# torch_geometric.utils¶

 degree Computes the (unweighted) degree of a given one-dimensional index tensor. softmax Computes a sparsely evaluated softmax. dropout_adj Randomly drops edges from the adjacency matrix (edge_index, edge_attr) with probability p using samples from a Bernoulli distribution. sort_edge_index Row-wise sorts edge indices edge_index. is_undirected Returns True if the graph given by edge_index is undirected. to_undirected Converts the graph given by edge_index to an undirected graph such that $$(j,i) \in \mathcal{E}$$ for every edge $$(i,j) \in \mathcal{E}$$. contains_self_loops Returns True if the graph given by edge_index contains self-loops. remove_self_loops Removes every self-loop in the graph given by edge_index, so that $$(i,i) \not\in \mathcal{E}$$ for every $$i \in \mathcal{V}$$. segregate_self_loops Segregates self-loops from the graph. add_self_loops Adds a self-loop $$(i,i) \in \mathcal{E}$$ to every node $$i \in \mathcal{V}$$ in the graph given by edge_index. add_remaining_self_loops Adds remaining self-loop $$(i,i) \in \mathcal{E}$$ to every node $$i \in \mathcal{V}$$ in the graph given by edge_index. contains_isolated_nodes Returns True if the graph given by edge_index contains isolated nodes. remove_isolated_nodes Removes the isolated nodes from the graph given by edge_index with optional edge attributes edge_attr. subgraph Returns the induced subgraph of (edge_index, edge_attr) containing the nodes in subset. k_hop_subgraph Computes the $$k$$-hop subgraph of edge_index around node node_idx. homophily The homophily of a graph characterizes how likely nodes with the same label are near each other in a graph. get_laplacian Computes the graph Laplacian of the graph given by edge_index and optional edge_weight. to_dense_batch Given a sparse batch of node features $$\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}$$ (with $$N_i$$ indicating the number of nodes in graph $$i$$), creates a dense node feature tensor $$\mathbf{X} \in \mathbb{R}^{B \times N_{\max} \times F}$$ (with $$N_{\max} = \max_i^B N_i$$). to_dense_adj Converts batched sparse adjacency matrices given by edge indices and edge attributes to a single dense batched adjacency matrix. dense_to_sparse Converts a dense adjacency matrix to a sparse adjacency matrix defined by edge indices and edge attributes. normalized_cut Computes the normalized cut $$\mathbf{e}_{i,j} \cdot \left( \frac{1}{\deg(i)} + \frac{1}{\deg(j)} \right)$$ of a weighted graph given by edge indices and edge attributes. grid Returns the edge indices of a two-dimensional grid graph with height height and width width and its node positions. geodesic_distance Computes (normalized) geodesic distances of a mesh given by pos and face. tree_decomposition The tree decomposition algorithm of molecules from the “Junction Tree Variational Autoencoder for Molecular Graph Generation” paper. to_scipy_sparse_matrix Converts a graph given by edge indices and edge attributes to a scipy sparse matrix. from_scipy_sparse_matrix Converts a scipy sparse matrix to edge indices and edge attributes. to_networkx Converts a torch_geometric.data.Data instance to a networkx.Graph if to_undirected is set to True, or a directed networkx.DiGraph otherwise. from_networkx Converts a networkx.Graph or networkx.DiGraph to a torch_geometric.data.Data instance. to_trimesh Converts a torch_geometric.data.Data instance to a trimesh.Trimesh. from_trimesh Converts a trimesh.Trimesh to a torch_geometric.data.Data instance. to_cugraph Converts a graph given by edge_index and optional edge_weight into a cugraph graph object. erdos_renyi_graph Returns the edge_index of a random Erdos-Renyi graph. stochastic_blockmodel_graph Returns the edge_index of a stochastic blockmodel graph. barabasi_albert_graph Returns the edge_index of a Barabasi-Albert preferential attachment model, where a graph of num_nodes nodes grows by attaching new nodes with num_edges edges that are preferentially attached to existing nodes with high degree. negative_sampling Samples random negative edges of a graph given by edge_index. structured_negative_sampling Samples a negative edge (i,k) for every positive edge (i,j) in the graph given by edge_index, and returns it as a tuple of the form (i,j,k). batched_negative_sampling Samples random negative edges of multiple graphs given by edge_index and batch. train_test_split_edges Splits the edges of a torch_geometric.data.Data object into positive and negative train/val/test edges. accuracy Computes the accuracy of predictions. true_positive Computes the number of true positive predictions. true_negative Computes the number of true negative predictions. false_positive Computes the number of false positive predictions. false_negative Computes the number of false negative predictions. precision Computes the precision $$\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}$$ of predictions. recall Computes the recall $$\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}$$ of predictions. f1_score Computes the $$F_1$$ score $$2 \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}} {\mathrm{precision}+\mathrm{recall}}$$ of predictions. intersection_and_union Computes intersection and union of predictions. mean_iou Computes the mean intersection over union score of predictions.
accuracy(pred, target)[source]

