[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

src/pytorch_tabular/categorical_encoders.py

@@ -58,9 +58,9 @@ def transform(self, X):
             raise ValueError("`fit` method must be called before `transform`.")
         assert all(c in X.columns for c in self.cols)
         if self.handle_missing == "error":
-            assert (
-                not X[self.cols].isnull().any().any()
-            ), "`handle_missing` = `error` and missing values found in columns to encode."
+            assert not X[self.cols].isnull().any().any(), (
+                "`handle_missing` = `error` and missing values found in columns to encode."
+            )
         X_encoded = X.copy(deep=True)
         category_cols = X_encoded.select_dtypes(include="category").columns
         X_encoded[category_cols] = X_encoded[category_cols].astype("object")
@@ -153,9 +153,9 @@ def fit(self, X, y=None):
         """
         self._before_fit_check(X, y)
         if self.handle_missing == "error":
-            assert (
-                not X[self.cols].isnull().any().any()
-            ), "`handle_missing` = `error` and missing values found in columns to encode."
+            assert not X[self.cols].isnull().any().any(), (
+                "`handle_missing` = `error` and missing values found in columns to encode."
+            )
         for col in self.cols:
             map = Series(unique(X[col].fillna(NAN_CATEGORY)), name=col).reset_index().rename(columns={"index": "value"})
             map["value"] += 1

src/pytorch_tabular/config/config.py

@@ -192,9 +192,9 @@ class DataConfig:
     )
 
     def __post_init__(self):
-        assert (
-            len(self.categorical_cols) + len(self.continuous_cols) + len(self.date_columns) > 0
-        ), "There should be at-least one feature defined in categorical, continuous, or date columns"
+        assert len(self.categorical_cols) + len(self.continuous_cols) + len(self.date_columns) > 0, (
+            "There should be at-least one feature defined in categorical, continuous, or date columns"
+        )
         _validate_choices(self)
         if os.name == "nt" and self.num_workers != 0:
             print("Windows does not support num_workers > 0. Setting num_workers to 0")
@@ -255,9 +255,9 @@ class InferredConfig:
 
     def __post_init__(self):
         if self.embedding_dims is not None:
-            assert all(
-                (isinstance(t, Iterable) and len(t) == 2) for t in self.embedding_dims
-            ), "embedding_dims must be a list of tuples (cardinality, embedding_dim)"
+            assert all((isinstance(t, Iterable) and len(t) == 2) for t in self.embedding_dims), (
+                "embedding_dims must be a list of tuples (cardinality, embedding_dim)"
+            )
             self.embedded_cat_dim = sum([t[1] for t in self.embedding_dims])
         else:
             self.embedded_cat_dim = 0

src/pytorch_tabular/models/category_embedding/config.py

@@ -98,7 +98,7 @@ class CategoryEmbeddingModelConfig(ModelConfig):
     )
     use_batch_norm: bool = field(
         default=False,
-        metadata={"help": ("Flag to include a BatchNorm layer after each Linear Layer+DropOut." " Defaults to False")},
+        metadata={"help": ("Flag to include a BatchNorm layer after each Linear Layer+DropOut. Defaults to False")},
     )
     initialization: str = field(
         default="kaiming",

src/pytorch_tabular/models/common/layers/embeddings.py

@@ -84,12 +84,12 @@ def forward(self, x: Dict[str, Any]) -> torch.Tensor:
             x.get("continuous", torch.empty(0, 0)),
             x.get("categorical", torch.empty(0, 0)),
         )
-        assert (
-            categorical_data.shape[1] == self.categorical_dim
-        ), "categorical_data must have same number of columns as categorical embedding layers"
-        assert (
-            continuous_data.shape[1] == self.continuous_dim
-        ), "continuous_data must have same number of columns as continuous dim"
+        assert categorical_data.shape[1] == self.categorical_dim, (
+            "categorical_data must have same number of columns as categorical embedding layers"
+        )
+        assert continuous_data.shape[1] == self.continuous_dim, (
+            "continuous_data must have same number of columns as continuous dim"
+        )
         embed = None
         if continuous_data.shape[1] > 0:
             if self.batch_norm_continuous_input:
@@ -141,12 +141,12 @@ def forward(self, x: Dict[str, Any]) -> torch.Tensor:
             x.get("continuous", torch.empty(0, 0)),
             x.get("categorical", torch.empty(0, 0)),
         )
-        assert categorical_data.shape[1] == len(
-            self.cat_embedding_layers
-        ), "categorical_data must have same number of columns as categorical embedding layers"
-        assert (
-            continuous_data.shape[1] == self.continuous_dim
-        ), "continuous_data must have same number of columns as continuous dim"
+        assert categorical_data.shape[1] == len(self.cat_embedding_layers), (
+            "categorical_data must have same number of columns as categorical embedding layers"
+        )
+        assert continuous_data.shape[1] == self.continuous_dim, (
+            "continuous_data must have same number of columns as continuous dim"
+        )
         embed = None
         if continuous_data.shape[1] > 0:
             if self.batch_norm_continuous_input:
@@ -273,12 +273,12 @@ def forward(self, x: Dict[str, Any]) -> torch.Tensor:
             x.get("continuous", torch.empty(0, 0)),
             x.get("categorical", torch.empty(0, 0)),
         )
-        assert categorical_data.shape[1] == len(
-            self.cat_embedding_layers
-        ), "categorical_data must have same number of columns as categorical embedding layers"
-        assert (
-            continuous_data.shape[1] == self.continuous_dim
-        ), "continuous_data must have same number of columns as continuous dim"
+        assert categorical_data.shape[1] == len(self.cat_embedding_layers), (
+            "categorical_data must have same number of columns as categorical embedding layers"
+        )
+        assert continuous_data.shape[1] == self.continuous_dim, (
+            "continuous_data must have same number of columns as continuous dim"
+        )
         embed = None
         if continuous_data.shape[1] > 0:
             cont_idx = torch.arange(self.continuous_dim, device=continuous_data.device).expand(

