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Unverified Commit f13b9b62 authored by Minjie Wang's avatar Minjie Wang Committed by GitHub
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[Doc] Scan the API docs and make many changes (#2080) 



* WIP: api

* dgl.sampling, dgl.data

* dgl.sampling; dgl.dataloading

* sampling packages

* convert

* subgraph

* deprecate

* subgraph APIs

* All docstrings for convert/subgraph/transform

* almost all funcs under dgl namespace

* WIP: DGLGraph

* done graph query

* message passing functions

* lint

* fix merge error

* fix test

* lint

* fix
Co-authored-by: default avatarQuan Gan <coin2028@hotmail.com>
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docs/_deprecate/5_giant_graph/1_sampling_mx.py deleted 100644 → 0
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"""
.. _model-sampling:
NodeFlow and Sampling
=======================================
**Author**: Ziyue Huang, Da Zheng, Quan Gan, Jinjing Zhou, Zheng Zhang
"""
################################################################################################
#
# Graph convolutional network
# ~~~
#
# In an :math:`L`-layer graph convolution network (GCN), given a graph
# :math:`G=(V, E)`, represented as an adjacency matrix :math:`A`, with
# node features :math:`H^{(0)} = X \in \mathbb{R}^{|V| \times d}`, the
# hidden feature of a node :math:`v` in :math:`(l+1)`-th layer
# :math:`h_v^{(l+1)}` depends on the features of all its neighbors in the
# previous layer :math:`h_u^{(l)}`:
#
# .. math::
#
#
# z_v^{(l+1)} = \sum_{u \in \mathcal{N}(v)} \tilde{A}_{uv} h_u^{(l)} \qquad h_v^{(l+1)} = \sigma ( z_v^{(l+1)} W^{(l)})
#
# where :math:`\mathcal{N}(v)` is the neighborhood of :math:`v`,
# :math:`\tilde{A}` could be any normalized version of :math:`A` such as
# :math:`D^{-1} A` in Kipf et al., :math:`\sigma(\cdot)` is an activation
# function, and :math:`W^{(l)}` is a trainable parameter of the
# :math:`l`-th layer.
#
# In the node classification task you minimize the following loss:
#
# .. math::
#
#
# \frac{1}{\vert \mathcal{V}_\mathcal{L} \vert} \sum_{v \in \mathcal{V}_\mathcal{L}} f(y_v, z_v^{(L)})
#
# where :math:`y_v` is the label of :math:`v`, and :math:`f(\cdot, \cdot)`
# is a loss function, e.g., cross entropy loss.
#
# While training GCN on the full graph, each node aggregates the hidden
# features of its neighbors to compute its hidden feature in the next
# layer.
#
# In this tutorial, you run GCN on the Reddit dataset constructed by `Hamilton et
# al. <https://arxiv.org/abs/1706.02216>`__, wherein the nodes are posts
# and edges are established if two nodes are commented by a same user. The
# task is to predict the category that a post belongs to. This graph has
# 233,000 nodes, 114.6 million edges and 41 categories. First load the Reddit graph.
#
import numpy as np
import dgl
import dgl.function as fn
from dgl import DGLGraph
from dgl.data import RedditDataset
import mxnet as mx
from mxnet import gluon
# Load MXNet as backend
dgl.load_backend('mxnet')
# load dataset
data = RedditDataset(self_loop=True)
train_nid = mx.nd.array(np.nonzero(data.train_mask)[0]).astype(np.int64)
features = mx.nd.array(data.features)
in_feats = features.shape[1]
labels = mx.nd.array(data.labels)
n_classes = data.num_labels
# construct DGLGraph and prepare related data
g = DGLGraph(data.graph, readonly=True)
g.ndata['features'] = features
################################################################################################
# Here you define the node UDF, which has a fully-connected layer:
#
class NodeUpdate(gluon.Block):
def __init__(self, in_feats, out_feats, activation=None):
super(NodeUpdate, self).__init__()
self.dense = gluon.nn.Dense(out_feats, in_units=in_feats)
self.activation = activation
def forward(self, node):
h = node.data['h']
h = self.dense(h)
if self.activation:
h = self.activation(h)
return {'activation': h}
################################################################################################
# In DGL, you implement GCN on the full graph with ``update_all`` in ``DGLGraph``.
# The following code performs two-layer GCN on the Reddit graph.
#
# number of GCN layers
L = 2
# number of hidden units of a fully connected layer
n_hidden = 64
layers = [NodeUpdate(g.ndata['features'].shape[1], n_hidden, mx.nd.relu),
NodeUpdate(n_hidden, n_hidden, mx.nd.relu)]
for layer in layers:
layer.initialize()
h = g.ndata['features']
for i in range(L):
g.ndata['h'] = h
g.update_all(message_func=fn.copy_src(src='h', out='m'),
reduce_func=fn.sum(msg='m', out='h'),
apply_node_func=lambda node: {'h': layers[i](node)['activation']})
h = g.ndata.pop('h')
##############################################################################
# NodeFlow
# ~~~~~~~~~~~~~~~~~
#
# As the graph scales up to billions of nodes or edges, training on the
# full graph would no longer be efficient or even feasible.
#
# Mini-batch training allows you to control the computation and memory
# usage within some budget. The training loss for each iteration is
#
# .. math::
#
# \frac{1}{\vert \tilde{\mathcal{V}}_\mathcal{L} \vert} \sum_{v \in \tilde{\mathcal{V}}_\mathcal{L}} f(y_v, z_v^{(L)})
#
# where :math:`\tilde{\mathcal{V}}_\mathcal{L}` is a subset sampled from
# the total labeled nodes :math:`\mathcal{V}_\mathcal{L}` uniformly at
# random.
#
# Stemming from the labeled nodes :math:`\tilde{\mathcal{V}}_\mathcal{L}`
# in a mini-batch and tracing back to the input forms a computational
# dependency graph (a directed acyclic graph [DAG]), which
# captures the computation flow of :math:`Z^{(L)}`.
#
# In the example below, a mini-batch to compute the hidden features of
# node D in layer 2 requires hidden features of A, B, E, G in layer 1,
# which in turn requires hidden features of C, D, F in layer 0.
#
# |image0|
#
# For that purpose, you define ``NodeFlow`` to represent this computation
# flow.
#
# ``NodeFlow`` is a type of layered graph, where nodes are organized in
# :math:`L + 1` sequential *layers*, and edges only exist between adjacent
# layers, forming *blocks*. You construct ``NodeFlow`` backwards, starting
# from the last layer with all the nodes whose hidden features are
# requested. The set of nodes the next layer depends on forms the previous
# layer. An edge connects a node in the previous layer to another in the
# next layer if the latter depends on the former. Repeat such process
# until all :math:`L + 1` layers are constructed. The feature of nodes in
# each layer, and that of edges in each block, are stored as separate
# tensors.
#
# .. raw:: html
#
# ``NodeFlow`` provides ``block_compute`` for per-block computation, which
# triggers computation and data propogation from the lower layer to the
# next upper layer.
#
##############################################################################
# Neighbor sampling
# ~~~~~~~~~~~~~~~~~
#
# Real-world graphs often have nodes with large degree, meaning that a
# moderately deep (e.g., three layers) GCN would often depend on input features
# of the entire graph, even if the computation only depends on outputs of
# a few nodes, hence its cost-ineffectiveness.
#
# Sampling methods mitigate this computational problem by reducing the
# receptive field effectively. Fig-c above shows one such example.
#
# Instead of using all the :math:`L`-hop neighbors of a node :math:`v`,
# `Hamilton et al. <https://arxiv.org/abs/1706.02216>`__ propose *neighbor
# sampling*, which randomly samples a few neighbors
# :math:`\hat{\mathcal{N}}^{(l)}(v)` to estimate the aggregation
# :math:`z_v^{(l+1)}` of its total neighbors :math:`\mathcal{N}(v)` in
# :math:`l`-th GCN layer, by an unbiased estimator
# :math:`\hat{z}_v^{(l+1)}`
#
# .. math::
#
#
# \hat{z}_v^{(l+1)} = \frac{\vert \mathcal{N}(v) \vert }{\vert \hat{\mathcal{N}}^{(l)}(v) \vert} \sum_{u \in \hat{\mathcal{N}}^{(l)}(v)} \tilde{A}_{uv} \hat{h}_u^{(l)} \qquad
# \hat{h}_v^{(l+1)} = \sigma ( \hat{z}_v^{(l+1)} W^{(l)} )
#
# Let :math:`D^{(l)}` be the number of neighbors to be sampled for each
# node at the :math:`l`-th layer, then the receptive field size of each
# node can be controlled under :math:`\prod_{i=0}^{L-1} D^{(l)}` by
# *neighbor sampling*.
#
##############################################################################
# You then implement *neighbor sampling* by ``NodeFlow``:
#
class GCNSampling(gluon.Block):
def __init__(self,
in_feats,
n_hidden,
n_classes,
n_layers,
activation,
dropout,
**kwargs):
super(GCNSampling, self).__init__(**kwargs)
self.dropout = dropout
self.n_layers = n_layers
with self.name_scope():
self.layers = gluon.nn.Sequential()
# input layer
self.layers.add(NodeUpdate(in_feats, n_hidden, activation))
# hidden layers
for i in range(1, n_layers-1):
self.layers.add(NodeUpdate(n_hidden, n_hidden, activation))
# output layer
self.layers.add(NodeUpdate(n_hidden, n_classes))
def forward(self, nf):
nf.layers[0].data['activation'] = nf.layers[0].data['features']
for i, layer in enumerate(self.layers):
h = nf.layers[i].data.pop('activation')
if self.dropout:
h = mx.nd.Dropout(h, p=self.dropout)
nf.layers[i].data['h'] = h
# block_compute() computes the feature of layer i given layer
# i-1, with the given message, reduce, and apply functions.
# Here, you essentially aggregate the neighbor node features in
# the previous layer, and update it with the `layer` function.
nf.block_compute(i,
fn.copy_src(src='h', out='m'),
lambda node : {'h': node.mailbox['m'].mean(axis=1)},
layer)
h = nf.layers[-1].data.pop('activation')
return h
##############################################################################
# DGL provides ``NeighborSampler`` to construct the ``NodeFlow`` for a
# mini-batch according to the computation logic of neighbor sampling.
# ``NeighborSampler``
# returns an iterator that generates a ``NodeFlow`` each time. This function
# has many options to give users opportunities to customize the behavior
# of the neighbor sampler, including the number of neighbors to sample or
# the number of hops to sample, for example. Please see `its API
# document <https://doc.dgl.ai/api/python/sampler.html>`__ for more
# details.
#
# dropout probability
dropout = 0.2
# batch size
batch_size = 1000
# number of neighbors to sample
num_neighbors = 4
# number of epochs
num_epochs = 1
# initialize the model and cross entropy loss
model = GCNSampling(in_feats, n_hidden, n_classes, L,
mx.nd.relu, dropout, prefix='GCN')
model.initialize()
loss_fcn = gluon.loss.SoftmaxCELoss()
# use adam optimizer
trainer = gluon.Trainer(model.collect_params(), 'adam',
{'learning_rate': 0.03, 'wd': 0})
for epoch in range(num_epochs):
i = 0
for nf in dgl.contrib.sampling.NeighborSampler(g, batch_size,
num_neighbors,
neighbor_type='in',
shuffle=True,
num_hops=L,
seed_nodes=train_nid):
# When `NodeFlow` is generated from `NeighborSampler`, it only contains
# the topology structure, on which there is no data attached.
# Users need to call `copy_from_parent` to copy specific data,
# such as input node features, from the original graph.
nf.copy_from_parent()
with mx.autograd.record():
# forward
pred = model(nf)
batch_nids = nf.layer_parent_nid(-1).astype('int64')
batch_labels = labels[batch_nids]
# cross entropy loss
loss = loss_fcn(pred, batch_labels)
loss = loss.sum() / len(batch_nids)
# backward
loss.backward()
# optimization
trainer.step(batch_size=1)
print("Epoch[{}]: loss {}".format(epoch, loss.asscalar()))
i += 1
# You only train the model with 32 mini-batches just for demonstration.
if i >= 32:
break
##############################################################################
