ruff: C4 (#222)

* ruff: C4

* apply

Author: Borda

examples/__only_for_dev__/to_test_node.py

@@ -55,7 +55,7 @@ def test_regression(
             continuous_feature_transform=continuous_feature_transform,
             normalize_continuous_features=normalize_continuous_features,
         )
-        model_config_params = dict(task="regression", depth=2, embed_categorical=embed_categorical)
+        model_config_params = {"task": "regression", "depth": 2, "embed_categorical": embed_categorical}
         model_config = NodeConfig(**model_config_params)
         # model_config_params = dict(task="regression")
         # model_config = NodeConfig(**model_config_params)
@@ -98,7 +98,7 @@ def test_classification(
         continuous_feature_transform=continuous_feature_transform,
         normalize_continuous_features=normalize_continuous_features,
     )
-    model_config_params = dict(task="classification", depth=2, embed_categorical=embed_categorical)
+    model_config_params = {"task": "classification", "depth": 2, "embed_categorical": embed_categorical}
     model_config = NodeConfig(**model_config_params)
     trainer_config = TrainerConfig(max_epochs=1, checkpoints=None, early_stopping=None)
     optimizer_config = OptimizerConfig()

examples/__only_for_dev__/to_test_regression.py

@@ -54,7 +54,7 @@
 #     batch_norm_continuous_input=True,
 #     attention_pooling=True,
 # )
-model_config = CategoryEmbeddingModelConfig(task="regression", dropout=0.2, head_config=dict(layers="32-16"))
+model_config = CategoryEmbeddingModelConfig(task="regression", dropout=0.2, head_config={"layers": "32-16"})
 
 trainer_config = TrainerConfig(
     # checkpoints=None,

pyproject.toml

@@ -22,6 +22,12 @@ select = [
 #    "D",  # see: https://pypi.org/project/pydocstyle
 #    "N",  # see: https://pypi.org/project/pep8-naming
 ]
+extend-select = [
+    "C4",  # see: https://pypi.org/project/flake8-comprehensions
+#    "SIM",  # see: https://pypi.org/project/flake8-simplify
+#    "RET",  # see: https://pypi.org/project/flake8-return
+#    "PT",  # see: https://pypi.org/project/flake8-pytest-style
+]
 ignore = [
     "E731",  # Do not assign a lambda expression, use a def
 ]

src/pytorch_tabular/config/config.py

@@ -231,7 +231,7 @@ 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]
+                (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:
@@ -468,7 +468,7 @@ class TrainerConfig:
         metadata={"help": "The number of epochs to wait until there is no further improvements in loss/metric"},
     )
     early_stopping_kwargs: Optional[Dict[str, Any]] = field(
-        default_factory=lambda: dict(),
+        default_factory=lambda: {},
         metadata={
             "help": "Additional keyword arguments for the early stopping callback."
             " See the documentation for the PyTorch Lightning EarlyStopping callback for more details."
@@ -505,7 +505,7 @@ class TrainerConfig:
         metadata={"help": "The number of best models to save"},
     )
     checkpoints_kwargs: Optional[Dict[str, Any]] = field(
-        default_factory=lambda: dict(),
+        default_factory=lambda: {},
         metadata={
             "help": "Additional keyword arguments for the checkpoints callback. See the documentation"
             " for the PyTorch Lightning ModelCheckpoint callback for more details."

src/pytorch_tabular/feature_extractor.py

@@ -67,7 +67,7 @@ def transform(self, X: pd.DataFrame, y=None) -> pd.DataFrame:
                     continue
                 batch[k] = v.to(self.tabular_model.model.device)
             if self.tabular_model.config.task == "ssl":
-                ret_value = dict(backbone_features=self.tabular_model.model.predict(batch, ret_model_output=True))
+                ret_value = {"backbone_features": self.tabular_model.model.predict(batch, ret_model_output=True)}
             else:
                 _, ret_value = self.tabular_model.model.predict(batch, ret_model_output=True)
             for k in self.extract_keys:

src/pytorch_tabular/models/base_model.py

@@ -486,7 +486,7 @@ def create_plotly_histogram(self, arr, name, bin_dict=None):
         # Overlay both histograms
         fig.update_layout(
             barmode="overlay",
-            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+            legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "right", "x": 1},
         )
         # Reduce opacity to see both histograms
         fig.update_traces(opacity=0.5)

