iicd/Graph-DiT
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update config and remove the randomforest judging

Browse Source
This commit is contained in:
mhz 2024-08-13 16:49:53 +02:00
parent b47e83330e
commit d800679b88
3 changed files with 186 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
configs/config.yaml
View File

@ -2,7 +2,7 @@ general:
name: 'graph_dit'
wandb: 'disabled'
gpus: 1
gpu_number: 3
gpu_number: 0
resume: null
test_only: null
sample_every_val: 2500
@ -31,7 +31,7 @@ model:
lambda_train: [1, 10] # node and edge training weight
ensure_connected: True
train:
n_epochs: 5000
n_epochs: 500
batch_size: 1200
lr: 0.0002
clip_grad: null

150
graph_dit/analysis/rdkit_functions.py
View File

@ -37,6 +37,144 @@ def selectivity_evaluation(gas1, gas2, prop_name):
y = np.log10(np.array(gas1) / np.array(gas2))
upper = (y - (a_dict[prop_name] * x + b_dict[prop_name])) > 0
return upper
class BasicGraphMetrics(object):
def __init__(self, graph_decoder, train_graphs=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
self.dataset_graphs_list = train_graphs
self.graph_decoder = graph_decoder
self.n_jobs = n_jobs
self.device = device
self.batch_size = batch_size
self.stat_ref = stat_ref
self.task_evaluator = task_evaluator
def compute_relaxed_validity(self, generated, ensure_connected):
valid = []
num_components = []
all_graphs = []
valid_graphs = []
covered_nodes = set()
direct_valid_count = 0
print(f"generated number: {len(generated)}")
for graph in generated:
node_types, edge_types = graph
direct_valid_flag = True
direct_valid_count += 1
valid.append(graph)
num_components.append(1)
covered_nodes.update(set(node_types))
all_graphs.append(graph)
return valid, len(valid) / len(generated), direct_valid_count / len(generated), np.array(num_components), all_graphs, covered_nodes
def evaluate(self, generated, targets, ensure_connected, active_atoms=None):
valid, validity, nc_validity, num_components, all_graphs, covered_nodes = self.compute_relaxed_validity(generated, ensure_connected=ensure_connected)
nc_mu = num_components.mean() if len(num_components) > 0 else 0
nc_min = num_components.min() if len(num_components) > 0 else 0
nc_max = num_components.max() if len(num_components) > 0 else 0
len_active = len(active_atoms) if active_atoms is not None else 1
cover_str = f"Cover {len(covered_nodes)} ({len(covered_nodes)/len_active * 100:.2f}%) atoms: {covered_nodes}"
print(f"Validity over {len(generated)} graphs: {validity * 100 :.2f}% (w/o correction: {nc_validity * 100 :.2f}%), cover {len(covered_nodes)} ({len(covered_nodes)/len_active * 100:.2f}%) nodes: {covered_nodes}")
print(f"Number of connected components of {len(generated)} graphs: min:{nc_min:.2f} mean:{nc_mu:.2f} max:{nc_max:.2f}")
if validity > 0:
dist_metrics = {'cover_str': cover_str ,'validity': validity, 'validity_nc': nc_validity}
unique = valid
close_pool = False
if self.n_jobs != 1:
pool = Pool(self.n_jobs)
close_pool = True
else:
pool = 1
# valid_graphs = mapper(pool)(get_mol, valid)
valid_graphs = valid
"""
Computes internal diversity as:
1/|A|^2 sum_{x, y in AxA} (1-tanimoto(x, y))
"""
# dist_metrics['interval_diversity'] = internal_diversity(valid_graphs, pool, device=self.device)
start_time = time.time()
if self.stat_ref is not None:
kwargs = {'n_jobs': pool, 'device': self.device, 'batch_size': self.batch_size}
kwargs_fcd = {'n_jobs': self.n_jobs, 'device': self.device, 'batch_size': self.batch_size}
try:
dist_metrics['sim/Frag'] = FragMetric(**kwargs)(gen=valid_graphs, pref=self.stat_ref['Frag'])
except:
print('error: ', 'pool', pool)
print('valid_graphs: ', valid_graphs)
dist_metrics['dist/FCD'] = FCDMetric(**kwargs_fcd)(gen=valid, pref=self.stat_ref['FCD'])
if self.task_evaluator is not None:
evaluation_list = list(self.task_evaluator.keys())
print('evaluation_list: ', evaluation_list)
evaluation_list = evaluation_list.copy()
assert 'meta_taskname' in evaluation_list
meta_taskname = self.task_evaluator['meta_taskname']
evaluation_list.remove('meta_taskname')
# meta_split = meta_taskname.split('-')
valid_index = np.array([True if graphs else False for graphs in all_graphs])
targets_log = {}
for i, name in enumerate(evaluation_list):
targets_log[f'input_{name}'] = np.array([float('nan')] * len(valid_index))
targets_log[f'input_{name}'] = targets[:, i]
targets = targets[valid_index]
# if len(meta_split) == 2:
# cached_perm = {meta_split[0]: None, meta_split[1]: None}
for i, name in enumerate(evaluation_list):
# if name == 'scs':
# continue
# elif name == 'sas':
# scores = calculateSAS(valid)
# else:
# scores = self.task_evaluator[name](valid)
# fix the scores
scores = np.random.rand(len(valid_index))
targets_log[f'output_{name}'] = np.array([float('nan')] * len(valid_index))
targets_log[f'output_{name}'][valid_index] = scores
# if name in ['O2', 'N2', 'CO2']:
# if len(meta_split) == 2:
# cached_perm[name] = scores
# scores, cur_targets = np.log10(scores), np.log10(targets[:, i])
# dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - cur_targets))
# elif name == 'sas':
# dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - targets[:, i]))
# else:
true_y = targets[:, i]
predicted_labels = (scores >= 0.5).astype(int)
acc = (predicted_labels == true_y).sum() / len(true_y)
dist_metrics[f'{name}/acc'] = acc
# if len(meta_split) == 2:
# if cached_perm[meta_split[0]] is not None and cached_perm[meta_split[1]] is not None:
# task_name = self.task_evaluator['meta_taskname']
# upper = selectivity_evaluation(cached_perm[meta_split[0]], cached_perm[meta_split[1]], task_name)
# dist_metrics[f'selectivity/{task_name}'] = np.sum(upper)
end_time = time.time()
elapsed_time = end_time - start_time
max_key_length = max(len(key) for key in dist_metrics)
print(f'Details over {len(valid)} ({len(generated)}) valid (total) graphs, calculating metrics using {elapsed_time:.2f} s:')
strs = ''
for i, (key, value) in enumerate(dist_metrics.items()):
if isinstance(value, (int, float, np.floating, np.integer)):
strs = strs + f'{key:>{max_key_length}}:{value:<7.4f}\t'
if i % 4 == 3:
strs = strs + '\n'
print(strs)
if close_pool:
pool.close()
pool.join()
else:
unique = []
dist_metrics = {}
targets_log = None
return unique, dict(nc_min=nc_min, nc_max=nc_max, nc_mu=nc_mu), all_graphs, dist_metrics, targets_log
class BasicMolecularMetrics(object):
def __init__(self, atom_decoder, train_smiles=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
@ -388,6 +526,18 @@ def connect_fragments(mol):
return combined_mol
#### connect fragements
def compute_graph_metrics(graph_list, targets, train_graphs, stat_ref, dataset_info, task_evaluator, comput_config):
""" graph_list: (dict) """
node_decoder = dataset_info.node_decoder
active_nodes = dataset_info.active_nodes
ensure_connected = dataset_info.ensure_connected
metrics = BasicGraphMetrics(node_decoder, train_graphs, stat_ref, task_evaluator, **comput_config)
evaluated_res = metrics.evaluate(graph_list, targets, ensure_connected, active_nodes)
all_graphs = evaluated_res[-3]
all_metrics = evaluated_res[-2]
targets_log = evaluated_res[-1]
unique_graphs = evaluated_res[0]
return unique_graphs, all_graphs, all_metrics, targets_log
def compute_molecular_metrics(molecule_list, targets, train_smiles, stat_ref, dataset_info, task_evaluator, comput_config):
""" molecule_list: (dict) """

