dsipts.models.DilatedConv module

class dsipts.models.DilatedConv.GLU(d_model)

Bases: Module

Gated Linear Unit, ‘Gate’ block in TFT paper Sub net of GRN: linear(x) * sigmoid(linear(x)) No dimension changes

Parameters:

d_model (int) – model dimension

__init__(d_model)

Gated Linear Unit, ‘Gate’ block in TFT paper Sub net of GRN: linear(x) * sigmoid(linear(x)) No dimension changes

Parameters:

d_model (int) – model dimension

forward(x)

Gated Linear Unit Sub net of GRN: linear(x) * sigmoid(linear(x)) No dimension changes: [bs, seq_len, d_model]

Parameters:

x (torch.Tensor)

Returns:

torch.Tensor

Return type:

Tensor

class dsipts.models.DilatedConv.Block(input_channels, kernel_size, output_channels, input_size, sum_layers)

Bases: Module

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

__init__(input_channels, kernel_size, output_channels, input_size, sum_layers)

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

forward(x)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class dsipts.models.DilatedConv.DilatedConv(past_steps, future_steps, past_channels, future_channels, embs, cat_emb_dim, hidden_RNN, num_layers_RNN, kind, kernel_size, sum_emb, out_channels, activation='torch.nn.ReLU', remove_last=False, persistence_weight=0.0, loss_type='l1', quantiles=[], dropout_rate=0.1, use_bn=False, use_glu=True, glu_percentage=1.0, n_classes=0, optim=None, optim_config=None, scheduler_config=None, **kwargs)

Bases: Base

Custom encoder-decoder

Parameters:

• past_steps (int) – number of past datapoints used

• future_steps (int) – number of future lag to predict

• past_channels (int) – number of numeric past variables, must be >0

• future_channels (int) – number of future numeric variables

• embs (List) – list of the initial dimension of the categorical variables

• cat_emb_dim (int) – final dimension of each categorical variable

• hidden_RNN (int) – hidden size of the RNN block

• num_layers_RNN (int) – number of RNN layers

• kind (str) – one among GRU or LSTM

• kernel_size (int) – kernel size in the encoder convolutional block

• sum_emb (bool) – if true the contribution of each embedding will be summed-up otherwise stacked

• out_channels (int) – number of output channels

• activation (str, optional) – activation fuction function pytorch. Default torch.nn.ReLU

• remove_last (bool, optional) – if True the model learns the difference respect to the last seen point

• persistence_weight (float) – weight controlling the divergence from persistence model. Default 0

• loss_type (str, optional) – this model uses custom losses or l1 or mse. Custom losses can be linear_penalization or exponential_penalization. Default l1,

• quantiles (List[int], optional) – we can use quantile loss il len(quantiles) = 0 (usually 0.1,0.5, 0.9) or L1loss in case len(quantiles)==0. Defaults to [].

• dropout_rate (float, optional) – dropout rate in Dropout layers

• use_bn (bool, optional) – if true BN layers will be added and dropouts will be removed

• use_glu (bool,optional) – use GLU for feature selection. Defaults to True.

• glu_percentage (float, optiona) – percentage of features to use. Defaults to 1.0.

• n_classes (int) – number of classes (0 in regression)

• optim (str, optional) – if not None it expects a pytorch optim method. Defaults to None that is mapped to Adam.

• optim_config (dict, optional) – configuration for Adam optimizer. Defaults to None.

• scheduler_config (dict, optional) – configuration for stepLR scheduler. Defaults to None.

handle_multivariate = True

handle_future_covariates = True

handle_categorical_variables = True

handle_quantile_loss = True

description = 'Can   handle multivariate output \nCan   handle future covariates\nCan   handle categorical covariates\nCan   handle Quantile loss function'

__init__(past_steps, future_steps, past_channels, future_channels, embs, cat_emb_dim, hidden_RNN, num_layers_RNN, kind, kernel_size, sum_emb, out_channels, activation='torch.nn.ReLU', remove_last=False, persistence_weight=0.0, loss_type='l1', quantiles=[], dropout_rate=0.1, use_bn=False, use_glu=True, glu_percentage=1.0, n_classes=0, optim=None, optim_config=None, scheduler_config=None, **kwargs)

Custom encoder-decoder

Parameters:

• past_steps (int) – number of past datapoints used

• future_steps (int) – number of future lag to predict

• past_channels (int) – number of numeric past variables, must be >0

• future_channels (int) – number of future numeric variables

• embs (List) – list of the initial dimension of the categorical variables

• cat_emb_dim (int) – final dimension of each categorical variable

• hidden_RNN (int) – hidden size of the RNN block

• num_layers_RNN (int) – number of RNN layers

• kind (str) – one among GRU or LSTM

• kernel_size (int) – kernel size in the encoder convolutional block

• sum_emb (bool) – if true the contribution of each embedding will be summed-up otherwise stacked

• out_channels (int) – number of output channels

• activation (str, optional) – activation fuction function pytorch. Default torch.nn.ReLU

• remove_last (bool, optional) – if True the model learns the difference respect to the last seen point

• persistence_weight (float) – weight controlling the divergence from persistence model. Default 0

• loss_type (str, optional) – this model uses custom losses or l1 or mse. Custom losses can be linear_penalization or exponential_penalization. Default l1,

• quantiles (List[int], optional) – we can use quantile loss il len(quantiles) = 0 (usually 0.1,0.5, 0.9) or L1loss in case len(quantiles)==0. Defaults to [].

• dropout_rate (float, optional) – dropout rate in Dropout layers

• use_bn (bool, optional) – if true BN layers will be added and dropouts will be removed

• use_glu (bool,optional) – use GLU for feature selection. Defaults to True.

• glu_percentage (float, optiona) – percentage of features to use. Defaults to 1.0.

• n_classes (int) – number of classes (0 in regression)

• optim (str, optional) – if not None it expects a pytorch optim method. Defaults to None that is mapped to Adam.

• optim_config (dict, optional) – configuration for Adam optimizer. Defaults to None.

• scheduler_config (dict, optional) – configuration for stepLR scheduler. Defaults to None.

forward(batch)

It is mandatory to implement this method

Parameters:

batch (dict) – batch of the dataloader

Returns:

result

Return type:

torch.tensor

inference(batch)

Usually it is ok to return the output of the forward method but sometimes not (e.g. RNN)

Parameters:

batch (dict) – batch

Returns:

result

Return type:

torch.tensor