libcity.model.trajectory_loc_prediction.HSTLSTM

class libcity.model.trajectory_loc_prediction.HSTLSTM.HSTLSTM(config, data_feature)

Bases: libcity.model.abstract_model.AbstractModel

RNN classifier using ST-LSTM as its core.

cal_inter(ld, hd, l, h, embed)

Calculate a linear interpolation. :param ld: Distances to lower bound, shape (batch_size, step) :param hd: Distances to higher bound, shape (batch_size, step) :param l: Lower bound indexes, shape (batch_size, step) :param h: Higher bound indexes, shape (batch_size, step)

calculate_loss(batch)

Train model using one batch of data and return loss value. :param model: One instance of STLSTMClassifier. :param batch_l: batch of input location sequences,

size (batch_size, time_step, input_size)

Parameters

• batch_t – batch of temporal interval value, size (batch_size, step)

• batch_d – batch of spatial distance value, size (batch_size, step)

• batch_label – batch of label, size (batch_size)

Returns

loss value.

forward(batch_l, batch_t, batch_d, origin_len)

Process forward propagation of ST-LSTM classifier. :param batch_l: batch of input location sequences,

size (batch_size, time_step, input_size)

Parameters

• batch_t – batch of temporal interval value, size (batch_size, step)

• batch_d – batch of spatial distance value, size (batch_size, step)

Returns

prediction result of this batch, size (batch_size, output_size, step).

place_parameters(ld, hd, l, h)

predict(batch)

Predict a batch of data. :param batch_l: batch of input location sequences,

size (batch_size, time_step, input_size)

Parameters

• batch_t – batch of temporal interval value, size (batch_size, step)

• batch_d – batch of spatial distance value, size (batch_size, step)

Returns

batch of predicted class indices, size (batch_size).

training: bool

class libcity.model.trajectory_loc_prediction.HSTLSTM.STLSTM(input_size, hidden_size, bias=True)

Bases: torch.nn.modules.module.Module

One layer, batch-first Spatial-Temporal LSTM network. Kong D, Wu F. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction[C]//IJCAI. 2018: 2341-2347. .. rubric:: Examples

>>> st_lstm = STLSTM(10, 20)
>>> input_l = torch.randn(6, 3, 10)
>>> input_s = torch.randn(6, 3, 10)
>>> input_q = torch.randn(6, 3, 10)
>>> hidden_out, cell_out = st_lstm(input_l, input_s, input_q)

check_forward_input(input_l, input_s, input_q)

forward(input_l, input_s, input_q, hc=None)

Proceed forward propagation of ST-LSTM network. :param input_l: input of location embedding vector, shape (batch_size, step, input_size) :param input_s: input of spatial embedding vector, shape (batch_size, step, input_size) :param input_q: input of temporal embedding vector, shape (batch_size, step, input_size) :param hc: tuple containing initial hidden state and cell state, optional. :return: hidden states and cell states produced by iterate through the steps.

training: bool

class libcity.model.trajectory_loc_prediction.HSTLSTM.STLSTMCell(input_size, hidden_size, bias=True)

Bases: torch.nn.modules.module.Module

A Spatial-Temporal Long Short Term Memory (ST-LSTM) cell. Kong D, Wu F. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction[C]//IJCAI. 2018: 2341-2347. .. rubric:: Examples

>>> st_lstm = STLSTMCell(10, 20)
>>> input_l = torch.randn(6, 3, 10)
>>> input_s = torch.randn(6, 3, 10)
>>> input_q = torch.randn(6, 3, 10)
>>> hc = (torch.randn(3, 20), torch.randn(3, 20))
>>> output = []
>>> for i in range(6):
>>>     hc = st_lstm(input_l[i], input_s[i], input_q[i], hc)
>>>     output.append(hc[0])

check_forward_hidden(input, hx, hidden_label='')

check_forward_input(input)

forward(input_l, input_s, input_q, hc=None)

Proceed one step forward propagation of ST-LSTM. :param input_l: input of location embedding vector, shape (batch_size, input_size) :param input_s: input of spatial embedding vector, shape (batch_size, input_size) :param input_q: input of temporal embedding vector, shape (batch_size, input_size) :param hc: tuple containing hidden state and cell state of previous step. :return: hidden state and cell state of this step.

reset_parameters()

st_lstm_cell_cal(input_l, input_s, input_q, hidden, cell, w_ih, w_hh, w_s, w_q, b_ih, b_hh)

Proceed calculation of one step of STLSTM. :param input_l: input of location embedding, shape (batch_size, input_size) :param input_s: input of spatial embedding, shape (batch_size, input_size) :param input_q: input of temporal embedding, shape (batch_size, input_size) :param hidden: hidden state from previous step, shape (batch_size, hidden_size) :param cell: cell state from previous step, shape (batch_size, hidden_size) :param w_ih: chunk of weights for process input tensor, shape (4 * hidden_size, input_size) :param w_hh: chunk of weights for process hidden state tensor, shape (4 * hidden_size, hidden_size) :param w_s: chunk of weights for process input of spatial embedding, shape (3 * hidden_size, input_size) :param w_q: chunk of weights for process input of temporal embedding, shape (3 * hidden_size, input_size) :param b_ih: chunk of biases for process input tensor, shape (4 * hidden_size) :param b_hh: chunk of biases for process hidden state tensor, shape (4 * hidden_size) :return: hidden state and cell state of this step.

training: bool

libcity.model.trajectory_loc_prediction.HSTLSTM.cal_slot_distance(value, slots)

Calculate a value’s distance with nearest lower bound and higher bound in slots. :param value: The value to be calculated. :param slots: values of slots, needed to be sorted. :return: normalized distance with lower bound and higher bound,

and index of lower bound and higher bound.

libcity.model.trajectory_loc_prediction.HSTLSTM.cal_slot_distance_batch(batch_value, slots)

Proceed cal_slot_distance on a batch of data. :param batch_value: a batch of value, size (batch_size, step) :param slots: values of slots, needed to be sorted. :return: batch of distances and indexes. All with shape (batch_size, step).

libcity.model.trajectory_loc_prediction.HSTLSTM.construct_slots(min_value, max_value, num_slots, type)

Construct values of slots given min value and max value. :param min_value: minimum value. :param max_value: maximum value. :param num_slots: number of slots to construct. :param type: type of slots to construct, ‘linear’ or ‘exp’. :return: values of slots.