Bump 1.0.4

PiperOrigin-RevId: 732156639

CHANGELOG.md

@@ -23,6 +23,8 @@ To release a new version (e.g. from `1.0.0` -> `2.0.0`):
 
 ## [Unreleased]
 
+## [1.0.4] - 2025-02-28
+
 * Fix a divide-by-zero error in spend ratio calculation when historical spend is
   zero, preventing a `ValueError` in `output_optimization_summary`.
 * Add `non_media_baseline_values` argument to `MediaSummary` visualizations.
@@ -203,4 +205,5 @@ To release a new version (e.g. from `1.0.0` -> `2.0.0`):
 [1.0.1]: https://github.com/google/meridian/releases/tag/v1.0.1
 [1.0.2]: https://github.com/google/meridian/releases/tag/v1.0.2
 [1.0.3]: https://github.com/google/meridian/releases/tag/v1.0.3
-[Unreleased]: https://github.com/google/meridian/compare/v1.0.3...HEAD
+[1.0.4]: https://github.com/google/meridian/releases/tag/v1.0.4
+[Unreleased]: https://github.com/google/meridian/compare/v1.0.4...HEAD

README.md

@@ -151,7 +151,7 @@ To cite this repository:
   author = {Google Meridian Marketing Mix Modeling Team},
   title = {Meridian: Marketing Mix Modeling},
   url = {https://github.com/google/meridian},
-  version = {1.0.3},
+  version = {1.0.4},
   year = {2025},
 }
 ```

meridian/__init__.py

@@ -14,7 +14,7 @@
 
 """Meridian API."""
 
-__version__ = "1.0.3"
+__version__ = "1.0.4"
 
 
 from meridian import analysis

meridian/analysis/optimizer.py

@@ -929,30 +929,33 @@ def optimize(
         specify either `target_roi` or `target_mroi`.
       budget: Number indicating the total budget for the fixed budget scenario.
         Defaults to the historical budget.
-      pct_of_spend: Numeric list of size `n_total_channels` containing the
+      pct_of_spend: Numeric list of size `n_paid_channels` containing the
         percentage allocation for spend for all media and RF channels. The order
-        must match `InputData.media` with values between 0-1, summing to 1. By
-        default, the historical allocation is used. Budget and allocation are
-        used in conjunction to determine the non-optimized media-level spend,
-        which is used to calculate the non-optimized performance metrics (for
-        example, ROI) and construct the feasible range of media-level spend with
-        the spend constraints. Consider using
-        `InputData.get_paid_channels_argument_builder()` to construct the
-        argument here.
-      spend_constraint_lower: Numeric list of size `n_total_channels` or float
+        must match `(InputData.media + InputData.reach)` with values between
+        0-1, summing to 1. By default, the historical allocation is used. Budget
+        and allocation are used in conjunction to determine the non-optimized
+        media-level spend, which is used to calculate the non-optimized
+        performance metrics (for example, ROI) and construct the feasible range
+        of media-level spend with the spend constraints. Consider using
+        `InputData.get_paid_channels_argument_builder()` to construct this
+        argument.
+      spend_constraint_lower: Numeric list of size `n_paid_channels` or float
         (same constraint for all channels) indicating the lower bound of
-        media-level spend. The lower bound of media-level spend is `(1 -
-        spend_constraint_lower) * budget * allocation)`. The value must be
-        between 0-1. Defaults to `0.3` for fixed budget and `1` for flexible.
-        Consider using `InputData.get_paid_channels_argument_builder()` to
-        construct the argument here.
-      spend_constraint_upper: Numeric list of size `n_total_channels` or float
+        media-level spend. If given as a channel-indexed array, the order must
+        match `(InputData.media + InputData.reach)`. The lower bound of
+        media-level spend is `(1 - spend_constraint_lower) * budget *
+        allocation)`. The value must be between 0-1. Defaults to `0.3` for fixed
+        budget and `1` for flexible. Consider using
+        `InputData.get_paid_channels_argument_builder()` to construct this
+        argument.
+      spend_constraint_upper: Numeric list of size `n_paid_channels` or float
         (same constraint for all channels) indicating the upper bound of
-        media-level spend. The upper bound of media-level spend is `(1 +
-        spend_constraint_upper) * budget * allocation)`. Defaults to `0.3` for
-        fixed budget and `1` for flexible. Consider using
-        `InputData.get_paid_channels_argument_builder()` to construct the
-        argument here.
+        media-level spend. If given as a channel-indexed array, the order must
+        match `(InputData.media + InputData.reach)`. The upper bound of
+        media-level spend is `(1 + spend_constraint_upper) * budget *
+        allocation)`. Defaults to `0.3` for fixed budget and `1` for flexible.
+        Consider using `InputData.get_paid_channels_argument_builder()` to
+        construct this argument.
       target_roi: Float indicating the target ROI constraint. Only used for
         flexible budget scenarios. The budget is constrained to when the ROI of
         the total spend hits `target_roi`.

meridian/analysis/optimizer_test.py

@@ -1867,8 +1867,20 @@ def test_budget_data_with_specified_pct_of_spend(
     )
     expected_pct_of_spend = [0.1, 0.2, 0.3, 0.3, 0.1]
 
+    idata = self.budget_optimizer_media_and_rf._meridian.input_data
+    paid_channels = list(
+        idata.get_all_paid_channels()
+    )
+    pct_of_spend = idata.get_paid_channels_argument_builder()(**{
+        paid_channels[0]: 0.1,
+        paid_channels[1]: 0.2,
+        paid_channels[2]: 0.3,
+        paid_channels[3]: 0.3,
+        paid_channels[4]: 0.1,
+    })
+
     optimization_results = self.budget_optimizer_media_and_rf.optimize(
-        pct_of_spend=expected_pct_of_spend, fixed_budget=True
+        pct_of_spend=pct_of_spend, fixed_budget=True
     )
 
     actual_spend = optimization_results.nonoptimized_data.spend