# Control variate
# ~~~~~~~~~~~~~~~
#
# The unbiased estimator :math:`\hat{Z}^{(\cdot)}` used in *neighbor
# sampling* might suffer from high variance, so it still requires a
# relatively large number of neighbors, e.g. \ :math:`D^{(0)}=25` and
# :math:`D^{(1)}=10` in `Hamilton et
# al. <https://arxiv.org/abs/1706.02216>`__. With *control variate*, a
# standard variance reduction technique widely used in Monte Carlo
# methods, 2 neighbors for a node seems sufficient.
#
# *Control variate* method works as follows: Given a random variable
# :math:`X` and you wish to estimate its expectation
# :math:`\mathbb{E} [X] = \theta`, it finds another random variable
# :math:`Y` which is highly correlated with :math:`X` and whose
# expectation :math:`\mathbb{E} [Y]` can be easily computed. The *control
# variate* estimator :math:`\tilde{X}` is
#
# .. math::
#
# \tilde{X} = X - Y + \mathbb{E} [Y] \qquad \mathbb{VAR} [\tilde{X}] = \mathbb{VAR} [X] + \mathbb{VAR} [Y] - 2 \cdot \mathbb{COV} [X, Y]
#
# If :math:`\mathbb{VAR} [Y] - 2\mathbb{COV} [X, Y] < 0`, then
# :math:`\mathbb{VAR} [\tilde{X}] < \mathbb{VAR} [X]`.
#
# `Chen et al. <https://arxiv.org/abs/1710.10568>`__ proposed a *control
# variate* based estimator used in GCN training, by using history
# :math:`\bar{H}^{(l)}` of the nodes which are not sampled, the modified
# estimator :math:`\hat{z}_v^{(l+1)}` is
#
# .. math::
#
#
# \hat{z}_v^{(l+1)} = \frac{\vert \mathcal{N}(v) \vert }{\vert \hat{\mathcal{N}}^{(l)}(v) \vert} \sum_{u \in \hat{\mathcal{N}}^{(l)}(v)} \tilde{A}_{uv} ( \hat{h}_u^{(l)} - \bar{h}_u^{(l)} ) + \sum_{u \in \mathcal{N}(v)} \tilde{A}_{uv} \bar{h}_u^{(l)} \\
# \hat{h}_v^{(l+1)} = \sigma ( \hat{z}_v^{(l+1)} W^{(l)} )
#
# This method can also be *conceptually* implemented in DGL as shown
# here.
#
have_large_memory = False
# The control-variate sampling code below needs to run on a large-memory
# machine for the Reddit graph.
if have_large_memory:
g.ndata['h_0'] = features
for i in range(L):
g.ndata['h_{}'.format(i+1)] = mx.nd.zeros((features.shape[0], n_hidden))
# With control-variate sampling, you only need to sample two neighbors to train GCN.
for nf in dgl.contrib.sampling.NeighborSampler(g, batch_size, expand_factor=2,
neighbor_type='in', num_hops=L,
seed_nodes=train_nid):
for i in range(nf.num_blocks):
# aggregate history on the original graph
g.pull(nf.layer_parent_nid(i+1),
fn.copy_src(src='h_{}'.format(i), out='m'),
lambda node: {'agg_h_{}'.format(i): node.mailbox['m'].mean(axis=1)})
nf.copy_from_parent()
h = nf.layers[0].data['features']
for i in range(nf.num_blocks):
prev_h = nf.layers[i].data['h_{}'.format(i)]
# compute delta_h, the difference of the current activation and the history
nf.layers[i].data['delta_h'] = h - prev_h
# refresh the old history
nf.layers[i].data['h_{}'.format(i)] = h.detach()
# aggregate the delta_h
nf.block_compute(i,
fn.copy_src(src='delta_h', out='m'),
lambda node: {'delta_h': node.data['m'].mean(axis=1)})
delta_h = nf.layers[i + 1].data['delta_h']
agg_h = nf.layers[i + 1].data['agg_h_{}'.format(i)]
# control variate estimator
nf.layers[i + 1].data['h'] = delta_h + agg_h
nf.apply_layer(i + 1, lambda node : {'h' : layer(node.data['h'])})
h = nf.layers[i + 1].data['h']
# update history
nf.copy_to_parent()
##############################################################################
# You can see full example here, `MXNet
# code <https://github.com/dmlc/dgl/blob/master/examples/mxnet/sampling/>`__
# and `PyTorch
# code <https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__.
#
# Below shows the performance of graph convolution network and GraphSage
# with neighbor sampling and control variate sampling on the Reddit
# dataset. Our GraphSage with control variate sampling, when sampling one
# neighbor, can achieve over 96 percent test accuracy. |image1|
#
# More APIs
# ~~~~~~~~~
#
# In fact, ``block_compute`` is one of the APIs that comes with
# ``NodeFlow``, which provides flexibility to research new ideas. The
# computation flow underlying a DAG can be executed in one sweep, by
# calling ``prop_flows``.
#
# ``prop_flows`` accepts a list of UDFs. The code below defines node update UDFs
# for each layer and computes a simplified version of GCN with neighbor sampling.
#
apply_node_funcs = [
lambda node : {'h' : layers[0](node)['activation']},
lambda node : {'h' : layers[1](node)['activation']},
]
for nf in dgl.contrib.sampling.NeighborSampler(g, batch_size, num_neighbors,
neighbor_type='in', num_hops=L,
seed_nodes=train_nid):
nf.copy_from_parent()
nf.layers[0].data['h'] = nf.layers[0].data['features']
nf.prop_flow(fn.copy_src(src='h', out='m'),
fn.sum(msg='m', out='h'), apply_node_funcs)
##############################################################################
# Internally, ``prop_flow`` triggers the computation by fusing together
# all the block computations, from the input to the top. The main
# advantages of this API are 1) simplicity, 2) allowing more system-level
# optimization in the future.
#
# .. |image0| image:: https://data.dgl.ai/tutorial/sampling/NodeFlow.png
# .. |image1| image:: https://data.dgl.ai/tutorial/sampling/sampling_result.png
#
docs/_deprecate/5_giant_graph/2_giant.py deleted 100644 → 0
View file @ 35e25914
"""
.. _model-graph-store:
Large-Scale Training of Graph Neural Networks
=============================================
**Author**: Da Zheng, Chao Ma, Zheng Zhang
"""
################################################################################################
#
# In real-world tasks, many graphs are very large. For example, a recent
# snapshot of the friendship network of Facebook contains 800 million
# nodes and over 100 billion links. We are facing challenges on
# large-scale training of graph neural networks.
#
# To accelerate training on a giant graph, DGL provides two additional
# components: sampler and graph store.
#
# - A sampler constructs small subgraphs (``NodeFlow``) from a given
# (giant) graph. The sampler can run on a local machine as well as on
# remote machines. Also, DGL can launch multiple parallel samplers
# across a set of machines.
#
# - The graph store contains graph embeddings of a giant graph, as well
# as the graph structure. So far, we provide a shared-memory graph
# store to support multi-processing training, which is important for
# training on multiple GPUs and on non-uniform memory access (NUMA)
# machines. The shared-memory graph store has a similar interface to
# ``DGLGraph`` for programming. DGL will also support a distributed
# graph store that can store graph embeddings across machines in the
# future release.
#
# The figure below shows the interaction of the trainer with the samplers
# and the graph store. The trainer takes subgraphs (``NodeFlow``) from the
# sampler and fetches graph embeddings from the graph store before
# training. The trainer can push new graph embeddings to the graph store
# afterward.
#
# |image0|
#
# In this tutorial, we use control-variate sampling to demonstrate how to
# use these three DGL components, extending `the original code of
# control-variate
# sampling <https://doc.dgl.ai/tutorials/models/5_giant_graph/1_sampling_mx.html#sphx-glr-tutorials-models-5-giant-graph-1-sampling-mx-py>`__.
# Because the graph store has a similar API to ``DGLGraph``, the code is
# similar. The tutorial will mainly focus on the difference.
#
# Graph Store
# -----------
#
# The graph store has two parts: the server and the client. We need to run
# the graph store server as a daemon before training. We provide a script
# ``run_store_server.py`` `(link) <https://github.com/dmlc/dgl/blob/master/examples/mxnet/sampling/run_store_server.py>`__
# that runs the graph store server and loads graph data. For example, the
# following command runs a graph store server that loads the reddit
# dataset and is configured to run with four trainers.
#
# ::
#
# python3 run_store_server.py --dataset reddit --num-workers 4
#
# The trainer uses the graph store client to access data in the graph
# store from the trainer process. A user only needs to write code in the
# trainer. We first create the graph store client that connects with the
# server. We specify ``store_type`` as “shared_memory” to connect with the
# shared-memory graph store server.
#
# .. code:: python
#
# g = dgl.contrib.graph_store.create_graph_from_store("reddit", store_type="shared_mem")
#
# The `sampling
# tutorial <https://doc.dgl.ai/tutorials/models/5_giant_graph/1_sampling_mx.html#sphx-glr-tutorials-models-5-giant-graph-1-sampling-mx-py>`__
# shows the detail of sampling methods and how they are used to train
# graph neural networks such as graph convolution network. As a recap, the
# graph convolution model performs the following computation in each
# layer.
#
# .. math::
#
#
# z_v^{(l+1)} = \sum_{u \in \mathcal{N}^{(l)}(v)} \tilde{A}_{uv} h_u^{(l)} \qquad
# h_v^{(l+1)} = \sigma ( z_v^{(l+1)} W^{(l)} )
#
# `Control variate sampling <https://arxiv.org/abs/1710.10568>`__
# approximates :math:`z_v^{(l+1)}` as follows:
#
# .. math::
#
#
# \hat{z}_v^{(l+1)} = \frac{\vert \mathcal{N}(v) \vert }{\vert \hat{\mathcal{N}}^{(l)}(v) \vert} \sum_{u \in \hat{\mathcal{N}}^{(l)}(v)} \tilde{A}_{uv} ( \hat{h}_u^{(l)} - \bar{h}_u^{(l)} ) + \sum_{u \in \mathcal{N}(v)} \tilde{A}_{uv} \bar{h}_u^{(l)} \\
# \hat{h}_v^{(l+1)} = \sigma ( \hat{z}_v^{(l+1)} W^{(l)} )
#
# In addition to the approximation, `Chen et.
# al. <https://arxiv.org/abs/1710.10568>`__ applies a preprocessing trick
# to reduce the number of hops for sampling neighbors by one. This trick
# works for models such as Graph Convolution Networks and GraphSage. It
# preprocesses the input layer. The original GCN takes :math:`X` as input.
# Instead of taking :math:`X` as the input of the model, the trick
# computes :math:`U^{(0)}=\tilde{A}X` and uses :math:`U^{(0)}` as the
# input of the first layer. In this way, the vertices in the first layer
# does not need to compute aggregation over their neighborhood and, thus,
# reduce the number of layers to sample by one.
#
# For a giant graph, both :math:`\tilde{A}` and :math:`X` can be very
# large. We need to perform this operation in a distributed fashion. That
# is, each trainer takes part of the computation and the computation is
# distributed among all trainers. We can use ``update_all`` in the graph
# store to perform this computation.
#
# .. code:: python
#
# g.update_all(fn.copy_src(src='features', out='m'),
# fn.sum(msg='m', out='preprocess'),
# lambda node : {'preprocess': node.data['preprocess'] * node.data['norm']})
#
# ``update_all`` in the graph store runs in a distributed fashion. That
# is, all trainers need to invoke this function and take part of the
# computation. When a trainer completes its portion, it will wait for
# other trainers to complete before proceeding with its other computation.
#
# The node/edge data now live in the graph store and the access to the
# node/edge data is now a little different. The graph store no longer
# supports data access with ``g.ndata``/``g.edata``, which reads the
# entire node/edge data tensor. Instead, users have to use
# ``g.nodes[node_ids].data[embed_name]`` to access data on some nodes.
# (Note: this method is also allowed in ``DGLGraph`` and ``g.ndata`` is
# simply a short syntax for ``g.nodes[:].data``). In addition, the graph
# store supports ``get_n_repr``/``set_n_repr`` for node data and
# ``get_e_repr``/``set_e_repr`` for edge data.
#
# To initialize the node/edge tensors more efficiently, we provide two new
# methods in the graph store client to initialize node data and edge data
# (i.e., ``init_ndata`` for node data or ``init_edata`` for edge data).
# What happened under the hood is that these two methods send
# initialization commands to the server and the graph store server
# initializes the node/edge tensors on behalf of trainers.
#
# Here we show how we should initialize node data for control-variate
# sampling. ``h_i`` stores the history of nodes in layer ``i``;
# ``agg_h_i`` stores the aggregation of the history of neighbor nodes in
# layer ``i``.
#
# .. code:: python
#
# for i in range(n_layers):
# g.init_ndata('h_{}'.format(i), (features.shape[0], args.n_hidden), 'float32')
# g.init_ndata('agg_h_{}'.format(i), (features.shape[0], args.n_hidden), 'float32')
#
# After we initialize node data, we train GCN with control-variate
# sampling as below. The training code takes advantage of preprocessed
# input data in the first layer and works identically to the
# single-process training procedure.
#
# .. code:: python
#
# for nf in NeighborSampler(g, batch_size, num_neighbors,
# neighbor_type='in', num_hops=L-1,
# seed_nodes=labeled_nodes):
# for i in range(nf.num_blocks):
# # aggregate history on the original graph
# g.pull(nf.layer_parent_nid(i+1),
# fn.copy_src(src='h_{}'.format(i), out='m'),
# lambda node: {'agg_h_{}'.format(i): node.data['m'].mean(axis=1)})
# # We need to copy data in the NodeFlow to the right context.
