.

Author: thornoe

gis/output/coastal_eco_imp.csv

@@ -1,9 +1 @@
 ,Sparse subset,Observed subset,All in VP3
-Small,0.7645461598138091,0.6499756137213462,0.6556765627331046
-Medium,0.18076027928626842,0.292635343846529,0.2827092346710428
-Large,0.01047323506594259,0.021947650788489675,0.022676413546173356
-Soft bottom,0.04422032583397983,0.03544139164363518,0.03893778904967925
-Natural,0.895655546935609,0.9270037392293936,0.9115321497836789
-DK2,0.07796741660201707,0.16387579255405624,0.1654483067283306
-basis,2.6574350234341715,2.6447368421052633,2.647725321888412
-n,2578.0,6151.0,6703.0

gis/output/coastal_eco_imp.pdf

@@ -1,14 +0,0 @@
-\begin{tabular}{llll}
-\toprule
- & Sparse subset & Observed subset & All in VP3 \\
-\midrule
-Small & 0.7645 & 0.6500 & 0.6557 \\
-Medium & 0.1808 & 0.2926 & 0.2827 \\
-Large & 0.0105 & 0.0219 & 0.0227 \\
-Soft bottom & 0.0442 & 0.0354 & 0.0389 \\
-Natural & 0.8957 & 0.9270 & 0.9115 \\
-DK2 & 0.0780 & 0.1639 & 0.1654 \\
-basis & 2.6574 & 2.6447 & 2.6477 \\
-n & 2578 & 6151 & 6703 \\
-\bottomrule
-\end{tabular}

gis/output/coastal_eco_imp_MA.csv

@@ -502,12 +502,12 @@ def process_string(s):
     # Imputed ecological status using a continuous scale
     dfEcoObs = dfIndicator.copy()
 
+# Save CSV of data on mean ecological status by water body and year
+dfEcoObs.to_csv("output\\" + j + "_eco_" + suffix + ".csv")
+
 # Merge observed ecological status each year with basis analysis for VP3
 dfEco = dfEcoObs.merge(dfVP[["basis"]], on="wb")
 
-# Save CSV of data on mean ecological status by water body and year
-dfEco.to_csv("output\\" + j + "_eco_" + suffix + ".csv")
-
 if suffix != "obs":
     # Prepare for statistics and missing values graph
     for t in dfEco.columns:

gis/output/coastal_eco_obs.csv

@@ -845,12 +845,12 @@ def ecological_status(self, j, dfIndicator, dfVP, suffix="obs", index=None):
                 # Imputed ecological status using a continuous scale
                 dfEcoObs = dfIndicator.copy()
 
+            # Save CSV of data on mean ecological status by water body and year
+            dfEcoObs.to_csv("output\\" + j + "_eco_" + suffix + ".csv")
+
             # Merge observed ecological status each year with basis analysis for VP3
             dfEco = dfEcoObs.merge(dfVP[["basis"]], on="wb")
 
-            # Save CSV of data on mean ecological status by water body and year
-            dfEco.to_csv("output\\" + j + "_eco_" + suffix + ".csv")
-
             if suffix != "obs":
                 # Prepare for statistics and missing values graph
                 for t in dfEco.columns:

gis/output/coastal_eco_obs.pdf

@@ -190,14 +190,14 @@ def stepwise_selection(subset, dummies, data, dfDummies, years, select_all=False
         s.loc["Total", "n"] = s["n"].sum()
 
