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spektral | spektral-master/spektral/layers/convolutional/general_conv.py | import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras.layers import BatchNormalization, Dropout, PReLU
from spektral.layers.convolutional.message_passing import MessagePassing
class GeneralConv(MessagePassing):
r"""
A general convolutional layer from the paper
> [Design ... | 5,435 | 32.975 | 84 | py |
spektral | spektral-master/spektral/layers/convolutional/gin_conv.py | import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras.layers import BatchNormalization, Dense
from tensorflow.keras.models import Sequential
from spektral.layers import ops
from spektral.layers.convolutional.message_passing import MessagePassing
class GINConv(MessagePassing):
r""... | 5,345 | 32.4125 | 86 | py |
spektral | spektral-master/spektral/layers/convolutional/graphsage_conv.py | from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.convolutional.message_passing import MessagePassing
class GraphSageConv(MessagePassing):
r"""
A GraphSAGE layer from the paper
> [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.0... | 3,941 | 31.04878 | 95 | py |
spektral | spektral-master/spektral/layers/convolutional/edge_conv.py | from tensorflow.keras import activations
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from spektral.layers.convolutional.message_passing import MessagePassing
class EdgeConv(MessagePassing):
r"""
An edge convolutional layer... | 4,206 | 31.612403 | 92 | py |
spektral | spektral-master/spektral/layers/convolutional/gated_graph_conv.py | import tensorflow as tf
from tensorflow.keras.layers import GRUCell
from spektral.layers.convolutional.message_passing import MessagePassing
class GatedGraphConv(MessagePassing):
r"""
A gated graph convolutional layer from the paper
> [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.054... | 4,254 | 32.242188 | 82 | py |
spektral | spektral-master/spektral/layers/convolutional/ecc_conv.py | import warnings
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.layers.ops import modes
class ECCConv(Conv):
r"""
An edge-conditioned convolutional ... | 6,994 | 34.871795 | 82 | py |
spektral | spektral-master/spektral/layers/convolutional/gcn_conv.py | from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.utils import gcn_filter
class GCNConv(Conv):
r"""
A graph convolutional layer (GCN) from the paper
> [Semi-Supervised Classification with Graph Convolutional Networ... | 3,695 | 30.322034 | 110 | py |
spektral | spektral-master/spektral/layers/convolutional/gtv_conv.py | import tensorflow as tf
from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
class GTVConv(Conv):
r"""
A graph total variation convolutional layer (GTVConv) from the paper
> [Total Variation Graph Neural Networks](https://arxiv.org... | 6,767 | 30.774648 | 121 | py |
spektral | spektral-master/spektral/layers/convolutional/gcs_conv.py | from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.utils import normalized_adjacency
class GCSConv(Conv):
r"""
A `GraphConv` layer with a trainable skip connection.
**Mode**: single, disjoint, mixed, batch.
Thi... | 3,852 | 29.824 | 89 | py |
spektral | spektral-master/spektral/layers/convolutional/crystal_conv.py | from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from spektral.layers.convolutional.message_passing import MessagePassing
class CrystalConv(MessagePassing):
r"""
A crystal graph convolutional layer from the paper
> [Crystal Graph Convolutional Neural Networks for an Ac... | 3,725 | 32.567568 | 90 | py |
spektral | spektral-master/spektral/layers/convolutional/censnet_conv.py | import tensorflow as tf
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.utils.convolution import gcn_filter, incidence_matrix, line_graph
class CensNetConv(Conv):
r"""
A CensNet convolutional layer from the paper
> [Co-embedding of Nodes and Edges with G... | 10,489 | 39.346154 | 104 | py |
spektral | spektral-master/spektral/layers/convolutional/__init__.py | from .agnn_conv import AGNNConv
from .appnp_conv import APPNPConv
from .arma_conv import ARMAConv
from .censnet_conv import CensNetConv
from .cheb_conv import ChebConv
from .crystal_conv import CrystalConv
from .diffusion_conv import DiffusionConv
from .ecc_conv import ECCConv
from .edge_conv import EdgeConv
from .gat_... | 723 | 33.47619 | 49 | py |
spektral | spektral-master/spektral/layers/convolutional/conv.py | import warnings
from functools import wraps
import tensorflow as tf
from tensorflow.keras.layers import Layer
from spektral.utils.keras import (
deserialize_kwarg,
is_keras_kwarg,
is_layer_kwarg,
serialize_kwarg,
)
class Conv(Layer):
r"""
A general class for convolutional layers.
