ).relu() + + + state_x = self.conv3( + (game_x, state_x), + edge_index_dict[('game_vertex', 'history', 'state_vertex')], + edge_attr[('game_vertex', 'history', 'state_vertex')], + ).relu() + + state_x = self.conv32( + (game_x, state_x), + edge_index_dict[('game_vertex', 'history', 'state_vertex')], + edge_attr[('game_vertex', 'history', 'state_vertex')], + ).relu() + + state_x = self.conv4( + (game_x, state_x), + edge_index_dict[('game_vertex', 'in', 'state_vertex')], + ).relu() + + state_x = self.conv42( + (game_x, state_x), + edge_index_dict[('game_vertex', 'in', 'state_vertex')], + ).relu() + + return self.lin(state_x) + + +class StateModelEncoder(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.state_encoder = StateGNNEncoderConvEdgeAttr(hidden_channels, out_channels) + + def forward(self, x_dict, edge_index_dict, edge_attr=None): + z_dict = {} + z_dict['state_vertex'] = self.state_encoder(x_dict, edge_index_dict, edge_attr) + z_dict['game_vertex'] = x_dict['game_vertex'] + return z_dict diff --git a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/GNN_state_pred_het_dict_StateGNNEncoderConvEdgeAttr_32ch b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/GNN_state_pred_het_dict_StateGNNEncoderConvEdgeAttr_32ch new file mode 100644 index 0000000000000000000000000000000000000000..53458d0a07fee220eaec1d525d6336a1eba3f95c GIT binary patch literal 48304 zcmb@t2|QKZ+y8IMTuG*okg*IIGVXO98c2$wB$Oc~4YbiH4U|e`N-|`sgi1;p*z4M) zfi%yhQL|F1G{^sRyTAAU>G!+8_w)Ro=Q*#};q^M_bKdJ(pSAW{*IIk+N$%njA|le# 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z-8Suy=9s3|zlt{R&V2fECL9t|? zYNO>#)-N&i_gy`Zc8T@%4dW2s+c>WMdnm9K`j5x=on~Y_kY&`Lb^HH(OMhl1O&Ww{ zGLV&&=z2Yj+nMi8jcflNlBNvC`eny7ViYC_iE;?84S?AV2K!~tZk2Eu-Tz0`k(C9N9_W;GD@qjB}me*oivL14c`#*ZezKeJ>v48r;) zFn%=D9l+W&2VlG&n(&UL0HxUC? s|2y}8(`gd_{2*8N&y?G4$oIK Date: Fri, 3 Nov 2023 19:31:13 +0300 Subject: [PATCH 5/7] delete old --- VSharp.ML.AIAgent/ml/data_loader.py | 297 ---------------------------- VSharp.ML.AIAgent/ml/models.py | 35 ++-- 2 files changed, 12 insertions(+), 320 deletions(-) delete mode 100644 VSharp.ML.AIAgent/ml/data_loader.py diff --git a/VSharp.ML.AIAgent/ml/data_loader.py b/VSharp.ML.AIAgent/ml/data_loader.py deleted file mode 100644 index b4436b567..000000000 --- a/VSharp.ML.AIAgent/ml/data_loader.py +++ /dev/null @@ -1,297 +0,0 @@ -import argparse -import json -import os.path -from torch_geometric.data import Data, HeteroData -from os import walk -from typing import Dict, Tuple -import torch -import numpy as np - -from common.game import GameState - -# NUM_NODE_FEATURES = 49 -NUM_NODE_FEATURES = 6 -EXPECTED_FILENAME = "expectedResults.txt" - - -class DataLoader: # TODO: inheritance and more ways to load (different predictions) - def __init__(self, data_dir): - self.data_dir = data_dir - self.graph_types_and_expected: Dict[ - str, Dict[int, float] - ] = {} # Dict[example name, Dict[graph №, expected]] - self.graph_types_and_data = {} - self.dataset = [] - self.process_directory(data_dir) - self.__process_files() - - def process_directory(self, data_dir): - example_dirs = next(walk(data_dir), (None, [], None))[1] - print(example_dirs) - for fldr in example_dirs: - fldr_path = os.path.join(data_dir, fldr) - graphs_to_convert = [] - for f in os.listdir(fldr_path): - if f != EXPECTED_FILENAME: - graphs_to_convert.append(f) - else: - self.graph_types_and_expected[fldr] = self.get_expected_values( - fldr_path - ) - graphs_to_convert.sort(key=lambda x: int(x)) - self.graph_types_and_data[fldr] = graphs_to_convert - - def __process_files(self): - for k, v in self.graph_types_and_data.items(): - for file in v: - print(os.path.join(self.data_dir, k, file)) - graph = self.convert_file_to_graph_homo( - os.path.join(self.data_dir, k, file), - self.graph_types_and_expected[k][int(file)], - ) - self.dataset.append(graph) - - @staticmethod - def get_expected_values(fldr_path: str) -> Dict[int, float]: - """Get TotalReachableRewardFromCurrentState for every graph - Headers: GraphID ExpectedStateNumber ExpectedRewardForStep TotalReachableRewardFromCurrentState - """ - expected = {} - with open(os.path.join(fldr_path, EXPECTED_FILENAME)) as f: - next(f) - for line in f: - split = line.split() - expected[int(split[0])] = int(split[-1]) - return expected - - @staticmethod - def convert_file_to_graph_homo(filepath, expected) -> Data: - """File headers: - Nodes: #VertexId InCoverageZone BasicBlockSize CoveredByTest - VisitedByState TouchedByState (State_i_ID State_i_Position)* - Edges: #VertexFrom VertexTo Terminal(0-CFG, 1-Call, 2-Return)""" - nodes = [] - edges = [] - edge_attr_ = [] - with open(filepath) as f: - parse_edges = False - for line in f: - if "#" not in line: # skip headers - if not parse_edges: # parse nodes - arr = np.zeros(NUM_NODE_FEATURES) - split = np.array(line.split()[1:]) - arr[0 : split.size] = split - nodes.append(arr) - else: # parse edges - split = list(map(lambda x: int(x), line.split())) - edges.append(split[:-1]) - edge_attr_.append(split[2]) - else: - if "#Edges" in line: - parse_edges = True - x = torch.tensor(np.array(nodes), dtype=torch.float) - edge_index = torch.tensor(edges, dtype=torch.long) - edge_attr = torch.tensor(np.array(edge_attr_), dtype=torch.long) - data = Data( - x=x, edge_index=edge_index.t().contiguous(), edge_attr=edge_attr, y=expected - ) - return data - - -class ServerDataloaderHetero(DataLoader): - def __init__(self, data_dir): - self.data_dir = data_dir - self.graph_types_and_expected: Dict[ - str, Dict[int, np.array] - ] = {} # Dict[example name, Dict[graph №, expected]] - self.graph_types_and_data = {} - self.dataset = [] - self.process_directory(data_dir) - self.__process_files() - - def __process_files(self): - for k, v in self.graph_types_and_data.items(): - for file in v: - with open(os.path.join(self.data_dir, k, file)) as f: - print(os.path.join(self.data_dir, k, file)) - data = json.load(f) - graph, state_map = self.convert_input_to_tensor( - GameState.from_dict(data) - ) - # add_expected values - expected = self.graph_types_and_expected[k][int(file)][0] - graph.y = state_map[expected] - self.dataset.append(graph) - - def get_expected_values(self, fldr_path: str) -> Dict[int, np.array]: - """Get TotalReachableRewardFromCurrentState for every graph - Headers: GraphID ExpectedStateNumber ExpectedRewardForCoveredInStep ExpectedRewardForVisitedInstructionsInStep - TotalReachableRewardFromCurrentState""" - expected = {} - with open(os.path.join(fldr_path, EXPECTED_FILENAME)) as f: - next(f) - for line in f: - split = line.split() - expected[int(split[0])] = np.array(split[1:], dtype=int) - return expected - - @staticmethod - def convert_input_to_tensor(input: GameState) -> Tuple[HeteroData, Dict[int, int]]: - """ - Converts game env to tensors and state_map - input later can be changed to - """ - mp = input.Map - data = HeteroData() - nodes_vertex_set = set() - nodes_state_set = set() - nodes_vertex = [] - nodes_state = [] - edges_index_v_v = [] - edges_index_s_s = [] - edges_index_s_v = [] - edges_index_v_s = [] - edges_attr_s_v = [] # 0 - history # 1 - state in - edges_attr_v_s = [] - edges_attr_v_v = [] - - state_map: Dict[int, int] = {} # Maps real state id to its index - vertex_map: Dict[int, int] = {} # Maps real vertex id to its index - vertex_index = 0 - state_index = 0 - for m in mp: - # process vertices - vertex_from, vertex_to = m.VertexFrom.__dict__, m.VertexTo.__dict__ - for v in [vertex_from, vertex_to]: - vertex_id = int(v["Id"]) - if vertex_id not in nodes_vertex_set: - nodes_vertex_set.add(vertex_id) - vertex_map[vertex_id] = vertex_index - vertex_index = vertex_index + 1 - nodes_vertex.append( - np.array( - [ - int(v["InCoverageZone"]), - v["BasicBlockSize"], - int(v["CoveredByTest"]), - int(v["VisitedByState"]), - int(v["TouchedByState"]), - ] - ) - ) - # proccess edge - edges_index_v_v.append( - np.array( - [ - vertex_map[int(vertex_from["Id"])], - vertex_map[int(vertex_to["Id"])], - ] - ) - ) - edges_attr_v_v.append(np.array([m.Label.Token])) - # dealing with states - # nodes_number = 0 #unique id for every node! - states_info = {} - for m in mp: - vertex_from, vertex_to = m.VertexFrom.