     # Overwrite CSV of accuracy scores and share with less than good ecological status
-    if subset is sparse:
-        scores.to_csv("output/streams_eco_imp_accuracy_sparse.csv")
-        status.to_csv("output/streams_eco_imp_LessThanGood_sparse.csv")
-        scores_all.to_csv("output/streams_eco_imp_accuracy_sparse_all.csv")
-        status_all.to_csv("output/streams_eco_imp_LessThanGood_sparse_all.csv")
-    else:
-        scores.to_csv("output/streams_eco_imp_accuracy.csv")
-        status.to_csv("output/streams_eco_imp_LessThanGood.csv")
+    # if subset is sparse:
+    #     scores.to_csv("output/streams_eco_imp_accuracy_sparse.csv")
+    #     status.to_csv("output/streams_eco_imp_LessThanGood_sparse.csv")
+    #     scores_all.to_csv("output/streams_eco_imp_accuracy_sparse_all.csv")
+    #     status_all.to_csv("output/streams_eco_imp_LessThanGood_sparse_all.csv")
+    # else:
+    #     scores.to_csv("output/streams_eco_imp_accuracy.csv")
+    #     status.to_csv("output/streams_eco_imp_LessThanGood.csv")
 
     return selected, scores, status  #  selected predictors; scores and stats by year
 
@@ -310,26 +310,25 @@ def stepwise_selection(subset, dummies, data, dfDummies, years, select_all=False
 #   2. Multivariate feature imputation (note: Forward Stepwise Selection takes ~5 days)
 ########################################################################################
 # # Example data for testing Forward Stepwise Selection with LOO-CV (takes ~5 seconds)
-# dfEcoObs = pd.DataFrame(
-#     {
-#         1988: [2.5, 3.0, 3.5, 4.0, np.nan, 5.0],
-#         1989: [2.6, 3.1, 3.6, np.nan, 4.6, 5.1],
-#         1990: [2.7, 3.2, np.nan, 4.2, 4.7, 5.2],
-#         1991: [2.8, np.nan, 3.8, 4.3, 4.8, 5.3],
-#         1992: [np.nan, 3.4, 3.9, 4.4, 4.9, 5.4],
-#         1993: [3.0, 3.5, 3.8, 4.4, 5.1, 5.5],
-#     }
-# )
-# dfEcoObs.index.name = "wb"
-# sparse = dfEcoObs[dfEcoObs.notna().sum(axis=1) == 5]
-# dfObs = dfEcoObs.copy()
-# dfTypology = dfObs.copy()
-# dfTypology["Small"] = [0, 0, 1, 1, 0, 0]  #  effect: 0.2 worse in 1993
-# dfNatural = dfTypology.copy()
-# dfNatural["Natural"] = [0, 0, 0, 1, 1, 0]  #  effect: 0.1 better in 1993
-# cols = ["Small", "Natural"]
-# years = list(range(1989, 1993 + 1))
-
+dfEcoObs = pd.DataFrame(
+    {
+        1988: [2.5, 3.0, 3.5, 4.0, np.nan, 5.0],
+        1989: [2.6, 3.1, 3.6, np.nan, 4.6, 5.1],
+        1990: [2.7, 3.2, np.nan, 4.2, 4.7, 5.2],
+        1991: [2.8, np.nan, 3.8, 4.3, 4.8, 5.3],
+        1992: [np.nan, 3.4, 3.9, 4.4, 4.9, 5.4],
+        1993: [3.0, 3.5, 3.8, 4.4, 5.1, 5.5],
+    }
+)
+dfEcoObs.index.name = "wb"
+sparse = dfEcoObs[dfEcoObs.notna().sum(axis=1) == 5]
+dfObs = dfEcoObs.copy()
+dfTypology = dfObs.copy()
+dfTypology["Small"] = [0, 0, 1, 1, 0, 0]  #  effect: 0.2 worse in 1993
+dfNatural = dfTypology.copy()
+dfNatural["Natural"] = [0, 0, 0, 1, 1, 0]  #  effect: 0.1 better in 1993
+cols = ["Small", "Natural"]
+years = list(range(1989, 1993 + 1))
 
 # Forward stepwise selection of dummies - CV over subset of sparsely observed streams
 kwargs = {"data": dfObs, "dfDummies": dfDistrict, "years": years}  #  shared arguments