# nf.copy_from_parent(ctx=right_context)
# nf.apply_layer(0, lambda node : {'h' : layer(node.data['preprocess'])})
# h = nf.layers[0].data['h']
#
# for i in range(nf.num_blocks):
# prev_h = nf.layers[i].data['h_{}'.format(i)]
# # compute delta_h, the difference of the current activation and the history
# nf.layers[i].data['delta_h'] = h - prev_h
# # refresh the old history
# nf.layers[i].data['h_{}'.format(i)] = h.detach()
# # aggregate the delta_h
# nf.block_compute(i,
# fn.copy_src(src='delta_h', out='m'),
# lambda node: {'delta_h': node.data['m'].mean(axis=1)})
# delta_h = nf.layers[i + 1].data['delta_h']
# agg_h = nf.layers[i + 1].data['agg_h_{}'.format(i)]
# # control variate estimator
# nf.layers[i + 1].data['h'] = delta_h + agg_h
# nf.apply_layer(i + 1, lambda node : {'h' : layer(node.data['h'])})
# h = nf.layers[i + 1].data['h']
# # update history
# nf.copy_to_parent()
#
# The complete example code can be found
# `here <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__.
#
# After showing how the shared-memory graph store is used with
# control-variate sampling, let’s see how to use it for multi-GPU training
# and how to optimize the training on a non-uniform memory access (NUMA)
# machine. A NUMA machine here means a machine with multiple processors
# and large memory. It works for all backend frameworks as long as the
# framework supports multi-processing training. If we use MXNet as the
# backend, we can use the distributed MXNet kvstore to aggregate gradients
# among processes and use the MXNet launch tool to launch multiple workers
# that run the training script. The command below launches our example
# code for multi-processing GCN training with control variate sampling and
# it runs 4 trainers.
#
# ::
#
# python3 ../incubator-mxnet/tools/launch.py -n 4 -s 1 --launcher local \
# python3 examples/mxnet/sampling/multi_process_train.py \
# --graph-name reddit \
# --model gcn_cv --num-neighbors 1 \
# --batch-size 2500 --test-batch-size 5000 \
# --n-hidden 64
#
# ..
#
# It is fairly easy to enable multi-GPU training. All we need to do is to
# copy data to a right GPU context and invoke NodeFlow computation in that
# GPU context. As shown above, we specify a context ``right_context`` in
# ``copy_from_parent``.
#
# To optimize the computation on a NUMA machine, we need to configure each
# process properly. For example, we should use the same number of
# processes as the number of NUMA nodes (usually equivalent to the number
# of processors) and bind the processes to NUMA nodes. In addition, we
# should reduce the number of OpenMP threads to the number of CPU cores in
# a processor and reduce the number of threads of the MXNet kvstore to a
# small number such as 4.
#
# .. code:: python
#
# import numa
# import os
# if 'DMLC_TASK_ID' in os.environ and int(os.environ['DMLC_TASK_ID']) < 4:
# # bind the process to a NUMA node.
# numa.bind([int(os.environ['DMLC_TASK_ID'])])
# # Reduce the number of OpenMP threads to match the number of
# # CPU cores of a processor.
# os.environ['OMP_NUM_THREADS'] = '16'
# else:
# # Reduce the number of OpenMP threads in the MXNet KVstore server to 4.
# os.environ['OMP_NUM_THREADS'] = '4'
#
# Given the configuration above, NUMA-aware multi-processing training can
# accelerate training almost by a factor of 4 as shown in the figure below
# on an X1.32xlarge instance where there are 4 processors, each of which
# has 16 physical CPU cores. We can see that NUMA-unaware training cannot
# take advantage of computation power of the machine. It is even slightly
# slower than just using one of the processors in the machine. NUMA-aware
# training, on the other hand, takes about only 20 seconds to converge to
# the accuracy of 96% with 20 iterations.
#
# |image1|
#
# Distributed Sampler
# -------------------
#
# For many tasks, we found that the sampling takes a significant amount of
# time for the training process on a giant graph. So DGL supports
# distributed samplers for speeding up the sampling process on giant
# graphs. DGL allows users to launch multiple samplers on different
# machines concurrently, and each sampler can send its sampled subgraph
# (``NodeFlow``) to trainer machines continuously.
#
# To use the distributed sampler on DGL, users start both trainer and
# sampler processes on different machines. Users can find the complete
# demo code and launch scripts `in this
# link <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling/dis_sampling>`__
# and this tutorial will focus on the main difference between
# single-machine code and distributed code.
#
# For the trainer, developers can easily migrate the existing
# single-machine sampler code to the distributed setting seamlessly by
# just changing a few lines of code. First, users need to create a
# distributed ``SamplerReceiver`` object before training:
#
# .. code:: python
#
# sampler = dgl.contrib.sampling.SamplerReceiver(graph, ip_addr, num_sampler)
#
# The ``SamplerReceiver`` class is used for receiving remote subgraph from
# other machines. This API has three arguments: ``parent_graph``,
# ``ip_address``, and ``number_of_samplers``.
#
# After that, developers can change just one line of existing
# single-machine training code like this:
#
# .. code:: python
#
# for nf in sampler:
# for i in range(nf.num_blocks):
# # aggregate history on the original graph
# g.pull(nf.layer_parent_nid(i+1),
# fn.copy_src(src='h_{}'.format(i), out='m'),
# lambda node: {'agg_h_{}'.format(i): node.data['m'].mean(axis=1)})
#
# ...
#
# Here, we use the code ``for nf in sampler`` to replace the original
# single-machine sampling code:
#
# .. code:: python
#
# for nf in NeighborSampler(g, batch_size, num_neighbors,
# neighbor_type='in', num_hops=L-1,
# seed_nodes=labeled_nodes):
#
# All the other parts of the original single-machine code is not changed.
#
# In addition, developers need to write sampling logic on the sampler
# machine. For neighbor-sampler, developers can just copy their existing
# single-machine code to sampler machines like this:
#
# .. code:: python
#
# sender = dgl.contrib.sampling.SamplerSender(trainer_address)
#
# ...
#
# for n in num_epoch:
# for nf in dgl.contrib.sampling.NeighborSampler(graph, batch_size, num_neighbors,
# neighbor_type='in',
# shuffle=shuffle,
# num_workers=num_workers,
# num_hops=num_hops,
# add_self_loop=add_self_loop,
# seed_nodes=seed_nodes):
# sender.send(nf, trainer_id)
# # tell trainer I have finished current epoch
# sender.signal(trainer_id)
#
# The figure below shows the overall performance improvement of training
# GCN and GraphSage on the Reddit dataset after deploying the
# optimizations in this tutorial. Our NUMA optimization speeds up the
# training by a factor of 4. The distributed sampling achieves additional
# 20%-40% speed improvement for different tasks.
#
# |image2|
#
# Scale to giant graphs
# ---------------------
#
# Finally, we would like to demonstrate the scalability of DGL with giant
# synthetic graphs. We create three large power-law graphs with
# `RMAT <http://www.cs.cmu.edu/~christos/PUBLICATIONS/siam04.pdf>`__. Each
# node is associated with 100 features and we compute node embeddings with
# 64 dimensions. Below shows the training speed and memory consumption of
# GCN with neighbor sampling.
#
# ====== ====== ================== ===========
# #Nodes #Edges Time per epoch (s) Memory (GB)
# ====== ====== ================== ===========
# 5M 250M 4.7 8
# 50M 2.5B 46 75
# 500M 25B 505 740
# ====== ====== ================== ===========
#
# We can see that DGL can scale to graphs with up to 500M nodes and 25B
# edges.
#
# .. |image0| image:: https://data.dgl.ai/tutorial/sampling/arch.png
# .. |image1| image:: https://data.dgl.ai/tutorial/sampling/NUMA_speedup.png
# .. |image2| image:: https://data.dgl.ai/tutorial/sampling/whole_speedup.png
#
docs/_deprecate/5_giant_graph/README.txt deleted 100644 → 0
View file @ 35e25914
.. _tutorials5-index:
Training on giant graphs
=============================
* **Sampling** `[paper] <https://arxiv.org/abs/1710.10568>`__ `[tutorial]
<5_giant_graph/1_sampling_mx.html>`__ `[MXNet code]
<https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__ `[Pytorch code]
<https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
You can perform neighbor sampling and control-variate sampling to train a
graph convolution network and its variants on a giant graph.
* **Scale to giant graphs** `[tutorial] <5_giant_graph/2_giant.html>`__
`[MXNet code] <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__
`[Pytorch code]
<https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
You can find two components (graph store and distributed sampler) to scale to
graphs with hundreds of millions of nodes.
docs/_deprecate/8_sse_mx.py deleted 100644 → 0
View file @ 35e25914
"""
.. _model-sse:
Stochastic steady-state embedding (SSE)
=======================================
**Author**: Gai Yu, Da Zheng, Quan Gan, Jinjing Zhou, Zheng Zhang
"""
################################################################################################
#
# .. math::
#
# \newcommand{\bfy}{\textbf{y}}
# \newcommand{\cale}{{\mathcal{E}}}
# \newcommand{\calg}{{\mathcal{G}}}
# \newcommand{\call}{{\mathcal{L}}}
# \newcommand{\caln}{{\mathcal{N}}}
# \newcommand{\calo}{{\mathcal{O}}}
# \newcommand{\calt}{{\mathcal{T}}}
# \newcommand{\calv}{{\mathcal{V}}}
# \newcommand{\until}{\text{until}\ }
#
# In this tutorial, you learn how to use the Deep Graph Library (DGL) with MXNet to implement the following:
#
# - Simple, steady-state algorithms with `stochastic steady-state
# embedding <https://www.cc.gatech.edu/~hdai8/pdf/equilibrium_embedding.pdf>`__
# (SSE)
# - Training with subgraph sampling
#
# Subgraph sampling is a technique to scale-up learning to
# gigantic graphs (for example, billions of nodes and edges). Subgraph sampling can apply to
# other algorithms, such as :doc:`Graph convolution
# network <1_gcn>`
# and :doc:`Relational graph convolution
# network <4_rgcn>`.
#
# Steady-state algorithms
# -----------------------
#
# Many algorithms for graph analytics are iterative procedures that
# end when a steady state is reached. Examples include
# PageRank or mean-field inference on Markov random fields.
#
# Flood-fill algorithm
# ~~~~~~~~~~~~~~~~~~~~
#
# A *Flood-fill algorithm* (or *infection* algorithm) can
# also be seen as a procedure. Specifically, the problem is that
# given a graph :math:`\calg = (\calv, \cale)` and a source node
# :math:`s \in \calv`, you need to mark all nodes that can be reached from
# :math:`s`. Let :math:`\calv = \{1, ..., n\}` and let :math:`y_v`
# indicate whether a node :math:`v` is marked. The flood-fill algorithm
# proceeds as follows.
#
# .. math::
#
#
# \begin{alignat}{2}
# & y_s^{(0)} \leftarrow 1 \tag{0} \\
# & y_v^{(0)} \leftarrow 0 \qquad && v \ne s \tag{1} \\
# & y_v^{(t + 1)} \leftarrow \max_{u \in \caln (v)} y_u^{(t)} \qquad && \until \forall v \in \calv, y_v^{(t + 1)} = y_v^{(t)} \tag{2}
# \end{alignat}
#
#
# where :math:`\caln (v)` denotes the neighborhood of :math:`v`, including
# :math:`v` itself.
#
# The flood-fill algorithm first marks the source node :math:`s`, and then
# repeatedly marks nodes with one or more marked neighbors until no node
# needs to be marked, that is, the steady state is reached.
#
# Flood-fill algorithm and steady-state operator
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# More abstractly, :math:`\begin{align}
# & y_v^{(0)} \leftarrow \text{constant} \\
# & \bfy^{(t + 1)} \leftarrow \calt (\bfy^{(t)}) \qquad \until \bfy^{(t + 1)} = \bfy^{(t)} \tag{3}
# \end{align}` where :math:`\bfy^{(t)} = (y_1^{(t)}, ..., y_n^{(t)})` and
# :math:`[\calt (\bfy^{(t)})]_v = \hat\calt (\{\bfy_u^{(t)} : u \in \caln (v)\})`.
# In the case of the flood-fill algorithm, :math:`\hat\calt = \max`. The
# condition “:math:`\until \bfy^{(t + 1)} = \bfy^{(t)}`” in :math:`(3)`
# implies that :math:`\bfy^*` is the solution to the problem if and only
# if :math:`\bfy^* = \calt (\bfy^*)`, that is \ :math:`\bfy^*` is steady
# under :math:`\calt`. Thus we call :math:`\calt` the *steady-state
# operator*.
#
# Implementing a flood-fill algorithm
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# You can implement flood-fill in DGL with the following code.
