Added 4 new graphs to Pareto Optimizer + bug fixes

python/src/robyn/data/entities/hyperparameters.py

@@ -51,8 +51,8 @@ class Hyperparameters:
         hyperparameters (Dict[str, Hyperparameter]): A dictionary of hyperparameters where the key is the channel name and the value is a Hyperparameter object.
     """
 
-    hyperparameters: Dict[str, ChannelHyperparameters] = (None,)
-    adstock: AdstockType = (None,)  # Mandatory. User provides this.
+    hyperparameters: Dict[str, ChannelHyperparameters] = field(default_factory=dict)
+    adstock: AdstockType = AdstockType.GEOMETRIC  # Mandatory. User provides this.
     lambda_: float = 0.0  # User does not provide this. Model run calculates it.
     train_size: List[float] = field(default_factory=lambda: [0.5, 0.8])
     hyper_bound_list_updated: Dict[str, List[float]] = field(default_factory=dict)

python/src/robyn/data/entities/mmmdata.py

@@ -249,3 +249,23 @@ def calculate_rolling_window_indices(self) -> None:
                 - self.mmmdata_spec.rolling_window_start_which
                 + 1
             )
+
+    def set_default_factor_vars(self) -> None:
+        """
+        Set the default factor variables.
+        """
+        factor_variables = self.mmmdata_spec.factor_vars
+        selected_columns = self.data[self.mmmdata_spec.context_vars]
+        non_numeric_columns = ~selected_columns.applymap(
+            lambda x: isinstance(x, (int, float))
+        ).all()
+        if non_numeric_columns.any():
+            non_factor_columns = non_numeric_columns[
+                ~non_numeric_columns.index.isin(factor_variables or [])
+            ]
+            non_factor_columns = non_factor_columns[non_factor_columns]
+            if len(non_factor_columns) > 0:
+                factor_variables = (
+                    factor_variables or []
+                ) + non_factor_columns.index.tolist()
+        self.mmmdata_spec.factor_vars = factor_variables

python/src/robyn/modeling/clustering/cluster_builder.py

@@ -219,7 +219,7 @@ def _calculate_confidence_intervals(
         cluster_collect = []
 
         self.logger.debug(f"Processing {config.k_clusters} clusters")
-        for j in range(1, config.k_clusters + 1):
+        for j in range(0, config.k_clusters):
             df_outcome = df_clusters_outcome[df_clusters_outcome["cluster"] == j]
             if len(df_outcome["sol_id"].unique()) < 3:
                 self.logger.warning(

python/src/robyn/modeling/feature_engineering.py

@@ -392,6 +392,8 @@ def _prophet_decomposition(self, dt_mod: pd.DataFrame) -> pd.DataFrame:
         dt_regressors["ds"] = pd.to_datetime(dt_regressors["ds"])
 
         # Handle factor variables
+        if self.mmm_data.mmmdata_spec.factor_vars is None:
+            self.mmm_data.set_default_factor_vars()
         factor_vars = self.mmm_data.mmmdata_spec.factor_vars
         if factor_vars:
             # Create dummy variables but keep original

python/src/robyn/modeling/pareto/pareto_optimizer.py

@@ -253,7 +253,7 @@ def optimize(
             self.logger.info("Pareto optimization completed successfully")
             return ParetoResult(
                 pareto_solutions=plotting_data["pareto_solutions"],
-                pareto_fronts=pareto_fronts,
+                pareto_fronts=max(pareto_data.pareto_fronts),
                 result_hyp_param=aggregated_data["result_hyp_param"],
                 result_calibration=aggregated_data["result_calibration"],
                 x_decomp_agg=pareto_data.x_decomp_agg,

python/src/robyn/reporting/onepager_reporting.py

@@ -10,7 +10,7 @@
 
 from robyn.modeling.entities.pareto_result import ParetoResult
 from robyn.modeling.entities.clustering_results import ClusteredResult
-from robyn.data.entities.hyperparameters import AdstockType
+from robyn.data.entities.hyperparameters import Hyperparameters
 from robyn.data.entities.mmmdata import MMMData
 from robyn.data.entities.enums import PlotType
 
@@ -27,13 +27,13 @@ def __init__(
         self,
         pareto_result: ParetoResult,
         clustered_result: Optional[ClusteredResult] = None,
-        adstock: Optional[AdstockType] = None,
+        hyperparameter: Optional[Hyperparameters] = None,
         mmm_data: Optional[MMMData] = None,
         holidays_data: Optional[HolidaysData] = None,
     ):
         self.pareto_result = pareto_result
         self.clustered_result = clustered_result
-        self.adstock = adstock
+        self.hyperparameter = hyperparameter
         self.mmm_data = mmm_data
         self.holidays_data = holidays_data
 