__dict__, m.VertexTo.__dict__ - for v in [vertex_from, vertex_to]: - vertex_id = v["Id"] - states = v["States"] - if states: # proccess states independently - for s in states: - dct = s.__dict__ - sid = int(dct["Id"]) - states_info[sid] = dct - if sid not in nodes_state_set: - nodes_state_set.add(sid) - state_map[sid] = state_index - state_index = state_index + 1 - nodes_state.append( - np.array( - [ - dct["Position"], - dct["PredictedUsefulness"], - dct["PathConditionSize"], - dct["VisitedAgainVertices"], - dct["VisitedNotCoveredVerticesInZone"], - dct["VisitedNotCoveredVerticesOutOfZone"], - ] - ) - ) - # fix state position - edges_index_s_v.append( - np.array([state_map[sid], vertex_map[vertex_id]]) - ) - edges_attr_s_v.append(np.array(1)) - edges_index_v_s.append( - np.array([vertex_map[vertex_id], state_map[sid]]) - ) - edges_attr_v_s.append(np.array(1)) - # children edges - history = dct["History"] - if history: - for h in history: - edges_index_s_v.append( - np.array([state_map[sid], vertex_map[int(h)]]) - ) - edges_attr_s_v.append(np.array(0)) - # children edges --- add after all states - for k, v in states_info.items(): - children = v["Children"] - if children: - for c in children: - edges_index_s_s.append(np.array([state_map[k], state_map[int(c)]])) - - # nodes_vertex = sorted(nodes_vertex, key=itemgetter(0)) - # nodes_state = sorted(nodes_state, key=itemgetter(0)) - 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 = ( - torch.tensor(np.array(edges_index_v_v), dtype=torch.long).t().contiguous() - ) - data["state_vertex", "to", "game_vertex"].edge_index = ( - torch.tensor(np.array(edges_index_s_v), dtype=torch.long).t().contiguous() - ) - data["game_vertex", "to", "state_vertex"].edge_index = ( - torch.tensor(np.array(edges_index_v_s), dtype=torch.long).t().contiguous() - ) - if edges_index_s_s: - data["state_vertex", "parent_of", "state_vertex"].edge_index = ( - torch.tensor(np.array(edges_index_s_s), dtype=torch.long) - .t() - .contiguous() - ) - # print(data['state', 'parent_of', 'state'].edge_index) - data["game_vertex", "to", "game_vertex"].edge_attr = torch.tensor( - np.array(edges_attr_v_v), dtype=torch.long - ) - data["state_vertex", "to", "game_vertex"].edge_attr = torch.tensor( - np.array(edges_attr_s_v), dtype=torch.long - ) - - # data.state_map = state_map - return data, state_map - - -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(): - dl = DataLoader("../../GNN_V#/") - return dl.dataset - - -def get_data_hetero(): - dl = ServerDataloaderHetero("../../../GNN_V#/Serialized_test_simple") - return dl.dataset - - -if __name__ == "__main__": - get_data_hetero() diff --git a/VSharp.ML.AIAgent/ml/models.py b/VSharp.ML.AIAgent/ml/models.py index fd5890858..45841c3b7 100644 --- a/VSharp.ML.AIAgent/ml/models.py +++ b/VSharp.ML.AIAgent/ml/models.py @@ -1,5 +1,4 @@ import torch -<<<<<<< HEAD import torch.nn.functional as F from torch import nn from torch.nn import Linear @@ -188,30 +187,23 @@ def forward(self, x_dict, edge_index_dict): x_dict = conv(x_dict, edge_index_dict) x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()} return self.lin(x_dict["author"]) -======= -from torch_geometric.nn import GATConv, Linear -from .data_loader import NUM_NODE_FEATURES -from torch.nn import Linear -import torch.nn.functional as F -from torch_geometric.nn import GCNConv, SAGEConv -from torch_geometric.nn import global_mean_pool - -NUM_PREDICTED_VALUES = 4 ->>>>>>> 45ee79dea533f9bff47007608c2d6e33aa9a78ff - - -class GNN_Het(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): +class GCN_SimpleNoEdgeLabels(torch.nn.Module): + def __init__(self): super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) + self.conv1 = GCNConv(NUM_NODE_FEATURES, 16) + self.conv2 = GCNConv(16, 1) - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() + def forward(self, data): + x, edge_index = data.x, data.edge_index + + x = self.conv1(x, edge_index) + x = F.relu(x) + x = F.dropout(x, training=self.training) x = self.conv2(x, edge_index) - return x + + return F.log_softmax(x, dim=1) class GNN_MultipleOutput(torch.nn.Module): @@ -347,7 +339,6 @@ def forward(self, x, edge_index): x = x.relu() x = self.conv2(x, edge_index) + self.lin2(x) return x -<<<<<<< HEAD class VertexGNNEncoder(torch.nn.Module): @@ -772,5 +763,3 @@ def forward(self, x_dict, edge_index_dict, edge_attr_dict): ).relu() x = self.mlp(in_x) return x -======= ->>>>>>> 45ee79dea533f9bff47007608c2d6e33aa9a78ff From d4f9bad96bac5c3607b5f2d45cf38e2dac0964b1 Mon Sep 17 00:00:00 2001 From: katyacyfra Date: Wed, 8 Nov 2023 20:55:47 +0300 Subject: [PATCH 6/7] fix codestyle --- VSharp.ML.AIAgent/ml/data_loader_compact.py | 47 +- VSharp.ML.AIAgent/ml/models.py | 765 ------------------ .../GNN_state_pred_het_dict_RCGN_Sage_64_100e | Bin 97083 -> 0 bytes .../GNN_state_pred_het_full_RCGN_Sage_64_100e | Bin 104699 -> 0 bytes .../GNN_state_pred_het_dict_RCGN_Sage_64_20e | Bin 97083 -> 0 bytes .../GNN_state_pred_het_full_RCGN_Sage_64_20e | Bin 104699 -> 0 bytes .../GNN_state_pred_het_dict_RCGN_Sage_64_50e | Bin 97083 -> 0 bytes .../GNN_state_pred_het_full_RCGN_Sage_64_50e | Bin 104699 -> 0 bytes .../EdgeTypeRGCNSageSubgraphs64ch/description | 210 ----- .../EdgeTypeRGCNSageSubgraphs64ch/model.py | 49 -- .../GNN_state_pred_het_dict_compact | Bin 153392 -> 0 bytes .../GNN_state_pred_het_full_compact | Bin 166256 -> 0 bytes .../description | 56 -- .../StateGNNEncoderConvEdgeAttrBasic/model.py | 64 -- ..._het_dict_StateGNNEncoderConvEdgeAttr_32ch | Bin 48304 -> 0 bytes ..._het_full_StateGNNEncoderConvEdgeAttr_32ch | Bin 61168 -> 0 bytes .../description | 56 -- .../model.py | 64 -- ...t__StateGNNEncoderConvEdgeAttr_state_after | Bin 58288 -> 0 bytes ...ll_StateGNNEncoderConvEdgeAttr_state_after | Bin 71152 -> 0 bytes .../description | 56 -- .../StateGNNEncoderConvEdgeAttrSPA/model.py | 62 -- .../20e/GNN_state_pred_het_dict | Bin 40892 -> 0 bytes .../20e/GNN_state_pred_het_full | Bin 53948 -> 0 bytes .../50e/GNN_state_pred_het_dict | Bin 40892 -> 0 bytes .../50e/GNN_state_pred_het_full | Bin 53948 -> 0 bytes .../StatesAfterAllCompact32ch/description | 113 --- .../models/StatesAfterAllCompact32ch/model.py | 50 -- .../32ch/20e/GNN_state_pred_het_dict | Bin 0 -> 38946 bytes .../32ch/20e/GNN_state_pred_het_full | Bin 0 -> 48674 bytes .../models/TAGSageSimple/32ch/20e/train_res | 20 + .../ml/models/TAGSageSimple/model.py | 47 ++ .../ml/predict_state_vector_hetero.py | 155 ++-- VSharp.ML.AIAgent/predict_state_hetero.py | 135 ---- VSharp.ML.AIAgent/pretrain.py | 16 + 35 files changed, 175 insertions(+), 1790 deletions(-) delete mode 100644 VSharp.ML.AIAgent/ml/models.py delete mode 100644 VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/100e/GNN_state_pred_het_dict_RCGN_Sage_64_100e delete mode 100644 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create mode 100644 VSharp.ML.AIAgent/ml/models/TAGSageSimple/model.py delete mode 100644 VSharp.ML.AIAgent/predict_state_hetero.py create mode 100644 VSharp.ML.AIAgent/pretrain.py diff --git a/VSharp.ML.AIAgent/ml/data_loader_compact.py b/VSharp.ML.AIAgent/ml/data_loader_compact.py index 95e5f0610..135998206 100644 --- a/VSharp.ML.AIAgent/ml/data_loader_compact.py +++ b/VSharp.ML.AIAgent/ml/data_loader_compact.py @@ -1,4 +1,3 @@ -import argparse import json import os.path import pickle @@ -78,9 +77,7 @@ 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]) - ) + edges_attr_v_v.append(np.array([e.Label.Token])) edges_types_v_v.append(e.Label.Token) state_doubles = 0 @@ -103,7 +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: + 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])) @@ -126,17 +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['game_vertex', 'to', 'game_vertex'].edge_attr = torch.tensor(np.array(edges_attr_v_v), dtype=torch.long) - 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 = ( + data["game_vertex_to_game_vertex"].edge_attr = torch.tensor( + np.array(edges_attr_v_v), dtype=torch.long + ) + 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() @@ -153,24 +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) @@ -246,19 +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 diff --git a/VSharp.ML.AIAgent/ml/models.py b/VSharp.ML.AIAgent/ml/models.py deleted file mode 100644 index 45841c3b7..000000000 --- a/VSharp.ML.AIAgent/ml/models.py +++ /dev/null @@ -1,765 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn -from torch.nn import Linear -from torch_geometric.nn import ( - ARMAConv, - FeaStConv, - GATConv, - GCNConv, - GraphConv, - HeteroConv, - Linear, - ResGatedGraphConv, - SAGEConv, - TAGConv, - TransformerConv, - global_mean_pool, - to_hetero, -) - -from torchvision.ops import MLP - - -from learning.timer.wrapper import timeit - -NUM_PREDICTED_VALUES = 4 - - -class ARMANet(torch.nn.Module): - def __init__(self, hidden_channels, num_classes, num_stacks=1, num_layers=1): - super(ARMANet, self).