excluded _prior suffix from CLV models

docs/source/notebooks/clv/bg_nbd.ipynb

@@ -280,7 +280,7 @@
    "id": "0a5fc96b-eedd-43ae-bd70-5ae388f1f636",
    "metadata": {},
    "source": [
-    "Notice the additional `phi_dropout` and `kappa_dropout` priors. These were added to the default configuration to improve performance, but can be omitted when specifying a custom `model_config` with `a_prior` and `b_prior`.\n",
+    "Notice the additional `phi_dropout` and `kappa_dropout` priors. These were added to the default configuration to improve performance, but can be omitted when specifying a custom `model_config` with `a` and `b`.\n",
     "\n",
     "The specified model structure can also be visualized:"
    ]

docs/source/notebooks/clv/clv_quickstart.ipynb

@@ -576,10 +576,10 @@
    "outputs": [],
    "source": [
     "model_config = {\n",
-    "    \"a_prior\": Prior(\"HalfNormal\", sigma=10),\n",
-    "    \"b_prior\": Prior(\"HalfNormal\", sigma=10),\n",
-    "    \"alpha_prior\": Prior(\"HalfNormal\", sigma=10),\n",
-    "    \"r_prior\": Prior(\"HalfNormal\", sigma=10),\n",
+    "    \"a\": Prior(\"HalfNormal\", sigma=10),\n",
+    "    \"b\": Prior(\"HalfNormal\", sigma=10),\n",
+    "    \"alpha\": Prior(\"HalfNormal\", sigma=10),\n",
+    "    \"r\": Prior(\"HalfNormal\", sigma=10),\n",
     "}"
    ]
   },

docs/source/notebooks/clv/dev/beta_geo_beta_binom.ipynb

@@ -52,10 +52,10 @@
    ],
    "source": [
     "model_config = {\n",
-    "    \"alpha_prior\": Prior(\"HalfFlat\"),\n",
-    "    \"beta_prior\": Prior(\"HalfFlat\"),\n",
-    "    \"gamma_prior\": Prior(\"HalfFlat\"),\n",
-    "    \"delta_prior\": Prior(\"HalfFlat\"),\n",
+    "    \"alpha\": Prior(\"HalfFlat\"),\n",
+    "    \"beta\": Prior(\"HalfFlat\"),\n",
+    "    \"gamma\": Prior(\"HalfFlat\"),\n",
+    "    \"delta\": Prior(\"HalfFlat\"),\n",
     "}\n",
     "\n",
     "model = BetaGeoBetaBinomModel(data=data,model_config=model_config)\n",

docs/source/notebooks/clv/mbg_nbd.ipynb

@@ -269,7 +269,7 @@
    "id": "0a5fc96b-eedd-43ae-bd70-5ae388f1f636",
    "metadata": {},
    "source": [
-    "Notice the additional `phi_dropout` and `kappa_dropout` priors. These were added to the default configuration to improve performance, but can be omitted when specifying a custom `model_config` with `a_prior` and `b_prior`.\n",
+    "Notice the additional `phi_dropout` and `kappa_dropout` priors. These were added to the default configuration to improve performance, but can be omitted when specifying a custom `model_config` with `a` and `b`.\n",
     "\n",
     "The specified model structure can also be visualized:"
    ]

docs/source/notebooks/clv/pareto_nbd.ipynb

@@ -588,10 +588,10 @@
    "outputs": [],
    "source": [
     "flat_config = {\n",
-    "    \"r_prior\": Prior(\"HalfFlat\"),\n",
-    "    \"alpha_prior\": Prior(\"HalfFlat\"),\n",
-    "    \"s_prior\": Prior(\"HalfFlat\"),\n",
-    "    \"beta_prior\": Prior(\"HalfFlat\"),\n",
+    "    \"r\": Prior(\"HalfFlat\"),\n",
+    "    \"alpha\": Prior(\"HalfFlat\"),\n",
+    "    \"s\": Prior(\"HalfFlat\"),\n",
+    "    \"beta\": Prior(\"HalfFlat\"),\n",
     "}\n",
     "\n",
     "pnbd_pymc = clv.ParetoNBDModel(data=rfm_data, model_config=flat_config)"

pymc_marketing/clv/models/beta_geo.py

@@ -57,12 +57,12 @@ class BetaGeoModel(CLVModel):
             * `T`: Time between the first purchase and the end of the observation period
     model_config : dict, optional
         Dictionary of model prior parameters:
-            * `alpha_prior`: Scale parameter for time between purchases; defaults to `Prior("Weibull", alpha=2, beta=10)`
-            * `r_prior`: Shape parameter for time between purchases; defaults to `Prior("Weibull", alpha=2, beta=1)`
-            * `a_prior`: Shape parameter of dropout process; defaults to `phi_purchase_prior` * `kappa_purchase_prior`
-            * `b_prior`: Shape parameter of dropout process; defaults to `1-phi_dropout_prior` * `kappa_dropout_prior`
-            * `phi_dropout_prior`: Nested prior for a and b priors; defaults to `Prior("Uniform", lower=0, upper=1)`
-            * `kappa_dropout_prior`: Nested prior for a and b priors; defaults to `Prior("Pareto", alpha=1, m=1)`
+            * `alpha`: Scale parameter for time between purchases; defaults to `Prior("Weibull", alpha=2, beta=10)`
+            * `r`: Shape parameter for time between purchases; defaults to `Prior("Weibull", alpha=2, beta=1)`
+            * `a`: Shape parameter of dropout process; defaults to `phi_purchase` * `kappa_purchase`
+            * `b`: Shape parameter of dropout process; defaults to `1-phi_dropout` * `kappa_dropout`
+            * `phi_dropout`: Nested prior for a and b priors; defaults to `Prior("Uniform", lower=0, upper=1)`
+            * `kappa_dropout`: Nested prior for a and b priors; defaults to `Prior("Pareto", alpha=1, m=1)`
     sampler_config : dict, optional
         Dictionary of sampler parameters. Defaults to *None*.
 
@@ -96,10 +96,10 @@ class BetaGeoModel(CLVModel):
         model = BetaGeoModel(
             data=data,
             model_config={
-                "r_prior": Prior("Weibull", alpha=2, beta=1),
-                "alpha_prior": Prior("HalfFlat"),
-                "a_prior": Prior("Beta", alpha=2, beta=3),
-                "b_prior": Prior("Beta", alpha=3, beta=2),
+                "r": Prior("Weibull", alpha=2, beta=1),
+                "alpha": Prior("HalfFlat"),
+                "a": Prior("Beta", alpha=2, beta=3),
+                "b": Prior("Beta", alpha=3, beta=2),
             },
             sampler_config={
                 "draws": 1000,
@@ -166,10 +166,10 @@ def __init__(
     def default_model_config(self) -> ModelConfig:
         """Default model configuration."""
         return {
-            "alpha_prior": Prior("Weibull", alpha=2, beta=10),
-            "r_prior": Prior("Weibull", alpha=2, beta=1),
-            "phi_dropout_prior": Prior("Uniform", lower=0, upper=1),
-            "kappa_dropout_prior": Prior("Pareto", alpha=1, m=1),
+            "alpha": Prior("Weibull", alpha=2, beta=10),
+            "r": Prior("Weibull", alpha=2, beta=1),
+            "phi_dropout": Prior("Uniform", lower=0, upper=1),
+            "kappa_dropout": Prior("Pareto", alpha=1, m=1),
         }
 
     def build_model(self) -> None:  # type: ignore[override]
@@ -180,21 +180,21 @@ def build_model(self) -> None:  # type: ignore[override]
         }
         with pm.Model(coords=coords) as self.model:
             # purchase rate priors
-            alpha = self.model_config["alpha_prior"].create_variable("alpha")
-            r = self.model_config["r_prior"].create_variable("r")
+            alpha = self.model_config["alpha"].create_variable("alpha")
+            r = self.model_config["r"].create_variable("r")
 
             # dropout priors
-            if "a_prior" in self.model_config and "b_prior" in self.model_config:
-                a = self.model_config["a_prior"].create_variable("a")
-                b = self.model_config["b_prior"].create_variable("b")
+            if "a" in self.model_config and "b" in self.model_config:
+                a = self.model_config["a"].create_variable("a")
+                b = self.model_config["b"].create_variable("b")
             else:
                 # hierarchical pooling of dropout rate priors
-                phi_dropout = self.model_config["phi_dropout_prior"].create_variable(
+                phi_dropout = self.model_config["phi_dropout"].create_variable(
                     "phi_dropout"
                 )
-                kappa_dropout = self.model_config[
-                    "kappa_dropout_prior"
-                ].create_variable("kappa_dropout")
+                kappa_dropout = self.model_config["kappa_dropout"].create_variable(
+                    "kappa_dropout"
+                )
 
                 a = pm.Deterministic("a", phi_dropout * kappa_dropout)
                 b = pm.Deterministic("b", (1.0 - phi_dropout) * kappa_dropout)

pymc_marketing/clv/models/beta_geo_beta_binom.py

@@ -59,14 +59,14 @@
     model_config : dict, optional
         Dictionary containing model parameters:
 