Computes the accuracy of predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

Return type

float

add_remaining_self_loops(edge_index, edge_weight: Optional[torch.Tensor] = None, fill_value: float = 1.0, num_nodes: Optional[int] = None)[source]

Adds remaining self-loop $$(i,i) \in \mathcal{E}$$ to every node $$i \in \mathcal{V}$$ in the graph given by edge_index. In case the graph is weighted and already contains a few self-loops, only non-existent self-loops will be added with edge weights denoted by fill_value.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_weight (Tensor, optional) – One-dimensional edge weights. (default: None)

• fill_value (float, optional) – If edge_weight is not None, will add self-loops with edge weights of fill_value to the graph. (default: 1.)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

(LongTensor, Tensor)

add_self_loops(edge_index, edge_weight: Optional[torch.Tensor] = None, fill_value: float = 1.0, num_nodes: Optional[int] = None)[source]

Adds a self-loop $$(i,i) \in \mathcal{E}$$ to every node $$i \in \mathcal{V}$$ in the graph given by edge_index. In case the graph is weighted, self-loops will be added with edge weights denoted by fill_value.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_weight (Tensor, optional) – One-dimensional edge weights. (default: None)

• fill_value (float, optional) – If edge_weight is not None, will add self-loops with edge weights of fill_value to the graph. (default: 1.)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

(LongTensor, Tensor)

barabasi_albert_graph(num_nodes, num_edges)[source]

Returns the edge_index of a Barabasi-Albert preferential attachment model, where a graph of num_nodes nodes grows by attaching new nodes with num_edges edges that are preferentially attached to existing nodes with high degree.

Parameters
• num_nodes (int) – The number of nodes.

• num_edges (int) – The number of edges from a new node to existing nodes.

batched_negative_sampling(edge_index, batch, num_neg_samples=None, method='sparse', force_undirected=False)[source]

Samples random negative edges of multiple graphs given by edge_index and batch.

Parameters
• edge_index (LongTensor) – The edge indices.

• batch (LongTensor) – Batch vector $$\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N$$, which assigns each node to a specific example.

• num_neg_samples (int, optional) – The number of negative samples to return. If set to None, will try to return a negative edge for every positive edge. (default: None)

• method (string, optional) – The method to use for negative sampling, i.e., "sparse" or "dense". This is a memory/runtime trade-off. "sparse" will work on any graph of any size, while "dense" can perform faster true-negative checks. (default: "sparse")

• force_undirected (bool, optional) – If set to True, sampled negative edges will be undirected. (default: False)

Return type

LongTensor

contains_isolated_nodes(edge_index, num_nodes=None)[source]

Returns True if the graph given by edge_index contains isolated nodes.

Parameters
• edge_index (LongTensor) – The edge indices.

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

bool

contains_self_loops(edge_index)[source]

Returns True if the graph given by edge_index contains self-loops.

Parameters

edge_index (LongTensor) – The edge indices.

Return type

bool

degree(index, num_nodes: Optional[int] = None, dtype: Optional[int] = None)[source]

Computes the (unweighted) degree of a given one-dimensional index tensor.

Parameters
• index (LongTensor) – Index tensor.

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of index. (default: None)

• dtype (torch.dtype, optional) – The desired data type of the returned tensor.

Return type

Tensor

Converts a dense adjacency matrix to a sparse adjacency matrix defined by edge indices and edge attributes.

Parameters

dropout_adj(edge_index, edge_attr=None, p=0.5, force_undirected=False, num_nodes=None, training=True)[source]

Randomly drops edges from the adjacency matrix (edge_index, edge_attr) with probability p using samples from a Bernoulli distribution.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• p (float, optional) – Dropout probability. (default: 0.5)

• force_undirected (bool, optional) – If set to True, will either drop or keep both edges of an undirected edge. (default: False)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

• training (bool, optional) – If set to False, this operation is a no-op. (default: True)

erdos_renyi_graph(num_nodes, edge_prob, directed=False)[source]

Returns the edge_index of a random Erdos-Renyi graph.