@@ -270,9 +270,12 @@ def _generate_solution_plots(
             # Initialize visualizers
             pareto_viz = (
                 ParetoVisualizer(
-                    self.pareto_result, self.adstock, self.mmm_data, self.holidays_data
+                    self.pareto_result,
+                    self.mmm_data,
+                    self.holidays_data,
+                    self.hyperparameter,
                 )
-                if self.adstock and self.holidays_data
+                if self.hyperparameter and self.holidays_data
                 else None
             )
             cluster_viz = (

python/src/robyn/robyn.py

@@ -4,9 +4,7 @@
 from logging.handlers import RotatingFileHandler
 from pathlib import Path
 from typing import Dict, Optional, List
-import numpy as np
-from robyn.modeling.entities.clustering_results import ClusteredResult
-from robyn.data.entities.enums import AdstockType, PlotType
+import copy
 from robyn.data.entities.mmmdata import MMMData
 from robyn.data.entities.holidays_data import HolidaysData
 from robyn.data.entities.hyperparameters import Hyperparameters
@@ -196,7 +194,6 @@ def train_models(
         try:
             logger.info("Training models")
             trials_config = trials_config or TrialsConfig(trials=5, iterations=2000)
-
             model_executor = ModelExecutor(
                 mmmdata=self.mmm_data,
                 holidays_data=self.holidays_data,
@@ -261,6 +258,7 @@ def evaluate_models(
                 holidays_data=self.holidays_data,
             )
             self.pareto_result = pareto_optimizer.optimize(**pareto_config)
+            unfiltered_pareto_result = copy.deepcopy(self.pareto_result)
 
             # Optional clustering
             is_clustered = False
@@ -274,10 +272,13 @@ def evaluate_models(
             )
             if display_plots or export_plots:
                 pareto_visualizer = ParetoVisualizer(
-                    self.pareto_result,
-                    self.hyperparameters.adstock,
-                    self.mmm_data,
-                    self.holidays_data,
+                    pareto_result=self.pareto_result,
+                    mmm_data=self.mmm_data,
+                    holiday_data=self.holidays_data,
+                    hyperparameter=self.hyperparameters,
+                    featurized_mmm_data=self.featurized_mmm_data,
+                    unfiltered_pareto_result=unfiltered_pareto_result,
+                    model_outputs=self.model_outputs,
                 )
                 pareto_visualizer.plot_all(display_plots, self.working_dir)
                 if self.cluster_result:
@@ -412,7 +413,7 @@ def generate_one_pager(self, solution_id: Optional[str] = None) -> None:
             onepager = OnePager(
                 pareto_result=self.pareto_result,
                 clustered_result=self.cluster_result,
-                adstock=self.hyperparameters.adstock,
+                hyperparameter=self.hyperparameters,
                 mmm_data=self.mmm_data,
                 holidays_data=self.holidays_data,
             )

python/src/robyn/tutorials/e2e_test/tutorial3_modeling_compare.ipynb

@@ -144,7 +144,7 @@
    "outputs": [],
    "source": [
     "hyperparameters = Hyperparameters(\n",
-    "    {\n",
+    "    hyperparameters={\n",
     "        \"facebook_S\": ChannelHyperparameters(\n",
     "            alphas=[0.5, 3],\n",
     "            gammas=[0.3, 1],\n",

python/src/robyn/tutorials/e2e_test/tutorial4_pareto_e2e_test.ipynb

@@ -291,17 +291,32 @@
    "source": [
     "from robyn.data.entities.enums import AdstockType\n",
     "from robyn.reporting.onepager_reporting import OnePager\n",
+    "from robyn.visualization.pareto_visualizer import ParetoVisualizer\n",
     "\n",
-    "visualizer = OnePager(\n",
-    "    pareto_result=filtered_pareto_results,\n",
-    "    clustered_result=cluster_results,\n",
-    "    adstock=AdstockType.GEOMETRIC,\n",
-    "    mmm_data=mmm_data,\n",
-    "    holidays_data=holidays_data,\n",
-    ")\n",
+    "\n",
+    "visualizer = OnePager(pareto_result=filtered_pareto_results, clustered_result=cluster_results, hyperparameter=hyperparameters, mmm_data=mmm_data, holidays_data=holidays_data)\n",
     "visualizer.generate_one_pager(top_pareto=True)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "visualizer = ParetoVisualizer(\n",
+    "    pareto_result=filtered_pareto_results, \n",
+    "    hyperparameter=hyperparameters, \n",
+    "    mmm_data=mmm_data, \n",
+    "    holiday_data=holidays_data,\n",
+    "    featurized_mmm_data=featurized_mmm_data,\n",
+    "    unfiltered_pareto_result=pareto_result,\n",
+    "    model_outputs=output_models)\n",
+    "\n",
+    "visualizer.plot_all(True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