import mxnet as mx
import os
import dgl
def T(g):
def message_func(edges):
return {'m': edges.src['y']}
def reduce_func(nodes):
# First compute the maximum of all neighbors...
m = mx.nd.max(nodes.mailbox['m'], axis=1)
# Then compare the maximum with the node itself.
# One can also add a self-loop to each node to avoid this
# additional max computation.
m = mx.nd.maximum(m, nodes.data['y'])
return {'y': m.reshape(m.shape[0], 1)}
g.update_all(message_func, reduce_func)
return g.ndata['y']
##############################################################################
# To run the algorithm, create a ``DGLGraph`` as in the example code here, consisting of two
# disjointed chains, each with ten nodes, and initialize it as specified in
# Eq. :math:`(0)` and Eq. :math:`(1)`.
#
import networkx as nx
def disjoint_chains(n_chains, length):
path_graph = nx.path_graph(n_chains * length).to_directed()
for i in range(n_chains - 1): # break the path graph into N chains
path_graph.remove_edge((i + 1) * length - 1, (i + 1) * length)
path_graph.remove_edge((i + 1) * length, (i + 1) * length - 1)
for n in path_graph.nodes:
path_graph.add_edge(n, n) # add self connections
return path_graph
N = 2 # the number of chains
L = 500 # the length of a chain
s = 0 # the source node
# The sampler (see the subgraph sampling section) only supports
# readonly graphs.
g = dgl.DGLGraph(disjoint_chains(N, L), readonly=True)
y = mx.nd.zeros([g.number_of_nodes(), 1])
y[s] = 1
g.ndata['y'] = y
##############################################################################
# Now apply ``T`` to ``g`` until convergence. You can see that nodes
# reachable from ``s`` are gradually infected (marked).
#
while True:
prev_y = g.ndata['y']
next_y = T(g)
if all(prev_y == next_y):
break
##############################################################################
# The update procedure is visualized as follows:
#
# |image0|
#
# Steady-state embedding
# ----------------------
#
# Neural flood-fill algorithm
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Next, you can design a neural network that simulates the
# flood-fill algorithm.
#
# Instead of using :math:`\calt` to update the states of nodes, use
# :math:`\calt_\Theta`, a graph neural network (and
# :math:`\hat\calt_\Theta` instead of :math:`\hat\calt`).
# The state of a node :math:`v` is no longer a Boolean value
# (:math:`y_v`), but, an embedding :math:`h_v` (a vector of some
# reasonable dimension, say, :math:`H`).
# You can also associate a feature vector :math:`x_v` with :math:`v`. For
# the flood-fill algorithm, simply use the one-hot encoding of a
# node’s ID as its feature vector, so that our algorithm can
# distinguish different nodes.
# Only iterate :math:`T` times instead of iterating until the
# steady-state condition is satisfied.
# After iteration, mark the nodes by passing the node embedding
# :math:`h_v` into another neural network to produce a probability
# :math:`p_v` of whether the node is reachable.
#
# Mathematically, :math:`\begin{align}
# & h_v^{(0)} \leftarrow \text{random embedding} \\
# & h_v^{(t + 1)} \leftarrow \calt_\Theta (h_1^{(t)}, ..., h_n^{(t)}) \qquad 1 \leq t \leq T \tag{4}
# \end{align}` where
# :math:`[\calt_\Theta (h_1^{(t)}, ..., h_n^{(t)})]_v = \hat\calt_\Theta (x_u, h_u^{(t)} : u \in \caln (v)\})`.
# :math:`\hat\calt_\Theta` is a two layer neural network, as follows:
#
# .. math::
#
#
# \hat\calt_\Theta (\{x_u, h_u^{(t)} : u \in \caln (v)\})
# = W_1 \sigma \left(W_2 \left[x_v, \sum_{u \in \caln (v)} \left[h_v, x_v\right]\right]\right)
#
# where :math:`[\cdot, \cdot]` denotes the concatenation of vectors, and
# :math:`\sigma` is a nonlinearity, e.g. ReLU. Essentially, for every
# node, :math:`\calt_\Theta` repeatedly gathers its neighbors’ feature
# vectors and embeddings, sums them up, and feeds the result along with
# the node’s own feature vector to a two layer neural network.
#
# Implementation of neural flood-fill
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Like the naive algorithm, the neural flood-fill algorithm can be
# partitioned into a ``message_func`` (neighborhood information gathering)
# and a ``reduce_func`` (:math:`\hat\calt_\Theta`). We define
# :math:`\hat\calt_\Theta` as a callable ``gluon.Block`` as in this example code.
#
import mxnet.gluon as gluon
class FullGraphSteadyStateOperator(gluon.Block):
def __init__(self, n_hidden, activation, **kwargs):
super(FullGraphSteadyStateOperator, self).__init__(**kwargs)
with self.name_scope():
self.dense1 = gluon.nn.Dense(n_hidden, activation=activation)
self.dense2 = gluon.nn.Dense(n_hidden)
def forward(self, g):
def message_func(edges):
x = edges.src['x']
h = edges.src['h']
return {'m' : mx.nd.concat(x, h, dim=1)}
def reduce_func(nodes):
m = mx.nd.sum(nodes.mailbox['m'], axis=1)
z = mx.nd.concat(nodes.data['x'], m, dim=1)
return {'h' : self.dense2(self.dense1(z))}
g.update_all(message_func, reduce_func)
return g.ndata['h']
##############################################################################
# In practice, Eq. :math:`(4)` may cause numerical instability. One
# solution is to update :math:`h_v` with a moving average, as follows:
#
# .. math::
#
#
# h_v^{(t + 1)} \leftarrow (1 - \alpha) h_v^{(t)} + \alpha \left[\calt_\Theta (h_0^{(t)}, ..., h_n^{(t)})\right]_v \qquad 0 < \alpha < 1
#
# Putting these together you have:
#
def update_embeddings(g, steady_state_operator):
prev_h = g.ndata['h']
next_h = steady_state_operator(g)
g.ndata['h'] = (1 - alpha) * prev_h + alpha * next_h
##############################################################################
# The last step involves implementing the predictor.
#
class Predictor(gluon.Block):
def __init__(self, n_hidden, activation, **kwargs):
super(Predictor, self).__init__(**kwargs)
with self.name_scope():
self.dense1 = gluon.nn.Dense(n_hidden, activation=activation)
self.dense2 = gluon.nn.Dense(2) ## binary classifier
def forward(self, h):
return self.dense2(self.dense1(h))
##############################################################################
# The predictor’s decision rule is just a decision rule for binary
# classification.
#
# .. math::
#
#
# \hat{y}_v = \text{argmax}_{i \in \{0, 1\}} \left[g_\Phi (h_v^{(T)})\right]_i \tag{5}
#
# where the predictor is denoted by :math:`g_\Phi` and :math:`\hat{y}_v`
# indicates whether the node :math:`v` is marked or not.
#
# Our implementation can be further accelerated using DGL's :mod:`built-in
# functions <dgl.function>`, which maps
# the computation to more efficient sparse operators in the backend
# framework (e.g., MXNet/Gluon, PyTorch). Please see
# the :doc:`Graph convolution network <1_gcn>` tutorial
# for more details.
#
# Efficient semi-supervised learning on graph
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# In this setting, you can observe the entire structure of one fixed graph as well
# as the feature vector of each node. However, you might only have access to the
# labels of some (very few) of the nodes. Train the neural
# flood-fill algorithm in this setting as well.
#
# Initialize feature vectors ``'x'`` and node embeddings ``'h'``
# first.
#
import numpy as np
import numpy.random as npr
n = g.number_of_nodes()
n_hidden = 16
g.ndata['x'] = mx.nd.eye(n, n)
g.ndata['y'] = mx.nd.concat(*[i * mx.nd.ones([L, 1], dtype='float32')
for i in range(N)], dim=0)
g.ndata['h'] = mx.nd.zeros([n, n_hidden])
r_train = 0.2 # the ratio of test nodes
n_train = int(r_train * n)
nodes_train = npr.choice(range(n), n_train, replace=True)
test_bitmap = np.ones(shape=(n))
test_bitmap[nodes_train] = 0
nodes_test = np.where(test_bitmap)[0]
##############################################################################
# Unrolling the iterations in Eq. :math:`(4)`, we have the following
# expression for updated node embeddings:
#
# .. math::
#
#
# h_v^{(T)} = \calt_\Theta^T (h_1^{(0)}, ..., h_n^{(0)}) \qquad v \in \calv \tag{6}
#
# where :math:`\calt_\Theta^T` means applying :math:`\calt_\Theta` for
# :math:`T` times. These updated node embeddings are fed to :math:`g_\Phi`
# as in Eq. :math:`(5)`. These steps are fully differentiable and the
# neural flood-fill algorithm can thus be trained in an end-to-end
# fashion. Denoting the binary cross-entropy loss by :math:`l`, you have a
# loss function in the following form:
#
# .. math::
#
#
# \call (\Theta, \Phi) = \frac1{\left|\calv_y\right|} \sum_{v \in \calv_y} l \left(g_\Phi \left(\left[\calt_\Theta^T (h_1^{(0)}, ..., h_n^{(0)})\right]_v \right), y_v\right) \tag{7}
#
# After computing :math:`\call (\Theta, \Phi)`, you can update
# :math:`\Theta` and :math:`\Phi` using the gradients
# :math:`\nabla_\Theta \call (\Theta, \Phi)` and
# :math:`\nabla_\Phi \call (\Theta, \Phi)`. One problem with Eq.
# :math:`(7)` is that computing :math:`\nabla_\Theta \call (\Theta, \Phi)`
# and :math:`\nabla_\Phi \call (\Theta, \Phi)` requires back-propagating
# :math:`T` times through :math:`\calt_\Theta`, which may be slow in
# practice. So, adopt the following steady-state loss function, which
# only incorporates the last node embedding update in back-propagation:
#
# .. math::
#
#
# \call_\text{SteadyState} (\Theta, \Phi) = \frac1{\left|\calv_y\right|} \sum_{v \in \calv_y} l \left(g_\Phi \left(\left[\calt_\Theta (h_1^{(T - 1)}, ..., h_n^{(T - 1)})\right]_v, y_v\right)\right) \tag{8}
#
# The following implements one step of training with
# :math:`\call_\text{SteadyState}`. Note that ``g`` in the following is
# :math:`\calg_y` instead of :math:`\calg`.
#
def fullgraph_update_parameters(g, label_nodes, steady_state_operator, predictor, trainer):
n = g.number_of_nodes()
with mx.autograd.record():
steady_state_operator(g)
z = predictor(g.ndata['h'][label_nodes])
y = g.ndata['y'].reshape(n)[label_nodes] # label
loss = mx.nd.softmax_cross_entropy(z, y)
loss.backward()
trainer.step(n) # divide gradients by the number of labelled nodes
return loss.asnumpy()[0]
##############################################################################
# You are now ready to implement the training procedure, which is in two
# phases.
#
# - The first phase updates node embeddings several times using
# :math:`\calt_\Theta` to attain an approximately steady state
# - The second phase trains :math:`\calt_\Theta` and :math:`g_\Phi` using
# this steady state.
#
# You update the node embeddings of :math:`\calg` instead of
# :math:`\calg_y` only. The reason lies in the semi-supervised learning
# setting. To do inference on :math:`\calg`, you need node embeddings on
# :math:`\calg` instead of on :math:`\calg_y` only.
#
def train(g, label_nodes, steady_state_operator, predictor, trainer):
# first phase
for i in range(n_embedding_updates):
update_embeddings(g, steady_state_operator)
# second phase
for i in range(n_parameter_updates):
loss = fullgraph_update_parameters(g, label_nodes, steady_state_operator,
predictor, trainer)
return loss
##############################################################################
# Scaling up with stochastic subgraph training
# --------------------------------------------
#
# The computation time per update is linear to the number of edges in a
# graph. If we have a gigantic graph with billions of nodes and edges, the
# update function would be inefficient.
#
# A possible improvement draws an analogy from mini-batch training on large
# datasets. Instead of computing gradients on the entire graph, only
# consider some subgraphs randomly sampled from the labelled nodes.
# Mathematically, you have the following loss function:
#
# .. math::
#
#
# \call_\text{StochasticSteadyState} (\Theta, \Phi) = \frac1{\left|\calv_y^{(k)}\right|} \sum_{v \in \calv_y^{(k)}} l \left(g_\Phi \left(\left[\calt_\Theta (h_1, ..., h_n)\right]_v\right), y_v\right)
#
# where :math:`\calv_y^{(k)}` is the subset sampled for iteration
# :math:`k`.
#
# In this training procedure, you also update node embeddings only on
# sampled subgraphs, which is perhaps not surprising if you know
# stochastic fixed-point iteration.
#
# Neighbor sampling
# ~~~~~~~~~~~~~~~~~
#
# You can use *neighbor sampling* as a subgraph sampling strategy. Neighbor
# sampling traverses small neighborhoods from seed nodes with breadth first search. For
# each newly sampled node, a small subset of neighboring nodes are sampled
# and added to the subgraph along with the connecting edges, unless the
# node reaches the maximum of :math:`k` hops from the seeding node.
#
# The following shows neighbor sampling with two seed nodes at a time, a
# maximum of two hops, and a maximum of three neighboring nodes.
#
# |image1|
#
# DGL supports very efficient subgraph sampling natively. This helps users
# scale algorithms to large graphs. Currently, DGL provides the
# :func:`~dgl.contrib.sampling.sampler.NeighborSampler`
# API, which returns a subgraph iterator that samples multiple subgraphs
# at a time with neighbor sampling.
#
# The following code demonstrates how to use the ``NeighborSampler`` to
# sample subgraphs, and stores the seed nodes of the subgraph in each iteration:
#
nx_G = nx.erdos_renyi_graph(36, 0.06)