Parameters
f1_score(pred, target, num_classes)[source]

Computes the $$F_1$$ score $$2 \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}} {\mathrm{precision}+\mathrm{recall}}$$ of predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

Tensor

false_negative(pred, target, num_classes)[source]

Computes the number of false negative predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

LongTensor

false_positive(pred, target, num_classes)[source]

Computes the number of false positive predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

LongTensor

from_networkx(G)[source]

Converts a networkx.Graph or networkx.DiGraph to a torch_geometric.data.Data instance.

Parameters

G (networkx.Graph or networkx.DiGraph) – A networkx graph.

from_scipy_sparse_matrix(A)[source]

Converts a scipy sparse matrix to edge indices and edge attributes.

Parameters

A (scipy.sparse) – A sparse matrix.

from_trimesh(mesh)[source]

Converts a trimesh.Trimesh to a torch_geometric.data.Data instance.

Parameters

mesh (trimesh.Trimesh) – A trimesh mesh.

geodesic_distance(pos, face, src=None, dest=None, norm=True, max_distance=None, num_workers=0)[source]

Computes (normalized) geodesic distances of a mesh given by pos and face. If src and dest are given, this method only computes the geodesic distances for the respective source and target node-pairs.

Note

This function requires the gdist package. To install, run pip install cython && pip install gdist.

Parameters
• pos (Tensor) – The node positions.

• face (LongTensor) – The face indices.

• src (LongTensor, optional) – If given, only compute geodesic distances for the specified source indices. (default: None)

• dest (LongTensor, optional) – If given, only compute geodesic distances for the specified target indices. (default: None)

• norm (bool, optional) – Normalizes geodesic distances by $$\sqrt{\textrm{area}(\mathcal{M})}$$. (default: True)

• max_distance (float, optional) – If given, only yields results for geodesic distances less than max_distance. This will speed up runtime dramatically. (default: None)

• num_workers (int, optional) – How many subprocesses to use for calculating geodesic distances. 0 means that computation takes place in the main process. -1 means that the available amount of CPU cores is used. (default: 0)

Return type

Tensor

get_laplacian(edge_index, edge_weight: Optional[torch.Tensor] = None, normalization: Optional[str] = None, dtype: Optional[int] = None, num_nodes: Optional[int] = None)[source]

Computes the graph Laplacian of the graph given by edge_index and optional edge_weight.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_weight (Tensor, optional) – One-dimensional edge weights. (default: None)

• normalization (str, optional) –

The normalization scheme for the graph Laplacian (default: None):

1. None: No normalization $$\mathbf{L} = \mathbf{D} - \mathbf{A}$$

2. "sym": Symmetric normalization $$\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}$$

3. "rw": Random-walk normalization $$\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1} \mathbf{A}$$

• dtype (torch.dtype, optional) – The desired data type of returned tensor in case edge_weight=None. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

grid(height, width, dtype=None, device=None)[source]

Returns the edge indices of a two-dimensional grid graph with height height and width width and its node positions.

Parameters
• height (int) – The height of the grid.

• width (int) – The width of the grid.

• dtype (torch.device, optional) – The desired data type of the returned position tensor.

• dtype – The desired device of the returned tensors.

Return type

(LongTensor, Tensor)

homophily(edge_index: Union[torch.Tensor, torch_sparse.tensor.SparseTensor], y: torch.Tensor, method: str = 'edge')[source]

The homophily of a graph characterizes how likely nodes with the same label are near each other in a graph. There are many measures of homophily that fits this definition. In particular:

Parameters
• edge_index (Tensor or SparseTensor) – The graph connectivity.

• y (Tensor) – The labels.

• method (str, optional) – The method used to calculate the homophily, either "edge" (first formula) or "node" (second formula). (default: "edge")

intersection_and_union(pred, target, num_classes, batch=None)[source]

Computes intersection and union of predictions.

Parameters
• pred (LongTensor) – The predictions.

• target (LongTensor) – The targets.

• num_classes (int) – The number of classes.

• batch (LongTensor) – The assignment vector which maps each pred-target pair to an example.

Return type

(LongTensor, LongTensor)

is_undirected(edge_index: torch.Tensor, edge_attr: Optional[torch.Tensor] = None, num_nodes: Optional[int] = None)bool[source]

Returns True if the graph given by edge_index is undirected.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

bool

k_hop_subgraph(node_idx, num_hops, edge_index, relabel_nodes=False, num_nodes=None, flow='source_to_target')[source]

Computes the $$k$$-hop subgraph of edge_index around node node_idx. It returns (1) the nodes involved in the subgraph, (2) the filtered edge_index connectivity, (3) the mapping from node indices in node_idx to their new location, and (4) the edge mask indicating which edges were preserved.