src/pytorch_tabular/models/gate/config.py

@@ -173,7 +173,7 @@ def __post_init__(self):
         assert self.tree_depth > 0, "tree_depth should be greater than 0"
         # Either gflu_stages or num_trees should be greater than 0
         assert self.num_trees > 0, (
-            "`num_trees` must be greater than 0." "If you want a lighter model which performs better, use GANDALF."
+            "`num_trees` must be greater than 0.If you want a lighter model which performs better, use GANDALF."
         )
         super().__post_init__()
 

src/pytorch_tabular/models/gate/gate_model.py

@@ -51,12 +51,12 @@ def __init__(
         embedding_dropout: float = 0.0,
     ):
         super().__init__()
-        assert (
-            binning_activation in self.BINARY_ACTIVATION_MAP.keys()
-        ), f"`binning_activation should be one of {self.BINARY_ACTIVATION_MAP.keys()}"
-        assert (
-            feature_mask_function in self.ACTIVATION_MAP.keys()
-        ), f"`feature_mask_function should be one of {self.ACTIVATION_MAP.keys()}"
+        assert binning_activation in self.BINARY_ACTIVATION_MAP.keys(), (
+            f"`binning_activation should be one of {self.BINARY_ACTIVATION_MAP.keys()}"
+        )
+        assert feature_mask_function in self.ACTIVATION_MAP.keys(), (
+            f"`feature_mask_function should be one of {self.ACTIVATION_MAP.keys()}"
+        )
 
         self.gflu_stages = gflu_stages
         self.num_trees = num_trees

src/pytorch_tabular/models/mixture_density/config.py

@@ -87,9 +87,9 @@ class MDNConfig(ModelConfig):
     _probabilistic: bool = field(default=True)
 
     def __post_init__(self):
-        assert (
-            self.backbone_config_class not in INCOMPATIBLE_BACKBONES
-        ), f"{self.backbone_config_class} is not a supported backbone for MDN head"
+        assert self.backbone_config_class not in INCOMPATIBLE_BACKBONES, (
+            f"{self.backbone_config_class} is not a supported backbone for MDN head"
+        )
         assert self.head == "MixtureDensityHead"
         return super().__post_init__()
 

src/pytorch_tabular/models/tabnet/config.py

@@ -90,7 +90,7 @@ class TabNetModelConfig(ModelConfig):
     )
     gamma: float = field(
         default=1.3,
-        metadata={"help": ("Float above 1, scaling factor for attention updates (usually between" " 1.0 to 2.0)")},
+        metadata={"help": ("Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0)")},
     )
     n_independent: int = field(
         default=2,

src/pytorch_tabular/models/tabnet/tabnet_model.py

@@ -104,4 +104,4 @@ def _build_network(self):
         self._head = nn.Identity()
 
     def extract_embedding(self):
-        raise ValueError("Extracting Embeddings is not supported by Tabnet. Please use another" " compatible model")
+        raise ValueError("Extracting Embeddings is not supported by Tabnet. Please use another compatible model")

src/pytorch_tabular/ssl_models/base_model.py

@@ -85,9 +85,9 @@ def __init__(
         self._setup_metrics()
 
     def _setup_encoder_decoder(self, encoder, encoder_config, decoder, decoder_config, inferred_config):
-        assert (encoder is not None) or (
-            encoder_config is not None
-        ), "Either encoder or encoder_config must be provided"
+        assert (encoder is not None) or (encoder_config is not None), (
+            "Either encoder or encoder_config must be provided"
+        )
         # assert (decoder is not None) or (decoder_config is not None),
         # "Either decoder or decoder_config must be provided"
         if encoder is not None: