ci: auto fixes from pre-commit.com hooks

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

notebooks/4.0c-mb-feature-importances.ipynb

@@ -1203,7 +1203,8 @@
     "plt.axis(\"off\")\n",
     "plt.tight_layout()\n",
     "plt.savefig(\n",
-    "    f\"../reports/Graphs/attention_head_{h+1}_layer_{l+1}_{key}.pdf\", bbox_inches=\"tight\"\n",
+    "    f\"../reports/Graphs/attention_head_{h + 1}_layer_{l + 1}_{key}.pdf\",\n",
+    "    bbox_inches=\"tight\",\n",
     ")"
    ]
   },
@@ -1277,7 +1278,7 @@
     "                    alpha=el.item(),\n",
     "                )\n",
     "\n",
-    "        axes[l, h].set_title(f\"head {l+1,h+1}\", size=\"xx-small\")\n",
+    "        axes[l, h].set_title(f\"head {l + 1, h + 1}\", size=\"xx-small\")\n",
     "        # fig.tight_layout()\n",
     "        axes[l, h].set_xticks([])\n",
     "        axes[l, h].set_yticks([])\n",

src/otc/data/dataset.py

@@ -52,15 +52,15 @@ def __init__(
         # infer feature names from dataframe.
         if isinstance(x, pd.DataFrame):
             feature_names = x.columns.tolist()
-        assert (
-            len(feature_names) == x.shape[1]
-        ), "`len('feature_names)` must match `X.shape[1]`"
+        assert len(feature_names) == x.shape[1], (
+            "`len('feature_names)` must match `X.shape[1]`"
+        )
 
         # calculate cat indices
         cat_features = cat_features if cat_features else []
-        assert set(cat_features).issubset(
-            feature_names
-        ), "Categorical features must be a subset of feature names."
+        assert set(cat_features).issubset(feature_names), (
+            "Categorical features must be a subset of feature names."
+        )
 
         self._cat_idx = [
             feature_names.index(i) for i in cat_features if i in feature_names
@@ -77,12 +77,12 @@ def __init__(
         y = y.to_numpy() if isinstance(y, pd.Series) else y
         weight = weight.to_numpy() if isinstance(weight, pd.Series) else weight
 
-        assert (
-            x.shape[0] == y.shape[0]
-        ), "Length of feature matrix must match length of target."
-        assert len(cat_features) == len(
-            self._cat_unique_counts
-        ), "For all categorical features the number of unique entries must be provided."
+        assert x.shape[0] == y.shape[0], (
+            "Length of feature matrix must match length of target."
+        )
+        assert len(cat_features) == len(self._cat_unique_counts), (
+            "For all categorical features the number of unique entries must be provided."
+        )
 
         # adjust target to be either 0 or 1
         self.y = torch.tensor(y).float()
@@ -105,9 +105,9 @@ def __init__(
             if weight is not None
             else torch.ones(len(self.y), requires_grad=False).float()
         )
-        assert (
-            y.shape[0] == weight.shape[0]
-        ), "Length of label must match length of weight."
+        assert y.shape[0] == weight.shape[0], (
+            "Length of label must match length of weight."
+        )
         self.weight = weight
 
     def __len__(self) -> int:

src/otc/models/callback.py

@@ -132,7 +132,7 @@ def on_train_end(
             name (str): name of study.
         """
         if study.best_trial == trial:
-            prefix_file = f"{study.study_name}_" f"{model.__class__.__name__}_{name}"
+            prefix_file = f"{study.study_name}_{model.__class__.__name__}_{name}"
 
