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Dataset_linear.py
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import os
import random
import dgl
import torch
import af_reader_py
from dgl.data import DGLDataset
from torch.utils.data import DataLoader
from sklearn.preprocessing import StandardScaler
from torch.utils.data import DataLoader, Dataset
MAX_ARG = 200000
af_data_root = "../af_dataset/"
result_root = "../af_dataset/all_result/"
def transfom_to_graph(label_path, n, device="cpu"):
f = open(label_path, 'r')
data = f.read()
target = [0.]*n
for n in data.split(','):
if n == '':
continue
target[int(n)] = 1.0
return torch.tensor(target, requires_grad=False, device=device)
def light_get_item(af_path, features_path, device= "cpu"):
att1, att2, nb_el = af_reader_py.reading_file_for_dgl(af_path)
graph = dgl.graph((torch.tensor(att1),torch.tensor(att2)), num_nodes = nb_el, device=device)
graph = dgl.add_self_loop(graph)
inputs = torch.load(features_path, map_location=device)
return graph, inputs, nb_el
def get_item(af_path, features_path, device="cpu", max_arg=MAX_ARG):
att1, att2, nb_el = af_reader_py.reading_file_for_dgl(af_path)
if nb_el > max_arg:
return None, None, 1000000
graph = dgl.graph((torch.tensor(att1),torch.tensor(att2)), num_nodes = nb_el, device=device)#.to(device)
raw_features = af_reader_py.compute_features(af_path, 10000, 0.000001 )
scaler = StandardScaler()
#print(len(raw_features))
features = scaler.fit_transform(raw_features)
inputs = torch.tensor(features, dtype=torch.float32, requires_grad=False).to(device)
torch.save(inputs, features_path)
#print("N : ",nb_el," ", graph.number_of_nodes())
graph = dgl.add_self_loop(graph)
return graph, inputs, nb_el
class TrainingGraphDataset(DGLDataset):
def __init__(self, af_dir, label_dir, task, max_arg=MAX_ARG, device="cpu"):
self.label_dir = label_dir
self.af_dir = af_dir
self.task = task
self.max_arg = max_arg
self.device=device
super().__init__(name="Af dataset")
def __len__(self):
return len(self.graphs)
def process(self):
list_year_dir = ["2017"]
self.af_dir = af_data_root+"dataset_af"
self.label_dir = result_root+"result_"+self.task
self.graphs = []
self.labels = []
list_unique_file = []
for year in list_year_dir:
iter = os.listdir(self.label_dir +"_"+ year)
for f in iter:
true_name = f.replace(".apx", "")
true_name = true_name.replace(".af", "")
true_name = true_name.replace(".tgf", "")
true_name = true_name.replace(".old", "")
if true_name not in list_unique_file:
list_unique_file.append(true_name)
af_path = self.af_dir+"_"+year+"/"+f
label_path = self.label_dir+"_"+year+"/"+f
features_path = af_data_root+"all_features/"+year+"/"+f+".pt"
if os.path.exists(af_data_root+"all_features/"+year+"/"+f+".pt"):
graph, features, nb_el = light_get_item(af_path, features_path, device=self.device)
else:
graph, features, nb_el = get_item(af_path, features_path, device=self.device)
if nb_el < self.max_arg :
graph.ndata["feat"] = features
graph.ndata["label"] = transfom_to_graph(label_path, nb_el, device=self.device)
self.graphs.append(graph)
def __getitem__(self, idx:int):
return self.graphs[idx]
class LarsMalmDataset(DGLDataset):
def __init__(self, task, device="cpu"):
self.label_dir = "../AFGraphLib/AFs/solutions/"
self.af_dir = "../AFGraphLib/AFs/"
self.features_dir = "../AFGraphLib/AFs/features_cache/"
self.device = device
self.task = task
super().__init__(name="Dataset of Lars Malm")
def __len__(self):
return len(self.graphs)
def process(self):
tot_file = 0
self.graphs = []
self.labels = []
list_unique_file = []
iter = os.listdir(self.af_dir)
for f in iter:
true_name = f.replace(".apx", "")
true_name = true_name.replace(".af", "")
true_name = true_name.replace(".tgf", "")
true_name = true_name.replace(".old", "")
if true_name not in list_unique_file:
af_path = self.af_dir+f
sem = self.task.split('-')[1]
problem_type = self.task.split('-')[0]
if sem == "PR":
label_path = self.label_dir + f +".txt"
else:
label_path = self.label_dir + f +".apx.EE-"+sem
if not os.path.exists(label_path):
continue
features_path = self.features_dir + f + ".pt"
list_unique_file.append(true_name)
print(f)
if os.path.exists(features_path):
graph, features, nb_el = light_get_item(af_path, features_path, device=self.device)
else:
graph, features, nb_el = get_item(af_path, features_path, device=self.device)
label = None
if problem_type == "DC":
label = af_reader_py.read_lars_solution_dc(label_path, af_path)
else :
label = af_reader_py.read_lars_solution_ds(label_path, af_path)
if nb_el < MAX_ARG:
tot_file += 1
graph.ndata["feat"] = features
graph.ndata["label"] = torch.Tensor(label).to(self.device)