G = dgl.DGLGraph(nx_G.to_directed(), readonly=True)
sampler = dgl.contrib.sampling.NeighborSampler(
G, 2, 3, num_hops=2, shuffle=True)
seeds = []
for subg in sampler:
seeds.append(subg.layer_parent_nid(-1))
##############################################################################
# Sample training with DGL
# ~~~~~~~~~~~~~~~~
#
# The code illustrates the training process in mini-batches.
#
class SubgraphSteadyStateOperator(gluon.Block):
def __init__(self, n_hidden, activation, **kwargs):
super(SubgraphSteadyStateOperator, self).__init__(**kwargs)
with self.name_scope():
self.dense1 = gluon.nn.Dense(n_hidden, activation=activation)
self.dense2 = gluon.nn.Dense(n_hidden)
def forward(self, subg):
def message_func(edges):
x = edges.src['x']
h = edges.src['h']
return {'m' : mx.nd.concat(x, h, dim=1)}
def reduce_func(nodes):
m = mx.nd.sum(nodes.mailbox['m'], axis=1)
z = mx.nd.concat(nodes.data['x'], m, dim=1)
return {'h' : self.dense2(self.dense1(z))}
subg.block_compute(0, message_func, reduce_func)
return subg.layers[-1].data['h']
def update_parameters_subgraph(subg, steady_state_operator, predictor, trainer):
n = subg.layer_size(-1)
with mx.autograd.record():
steady_state_operator(subg)
z = predictor(subg.layers[-1].data['h'])
y = subg.layers[-1].data['y'].reshape(n) # label
loss = mx.nd.softmax_cross_entropy(z, y)
loss.backward()
trainer.step(n) # divide gradients by the number of labelled nodes
return loss.asnumpy()[0]
def update_embeddings_subgraph(g, steady_state_operator):
# Note that we are only updating the embeddings of seed nodes here.
# The reason is that only the seed nodes have ample information
# from neighbors, especially if the subgraph is small (e.g. 1-hops)
prev_h = g.layers[-1].data['h']
next_h = steady_state_operator(g)
g.layers[-1].data['h'] = (1 - alpha) * prev_h + alpha * next_h
def train_on_subgraphs(g, label_nodes, batch_size,
steady_state_operator, predictor, trainer):
# To train SSE, we create two subgraph samplers with the
# `NeighborSampler` API for each phase.
# The first phase samples from all vertices in the graph.
sampler = dgl.contrib.sampling.NeighborSampler(
g, batch_size, g.number_of_nodes(), num_hops=1)
sampler_iter = iter(sampler)
# The second phase only samples from labeled vertices.
sampler_train = dgl.contrib.sampling.NeighborSampler(
g, batch_size, g.number_of_nodes(), seed_nodes=label_nodes, num_hops=1)
sampler_train_iter = iter(sampler_train)
for i in range(n_embedding_updates):
subg = next(sampler_iter)
# Currently, subgraphing does not copy or share features
# automatically. Therefore, we need to copy the node
# embeddings of the subgraph from the parent graph with
# `copy_from_parent()` before computing...
subg.copy_from_parent()
update_embeddings_subgraph(subg, steady_state_operator)
# ... and copy them back to the parent graph.
g.ndata['h'][subg.layer_parent_nid(-1)] = subg.layers[-1].data['h']
for i in range(n_parameter_updates):
try:
subg = next(sampler_train_iter)
except:
break
# Again we need to copy features from parent graph
subg.copy_from_parent()
loss = update_parameters_subgraph(subg, steady_state_operator, predictor, trainer)
# We don't need to copy the features back to parent graph.
return loss
##############################################################################
# You can also define a helper function that reports prediction accuracy.
def test(g, test_nodes, predictor):
z = predictor(g.ndata['h'][test_nodes])
y_bar = mx.nd.argmax(z, axis=1)
y = g.ndata['y'].reshape(n)[test_nodes]
accuracy = mx.nd.sum(y_bar == y) / len(test_nodes)
return accuracy.asnumpy()[0], z
##############################################################################
# Some routine preparations for training.
#
lr = 1e-3
activation = 'relu'
subgraph_steady_state_operator = SubgraphSteadyStateOperator(n_hidden, activation)
predictor = Predictor(n_hidden, activation)
subgraph_steady_state_operator.initialize()
predictor.initialize()
params = subgraph_steady_state_operator.collect_params()
params.update(predictor.collect_params())
trainer = gluon.Trainer(params, 'adam', {'learning_rate' : lr})
##############################################################################
# Now train it. As before, nodes reachable from :math:`s` are
# gradually infected, except that in the background is a neural network.
#
n_epochs = 35
n_embedding_updates = 8
n_parameter_updates = 5
alpha = 0.1
batch_size = 64
y_bars = []
for i in range(n_epochs):
loss = train_on_subgraphs(g, nodes_train, batch_size, subgraph_steady_state_operator,
predictor, trainer)
accuracy_train, _ = test(g, nodes_train, predictor)
accuracy_test, z = test(g, nodes_test, predictor)
print("Iter {:05d} | Train acc {:.4} | Test acc {:.4f}".format(i, accuracy_train, accuracy_test))
y_bar = mx.nd.argmax(z, axis=1)
y_bars.append(y_bar)
##############################################################################
# |image2|
#
# In this tutorial, you used a very small example graph to demonstrate the
# subgraph training for easy visualization. Subgraph training actually
# helps you scale to gigantic graphs. For instance,
# scaling SSE to a graph with 50 million nodes and 150 million edges in a
# single P3.8x large instance, and one epoch, only takes about 160 seconds.
#
# For full examples, see `Benchmark SSE on multi-GPUs <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sse>`_ on Github.
#
# .. |image0| image:: https://data.dgl.ai/tutorial/img/floodfill-paths.gif
# .. |image1| image:: https://data.dgl.ai/tutorial/img/neighbor-sampling.gif
# .. |image2| image:: https://data.dgl.ai/tutorial/img/sse.gif
docs/source/api/python/dgl.DGLGraph.rst
View file @ f13b9b62
...@@ -6,9 +6,24 @@ dgl.DGLGraph ...@@ -6,9 +6,24 @@ dgl.DGLGraph
.. currentmodule:: dgl .. currentmodule:: dgl
.. class:: DGLGraph .. class:: DGLGraph
Class for storing graph structure and node/edge feature data.
There are a few ways to create create a DGLGraph:
* To create a homogeneous graph from Tensor data, use :func:`dgl.graph`.
* To create a heterogeneous graph from Tensor data, use :func:`dgl.heterograph`.
* To create a graph from other data sources, use ``dgl.*`` create ops. See
:ref:`api-graph-create-ops`.
Read the user guide chapter :ref:`guide-graph` for an in-depth explanation about its
usage.
Querying metagraph structure Querying metagraph structure
---------------------------- ----------------------------
Methods for getting information about the node and edge types. They are typically useful
when the graph is heterogeneous.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -19,12 +34,13 @@ Querying metagraph structure ...@@ -19,12 +34,13 @@ Querying metagraph structure
DGLGraph.canonical_etypes DGLGraph.canonical_etypes
DGLGraph.metagraph DGLGraph.metagraph
DGLGraph.to_canonical_etype DGLGraph.to_canonical_etype
DGLGraph.get_ntype_id
DGLGraph.get_etype_id
Querying graph structure Querying graph structure
------------------------ ------------------------
Methods for getting information about the graph structure such as capacity, connectivity,
neighborhood, etc.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -53,15 +69,20 @@ Querying graph structure ...@@ -53,15 +69,20 @@ Querying graph structure
Querying and manipulating sparse format Querying and manipulating sparse format
--------------------------------------- ---------------------------------------
Methods for getting or manipulating the internal storage formats of a ``DGLGraph``.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
DGLGraph.formats DGLGraph.formats
DGLGraph.create_format_ DGLGraph.create_format_
Querying and manipulating index data type Querying and manipulating node/edge ID type
----------------------------------------- -----------------------------------------
Methods for getting or manipulating the data type for storing structure-related
data such as node and edge IDs.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -72,6 +93,9 @@ Querying and manipulating index data type ...@@ -72,6 +93,9 @@ Querying and manipulating index data type
Using Node/edge features Using Node/edge features
------------------------ ------------------------
Methods for getting or setting the data type for storing structure-related
data such as node and edge IDs.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -85,12 +109,14 @@ Using Node/edge features ...@@ -85,12 +109,14 @@ Using Node/edge features
DGLGraph.dstnodes DGLGraph.dstnodes
DGLGraph.srcdata DGLGraph.srcdata
DGLGraph.dstdata DGLGraph.dstdata
DGLGraph.local_var
DGLGraph.local_scope
Transforming graph Transforming graph
------------------ ------------------
Methods for generating a new graph by transforming the current ones. Most of them
are alias of the :ref:`api-subgraph-extraction` and :ref:`api-transform`
under the ``dgl`` namespace.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -99,9 +125,16 @@ Transforming graph ...@@ -99,9 +125,16 @@ Transforming graph
DGLGraph.node_type_subgraph DGLGraph.node_type_subgraph
DGLGraph.edge_type_subgraph DGLGraph.edge_type_subgraph
DGLGraph.__getitem__ DGLGraph.__getitem__
DGLGraph.line_graph
DGLGraph.reverse
DGLGraph.add_self_loop
DGLGraph.remove_self_loop
DGLGraph.to_simple
Adjacency and incidence matrix
---------------------------------
Converting to other formats Methods for getting the adjacency and the incidence matrix of the graph.
---------------------------
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -114,6 +147,8 @@ Converting to other formats ...@@ -114,6 +147,8 @@ Converting to other formats
Computing with DGLGraph Computing with DGLGraph
----------------------------- -----------------------------
Methods for performing message passing, applying functions on node/edge features, etc.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -130,7 +165,11 @@ Computing with DGLGraph ...@@ -130,7 +165,11 @@ Computing with DGLGraph
DGLGraph.filter_edges DGLGraph.filter_edges
Querying batch summary Querying batch summary
---------------------- ---------------------------------
Methods for getting the batching information if the current graph is a batched
graph generated from :func:`dgl.batch`. They are also widely used in the
:ref:`api-batch`.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -142,6 +181,8 @@ Querying batch summary ...@@ -142,6 +181,8 @@ Querying batch summary
Mutating topology Mutating topology
----------------- -----------------
Methods for mutating the graph structure *in-place*.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -153,8 +194,20 @@ Mutating topology ...@@ -153,8 +194,20 @@ Mutating topology
Device Control Device Control
-------------- --------------
Methods for getting or changing the device on which the graph is hosted.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
DGLGraph.to DGLGraph.to
DGLGraph.device DGLGraph.device
Misc
----
Other utility methods.
.. autosummary::
:toctree: ../../generated/
DGLGraph.local_scope
docs/source/api/python/dgl.data.rst
View file @ f13b9b62
...@@ -4,45 +4,42 @@ dgl.data ...@@ -4,45 +4,42 @@ dgl.data
========= =========
.. currentmodule:: dgl.data .. currentmodule:: dgl.data
.. automodule:: dgl.data
Dataset Classes Quick links:
---------------
DGL dataset * `Node Prediction Datasets`_
``````````` * `Edge Prediction Datasets`_
* `Graph Prediction Datasets`_
Base Dataset Class
---------------------------
.. autoclass:: DGLDataset .. autoclass:: DGLDataset