__init__() - - self.conv1 = ARMAConv( - -1, - out_channels=hidden_channels, - num_stacks=num_stacks, - num_layers=num_layers, - ) - - self.conv2 = ARMAConv( - -1, - out_channels=hidden_channels, - num_stacks=num_stacks, - num_layers=num_layers, - ) - - self.fc1 = nn.Linear(64, num_classes) - - """def forward(self, x, edge_index): - x = F.relu(self.conv1(x, edge_index)) - x = F.relu(self.conv2(x, edge_index)) - #x = global_mean_pool(x, x.batch) - x = F.dropout(x) - x = self.fc1(x) - return x""" - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() - x = self.conv2(x, edge_index) - return x - - -class GATNet(torch.nn.Module): - def __init__(self, in_channels, hidden_channels=512, num_classes=2): - super(GATNet, self).__init__() - - # self.fc0 = nn.Linear(in_channels, hidden_channels) - self.conv1 = GATConv(in_channels, hidden_channels) - self.conv2 = GATConv(hidden_channels, 64) - self.fc1 = nn.Linear(64, num_classes) - - self.reset_parameters() - - def reset_parameters(self): - self.conv1.reset_parameters() - self.conv2.reset_parameters() - - def forward(self, x, edge_index): - # x = F.relu(self.fc0(x)) - x = F.relu(self.conv1(x, edge_index)) - x = F.relu(self.conv2(x, edge_index)) - # x = global_mean_pool(x, data.batch) - x = F.dropout(x, training=self.training) - x = self.fc1(x) - return x - - -class FeaStNet(torch.nn.Module): - def __init__( - self, in_channels, hidden_channels=512, num_classes=2, heads=1, t_inv=True - ): - super(FeaStNet, self).__init__() - - # self.fc0 = nn.Linear(in_channels, hidden_channels) - self.conv1 = FeaStConv(in_channels, hidden_channels, heads=heads, t_inv=t_inv) - self.conv2 = FeaStConv(hidden_channels, 64, heads=heads, t_inv=t_inv) - self.fc1 = nn.Linear(64, num_classes) - - self.reset_parameters() - - def reset_parameters(self): - self.conv1.reset_parameters() - self.conv2.reset_parameters() - - def forward(self, data): - x, edge_index = data.x, data.edge_index - - # x = F.relu(self.fc0(x)) - x = F.relu(self.conv1(x, edge_index)) - x = F.relu(self.conv2(x, edge_index)) - x = global_mean_pool(x, data.batch) - x = F.dropout(x, training=self.training) - x = self.fc1(x) - return x - - -class RGGCN(torch.nn.Module): - def __init__(self, in_channels, hidden_channels=512, num_classes=2): - super(RGGCN, self).__init__() - - # self.fc0 = nn.Linear(in_channels, hidden_channels) - self.conv1 = ResGatedGraphConv(in_channels, hidden_channels) - self.conv2 = ResGatedGraphConv(hidden_channels, 64) - self.fc1 = nn.Linear(64, num_classes) - - self.reset_parameters() - - def reset_parameters(self): - self.conv1.reset_parameters() - self.conv2.reset_parameters() - - def forward(self, data): - x, edge_index = data.x, data.edge_index - - # x = F.relu(self.fc0(x)) - x = F.relu(self.conv1(x, edge_index)) - x = F.relu(self.conv2(x, edge_index)) - x = global_mean_pool(x, data.batch) - x = F.dropout(x, training=self.training) - x = self.fc1(x) - return x - - -class UniMP(torch.nn.Module): - def __init__(self, in_channels, hidden_channels=512, num_classes=2): - super(UniMP, self).__init__() - # self.fc0 = nn.Linear(in_channels, hidden_channels) - self.conv1 = TransformerConv(in_channels, hidden_channels) - self.conv2 = TransformerConv(hidden_channels, 64) - self.fc1 = nn.Linear(64, num_classes) - - self.reset_parameters() - - def reset_parameters(self): - self.conv1.reset_parameters() - self.conv2.reset_parameters() - - def forward(self, data): - x, edge_index = data.x, data.edge_index - # x = F.relu(self.fc0(x)) - x = F.relu(self.conv1(x, edge_index)) - x = F.relu(self.conv2(x, edge_index)) - x = global_mean_pool(x, data.batch) - x = F.dropout(x, training=self.training) - x = self.fc1(x) - return x - - -class HeteroGNN(torch.nn.Module): - def __init__(self, metadata, hidden_channels, out_channels, num_layers): - super().__init__() - - self.convs = torch.nn.ModuleList() - for _ in range(num_layers): - conv = HeteroConv( - { - edge_type: SAGEConv((-1, -1), hidden_channels) - for edge_type in metadata[1] - } - ) - self.convs.append(conv) - - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - for conv in self.convs: - x_dict = conv(x_dict, edge_index_dict) - x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()} - return self.lin(x_dict["author"]) - - -class GCN_SimpleNoEdgeLabels(torch.nn.Module): - def __init__(self): - super().__init__() - self.conv1 = GCNConv(NUM_NODE_FEATURES, 16) - self.conv2 = GCNConv(16, 1) - - def forward(self, data): - x, edge_index = data.x, data.edge_index - - x = self.conv1(x, edge_index) - x = F.relu(x) - x = F.dropout(x, training=self.training) - x = self.conv2(x, edge_index) - - return F.log_softmax(x, dim=1) - - -class GNN_MultipleOutput(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) - - def forward(self, x, edge_index): - x1 = self.conv1(x, edge_index).relu() - x1 = self.conv2(x1, edge_index) - - x2 = self.conv1(x, edge_index).relu() - x2 = self.conv2(x2, edge_index) - - x3 = self.conv1(x, edge_index).relu() - x3 = self.conv2(x3, edge_index) - - x4 = self.conv1(x, edge_index).relu() - x4 = self.conv2(x4, edge_index) - return x1, x2, x3, x4 - - -class GCN_SimpleMultipleOutput(torch.nn.Module): - # https://discuss.pytorch.org/t/a-model-with-multiple-outputs/10440 - def __init__(self, hidden_channels): - super(GCN_SimpleMultipleOutput, self).__init__() - torch.manual_seed(12345) - self.conv1 = GCNConv(NUM_NODE_FEATURES, hidden_channels) - self.conv1.add_self_loops = False - self.conv2 = GCNConv(hidden_channels, NUM_PREDICTED_VALUES) - self.conv2.add_self_loops = False - - def forward(self, x, edge_index): - x1 = self.conv1(x, edge_index) - x1 = F.relu(x1) - x1 = F.dropout(x1, training=self.training) - x1 = self.conv2(x1, edge_index) - - x2 = self.conv1(x, edge_index) - x2 = F.relu(x2) - x2 = F.dropout(x2, training=self.training) - x2 = self.conv2(x2, edge_index) - - x3 = self.conv1(x, edge_index) - x3 = F.relu(x3) - x3 = F.dropout(x3, training=self.training) - x3 = self.conv2(x3, edge_index) - - x4 = self.conv1(x, edge_index) - x4 = F.relu(x4) - x4 = F.dropout(x4, training=self.training) - x4 = self.conv2(x4, edge_index) - - return ( - F.log_softmax(x1, dim=1), - F.log_softmax(x2, dim=1), - F.log_softmax(x3, dim=1), - F.log_softmax(x4, dim=1), - ) - - -class GNN_Het(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() - x = self.conv2(x, edge_index) - return x - - -class ARMA_Het(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = ARMAConv(-1, hidden_channels) - self.conv2 = ARMAConv(-1, out_channels) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() - x = self.conv2(x, edge_index) - return x - - -class GNN_Het_EA(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) - - def forward(self, x, edge_index, edge_attr): - x = self.conv1(x, edge_index, edge_attr).relu() - x = self.conv2(x, edge_index, edge_attr) - return x - - -class GCN(torch.nn.Module): - def __init__(self, hidden_channels): - super(GCN, self).__init__() - torch.manual_seed(12345) - self.conv1 = GCNConv(NUM_NODE_FEATURES, hidden_channels) - self.conv2 = GCNConv(hidden_channels, hidden_channels) - self.conv3 = GCNConv(hidden_channels, hidden_channels) - self.lin = Linear(hidden_channels, 3421) - - def forward(self, x, edge_index, batch): - x = self.conv1(x, edge_index) - x = x.relu() - x = self.conv2(x, edge_index) - x = x.relu() - x = self.conv3(x, edge_index) - - x = global_mean_pool(x, batch) # [batch_size, hidden_channels] - - x = F.dropout(x, p=0.5, training=self.training) - x = self.lin(x) - - return x - - -class GAT(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = GATConv((-1, -1), hidden_channels, add_self_loops=False) - self.lin1 = Linear(-1, hidden_channels) - self.conv2 = GATConv((-1, -1), out_channels, add_self_loops=False) - self.lin2 = Linear(-1, out_channels) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index) + self.lin1(x) - x = x.relu() - x = self.conv2(x, edge_index) + self.lin2(x) - return x - - -class VertexGNNEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - - self.conv1 = SAGEConv(-1, hidden_channels) - self.conv2 = SAGEConv(hidden_channels, hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() - x = self.conv2(x, edge_index).relu() - return self.lin(x) - - -class StateGNNEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), hidden_channels) - self.conv3 = SAGEConv((-1, -1), hidden_channels) - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - game_x = self.conv1( - x_dict["game_vertex"], - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - state_x = self.conv2( - x_dict["state_vertex"], - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - return self.lin(state_x) - - -class StateGNNEncoderConv(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - # self.conv1 = GCNConv(5, hidden_channels) - # self.conv2 = GCNConv(6, hidden_channels) - # GravNetConv - # self.conv1 = GravNetConv(-1, hidden_channels, 2, 2, 2) - # self.conv2 = GravNetConv(-1, hidden_channels, 2, 2, 2) - # GatedGraphConv - self.conv1 = TAGConv(5, hidden_channels) - self.conv2 = TAGConv(6, hidden_channels) - self.conv3 = SAGEConv((-1, -1), hidden_channels) # SAGEConv - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - game_x = self.conv1( - x_dict["game_vertex"], - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - state_x = self.conv2( - x_dict["state_vertex"], - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - return self.lin(state_x) - - -class StateGNNEncoderConvTAG100hops(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = TAGConv(5, hidden_channels, k=100) - self.conv2 = TAGConv(6, hidden_channels, k=100) - self.conv3 = SAGEConv((-1, -1), hidden_channels) # SAGEConv - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - game_x = self.conv1( - x_dict["game_vertex"], - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - state_x = self.conv2( - x_dict["state_vertex"], - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - return self.lin(state_x) - - -class StateGNNEncoderConvEdgeAttr(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = TAGConv(5, hidden_channels, 2) - self.conv2 = TAGConv(6, hidden_channels, 3) # TAGConv - self.conv3 = GraphConv((-1, -1), hidden_channels) # SAGEConv - self.conv32 = GraphConv((-1, -1), hidden_channels) - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.conv42 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict, edge_attr=None): - game_x = self.conv1( - x_dict["game_vertex"], - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - state_x = self.conv2( - x_dict["state_vertex"], - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - edge_attr[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv32( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - edge_attr[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - state_x = self.conv42( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - return self.lin(state_x) - - -class StateGNNEncoderConvExp(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - # self.conv1 = GCNConv(5, hidden_channels) - # self.conv2 = GCNConv(6, hidden_channels) - # GravNetConv - # self.conv1 = GravNetConv(-1, hidden_channels, 2, 2, 2) - # self.conv2 = GravNetConv(-1, hidden_channels, 2, 2, 2) - # GatedGraphConv - self.conv1 = TAGConv(5, hidden_channels, 10) - self.conv12 = TAGConv(hidden_channels, hidden_channels, 10) # TAGConv - self.conv22 = TAGConv(hidden_channels, hidden_channels, 10) # TAGConv - self.conv2 = TAGConv(6, hidden_channels, 10) # TAGConv - self.conv3 = SAGEConv((-1, -1), hidden_channels) # SAGEConv - self.conv32 = SAGEConv((-1, -1), hidden_channels) - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.conv42 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - game_x = self.conv1( - x_dict["game_vertex"], - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - game_x = self.conv12( - game_x, - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - state_x = self.conv2( - x_dict["state_vertex"], - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv22( - state_x, - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv32( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - state_x = self.conv42( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - return self.lin(state_x) - - -class StateGNNEncoderConv(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = GCNConv(-1, hidden_channels) - self.conv2 = GCNConv(-1, hidden_channels) - self.conv12 = GCNConv(-1, hidden_channels) - self.conv22 = GCNConv(-1, hidden_channels) - self.conv3 = SAGEConv((-1, -1), hidden_channels) # SAGEConv - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.conv32 = SAGEConv((-1, -1), hidden_channels) # SAGEConv - self.conv42 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict): - game_x = self.conv1( - x_dict["game_vertex"], - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - game_x = self.conv12( - game_x, - edge_index_dict[("game_vertex", "to", "game_vertex")], - ).relu() - - state_x = self.conv2( - x_dict["state_vertex"], - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv22( - state_x, - edge_index_dict[("state_vertex", "parent_of", "state_vertex")], - ).relu() - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv32( - (game_x, state_x), - edge_index_dict[("game_vertex", "history", "state_vertex")], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - state_x = self.conv42( - (game_x, state_x), - edge_index_dict[("game_vertex", "in", "state_vertex")], - ).relu() - - return self.lin(state_x) - - -class VerStateModel(torch.nn.Module): - def __init__(self, metadata, hidden_channels, out_channels): - super().__init__() - self.vertex_encoder = VertexGNNEncoder(hidden_channels, out_channels) - self.state_encoder = StateGNNEncoder(hidden_channels, out_channels) - self.decoder = GNN_Het(hidden_channels, out_channels) - self.decoder = to_hetero(self.decoder, metadata, aggr="sum") - - def forward(self, x_dict, edge_index_dict): - z_dict = {} - # x_dict['game_vertex'] = self.user_emb(x_dict['game_vertex']) - z_dict["state_vertex"] = self.state_encoder(x_dict, edge_index_dict) - z_dict["game_vertex"] = x_dict["game_vertex"] - # print(edge_index_dict) - # z_dict['state_vertex'] = self.state_encoder( - # x_dict['state_vertex'], - # edge_index_dict[('state_vertex', 'parent_of', 'state_vertex')], - # ) - - # return self.decoder(z_dict, edge_index_dict) # TODO: process separately - return z_dict - - -class StateModelEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - # self.vertex_encoder = VertexGNNEncoder(hidden_channels, out_channels) - self.state_encoder = StateGNNEncoderConvEdgeAttr(hidden_channels, out_channels) - # self.decoder = GNN_Het(hidden_channels, out_channels) - # self.decoder = to_hetero(self.decoder, metadata, aggr='sum') - - @timeit - def forward(self, x_dict, edge_index_dict, edge_attr=None): - z_dict = {} - # x_dict['game_vertex'] = self.user_emb(x_dict['game_vertex']) - # print(x_dict, edge_index_dict) - z_dict["state_vertex"] = self.state_encoder(x_dict, edge_index_dict, edge_attr) - z_dict["game_vertex"] = x_dict["game_vertex"] - # print(edge_index_dict) - # z_dict['state_vertex'] = self.state_encoder( - # x_dict['state_vertex'], - # edge_index_dict[('state_vertex', 'parent_of', 'state_vertex')], - # ) - - # return self.decoder(z_dict, edge_index_dict) # TODO: process separately - return z_dict - - -class StateModelEncoderTAG100hops(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.state_encoder = StateGNNEncoderConvTAG100hops( - hidden_channels, out_channels - ) - - def forward(self, x_dict, edge_index_dict): - z_dict = {} - z_dict["state_vertex"] = self.state_encoder(x_dict, edge_index_dict) - z_dict["game_vertex"] = x_dict["game_vertex"] - return z_dict - - -class SAGEConvModel(torch.nn.Module): - def __init__( - self, - hidden_channels, - num_gv_layers=2, - num_sv_layers=2, - ): - super().