IOHprofiler · jacobdenobel · Jan 6, 2026 · Jun 5, 2025 · Jun 5, 2025 · Jun 18, 2025
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -30,9 +30,9 @@ jobs:
         coverage run -m --source=iohinspector unittest discover
         coverage report -m 
     - name: Upload coverage report
-      if: ${{ (matrix.python-version == 3.12) && (matrix.os == 'ubuntu-20.04') }}
+      if: ${{ (matrix.python-version == 3.12) && (matrix.os == 'ubuntu-22.04') }}
       env:
         CODACY_PROJECT_TOKEN: ${{ secrets.CODACY_PROJECT_TOKEN }}
       run: |
           coverage xml -o cobertura.xml
-          bash <(curl -Ls https://coverage.codacy.com/get.sh) report
+          bash <(curl -Ls https://coverage.codacy.com/get.sh) report
diff --git a/.gitignore b/.gitignore
@@ -3,6 +3,7 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+
 # C extensions
 *.so
 
@@ -161,5 +162,5 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
-data
-aux/*
+data/
+aux/
diff --git a/FUNCTION_REFERENCE.md b/FUNCTION_REFERENCE.md
diff --git a/example_plots/attractor_network.png b/example_plots/attractor_network.png
diff --git a/example_plots/eaf_differences.png b/example_plots/eaf_differences.png
diff --git a/example_plots/eaf_diffs.png b/example_plots/eaf_diffs.png
diff --git a/example_plots/eaf_pareto.png b/example_plots/eaf_pareto.png
diff --git a/example_plots/eaf_single_objective.png b/example_plots/eaf_single_objective.png
diff --git a/example_plots/ecdf.png b/example_plots/ecdf.png
diff --git a/example_plots/ecdf_comparison.png b/example_plots/ecdf_comparison.png
diff --git a/example_plots/fixed_budget.png b/example_plots/fixed_budget.png
diff --git a/example_plots/fixed_budget_convergence.png b/example_plots/fixed_budget_convergence.png
diff --git a/example_plots/fixed_target.png b/example_plots/fixed_target.png
diff --git a/example_plots/fixed_target_ert.png b/example_plots/fixed_target_ert.png
diff --git a/example_plots/heatmap_single_run.png b/example_plots/heatmap_single_run.png
diff --git a/example_plots/indicator_over_time.png b/example_plots/indicator_over_time.png
diff --git a/example_plots/pareto_fronts.png b/example_plots/pareto_fronts.png
diff --git a/example_plots/robustrank_changes.png b/example_plots/robustrank_changes.png
diff --git a/example_plots/robustrank_over_time.png b/example_plots/robustrank_over_time.png
diff --git a/example_plots/tournament_rankings.png b/example_plots/tournament_rankings.png
diff --git a/examples/MO_Examples.ipynb b/examples/MO_Examples.ipynb
diff --git a/examples/SO_Examples.ipynb b/examples/SO_Examples.ipynb
diff --git a/image.png b/image.png
diff --git a/pyproject.toml b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "iohinspector"
-version = "0.0.5"
+version = "0.1.0"
 authors = [
   { name="Diederick Vermetten", email="d.vermetten@gmail.com" },
   { name="Jacob de Nobel", email="jacobdenobel@gmail.com" },
@@ -26,7 +26,8 @@ dependencies = [
   "pyarrow", 
   "robustranking", 
   "seaborn",
-  "skelo"
+  "skelo",
+  "networkx"
 ]
 
 [project.optional-dependencies]

diff --git a/src/iohinspector/__init__.py b/src/iohinspector/__init__.py
@@ -1,6 +1,6 @@
 from .align import *
 from .data import *
 from .manager import *
-from .metrics import *
 from .indicators import *
-from .plot import *
+from .metrics import *
+from .plots import *
diff --git a/src/iohinspector/indicators/__init__.py b/src/iohinspector/indicators/__init__.py
@@ -8,7 +8,7 @@
 from .final import *
 
 def add_indicator(
-    df: pl.DataFrame, indicator: Callable, objective_columns: Iterable, **kwargs
+    df: pl.DataFrame, indicator: Callable, obj_vars: Iterable, **kwargs
 ) -> pl.DataFrame:
     """Adds an indicator to a Polars DataFrame.
 
