"""
自定义数据集
=========================

"""
# %%
# node_classification的数据集
# -------------------------------
import torch
from cogdl import experiment
from cogdl.data import Graph
from cogdl.datasets import NodeDataset, generate_random_graph

class MyNodeDataset(NodeDataset):
    def __init__(self, path="data.pt"):
        self.path = path
        super(MyNodeDataset, self).__init__(path, scale_feat=False, metric="accuracy")

    def process(self):
        """You need to load your dataset and transform to `Graph`"""
        num_nodes, num_edges, feat_dim = 100, 300, 30

        # load or generate your dataset
        edge_index = torch.randint(0, num_nodes, (2, num_edges))
        x = torch.randn(num_nodes, feat_dim)
        y = torch.randint(0, 2, (num_nodes,))

        # set train/val/test mask in node_classification task
        train_mask = torch.zeros(num_nodes).bool()
        train_mask[0 : int(0.3 * num_nodes)] = True
        val_mask = torch.zeros(num_nodes).bool()
        val_mask[int(0.3 * num_nodes) : int(0.7 * num_nodes)] = True
        test_mask = torch.zeros(num_nodes).bool()
        test_mask[int(0.7 * num_nodes) :] = True
        data = Graph(x=x, edge_index=edge_index, y=y, train_mask=train_mask, val_mask=val_mask, test_mask=test_mask)
        return data

if __name__ == "__main__":
    # Train customized dataset via defining a new class
    dataset = MyNodeDataset()
    experiment(dataset=dataset, model="gcn")

    # Train customized dataset via feeding the graph data to NodeDataset
    data = generate_random_graph(num_nodes=100, num_edges=300, num_feats=30)
    dataset = NodeDataset(data=data)
    experiment(dataset=dataset, model="gcn")

# %%
# graph_classification的数据集
# ---------------------------------
from cogdl.data import Graph
from cogdl.datasets import GraphDataset

class MyGraphDataset(GraphDataset):
    def __init__(self, path="data.pt"):
        self.path = path
        super(MyGraphDataset, self).__init__(path, metric="accuracy")

    def process(self):
        # Load and preprocess data
        # Here we randomly generate several graphs for simplicity as an example
        graphs = []
        for i in range(10):
            edges = torch.randint(0, 20, (2, 30))
            label = torch.randint(0, 7, (1,))
            graphs.append(Graph(edge_index=edges, y=label))
        return graphs

if __name__ == "__main__":
    dataset = MyGraphDataset()
    experiment(model="gin", dataset=dataset)