__init__() - self.gv_layers = nn.ModuleList() - 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(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]) - - @timeit - 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 diff --git a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/100e/GNN_state_pred_het_dict_RCGN_Sage_64_100e b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/100e/GNN_state_pred_het_dict_RCGN_Sage_64_100e 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a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description deleted file mode 100644 index 54c02ebba..000000000 --- a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/description +++ /dev/null @@ -1,210 +0,0 @@ -=========================================================================== -Layer (type:depth-idx) Param # -=========================================================================== -StateModelEncoder -- -├─StateGNNEncoderConvEdgeAttrGraphConvAll: 1-1 -- -│ └─RGCNConv: 2-1 1,344 -│ │ └─MeanAggregation: 3-1 -- -│ └─SAGEConv: 2-2 -- -│ │ └─MeanAggregation: 3-2 -- -│ │ └─Linear: 3-3 4,160 -│ │ └─Linear: 3-4 4,096 -│ └─GraphConv: 2-3 -- -│ │ └─SumAggregation: 3-5 -- -│ │ └─Linear: 3-6 4,160 -│ │ └─Linear: 3-7 384 -│ └─SAGEConv: 2-4 -- -│ │ └─MeanAggregation: 3-8 -- -│ │ └─Linear: 3-9 4,160 -│ │ └─Linear: 3-10 4,096 -│ └─Linear: 2-5 520 -=========================================================================== -Total params: 22,920 -Trainable params: 22,920 -Non-trainable params: 0 -=========================================================================== -20 epochs -====================================================================== -Number of training graphs: 11159 -Number of test graphs: 1969 -Epoch: 001, Train Loss: 0.090079, Test Loss: 0.093262 -Epoch: 002, Train Loss: 0.045897, Test Loss: 0.048197 -Epoch: 003, Train Loss: 0.031259, Test Loss: 0.032865 -Epoch: 004, Train Loss: 0.026366, Test Loss: 0.027732 -Epoch: 005, Train Loss: 0.023653, Test Loss: 0.024858 -Epoch: 006, Train Loss: 0.019886, Test Loss: 0.021016 -Epoch: 007, Train Loss: 0.017092, Test Loss: 0.018127 -Epoch: 008, Train Loss: 0.016015, Test Loss: 0.016991 -Epoch: 009, Train Loss: 0.014284, Test Loss: 0.015263 -Epoch: 010, Train Loss: 0.013691, Test Loss: 0.014667 -Epoch: 011, Train Loss: 0.013650, Test Loss: 0.014604 -Epoch: 012, Train Loss: 0.012289, Test Loss: 0.013232 -Epoch: 013, Train Loss: 0.012020, Test Loss: 0.012884 -Epoch: 014, Train Loss: 0.011039, Test Loss: 0.011909 -Epoch: 015, Train Loss: 0.010677, Test Loss: 0.011484 -Epoch: 016, Train Loss: 0.010178, Test Loss: 0.011045 -Epoch: 017, Train Loss: 0.010390, Test Loss: 0.011162 -Epoch: 018, Train Loss: 0.009788, Test Loss: 0.010556 -Epoch: 019, Train Loss: 0.009483, Test Loss: 0.010253 -Epoch: 020, Train Loss: 0.009276, Test Loss: 0.009997 -====================================================================== -50 epochs -====================================================================== -Number of training graphs: 11159 -Number of test graphs: 1969 -Epoch: 001, Train Loss: 0.106262, Test Loss: 0.101664 -Epoch: 002, Train Loss: 0.045628, Test Loss: 0.045041 -Epoch: 003, Train Loss: 0.029963, Test Loss: 0.029063 -Epoch: 004, Train Loss: 0.025636, Test Loss: 0.024923 -Epoch: 005, Train Loss: 0.022738, Test Loss: 0.022126 -Epoch: 006, Train Loss: 0.019849, Test Loss: 0.019430 -Epoch: 007, Train Loss: 0.018097, Test Loss: 0.017772 -Epoch: 008, Train Loss: 0.017498, Test Loss: 0.017380 -Epoch: 009, Train Loss: 0.015480, Test Loss: 0.015368 -Epoch: 010, Train Loss: 0.014630, Test Loss: 0.014567 -Epoch: 011, Train Loss: 0.013638, Test Loss: 0.013591 -Epoch: 012, Train Loss: 0.012503, Test Loss: 0.012464 -Epoch: 013, Train Loss: 0.011956, Test Loss: 0.011889 -Epoch: 014, Train Loss: 0.011676, Test Loss: 0.011726 -Epoch: 015, Train Loss: 0.010620, Test Loss: 0.010660 -Epoch: 016, Train Loss: 0.010023, Test Loss: 0.010080 -Epoch: 017, Train Loss: 0.009940, Test Loss: 0.010015 -Epoch: 018, Train Loss: 0.008979, Test Loss: 0.009042 -Epoch: 019, Train Loss: 0.008818, Test Loss: 0.008894 -Epoch: 020, Train Loss: 0.008471, Test Loss: 0.008537 -Epoch: 021, Train Loss: 0.008566, Test Loss: 0.008620 -Epoch: 022, Train Loss: 0.008271, Test Loss: 0.008342 -Epoch: 023, Train Loss: 0.008117, Test Loss: 0.008185 -Epoch: 024, Train Loss: 0.008082, Test Loss: 0.008158 -Epoch: 025, Train Loss: 0.007669, Test Loss: 0.007761 -Epoch: 026, Train Loss: 0.007696, Test Loss: 0.007773 -Epoch: 027, Train Loss: 0.007788, Test Loss: 0.007853 -Epoch: 028, Train Loss: 0.007624, Test Loss: 0.007743 -Epoch: 029, Train Loss: 0.007382, Test Loss: 0.007479 -Epoch: 030, Train Loss: 0.007387, Test Loss: 0.007493 -Epoch: 031, Train Loss: 0.007157, Test Loss: 0.007255 -Epoch: 032, Train Loss: 0.007329, Test Loss: 0.007456 -Epoch: 033, Train Loss: 0.007319, Test Loss: 0.007407 -Epoch: 034, Train Loss: 0.007313, Test Loss: 0.007414 -Epoch: 035, Train Loss: 0.007181, Test Loss: 0.007287 -Epoch: 036, Train Loss: 0.006902, Test Loss: 0.006980 -Epoch: 037, Train Loss: 0.006935, Test Loss: 0.007019 -Epoch: 038, Train Loss: 0.006700, Test Loss: 0.006774 -Epoch: 039, Train Loss: 0.006823, Test Loss: 0.006906 -Epoch: 040, Train Loss: 0.006726, Test Loss: 0.006817 -Epoch: 041, Train Loss: 0.006584, Test Loss: 0.006677 -Epoch: 042, Train Loss: 0.006661, Test Loss: 0.006756 -Epoch: 043, Train Loss: 0.006629, Test Loss: 0.006735 -Epoch: 044, Train Loss: 0.006494, Test Loss: 0.006589 -Epoch: 045, Train Loss: 0.006597, Test Loss: 0.006696 -Epoch: 046, Train Loss: 0.006693, Test Loss: 0.006806 -Epoch: 047, Train Loss: 0.006161, Test Loss: 0.006259 -Epoch: 048, Train Loss: 0.006176, Test Loss: 0.006275 -Epoch: 049, Train Loss: 0.006586, Test Loss: 0.006698 -Epoch: 050, Train Loss: 0.006144, Test Loss: 0.006247 -====================================================================== -100 epochs -====================================================================== -Number of training graphs: 11159 -Number of test graphs: 1969 -Epoch: 001, Train Loss: 0.088840, Test Loss: 0.090856 -Epoch: 002, Train Loss: 0.048259, Test Loss: 0.049335 -Epoch: 003, Train Loss: 0.033102, Test Loss: 0.033849 -Epoch: 004, Train Loss: 0.027116, Test Loss: 0.027763 -Epoch: 005, Train Loss: 0.022399, Test Loss: 0.022726 -Epoch: 006, Train Loss: 0.020036, Test Loss: 0.020331 -Epoch: 007, Train Loss: 0.017806, Test Loss: 0.018003 -Epoch: 008, Train Loss: 0.016133, Test Loss: 0.016251 -Epoch: 009, Train Loss: 0.015785, Test Loss: 0.015922 -Epoch: 010, Train Loss: 0.014697, Test Loss: 0.014795 -Epoch: 011, Train Loss: 0.013341, Test Loss: 0.013371 -Epoch: 012, Train Loss: 0.012576, Test Loss: 0.012609 -Epoch: 013, Train Loss: 0.012214, Test Loss: 0.012219 -Epoch: 014, Train Loss: 0.011537, Test Loss: 0.011597 -Epoch: 015, Train Loss: 0.011197, Test Loss: 0.011236 -Epoch: 016, Train Loss: 0.010738, Test Loss: 0.010762 -Epoch: 017, Train Loss: 0.010890, Test Loss: 0.010922 -Epoch: 018, Train Loss: 0.010255, Test Loss: 0.010338 -Epoch: 019, Train Loss: 0.009713, Test Loss: 0.009780 -Epoch: 020, Train Loss: 0.009852, Test Loss: 0.009901 -Epoch: 021, Train Loss: 0.009622, Test Loss: 0.009652 -Epoch: 022, Train Loss: 0.009537, Test Loss: 0.009565 -Epoch: 023, Train Loss: 