You ca... | 2,918 | 26.280374 | 86 | py |
spektral | spektral-master/spektral/layers/convolutional/tag_conv.py | from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from spektral.layers.convolutional.message_passing import MessagePassing
from spektral.utils import normalized_adjacency
class TAGConv(MessagePassing):
r"""
A Topology Adaptive Graph Convolutional layer (TAG) from the paper
... | 3,772 | 29.92623 | 92 | py |
spektral | spektral-master/spektral/layers/convolutional/message_passing.py | import inspect
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer
from spektral.layers.ops.scatter import deserialize_scatter, serialize_scatter
from spektral.utils.keras import (
deserialize_kwarg,
is_keras_kwarg,
is_layer_kwarg,
serialize_kwar... | 7,175 | 34.176471 | 90 | py |
spektral | spektral-master/spektral/layers/convolutional/gat_conv.py | import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras import constraints, initializers, regularizers
from tensorflow.keras.layers import Dropout
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.layers.ops import modes
class GATConv(Co... | 10,279 | 36.933579 | 85 | py |
spektral | spektral-master/spektral/layers/ops/sparse.py | import tensorflow as tf
from tensorflow.python.ops import gen_sparse_ops
from . import ops
def add_self_loops(a, fill=1.0):
"""
Adds self-loops to the given adjacency matrix. Self-loops are added only for
those node that don't have a self-loop already, and are assigned a weight
of `fill`.
:param ... | 7,920 | 36.719048 | 85 | py |
spektral | spektral-master/spektral/layers/ops/modes.py | import tensorflow as tf
from tensorflow.keras import backend as K
SINGLE = 1 # Single mode rank(x) = 2, rank(a) = 2
DISJOINT = SINGLE # Disjoint mode rank(x) = 2, rank(a) = 2
BATCH = 3 # Batch mode rank(x) = 3, rank(a) = 3
MIXED = 4 # Mixed mode rank(x) = 3, rank(a) = 2
def disjoint_signal_to_batch(X... | 3,567 | 32.345794 | 89 | py |
spektral | spektral-master/spektral/layers/ops/graph.py | import tensorflow as tf
from tensorflow.keras import backend as K
from . import ops
def normalize_A(A):
"""
Computes symmetric normalization of A, dealing with sparse A and batch mode
automatically.
:param A: Tensor or SparseTensor with rank k = {2, 3}.
:return: Tensor or SparseTensor of rank k.
... | 2,116 | 29.242857 | 82 | py |
spektral | spektral-master/spektral/layers/ops/scatter.py | import tensorflow as tf
def mixed_mode_support(scatter_fn):
def _wrapper_mm_support(updates, indices, N):
if len(updates.shape) == 3:
updates = tf.transpose(updates, perm=(1, 0, 2))
out = scatter_fn(updates, indices, N)
if len(out.shape) == 3:
out = tf.transpose(out... | 8,807 | 38.497758 | 87 | py |
spektral | spektral-master/spektral/layers/ops/matmul.py | import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.python.ops.linalg.sparse import sparse as tfsp
from . import ops
def dot(a, b):
"""
Computes a @ b, for a, b of the same rank (both 2 or both 3).
If the rank is 2, then the innermost dimension of `a` must match the
out... | 6,354 | 33.726776 | 81 | py |
spektral | spektral-master/spektral/layers/ops/__init__.py | from .graph import *
from .matmul import *
from .modes import *
from .ops import *
from .scatter import *
from .sparse import *
| 128 | 17.428571 | 22 | py |
spektral | spektral-master/spektral/layers/ops/ops.py | import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
def transpose(a, perm=None, name=None):
"""
Transposes a according to perm, dealing automatically with sparsity.
:param a: Tensor or SparseTensor with rank k.
:param perm: permutation indices of size k.
