Huge chunck of changes requested by William

Author: cetagostini

data/config_files/basic_model.yml

@@ -1,6 +1,3 @@
-schema_version: 1
-
-# ----------------------------------------------------------------------
 model:
   class: pymc_marketing.mmm.multidimensional.MMM
   kwargs:

data/config_files/example_with_original_scale_vars.yml

@@ -1,6 +1,3 @@
-schema_version: 1
-
-# ----------------------------------------------------------------------
 model:
   class: pymc_marketing.mmm.multidimensional.MMM
   kwargs:

data/config_files/multi_dimensiona_hierarchical_model_nested_config.yml

@@ -1,6 +1,3 @@
-schema_version: 1
-
-# ----------------------------------------------------------------------
 model:
   class: pymc_marketing.mmm.multidimensional.MMM
   kwargs:

data/config_files/multi_dimensional_hierarchical_model.yml

@@ -1,6 +1,3 @@
-schema_version: 1
-
-# ----------------------------------------------------------------------
 model:
   class: pymc_marketing.mmm.multidimensional.MMM
   kwargs:

data/config_files/multi_dimensional_hierarchical_with_arbitrary_effects_model.yml

@@ -1,6 +1,3 @@
-schema_version: 1
-
-# ----------------------------------------------------------------------
 model:
   class: pymc_marketing.mmm.multidimensional.MMM
   kwargs:

data/config_files/multi_dimensional_model.yml

@@ -1,6 +1,3 @@
-schema_version: 1
-
-# ----------------------------------------------------------------------
 model:
   class: pymc_marketing.mmm.multidimensional.MMM
   kwargs:

docs/source/notebooks/mmm/mmm_build_from_yml_example.ipynb

@@ -32,7 +32,7 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
-    "from pymc_marketing.mmm.builders.yaml import build_from_yaml\n",
+    "from pymc_marketing.mmm.builders.yaml import build_mmm_from_yaml\n",
     "from pymc_marketing.paths import data_dir\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
@@ -205,7 +205,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmm = build_from_yaml(\n",
+    "mmm = build_mmm_from_yaml(\n",
     "    X=X, y=y, config_path=data_dir / \"config_files\" / \"basic_model.yml\"\n",
     ")"
    ]
@@ -1055,7 +1055,7 @@
     "3. sample_kwargs: Optional parameters for the sampling process\n",
     "4. data: Optional paths to data files\n",
     "\n",
-    "The build_from_yaml function:\n",
+    "The build_mmm_from_yaml function:\n",
     "- Parses this YAML configuration\n",
     "- Uses the 'build' function to instantiate objects recursively\n",
     "- Handles special cases like priors and distributions\n",
@@ -1076,7 +1076,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmm2 = build_from_yaml(\n",
+    "mmm2 = build_mmm_from_yaml(\n",
     "    X=X, y=y, config_path=data_dir / \"config_files\" / \"multi_dimensional_model.yml\"\n",
     ")"
    ]
@@ -1333,7 +1333,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmm3 = build_from_yaml(\n",
+    "mmm3 = build_mmm_from_yaml(\n",
     "    X=X,\n",
     "    y=y,\n",
     "    config_path=data_dir / \"config_files\" / \"multi_dimensional_hierarchical_model.yml\",\n",
@@ -1625,7 +1625,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmm4 = build_from_yaml(\n",
+    "mmm4 = build_mmm_from_yaml(\n",
     "    X=X,\n",
     "    y=y,\n",
     "    config_path=data_dir\n",

pymc_marketing/mmm/builders/__init__.py

@@ -16,9 +16,9 @@
 # Expose key functionality
 from pymc_marketing.deserialize import register_deserialization
 from pymc_marketing.mmm.builders.factories import build, create_prior_from_dict
-from pymc_marketing.mmm.builders.yaml import build_from_yaml
+from pymc_marketing.mmm.builders.yaml import build_mmm_from_yaml
 
-__all__ = ["build", "build_from_yaml"]
+__all__ = ["build", "build_mmm_from_yaml"]
 
 # Register deserializers
 register_deserialization(

pymc_marketing/mmm/builders/deserializers.py

@@ -26,36 +26,6 @@
 logger = logging.getLogger(__name__)
 
 
-def is_prior_dict(data: Any) -> bool:
-    """Check if the data is a dictionary representing a Prior."""
-    if not isinstance(data, dict):
-        return False
-    return "distribution" in data
-
-
-def deserialize_prior(data: dict[str, Any]) -> Prior:
-    """Deserialize a Prior from the dictionary representation."""
-    # Make a copy to avoid modifying the original
-    data_copy = data.copy()
-
-    # Extract distribution
-    distribution = data_copy.pop("distribution")
-
-    # Process nested priors in the data before creating the Prior
-    for key, value in data_copy.items():
-        if isinstance(value, dict) and is_prior_dict(value):
-            data_copy[key] = deserialize_prior(value)
-        elif (
-            isinstance(value, dict)
-            and "class" in value
-            and value["class"] == "pymc_marketing.prior.Prior"
-        ):
-            data_copy[key] = deserialize_standard_prior(value)
-
-    # Create Prior
-    return Prior(distribution, **data_copy)
-
-
 def is_alternative_prior(data: Any) -> bool:
     """Check if the data is a dictionary representing a Prior (alternative check)."""
     return isinstance(data, dict) and "distribution" in data
@@ -105,72 +75,14 @@ def deserialize_alternative_prior(data: dict[str, Any]) -> Prior:
     )
 