0.008998, Test Loss: 0.009045 -Epoch: 024, Train Loss: 0.008879, Test Loss: 0.008931 -Epoch: 025, Train Loss: 0.008473, Test Loss: 0.008513 -Epoch: 026, Train Loss: 0.008202, Test Loss: 0.008260 -Epoch: 027, Train Loss: 0.008891, Test Loss: 0.008965 -Epoch: 028, Train Loss: 0.007975, Test Loss: 0.008049 -Epoch: 029, Train Loss: 0.007944, Test Loss: 0.008015 -Epoch: 030, Train Loss: 0.008001, Test Loss: 0.008064 -Epoch: 031, Train Loss: 0.007588, Test Loss: 0.007652 -Epoch: 032, Train Loss: 0.007737, Test Loss: 0.007803 -Epoch: 033, Train Loss: 0.007688, Test Loss: 0.007752 -Epoch: 034, Train Loss: 0.007518, Test Loss: 0.007564 -Epoch: 035, Train Loss: 0.007256, Test Loss: 0.007309 -Epoch: 036, Train Loss: 0.007205, Test Loss: 0.007280 -Epoch: 037, Train Loss: 0.006978, Test Loss: 0.007023 -Epoch: 038, Train Loss: 0.006971, Test Loss: 0.007041 -Epoch: 039, Train Loss: 0.007132, Test Loss: 0.007174 -Epoch: 040, Train Loss: 0.006833, Test Loss: 0.006906 -Epoch: 041, Train Loss: 0.006963, Test Loss: 0.007022 -Epoch: 042, Train Loss: 0.006651, Test Loss: 0.006716 -Epoch: 043, Train Loss: 0.006437, Test Loss: 0.006495 -Epoch: 044, Train Loss: 0.006563, Test Loss: 0.006648 -Epoch: 045, Train Loss: 0.006508, Test Loss: 0.006566 -Epoch: 046, Train Loss: 0.006333, Test Loss: 0.006419 -Epoch: 047, Train Loss: 0.006342, Test Loss: 0.006418 -Epoch: 048, Train Loss: 0.006346, Test Loss: 0.006426 -Epoch: 049, Train Loss: 0.005944, Test Loss: 0.006024 -Epoch: 050, Train Loss: 0.006072, Test Loss: 0.006177 -Epoch: 051, Train Loss: 0.006027, Test Loss: 0.006113 -Epoch: 052, Train Loss: 0.005844, Test Loss: 0.005947 -Epoch: 053, Train Loss: 0.005841, Test Loss: 0.005907 -Epoch: 054, Train Loss: 0.005795, Test Loss: 0.005874 -Epoch: 055, Train Loss: 0.005652, Test Loss: 0.005740 -Epoch: 056, Train Loss: 0.005787, Test Loss: 0.005880 -Epoch: 057, Train Loss: 0.005617, Test Loss: 0.005701 -Epoch: 058, Train Loss: 0.005593, Test Loss: 0.005684 -Epoch: 059, Train Loss: 0.005616, Test Loss: 0.005713 -Epoch: 060, Train Loss: 0.005459, Test Loss: 0.005549 -Epoch: 061, Train Loss: 0.005517, Test Loss: 0.005620 -Epoch: 062, Train Loss: 0.005553, Test Loss: 0.005662 -Epoch: 063, Train Loss: 0.005391, Test Loss: 0.005493 -Epoch: 064, Train Loss: 0.005434, Test Loss: 0.005547 -Epoch: 065, Train Loss: 0.005258, Test Loss: 0.005361 -Epoch: 066, Train Loss: 0.005369, Test Loss: 0.005469 -Epoch: 067, Train Loss: 0.005316, Test Loss: 0.005427 -Epoch: 068, Train Loss: 0.005216, Test Loss: 0.005315 -Epoch: 069, Train Loss: 0.005302, Test Loss: 0.005403 -Epoch: 070, Train Loss: 0.005128, Test Loss: 0.005224 -Epoch: 071, Train Loss: 0.005114, Test Loss: 0.005220 -Epoch: 072, Train Loss: 0.005161, Test Loss: 0.005271 -Epoch: 073, Train Loss: 0.005106, Test Loss: 0.005205 -Epoch: 074, Train Loss: 0.005157, Test Loss: 0.005254 -Epoch: 075, Train Loss: 0.005084, Test Loss: 0.005184 -Epoch: 076, Train Loss: 0.005150, Test Loss: 0.005252 -Epoch: 077, Train Loss: 0.005019, Test Loss: 0.005114 -Epoch: 078, Train Loss: 0.005175, Test Loss: 0.005285 -Epoch: 079, Train Loss: 0.005062, Test Loss: 0.005166 -Epoch: 080, Train Loss: 0.005217, Test Loss: 0.005319 -Epoch: 081, Train Loss: 0.004961, Test Loss: 0.005047 -Epoch: 082, Train Loss: 0.005292, Test Loss: 0.005405 -Epoch: 083, Train Loss: 0.005039, Test Loss: 0.005148 -Epoch: 084, Train Loss: 0.004908, Test Loss: 0.005010 -Epoch: 085, Train Loss: 0.005187, Test Loss: 0.005285 -Epoch: 086, Train Loss: 0.004868, Test Loss: 0.004969 -Epoch: 087, Train Loss: 0.005173, Test Loss: 0.005261 -Epoch: 088, Train Loss: 0.004844, Test Loss: 0.004941 -Epoch: 089, Train Loss: 0.005036, Test Loss: 0.005125 -Epoch: 090, Train Loss: 0.004846, Test Loss: 0.004941 -Epoch: 091, Train Loss: 0.004800, Test Loss: 0.004897 -Epoch: 092, Train Loss: 0.005034, Test Loss: 0.005135 -Epoch: 093, Train Loss: 0.004912, Test Loss: 0.005009 -Epoch: 094, Train Loss: 0.004924, Test Loss: 0.005011 -Epoch: 095, Train Loss: 0.004784, Test Loss: 0.004886 -Epoch: 096, Train Loss: 0.004772, Test Loss: 0.004874 -Epoch: 097, Train Loss: 0.004694, Test Loss: 0.004792 -Epoch: 098, Train Loss: 0.004661, Test Loss: 0.004745 -Epoch: 099, Train Loss: 0.004699, Test Loss: 0.004792 -Epoch: 100, Train Loss: 0.004633, Test Loss: 0.004714 - diff --git a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py b/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py deleted file mode 100644 index b67c4797a..000000000 --- a/VSharp.ML.AIAgent/ml/models/EdgeTypeRGCNSageSubgraphs64ch/model.py +++ /dev/null @@ -1,49 +0,0 @@ -import torch -from torch.nn import Linear -from torch_geometric.nn import TAGConv, GraphConv, SAGEConv - - -class StateGNNEncoderConvEdgeAttrGraphConvAll(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = RGCNConv(5, hidden_channels, 3) - self.conv2 = SAGEConv(hidden_channels, hidden_channels) - self.conv3 = GraphConv((-1, -1), hidden_channels) - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict, edge_attr=None, edge_type=None): - game_x = self.conv1( - x_dict['game_vertex'], - edge_index_dict[('game_vertex', 'to', 'game_vertex')], - edge_type - ).relu() - - state_x = self.conv3( - (game_x, x_dict['state_vertex']), - edge_index_dict[('game_vertex', 'history', 'state_vertex')], - edge_attr[('game_vertex', 'history', 'state_vertex')], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[('game_vertex', 'in', 'state_vertex')], - ).relu() - - state_x = self.conv2( - state_x, - edge_index_dict[('state_vertex', 'parent_of', 'state_vertex')], - ).relu() - - return self.lin(state_x) - -class StateModelEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.state_encoder = StateGNNEncoderConvEdgeAttrGraphConvAll(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict, edge_attr=None): - z_dict = {} - z_dict['state_vertex'] = self.state_encoder(x_dict, edge_index_dict, edge_attr, edge_type) - z_dict['game_vertex'] = x_dict['game_vertex'] - return z_dict diff --git a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/GNN_state_pred_het_dict_compact b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/GNN_state_pred_het_dict_compact deleted file mode 100644 index 8d45e5afc2b2b15dbdc2e73d30f869b59a31aa73..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 153392 zcmbrl2{@JO+y8BdL`9S`WJ-gSS%v$$R>_#Clp&2OMVXe+Af-g6h(wV{niCSqy3g~b ziPE4lrVp0f>uFrYi-|M=sdChhZ?))$qJw4!TKvagFW>E*ZA64QW1~;Uylf}<;w$n zeZ7_k`vmx}m7W!}!b=#*N*F|%FQVhj7aiw5LYP`#5?=nx1BBn}Ef4SyHPlXt}rEl8|5@-?h?9g1nZ6`1r0^671!_HXvw8sF5^ZeB3zq{(mq6f;?Aw@%#1t zMB2tTz%zKR@aMu0rTG%>qC$-UU(#J%_`W%4qcc?a_cdkYir?&sYXz>qKN-QRJlXfR*y53++q zG=GSLgo9`_U%rp75X>Ly%pdmebi@D96@Q}}@rVCOAMqDmsgJJQM^_2vt2*;X{yW|1 zKXkR<=*E2YKj|8O(KY+%T7C3U!F+9J{^)-wGNZmC8}l2~gg@5%52wfd>9o!-r^om4 zbc6X5ocR;~AzLH8-<{U`4Q$HS7iJqFblRXVTghm?p@ZZvr;YmP#=(3OXTIsb)BkdM 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-================================================================= -StateModelEncoder -- -├─StateGNNEncoderConvEdgeAttr: 1-1 -- -│ └─TAGConv: 2-1 64 -│ │ └─SumAggregation: 3-1 -- -│ │ └─ModuleList: 3-2 960 -│ └─TAGConv: 2-2 64 -│ │ └─SumAggregation: 3-3 -- -│ │ └─ModuleList: 3-4 1,536 -│ └─GraphConv: 2-3 -- -│ │ └─SumAggregation: 3-5 -- -│ │ └─Linear: 3-6 4,160 -│ │ └─Linear: 3-7 4,096 -│ └─GraphConv: 2-4 -- -│ │ └─SumAggregation: 3-8 -- -│ │ └─Linear: 3-9 4,160 -│ │ └─Linear: 3-10 4,096 -│ └─SAGEConv: 2-5 -- -│ │ └─MeanAggregation: 3-11 -- -│ │ └─Linear: 3-12 4,160 -│ │ └─Linear: 3-13 4,096 -│ └─SAGEConv: 2-6 -- -│ │ └─MeanAggregation: 3-14 -- -│ │ └─Linear: 3-15 4,160 -│ │ └─Linear: 3-16 4,096 -│ └─Linear: 2-7 520 -================================================================= -Total params: 36,168 -Trainable params: 36,168 -Non-trainable params: 0 -================================================================= -================================================================= -Number of training graphs: 170186 -Number of test graphs: 30033 -Epoch: 001, Train Loss: 0.021431, Test Loss: 0.021463 -Epoch: 002, Train Loss: 0.014803, Test Loss: 0.014820 -Epoch: 