@@ -39,6 +39,6 @@ def add_indicator(
             group of data.
     """
     indicator_callable = partial(
-        indicator, objective_columns=objective_columns, **kwargs
+        indicator, obj_vars=obj_vars, **kwargs
     )
     return df.group_by("data_id").map_groups(indicator_callable)
diff --git a/src/iohinspector/indicators/anytime.py b/src/iohinspector/indicators/anytime.py
@@ -51,8 +51,8 @@ def _r2(weight_vec_set, ideal_point, point_set):
 
 
 class NonDominated:
-    def __call__(self, group: pl.DataFrame, objective_columns: Iterable):
-        objectives = np.array(group[objective_columns])
+    def __call__(self, group: pl.DataFrame, obj_vars: Iterable):
+        objectives = np.array(group[obj_vars])
         is_efficient = np.ones(objectives.shape[0], dtype=bool)
         for i, c in enumerate(objectives[1:]):
             if is_efficient[i + 1]:
@@ -82,20 +82,20 @@ def minimize(self):
         return False
 
     def __call__(
-        self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int]
+        self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int]
     ) -> pl.DataFrame:
         """
         Args:
             group (pl.DataFrame): The DataFrame on which the indicator will be added (should be 1 optimization run only)
-            objective_columns (Iterable): Which columns are the objectives
+            obj_vars (Iterable): Which columns are the objectives
             evals (Iterable[int]): At which evaluations the operation should be performed.
             Note that using more evaluations will make the code slower.
 
         Returns:
             pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+
         """
         obj_vals = np.clip(
-            np.array(group[objective_columns]), None, self.reference_point
+            np.array(group[obj_vars]), None, self.reference_point
         )
         evals_dt = group["evaluations"]
         hvs = [
@@ -111,7 +111,7 @@ def __call__(
             )
             .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward")
             .fill_null(np.inf)
-            .drop(objective_columns)
+            .drop(obj_vars)
         )
 
 
@@ -140,20 +140,20 @@ def minimize(self):
         return True
 
     def __call__(
-        self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int]
+        self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int]
     ) -> pl.DataFrame:
         """
         Args:
             group (pl.DataFrame): The DataFrame on which the indicator will be added (should be 1 optimization run only)
-            objective_columns (Iterable): Which columns are the objectives
+            obj_vars (Iterable): Which columns are the objectives
             evals (Iterable[int]): At which evaluations the operation should be performed.
             Note that using more evaluations will make the code slower.
 
         Returns:
             pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+
         """
         obj_vals = np.clip(
-            np.array(group[objective_columns]), None, self.reference_point
+            np.array(group[obj_vars]), None, self.reference_point
         )
         evals_dt = group["evaluations"]
         hvs = [
@@ -172,7 +172,7 @@ def __call__(
             )
             .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward")
             .fill_null(np.inf)
-            .drop(objective_columns)
+            .drop(obj_vars)
         )
 
 
@@ -195,7 +195,7 @@ def var_name(self):
         return "IGD+"
 
     def __call__(
-        self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int]
+        self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int]
     ) -> pl.DataFrame:
         """
 
@@ -208,7 +208,7 @@ def __call__(
         Returns:
             pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+
         """
-        obj_vals = np.array(group[objective_columns])
+        obj_vals = np.array(group[obj_vars])
         evals_dt = group["evaluations"]
         igds = [
             igd_plus(
@@ -226,7 +226,7 @@ def __call__(
             )
             .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward")
             .fill_null(np.inf)
-            .drop(objective_columns)
+            .drop(obj_vars)
         )
 
 try:
@@ -251,7 +251,7 @@ def minimize(self):
             return True
 
         def __call__(
-            self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int]
+            self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int]
         ) -> pl.DataFrame:
             """
 
@@ -264,7 +264,7 @@ def __call__(
             Returns:
                 pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+
             """
-            obj_vals = np.array(group[objective_columns])
+            obj_vals = np.array(group[obj_vars])
             evals_dt = group["evaluations"]
             igds = [
                 _r2(
@@ -283,7 +283,7 @@ def __call__(
                 )
                 .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward")
                 .fill_null(np.inf)
-                .drop(objective_columns)
+                .drop(obj_vars)
             )
 
 except ImportError:

diff --git a/src/iohinspector/indicators/final.py b/src/iohinspector/indicators/final.py
@@ -6,8 +6,8 @@
 
 
 class NonDominated:
-    def __call__(self, group: pl.DataFrame, objective_columns: Iterable):
-        objectives = np.array(group[objective_columns])
+    def __call__(self, group: pl.DataFrame, obj_vars: Iterable):
+        objectives = np.array(group[obj_vars])
         return group.with_columns(
             pl.Series(name="final_nondominated", values=is_nondominated(objectives))
         )