:param nam... | 3,729 | 34.52381 | 78 | py |
spektral | spektral-master/spektral/utils/keras.py | from tensorflow.keras import activations, constraints, initializers, regularizers
LAYER_KWARGS = {"activation", "use_bias"}
KERAS_KWARGS = {
"trainable",
"name",
"dtype",
"dynamic",
"input_dim",
"input_shape",
"batch_input_shape",
"batch_size",
"weights",
"activity_regularizer",... | 1,372 | 23.517857 | 81 | py |
spektral | spektral-master/spektral/utils/sparse.py | import numpy as np
import tensorflow as tf
from scipy import sparse as sp
def reorder(edge_index, edge_weight=None, edge_features=None):
"""
Reorders `edge_index`, `edge_weight`, and `edge_features` according to the row-major
ordering of `edge_index`.
:param edge_index: np.array of shape `[n_edges, 2]... | 2,843 | 34.111111 | 88 | py |
spektral | spektral-master/spektral/utils/logging.py | import os
import time
from pprint import pformat
LOGFILE = None
TIME_STACK = []
def init_logging(name=None):
"""
Creates a log directory with an empty log.txt file.
:param name: custom name for the log directory (default \"%Y-%m-%d-%H-%M-%S\")
:return: string, the relative path to the log directory
... | 2,314 | 26.891566 | 82 | py |
spektral | spektral-master/spektral/utils/misc.py | import numpy as np
def pad_jagged_array(x, target_shape):
"""
Given a jagged array of arbitrary dimensions, zero-pads all elements in the
array to match the provided `target_shape`.
:param x: a list or np.array of dtype object, containing np.arrays with
variable dimensions;
:param target_shape... | 2,923 | 32.227273 | 81 | py |
spektral | spektral-master/spektral/utils/convolution.py | import copy
import warnings
from functools import partial
import numpy as np
import tensorflow as tf
from scipy import linalg
from scipy import sparse as sp
from scipy.sparse.linalg import ArpackNoConvergence
def degree_matrix(A):
"""
Computes the degree matrix of the given adjacency matrix.
:param A: ra... | 11,683 | 33.26393 | 88 | py |
spektral | spektral-master/spektral/utils/__init__.py | from .convolution import *
from .io import *
from .logging import *
from .misc import *
from .sparse import *
| 110 | 17.5 | 26 | py |
spektral | spektral-master/spektral/utils/io.py | import ast
import sys
import joblib
import networkx as nx
import numpy as np
import pandas as pd
import scipy.sparse as sp
from spektral.data.graph import Graph
def load_binary(filename):
"""
Loads a pickled file.
:param filename: a string or file-like object
:return: the loaded object
"""
t... | 12,804 | 24.921053 | 114 | py |
spektral | spektral-master/spektral/data/loaders.py | import numpy as np
import tensorflow as tf
from spektral.data.utils import (
batch_generator,
collate_labels_batch,
collate_labels_disjoint,
get_spec,
prepend_none,
sp_matrices_to_sp_tensors,
to_batch,
to_disjoint,
to_mixed,
to_tf_signature,
)
version = tf.__version__.split("."... | 21,819 | 33.416404 | 88 | py |
spektral | spektral-master/spektral/data/utils.py | import numpy as np
import tensorflow as tf
from scipy import sparse as sp
from spektral.utils import pad_jagged_array
from spektral.utils.sparse import sp_matrix_to_sp_tensor
def to_disjoint(x_list=None, a_list=None, e_list=None):
"""
Converts lists of node features, adjacency matrices and edge features to
... | 10,658 | 34.768456 | 88 | py |
spektral | spektral-master/spektral/data/dataset.py | import copy
import os.path as osp
import warnings
import numpy as np
import tensorflow as tf
from spektral.data.graph import Graph
from spektral.data.utils import get_spec
from spektral.datasets.utils import DATASET_FOLDER
class Dataset:
"""
A container for Graph objects. This class can be extended to repre... | 10,277 | 33.840678 | 84 | py |
spektral | spektral-master/spektral/data/graph.py | import warnings
import numpy as np
import scipy.sparse as sp
class Graph:
"""
A container to represent a graph. The data associated with the Graph is
stored in its attributes:
- `x`, for the node features;
- `a`, for the adjacency matrix;
- `e`, for the edge attributes;
-... | 5,610 | 33.006061 | 117 | py |
spektral | spektral-master/spektral/data/__init__.py | from .dataset import Dataset
from .graph import Graph
from .loaders import (
BatchLoader,
DisjointLoader,
Loader,
MixedLoader,
PackedBatchLoader,
SingleLoader,
)
| 186 | 16 | 28 | py |
spektral | spektral-master/spektral/transforms/one_hot.py | from spektral.utils import label_to_one_hot, one_hot
class OneHotLabels:
"""
One-hot encodes the graph labels along the innermost dimension (also if they
are simple scalars).