python/src/robyn/tutorials/tutorial1.ipynb

@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a1cce14",
+   "id": "eefbc5da",
    "metadata": {},
    "source": [
     "## 2.2. Initialize Robyn\n",
@@ -126,7 +126,7 @@
     "\n",
     "# Create Hyperparameters\n",
     "hyperparameters = Hyperparameters(\n",
-    "    {\n",
+    "    hyperparameters={\n",
     "        \"facebook_S\": ChannelHyperparameters(\n",
     "            alphas=[0.5, 3],\n",
     "            gammas=[0.3, 1],\n",
@@ -258,10 +258,8 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "\n",
     "from robyn.modeling.clustering.clustering_config import ClusterBy, ClusteringConfig\n",
     "\n",
-    "\n",
     "configs = ClusteringConfig(\n",
     "    dep_var_type= DependentVarType(mmm_data.mmmdata_spec.dep_var_type),\n",
     "    cluster_by = ClusterBy.HYPERPARAMETERS,\n",
@@ -388,7 +386,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

python/src/robyn/tutorials/tutorial1_src.ipynb

@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,6 +333,25 @@
     "print(cluster_results)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reestablish Pareto Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from robyn.modeling.pareto.pareto_utils import ParetoUtils\n",
+    "\n",
+    "utils = ParetoUtils()\n",
+    "pareto_result = utils.process_pareto_clustered_results(pareto_result, clustered_result=cluster_results, ran_cluster=True, ran_calibration= False)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -495,7 +514,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

python/src/robyn/tutorials/tutorial2_feature_engineering.ipynb

@@ -313,7 +313,7 @@
    ],
    "source": [
     "hyperparameters = Hyperparameters(\n",
-    "    {\n",
+    "    hyperparameters={\n",
     "        \"facebook_S\": ChannelHyperparameters(\n",
     "            alphas=[0.5, 3],\n",
     "            gammas=[0.3, 1],\n",

python/src/robyn/tutorials/tutorial3_modeling.ipynb

@@ -301,7 +301,7 @@
    ],
    "source": [
     "hyperparameters = Hyperparameters(\n",
-    "    {\n",
+    "    hyperparameters={\n",
     "        \"facebook_S\": ChannelHyperparameters(\n",
     "            alphas=[0.5, 3],\n",
     "            gammas=[0.3, 1],\n",

python/src/robyn/tutorials/tutorial5_calibration.ipynb

@@ -403,7 +403,7 @@
    ],
    "source": [
     "hyperparameters = Hyperparameters(\n",
-    "    {\n",
+    "    hyperparameters={\n",
     "        \"facebook_S\": ChannelHyperparameters(\n",
     "            alphas=[0.5, 3],\n",
     "            gammas=[0.3, 1],\n",

python/src/robyn/tutorials/tutorial7_clustering.ipynb

@@ -137,7 +137,7 @@
     "from robyn.modeling.pareto.pareto_utils import ParetoUtils\n",
     "\n",
     "utils = ParetoUtils()\n",
-    "new_pareto_results = utils.process_pareto_clustered_results(pareto_result, clustered_result=cluster_results, ran_cluster=False, ran_calibration= False)"
+    "new_pareto_results = utils.process_pareto_clustered_results(pareto_result, clustered_result=cluster_results, ran_cluster=True, ran_calibration= False)"
    ]
   },
   {
@@ -147,9 +147,10 @@
    "outputs": [],
    "source": [
     "from robyn.data.entities.enums import AdstockType   \n",
+    "from robyn.data.entities.hyperparameters import Hyperparameters\n",
     "from robyn.reporting.onepager_reporting import OnePager\n",
     "\n",
-    "visualizer = OnePager(pareto_result=new_pareto_results, clustered_result=cluster_results, adstock=AdstockType.GEOMETRIC, mmm_data=mmm_data, holidays_data=holidays_data)\n",
+    "visualizer = OnePager(pareto_result=new_pareto_results, clustered_result=cluster_results, hyperparameter=Hyperparameters(adstock=AdstockType.GEOMETRIC), mmm_data=mmm_data, holidays_data=holidays_data)\n",
     "visualizer.generate_one_pager(top_pareto=True)"
    ]
   }