[pre-commit.ci] pre-commit autoupdate (#629)

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.3.5 → v0.3.7](astral-sh/ruff-pre-commit@v0.3.5...v0.3.7)

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

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.5
+    rev: v0.3.7
     hooks:
       - id: ruff
         args: ["--fix", "--output-format=full"]

docs/source/notebooks/clv/sBG.ipynb

@@ -1169,8 +1169,8 @@
    "source": [
     "weeks = xr.DataArray(np.arange(1, 14), dims=[\"weeks\"])\n",
     "\n",
-    "survival_perc_highend = 100-100*((churn_highend < weeks))\n",
-    "survival_perc_regular = 100-100*((churn_regular < weeks))"
+    "survival_perc_highend = 100-100*(churn_highend < weeks)\n",
+    "survival_perc_regular = 100-100*(churn_regular < weeks)"
    ]
   },
   {

pymc_marketing/clv/models/basic.py

@@ -1,7 +1,8 @@
 import json
 import warnings
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Dict, Optional, Sequence, cast
+from typing import cast
 
 import arviz as az
 import pandas as pd
@@ -21,8 +22,8 @@ def __init__(
         self,
         data: pd.DataFrame,
         *,
-        model_config: Optional[Dict] = None,
-        sampler_config: Optional[Dict] = None,
+        model_config: dict | None = None,
+        sampler_config: dict | None = None,
     ):
         super().__init__(model_config, sampler_config)
         self.data = data
@@ -211,18 +212,18 @@ def thin_fit_result(self, keep_every: int):
         return type(self)._build_with_idata(new_idata)
 
     @staticmethod
-    def _create_distribution(dist: Dict, shape=()):
+    def _create_distribution(dist: dict, shape=()):
         try:
             return getattr(pm, dist["dist"]).dist(**dist.get("kwargs", {}), shape=shape)
         except AttributeError:
             raise ValueError(f"Distribution {dist['dist']} does not exist in PyMC")
 
     @property
-    def default_sampler_config(self) -> Dict:
+    def default_sampler_config(self) -> dict:
         return {}
 
     @property
-    def _serializable_model_config(self) -> Dict:
+    def _serializable_model_config(self) -> dict:
         return self.model_config
 
     @property

pymc_marketing/clv/models/beta_geo.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional, Sequence, Union
+from collections.abc import Sequence
 
 import numpy as np
 import pandas as pd
@@ -117,8 +117,8 @@ class BetaGeoModel(CLVModel):
     def __init__(
         self,
         data: pd.DataFrame,
-        model_config: Optional[Dict] = None,
-        sampler_config: Optional[Dict] = None,
+        model_config: dict | None = None,
+        sampler_config: dict | None = None,
     ):
         self._validate_cols(
             data,
@@ -132,7 +132,7 @@ def __init__(
         )
 
     @property
-    def default_model_config(self) -> Dict[str, Dict]:
+    def default_model_config(self) -> dict[str, dict]:
         return {
             "a_prior": {"dist": "HalfFlat", "kwargs": {}},
             "b_prior": {"dist": "HalfFlat", "kwargs": {}},
@@ -211,11 +211,11 @@ def _unload_params(self):
     # taken from https://lifetimes.readthedocs.io/en/latest/lifetimes.fitters.html
     def expected_num_purchases(
         self,
-        customer_id: Union[np.ndarray, pd.Series],
-        t: Union[np.ndarray, pd.Series, TensorVariable],
-        frequency: Union[np.ndarray, pd.Series, TensorVariable],
-        recency: Union[np.ndarray, pd.Series, TensorVariable],
-        T: Union[np.ndarray, pd.Series, TensorVariable],
+        customer_id: np.ndarray | pd.Series,
+        t: np.ndarray | pd.Series | TensorVariable,
+        frequency: np.ndarray | pd.Series | TensorVariable,
+        recency: np.ndarray | pd.Series | TensorVariable,
+        T: np.ndarray | pd.Series | TensorVariable,
     ) -> xr.DataArray:
         r"""
         Given a purchase history/profile of :math:`x` and :math:`t_x` for an individual
@@ -273,10 +273,10 @@ def expected_num_purchases(
 
