add SA tutorial, minor dixes

.gitignore

@@ -106,6 +106,7 @@ celerybeat.pid
 # Environments
 .env
 .venv
+.venv*
 env/
 venv/
 ENV/

cases/synthetic/circle/single_objective.py

@@ -16,20 +16,19 @@ def __init__(self, domain: Domain, estimator: Estimator = None) -> None:
 
     def _evaluate(self, ind: Structure) -> float:
 
-        num = 3
         num_polys = len(ind.polygons)
         loss = 0
         for poly in ind.polygons:
             area = self.domain.geometry.get_square(poly)
             length = self.domain.geometry.get_length(poly)
             if area == 0:
-                ratio = None
+                ratio = float('inf')
             else:
                 ratio = 1 - 4 * np.pi * area / length ** 2
 
             loss += ratio
-
-        loss = loss + 20 * abs(num_polys - num)
+        if num_polys > 1:
+            loss += 20 * abs(num_polys - 1)
         return loss
 
 
@@ -61,7 +60,7 @@ def _evaluate(self, ind: Structure) -> float:
 
 tuner_cfg = TunerParams(
     tuner_type='optuna',
-    n_steps_tune=10,
+    n_steps_tune=25,
     hyperopt_dist='uniform',
     verbose=True,
     timeout_minutes=60,
@@ -101,6 +100,7 @@ def _evaluate(self, ind: Structure) -> float:
         'not_too_close_points',
     ],
     extra=5,
+    estimation_n_jobs=-1,
     n_jobs=-1,
     log_dir='logs',
     run_name='run_name',

docs/source/tutorials/index.rst

@@ -7,3 +7,4 @@ Tutorials
 
    optimisation
    tuning
+   sa

docs/source/tutorials/sa.rst

@@ -0,0 +1,39 @@
+Sensitivity analysis
+====================
+
+SA
+--
+
+Use SA to run local search near an optimized structure.
+
+
+.. code-block:: python
+
+   from gefest.core.configs.optimization_params import OptimizationParams
+   from gefest.core.geometry.datastructs.structure import Structure
+   from gefest.tools.tuners.sa import SensitivityAnalysis, report_viz
+   from matplotlib import pyplot as plt
+
+   structure: list[Structure] | Structure = ...
+   opt_params: OptimizationParams = ...
+
+   sa = SensitivityAnalysis([optimized_struct], opt_params)
+   res = sa.analysis()
+
+   # plot analysis history
+   report_viz(res)
+
+
+   # plot initial and best structure
+   sv = StructVizualizer()
+   sv.plot_structure(res[1][0])
+   sv.plot_structure(res[1][1])
+
+   plt.show(block=True)
+
+   # animated history of structures during SA
+   gm = GIFMaker()
+   for st in tqdm(res[1]):
+       gm.create_frame(st, {'sa': st.fitness})
+
+   gm.make_gif('sa individuals', 500)