Update README.md
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araison
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README.md
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README.md
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Here is the an example code for using EiXGNN from the `"EiX-GNN: Concept-level eigencentrality explainer for graph neural
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networks"<https://arxiv.org/abs/2206.03491>`_ paper
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```python
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from torch_geometric.datasets import TUDataset
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dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
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data = dataset[0]
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import torch.nn.functional as F
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from torch_geometric.nn import GCNConv, global_mean_pool
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class GCN(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = GCNConv(dataset.num_node_features, 20)
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self.conv2 = GCNConv(20, dataset.num_classes)
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def forward(self, data):
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x, edge_index, batch = data.x, data.edge_index, data.batch
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x = self.conv1(x, edge_index)
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x = F.relu(x)
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x = global_mean_pool(x, batch)
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x = F.softmax(x, dim=1)
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return x
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = GCN().to(device)
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model.eval()
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data = dataset[0].to(device)
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explainer = EiXGNN()
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explained = explainer.forward(model, data.x, data.edge_index)
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```
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115
eixgnn/eixgnn.py
115
eixgnn/eixgnn.py
@ -11,7 +11,6 @@ from torch.nn import KLDivLoss, Softmax
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from torch_geometric.data import Batch, Data
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from torch_geometric.explain import Explanation
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from torch_geometric.explain.algorithm.base import ExplainerAlgorithm
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# from torch_geometric.explain.algorithm.utils import clear_masks, set_masks
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from torch_geometric.explain.config import (ExplainerConfig, MaskType,
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ModelConfig, ModelMode,
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ModelTaskLevel)
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@ -129,7 +128,7 @@ class EiXGNN(ExplainerAlgorithm):
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f"This signal similarity metric : {signal_similarity['metric']} is not supported yet. No explanation provided."
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)
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return False
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# ADD OTHER CASE
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# TODO ADD OTHER CASE
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return True
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def get_mc_shapley(self, subset_node: list, data: Data) -> Tensor:
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@ -216,16 +215,6 @@ class EiXGNN(ExplainerAlgorithm):
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extended_map[i, indexes[i][j]] += abs(shap_vals[i][j])
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return extended_map
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# def _shapley_value_extended(self, concepts, shap_val, size=None):
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# extended_shap_val_matrix = np.zeros(size)
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# nodes_lists = [self._subgraph_node_mapping(concept) for concept in concepts]
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# for i in range(extended_shap_val_matrix.shape[0]):
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# concept_shap_val = shap_val[i]
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# node_list = nodes_lists[i]
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# for j, val in zip(node_list, concept_shap_val):
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# extended_shap_val_matrix[i, j] = val
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# return extended_shap_val_matrix
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def _global_concept_similarity_matrix(self, concepts):
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A = np.zeros((len(concepts), len(concepts)))
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concepts_pred = self.model(Batch().from_data_list(concepts))
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@ -249,80 +238,6 @@ class EiXGNN(ExplainerAlgorithm):
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pr = list(pagerank(G).values())
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return pr
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# def _shapley_value_concept(
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# self,
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# concept: Data,
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# ) -> np.ndarray:
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# g_plus_list = []
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# g_minus_list = []
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# val_shap = np.zeros(concept.x.shape[0])
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# for node_ind in range(concept.x.shape[0]):
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# for _ in range(self.shap_val_approx):
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# perm = torch.randperm(concept.x.shape[0])
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# x_perm = torch.clone(concept.x)
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# x_perm = x_perm[perm]
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# x_minus = torch.clone(concept.x)
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# x_plus = torch.clone(concept.x)
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# x_plus = torch.cat(
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# (x_plus[: node_ind + 1], x_perm[node_ind + 1 :]), axis=0
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# )
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# if node_ind == 0:
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# x_minus = torch.clone(x_perm)
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# else:
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# x_minus = torch.cat((x_minus[:node_ind], x_perm[node_ind:]), axis=0)
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# g_plus = concept.__copy__()
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# g_plus.x = x_plus
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# g_minus = concept.__copy__()
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# g_minus.x = x_minus
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# g_plus_list.append(g_plus)
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# g_minus_list.append(g_minus)
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# g_plus = Batch().from_data_list(g_plus_list)
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# g_minus = Batch().from_data_list(g_minus_list)
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# with torch.no_grad():
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# g_plus = g_plus.to(concept.x.device)
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# g_minus = g_minus.to(concept.x.device)
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# out_g_plus = self.model(g_plus)
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# out_g_minus = self.model(g_minus)
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# g_plus_score = F.softmax(out_g_plus, dim=1)
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# g_minus_score = F.softmax(out_g_minus, dim=1)
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# g_plus_score = g_plus_score.reshape(
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# (1, concept.x.shape[0], self.shap_val_approx, -1)
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# )
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# g_minus_score = g_minus_score.reshape(
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# (1, concept.x.shape[0], self.shap_val_approx, -1)
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# )
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# for node_ind in range(concept.x.shape[0]):
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# score = torch.mean(
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# torch.FloatTensor(
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# [
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# torch.norm(
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# input=g_plus_score[0, node_ind, i]
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# - g_minus_score[0, node_ind, i],
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# p=1,
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# )
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# for i in range(self.shap_val_approx)
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# ]
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# )
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# )
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# val_shap[node_ind] = score.item()
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# return val_shap
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# def _subgraph_node_mapping(self, concept):
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# nodes_list = torch.unique(concept.edge_index)
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# return nodes_list
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def _compute_node_ablation_prior(
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self,
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x: torch.Tensor,
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@ -353,32 +268,4 @@ class EiXGNN(ExplainerAlgorithm):
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if __name__ == "__main__":
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from torch_geometric.datasets import TUDataset
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dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
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data = dataset[0]
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import torch.nn.functional as F
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from torch_geometric.nn import GCNConv, global_mean_pool
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class GCN(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = GCNConv(dataset.num_node_features, 20)
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self.conv2 = GCNConv(20, dataset.num_classes)
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def forward(self, data):
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x, edge_index, batch = data.x, data.edge_index, data.batch
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x = self.conv1(x, edge_index)
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x = F.relu(x)
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x = global_mean_pool(x, batch)
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x = F.softmax(x, dim=1)
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return x
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = GCN().to(device)
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model.eval()
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data = dataset[0].to(device)
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# optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
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# explainer = EiXGNN()
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# explained = explainer.forward(model, data.x, data.edge_index)
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