     def expected_probability_alive(
         self,
-        customer_id: Union[np.ndarray, pd.Series],
-        frequency: Union[np.ndarray, pd.Series],
-        recency: Union[np.ndarray, pd.Series],
-        T: Union[np.ndarray, pd.Series],
+        customer_id: np.ndarray | pd.Series,
+        frequency: np.ndarray | pd.Series,
+        recency: np.ndarray | pd.Series,
+        T: np.ndarray | pd.Series,
     ) -> xr.DataArray:
         r"""
         Posterior expected value of the probability of being alive at time T. The
@@ -310,7 +310,7 @@ def expected_probability_alive(
 
     def expected_num_purchases_new_customer(
         self,
-        t: Union[np.ndarray, pd.Series],
+        t: np.ndarray | pd.Series,
     ):
         r"""
         Posterior expected number of purchases for any interval of length :math:`t`. See
@@ -345,7 +345,7 @@ def expected_num_purchases_new_customer(
 
     def _distribution_new_customers(
         self,
-        random_seed: Optional[RandomState] = None,
+        random_seed: RandomState | None = None,
         var_names: Sequence[str] = ("population_dropout", "population_purchase_rate"),
     ) -> xr.Dataset:
         with pm.Model():
@@ -372,7 +372,7 @@ def _distribution_new_customers(
 
     def distribution_new_customer_dropout(
         self,
-        random_seed: Optional[RandomState] = None,
+        random_seed: RandomState | None = None,
     ) -> xr.Dataset:
         """Sample the Beta distribution for the population-level dropout rate.
 
@@ -396,7 +396,7 @@ def distribution_new_customer_dropout(
 
     def distribution_new_customer_purchase_rate(
         self,
-        random_seed: Optional[RandomState] = None,
+        random_seed: RandomState | None = None,
     ) -> xr.Dataset:
         """Sample the Gamma distribution for the population-level purchase rate.
 

pymc_marketing/clv/models/gamma_gamma.py

@@ -1,5 +1,3 @@
-from typing import Dict, Optional, Union
-
 import numpy as np
 import pandas as pd
 import pymc as pm
@@ -15,10 +13,10 @@
 class BaseGammaGammaModel(CLVModel):
     def distribution_customer_spend(
         self,
-        customer_id: Union[np.ndarray, pd.Series],
-        mean_transaction_value: Union[np.ndarray, pd.Series, TensorVariable],
-        frequency: Union[np.ndarray, pd.Series, TensorVariable],
-        random_seed: Optional[RandomState] = None,
+        customer_id: np.ndarray | pd.Series,
+        mean_transaction_value: np.ndarray | pd.Series | TensorVariable,
+        frequency: np.ndarray | pd.Series | TensorVariable,
+        random_seed: RandomState | None = None,
     ) -> xarray.DataArray:
         """Posterior distribution of transaction value per customer"""
 
@@ -44,9 +42,9 @@ def distribution_customer_spend(
 
     def expected_customer_spend(
         self,
-        customer_id: Union[np.ndarray, pd.Series],
-        mean_transaction_value: Union[np.ndarray, pd.Series],
-        frequency: Union[np.ndarray, pd.Series],
+        customer_id: np.ndarray | pd.Series,
+        mean_transaction_value: np.ndarray | pd.Series,
+        frequency: np.ndarray | pd.Series,
     ) -> xarray.DataArray:
         """Expected transaction value per customer
 
