Improved directed search tutorial (#194)

Building on #193, this substantially expands the directed search tutorial with improved discussion on convergence, seed analysis, and robust optimization. In addition, various links to relevant literature have been added.
quaquel · Oct 25, 2022 · 76dcc45 · 76dcc45
1 parent 54ba5e6
commit 76dcc45
Show file tree

Hide file tree

Showing 10 changed files with 456 additions and 163 deletions.
diff --git a/docs/source/indepth_tutorial/archives/0.tar.gz b/docs/source/indepth_tutorial/archives/0.tar.gz
diff --git a/docs/source/indepth_tutorial/archives/1.tar.gz b/docs/source/indepth_tutorial/archives/1.tar.gz
diff --git a/docs/source/indepth_tutorial/archives/2.tar.gz b/docs/source/indepth_tutorial/archives/2.tar.gz
diff --git a/docs/source/indepth_tutorial/archives/3.tar.gz b/docs/source/indepth_tutorial/archives/3.tar.gz
diff --git a/docs/source/indepth_tutorial/archives/4.tar.gz b/docs/source/indepth_tutorial/archives/4.tar.gz
diff --git a/docs/source/indepth_tutorial/archives/archives.tar.gz b/docs/source/indepth_tutorial/archives/archives.tar.gz
diff --git a/docs/source/indepth_tutorial/directed-search.ipynb b/docs/source/indepth_tutorial/directed-search.ipynb
diff --git a/ema_workbench/__init__.py b/ema_workbench/__init__.py
@@ -10,17 +10,23 @@
     optimize,
     ScalarOutcome,
     TimeSeriesOutcome,
+    ArrayOutcome,
+    Constraint,
     Constant,
     Scenario,
     Policy,
     MultiprocessingEvaluator,
     IpyparallelEvaluator,
     SequentialEvaluator,
     ReplicatorModel,
-    Constraint,
-    ArrayOutcome,
     Samplers,
     OutputSpaceExploration,
+    HypervolumeMetric,
+    GenerationalDistanceMetric,
+    EpsilonIndicatorMetric,
+    InvertedGenerationalDistanceMetric,
+    SpacingMetric,
+    epsilon_nondominated,
 )
 from .util import save_results, load_results, ema_logging, EMAError, process_replications
 

diff --git a/ema_workbench/examples/data/archives.tar.gz b/ema_workbench/examples/data/archives.tar.gz
diff --git a/ema_workbench/examples/lake_model_intertemporal.py b/ema_workbench/examples/lake_model_intertemporal.py
@@ -297,7 +297,7 @@ def lake_problem(
         ScalarOutcome("reliability", kind=ScalarOutcome.MAXIMIZE, expected_range=(0, 1)),
     ]
 
-    convergence_metrics = [HyperVolume.from_outcomes(lake_model.outcomes), EpsilonProgress()]
+    convergence_metrics = [EpsilonProgress()]
 
     constraints = [
         Constraint("max pollution", outcome_names="max_P", function=lambda x: max(0, x - 5))