-        * `alpha_prior`: Shape parameter of purchase process; defaults to `phi_purchase_prior` * `kappa_purchase_prior`
-        * `beta_prior`: Shape parameter of purchase process; defaults to `1-phi_purchase_prior` * `kappa_purchase_prior`
-        * `gamma_prior`: Shape parameter of dropout process; defaults to `phi_purchase_prior` * `kappa_purchase_prior`
-        * `delta_prior`: Shape parameter of dropout process; defaults to `1-phi_dropout_prior` * `kappa_dropout_prior`
-        * `phi_purchase_prior`: Nested prior for alpha and beta priors; defaults to `Prior("Uniform", lower=0, upper=1)`
-        * `kappa_purchase_prior`: Nested prior for alpha and beta priors; defaults to `Prior("Pareto", alpha=1, m=1)`
-        * `phi_dropout_prior`: Nested prior for gamma and delta priors; defaults to `Prior("Uniform", lower=0, upper=1)`
-        * `kappa_dropout_prior`: Nested prior for gamma and delta priors; defaults to `Prior("Pareto", alpha=1, m=1)`
+        * `alpha`: Shape parameter of purchase process; defaults to `phi_purchase` * `kappa_purchase`
+        * `beta`: Shape parameter of purchase process; defaults to `1-phi_purchase` * `kappa_purchase`
+        * `gamma`: Shape parameter of dropout process; defaults to `phi_purchase` * `kappa_purchase`
+        * `delta`: Shape parameter of dropout process; defaults to `1-phi_dropout` * `kappa_dropout`
+        * `phi_purchase`: Nested prior for alpha and beta priors; defaults to `Prior("Uniform", lower=0, upper=1)`
+        * `kappa_purchase`: Nested prior for alpha and beta priors; defaults to `Prior("Pareto", alpha=1, m=1)`
+        * `phi_dropout`: Nested prior for gamma and delta priors; defaults to `Prior("Uniform", lower=0, upper=1)`
+        * `kappa_dropout`: Nested prior for gamma and delta priors; defaults to `Prior("Pareto", alpha=1, m=1)`
 
         If not provided, the model will use default priors specified in the `default_model_config` class attribute.
     sampler_config : dict, optional
@@ -88,10 +88,10 @@
         model = BetaGeoBetaBinomModel(
             data=rfm_df,
             model_config={
-                "alpha_prior": Prior("HalfFlat"),
-                "beta_prior": Prior("HalfFlat"),
-                "gamma_prior": Prior("HalfFlat"),
-                "delta_prior": Prior("HalfFlat"),
+                "alpha": Prior("HalfFlat"),
+                "beta": Prior("HalfFlat"),
+                "gamma": Prior("HalfFlat"),
+                "delta": Prior("HalfFlat"),
             },
         )
 
@@ -175,10 +175,10 @@
     def default_model_config(self) -> ModelConfig:
         """Default model configuration."""
         return {
-            "phi_purchase_prior": Prior("Uniform", lower=0, upper=1),
-            "kappa_purchase_prior": Prior("Pareto", alpha=1, m=1),
-            "phi_dropout_prior": Prior("Uniform", lower=0, upper=1),
-            "kappa_dropout_prior": Prior("Pareto", alpha=1, m=1),
+            "phi_purchase": Prior("Uniform", lower=0, upper=1),
+            "kappa_purchase": Prior("Pareto", alpha=1, m=1),
+            "phi_dropout": Prior("Uniform", lower=0, upper=1),
+            "kappa_dropout": Prior("Pareto", alpha=1, m=1),
         }
 
     def build_model(self) -> None:  # type: ignore[override]
@@ -189,36 +189,33 @@
         }
         with pm.Model(coords=coords) as self.model:
             # purchase rate priors
-            if "alpha_prior" in self.model_config and "beta_prior" in self.model_config:
-                alpha = self.model_config["alpha_prior"].create_variable("alpha")
-                beta = self.model_config["beta_prior"].create_variable("beta")
+            if "alpha" in self.model_config and "beta" in self.model_config:
+                alpha = self.model_config["alpha"].create_variable("alpha")
+                beta = self.model_config["beta"].create_variable("beta")
             else:
                 # hierarchical pooling of purchase rate priors
-                phi_purchase = self.model_config["phi_purchase_prior"].create_variable(
+                phi_purchase = self.model_config["phi_purchase"].create_variable(
                     "phi_purchase"
                 )
-                kappa_purchase = self.model_config[
-                    "kappa_purchase_prior"
-                ].create_variable("kappa_purchase")
+                kappa_purchase = self.model_config["kappa_purchase"].create_variable(
+                    "kappa_purchase"
+                )
 
                 alpha = pm.Deterministic("alpha", phi_purchase * kappa_purchase)
                 beta = pm.Deterministic("beta", (1.0 - phi_purchase) * kappa_purchase)
 
