Meta Models

class flood_forecast.meta_models.merging_model.MergingModel(method: str, other_params: Dict)

__init__(method: str, other_params: Dict)

A model meant to help merge meta-data with the temporal data

Parameters:

• method (str) – The method you want to use (Bilinear, Bilinear2, MultiAttn, Concat)

• other_params (Dict) – A dictionary of the additional parameters necessary to init the inner part.

..code-block:: python

merging_mod = MergingModel(“Bilinear”, {“in_features1”: 5, “in_features_2”:1, “out_features”:40 }) print(merging_mod(torch.rand(4, 5, 128), torch.rand(128)).shape) # (4, 40, 128)

…

forward(temporal_data: Tensor, meta_data: Tensor)

Performs the forward pass on both meta and temporal data. Returns merged tensor.

Parameters:

• temporal_data (torch.Tensor) – The temporal data should be in shape (batch_size, n_time_series, n_feats)

• meta_data (torch.Tensor) – The meta-data passed to the model will have dimension (d_meta)

Returns:

The combined tensor with both the meta-data and temporal data. Shape will vary.

Return type:

torch.Tensor

class flood_forecast.meta_models.merging_model.Concatenation(cat_dim: int, repeat: bool = True, use_layer: bool = False, combined_d: int = 1, out_shape: int = 1)

__init__(cat_dim: int, repeat: bool = True, use_layer: bool = False, combined_d: int = 1, out_shape: int = 1)

A function to combine two tensors together via concantenation

Parameters:

• cat_dim (int) – The dimension that you want to concatenate along (e.g. 0, 1, 2)

• repeat (bool, optional) – boolean of whether to repeate meta_data along temporal_dim , defaults to True

• use_layer (bool, optional) – to use a layer to get the final out_shape , defaults to False

• combined_shape (int, optional) – The final combined shape, defaults to 1

• out_shape (int, optional) – The output shape you want, defaults to 1

forward(temporal_data: Tensor, meta_data: Tensor) → Tensor

Args:

temporal_data: (batch_size, seq_len, d_model) meta_data (batch_size, d_embedding)

class flood_forecast.meta_models.merging_model.MultiModalSelfAttention(d_model: int, n_heads: int, dropout: float)

__init__(d_model: int, n_heads: int, dropout: float)

Uses self-attention to combine the meta-data and the temporal data.

Parameters:

• d_model (int) – The dimension of the meta-data

• n_heads (int) – The number of heads to use in multi-head mechanism

• dropout (float) – The dropout score as a flow

forward(temporal_data: Tensor, meta_data: Tensor) → Tensor

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.