libcity.data.dataset.eta_encoder.abstract_eta_encoder¶
-
class
libcity.data.dataset.eta_encoder.abstract_eta_encoder.AbstractETAEncoder(config)[source]¶ Bases:
objectETA Encoder
ETA Encoder is used to encode the spatiotemporal information in trajectory. We abstract the encoding operation from the Dataset Module to facilitate developers to achive more flexible and diverse trajectory representation extraction. It is worth noting that the representation extraction involved here is not learnable and fixed. Any learnable representation extraction, e.g. embedding, should be emplemented in Model Module.
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config¶ The configuration of the encoder.
- Type
libcity.ConfigParser
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pad_item¶ The key is a feature’s name and the value should be corresponding padding value. If a feature dose not need to be padded, don’t insert it into this dict. In other word, libcity.dataset.Batch will pad all features in pad_item.keys().
- Type
dict
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feature_dict¶ The key is a feature’s name and the value should be the data type of the corresponding feature. When libcity.dataset.Batch converts the encoded trajectory tuple to tensor, It will refer to this attribute to know the feature name and data type corresponding to each element in the tuple.
- Type
dict
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data_feature¶ The data_feature contains the statistics features of the encoded dataset, which is used to init the model. For example, if the model use torch.nn.Embedding to embed location id and time id, the data_feature should contain loc_size and time_size to tell model how to init the embedding layer.
- Type
dict
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encode(uid, trajectories, dyna_feature_column)[source]¶ Encode trajectories of user uid.
- Parameters
uid (int) – The uid of user. If there is no need to encode uid, just keep it.
trajectories (list of trajectory) – The trajectories of user. Each trajectory is
sequence of spatiotemporal point. The spatiotemporal point is represented by (a) –
list. Thus (a) –
- trajectory1 = [
[dyna_id, type, time, entity_id, traj_id, coordinates/location, properties], [dyna_id, type, time, entity_id, traj_id, coordinates/location, properties], …..
]
trajectory is represented by a list of lists. For example (a) –
- trajectory1 = [
[dyna_id, type, time, entity_id, traj_id, coordinates/location, properties], [dyna_id, type, time, entity_id, traj_id, coordinates/location, properties], …..
]
spatiotemporal tuple contains all useful information in a record of the Raw (Every) –
Data (refer to corresponding .dyna file for details) –
represented as (are) –
- [
- [ # trajectory1
[dyna_id, type, time, entity_id, traj_id, coordinates/location, properties], [dyna_id, type, time, entity_id, traj_id, coordinates/location, properties], …
], trajectory2, …
]
dyna_feature_column (dict) – The key is a feature’s name and the value should be corresponding column id in .dyna file.
- Returns
The return value of this function is the list of encoded trajectories. Same as the input format, each encoded trajectory should be a tuple, which contains all features extracted from the input trajectory. The encoded trajectory will subsequently be converted to a torch.tensor and then directly input to the model. (see more in libcity.Batch) Take the DeeptteEncoder as an example.
- encoded_trajectory = [current_longi, current_lati, current_tim, current_dis, current_state,
uid, weekid, timeid, dist, time]
Please make sure the order of the features in the list is consistent with the order of the features in self.feature_dict.
- Return type
list
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