Apply fixes to pass ruff tests

Author: stes

cebra/attribution/attribution_models.py

@@ -5,14 +5,15 @@
 # Code will be open-sourced upon publication.
 #
 import dataclasses
-import sys
 import time
 
+import cvxpy as cp
 import numpy as np
 import scipy.linalg
 import sklearn.metrics
 import torch
 import torch.nn as nn
+import tqdm
 from captum.attr import NeuronFeatureAblation
 from captum.attr import NeuronGradient
 from captum.attr import NeuronGradientShap
@@ -172,10 +173,11 @@ def _inverse_lsq_cvxpy(matrix: np.ndarray,
         matrix_param = cp.Parameter((matrix.shape[0], matrix.shape[1]))
         matrix_param.value = matrix
 
-        I = np.eye(matrix.shape[0])
+        identity = np.eye(matrix.shape[0])
         matrix_inverse = cp.Variable((matrix.shape[1], matrix.shape[0]))
 
-        objective = cp.Minimize(cp.norm(matrix @ matrix_inverse - I, "fro"))
+        objective = cp.Minimize(
+            cp.norm(matrix @ matrix_inverse - identity, "fro"))
         prob = cp.Problem(objective)
         prob.solve(verbose=False, solver=solver)
 

cebra/data/multiobjective.py

@@ -4,7 +4,6 @@
 # Not licensed yet. Distribution for review.
 # Code will be open-sourced upon publication.
 #
-from typing import List
 
 import literate_dataclasses as dataclasses
 

cebra/models/jacobian_regularizer.py

@@ -13,7 +13,6 @@
 from __future__ import division
 
 import torch
-import torch.autograd as autograd
 import torch.nn as nn
 
 

cebra/models/multi_criterions.py

@@ -4,7 +4,7 @@
 # Not licensed yet. Distribution for review.
 # Code will be open-sourced upon publication.
 #
-from typing import Optional, Tuple, Union
+from typing import Tuple
 
 import torch
 from torch import nn

cebra/solver/base.py

@@ -34,10 +34,9 @@
 import logging
 import os
 import time
-from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Callable, Dict, List, Literal, Optional
 
 import literate_dataclasses as dataclasses
-import numpy as np
 import torch
 
 import cebra

cebra/solver/multiobjective.py

@@ -6,25 +6,22 @@
 #
 """Multiobjective contrastive learning."""
 
-import abc
 import logging
-import os
 import time
 import warnings
-from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Callable, Dict, List, Optional, Tuple
 
 import literate_dataclasses as dataclasses
 import numpy as np
 import torch
-import tqdm
 
 import cebra
 import cebra.data
 import cebra.io
 import cebra.models
-import cebra.solver.base as abc_
 from cebra.solver import register
 from cebra.solver.base import Solver
+from cebra.solver.schedulers import Scheduler
 from cebra.solver.util import Meter
 
 
@@ -43,9 +40,8 @@ def __init__(self, loader):
     def _check_overwriting_key(self, key):
         if key in self.current_info:
             warnings.warn(
-                f"Configuration key already exists. Overwriting existing value. "
-                f"If you don't want to overwrite you should call push() before."
-            )
+                "Configuration key already exists. Overwriting existing value. "
+                "If you don't want to overwrite you should call push() before.")
 
     def _check_pushed_status(self):
         if "slice" not in self.current_info:

cebra/solver/regularized.py

@@ -6,15 +6,14 @@
 #
 """Regularized contrastive learning."""
 
-from typing import Callable, Dict, List, Literal, Optional, Union
+from typing import Dict, Optional
 
 import literate_dataclasses as dataclasses
 import torch
 
 import cebra
 import cebra.data
 import cebra.models
-import cebra.solver.multiobjective as abc_
 from cebra.solver import register
 from cebra.solver.single_session import SingleSessionSolver
 

cebra/solver/single_session.py

@@ -22,9 +22,7 @@
 """Single session solvers embed a single pair of time series."""
 
 import copy
-import os
-from collections.abc import Iterable
-from typing import Callable, Dict, List, Literal, Optional, Union
+from typing import Dict
 
 import literate_dataclasses as dataclasses
 import torch

examples/train_and_evaluate.ipynb

@@ -20,7 +20,6 @@
     "import cebra.models\n",
     "import cebra.solver\n",
     "\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import torch"
    ]
@@ -50,7 +49,6 @@
     }
    ],
    "source": [
-    "import cebra.solver\n",
     "\n",
     "cebra.solver.get_options()"
    ]
@@ -386,7 +384,6 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "# Create and fit the linear regression model\n",
@@ -534,7 +531,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "R2_dict = {}\n",

tests/test_models.py

@@ -23,7 +23,6 @@
 
 import pytest
 import torch
-from torch import nn
 
 import cebra.models
 import cebra.models.model