@@ -107,11 +105,11 @@ def expected_new_customer_spend(self) -> xarray.DataArray:
     def expected_customer_lifetime_value(
         self,
         transaction_model: CLVModel,
-        customer_id: Union[np.ndarray, pd.Series],
-        mean_transaction_value: Union[np.ndarray, pd.Series],
-        frequency: Union[np.ndarray, pd.Series],
-        recency: Union[np.ndarray, pd.Series],
-        T: Union[np.ndarray, pd.Series],
+        customer_id: np.ndarray | pd.Series,
+        mean_transaction_value: np.ndarray | pd.Series,
+        frequency: np.ndarray | pd.Series,
+        recency: np.ndarray | pd.Series,
+        T: np.ndarray | pd.Series,
         time: int = 12,
         discount_rate: float = 0.01,
         freq: str = "D",
@@ -226,8 +224,8 @@ class GammaGammaModel(BaseGammaGammaModel):
     def __init__(
         self,
         data: pd.DataFrame,
-        model_config: Optional[Dict] = None,
-        sampler_config: Optional[Dict] = None,
+        model_config: dict | None = None,
+        sampler_config: dict | None = None,
     ):
         self._validate_cols(
             data,
@@ -239,7 +237,7 @@ def __init__(
         )
 
     @property
-    def default_model_config(self) -> Dict:
+    def default_model_config(self) -> dict:
         return {
             "p_prior": {"dist": "HalfFlat", "kwargs": {}},
             "q_prior": {"dist": "HalfFlat", "kwargs": {}},
@@ -362,8 +360,8 @@ class GammaGammaModelIndividual(BaseGammaGammaModel):
     def __init__(
         self,
         data: pd.DataFrame,
-        model_config: Optional[Dict] = None,
-        sampler_config: Optional[Dict] = None,
+        model_config: dict | None = None,
+        sampler_config: dict | None = None,
     ):
         self._validate_cols(
             data, required_cols=["customer_id", "individual_transaction_value"]
@@ -373,7 +371,7 @@ def __init__(
         )
 
     @property
-    def default_model_config(self) -> Dict:
+    def default_model_config(self) -> dict:
         return {
             "p_prior": {"dist": "HalfFlat", "kwargs": {}},
             "q_prior": {"dist": "HalfFlat", "kwargs": {}},
@@ -419,9 +417,9 @@ def _summarize_mean_data(self, customer_id, individual_transaction_value):
 
     def distribution_customer_spend(  # type: ignore [override]
         self,
-        customer_id: Union[np.ndarray, pd.Series],
-        individual_transaction_value: Union[np.ndarray, pd.Series, TensorVariable],
-        random_seed: Optional[RandomState] = None,
+        customer_id: np.ndarray | pd.Series,
+        individual_transaction_value: np.ndarray | pd.Series | TensorVariable,
+        random_seed: RandomState | None = None,
     ) -> xarray.DataArray:
         """Return distribution of transaction value per customer"""
 
@@ -438,9 +436,9 @@ def distribution_customer_spend(  # type: ignore [override]
 
     def expected_customer_spend(
         self,
-        customer_id: Union[np.ndarray, pd.Series],
-        individual_transaction_value: Union[np.ndarray, pd.Series, TensorVariable],
-        random_seed: Optional[RandomState] = None,
+        customer_id: np.ndarray | pd.Series,
+        individual_transaction_value: np.ndarray | pd.Series | TensorVariable,
+        random_seed: RandomState | None = None,
     ) -> xarray.DataArray:
         """Return expected transaction value per customer"""
 
@@ -458,10 +456,10 @@ def expected_customer_spend(
     def expected_customer_lifetime_value(  # type: ignore [override]
         self,
         transaction_model: CLVModel,
-        customer_id: Union[np.ndarray, pd.Series],
-        individual_transaction_value: Union[np.ndarray, pd.Series, TensorVariable],
-        recency: Union[np.ndarray, pd.Series],
-        T: Union[np.ndarray, pd.Series],
+        customer_id: np.ndarray | pd.Series,
+        individual_transaction_value: np.ndarray | pd.Series | TensorVariable,
+        recency: np.ndarray | pd.Series,
+        T: np.ndarray | pd.Series,
         time: int = 12,
         discount_rate: float = 0.01,
         freq: str = "D",