Either `depth` or `labels` must be passed as argument.
**Arguments**
- `depth`: int, the size of the one-hot vector ... | 996 | 30.15625 | 80 | py |
spektral | spektral-master/spektral/transforms/gcn_filter.py | from spektral.utils import gcn_filter
class GCNFilter:
r"""
Normalizes the adjacency matrix as described by
[Kipf & Welling (2017)](https://arxiv.org/abs/1609.02907):
$$
\A \leftarrow \hat\D^{-\frac{1}{2}} (\A + \I) \hat\D^{-\frac{1}{2}}
$$
where \( \hat\D_{ii} = 1 + \... | 692 | 24.666667 | 84 | py |
spektral | spektral-master/spektral/transforms/constant.py | import numpy as np
class Constant:
"""
Concatenates a constant value to the node attributes.
**Arguments**
- `value`: the value to concatenate to the node attributes.
"""
def __init__(self, value):
self.value = value
def __call__(self, graph):
value = np.zeros((graph.n_... | 500 | 19.875 | 63 | py |
spektral | spektral-master/spektral/transforms/layer_preprocess.py | class LayerPreprocess(object):
"""
Applies the `preprocess` function of a convolutional Layer to the adjacency
matrix.
**Arguments**
- `layer_class`: the class of a layer from `spektral.layers.convolutional`,
or any Layer that implements a `preprocess(adj)` method.
"""
def __init__(se... | 566 | 27.35 | 79 | py |
spektral | spektral-master/spektral/transforms/laplacian_pe.py | import numpy as np
from scipy.sparse.linalg import eigsh
from spektral.utils import normalized_laplacian
class LaplacianPE:
r"""
Adds Laplacian positional encodings to the nodes.
The first `k` eigenvectors are computed and concatenated to the node features.
Each node will be extended with its corres... | 992 | 26.583333 | 82 | py |
spektral | spektral-master/spektral/transforms/normalize_adj.py | from spektral.utils import normalized_adjacency
class NormalizeAdj:
r"""
Normalizes the adjacency matrix as:
$$
\A \leftarrow \D^{-1/2}\A\D^{-1/2}
$$
**Arguments**
- `symmetric`: If False, then it computes \(\D^{-1}\A\) instead.
"""
def __init__(self, symmetr... | 519 | 20.666667 | 72 | py |
spektral | spektral-master/spektral/transforms/adj_to_sp_tensor.py | from spektral.utils.sparse import sp_matrix_to_sp_tensor
class AdjToSpTensor:
"""
Converts the adjacency matrix to a SparseTensor.
"""
def __call__(self, graph):
if graph.a is not None:
graph.a = sp_matrix_to_sp_tensor(graph.a)
return graph
| 289 | 19.714286 | 56 | py |
spektral | spektral-master/spektral/transforms/degree.py | import numpy as np
from spektral.utils import one_hot
class Degree:
"""
Concatenates to each node attribute the one-hot degree of the corresponding
node.
The adjacency matrix is expected to have integer entries and the degree is
cast to integer before one-hot encoding.
**Arguments**
- ... | 1,056 | 24.780488 | 79 | py |
spektral | spektral-master/spektral/transforms/__init__.py | from .adj_to_sp_tensor import AdjToSpTensor
from .clustering_coefficient import ClusteringCoeff
from .constant import Constant
from .degree import Degree
from .delaunay import Delaunay
from .gcn_filter import GCNFilter
from .laplacian_pe import LaplacianPE
from .layer_preprocess import LayerPreprocess
from .normalize_a... | 463 | 34.692308 | 51 | py |
spektral | spektral-master/spektral/transforms/normalize_sphere.py | import numpy as np
class NormalizeSphere:
r"""
Normalizes the node attributes so that they are centered at the origin and
contained within a sphere of radius 1:
$$
\X_{i} \leftarrow \frac{\X_{i} - \bar\X}{\max_{i,j} \X_{ij}}
$$
where \( \bar\X \) is the centroid of... | 538 | 24.666667 | 82 | py |
spektral | spektral-master/spektral/transforms/normalize_one.py | import numpy as np
class NormalizeOne:
r"""
Normalizes the node attributes by dividing each row by its sum, so that it
sums to 1:
$$
\X_i \leftarrow \frac{\X_i}{\sum_{j=1}^{N} \X_{ij}}
$$
"""
def __call__(self, graph):
x_sum = np.sum(graph.x, -1)
x_sum[x_sum == 0]... | 392 | 18.65 | 78 | py |
spektral | spektral-master/spektral/transforms/clustering_coefficient.py | import networkx as nx
import numpy as np
class ClusteringCoeff:
"""
Concatenates to each node attribute the clustering coefficient of the
corresponding node.