Parameters
• node_idx (int, list, tuple or torch.Tensor) – The central node(s).

• num_hops – (int): The number of hops $$k$$.

• edge_index (LongTensor) – The edge indices.

• relabel_nodes (bool, optional) – If set to True, the resulting edge_index will be relabeled to hold consecutive indices starting from zero. (default: False)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

• flow (string, optional) – The flow direction of $$k$$-hop aggregation ("source_to_target" or "target_to_source"). (default: "source_to_target")

Return type

(LongTensor, LongTensor, LongTensor, BoolTensor)

mean_iou(pred, target, num_classes, batch=None)[source]

Computes the mean intersection over union score of predictions.

Parameters
• pred (LongTensor) – The predictions.

• target (LongTensor) – The targets.

• num_classes (int) – The number of classes.

• batch (LongTensor) – The assignment vector which maps each pred-target pair to an example.

Return type

Tensor

negative_sampling(edge_index, num_nodes=None, num_neg_samples=None, method='sparse', force_undirected=False)[source]

Samples random negative edges of a graph given by edge_index.

Parameters
• edge_index (LongTensor) – The edge indices.

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

• num_neg_samples (int, optional) – The (approximate) number of negative samples to return. If set to None, will try to return a negative edge for every positive edge. (default: None)

• method (string, optional) – The method to use for negative sampling, i.e., "sparse" or "dense". This is a memory/runtime trade-off. "sparse" will work on any graph of any size, while "dense" can perform faster true-negative checks. (default: "sparse")

• force_undirected (bool, optional) – If set to True, sampled negative edges will be undirected. (default: False)

Return type

LongTensor

normalized_cut(edge_index, edge_attr, num_nodes: Optional[int] = None)[source]

Computes the normalized cut $$\mathbf{e}_{i,j} \cdot \left( \frac{1}{\deg(i)} + \frac{1}{\deg(j)} \right)$$ of a weighted graph given by edge indices and edge attributes.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor) – Edge weights or multi-dimensional edge features.

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

Tensor

precision(pred, target, num_classes)[source]

Computes the precision $$\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}$$ of predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

Tensor

recall(pred, target, num_classes)[source]

Computes the recall $$\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}$$ of predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

Tensor

remove_isolated_nodes(edge_index, edge_attr=None, num_nodes=None)[source]

Removes the isolated nodes from the graph given by edge_index with optional edge attributes edge_attr. In addition, returns a mask of shape [num_nodes] to manually filter out isolated node features later on. Self-loops are preserved for non-isolated nodes.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

(LongTensor, Tensor, BoolTensor)

remove_self_loops(edge_index, edge_attr: Optional[torch.Tensor] = None)[source]

Removes every self-loop in the graph given by edge_index, so that $$(i,i) \not\in \mathcal{E}$$ for every $$i \in \mathcal{V}$$.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

Return type

(LongTensor, Tensor)

segregate_self_loops(edge_index, edge_attr: Optional[torch.Tensor] = None)[source]

Segregates self-loops from the graph.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

Return type

(LongTensor, Tensor, LongTensor, Tensor)

sort_edge_index(edge_index, edge_attr=None, num_nodes=None)[source]

Row-wise sorts edge indices edge_index.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

(LongTensor, Tensor)

stochastic_blockmodel_graph(block_sizes, edge_probs, directed=False)[source]

Returns the edge_index of a stochastic blockmodel graph.

Parameters
• block_sizes ([int] or LongTensor) – The sizes of blocks.

• edge_probs ([[float]] or FloatTensor) – The density of edges going

• each block to each other block. Must be symmetric if the graph is (from) – undirected.

• directed (bool, optional) – If set to True, will return a directed graph. (default: False)

structured_negative_sampling(edge_index, num_nodes=None)[source]

Samples a negative edge (i,k) for every positive edge (i,j) in the graph given by edge_index, and returns it as a tuple of the form (i,j,k).

Parameters
• edge_index (LongTensor) – The edge indices.

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

(LongTensor, LongTensor, LongTensor)

subgraph(subset, edge_index, edge_attr=None, relabel_nodes=False, num_nodes=None)[source]

Returns the induced subgraph of (edge_index, edge_attr) containing the nodes in subset.

Parameters
• subset (LongTensor, BoolTensor or [int]) – The nodes to keep.

• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• relabel_nodes (bool, optional) – If set to True, the resulting edge_index will be relabeled to hold consecutive indices starting from zero. (default: False)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type

(LongTensor, Tensor)

to_cugraph(edge_index: torch.Tensor, edge_weight: Optional[torch.Tensor] = None, relabel_nodes: bool = True)[source]

Converts a graph given by edge_index and optional edge_weight into a cugraph graph object.