 
-def is_standard_prior_dict(data: Any) -> tuple[bool, str]:
-    """
-    Check if the data is a standard dictionary in the format used by factories.py.
-
-    Returns a tuple of (is_match, target_class_name)
-    """
-    if not isinstance(data, dict):
-        return False, ""
-
-    if (
-        "class" in data
-        and data["class"] == "pymc_marketing.prior.Prior"
-        and "kwargs" in data
-    ):
-        return True, "Prior"
-
-    return False, ""
-
-
-def deserialize_standard_prior(data: dict[str, Any]) -> Prior:
-    """
-    Deserialize a prior from the standard format used by factories.py.
-
-    The expected format is:
-    {
-        "class": "pymc_marketing.prior.Prior",
-        "kwargs": {
-            "args": ["Distribution"],
-            "param1": value1,
-            ...
-        }
-    }
-    """
-    kwargs = data.get("kwargs", {})
-
-    # Get distribution from args
-    args = kwargs.get("args", ["Normal"])
-    distribution = args[0]
-
-    # Create a new kwargs dict without args
-    new_kwargs = {k: v for k, v in kwargs.items() if k != "args"}
-
-    # Process nested priors in kwargs
-    for key, value in new_kwargs.items():
-        if isinstance(value, dict):
-            if "distribution" in value:
-                new_kwargs[key] = deserialize_prior(value)
-            elif "class" in value and value["class"] == "pymc_marketing.prior.Prior":
-                new_kwargs[key] = deserialize_standard_prior(value)
-
-    # Create Prior
-    logger.info(f"Creating Prior with distribution={distribution}, kwargs={new_kwargs}")
-    return Prior(distribution, **new_kwargs)
-
-
 def is_priors_dict(data: Any) -> bool:
     """Check if the data is a dictionary of priors."""
     if not isinstance(data, dict):
         return False
 
     # Check if any value is a prior-like dictionary
     for _key, value in data.items():
-        if isinstance(value, dict) and (
-            "distribution" in value
-            or ("class" in value and value["class"] == "pymc_marketing.prior.Prior")
-        ):
+        if isinstance(value, dict) and "distribution" in value:
             return True
     return False
 
@@ -181,26 +93,16 @@ def deserialize_priors_dict(data: dict[str, Any]) -> dict[str, Any]:
     for key, value in data.items():
         if isinstance(value, dict):
             if "distribution" in value:
-                result[key] = deserialize_prior(value)
-            elif "class" in value and value["class"] == "pymc_marketing.prior.Prior":
-                result[key] = deserialize_standard_prior(value)
+                result[key] = deserialize(value)
             else:
                 result[key] = value
         else:
             result[key] = value
     return result
 
 
-# Register the simple prior deserializer for distribution-based format
-register_deserialization(is_prior_dict, deserialize_prior)
-
 # Register the alternative prior deserializer for more complex nested cases
 register_deserialization(is_alternative_prior, deserialize_alternative_prior)
 
-# Register the standard deserializer used by factories.py
-register_deserialization(
-    lambda x: is_standard_prior_dict(x)[0], deserialize_standard_prior
-)
-
 # Register the priors dictionary deserializer
 register_deserialization(is_priors_dict, deserialize_priors_dict)

pymc_marketing/mmm/builders/factories.py

@@ -19,6 +19,7 @@
 from collections.abc import Mapping, MutableMapping, Sequence
 from typing import Any
 
+from pymc_marketing.deserialize import deserialize
 from pymc_marketing.prior import Prior
 
 # Optional short-name registry -------------------------------------------------
@@ -126,13 +127,23 @@ def build(spec: Mapping[str, Any]) -> Any:
                             isinstance(prior_value, dict)
                             and "distribution" in prior_value
                         ):
-                            priors_dict[prior_key] = create_prior_from_dict(prior_value)
+                            # Use deserialize for individual priors
+                            try:
+                                priors_dict[prior_key] = deserialize(prior_value)
+                            except Exception:
+                                # Fall back to create_prior_from_dict if deserialize fails
+                                priors_dict[prior_key] = create_prior_from_dict(
+                                    prior_value
+                                )
                         else:
                             priors_dict[prior_key] = prior_value
                     kwargs[k] = priors_dict
                 elif k == "prior" and "distribution" in v:
-                    # Create a single prior object
-                    kwargs[k] = create_prior_from_dict(v)  # type: ignore
+                    # Use deserialize for a single prior, with fallback
+                    try:
+                        kwargs[k] = deserialize(v)
+                    except Exception:
+                        kwargs[k] = create_prior_from_dict(v)  # type: ignore
                 else:
                     kwargs[k] = resolve(v)
             else: