libcity.model.road_representation¶
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class
libcity.model.road_representation.ChebConv(config, data_feature)[source]¶ Bases:
libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel-
calculate_loss(batch)[source]¶ - Parameters
batch – dict, need key ‘node_features’, ‘node_labels’, ‘mask’
Returns:
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forward(batch)[source]¶ 自回归任务
- Parameters
batch – dict, need key ‘node_features’ contains tensor shape=(N, feature_dim)
- Returns
N, feature_dim
- Return type
torch.tensor
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training: bool¶
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class
libcity.model.road_representation.DeepWalk(config, data_feature)[source]¶ Bases:
libcity.model.abstract_traffic_tradition_model.AbstractTraditionModel
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class
libcity.model.road_representation.GAT(config, data_feature)[source]¶ Bases:
libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel-
calculate_loss(batch)[source]¶ - Parameters
batch – dict, need key ‘node_features’, ‘node_labels’, ‘mask’
Returns:
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forward(batch)[source]¶ 自回归任务 :param batch: dict, need key ‘node_features’ contains tensor shape=(N, feature_dim)
- Returns
N, feature_dim
- Return type
torch.tensor
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training: bool¶
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class
libcity.model.road_representation.GeomGCN(config, data_feature)[source]¶ Bases:
libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel-
calculate_loss(batch)[source]¶ - Parameters
batch – dict, need key ‘node_features’, ‘node_labels’, ‘mask’
Returns:
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forward(batch)[source]¶ 自回归任务
- Parameters
batch – dict, need key ‘node_features’ contains tensor shape=(N, feature_dim)
- Returns
N, output_classes
- Return type
torch.tensor
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training: bool¶
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class
libcity.model.road_representation.LINE(config, data_feature)[source]¶ Bases:
libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel-
calculate_loss(batch)[source]¶ 输入一个batch的数据，返回训练过程的loss，也就是需要定义一个loss函数
- Parameters
batch (Batch) – a batch of input
- Returns
return training loss
- Return type
torch.tensor
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forward(I, J)[source]¶ - Parameters
I – origin indices of node i ; (B,)
J – origin indices of node j ; (B,)
- Returns
[u_j^T * u_i for (i,j) in zip(I, J)]; (B,) elif order == ‘second’:
[u’_j^T * v_i for (i,j) in zip(I, J)]; (B,)
- Return type
if order == ‘first’
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training: bool¶
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class
libcity.model.road_representation.Node2Vec(config, data_feature)[source]¶ Bases:
libcity.model.abstract_traffic_tradition_model.AbstractTraditionModel