Parameters

relabel_nodes (bool, optional) – If set to True, cugraph will remove any isolated nodes, leading to a relabeling of nodes. (default: True)

to_dense_adj(edge_index, batch=None, edge_attr=None, max_num_nodes=None)[source]

Converts batched sparse adjacency matrices given by edge indices and edge attributes to a single dense batched adjacency matrix.

Parameters
• edge_index (LongTensor) – The edge indices.

• batch (LongTensor, optional) – Batch vector $$\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N$$, which assigns each node to a specific example. (default: None)

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• max_num_nodes (int, optional) – The size of the output node dimension. (default: None)

Return type

Tensor

to_dense_batch(x, batch=None, fill_value=0, max_num_nodes=None)[source]

Given a sparse batch of node features $$\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}$$ (with $$N_i$$ indicating the number of nodes in graph $$i$$), creates a dense node feature tensor $$\mathbf{X} \in \mathbb{R}^{B \times N_{\max} \times F}$$ (with $$N_{\max} = \max_i^B N_i$$). In addition, a second tensor holding $$[N_1, \ldots, N_B] \in \mathbb{N}^B$$ is returned.

Parameters
• x (Tensor) – Node feature matrix $$\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}$$.

• batch (LongTensor, optional) – Batch vector $$\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N$$, which assigns each node to a specific example. (default: None)

• fill_value (float, optional) – The value for invalid entries in the resulting dense output tensor. (default: 0)

• max_num_nodes (int, optional) – The size of the output node dimension. (default: None)

Return type

(Tensor, BoolTensor)

to_networkx(data, node_attrs=None, edge_attrs=None, to_undirected=False, remove_self_loops=False)[source]

Converts a torch_geometric.data.Data instance to a networkx.Graph if to_undirected is set to True, or a directed networkx.DiGraph otherwise.

Parameters
to_scipy_sparse_matrix(edge_index, edge_attr=None, num_nodes=None)[source]

Converts a graph given by edge indices and edge attributes to a scipy sparse matrix.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of index. (default: None)

to_trimesh(data)[source]

Converts a torch_geometric.data.Data instance to a trimesh.Trimesh.

Parameters

data (torch_geometric.data.Data) – The data object.

to_undirected(edge_index: torch.Tensor, edge_attr: Optional[torch.Tensor] = None, num_nodes: Optional[int] = None, reduce: str = 'add')Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]][source]

Converts the graph given by edge_index to an undirected graph such that $$(j,i) \in \mathcal{E}$$ for every edge $$(i,j) \in \mathcal{E}$$.

Parameters
• edge_index (LongTensor) – The edge indices.

• edge_attr (Tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

• reduce (string, optional) – The reduce operation to use for merging edge features. (default: "add")

Return type

LongTensor if edge_attr is None, else (LongTensor, Tensor)

train_test_split_edges(data, val_ratio: float = 0.05, test_ratio: float = 0.1)[source]

Splits the edges of a torch_geometric.data.Data object into positive and negative train/val/test edges. As such, it will replace the edge_index attribute with train_pos_edge_index, train_pos_neg_adj_mask, val_pos_edge_index, val_neg_edge_index and test_pos_edge_index attributes. If data has edge features named edge_attr, then train_pos_edge_attr, val_pos_edge_attr and test_pos_edge_attr will be added as well.

Parameters
• data (Data) – The data object.

• val_ratio (float, optional) – The ratio of positive validation edges. (default: 0.05)

• test_ratio (float, optional) – The ratio of positive test edges. (default: 0.1)

Return type

torch_geometric.data.Data

tree_decomposition(mol, return_vocab=False)[source]

The tree decomposition algorithm of molecules from the “Junction Tree Variational Autoencoder for Molecular Graph Generation” paper. Returns the graph connectivity of the junction tree, the assignment mapping of each atom to the clique in the junction tree, and the number of cliques.

Parameters
• mol (rdkit.Chem.Mol) – A rdkit molecule.

• return_vocab (bool, optional) – If set to True, will return an identifier for each clique (ring, bond, bridged compounds, single). (default: False)

Return type

(LongTensor, LongTensor, int)

true_negative(pred, target, num_classes)[source]

Computes the number of true negative predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

LongTensor

true_positive(pred, target, num_classes)[source]

Computes the number of true positive predictions.

Parameters
• pred (Tensor) – The predictions.

• target (Tensor) – The targets.

• num_classes (int) – The number of classes.

Return type

LongTensor