import math import torch import torch.nn as nn from utils import normalize_data import torch.nn.functional as F from torch.nn import TransformerEncoder, TransformerEncoderLayer class StyleEncoder(nn.Module): def __init__(self, em_size, hyperparameter_definitions): super().__init__() # self.embeddings = {} self.em_size = em_size # self.hyperparameter_definitions = {} # for hp in hyperparameter_definitions: # self.embeddings[hp] = nn.Linear(1, self.em_size) # self.embeddings = nn.ModuleDict(self.embeddings) self.embedding = nn.Linear(hyperparameter_definitions.shape[0], self.em_size) def forward(self, hyperparameters): # T x B x num_features # Make faster by using matrices # sampled_embeddings = [torch.stack([ # self.embeddings[hp](torch.tensor([batch[hp]], device=self.embeddings[hp].weight.device, dtype=torch.float)) # for hp in batch # ], -1).sum(-1) for batch in hyperparameters] # return torch.stack(sampled_embeddings, 0) return self.embedding(hyperparameters) class _PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.): super().__init__() self.dropout = nn.Dropout(p=dropout) self.d_model = d_model self.device_test_tensor = nn.Parameter(torch.tensor(1.)) def forward(self, x):# T x B x num_features assert self.d_model % x.shape[-1]*2 == 0 d_per_feature = self.d_model // x.shape[-1] pe = torch.zeros(*x.shape, d_per_feature, device=self.device_test_tensor.device) #position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) interval_size = 10 div_term = (1./interval_size) * 2*math.pi*torch.exp(torch.arange(0, d_per_feature, 2, device=self.device_test_tensor.device).float()*math.log(math.sqrt(2))) #print(div_term/2/math.pi) pe[..., 0::2] = torch.sin(x.unsqueeze(-1) * div_term) pe[..., 1::2] = torch.cos(x.unsqueeze(-1) * div_term) return self.dropout(pe).view(x.shape[0],x.shape[1],self.d_model) Positional = lambda _, emsize: _PositionalEncoding(d_model=emsize) class EmbeddingEncoder(nn.Module): def __init__(self, num_features, em_size, num_embs=100): super().__init__() self.num_embs = num_embs self.embeddings = nn.Embedding(num_embs * num_features, em_size, max_norm=True) self.init_weights(.1) self.min_max = (-2,+2) @property def width(self): return self.min_max[1] - self.min_max[0] def init_weights(self, initrange): self.embeddings.weight.data.uniform_(-initrange, initrange) def discretize(self, x): split_size = self.width / self.num_embs return (x - self.min_max[0] // split_size).int().clamp(0, self.num_embs - 1) def forward(self, x): # T x B x num_features x_idxs = self.discretize(x) x_idxs += torch.arange(x.shape[-1], device=x.device).view(1, 1, -1) * self.num_embs # print(x_idxs,self.embeddings.weight.shape) return self.embeddings(x_idxs).mean(-2) class Normalize(nn.Module): def __init__(self, mean, std): super().__init__() self.mean = mean self.std = std def forward(self, x): return (x-self.mean)/self.std def get_normalized_uniform_encoder(encoder_creator): """ This can be used to wrap an encoder that is fed uniform samples in [0,1] and normalizes these to 0 mean and 1 std. For example, it can be used as `encoder_creator = get_normalized_uniform_encoder(encoders.Linear)`, now this can be initialized with `encoder_creator(feature_dim, in_dim)`. :param encoder: :return: """ return lambda in_dim, out_dim: nn.Sequential(Normalize(.5, math.sqrt(1/12)), encoder_creator(in_dim, out_dim)) Linear = nn.Linear MLP = lambda num_features, emsize: nn.Sequential(nn.Linear(num_features+1,emsize*2), nn.ReLU(), nn.Linear(emsize*2,emsize)) class NanHandlingEncoder(nn.Module): def __init__(self, num_features, emsize, keep_nans=True): super().