:members: download, save, load, process, has_cache, __getitem__, __len__ :members: download, save, load, process, has_cache, __getitem__, __len__
DGL builtin dataset .. _sstdata:
```````````````````
.. autoclass:: DGLBuiltinDataset Node Prediction Datasets
:members: download ---------------------------------------
.. _sstdata: DGL hosted datasets for node classification/regression tasks.
Stanford sentiment treebank dataset Stanford sentiment treebank dataset
``````````````````````````````````` ```````````````````````````````````
For more information about the dataset, see `Sentiment Analysis <https://nlp.stanford.edu/sentiment/index.html>`__.
.. autoclass:: SSTDataset .. autoclass:: SSTDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _karateclubdata:
.. _karateclubdata:
Karate club dataset Karate club dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: KarateClubDataset .. autoclass:: KarateClubDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _citationdata: .. _citationdata:
Citation network dataset Citation network dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: CoraGraphDataset .. autoclass:: CoraGraphDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
...@@ -52,22 +49,13 @@ Citation network dataset ...@@ -52,22 +49,13 @@ Citation network dataset
.. autoclass:: PubmedGraphDataset .. autoclass:: PubmedGraphDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _kgdata: .. _corafulldata:
CoraFull dataset
Knowlege graph dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: CoraFullDataset
.. autoclass:: FB15k237Dataset
:members: __getitem__, __len__
.. autoclass:: FB15kDataset
:members: __getitem__, __len__
.. autoclass:: WN18Dataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _rdfdata: .. _rdfdata:
RDF datasets RDF datasets
``````````````````````````````````` ```````````````````````````````````
...@@ -83,19 +71,9 @@ RDF datasets ...@@ -83,19 +71,9 @@ RDF datasets
.. autoclass:: AMDataset .. autoclass:: AMDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _corafulldata:
CoraFull dataset
```````````````````````````````````
.. autoclass:: CoraFullDataset
:members: __getitem__, __len__
.. _amazoncobuydata: .. _amazoncobuydata:
Amazon Co-Purchase dataset Amazon Co-Purchase dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: AmazonCoBuyComputerDataset .. autoclass:: AmazonCoBuyComputerDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
...@@ -103,60 +81,90 @@ Amazon Co-Purchase dataset ...@@ -103,60 +81,90 @@ Amazon Co-Purchase dataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _coauthordata: .. _coauthordata:
Coauthor dataset Coauthor dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: CoauthorCSDataset .. autoclass:: CoauthorCSDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. autoclass:: CoauthorPhysicsDataset .. autoclass:: CoauthorPhysicsDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _bitcoinotcdata: .. _ppidata:
Protein-Protein Interaction dataset
BitcoinOTC dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: PPIDataset
:members: __getitem__, __len__
.. autoclass:: BitcoinOTCDataset .. _redditdata:
Reddit dataset
``````````````
.. autoclass:: RedditDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _sbmdata:
Symmetric Stochastic Block Model Mixture dataset
````````````````````````````````````````````````
.. autoclass:: SBMMixtureDataset
:members: __getitem__, __len__, collate_fn
ICEWS18 dataset
```````````````````````````````````
.. autoclass:: ICEWS18Dataset Edge Prediction Datasets
:members: __getitem__, __len__ ---------------------------------------
.. _qm7bdata: DGL hosted datasets for edge classification/regression and link prediction tasks.
QM7b dataset .. _kgdata:
Knowlege graph dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: QM7bDataset .. autoclass:: FB15k237Dataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. autoclass:: FB15kDataset
:members: __getitem__, __len__
.. autoclass:: WN18Dataset
:members: __getitem__, __len__
GDELT dataset .. _bitcoinotcdata:
BitcoinOTC dataset
```````````````````````````````````
.. autoclass:: BitcoinOTCDataset
:members: __getitem__, __len__
ICEWS18 dataset
``````````````````````````````````` ```````````````````````````````````
.. autoclass:: ICEWS18Dataset
:members: __getitem__, __len__
GDELT dataset
```````````````````````````````````
.. autoclass:: GDELTDataset .. autoclass:: GDELTDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _minigcdataset:
Graph Prediction Datasets
---------------------------------------
DGL hosted datasets for graph classification/regression tasks.
.. _qm7bdata:
QM7b dataset
```````````````````````````````````
.. autoclass:: QM7bDataset
:members: __getitem__, __len__
.. _minigcdataset:
Mini graph classification dataset Mini graph classification dataset
````````````````````````````````` `````````````````````````````````
.. autoclass:: MiniGCDataset .. autoclass:: MiniGCDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _tudata: .. _tudata:
TU dataset TU dataset
`````````` ``````````
.. autoclass:: TUDataset .. autoclass:: TUDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
...@@ -164,41 +172,14 @@ TU dataset ...@@ -164,41 +172,14 @@ TU dataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _gindataset: .. _gindataset:
Graph isomorphism network dataset Graph isomorphism network dataset
``````````````````````````````````` ```````````````````````````````````
A compact subset of graph kernel dataset A compact subset of graph kernel dataset
.. autoclass:: GINDataset .. autoclass:: GINDataset
:members: __getitem__, __len__ :members: __getitem__, __len__
.. _ppidata: Utilities
-----------------
Protein-Protein Interaction dataset
```````````````````````````````````
.. autoclass:: PPIDataset
:members: __getitem__, __len__
.. _redditdata:
Reddit dataset
``````````````
.. autoclass:: RedditDataset
:members: __getitem__, __len__
.. _sbmdata:
Symmetric Stochastic Block Model Mixture dataset
````````````````````````````````````````````````
.. autoclass:: SBMMixtureDataset
:members: __getitem__, __len__, collate_fn
Utils
-----
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -214,4 +195,3 @@ Utils ...@@ -214,4 +195,3 @@ Utils
.. autoclass:: dgl.data.utils.Subset .. autoclass:: dgl.data.utils.Subset
:members: __getitem__, __len__ :members: __getitem__, __len__
docs/source/api/python/dgl.dataloading.rst
View file @ f13b9b62
...@@ -7,47 +7,29 @@ dgl.dataloading ...@@ -7,47 +7,29 @@ dgl.dataloading
DataLoaders DataLoaders
----------- -----------
PyTorch node/edge DataLoaders
`````````````````````````````
.. currentmodule:: dgl.dataloading.pytorch .. currentmodule:: dgl.dataloading.pytorch
DGL DataLoader for mini-batch training works similarly to PyTorch's DataLoader.
It has a generator interface that returns mini-batches sampled from some given graphs.
DGL provides two DataLoaders: a ``NodeDataLoader`` for node classification task
and an ``EdgeDataLoader`` for edge/link prediction task.
.. autoclass:: NodeDataLoader .. autoclass:: NodeDataLoader
.. autoclass:: EdgeDataLoader .. autoclass:: EdgeDataLoader
General collating functions
```````````````````````````
.. currentmodule:: dgl.dataloading.dataloader
.. autoclass:: Collator
:members: dataset, collate
.. autoclass:: NodeCollator
:members: dataset, collate
:show-inheritance:
.. autoclass:: EdgeCollator
:members: dataset, collate
:show-inheritance:
.. _api-dataloading-neighbor-sampling: .. _api-dataloading-neighbor-sampling:
Neighbor Sampler
Neighborhood Sampling Classes
----------------------------- -----------------------------
.. currentmodule:: dgl.dataloading.neighbor
Base Multi-layer Neighborhood Sampling Class Neighbor samplers are classes that control the behavior of ``DataLoader`` s
```````````````````````````````````````````` to sample neighbors. All of them inherit the base :class:`BlockSampler` class, but implement
different neighbor sampling strategies by overriding the ``sample_frontier`` or
the ``sample_blocks`` methods.
.. autoclass:: BlockSampler .. autoclass:: BlockSampler
:members: sample_frontier, sample_blocks :members: sample_frontier, sample_blocks
Uniform Node-wise Neighbor Sampling (GraphSAGE style)
`````````````````````````````````````````````````````
.. currentmodule:: dgl.dataloading.neighbor
.. autoclass:: MultiLayerNeighborSampler .. autoclass:: MultiLayerNeighborSampler
:members: sample_frontier :members: sample_frontier
:show-inheritance: :show-inheritance:
...@@ -59,8 +41,10 @@ Uniform Node-wise Neighbor Sampling (GraphSAGE style) ...@@ -59,8 +41,10 @@ Uniform Node-wise Neighbor Sampling (GraphSAGE style)
Negative Samplers for Link Prediction Negative Samplers for Link Prediction
------------------------------------- -------------------------------------
.. currentmodule:: dgl.dataloading.negative_sampler .. currentmodule:: dgl.dataloading.negative_sampler
Negative samplers are classes that control the behavior of the ``EdgeDataLoader``
to generate negative edges.
.. autoclass:: Uniform .. autoclass:: Uniform
:members: __call__ :members: __call__
docs/source/api/python/dgl.function.rst
View file @ f13b9b62
...@@ -74,6 +74,11 @@ can be used in any `autograd` system. Also, built-in functions can be used not o ...@@ -74,6 +74,11 @@ can be used in any `autograd` system. Also, built-in functions can be used not o
or ``apply_edges`` as shown in the example, but wherever message and reduce functions are or ``apply_edges`` as shown in the example, but wherever message and reduce functions are
required (e.g. ``pull``, ``push``, ``send_and_recv``). required (e.g. ``pull``, ``push``, ``send_and_recv``).
.. _api-built-in:
DGL Built-in Function
-------------------------
Here is a cheatsheet of all the DGL built-in functions. Here is a cheatsheet of all the DGL built-in functions.
+-------------------------+-----------------------------------------------------------------+-----------------------+ +-------------------------+-----------------------------------------------------------------+-----------------------+
......
docs/source/api/python/dgl.rst
View file @ f13b9b62
...@@ -4,10 +4,15 @@ dgl ...@@ -4,10 +4,15 @@ dgl
============================= =============================
.. currentmodule:: dgl .. currentmodule:: dgl
.. automodule:: dgl
.. _api-graph-create-ops:
Graph Create Ops Graph Create Ops
------------------------- -------------------------
Operators for constructing :class:`DGLGraph` from raw data formats.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -24,9 +29,11 @@ Graph Create Ops ...@@ -24,9 +29,11 @@ Graph Create Ops
.. _api-subgraph-extraction: .. _api-subgraph-extraction:
Subgraph Extraction Routines Subgraph Extraction Ops
------------------------------------- -------------------------------------
Operators for extracting and returning subgraphs.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -37,8 +44,12 @@ Subgraph Extraction Routines ...@@ -37,8 +44,12 @@ Subgraph Extraction Routines
in_subgraph in_subgraph
out_subgraph out_subgraph
Graph Mutation Routines .. _api-transform:
---------------------------------
Graph Transform Ops
----------------------------------
Operators for generating new graphs by manipulating the structure of the existing ones.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -50,13 +61,6 @@ Graph Mutation Routines ...@@ -50,13 +61,6 @@ Graph Mutation Routines
add_self_loop add_self_loop
remove_self_loop remove_self_loop
add_reverse_edges add_reverse_edges
Graph Transform Routines
----------------------------------
.. autosummary::
:toctree: ../../generated/
reverse reverse
to_bidirected to_bidirected
to_simple to_simple
...@@ -69,9 +73,14 @@ Graph Transform Routines ...@@ -69,9 +73,14 @@ Graph Transform Routines
khop_graph khop_graph
metapath_reachable_graph metapath_reachable_graph
Batching and Reading Out .. _api-batch:
Batching and Reading Out Ops
------------------------------- -------------------------------
Operators for batching multiple graphs into one for batch processing and
operators for computing graph-level representation for both single and batched graphs.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -92,18 +101,22 @@ Batching and Reading Out ...@@ -92,18 +101,22 @@ Batching and Reading Out
topk_nodes topk_nodes
topk_edges topk_edges