src/pytorch_tabular/models/common/layers.py

@@ -150,7 +150,7 @@ def __init__(
     def forward(self, x):
         h = self.n_heads
         q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+        q, k, v = (rearrange(t, "b n (h d) -> b h n d", h=h) for t in (q, k, v))
         sim = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
 
         attn = sim.softmax(dim=-1)

src/pytorch_tabular/models/ft_transformer/config.py

@@ -254,7 +254,7 @@ def __post_init__(self):
                 " and `out_ff_initialization` as head_config is passed."
             )
         else:
-            if any([p is not None for p in deprecated_args]):
+            if any(p is not None for p in deprecated_args):
                 warnings.warn(
                     "The `out_ff_layers`, `out_ff_activation`, `out_ff_dropoout`, and `out_ff_initialization`"
                     " arguments are deprecated and will be removed next release."
@@ -263,13 +263,13 @@ def __post_init__(self):
                 )
                 # TODO: Remove this once we deprecate the old config
                 # Fill the head_config using deprecated parameters
-                self.head_config = dict(
-                    layers=ifnone(self.out_ff_layers, ""),
-                    activation=ifnone(self.out_ff_activation, "ReLU"),
-                    dropout=ifnone(self.out_ff_dropout, 0.0),
-                    use_batch_norm=False,
-                    initialization=ifnone(self.out_ff_initialization, "kaiming"),
-                )
+                self.head_config = {
+                    "layers": ifnone(self.out_ff_layers, ""),
+                    "activation": ifnone(self.out_ff_activation, "ReLU"),
+                    "dropout": ifnone(self.out_ff_dropout, 0.0),
+                    "use_batch_norm": False,
+                    "initialization": ifnone(self.out_ff_initialization, "kaiming"),
+                }
 
         return super().__post_init__()
 

src/pytorch_tabular/models/mixture_density/mdn.py

@@ -86,8 +86,8 @@ def _build_network(self):
     def forward(self, x: Dict):
         if isinstance(self.backbone, TabTransformerBackbone):
             if self.hparams.categorical_dim > 0:
-                x_cat = self.embed_input(dict(categorical=x["categorical"]))
-            x = self.compute_backbone(dict(categorical=x_cat, continuous=x["continuous"]))
+                x_cat = self.embed_input({"categorical": x["categorical"]})
+            x = self.compute_backbone({"categorical": x_cat, "continuous": x["continuous"]})
         else:
             x = self.embedding_layer(x)
             x = self.compute_backbone(x)
@@ -230,7 +230,7 @@ def validation_epoch_end(self, outputs) -> None:
                 commit=False,
             )
             if self.head.hparams.log_debug_plot:
-                fig = self.create_plotly_histogram(pi, "pi", bin_dict=dict(start=0.0, end=1.0, size=0.1))
+                fig = self.create_plotly_histogram(pi, "pi", bin_dict={"start": 0.0, "end": 1.0, "size": 0.1})
                 wandb.log(
                     {
                         "valid_pi": fig,

src/pytorch_tabular/models/tab_transformer/config.py

@@ -243,7 +243,7 @@ def __post_init__(self):
                 " and `out_ff_initialization` as head_config is passed."
             )
         else:
-            if any([p is not None for p in deprecated_args]):
+            if any(p is not None for p in deprecated_args):
                 warnings.warn(
                     "The `out_ff_layers`, `out_ff_activation`, `out_ff_dropoout`, and `out_ff_initialization`"
                     " arguments are deprecated and will be removed next release."
@@ -252,13 +252,13 @@ def __post_init__(self):
                 )
                 # TODO: Remove this once we deprecate the old config
                 # Fill the head_config using deprecated parameters
-                self.head_config = dict(
-                    layers=ifnone(self.out_ff_layers, ""),
-                    activation=ifnone(self.out_ff_activation, "ReLU"),
-                    dropout=ifnone(self.out_ff_dropout, 0.0),
-                    use_batch_norm=False,
-                    initialization=ifnone(self.out_ff_initialization, "kaiming"),
-                )
+                self.head_config = {
+                    "layers": ifnone(self.out_ff_layers, ""),
+                    "activation": ifnone(self.out_ff_activation, "ReLU"),
+                    "dropout": ifnone(self.out_ff_dropout, 0.0),
+                    "use_batch_norm": False,
+                    "initialization": ifnone(self.out_ff_initialization, "kaiming"),
+                }
         return super().__post_init__()