update config and remove the randomforest judging

configs/config.yaml

@@ -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

graph_dit/analysis/rdkit_functions.py

@@ -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) """

graph_dit/analysis/visualization.py

@@ -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

graph_dit/output_graphs/final_graphs1212.txt

@@ -0,0 +1,101 @@
+graph, input_nasbench201, output_nasbench201
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graph_dit/output_graphs/final_graphs122318.txt

@@ -0,0 +1,101 @@
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graph_dit/output_graphs/final_graphs130942.txt

@@ -0,0 +1,101 @@
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