patch values stacked plot (pymc-labs#1181)

* clip values

* tested soluton

* return figure

pymc_marketing/mmm/base.py

@@ -13,6 +13,7 @@
 #   limitations under the License.
 """Base class for Marketing Mix Models (MMM)."""
 
+import warnings
 from collections.abc import Callable
 from inspect import (
     getattr_static,
@@ -1126,7 +1127,17 @@ def plot_grouped_contribution_breakdown_over_time(
         area_params = dict(stacked=True, ax=ax)
         if area_kwargs is not None:
             area_params.update(area_kwargs)
-        all_contributions_over_time.plot.area(**area_params)
+        try:
+            all_contributions_over_time.plot.area(**area_params)
+        except ValueError:
+            warnings.warn(
+                """
+                Each contribution value must be either all positive or all negative.
+                Try deselecting variables with negative contributions.
+                """,
+                stacklevel=2,
+            )
+            return fig
         ax.legend(title="groups", loc="center left", bbox_to_anchor=(1, 0.5))
         return fig
 

tests/mmm/test_plotting.py

@@ -31,8 +31,9 @@
 rng: np.random.Generator = np.random.default_rng(seed=seed)
 
 
-@pytest.fixture(scope="module")
-def toy_X() -> pd.DataFrame:
+@pytest.fixture(scope="module", params=[0, 42], ids=["seed_0", "seed_42"])
+def toy_X(request) -> pd.DataFrame:
+    local_rng: np.random.Generator = np.random.default_rng(seed=request.param)
     date_data: pd.DatetimeIndex = pd.date_range(
         start="2019-06-01", end="2021-12-31", freq="W-MON"
     )
@@ -42,12 +43,12 @@ def toy_X() -> pd.DataFrame:
     return pd.DataFrame(
         data={
             "date": date_data,
-            "channel_1": rng.integers(low=0, high=400, size=n),
-            "channel_2": rng.integers(low=0, high=50, size=n),
-            "control_1": rng.gamma(shape=1000, scale=500, size=n),
-            "control_2": rng.gamma(shape=100, scale=5, size=n),
-            "other_column_1": rng.integers(low=0, high=100, size=n),
-            "other_column_2": rng.normal(loc=0, scale=1, size=n),
+            "channel_1": local_rng.integers(low=0, high=400, size=n),
+            "channel_2": local_rng.integers(low=0, high=50, size=n),
+            "control_1": local_rng.gamma(shape=1000, scale=500, size=n),
+            "control_2": local_rng.normal(loc=0, scale=2, size=n),
+            "other_column_1": local_rng.integers(low=0, high=100, size=n),
+            "other_column_2": local_rng.normal(loc=0, scale=1, size=n),
         }
     )