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update print and output json statements

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mhz 2024-07-03 15:26:12 +02:00
parent 73324083ce
commit d44900c8ba
2 changed files with 74 additions and 7 deletions
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30
graph_dit/metrics/molecular_metrics_sampling.py
View File

@ -1,5 +1,6 @@
### packages for visualization
from analysis.rdkit_functions import compute_molecular_metrics
from analysis.rdkit_functions import compute_graph_metrics
from mini_moses.metrics.metrics import compute_intermediate_statistics
from metrics.property_metric import TaskModel
@ -49,8 +50,8 @@ class SamplingGraphMetrics(nn.Module):
self.task_evaluator = {
'meta_taskname': dataset_infos.task,
'sas': None,
'scs': None
# 'sas': None,
# 'scs': None
}
for cur_task in dataset_infos.task.split("-")[:]:
@ -62,13 +63,14 @@ class SamplingGraphMetrics(nn.Module):
self.task_evaluator[cur_task] = evaluator
def forward(self, graphs, targets, name, current_epoch, val_counter, test=False):
test = True
if isinstance(targets, list):
targets_cat = torch.cat(targets, dim=0)
targets_np = targets_cat.detach().cpu().numpy()
else:
targets_np = targets.detach().cpu().numpy()
unique_graphs, all_graphs, all_graphs, targets_log = compute_molecular_metrics(
unique_graphs, all_graphs, all_metrics, targets_log = compute_graph_metrics(
graphs,
targets_np,
self.train_graphs,
@ -77,6 +79,22 @@ class SamplingGraphMetrics(nn.Module):
self.task_evaluator,
self.compute_config,
)
print(f"all graphs: {all_graphs}")
print(f"all graphs[0]: {all_graphs[0]}")
tmp_graphs = all_graphs.copy()
str_graphs = []
for graph in tmp_graphs:
node_types = graph[0]
edge_types = graph[1]
node_str = " ".join([str(node) for node in node_types])
edge_str_list = []
for i in range(len(node_types)):
for j in range(len(node_types)):
edge_str_list.append(str(edge_types[i][j]))
edge_str_list.append("/n")
edge_str = " ".join(edge_str_list)
str_graphs.append(f"nodes: {node_str} /n edges: /n{edge_str}")
if test:
file_name = "final_graphs.txt"
@ -88,7 +106,7 @@ class SamplingGraphMetrics(nn.Module):
all_tasks_str = "graph, " + ", ".join([f"input_{task}" for task in all_tasks_name] + [f"output_{task}" for task in all_tasks_name])
fp.write(all_tasks_str + "\n")
for i, graph in enumerate(all_graphs):
for i, graph in enumerate(str_graphs):
if targets_log is not None:
all_result_str = f"{graph}, " + ", ".join([f"{targets_log['input_'+task][i]}" for task in all_tasks_name] + [f"{targets_log['output_'+task][i]}" for task in all_tasks_name])
fp.write(all_result_str + "\n")
@ -107,7 +125,7 @@ class SamplingGraphMetrics(nn.Module):
textfile.write(graph + "\n")
textfile.close()
all_logs = all_graphs
all_logs = all_metrics
if test:
all_logs["log_name"] = "test"
else:
@ -116,7 +134,7 @@ class SamplingGraphMetrics(nn.Module):
)
result_to_csv("output.csv", all_logs)
return all_graphs
return str_graphs
def reset(self):
pass

51
graph_dit/metrics/property_metric.py
View File

@ -102,6 +102,7 @@ class TaskModel():
mask = ~np.isnan(labels)
labels = labels[mask]
features = features[mask]
# features = str(features)
self.model.fit(features, labels)
y_pred = self.model.predict(features)
perf = self.metric_func(labels, y_pred)
@ -136,7 +137,7 @@ class TaskModel():
print(f'{self.task_name} performance: {perf}')
return perf
def __call__(self, smiles_list):
def __call(self, smiles_list):
fps = []
mask = []
for i,smiles in enumerate(smiles_list):
@ -153,6 +154,54 @@ class TaskModel():
scores = scores * np.array(mask)
return np.float32(scores)
def __call__(self, graph_list):
# def read_adj_ops_from_json(filename):
# with open(filename, 'r') as json_file:
# data = json.load(json_file)
# adj_ops_pairs = []
# for item in data:
# adj_matrix = np.array(item['adj_matrix'])
# ops = item['ops']
# acc = item['train'][0]['accuracy']
# adj_ops_pairs.append((adj_matrix, ops, acc))
# return adj_ops_pairs
def feature_from_adj_and_ops(ops, adj):
return np.concatenate([adj.flatten(), ops])
# filename = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
# graphs = read_adj_ops_from_json(filename)
# adjs = []
# opss = []
# accs = []
# features = []
# for graph in graphs:
# adj, ops, acc=graph
# op_code = [op_type[op] for op in ops]
# adjs.append(adj)
# opss.append(op_code)
# accs.append(acc)
features = []
print(f"graphlist: {graph_list[0]}")
print(f"len graphlist: {len(graph_list)}")
for op_code, adj in graph_list:
features.append(feature_from_adj_and_ops(op_code, adj))
print(f"len features: {len(features)}")
# print(f"features: {features[0].shape}")
features = np.stack(features)
features = features.astype(np.float32)
print(f"features shape: {features.shape}")
fps = features
if 'classification' in self.task_type:
scores = self.model.predict_proba(fps)[:, 1]
else:
scores = self.model.predict(fps)
# scores = scores * np.array(mask)
return np.float32(scores)
@classmethod
def fingerprints_from_mol(cls, mol): # use ECFP4
features_vec = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048)