             # dropout priors
-            if (
-                "gamma_prior" in self.model_config
-                and "delta_prior" in self.model_config
-            ):
-                gamma = self.model_config["gamma_prior"].create_variable("gamma")
-                delta = self.model_config["delta_prior"].create_variable("delta")
+            if "gamma" in self.model_config and "delta" in self.model_config:
+                gamma = self.model_config["gamma"].create_variable("gamma")
+                delta = self.model_config["delta"].create_variable("delta")
             else:
                 # hierarchical pooling of dropout rate priors
-                phi_dropout = self.model_config["phi_dropout_prior"].create_variable(
+                phi_dropout = self.model_config["phi_dropout"].create_variable(
                     "phi_dropout"
                 )
-                kappa_dropout = self.model_config[
-                    "kappa_dropout_prior"
-                ].create_variable("kappa_dropout")
+                kappa_dropout = self.model_config["kappa_dropout"].create_variable(
+                    "kappa_dropout"
+                )
 
                 gamma = pm.Deterministic("gamma", phi_dropout * kappa_dropout)
                 delta = pm.Deterministic("delta", (1.0 - phi_dropout) * kappa_dropout)

pymc_marketing/clv/models/gamma_gamma.py

@@ -254,9 +254,9 @@ class GammaGammaModel(BaseGammaGammaModel):
                 "frequency": [6, 8, 2, 1, ...],
             }),
             model_config={
-                "p_prior": {'dist': 'HalfNormal', kwargs: {}},
-                "q_prior": {'dist': 'HalfStudentT', kwargs: {"nu": 4, "sigma": 10}},
-                "v_prior": {'dist': 'HalfCauchy', kwargs: {"beta":1}},
+                "p": {'dist': 'HalfNormal', kwargs: {}},
+                "q": {'dist': 'HalfStudentT', kwargs: {"nu": 4, "sigma": 10}},
+                "v": {'dist': 'HalfCauchy', kwargs: {"beta":1}},
             },
             sampler_config={
                 "draws": 1000,
@@ -318,9 +318,9 @@ def __init__(
     def default_model_config(self) -> ModelConfig:
         """Default model configuration."""
         return {
-            "p_prior": Prior("HalfFlat"),
-            "q_prior": Prior("HalfFlat"),
-            "v_prior": Prior("HalfFlat"),
+            "p": Prior("HalfFlat"),
+            "q": Prior("HalfFlat"),
+            "v": Prior("HalfFlat"),
         }
 
     def build_model(self) -> None:  # type: ignore[override]
@@ -330,9 +330,9 @@ def build_model(self) -> None:  # type: ignore[override]
 
         coords = {"customer_id": self.data["customer_id"]}
         with pm.Model(coords=coords) as self.model:
-            p = self.model_config["p_prior"].create_variable("p")
-            q = self.model_config["q_prior"].create_variable("q")
-            v = self.model_config["v_prior"].create_variable("v")
+            p = self.model_config["p"].create_variable("p")
+            q = self.model_config["q"].create_variable("q")
+            v = self.model_config["v"].create_variable("v")
 
             # Likelihood for mean_spend, marginalizing over nu
             # Eq 1a from [1], p.2
@@ -393,9 +393,9 @@ class GammaGammaModelIndividual(BaseGammaGammaModel):
                 }
             ),
             model_config={
-                "p_prior": {dist: 'HalfNorm', kwargs: {}},
-                "q_prior": {dist: 'HalfStudentT', kwargs: {"nu": 4, "sigma": 10}},
-                "v_prior": {dist: 'HalfCauchy', kwargs: {}},
+                "p": {dist: 'HalfNorm', kwargs: {}},
+                "q": {dist: 'HalfStudentT', kwargs: {"nu": 4, "sigma": 10}},
+                "v": {dist: 'HalfCauchy', kwargs: {}},
             },
             sampler_config={
                 "draws": 1000,
@@ -455,9 +455,9 @@ def __init__(
     def default_model_config(self) -> dict:
         """Default model configuration."""
         return {
-            "p_prior": Prior("HalfFlat"),
-            "q_prior": Prior("HalfFlat"),
-            "v_prior": Prior("HalfFlat"),
+            "p": Prior("HalfFlat"),
+            "q": Prior("HalfFlat"),
+            "v": Prior("HalfFlat"),
         }
 
     def build_model(self) -> None:  # type: ignore[override]
@@ -469,9 +469,9 @@ def build_model(self) -> None:  # type: ignore[override]
             "obs": range(self.data.shape[0]),
         }
         with pm.Model(coords=coords) as self.model:
-            p = self.model_config["p_prior"].create_variable("p")
-            q = self.model_config["q_prior"].create_variable("q")
-            v = self.model_config["v_prior"].create_variable("v")
+            p = self.model_config["p"].create_variable("p")
+            q = self.model_config["q"].create_variable("q")
+            v = self.model_config["v"].create_variable("v")
 
             nu = pm.Gamma("nu", q, v, dims=("customer_id",))
             pm.Gamma(