"""
def __call__(self, graph):
if "a" not in graph:
raise ValueError("The graph must have an adjacency matrix")
... | 673 | 25.96 | 74 | py |
spektral | spektral-master/spektral/transforms/delaunay.py | import numpy as np
import scipy.sparse as sp
from scipy.spatial import Delaunay as DelaunaySP
class Delaunay:
"""
Computes the Delaunay triangulation of the node features.
The adjacency matrix is obtained from the edges of the triangulation and
replaces the previous adjacency matrix.
Duplicate ed... | 1,022 | 30.96875 | 84 | py |
spektral | spektral-master/examples/other/explain_graph_predictions.py | import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.metrics import categorical_accuracy
from tensorflow.keras.optimizers import Adam
from spektral.data import DisjointLoader
from spektral.datasets import TUDataset
... | 2,857 | 29.084211 | 86 | py |
spektral | spektral-master/examples/other/explain_node_predictions.py | import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.optimizers import Adam
from spektral.data.loaders import SingleLoader
from spektral.datasets.citation import ... | 2,137 | 28.694444 | 81 | py |
spektral | spektral-master/examples/other/node_clustering_mincut.py | """
This example implements the experiments for node clustering on citation networks
from the paper:
Mincut pooling in Graph Neural Networks (https://arxiv.org/abs/1907.00481)
Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi
"""
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from s... | 3,447 | 30.345455 | 85 | py |
spektral | spektral-master/examples/other/graph_signal_classification_mnist.py | import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.metrics import sparse_categorical_accuracy
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.re... | 4,254 | 30.058394 | 81 | py |
spektral | spektral-master/examples/other/node_clustering_tvgnn.py | """
This example implements the node clustering experiment on citation networks
from the paper:
Total Variation Graph Neural Networks (https://arxiv.org/abs/2211.06218)
Jonas Berg Hansen and Filippo Maria Bianchi
"""
import numpy as np
import tensorflow as tf
from sklearn.metrics.cluster import (
completeness_sco... | 3,374 | 23.816176 | 85 | py |
spektral | spektral-master/examples/graph_prediction/ogbg-mol-hiv_ecc.py | """
This example shows how to perform molecule classification with the
[Open Graph Benchmark](https://ogb.stanford.edu) `mol-hiv` dataset, using a
simple ECC-based GNN in disjoint mode. The model does not perform really well
but should give you a starting point if you want to implement a more
sophisticated one.
"""
im... | 3,971 | 34.783784 | 86 | py |
spektral | spektral-master/examples/graph_prediction/qm9_ecc_batch.py | """
This example shows how to perform regression of molecular properties with the
QM9 database, using a GNN based on edge-conditioned convolutions in batch mode.
"""
import numpy as np
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from... | 2,883 | 35.506329 | 85 | py |
spektral | spektral-master/examples/graph_prediction/custom_dataset.py | """
This example shows how to define your own dataset and use it to train a
non-trivial GNN with message-passing and pooling layers.
The script also shows how to implement fast training and evaluation functions
in disjoint mode, with early stopping and accuracy monitoring.
The dataset that we create is a simple synthe... | 6,894 | 33.133663 | 94 | py |
spektral | spektral-master/examples/graph_prediction/tud_mincut.py | import numpy as np
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from spektral.data import BatchLoader
from spektral.datasets import TUDataset
from spektral.layers import GCSConv, Glo... | 3,272 | 35.775281 | 80 | py |
spektral | spektral-master/examples/graph_prediction/general_gnn.py | """
This example implements the model from the paper
> [Design Space for Graph Neural Networks](https://arxiv.org/abs/2011.08843)<br>
> Jiaxuan You, Rex Ying, Jure Leskovec
using the PROTEINS dataset.