self.graphs.append(graph)
print("TOTAL number of file : ", tot_file)
def __getitem__(self, idx:int):
return self.graphs[idx]
class ValisationDataset(DGLDataset):
def __init__(self, af_dir, label_dir, task, device = "cpu"):
self.label_dir = label_dir
self.af_dir = af_dir
self.task = task
self.device = device
super().__init__(name="Validation Dataset")
def __len__(self):
return len(self.graphs)
def process(self):
#list_year_dir = ["2017", "2023"]
list_year_dir = ["2023"]
self.af_dir = af_data_root+"dataset_af"
self.label_dir = result_root+"result_"+self.task
self.graphs = []
self.labels = []
list_unique_file = []
print("device :", self.device)
for year in list_year_dir:
iter = os.listdir(self.label_dir +"_"+ year)
for f in iter:
true_name = f.replace(".apx", "")
true_name = true_name.replace(".af", "")
true_name = true_name.replace(".tgf", "")
true_name = true_name.replace(".old", "")
if true_name not in list_unique_file:
af_path = self.af_dir+"_"+year+"/"+f
label_path = self.label_dir+"_"+year+"/"+f
features_path = af_data_root+"all_features/"+year+"/"+f+".pt"
list_unique_file.append(true_name)
if os.path.exists(af_data_root+"all_features/"+year+"/"+f+".pt"):
graph, features, nb_el = light_get_item(af_path, features_path, device=self.device)
else:
graph, features, nb_el = get_item(af_path, features_path, device=self.device)
if nb_el < MAX_ARG:
graph.ndata["feat"] = features
graph.ndata["label"] = transfom_to_graph(label_path, nb_el, device=self.device)
self.graphs.append(graph)
def __getitem__(self, idx:int):
return self.graphs[idx]
def test(model, task, device="cpu", rand=False):
af_dataset = ValisationDataset(af_data_root+"dataset_af/", af_data_root+"result/", task=task, device=device)
model.eval()
acc_yes = 0
acc_no = 0
tot_el_yes = 0
tot_el_no = 0
mean_acc = 0
mean_acc_yes = 0
mean_acc_no = 0
tot_yes_count = 0
tot_no_count = 0
with torch.no_grad():
for graph in af_dataset:
inputs = graph.ndata["feat"]
if rand == True:
inputs_rand = torch.randn(graph.number_of_nodes(), 128 , dtype=torch.float, device=device)
num_rows_to_overwrite = inputs.size(0)
num_columns_in_features = inputs.size(1)
inputs_to_overwrite = inputs_rand.narrow(0, 0, num_rows_to_overwrite).narrow(1, 0, num_columns_in_features)
inputs_to_overwrite.copy_(inputs)
inputs = inputs_rand
label = graph.ndata["label"]
out = model(graph, inputs)
predicted = (torch.sigmoid(out.squeeze())>0.5).float()
one_acc_yes = sum(element1 == element2 == 1.0 for element1, element2 in zip(predicted, label)).item()
one_acc_no = sum(element1 == element2 == 0.0 for element1, element2 in zip(predicted, label)).item()
acc_yes += one_acc_yes
acc_no += one_acc_no
tot_yes = sum(element1 == 1.0 for element1 in label).item()
tot_no = sum(element1 == 0.0 for element1 in label).item()
tot_el_yes += tot_yes
tot_el_no += tot_no
mean_acc += ((one_acc_yes+one_acc_no)/(tot_yes+tot_no))
if tot_yes != 0:
mean_acc_yes += ((one_acc_yes)/(tot_yes))
tot_yes_count += 1
if tot_no != 0:
mean_acc_no += ((one_acc_no)/(tot_no))
tot_no_count += 1
print("acc : ", (acc_yes+acc_no)/(tot_el_no+tot_el_yes) ,"acc yes : ", acc_yes/tot_el_yes, "acc no : ", acc_no/tot_el_no )
print("acc mean : ", mean_acc/len(af_dataset), " acc mean y : ", mean_acc_yes/tot_yes_count, " acc mean no : ", mean_acc_no/tot_no_count)
print(task)
class TrainingLinearDataset(DGLDataset):
def __init__(self, task, max_arg=MAX_ARG, device="cpu"):
self.task = task
self.max_arg = max_arg
self.device=device
super().__init__(name="Af dataset")
def __len__(self):
return len(self.instances)
def process(self):
list_year_dir = ["2017"]
self.af_dir = af_data_root+"dataset_af"
self.label_dir = result_root+"result_"+self.task
self.instances = []
self.labels = []
list_unique_file = []
for year in list_year_dir:
iter = os.listdir(self.label_dir +"_"+ year)
for f in iter:
true_name = f.replace(".apx", "")
true_name = true_name.replace(".af", "")
true_name = true_name.replace(".tgf", "")
true_name = true_name.replace(".old", "")
if true_name not in list_unique_file:
list_unique_file.append(true_name)
af_path = self.af_dir+"_"+year+"/"+f
label_path = self.label_dir+"_"+year+"/"+f
features_path = af_data_root+"all_features/"+year+"/"+f+".pt"
gs = get_gs(af_path, device=self.device)
label = transfom_to_graph(label_path, len(gs), device=self.device)
self.labels.append(label)
self.instances.append(torch.tensor(gs,requires_grad=True ,device=self.device))
def __getitem__(self, idx:int):
r = random.randint(0,len(self.instances[idx])-1)
return (self.instances[idx][r], torch.tensor([self.labels[idx][r]], device=self.device))
def get_gs(af_path, device="cpu"):
gs = af_reader_py.compute_only_gs(af_path)
return gs