mwptoolkit.model.Graph2Tree.graph2tree

class mwptoolkit.model.Graph2Tree.graph2tree.Graph2Tree(config, dataset)

Bases: Module

Reference:

Zhang et al.”Graph-to-Tree Learning for Solving Math Word Problems” in ACL 2020.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

build_graph(seq_length, num_list, num_pos, group_nums)

calculate_loss(batch_data: dict) → float

Finish forward-propagating, calculating loss and back-propagation.

Parameters

batch_data – one batch data.

Returns

loss value.

batch_data should include keywords ‘question’, ‘ques len’, ‘equation’, ‘equ len’, ‘num stack’, ‘num size’, ‘num pos’, ‘num list’, ‘group nums’

convert_idx2symbol(output, num_list, num_stack)

batch_size=1

decoder_forward(encoder_outputs, problem_output, all_nums_encoder_outputs, nums_stack, seq_mask, num_mask, target=None, output_all_layers=False)

encoder_forward(seq_emb, input_length, graph, output_all_layers=False)

forward(seq, seq_length, nums_stack, num_size, num_pos, num_list, group_nums, target=None, output_all_layers=False) → Tuple[Tensor, Tensor, Dict[str, Any]]

Parameters

• seq (torch.Tensor) – input sequence, shape: [batch_size, seq_length].

• seq_length (torch.Tensor) – the length of sequence, shape: [batch_size].

• nums_stack (list) – different positions of the same number, length:[batch_size]

• num_size (list) – number of numbers of input sequence, length:[batch_size].

• num_pos (list) – number positions of input sequence, length:[batch_size].

• num_list (list) – numbers of input sequence, length:[batch_size].

• group_nums (list) – group numbers of input sequence, length:[batch_size].

• target (torch.Tensor | None) – target, shape: [batch_size, target_length], default None.

• output_all_layers (bool) – return output of all layers if output_all_layers is True, default False.

:return : token_logits:[batch_size, output_length, output_size], symbol_outputs:[batch_size,output_length], model_all_outputs. :rtype: tuple(torch.Tensor, torch.Tensor, dict)

generate_tree_input(target, decoder_output, nums_stack_batch, num_start, unk)

get_all_number_encoder_outputs(encoder_outputs, num_pos, batch_size, num_size, hidden_size)

model_test(batch_data: dict) → tuple

Model test.

Parameters

batch_data – one batch data.

Returns

predicted equation, target equation.

batch_data should include keywords ‘question’, ‘ques len’, ‘equation’, ‘num stack’, ‘num pos’, ‘num list’, ‘num size’, ‘group nums’

predict(batch_data: dict, output_all_layers=False)

predict samples without target.

Parameters

• batch_data (dict) – one batch data.

• output_all_layers (bool) – return all layer outputs of model.

Returns

token_logits, symbol_outputs, all_layer_outputs

training: bool