003, Train Loss: 0.012827, Test Loss: 0.012861 -Epoch: 004, Train Loss: 0.011392, Test Loss: 0.011396 -Epoch: 005, Train Loss: 0.011159, Test Loss: 0.011139 -Epoch: 006, Train Loss: 0.009657, Test Loss: 0.009621 -Epoch: 007, Train Loss: 0.010212, Test Loss: 0.010188 -Epoch: 008, Train Loss: 0.009641, Test Loss: 0.009652 -Epoch: 009, Train Loss: 0.008862, Test Loss: 0.008845 -Epoch: 010, Train Loss: 0.008803, Test Loss: 0.008784 -Epoch: 011, Train Loss: 0.008282, Test Loss: 0.008267 -Epoch: 012, Train Loss: 0.008095, Test Loss: 0.008067 -Epoch: 013, Train Loss: 0.007825, Test Loss: 0.007809 -Epoch: 014, Train Loss: 0.007740, Test Loss: 0.007749 -Epoch: 015, Train Loss: 0.007546, Test Loss: 0.007539 -Epoch: 016, Train Loss: 0.007591, Test Loss: 0.007591 -Epoch: 017, Train Loss: 0.007772, Test Loss: 0.007759 -Epoch: 018, Train Loss: 0.007730, Test Loss: 0.007716 -Epoch: 019, Train Loss: 0.007357, Test Loss: 0.007346 -Epoch: 020, Train Loss: 0.007512, Test Loss: 0.007499 diff --git a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py deleted file mode 100644 index e26f9dcf9..000000000 --- a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic/model.py +++ /dev/null @@ -1,64 +0,0 @@ -import torch -from torch.nn import Linear -from torch_geometric.nn import TAGConv, GraphConv, SAGEConv - - -class StateGNNEncoderConvEdgeAttr(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = TAGConv(5, hidden_channels, 2) - self.conv2 = TAGConv(6, hidden_channels, 3) #TAGConv - self.conv3 = GraphConv((-1, -1), hidden_channels) # SAGEConv - self.conv32 = GraphConv((-1, -1), hidden_channels) - self.conv4 = SAGEConv((-1, -1), hidden_channels) - self.conv42 = SAGEConv((-1, -1), hidden_channels) - self.lin = Linear(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict, edge_attr=None): - game_x = self.conv1( - x_dict['game_vertex'], - edge_index_dict[('game_vertex', 'to', 'game_vertex')], - ).relu() - - - state_x = self.conv2( - x_dict['state_vertex'], - edge_index_dict[('state_vertex', 'parent_of', 'state_vertex')], - ).relu() - - - state_x = self.conv3( - (game_x, state_x), - edge_index_dict[('game_vertex', 'history', 'state_vertex')], - edge_attr[('game_vertex', 'history', 'state_vertex')], - ).relu() - - state_x = self.conv32( - (game_x, state_x), - edge_index_dict[('game_vertex', 'history', 'state_vertex')], - edge_attr[('game_vertex', 'history', 'state_vertex')], - ).relu() - - state_x = self.conv4( - (game_x, state_x), - edge_index_dict[('game_vertex', 'in', 'state_vertex')], - ).relu() - - state_x = self.conv42( - (game_x, state_x), - edge_index_dict[('game_vertex', 'in', 'state_vertex')], - ).relu() - - return self.lin(state_x) - - -class StateModelEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.state_encoder = StateGNNEncoderConvEdgeAttr(hidden_channels, out_channels) - - def forward(self, x_dict, edge_index_dict, edge_attr=None): - z_dict = {} - z_dict['state_vertex'] = self.state_encoder(x_dict, edge_index_dict, edge_attr) - z_dict['game_vertex'] = x_dict['game_vertex'] - return z_dict diff --git a/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/GNN_state_pred_het_dict_StateGNNEncoderConvEdgeAttr_32ch b/VSharp.ML.AIAgent/ml/models/StateGNNEncoderConvEdgeAttrBasic32Ch/GNN_state_pred_het_dict_StateGNNEncoderConvEdgeAttr_32ch deleted file mode 100644 index 53458d0a07fee220eaec1d525d6336a1eba3f95c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 48304 zcmb@t2|QKZ+y8IMTuG*okg*IIGVXO98c2$wB$Oc~4YbiH4U|e`N-|`sgi1;p*z4M) 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z9Ni*GO#LgN;avgaPlV`ECv$o|v<{rgtU%+g5lvli2|v`1qC=ZJFu?F6PB#o;eWiKW zK4%-wxZc6}Lu>|-U5N~>Y-5rurKqDq05d^Xf$(=$FrQ3j|26-hb0ytj;Q0p)A@l$0 zuN)q*A}na>lK+SQv$^CC1<1Y3MP5A4{qje1pE~HrK}cNu$6iQCOa6$MypfR5kEZ$8 zD}8rPZf<^IE9b2Y@LRSbV1Zv?z)HV`t3pGK7tC8Z&uCdlXn$JK4DG5Y4)CL?bL~Id z5sv?V(Tq*{v#w|=ulcuc>CdcW;{jNv{aHHp@|jWG$^2*H|O^5s$CSyGi%&h;%>>IWCGc4I@Aeec7Sl`@)elX{OU>5yheNE7R zhROI21hec9>+4wlGc0+|KrpNRu)g-tKVN1ha|eL^(*J#}c7JBc%o~99OZWG+t?kEJ zFaYb9-tTKc(~lJ}0PB~|?<*(!nU%b10M;*k-?udEXO>LN04(eN-(%lt-k({?DFd)< z`m_2D**~*nat2`8_Gk4SeEVIjrT?Av&yepvRZPtApTSC9c-;RETXO$A`uDL=^zRpB k%l 5000: + dat = dat[:5000] + print("Part of the dataset is chosen!") + dataset.extend(dat) - def start(self): - dataset = data_loader_compact.get_data_hetero_vector() torch.manual_seed(12345) + shuffle(dataset) split_at = round(len(dataset) * 0.85) @@ -34,107 +50,88 @@ def start(self): print(f"Number of test graphs: {len(test_dataset)}") train_loader = DataLoader( - train_dataset, batch_size=1, shuffle=True + train_dataset, batch_size=1, shuffle=False ) # TODO: add learning by batches! test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False) - model = GNN_Het(hidden_channels=64, out_channels=8) - model = to_hetero(model, dataset[0].metadata(), aggr="sum") - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + model = self.model_class(hidden_channels=self.hidden, out_channels=8).to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=0.0001) - for epoch in range(1, 31): - self.train(model, train_loader, optimizer) + for epoch in range(1, epochs + 1): + self.train(model, train_loader, optimizer, device) train_acc = self.tst(model, train_loader) test_acc = self.tst(model, test_loader) print( - f"Epoch: {epoch:03d}, Train Loss: {train_acc:.4f}, Test Loss: {test_acc:.4f}" + f"Epoch: {epoch:03d}, Train Loss: {train_acc:.6f}, Test Loss: {test_acc:.6f}" ) - self.save(model, "./saved_models") + self.save_simple(model, dir_to_save, epochs) # loss function from link prediction example def weighted_mse_loss(self, pred, target, weight=None): weight = 1.0 if weight is None else weight[target].to(pred.dtype) return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() - def train(self, model, train_loader, optimizer): + def train(self, model, train_loader, optimizer, device): model.train() - for data in train_loader: # Iterate in batches over the training dataset. - out = model(data.x_dict, data.edge_index_dict) - pred = out["state_vertex"] + data = data.to(device) + optimizer.zero_grad() # Clear gradients. + out = model( + data.x_dict["game_vertex"], + data.x_dict["state_vertex"], + data.edge_index_dict["game_vertex_to_game_vertex"], + data["game_vertex_to_game_vertex"].edge_type, + data["game_vertex_history_state_vertex"].edge_index, + data["game_vertex_history_state_vertex"].edge_attr, + data["game_vertex_in_state_vertex"].edge_index, + data["state_vertex_parent_of_state_vertex"].edge_index, + ) target = data.y - loss = F.mse_loss(pred, target) + loss = F.mse_loss(out, target) loss.backward() # Derive gradients. optimizer.step() # Update parameters based on gradients. - optimizer.zero_grad() # Clear gradients. + @torch.no_grad() def tst(self, model, loader): model.eval() + total_loss = 0 + number_of_states_total = 0 for data in loader: - out = model(data.x_dict, data.edge_index_dict) - pred = out["state_vertex"] + out = model( + data.x_dict["game_vertex"], + data.x_dict["state_vertex"], + data["game_vertex_to_game_vertex"].edge_index, + data["game_vertex_to_game_vertex"].edge_type, + data["game_vertex_history_state_vertex"].edge_index, + data["game_vertex_history_state_vertex"].edge_attr, + data["game_vertex_in_state_vertex"].edge_index, + data["state_vertex_parent_of_state_vertex"].edge_index, + ) target = data.y - loss = F.mse_loss(pred, target) - return loss + for i, x in enumerate(out): + loss = F.mse_loss(x, target[i]) + total_loss += loss + number_of_states_total += 1 + return total_loss / number_of_states_total # correct / len(loader.dataset) @staticmethod - def predict_state(model, data: HeteroData, state_map: dict[int, int]) -> int: + def predict_state(model, data: HeteroData, state_map: Dict[int, int]) -> int: """Gets state id from model and heterogeneous graph data.state_map - maps real state id to state index""" state_map = {v: k for k, v in state_map.items()} # inversion for prediction out = model(data.x_dict, data.edge_index_dict) return state_map[int(out["state_vertex"].argmax(dim=0)[0])] - @staticmethod - def