#!/usr/bin/env python
# coding: utf-8

# ## 论文引用网络中的节点分类任务
# 
# 在这一教程中，我们将展示 GraphScope 如何结合图分析、图查询和图学习的能力，处理论文引用网络中的节点分类任务。
# 
# 
# 在这个例子中我们使用的是 [ogbn-mag](https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag) 数据集。"ogbn" 是由微软学术关系图（Microsoft Academic Graph）的子集组成的异构图网络。该图中包含4种类型的实体（即论文、作者、机构和研究领域），以及连接两个实体的四种类型的有向关系边。
# 
# 我们需要处理的任务是，给出异构的 ogbn-mag 数据，在该图上预测每篇论文的类别。这是一个节点分类任务，该任务可以归类在各个领域、各个方向或研究小组的论文，通过对论文属性和引用图上的结构信息对论文进行分类。在该数据中，每个论文节点包含了一个从论文标题、摘要抽取的 128 维 word2vec 向量作为表征，该表征是经过预训练提前获取的；而结构信息是在计算过程中即时计算的。
# 
# 这一教程将会分为以下几个步骤：
# 
# - 通过gremlin交互式查询图；
# - 执行图算法做图分析；
# - 执行基于图数据的机器学习任务；

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# Install graphscope package if you are NOT in the Playground

get_ipython().system('pip3 install graphscope')
get_ipython().system('pip3 uninstall -y importlib_metadata  # Address an module conflict issue on colab.google. Remove this line if you are not on colab.')


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# Import the graphscope module

import graphscope

graphscope.set_option(show_log=False)  # enable logging


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# Load the obgn_mag dataset as a graph

from graphscope.dataset import load_ogbn_mag

graph = load_ogbn_mag()


# ## Interactive query with gremlin
# 
# 在此示例中，我们启动了一个交互查询引擎，然后使用图遍历来查看两位给定作者共同撰写的论文数量。为了简化查询，我们假设作者可以分别由ID 2 和 4307 唯一标识。

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# Get the entrypoint for submitting Gremlin queries on graph g.
interactive = graphscope.gremlin(graph)

# Count the number of papers two authors (with id 2 and 4307) have co-authored.
papers = interactive.execute(
    "g.V().has('author', 'id', 2).out('writes').where(__.in('writes').has('id', 4307)).count()"
).one()
print("result", papers)


# ## Graph analytics with analytical engine
# 
# 继续我们的示例，下面我们在图数据中进行图分析来生成节点结构特征。我们首先通过在特定周期内从全图中提取论文（使用Gremlin！）来导出一个子图，然后运行 k-core 分解和三角形计数以生成每个论文节点的结构特征。

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# Exact a subgraph of publication within a time range.
sub_graph = interactive.subgraph("g.V().has('year', inside(2014, 2020)).outE('cites')")

# Project the subgraph to simple graph by selecting papers and their citations.
simple_g = sub_graph.project(vertices={"paper": []}, edges={"cites": []})
# compute the kcore and triangle-counting.
kc_result = graphscope.k_core(simple_g, k=5)
tc_result = graphscope.triangles(simple_g)

# Add the results as new columns to the citation graph.
sub_graph = sub_graph.add_column(kc_result, {"kcore": "r"})
sub_graph = sub_graph.add_column(tc_result, {"tc": "r"})


# ## Graph neural networks (GNNs)
# 
# 接着我们利用生成的结构特征和原有特征通过 GraphScope 的学习引擎来训练一个学习模型。
# 
# 在本例中，我们训练了 GCN 模型，将节点（论文）分类为349个类别，每个类别代表一个出处（例如预印本和会议）。

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# Define the features for learning,
# we chose original 128-dimension feature and k-core, triangle count result as new features.
paper_features = []
for i in range(128):
    paper_features.append("feat_" + str(i))
paper_features.append("kcore")
paper_features.append("tc")

# Launch a learning engine. here we split the dataset, 75% as train, 10% as validation and 15% as test.
lg = graphscope.graphlearn(
    sub_graph,
    nodes=[("paper", paper_features)],
    edges=[("paper", "cites", "paper")],
    gen_labels=[
        ("train", "paper", 100, (0, 75)),
        ("val", "paper", 100, (75, 85)),
        ("test", "paper", 100, (85, 100)),
    ],
)


# Then we define the training process, use internal GCN model.
from graphscope.learning.examples import GCN
from graphscope.learning.graphlearn.python.model.tf.optimizer import get_tf_optimizer
from graphscope.learning.graphlearn.python.model.tf.trainer import LocalTFTrainer


def train(config, graph):
    def model_fn():
        return GCN(
            graph,
            config["class_num"],
            config["features_num"],
            config["batch_size"],
            val_batch_size=config["val_batch_size"],
            test_batch_size=config["test_batch_size"],
            categorical_attrs_desc=config["categorical_attrs_desc"],
            hidden_dim=config["hidden_dim"],
            in_drop_rate=config["in_drop_rate"],
            neighs_num=config["neighs_num"],
            hops_num=config["hops_num"],
            node_type=config["node_type"],
            edge_type=config["edge_type"],
            full_graph_mode=config["full_graph_mode"],
        )

    trainer = LocalTFTrainer(
        model_fn,
        epoch=config["epoch"],
        optimizer=get_tf_optimizer(
            config["learning_algo"], config["learning_rate"], config["weight_decay"]
        ),
    )
    trainer.train_and_evaluate()


# hyperparameters config.
config = {
    "class_num": 349,  # output dimension
    "features_num": 130,  # 128 dimension + kcore + triangle count
    "batch_size": 500,
    "val_batch_size": 100,
    "test_batch_size": 100,
    "categorical_attrs_desc": "",
    "hidden_dim": 256,
    "in_drop_rate": 0.5,
    "hops_num": 2,
    "neighs_num": [5, 10],
    "full_graph_mode": False,
    "agg_type": "gcn",  # mean, sum
    "learning_algo": "adam",
    "learning_rate": 0.01,
    "weight_decay": 0.0005,
    "epoch": 5,
    "node_type": "paper",
    "edge_type": "cites",
}

# Start traning and evaluating
train(config, lg)


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