34
graph_dit/analysis/visualization.py
View File

@ -10,7 +10,41 @@ import numpy as np
import rdkit.Chem
import matplotlib.pyplot as plt
class GraphVisualization:
def __init__(self, dataset_infos):
self.dataset_infos = dataset_infos
def graph_from_graphs(self, node_list, adjency_matrix):
"""
Convert graphs to networkx graphs
node_list: the nodes of a batch of nodes (bs x n)
adjacency_matrix: the adjacency_matrix of the molecule (bs x n x n)
"""
graph = nx.Graph()
for i in range(len(node_list)):
if node_list[i] == -1:
continue
graph.add_node(i, number=i, symbol=node_list[i], color_val=node_list[i])
rows, cols = np.where(adjency_matrix >= 1)
edges = zip(rows.tolist(), cols.tolist())
for edge in edges:
edge_type = adjency_matrix[edge[0]][edge[1]]
graph.add_edge(edge[0], edge[1], color=float(edge_type), weight=3 * edge_type)
return graph
def visualize(self, path: str, graphs: list, num_graphs_to_visualize: int, log='graph'):
# define path to save figures
if not os.path.exists(path):
os.makedirs(path)
# visualize the final molecules
for i in range(num_graphs_to_visualize):
file_path = os.path.join(path, 'graph_{}.png'.format(i))
graph = self.graph_from_graphs(graphs[i][0].numpy(), graphs[i][1].numpy())
self.visualize_graph(graph=graph, pos=None, path=file_path)
im = plt.imread(file_path)
class MolecularVisualization:
def __init__(self, dataset_infos):
self.dataset_infos = dataset_infos