Add ruff for linting, remove flake8, remove isort, remove pylint

Makefile

@@ -79,30 +79,17 @@ install-develop: clean-build clean-pyc ## install the package in editable mode a
 
 # LINT TARGETS
 
-.PHONY: lint-deepecho
-lint-deepecho: ## check style with flake8 and isort
-	flake8 deepecho
-	isort -c --recursive deepecho
-	pylint deepecho --rcfile=setup.cfg
-
-.PHONY: lint-tests
-lint-tests: ## check style with flake8 and isort
-	flake8 --ignore=D tests
-	isort -c --recursive tests
-
 .PHONY: lint
-lint:  ## Run all code style checks
-	invoke lint
+lint:
+	ruff check .
+	ruff format .  --check
 
 .PHONY: fix-lint
-fix-lint: ## fix lint issues using autoflake, autopep8, and isort
-	find deepecho tests -name '*.py' | xargs autoflake --in-place --remove-all-unused-imports --remove-unused-variables
-	autopep8 --in-place --recursive --aggressive deepecho tests
-	isort --apply --atomic --recursive deepecho tests
-
+fix-lint:
+	ruff check --fix .
+	ruff format .
 
 # TEST TARGETS
-
 .PHONY: test-unit
 test-unit: ## run unit tests using pytest
 	invoke unit

deepecho/models/base.py

@@ -6,7 +6,7 @@
 from deepecho.sequences import assemble_sequences
 
 
-class DeepEcho():
+class DeepEcho:
     """The base class for DeepEcho models."""
 
     _verbose = True
@@ -28,7 +28,13 @@ def _validate(sequences, context_types, data_types):
             data_types:
                 See `fit`.
         """
-        dtypes = set(['continuous', 'categorical', 'ordinal', 'count', 'datetime'])
+        dtypes = set([
+            'continuous',
+            'categorical',
+            'ordinal',
+            'count',
+            'datetime',
+        ])
         assert all(dtype in dtypes for dtype in context_types)
         assert all(dtype in dtypes for dtype in data_types)
 
@@ -99,8 +105,15 @@ def _get_data_types(data, data_types, columns):
 
         return dtypes_list
 
-    def fit(self, data, entity_columns=None, context_columns=None,
-            data_types=None, segment_size=None, sequence_index=None):
+    def fit(
+        self,
+        data,
+        entity_columns=None,
+        context_columns=None,
+        data_types=None,
+        segment_size=None,
+        sequence_index=None,
+    ):
         """Fit the model to a dataframe containing time series data.
 
         Args:
@@ -135,8 +148,7 @@ def fit(self, data, entity_columns=None, context_columns=None,
         if segment_size is not None and not isinstance(segment_size, int):
             if sequence_index is None:
                 raise TypeError(
-                    '`segment_size` must be of type `int` if '
-                    'no `sequence_index` is given.'
+                    '`segment_size` must be of type `int` if ' 'no `sequence_index` is given.'
                 )
             if data[sequence_index].dtype.kind != 'M':
                 raise TypeError(
@@ -161,7 +173,12 @@ def fit(self, data, entity_columns=None, context_columns=None,
         data_types = self._get_data_types(data, data_types, self._data_columns)
         context_types = self._get_data_types(data, data_types, self._context_columns)
         sequences = assemble_sequences(
-            data, self._entity_columns, self._context_columns, segment_size, sequence_index)
+            data,
+            self._entity_columns,
+            self._context_columns,
+            segment_size,
+            sequence_index,
+        )
 
         # Validate and fit
         self._validate(sequences, context_types, data_types)
@@ -242,7 +259,7 @@ def sample(self, num_entities=None, context=None, sequence_length=None):
             # Reformat as a DataFrame
             group = pd.DataFrame(
                 dict(zip(self._data_columns, sequence)),
-                columns=self._data_columns
+                columns=self._data_columns,
             )
             group[self._entity_columns] = entity_values
             for column, value in zip(self._context_columns, context_values):

deepecho/models/basic_gan.py

@@ -13,10 +13,13 @@
 
 
 def _expand_context(data, context):
-    return torch.cat([
-        data,
-        context.unsqueeze(0).expand(data.shape[0], context.shape[0], context.shape[1])
-    ], dim=2)
+    return torch.cat(
+        [
+            data,
+            context.unsqueeze(0).expand(data.shape[0], context.shape[0], context.shape[1]),
+        ],
+        dim=2,
+    )
 
 
 class BasicGenerator(torch.nn.Module):
@@ -65,7 +68,7 @@ def forward(self, context=None, sequence_length=None):
         """
         latent = torch.randn(
             size=(sequence_length, context.size(0), self.latent_size),
-            device=self.device
+            device=self.device,
         )
         latent = _expand_context(latent, context)
 
@@ -150,8 +153,16 @@ class BasicGANModel(DeepEcho):
     _model_data_size = None
     _generator = None
 
-    def __init__(self, epochs=1024, latent_size=32, hidden_size=16,
-                 gen_lr=1e-3, dis_lr=1e-3, cuda=True, verbose=True):
+    def __init__(
+        self,
+        epochs=1024,
+        latent_size=32,
+        hidden_size=16,
+        gen_lr=1e-3,
+        dis_lr=1e-3,
+        cuda=True,
+        verbose=True,
+    ):
         self._epochs = epochs
         self._gen_lr = gen_lr
         self._dis_lr = dis_lr
@@ -211,7 +222,7 @@ def _index_map(columns, types):
                     'type': column_type,
                     'min': np.min(values),
                     'max': np.max(values),
-                    'indices': (dimensions, dimensions + 1)
+                    'indices': (dimensions, dimensions + 1),
                 }
                 dimensions += 2
 
@@ -221,10 +232,7 @@ def _index_map(columns, types):
                     indices[value] = dimensions
                     dimensions += 1
 
-                mapping[column] = {
-                    'type': column_type,
-                    'indices': indices
-                }
+                mapping[column] = {'type': column_type, 'indices': indices}
 
             else:
                 raise ValueError(f'Unsupported type: {column_type}')
@@ -317,7 +325,7 @@ def _value_to_tensor(self, tensor, value, properties):
             self._one_hot_encode(tensor, value, properties)
 
         else:
-            raise ValueError()   # Theoretically unreachable
+            raise ValueError()  # Theoretically unreachable
 
     def _data_to_tensor(self, data):
         """Convert the input data to the corresponding tensor.
@@ -370,7 +378,7 @@ def _tensor_to_data(self, tensor):
                 elif column_type in ('categorical', 'ordinal'):
                     value = self._one_hot_decode(tensor, row, properties)
                 else:
-                    raise ValueError()   # Theoretically unreachable
+                    raise ValueError()  # Theoretically unreachable
 
                 column_data.append(value)
 
@@ -412,7 +420,7 @@ def _truncate(self, generated):
             end_flag = sequence[:, self._data_size]
             if (end_flag == 1.0).any():
                 cut_idx = end_flag.detach().cpu().numpy().argmax()
-                sequence[cut_idx + 1:] = 0.0
+                sequence[cut_idx + 1 :] = 0.0
 
     def _generate(self, context, sequence_length=None):
         generated = self._generator(