Predict using model fitted on historical period for mw model

ghislainv · Jun 13, 2024 · fd7de21 · fd7de21
1 parent 9328fa8
commit fd7de21
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Showing 2 changed files with 6 additions and 4 deletions.
diff --git a/riskmapjnr/benchmark/defrate_per_class.py b/riskmapjnr/benchmark/defrate_per_class.py
@@ -115,10 +115,12 @@ class is created (see ``tab_file_defrate``).
             data_for = defor_cat_data[fcc_data > 0]
             data_defor = defor_cat_data[np.isin(fcc_data, [1, 2])]
         # nfor_per_cat
-        cat_for = pd.Categorical(data_for.flatten(), categories=cat)
+        cat_for = pd.Categorical(data_for.flatten(),
+                                 categories=cat)
         df["nfor"] += cat_for.value_counts().values
         # ndefor_per_cat
-        cat_defor = pd.Categorical(data_defor.flatten(), categories=cat)
+        cat_defor = pd.Categorical(data_defor.flatten(),
+                                   categories=cat)
         df["ndefor"] += cat_defor.value_counts().values
 
     # Remove classes with no forest

diff --git a/riskmapjnr/defrate_per_cat.py b/riskmapjnr/defrate_per_cat.py
@@ -113,10 +113,10 @@ def defrate_per_cat(fcc_file, riskmap_file, time_interval,
         if period == "calibration":
             data_for = defor_cat_data[fcc_data > 0]
             data_defor = defor_cat_data[fcc_data == 1]
-        elif period in ["validation", "confirmation"]:
+        elif period == "validation":
             data_for = defor_cat_data[fcc_data > 1]
             data_defor = defor_cat_data[fcc_data == 2]
-        elif period == "historical":
+        elif period in ["historical", "forecast"]:
             data_for = defor_cat_data[fcc_data > 0]
             data_defor = defor_cat_data[np.isin(fcc_data, [1, 2])]
         # nfor_per_cat