Adjacency Related Routines Adjacency Related Utilities
------------------------------- -------------------------------
Utilities for computing adjacency matrix and Lapacian matrix.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
khop_adj khop_adj
laplacian_lambda_max laplacian_lambda_max
Propagate Messages by Traversals Traversals
------------------------------------------ ------------------------------------------
Utilities for traversing graphs.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
...@@ -115,6 +128,9 @@ Propagate Messages by Traversals ...@@ -115,6 +128,9 @@ Propagate Messages by Traversals
Utilities Utilities
----------------------------------------------- -----------------------------------------------
Other utilities for controlling randomness, saving and loading graphs, etc.
.. autosummary:: .. autosummary::
:toctree: ../../generated/ :toctree: ../../generated/
......
docs/source/api/python/sampling.rst → docs/source/api/python/dgl.sampling.rst
View file @ f13b9b62
...@@ -5,7 +5,7 @@ dgl.sampling ...@@ -5,7 +5,7 @@ dgl.sampling
.. automodule:: dgl.sampling .. automodule:: dgl.sampling
Random walk sampling functions Random walk
------------------------------ ------------------------------
.. autosummary:: .. autosummary::
...@@ -14,7 +14,7 @@ Random walk sampling functions ...@@ -14,7 +14,7 @@ Random walk sampling functions
random_walk random_walk
pack_traces pack_traces
Neighbor sampling functions Neighbor sampling
--------------------------- ---------------------------
.. autosummary:: .. autosummary::
...@@ -22,8 +22,4 @@ Neighbor sampling functions ...@@ -22,8 +22,4 @@ Neighbor sampling functions
sample_neighbors sample_neighbors
select_topk select_topk
PinSAGESampler
Builtin sampler classes for more complicated sampling algorithms
----------------------------------------------------------------
.. autoclass:: RandomWalkNeighborSampler
.. autoclass:: PinSAGESampler
docs/source/api/python/index.rst
View file @ f13b9b62
...@@ -5,11 +5,12 @@ API Reference ...@@ -5,11 +5,12 @@ API Reference
:maxdepth: 2 :maxdepth: 2
dgl dgl
dgl.DGLGraph
dgl.data dgl.data
nn
dgl.ops
dgl.function
sampling
dgl.dataloading dgl.dataloading
dgl.DGLGraph
dgl.distributed dgl.distributed
dgl.function
nn
dgl.ops
dgl.sampling
udf
docs/source/api/python/nn.mxnet.rst
View file @ f13b9b62
...@@ -3,15 +3,6 @@ ...@@ -3,15 +3,6 @@
NN Modules (MXNet) NN Modules (MXNet)
=================== ===================
.. contents:: Contents
:local:
We welcome your contribution! If you want a model to be implemented in DGL as a NN module,
please `create an issue <https://github.com/dmlc/dgl/issues>`_ started with "[Feature Request] NN Module XXXModel".
If you want to contribute a NN module, please `create a pull request <https://github.com/dmlc/dgl/pulls>`_ started
with "[NN] XXXModel in MXNet NN Modules" and our team member would review this PR.
Conv Layers Conv Layers
---------------------------------------- ----------------------------------------
......
docs/source/api/python/nn.pytorch.rst
View file @ f13b9b62
...@@ -3,15 +3,6 @@ ...@@ -3,15 +3,6 @@
NN Modules (PyTorch) NN Modules (PyTorch)
==================== ====================
.. contents:: Contents
:local:
We welcome your contribution! If you want a model to be implemented in DGL as a NN module,
please `create an issue <https://github.com/dmlc/dgl/issues>`_ started with "[Feature Request] NN Module XXXModel".
If you want to contribute a NN module, please `create a pull request <https://github.com/dmlc/dgl/pulls>`_ started
with "[NN] XXXModel in PyTorch NN Modules" and our team member would review this PR.
.. _apinn-pytorch-conv: .. _apinn-pytorch-conv:
Conv Layers Conv Layers
......
docs/source/api/python/nn.tensorflow.rst
View file @ f13b9b62
...@@ -3,15 +3,6 @@ ...@@ -3,15 +3,6 @@
NN Modules (Tensorflow) NN Modules (Tensorflow)
==================== ====================
.. contents:: Contents
:local:
We welcome your contribution! If you want a model to be implemented in DGL as a NN module,
please `create an issue <https://github.com/dmlc/dgl/issues>`_ started with "[Feature Request] NN Module XXXModel".
If you want to contribute a NN module, please `create a pull request <https://github.com/dmlc/dgl/pulls>`_ started
with "[NN] XXXModel in tensorflow NN Modules" and our team member would review this PR.
Conv Layers Conv Layers
---------------------------------------- ----------------------------------------
......
docs/source/api/python/udf.rst
View file @ f13b9b62
.. _apiudf: .. _apiudf:
dgl.udf User-defined Function
================================================== ==================================================
.. currentmodule:: dgl.udf .. currentmodule:: dgl.udf
......
docs/source/index.rst
View file @ f13b9b62
...@@ -103,14 +103,15 @@ Getting Started ...@@ -103,14 +103,15 @@ Getting Started
:glob: :glob:
api/python/dgl api/python/dgl
api/python/dgl.DGLGraph
api/python/dgl.data api/python/dgl.data
api/python/nn
api/python/dgl.ops
api/python/dgl.function
api/python/sampling
api/python/dgl.dataloading api/python/dgl.dataloading
api/python/dgl.DGLGraph
api/python/dgl.distributed api/python/dgl.distributed
api/python/dgl.function
api/python/nn
api/python/dgl.ops
api/python/dgl.sampling
api/python/udf
.. toctree:: .. toctree::
:maxdepth: 3 :maxdepth: 3
......
python/dgl/__init__.py
View file @ f13b9b62
"""DGL root package.""" """
The ``dgl`` package contains data structure for storing structural and feature data
(i.e., the :class:`DGLGraph` class) and also utilities for generating, manipulating
and transforming graphs.
"""
# Windows compatibility # Windows compatibility
# This initializes Winsock and performs cleanup at termination as required # This initializes Winsock and performs cleanup at termination as required
import socket import socket
......
python/dgl/batch.py
View file @ f13b9b62
...@@ -10,7 +10,7 @@ from . import utils ...@@ -10,7 +10,7 @@ from . import utils
__all__ = ['batch', 'unbatch', 'batch_hetero', 'unbatch_hetero'] __all__ = ['batch', 'unbatch', 'batch_hetero', 'unbatch_hetero']
def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None): def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None):
r"""Batch a collection of ``DGLGraph``s into one graph for more efficient r"""Batch a collection of :class:`DGLGraph` s into one graph for more efficient
graph computation. graph computation.
Each input graph becomes one disjoint component of the batched graph. The nodes Each input graph becomes one disjoint component of the batched graph. The nodes
...@@ -35,8 +35,8 @@ def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None): ...@@ -35,8 +35,8 @@ def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None):
The numbers of nodes and edges of the input graphs are accessible via the The numbers of nodes and edges of the input graphs are accessible via the
:func:`DGLGraph.batch_num_nodes` and :func:`DGLGraph.batch_num_edges` attributes :func:`DGLGraph.batch_num_nodes` and :func:`DGLGraph.batch_num_edges` attributes
of the result graph. For homographs, they are 1D integer tensors, with each element of the resulting graph. For homogeneous graphs, they are 1D integer tensors,
being the number of nodes/edges of the corresponding input graph. For with each element being the number of nodes/edges of the corresponding input graph. For
heterographs, they are dictionaries of 1D integer tensors, with node heterographs, they are dictionaries of 1D integer tensors, with node
type or edge type as the keys. type or edge type as the keys.
...@@ -46,7 +46,7 @@ def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None): ...@@ -46,7 +46,7 @@ def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None):
By default, node/edge features are batched by concatenating the feature tensors By default, node/edge features are batched by concatenating the feature tensors
of all input graphs. This thus requires features of the same name to have of all input graphs. This thus requires features of the same name to have
the same data type and feature size. One can pass ``None`` to the ``ndata`` the same data type and feature size. One can pass ``None`` to the ``ndata``
or ``edata`` argument to prevent feature batching, or pass a list of string or ``edata`` argument to prevent feature batching, or pass a list of strings
to specify which features to batch. to specify which features to batch.
To unbatch the graph back to a list, use the :func:`dgl.unbatch` function. To unbatch the graph back to a list, use the :func:`dgl.unbatch` function.
...@@ -68,7 +68,7 @@ def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None): ...@@ -68,7 +68,7 @@ def batch(graphs, ndata=ALL, edata=ALL, *, node_attrs=None, edge_attrs=None):
Examples Examples
-------- --------
Batch homographs Batch homogeneous graphs
>>> import dgl >>> import dgl
>>> import torch as th >>> import torch as th
...@@ -251,13 +251,13 @@ def unbatch(g, node_split=None, edge_split=None): ...@@ -251,13 +251,13 @@ def unbatch(g, node_split=None, edge_split=None):
"""Revert the batch operation by split the given graph into a list of small ones. """Revert the batch operation by split the given graph into a list of small ones.
This is the reverse operation of :func:``dgl.batch``. If the ``node_split`` This is the reverse operation of :func:``dgl.batch``. If the ``node_split``
or the ``edge_split`` is not given, it uses the :func:`DGLGraph.batch_num_nodes` or the ``edge_split`` is not given, it calls :func:`DGLGraph.batch_num_nodes`
and :func:`DGLGraph.batch_num_edges` of the input graph. and :func:`DGLGraph.batch_num_edges` of the input graph to get the information.
If the ``node_split`` or the ``edge_split`` arguments are given, If the ``node_split`` or the ``edge_split`` arguments are given,
it will partition the graph according to the given segments. One must assure it will partition the graph according to the given segments. One must assure
that the partition is valid -- edges of the i^th graph only connect nodes that the partition is valid -- edges of the i^th graph only connect nodes
belong to the i^th graph. Otherwise, an error will be thrown. belong to the i^th graph. Otherwise, DGL will throw an error.
The function supports heterograph input, in which case the two split The function supports heterograph input, in which case the two split
section arguments shall be of dictionary type -- similar to the section arguments shall be of dictionary type -- similar to the
......
python/dgl/convert.py
View file @ f13b9b62
...@@ -35,7 +35,7 @@ def graph(data, ...@@ -35,7 +35,7 @@ def graph(data,
idtype=None, idtype=None,
device=None, device=None,
**deprecated_kwargs): **deprecated_kwargs):
"""Create a graph. """Create a graph and return.
Parameters Parameters
---------- ----------
...@@ -199,7 +199,7 @@ def heterograph(data_dict, ...@@ -199,7 +199,7 @@ def heterograph(data_dict,
num_nodes_dict=None, num_nodes_dict=None,
idtype=None, idtype=None,
device=None): device=None):
"""Create a heterogeneous graph. """Create a heterogeneous graph and return.
Parameters Parameters
---------- ----------
...@@ -354,33 +354,34 @@ def heterograph(data_dict, ...@@ -354,33 +354,34 @@ def heterograph(data_dict,
def to_heterogeneous(G, ntypes, etypes, ntype_field=NTYPE, def to_heterogeneous(G, ntypes, etypes, ntype_field=NTYPE,
etype_field=ETYPE, metagraph=None): etype_field=ETYPE, metagraph=None):
"""Convert the given homogeneous graph to a heterogeneous graph. """Convert a homogeneous graph to a heterogeneous graph and return.
The input graph should have only one type of nodes and edges. Each node and edge The input graph should have only one type of nodes and edges. Each node and edge
stores an integer feature (under ``ntype_field`` and ``etype_field``), representing stores an integer feature as its type ID
the type id, which can be used to retrieve the type names stored (specified by :attr:`ntype_field` and :attr:`etype_field`).
in the given ``ntypes`` and ``etypes`` arguments. DGL uses it to retrieve the type names stored in the given
:attr:`ntypes` and :attr:`etypes` arguments.
The function will automatically distinguish edge types that have the same given The function will automatically distinguish edge types that have the same given
