Updated style libraries

examples/scripts/adult_census_bayesian_tabmlp.py

@@ -61,9 +61,9 @@
 
                 model = BayesianWide(
                     input_dim=np.unique(X_tab).shape[0],
-                    pred_dim=df["age_buckets"].nunique()
-                    if objective == "multiclass"
-                    else 1,
+                    pred_dim=(
+                        df["age_buckets"].nunique() if objective == "multiclass" else 1
+                    ),
                     prior_sigma_1=1.0,
                     prior_sigma_2=0.002,
                     prior_pi=0.8,
@@ -88,9 +88,9 @@
                     prior_pi=0.8,
                     posterior_mu_init=0,
                     posterior_rho_init=-7.0,
-                    pred_dim=df["age_buckets"].nunique()
-                    if objective == "multiclass"
-                    else 1,
+                    pred_dim=(
+                        df["age_buckets"].nunique() if objective == "multiclass" else 1
+                    ),
                 )
 
             model_checkpoint = ModelCheckpoint(

pytorch_widedeep/bayesian_models/tabular/bayesian_mlp/bayesian_tab_mlp.py

@@ -226,9 +226,9 @@ def __init__(
                     prior_pi=self.prior_pi,
                     posterior_mu_init=self.posterior_mu_init,
                     posterior_rho_init=self.posterior_rho_init,
-                    use_bias=False
-                    if self.use_cont_bias is None
-                    else self.use_cont_bias,
+                    use_bias=(
+                        False if self.use_cont_bias is None else self.use_cont_bias
+                    ),
                     activation_fn=self.cont_embed_activation,
                 )
                 self.cont_out_dim = len(self.continuous_cols) * self.cont_embed_dim

pytorch_widedeep/callbacks.py

@@ -3,6 +3,7 @@
 
 CREDIT TO THE TORCHSAMPLE AND KERAS TEAMS
 """
+
 import os
 import copy
 import datetime

pytorch_widedeep/models/tabular/_base_tabular_model.py

@@ -101,9 +101,9 @@ def __init__(
             self.cat_embed = DiffSizeCatEmbeddings(
                 column_idx=self.column_idx,
                 embed_input=self.cat_embed_input,
-                embed_dropout=0.0
-                if self.cat_embed_dropout is None
-                else self.cat_embed_dropout,
+                embed_dropout=(
+                    0.0 if self.cat_embed_dropout is None else self.cat_embed_dropout
+                ),
                 use_bias=False if self.use_cat_bias is None else self.use_cat_bias,
                 activation_fn=self.cat_embed_activation,
             )
@@ -253,20 +253,22 @@ def __init__(
                 embed_dim=self.input_dim,
                 column_idx=self.column_idx,
                 embed_input=self.cat_embed_input,
-                embed_dropout=0.0
-                if self.cat_embed_dropout is None
-                else self.cat_embed_dropout,
-                full_embed_dropout=False
-                if self.full_embed_dropout is None
-                else self.full_embed_dropout,
+                embed_dropout=(
+                    0.0 if self.cat_embed_dropout is None else self.cat_embed_dropout
+                ),
+                full_embed_dropout=(
+                    False
+                    if self.full_embed_dropout is None
+                    else self.full_embed_dropout
+                ),
                 use_bias=False if self.use_cat_bias is None else self.use_cat_bias,
                 shared_embed=False if self.shared_embed is None else self.shared_embed,
-                add_shared_embed=False
-                if self.add_shared_embed is None
-                else self.add_shared_embed,
-                frac_shared_embed=0.0
-                if self.frac_shared_embed is None
-                else self.frac_shared_embed,
+                add_shared_embed=(
+                    False if self.add_shared_embed is None else self.add_shared_embed
+                ),
+                frac_shared_embed=(
+                    0.0 if self.frac_shared_embed is None else self.frac_shared_embed
+                ),
                 activation_fn=self.cat_embed_activation,
             )
 
@@ -359,9 +361,9 @@ def _set_continous_embeddings_layer(
             n_cont_cols=len(continuous_cols),
             embed_dim=cont_embed_dim,
             embed_dropout=0.0 if cont_embed_dropout is None else cont_embed_dropout,
-            full_embed_dropout=False
-            if full_embed_dropout is None
-            else full_embed_dropout,
+            full_embed_dropout=(
+                False if full_embed_dropout is None else full_embed_dropout
+            ),
             activation_fn=cont_embed_activation,
         )
 
@@ -377,9 +379,9 @@ def _set_continous_embeddings_layer(
             quantization_setup=quantization_setup,
             embed_dim=cont_embed_dim,
             embed_dropout=0.0 if cont_embed_dropout is None else cont_embed_dropout,
-            full_embed_dropout=False
-            if full_embed_dropout is None
-            else full_embed_dropout,
+            full_embed_dropout=(
+                False if full_embed_dropout is None else full_embed_dropout
+            ),
             activation_fn=cont_embed_activation,
         )
 