The configuration at the top of the file is the best one identified in the
paper, and should work well for m... | 3,934 | 34.133929 | 96 | py |
spektral | spektral-master/examples/graph_prediction/tud_gin.py | """
This example shows how to perform graph classification with a simple Graph
Isomorphism Network.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.metrics import categorical_accuracy
fro... | 4,168 | 33.741667 | 86 | py |
spektral | spektral-master/examples/graph_prediction/qm9_ecc.py | """
This example shows how to perform regression of molecular properties with the
QM9 database, using a simple GNN in disjoint mode.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.losses import MeanSquaredError
from tens... | 3,524 | 33.223301 | 86 | py |
spektral | spektral-master/examples/node_prediction/citation_gat_custom.py | """
This script is an extension of the citation_gcn_custom.py script.
It shows how to train GAT (with the same experimental setting of the original
paper), using faster training and test functions.
"""
import tensorflow as tf
from tensorflow.keras.layers import Dropout, Input
from tensorflow.keras.losses import Catego... | 3,234 | 28.144144 | 88 | py |
spektral | spektral-master/examples/node_prediction/citation_gcn.py | """
This example implements the experiments on citation networks from the paper:
Semi-Supervised Classification with Graph Convolutional Networks (https://arxiv.org/abs/1609.02907)
Thomas N. Kipf, Max Welling
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping
from tenso... | 2,097 | 30.313433 | 99 | py |
spektral | spektral-master/examples/node_prediction/citation_cheby.py | """
This example implements the experiments on citation networks from the paper:
Semi-Supervised Classification with Graph Convolutional Networks (https://arxiv.org/abs/1609.02907)
Thomas N. Kipf, Max Welling
using the convolutional layers described in:
Convolutional Neural Networks on Graphs with Fast Localized Spe... | 3,207 | 33.494624 | 113 | py |
spektral | spektral-master/examples/node_prediction/citation_arma.py | """
This example implements the experiments on citation networks from the paper:
Graph Neural Networks with convolutional ARMA filters (https://arxiv.org/abs/1901.01343)
Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi, Lorenzo Livi
"""
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.kera... | 3,057 | 32.604396 | 88 | py |
spektral | spektral-master/examples/node_prediction/citation_gcn_custom.py | """
This script is a proof of concept to train GCN as fast as possible and with as
little lines of code as possible.
It uses a custom training function instead of the standard Keras fit(), and
can train GCN for 200 epochs in a few tenths of a second (~0.20 on a GTX 1050).
"""
import tensorflow as tf
from tensorflow.ker... | 1,637 | 32.428571 | 88 | py |
spektral | spektral-master/examples/node_prediction/citation_simple_gc.py | """
This example implements the experiments on citation networks from the paper:
Simplifying Graph Convolutional Networks (https://arxiv.org/abs/1902.07153)
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
To implement it, we define a custom transform for the adjace... | 2,774 | 31.267442 | 100 | py |
spektral | spektral-master/examples/node_prediction/ogbn-arxiv_gcn.py | """
This example implements the same GCN example for node classification provided
with the [Open Graph Benchmark](https://ogb.stanford.edu).
See https://github.com/snap-stanford/ogb/blob/master/examples/nodeproppred/arxiv/gnn.py
for the reference implementation.
