[pre-commit.ci] pre-commit autoupdate and reduce update frequency (#98)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.284 → v0.0.290](astral-sh/ruff-pre-commit@v0.0.284...v0.0.290)
- [github.com/psf/black: 23.7.0 → 23.9.1](psf/black@23.7.0...23.9.1)

* use isinstance instead of type

* set pre-commit auto-update schedule to monthly

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: niksirbi <[email protected]>

.pre-commit-config.yaml

@@ -1,4 +1,7 @@
 exclude: '^neuralplayground/experiments/.*_20.*|^documents/'
+# Configuring https://pre-commit.ci/ bot
+ci:
+    autoupdate_schedule: monthly
 repos:
     - repo: https://github.com/pre-commit/pre-commit-hooks
       rev: v4.4.0
@@ -14,10 +17,10 @@ repos:
           - id: trailing-whitespace
             exclude: 'README.md'
     - repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.0.284
+      rev: v0.0.290
       hooks:
         - id: ruff
     - repo: https://github.com/psf/black
-      rev: 23.7.0
+      rev: 23.9.1
       hooks:
           - id: black

neuralplayground/agents/whittington_2020_extras/whittington_2020_model.py

@@ -1014,7 +1014,7 @@ def f_p(self, p):
         """
         activation = (
             [utils.leaky_relu(torch.clamp(p_f, min=-1, max=1)) for p_f in p]
-            if type(p) is list
+            if isinstance(p, list)
             else utils.leaky_relu(torch.clamp(p, min=-1, max=1))
         )
         return activation
@@ -1099,7 +1099,7 @@ def __init__(self, in_dim, out_dim, activation=(torch.nn.functional.elu, None),
         super(MLP, self).__init__()
         # Check if this network consists of module: are input and output dimensions lists? If not, make them
         # (but remember it wasn't)
-        if type(in_dim) is list:
+        if isinstance(in_dim, list):
             self.is_list = True
         else:
             in_dim = [in_dim]
@@ -1142,7 +1142,7 @@ def set_weights(self, from_layer, value):
             value: value to set weights to
         """
         # If single value is provided: copy it for each module
-        if type(value) is not list:
+        if not isinstance(value, list):
             input_value = [value for n in range(self.N)]
         else:
             input_value = value

neuralplayground/agents/whittington_2020_extras/whittington_2020_utils.py

@@ -75,7 +75,7 @@ def squared_error(value, target):
             Tensor of mean squared errors
     """
     # Return torch MSE loss
-    if type(value) is list:
+    if isinstance(value, list):
         loss = [0.5 * torch.sum(torch.nn.MSELoss(reduction="none")(value[i], target[i]), dim=-1) for i in range(len(value))]
     else:
         loss = 0.5 * torch.sum(torch.nn.MSELoss(reduction="none")(value, target), dim=-1)
@@ -97,7 +97,7 @@ def cross_entropy(value, target):
             Tensor of binary cross entropies
     """
     # Return torch BCE loss
-    if type(value) is list:
+    if isinstance(value, list):
         loss = [torch.nn.CrossEntropyLoss(reduction="none")(val, targ) for val, targ in zip(value, target)]
     else:
         loss = torch.nn.CrossEntropyLoss(reduction="none")(value, target)