@@ -396,9 +398,9 @@ def _set_continous_embeddings_layer(
             n_cont_cols=len(continuous_cols),
             embed_dim=cont_embed_dim,
             embed_dropout=0.0 if cont_embed_dropout is None else cont_embed_dropout,
-            full_embed_dropout=False
-            if full_embed_dropout is None
-            else full_embed_dropout,
+            full_embed_dropout=(
+                False if full_embed_dropout is None else full_embed_dropout
+            ),
             n_frequencies=n_frequencies,
             sigma=sigma,
             share_last_layer=share_last_layer,

pytorch_widedeep/models/tabular/resnet/tab_resnet.py

@@ -252,17 +252,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.blocks_dims[-1]] + self.mlp_hidden_dims,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=True
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    True if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
         else:
             self.mlp = None
@@ -399,17 +401,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.mlp_first_hidden_dim] + self.mlp_hidden_dim,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=True
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    True if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
             self.decoder = DenseResnet(
                 self.mlp_hidden_dims[-1],

pytorch_widedeep/models/tabular/self_supervised/_augmentations.py

@@ -5,6 +5,7 @@
 - CutMix: https://github.com/clovaai/CutMix-PyTorch
 - MixUp: https://github.com/facebookresearch/mixup-cifar10
 """
+
 import numpy as np
 import torch
 

pytorch_widedeep/models/tabular/self_supervised/contrastive_denoising_model.py

@@ -71,9 +71,7 @@ def __init__(
                 self.denoise_cont_mlp,
             ) = self._set_cat_and_cont_denoise_mlps()
 
-    def forward(
-        self, X: Tensor
-    ) -> Tuple[
+    def forward(self, X: Tensor) -> Tuple[
         Optional[Tuple[Tensor, Tensor]],
         Optional[Tuple[Tensor, Tensor]],
         Optional[Tuple[Tensor, Tensor]],
@@ -190,13 +188,13 @@ def _set_cat_and_cont_denoise_mlps(
 
         if self.model.continuous_cols is not None:
             if self.cont_mlp_type == "single":
-                denoise_cont_mlp: Union[
-                    ContSingleMlp, ContMlpPerFeature
-                ] = ContSingleMlp(
-                    self.model.input_dim,
-                    self.model.continuous_cols,
-                    self.model.column_idx,
-                    self.denoise_mlps_activation,
+                denoise_cont_mlp: Union[ContSingleMlp, ContMlpPerFeature] = (
+                    ContSingleMlp(
+                        self.model.input_dim,
+                        self.model.continuous_cols,
+                        self.model.column_idx,
+                        self.denoise_mlps_activation,
+                    )
                 )
             elif self.cont_mlp_type == "multiple":
                 denoise_cont_mlp = ContMlpPerFeature(

pytorch_widedeep/models/tabular/tabnet/_layers.py

@@ -206,9 +206,9 @@ def __init__(
             self.bn = nn.BatchNorm1d(output_dim, momentum=momentum)
 
         if mask_type == "sparsemax":
-            self.mask: Union[
-                sparsemax.Sparsemax, sparsemax.Entmax15
-            ] = sparsemax.Sparsemax(dim=-1)
+            self.mask: Union[sparsemax.Sparsemax, sparsemax.Entmax15] = (
+                sparsemax.Sparsemax(dim=-1)
+            )
         elif mask_type == "entmax":
             self.mask = sparsemax.Entmax15(dim=-1)
         else:

pytorch_widedeep/models/tabular/transformers/ft_transformer.py

@@ -308,17 +308,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.mlp_first_hidden_dim] + self.mlp_hidden_dim,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=False
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    False if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
         else:
             self.mlp = None

pytorch_widedeep/models/tabular/transformers/saint.py

@@ -289,17 +289,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.mlp_first_hidden_dim] + self.mlp_hidden_dim,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=False
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    False if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
         else:
             self.mlp = None

pytorch_widedeep/models/tabular/transformers/tab_fastformer.py

@@ -325,17 +325,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.mlp_first_hidden_dim] + self.mlp_hidden_dim,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=False
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    False if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
         else:
             self.mlp = None

pytorch_widedeep/models/tabular/transformers/tab_perceiver.py

@@ -322,17 +322,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.mlp_first_hidden_dim] + self.mlp_hidden_dim,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=False
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    False if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
         else:
             self.mlp = None

pytorch_widedeep/models/tabular/transformers/tab_transformer.py

@@ -312,17 +312,19 @@ def __init__(
         if self.mlp_hidden_dims is not None:
             self.mlp = MLP(
                 d_hidden=[self.mlp_first_hidden_dim] + self.mlp_hidden_dim,
-                activation="relu"
-                if self.mlp_activation is None
-                else self.mlp_activation,
+                activation=(
+                    "relu" if self.mlp_activation is None else self.mlp_activation
+                ),
                 dropout=0.0 if self.mlp_dropout is None else self.mlp_dropout,
                 batchnorm=False if self.mlp_batchnorm is None else self.mlp_batchnorm,
-                batchnorm_last=False
-                if self.mlp_batchnorm_last is None
-                else self.mlp_batchnorm_last,
-                linear_first=False
-                if self.mlp_linear_first is None
-                else self.mlp_linear_first,
+                batchnorm_last=(
+                    False
+                    if self.mlp_batchnorm_last is None
+                    else self.mlp_batchnorm_last
+                ),
+                linear_first=(
+                    False if self.mlp_linear_first is None else self.mlp_linear_first
+                ),
             )
         else:
             self.mlp = None