"""
import numpy as np
import tensorflow as tf
from ogb.n... | 3,535 | 33 | 87 | py |
spektral | spektral-master/examples/node_prediction/citation_gat.py | """
This example implements the experiments on citation networks from the paper:
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
"""
import numpy as np
from tensorflow.keras.callbacks import EarlyStopping
from t... | 3,212 | 30.194175 | 95 | py |
spektral | spektral-master/tests/test_datasets.py | from spektral import datasets
from spektral.data import BatchLoader, DisjointLoader, SingleLoader
batch_size = 3
def test_citation():
dataset = datasets.Cora()
dataset = datasets.Citeseer(random_split=True)
dataset = datasets.Pubmed(normalize_x=True)
sl = SingleLoader(dataset)
sl.load()
def tes... | 1,702 | 22.328767 | 68 | py |
spektral | spektral-master/tests/__init__.py | 0 | 0 | 0 | py | |
spektral | spektral-master/tests/test_layers/test_ops.py | import numpy as np
import scipy.sparse as sp
import tensorflow as tf
from spektral.data.utils import to_disjoint
from spektral.layers import ops
from spektral.utils import convolution
from spektral.utils.sparse import sp_batch_to_sp_tensor, sp_matrix_to_sp_tensor
batch_size = 10
N = 3
M = 5
tol = 1e-5
def _assert_a... | 14,358 | 32.627635 | 88 | py |
spektral | spektral-master/tests/test_layers/__init__.py | 0 | 0 | 0 | py | |
spektral | spektral-master/tests/test_layers/test_base.py | import numpy as np
import scipy.sparse as sp
import tensorflow as tf
from spektral import layers
from spektral.utils.sparse import sp_matrix_to_sp_tensor
from tests.test_layers.convolutional.core import _test_get_config
tol = 1e-6
def test_disjoint_2_batch():
X = np.array([[1, 0], [0, 1], [1, 1], [0, 0], [1, 2]... | 2,107 | 28.277778 | 85 | py |
spektral | spektral-master/tests/test_layers/pooling/test_diff_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.DiffPool,
"modes": [MODES["SINGLE"], MODES["BATCH"]],
"kwargs": {"k": 5, "return_selection": True},
"dense": True,
"sparse": True,
}
def test_layer():
run_layer(config)
| 311 | 19.8 | 59 | py |
spektral | spektral-master/tests/test_layers/pooling/core.py | import numpy as np
import scipy.sparse as sp
import tensorflow as tf
from tensorflow.keras import Input, Model
from spektral.utils.sparse import sp_matrix_to_sp_tensor
from tests.test_layers.convolutional.core import _test_get_config
tf.keras.backend.set_floatx("float64")
MODES = {
"SINGLE": 0,
"BATCH": 1,
... | 5,234 | 29.086207 | 85 | py |
spektral | spektral-master/tests/test_layers/pooling/test_topk_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.TopKPool,
"modes": [MODES["SINGLE"], MODES["DISJOINT"]],
"kwargs": {"ratio": 0.5, "return_selection": True},
"dense": False,
"sparse": True,
}
def test_layer():
run_layer(config)... | 321 | 20.466667 | 59 | py |
spektral | spektral-master/tests/test_layers/pooling/test_sag_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.SAGPool,
"modes": [MODES["SINGLE"], MODES["DISJOINT"]],
"kwargs": {"ratio": 0.5, "return_selection": True},
"dense": False,
"sparse": True,
}
def test_layer():
run_layer(config)
| 320 | 20.4 | 59 | py |
spektral | spektral-master/tests/test_layers/pooling/test_dmon_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.DMoNPool,
"modes": [MODES["SINGLE"], MODES["BATCH"]],
"kwargs": {"k": 5, "return_selection": True},
"dense": True,
"sparse": True,
}
def test_layer():
run_layer(config)
| 311 | 19.8 | 59 | py |
spektral | spektral-master/tests/test_layers/pooling/test_global_pooling.py | import numpy as np
import tensorflow as tf
from tensorflow.keras import Input, Model
from spektral.layers import (
GlobalAttentionPool,
GlobalAttnSumPool,
GlobalAvgPool,
GlobalMaxPool,
GlobalSumPool,
SortPool,
)
from tests.test_layers.convolutional.core import _test_get_config
tf.keras.backend... | 4,319 | 30.304348 | 87 | py |
spektral | spektral-master/tests/test_layers/pooling/test_asym_cheeger_cut_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.AsymCheegerCutPool,
"modes": [MODES["SINGLE"], MODES["BATCH"]],
"kwargs": {
"k": 5,
"return_selection": True,
"mlp_hidden": [32],
"totvar_coeff": 1.0,
"... | 431 | 19.571429 | 59 | py |
spektral | spektral-master/tests/test_layers/pooling/test_la_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.LaPool,
"modes": [MODES["SINGLE"], MODES["DISJOINT"]],
"kwargs": {"return_selection": True},
"dense": False,
"sparse": True,
}
def test_layer():
run_layer(config)
| 305 | 19.4 | 59 | py |
spektral | spektral-master/tests/test_layers/pooling/test_mincut_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.MinCutPool,
"modes": [MODES["SINGLE"], MODES["BATCH"]],
"kwargs": {"k": 5, "return_selection": True, "mlp_hidden": [32]},
"dense": True,
"sparse": True,
}
def test_layer():
run_l... | 333 | 21.266667 | 69 | py |
spektral | spektral-master/tests/test_layers/pooling/test_just_balance_pool.py | from spektral import layers
from tests.test_layers.pooling.core import MODES, run_layer
config = {
"layer": layers.JustBalancePool,
"modes": [MODES["SINGLE"], MODES["BATCH"]],
"kwargs": {"k": 5, "return_selection": True},
"dense": True,
"sparse": True,
}
def test_layer():
run_layer(config)
| 318 | 20.266667 | 59 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_censnet_conv.py | import enum
import networkx as nx
import numpy as np
import pytest
from core import A, F, S, batch_size
from tensorflow.keras import Input, Model
from spektral.layers import CensNetConv
NODE_CHANNELS = 8
"""
Number of node output channels to use for testing.