predict_state_with_dict( - model: torch.nn.Module, data: HeteroData, state_map: dict[int, int] - ) -> int: - """Gets state id from model and heterogeneous graph - data.state_map - maps real state id to state index""" - - data.to(GeneralConfig.DEVICE) - reversed_state_map = {v: k for k, v in state_map.items()} - - with torch.no_grad(): - out = model.forward(data.x_dict, data.edge_index_dict, data.edge_attr_dict) - - remapped = [] - - for index, vector in enumerate(out["state_vertex"]): - state_vector_mapping = StateVectorMapping( - state=reversed_state_map[index], - vector=(vector.detach().cpu().numpy()).tolist(), - ) - remapped.append(state_vector_mapping) - - return max(remapped, key=lambda mapping: sum(mapping.vector)).state - - def predict_state_single_out( - model: torch.nn.Module, data: HeteroData, state_map: dict[int, int] - ) -> int: - """Gets state id from model and heterogeneous graph - data.state_map - maps real state id to state index""" - - data.to(GeneralConfig.DEVICE) - reversed_state_map = {v: k for k, v in state_map.items()} - - with torch.no_grad(): - out = model.forward(data.x_dict, data.edge_index_dict, data.edge_attr_dict) - - remapped = [] - if type(out) is dict: - out = out["state_vertex"] - for index, vector in enumerate(out): - state_vector_mapping = StateVectorMapping( - state=reversed_state_map[index], - vector=(vector.detach().cpu().numpy()).tolist(), - ) - remapped.append(state_vector_mapping) - - return max(remapped, key=lambda mapping: sum(mapping.vector)).state - - def save(self, model, dir): + def save_simple(self, model, dir, epochs): + dir = os.path.join( + dir, + get_module_name(self.model_class), + str(self.hidden) + "ch", + str(epochs) + "e", + ) + if not os.path.exists(dir): + os.makedirs(dir) filepath = os.path.join(dir, "GNN_state_pred_het_dict") # case 1 torch.save(model.state_dict(), filepath) diff --git a/VSharp.ML.AIAgent/predict_state_hetero.py b/VSharp.ML.AIAgent/predict_state_hetero.py deleted file mode 100644 index 05556d0b1..000000000 --- a/VSharp.ML.AIAgent/predict_state_hetero.py +++ /dev/null @@ -1,135 +0,0 @@ -import os.path - -from typing import Dict - -import torch -from torch.autograd import Variable -from torch_geometric.nn import to_hetero, to_hetero_with_bases -from torch_geometric.data import HeteroData - -from ml.data_loader import get_data_hetero -from ml.models import GNN_Het -from random import shuffle -from torch_geometric.loader import DataLoader -import torch.nn.functional as F - - -class PredictStateHetGNN: - """predicts ExpectedStateNumber using Heterogeneous GNN""" - - def __init__(self): - self.state_maps = {} - self.start() - - def start(self): - dataset = get_data_hetero() - torch.manual_seed(12345) - - # shuffle(dataset) - - split_at = round(len(dataset) * 0.85) - - train_dataset = dataset[:split_at] - test_dataset = dataset[split_at:] - - print(f"Number of training graphs: {len(train_dataset)}") - print(f"Number of test graphs: {len(test_dataset)}") - - train_loader = DataLoader( - train_dataset, batch_size=1, shuffle=True - ) # TODO: add learning by batches! - test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False) - - # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - model = GNN_Het(hidden_channels=64, out_channels=1) - model = to_hetero(model, dataset[0].metadata(), aggr="sum") - # model = to_hetero_with_bases(model, dataset[0].metadata(), num_bases=3) - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - # criterion = torch.nn.CrossEntropyLoss() - - for epoch in range(1, 21): - self.train(model, train_loader, optimizer) - train_acc = self.tst(model, train_loader) - test_acc = self.tst(model, test_loader) - print( - f"Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}" - ) - - path = "./saved_models" - if not os.path.exists(path): - os.makedirs(path) - - self.save(model, path) - - # loss function from link prediction example - def weighted_mse_loss(self, pred, target, weight=None): - weight = 1.0 if weight is None else weight[target].to(pred.dtype) - return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() - - def train(self, model, train_loader, optimizer): - model.train() - - for data in train_loader: # Iterate in batches over the training dataset. - out = model(data.x_dict, data.edge_index_dict) - pred = out["state_vertex"] - target = torch.zeros(pred.size()) - state_to_move = data.y.item() - # TODO: more intellectual - target[state_to_move][ - 0 - ] = 5000.0 # pred.size()[0]*100 # should be substantially greater than the total number of states! - loss = F.mse_loss(pred, target) - loss.backward() # Derive gradients. - optimizer.step() # Update parameters based on gradients. - optimizer.zero_grad() # Clear gradients. - - def train_single_value(self, model, train_loader, optimizer): - # It does not work properly now --- no actual learning!!!''' - model.train() - - for data in train_loader: # Iterate in batches over the training dataset. - out = model(data.x_dict, data.edge_index_dict) - pred = out["state_vertex"].argmax(dim=0)[0].float() - target = torch.tensor(data.y[0][0]).float() - # loss = self.weighted_mse_loss(torch.tensor(pred, dtype=float), - # torch.tensor(pred, dtype=float)).sqrt() - loss = self.weighted_mse_loss(pred, target) - # print("loss", loss) - # loss = Variable(loss, requires_grad=True) #TODO: dirty hack, fix it later - loss.requires_grad = True - # print(loss) - # loss = criterion(torch.tensor(pred, dtype=float), torch.tensor(data.y[0][0])) # Compute the loss. - loss.backward() # Derive gradients. - optimizer.step() # Update parameters based on gradients. - optimizer.zero_grad() # Clear gradients. - - def tst(self, model, loader): - model.eval() - correct = 0 - for data in loader: - out = model(data.x_dict, data.edge_index_dict) - pred = int(out["state_vertex"].argmax(dim=0)[0]) - target = data.y.item() - # print(pred, target) - correct += int(pred == target) - return correct / len(loader.dataset) - - @staticmethod - def predict_state(model, data: HeteroData, state_map: Dict[int, int]) -> int: - """Gets state id from model and heterogeneous graph - data.state_map - maps real state id to state index""" - state_map = {v: k for k, v in state_map.items()} # inversion for prediction - out = model(data.x_dict, data.edge_index_dict) - return state_map[int(out["state_vertex"].argmax(dim=0)[0])] - - def save(self, model, dir): - filepath = os.path.join(dir, "GNN_state_pred_het_dict") - # case 1 - torch.save(model.state_dict(), filepath) - # case 2 - filepath = os.path.join(dir, "GNN_state_pred_het_full") - torch.save(model, filepath) - - -if __name__ == "__main__": - PredictStateHetGNN() diff --git a/VSharp.ML.AIAgent/pretrain.py b/VSharp.ML.AIAgent/pretrain.py new file mode 100644 index 000000000..a341bbef3 --- /dev/null +++ b/VSharp.ML.AIAgent/pretrain.py @@ -0,0 +1,16 @@ +from ml.data_loader_compact import ServerDataloaderHeteroVector +from ml.models.TAGSageSimple.model import StateModelEncoder +from ml.predict_state_vector_hetero import PredictStateVectorHetGNN + + +def get_data_hetero_vector(): + dl = ServerDataloaderHeteroVector( + "/home/cyfra/PycharmProjects/symbolic_exec_GNN-main/SerializedEpisodes_10.10.2023" + ) + return dl.dataset + + +if __name__ == "__main__": + # get_data_hetero_vector() + pr = PredictStateVectorHetGNN(StateModelEncoder, 32) + pr.train_and_save("../dataset", 20, "./ml/models/") From a31cb410db752bb3e05ae8bc4b161da863685c76 Mon Sep 17 00:00:00 2001 From: katyacyfra Date: Wed, 8 Nov 2023 20:59:16 +0300 Subject: [PATCH 7/7] delete hardcoded path --- VSharp.ML.AIAgent/pretrain.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/VSharp.ML.AIAgent/pretrain.py b/VSharp.ML.AIAgent/pretrain.py index a341bbef3..fdee3e77a 100644 --- a/VSharp.ML.AIAgent/pretrain.py +++ b/VSharp.ML.AIAgent/pretrain.py @@ -4,9 +4,7 @@ def get_data_hetero_vector(): - dl = ServerDataloaderHeteroVector( - "/home/cyfra/PycharmProjects/symbolic_exec_GNN-main/SerializedEpisodes_10.10.2023" - ) + dl = ServerDataloaderHeteroVector("../serialized") return dl.dataset