__init__() self.num_features = 2 * num_features if keep_nans else num_features self.emsize = emsize self.keep_nans = keep_nans self.layer = nn.Linear(self.num_features, self.emsize) def forward(self, x): if self.keep_nans: x = torch.cat([torch.nan_to_num(x, nan=0.0), normalize_data(torch.isnan(x) * -1 + torch.logical_and(torch.isinf(x), torch.sign(x) == 1) * 1 + torch.logical_and(torch.isinf(x), torch.sign(x) == -1) * 2 )], -1) else: x = torch.nan_to_num(x, nan=0.0) return self.layer(x) class Linear(nn.Linear): def __init__(self, num_features, emsize): super().__init__(num_features, emsize) self.num_features = num_features self.emsize = emsize def forward(self, x): x = torch.nan_to_num(x, nan=0.0) return super().forward(x) class SequenceSpanningEncoder(nn.Module): # Regular Encoder transforms Seq_len, B, S -> Seq_len, B, E attending only to last dimension # This Encoder accesses the Seq_Len dimension additionally # Why would we want this? We can learn normalization and embedding of features # , this might be more important for e.g. categorical, ordinal feats, nan detection # However maybe this can be easily learned through transformer as well? # A problem is to make this work across any sequence length and be independent of ordering # We could use average and maximum pooling and use those with a linear layer # Another idea !! Similar to this we would like to encode features so that their number is variable # We would like to embed features, also using knowledge of the features in the entire sequence # We could use convolution or another transformer # Convolution: # Transformer/Conv across sequence dimension that encodes and normalizes features # -> Transformer across feature dimension that encodes features to a constant size # Conv with flexible features but no sequence info: S,B,F -(reshape)-> S*B,1,F # -(Conv1d)-> S*B,N,F -(AvgPool,MaxPool)-> S*B,N,1 -> S,B,N # This probably won't work since it's missing a way to recognize which feature is encoded # Transformer with flexible features: S,B,F -> F,B*S,1 -> F2,B*S,1 -> S,B,F2 def __init__(self, num_features, em_size): super().__init__() raise NotImplementedError() # Seq_len, B, S -> Seq_len, B, E # self.convs = torch.nn.ModuleList([nn.Conv1d(64 if i else 1, 64, 3) for i in range(5)]) # self.linear = nn.Linear(64, emsize) class TransformerBasedFeatureEncoder(nn.Module): def __init__(self, num_features, emsize): super().__init__() hidden_emsize = emsize encoder = Linear(1, hidden_emsize) n_out = emsize nhid = 2*emsize dropout =0.0 nhead=4 nlayers=4 model = nn.Transformer(nhead=nhead, num_encoder_layers=4, num_decoder_layers=4, d_model=1) def forward(self, *input): # S,B,F -> F,S*B,1 -> F2,S*B,1 -> S,B,F2 input = input.transpose() self.model(input) class Conv(nn.Module): def __init__(self, input_size, emsize): super().__init__() self.convs = torch.nn.ModuleList([nn.Conv2d(64 if i else 1, 64, 3) for i in range(5)]) self.linear = nn.Linear(64,emsize) def forward(self, x): size = math.isqrt(x.shape[-1]) assert size*size == x.shape[-1] x = x.reshape(*x.shape[:-1], 1, size, size) for conv in self.convs: if x.shape[-1] < 4: break x = conv(x) x.relu_() x = nn.AdaptiveAvgPool2d((1,1))(x).squeeze(-1).squeeze(-1) return self.linear(x) class CanEmb(nn.Embedding): def __init__(self, num_features, num_embeddings: int, embedding_dim: int, *args, **kwargs): assert embedding_dim % num_features == 0 embedding_dim = embedding_dim // num_features super().__init__(num_embeddings, embedding_dim, *args, **kwargs) def forward(self, x): lx = x.long() assert (lx == x).all(), "CanEmb only works with tensors of whole numbers" x = super().forward(lx) return x.view(*x.shape[:-2], -1) def get_Canonical(num_classes): return lambda num_features, emsize: CanEmb(num_features, num_classes, emsize) def get_Embedding(num_embs_per_feature=100): return lambda num_features, emsize: EmbeddingEncoder(num_features, emsize, num_embs=num_embs_per_feature)