             uri_model: str
             file_model: str

src/otc/models/fttransformer.py

@@ -283,9 +283,9 @@ def __init__(
         """
         super().__init__()
         assert num_continous >= 0, "n_num_features must be non-negative"
-        assert (
-            num_continous or cat_cardinalities
-        ), "at least one of n_num_features or cat_cardinalities must be positive"
+        assert num_continous or cat_cardinalities, (
+            "at least one of n_num_features or cat_cardinalities must be positive"
+        )
         "and non-empty"
         self.initialization = "uniform"
         self.num_tokenizer = (
@@ -347,15 +347,15 @@ def forward(
         Returns:
             torch.Tensor: tokens.
         """
-        assert (
-            x_num is not None or x_cat is not None
-        ), "At least one of x_num and x_cat must be presented"
-        assert _all_or_none(
-            [self.num_tokenizer, x_num]
-        ), "If self.num_tokenizer is (not) None, then x_num must (not) be None"
-        assert _all_or_none(
-            [self.cat_tokenizer, x_cat]
-        ), "If self.cat_tokenizer is (not) None, then x_cat must (not) be None"
+        assert x_num is not None or x_cat is not None, (
+            "At least one of x_num and x_cat must be presented"
+        )
+        assert _all_or_none([self.num_tokenizer, x_num]), (
+            "If self.num_tokenizer is (not) None, then x_num must (not) be None"
+        )
+        assert _all_or_none([self.cat_tokenizer, x_cat]), (
+            "If self.cat_tokenizer is (not) None, then x_cat must (not) be None"
+        )
         x = []
         if self.num_tokenizer is not None:
             x.append(self.num_tokenizer(x_num))
@@ -559,9 +559,9 @@ def forward(
             Tuple[torch.Tensor, Dict[str, torch.Tensor]]: Tuple with tokens and
             attention_stats
         """
-        assert _all_or_none(
-            [key_compression, value_compression]
-        ), "If key_compression is (not) None, then value_compression must (not) be None"
+        assert _all_or_none([key_compression, value_compression]), (
+            "If key_compression is (not) None, then value_compression must (not) be None"
+        )
         q, k, v = self.W_q(x_q), self.W_k(x_kv), self.W_v(x_kv)
         for tensor in [q, k, v]:
             assert tensor.shape[-1] % self.n_heads == 0, _INTERNAL_ERROR_MESSAGE
@@ -756,9 +756,9 @@ def __init__(
                 f"Do you mean last_layer_query_idx=[{last_layer_query_idx}] ?"
             )
         if not prenormalization:
-            assert (
-                not first_prenormalization
-            ), "If `prenormalization` is False, then `first_prenormalization`"
+            assert not first_prenormalization, (
+                "If `prenormalization` is False, then `first_prenormalization`"
+            )
             "must be False"
         assert _all_or_none([n_tokens, kv_compression_ratio, kv_compression_sharing]), (
             "If any of the following arguments is (not) None, then all of them must "
@@ -814,9 +814,9 @@ def make_kv_compression() -> nn.Module:
                 if kv_compression_sharing == "headwise":
                     layer["value_compression"] = make_kv_compression()
                 else:
-                    assert (
-                        kv_compression_sharing == "key-value"
-                    ), _INTERNAL_ERROR_MESSAGE
+                    assert kv_compression_sharing == "key-value", (
+                        _INTERNAL_ERROR_MESSAGE
+                    )
             self.blocks.append(layer)
 
         self.head = Transformer.Head(
@@ -874,9 +874,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         Returns:
             torch.Tensor: output tensor.
         """
-        assert (
-            x.ndim == 3
-        ), "The input must have 3 dimensions: (n_objects, n_tokens, d_token)"
+        assert x.ndim == 3, (
+            "The input must have 3 dimensions: (n_objects, n_tokens, d_token)"
+        )
         for layer_idx, layer in enumerate(self.blocks):
             layer = cast(nn.ModuleDict, layer)
 

src/otc/models/objective.py

@@ -447,7 +447,7 @@ def __call__(self, trial: optuna.Trial) -> float:
         # https://catboost.ai/en/docs/features/training-on-gpu
         gpu_count = get_gpu_device_count()
         task_type = "GPU" if gpu_count > 0 else "CPU"
-        devices = f"0-{gpu_count-1}"
+        devices = f"0-{gpu_count - 1}"
 
         # kaggle book + https://catboost.ai/en/docs/concepts/parameter-tuning
         # friedman paper