[pre-commit.ci] pre-commit autoupdate (#1313)

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

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.8.3 → v0.8.4](astral-sh/ruff-pre-commit@v0.8.3...v0.8.4)
- [github.com/pre-commit/mirrors-mypy: v1.13.0 → v1.14.0](pre-commit/mirrors-mypy@v1.13.0...v1.14.0)

* remove precision numpy typing

* minor fix

* ignore error

---------

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

Author: 3 people

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
           - --exclude=docs/
           - --exclude=scripts/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.3
+    rev: v0.8.4
     hooks:
       - id: ruff
         types_or: [python, pyi, jupyter]
@@ -21,7 +21,7 @@ repos:
         types_or: [python, pyi, jupyter]
         exclude: ^docs/source/notebooks/clv/dev/
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.13.0
+    rev: v1.14.0
     hooks:
       - id: mypy
         args: [--ignore-missing-imports]

docs/source/notebooks/mmm/mmm_tvp_example.ipynb

@@ -379,14 +379,14 @@ def rfm_summary(
     customers["frequency"] = customers["count"] - 1
 
     customers["recency"] = (
-        (pandas.to_datetime(customers["max"]) - pandas.to_datetime(customers["min"]))  # type: ignore
-        / np.timedelta64(1, time_unit)
+        (pandas.to_datetime(customers["max"]) - pandas.to_datetime(customers["min"]))
+        / np.timedelta64(1, time_unit)  # type: ignore[call-overload]
         / time_scaler
     )
 
     customers["T"] = (
         (observation_period_end_ts - customers["min"])
-        / np.timedelta64(1, time_unit)
+        / np.timedelta64(1, time_unit)  # type: ignore[call-overload]
         / time_scaler
     )
 

pymc_marketing/clv/utils.py

@@ -32,7 +32,7 @@
 
 Index = Sequence[int]
 Indices = dict[str, Index]
-Values = npt.NDArray[np.int_] | npt.NDArray[np.float64] | npt.NDArray[np.str_]
+Values = npt.NDArray[np.int_] | npt.NDArray | npt.NDArray[np.str_]
 
 
 def _find_unaligned_values(same_value: npt.NDArray[np.int_]) -> list[int]:

pymc_marketing/mmm/lift_test.py

@@ -308,9 +308,7 @@ def create_idata_attrs(self) -> dict[str, str]:
 
         return attrs
 
-    def forward_pass(
-        self, x: pt.TensorVariable | npt.NDArray[np.float64]
-    ) -> pt.TensorVariable:
+    def forward_pass(self, x: pt.TensorVariable | npt.NDArray) -> pt.TensorVariable:
         """Transform channel input into target contributions of each channel.
 
         This method handles the ordering of the adstock and saturation
@@ -322,7 +320,7 @@ def forward_pass(
 
         Parameters
         ----------
-        x : pt.TensorVariable | npt.NDArray[np.float64]
+        x : pt.TensorVariable | npt.NDArray
             The channel input which could be spends or impressions
 
         Returns
@@ -586,9 +584,9 @@ def default_model_config(self) -> dict:
 
     def channel_contributions_forward_pass(
         self,
-        channel_data: npt.NDArray[np.float64],
+        channel_data: npt.NDArray,
         disable_logger_stdout: bool | None = False,
-    ) -> npt.NDArray[np.float64]:
+    ) -> npt.NDArray:
         """Evaluate the channel contribution for a given channel data and a fitted model, ie. the forward pass.
 
         Parameters
@@ -945,9 +943,9 @@ class MMM(
 
     def channel_contributions_forward_pass(
         self,
-        channel_data: npt.NDArray[np.float64],
+        channel_data: npt.NDArray,
         disable_logger_stdout: bool | None = False,
-    ) -> npt.NDArray[np.float64]:
+    ) -> npt.NDArray:
         """Evaluate the channel contribution for a given channel data and a fitted model, ie. the forward pass.
 
         We return the contribution in the original scale of the target variable.

pymc_marketing/mmm/mmm.py

@@ -448,7 +448,7 @@ def weibull_adstock(
     return batched_convolution(x, w, axis=axis, mode=mode)
 
 
-def logistic_saturation(x, lam: npt.NDArray[np.float64] | float = 0.5):
+def logistic_saturation(x, lam: npt.NDArray | float = 0.5):
     r"""Logistic saturation transformation.
 
     .. math::
@@ -492,7 +492,7 @@ def logistic_saturation(x, lam: npt.NDArray[np.float64] | float = 0.5):
 
 
 def inverse_scaled_logistic_saturation(
-    x, lam: npt.NDArray[np.float64] | float = 0.5, eps: float = np.log(3)
+    x, lam: npt.NDArray | float = 0.5, eps: float = np.log(3)
 ):
     r"""Inverse scaled logistic saturation transformation.
 
@@ -827,9 +827,9 @@ def tanh_saturation_baselined(
 
 
 def michaelis_menten(
-    x: float | np.ndarray | npt.NDArray[np.float64],
-    alpha: float | np.ndarray | npt.NDArray[np.float64],
-    lam: float | np.ndarray | npt.NDArray[np.float64],
+    x: float | np.ndarray | npt.NDArray,
+    alpha: float | np.ndarray | npt.NDArray,
+    lam: float | np.ndarray | npt.NDArray,
 ) -> float | Any:
     r"""Evaluate the Michaelis-Menten function for given values of x, alpha, and lambda.
 

pymc_marketing/mmm/transformers.py

@@ -90,9 +90,9 @@ def transform_1d_array(
 
 
 def sigmoid_saturation(
-    x: float | np.ndarray | npt.NDArray[np.float64],
-    alpha: float | np.ndarray | npt.NDArray[np.float64],
-    lam: float | np.ndarray | npt.NDArray[np.float64],
+    x: float | np.ndarray | npt.NDArray,
+    alpha: float | np.ndarray | npt.NDArray,
+    lam: float | np.ndarray | npt.NDArray,
 ) -> float | Any:
     """Sigmoid saturation function.
 

pymc_marketing/mmm/utils.py

@@ -298,7 +298,7 @@ def random_samples(
     combinations = list(product(range(n_chains), range(n_draws)))
 
     return [
-        tuple(pair) for pair in rng.choice(combinations, size=n, replace=False).tolist()
+        tuple(pair) for pair in list(rng.choice(combinations, size=n, replace=False))
     ]