type IDs but different src and dst type IDs. For example, we allow both edges A and B type IDs but different src and dst type IDs. For example, it allows both edges A and B
to have the same type ID 0, but one has (0, 1) and the other as (2, 3) as the to have the same type ID 0, but one has (0, 1) and the other as (2, 3) as the
(src, dst) type IDs. In this case, the function will "split" edge type 0 into two types: (src, dst) type IDs. In this case, the function will "split" edge type 0 into two types:
(0, ty_A, 1) and (2, ty_B, 3). In another word, these two edges share the same edge (0, ty_A, 1) and (2, ty_B, 3). In another word, these two edges share the same edge
type name, but can be distinguished by a canonical edge type tuple. type name, but can be distinguished by an edge type triplet.
This function will copy any node/edge features from :attr:`G` to the returned heterogeneous
graph, except for node/edge types to recover the heterogeneous graph.
One can retrieve the IDs of the nodes/edges in :attr:`G` from the returned heterogeneous The function stores the node and edge IDs in the input graph using the ``dgl.NID``
graph with node feature ``dgl.NID`` and edge feature ``dgl.EID`` respectively. and ``dgl.EID`` names in the ``ndata`` and ``edata`` of the resulting graph.
It also copies any node/edge features from :attr:`G` to the returned heterogeneous
graph, except for reserved fields for storing type IDs (``dgl.NTYPE`` and ``dgl.ETYPE``)
and node/edge IDs (``dgl.NID`` and ``dgl.EID``).
Parameters Parameters
---------- ----------
G : DGLGraph G : DGLGraph
The homogeneous graph. The homogeneous graph.
ntypes : list of str ntypes : list[str]
The node type names. The node type names.
etypes : list of str etypes : list[str]
The edge type names. The edge type names.
ntype_field : str, optional ntype_field : str, optional
The feature field used to store node type. (Default: ``dgl.NTYPE``) The feature field used to store node type. (Default: ``dgl.NTYPE``)
...@@ -389,24 +390,18 @@ def to_heterogeneous(G, ntypes, etypes, ntype_field=NTYPE, ...@@ -389,24 +390,18 @@ def to_heterogeneous(G, ntypes, etypes, ntype_field=NTYPE,
metagraph : networkx MultiDiGraph, optional metagraph : networkx MultiDiGraph, optional
Metagraph of the returned heterograph. Metagraph of the returned heterograph.
If provided, DGL assumes that G can indeed be described with the given metagraph. If provided, DGL assumes that G can indeed be described with the given metagraph.
If None, DGL will infer the metagraph from the given inputs, which would be If None, DGL will infer the metagraph from the given inputs, which could be
potentially slower for large graphs. costly for large graphs.
Returns Returns
------- -------
DGLGraph DGLGraph
A heterogeneous graph. The parent node and edge ID are stored in the column A heterogeneous graph.
``dgl.NID`` and ``dgl.EID`` respectively for all node/edge types.
Notes Notes
----- -----
The returned node and edge types may not necessarily be in the same order as The returned node and edge types may not necessarily be in the same order as
``ntypes`` and ``etypes``. And edge types may be duplicated if the source ``ntypes`` and ``etypes``.
and destination types differ.
The node IDs of a single type in the returned heterogeneous graph is ordered
the same as the nodes with the same ``ntype_field`` feature. Edge IDs of
a single type is similar.
Examples Examples
-------- --------
...@@ -568,15 +563,15 @@ def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE, ...@@ -568,15 +563,15 @@ def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE,
etype_field=etype_field, metagraph=metagraph) etype_field=etype_field, metagraph=metagraph)
def to_homogeneous(G, ndata=None, edata=None): def to_homogeneous(G, ndata=None, edata=None):
"""Convert the given heterogeneous graph to a homogeneous graph. """Convert a heterogeneous graph to a homogeneous graph and return.
The returned graph has only one type of nodes and edges.
Node and edge types are stored as features in the returned graph. Each feature Node and edge types of the input graph are stored as the ``dgl.NTYPE``
is an integer representing the type id, which can be used to retrieve the type and ``dgl.ETYPE`` features in the returned graph.
names stored in ``G.ntypes`` and ``G.etypes`` arguments. Each feature is an integer representing the type id, determined by the
:meth:`DGLGraph.get_ntype_id` and :meth:`DGLGraph.get_etype_id` methods.
If all The function also stores the original node/edge IDs as the ``dgl.NID``
and ``dgl.EID`` features in the returned graph.
Parameters Parameters
---------- ----------
...@@ -596,8 +591,7 @@ def to_homogeneous(G, ndata=None, edata=None): ...@@ -596,8 +591,7 @@ def to_homogeneous(G, ndata=None, edata=None):
Returns Returns
------- -------
DGLGraph DGLGraph
A homogeneous graph. The parent node and edge type/ID are stored in A homogeneous graph.
columns ``dgl.NTYPE/dgl.NID`` and ``dgl.ETYPE/dgl.EID`` respectively.
Examples Examples
-------- --------
...@@ -695,7 +689,7 @@ def from_scipy(sp_mat, ...@@ -695,7 +689,7 @@ def from_scipy(sp_mat,
eweight_name=None, eweight_name=None,
idtype=None, idtype=None,
device=None): device=None):
"""Create a graph from a SciPy sparse matrix. """Create a graph from a SciPy sparse matrix and return.
Parameters Parameters
---------- ----------
...@@ -785,7 +779,7 @@ def bipartite_from_scipy(sp_mat, ...@@ -785,7 +779,7 @@ def bipartite_from_scipy(sp_mat,
eweight_name=None, eweight_name=None,
idtype=None, idtype=None,
device=None): device=None):
"""Create a unidirectional bipartite graph from a SciPy sparse matrix. """Create a uni-directional bipartite graph from a SciPy sparse matrix and return.
The created graph will have two types of nodes ``utype`` and ``vtype`` as well as one The created graph will have two types of nodes ``utype`` and ``vtype`` as well as one
edge type ``etype`` whose edges are from ``utype`` to ``vtype``. edge type ``etype`` whose edges are from ``utype`` to ``vtype``.
...@@ -881,11 +875,12 @@ def from_networkx(nx_graph, ...@@ -881,11 +875,12 @@ def from_networkx(nx_graph,
edge_id_attr_name=None, edge_id_attr_name=None,
idtype=None, idtype=None,
device=None): device=None):
"""Create a graph from a NetworkX graph. """Create a graph from a NetworkX graph and return.
Creating a DGLGraph from a NetworkX graph is not fast especially for large scales. .. note::
It is recommended to first convert a NetworkX graph into a tuple of node-tensors Creating a DGLGraph from a NetworkX graph is not fast especially for large scales.
and then construct a DGLGraph with :func:`dgl.graph`. It is recommended to first convert a NetworkX graph into a tuple of node-tensors
and then construct a DGLGraph with :func:`dgl.graph`.
Parameters Parameters
---------- ----------
...@@ -903,7 +898,7 @@ def from_networkx(nx_graph, ...@@ -903,7 +898,7 @@ def from_networkx(nx_graph,
The names of the edge attributes to retrieve from the NetworkX graph. If given, DGL The names of the edge attributes to retrieve from the NetworkX graph. If given, DGL
stores the retrieved edge attributes in ``edata`` of the returned graph using their stores the retrieved edge attributes in ``edata`` of the returned graph using their
original names. The attribute data must be convertible to Tensor type (e.g., scalar, original names. The attribute data must be convertible to Tensor type (e.g., scalar,
numpy.ndarray, list, etc.). It must be None if :attr:`nx_graph` is undirected. ``numpy.ndarray``, list, etc.). It must be None if :attr:`nx_graph` is undirected.
edge_id_attr_name : str, optional edge_id_attr_name : str, optional
The name of the edge attribute that stores the edge IDs. If given, DGL will assign edge The name of the edge attribute that stores the edge IDs. If given, DGL will assign edge
IDs accordingly when creating the graph, so the attribute must be valid IDs, i.e. IDs accordingly when creating the graph, so the attribute must be valid IDs, i.e.
...@@ -1046,14 +1041,15 @@ def bipartite_from_networkx(nx_graph, ...@@ -1046,14 +1041,15 @@ def bipartite_from_networkx(nx_graph,
edge_id_attr_name=None, edge_id_attr_name=None,
idtype=None, idtype=None,
device=None): device=None):
"""Create a unidirectional bipartite graph from a NetworkX graph. """Create a unidirectional bipartite graph from a NetworkX graph and return.
The created graph will have two types of nodes ``utype`` and ``vtype`` as well as one The created graph will have two types of nodes ``utype`` and ``vtype`` as well as one
edge type ``etype`` whose edges are from ``utype`` to ``vtype``. edge type ``etype`` whose edges are from ``utype`` to ``vtype``.
Creating a DGLGraph from a NetworkX graph is not fast especially for large scales. .. note::
It is recommended to first convert a NetworkX graph into a tuple of node-tensors Creating a DGLGraph from a NetworkX graph is not fast especially for large scales.
and then construct a DGLGraph with :func:`dgl.heterograph`. It is recommended to first convert a NetworkX graph into a tuple of node-tensors
and then construct a DGLGraph with :func:`dgl.heterograph`.
Parameters Parameters
---------- ----------
...@@ -1074,7 +1070,7 @@ def bipartite_from_networkx(nx_graph, ...@@ -1074,7 +1070,7 @@ def bipartite_from_networkx(nx_graph,
The names of the node attributes for node type :attr:`utype` to retrieve from the The names of the node attributes for node type :attr:`utype` to retrieve from the
NetworkX graph. If given, DGL stores the retrieved node attributes in NetworkX graph. If given, DGL stores the retrieved node attributes in
``nodes[utype].data`` of the returned graph using their original names. The attribute ``nodes[utype].data`` of the returned graph using their original names. The attribute
data must be convertible to Tensor type (e.g., scalar, numpy.array, list, etc.). data must be convertible to Tensor type (e.g., scalar, ``numpy.ndarray``, list, etc.).
e_attrs : list[str], optional e_attrs : list[str], optional
The names of the edge attributes to retrieve from the NetworkX graph. If given, DGL The names of the edge attributes to retrieve from the NetworkX graph. If given, DGL
stores the retrieved edge attributes in ``edata`` of the returned graph using their stores the retrieved edge attributes in ``edata`` of the returned graph using their
...@@ -1242,14 +1238,16 @@ def bipartite_from_networkx(nx_graph, ...@@ -1242,14 +1238,16 @@ def bipartite_from_networkx(nx_graph,
return g.to(device) return g.to(device)
def to_networkx(g, node_attrs=None, edge_attrs=None): def to_networkx(g, node_attrs=None, edge_attrs=None):
"""Convert a homogeneous graph to a NetworkX graph. """Convert a homogeneous graph to a NetworkX graph and return.
It will save the edge IDs as the ``'id'`` edge attribute in the returned NetworkX graph. The resulting NetworkX graph also contains the node/edge features of the input graph.
Additionally, DGL saves the edge IDs as the ``'id'`` edge attribute in the
returned NetworkX graph.
Parameters Parameters
---------- ----------
g : DGLGraph g : DGLGraph
A homogeneous graph on CPU. A homogeneous graph.
node_attrs : iterable of str, optional node_attrs : iterable of str, optional
The node attributes to copy from ``g.ndata``. (Default: None) The node attributes to copy from ``g.ndata``. (Default: None)
edge_attrs : iterable of str, optional edge_attrs : iterable of str, optional
...@@ -1260,6 +1258,10 @@ def to_networkx(g, node_attrs=None, edge_attrs=None): ...@@ -1260,6 +1258,10 @@ def to_networkx(g, node_attrs=None, edge_attrs=None):
networkx.DiGraph networkx.DiGraph
The converted NetworkX graph. The converted NetworkX graph.
Notes
-----
The function only supports CPU graph input.
Examples Examples
-------- --------
The following example uses PyTorch backend. The following example uses PyTorch backend.
......
python/dgl/data/__init__.py
View file @ f13b9b62
"""Data related package.""" """The ``dgl.data`` package contains datasets hosted by DGL and also utilities
for downloading, processing, saving and loading data from external resources.
"""
from __future__ import absolute_import from __future__ import absolute_import
from . import citation_graph as citegrh from . import citation_graph as citegrh
......
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