"""
EDGE_CHANNELS = 10
"""
Number of edge output channels ... | 6,138 | 30.64433 | 83 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_gcs_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GCSConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "relu"},
"dense": True,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(conf... | 324 | 18.117647 | 63 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_gin_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GINConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "relu", "mlp_hidden": [16]},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer... | 389 | 19.526316 | 72 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_tag_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.TAGConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 7, "K": 3},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
| 295 | 16.411765 | 47 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_message_passing.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.MessagePassing,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 7},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
| 294 | 16.352941 | 47 | py |
spektral | spektral-master/tests/test_layers/convolutional/core.py | import itertools
import numpy as np
import tensorflow as tf
from tensorflow.keras import Input, Model
from spektral.utils.sparse import sp_matrix_to_sp_tensor
tf.keras.backend.set_floatx("float64")
MODES = {
"SINGLE": 0,
"BATCH": 1,
"MIXED": 2,
}
batch_size = 32
N = 11
F = 7
S = 3
A = np.ones((N, N))
X ... | 7,676 | 28.413793 | 87 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_xenet_conv.py | import numpy as np
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from spektral.layers import XENetConv, XENetConvBatch
from spektral.utils.sparse import sp_matrix_to_sp_tensor
# Not using these tests because they assume certain behaviors that we
# don't follow
"""
dense_config = ... | 6,662 | 32.822335 | 124 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_gated_graph_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GatedGraphConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 10, "n_layers": 3},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
| 310 | 17.294118 | 47 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_appnp_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.APPNPConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "relu", "mlp_hidden": [16]},
"dense": True,
"sparse": True,
"edges": False,
}
def test_layer... | 346 | 19.411765 | 72 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_graphsage_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GraphSageConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "relu"},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
| 315 | 17.588235 | 52 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_diffusion_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.DiffusionConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "tanh", "num_diffusion_steps": 5},
"dense": True,
"sparse": False,
"edges": False,
}
def... | 357 | 20.058824 | 78 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_ecc_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.ECCConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {"kernel_network": [8], "channels": 8, "activation": "relu"},
"dense": True,
"sparse": True,
"edges": True,
}
def test_layer... | 346 | 19.411765 | 75 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_edge_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.EdgeConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "relu", "mlp_hidden": [16]},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_laye... | 330 | 18.470588 | 72 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_crystal_conv.py | from core import MODES, F, run_layer
from spektral import layers
config = {
"layer": layers.CrystalConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": F}, # Set channels same as node features
"dense": False,
"sparse": True,
"edges": True,
}
def test_layer():
run_lay... | 331 | 18.529412 | 68 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_gin_conv_batch.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GINConvBatch,
"modes": [MODES["BATCH"]],
"kwargs": {"channels": 8, "activation": "relu", "mlp_hidden": [16]},
"dense": True,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
... | 376 | 18.842105 | 72 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_gcn_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GCNConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {"channels": 8, "activation": "relu"},
"dense": True,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(conf... | 324 | 18.117647 | 63 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_cheb_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.ChebConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {"K": 3, "channels": 8, "activation": "relu"},
"dense": True,
"sparse": True,
"edges": False,
}
def test_layer():
run_l... | 333 | 18.647059 | 63 | py |
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