Implement task selection (#383)

* commented out legacy numerical solver

* added comments and task_scheduling for selecting which task to serve to users

* removed standalone task weighting

* pre-commit hook rerun

Co-authored-by: Alexander Mattick <alex.mattick@fau.de>

scripts/postprocessing/infogain_selector.py

@@ -1,9 +1,11 @@
 import numpy as np
-from scipy import log2
-from scipy.integrate import nquad
 from scipy.special import gammaln, psi
 from scipy.stats import dirichlet
 
+'''
+Legacy numerical solution.
+Should not be used as it is probably broken
+
 
 def make_range(*x):
     """
@@ -38,6 +40,23 @@ def naive_monte_carlo_integral(fun, dim, samples=10_000_000):
     res = fun(pos)
     return np.mean(res)
 
+def infogain(a_post, a_prior):
+    raise (
+        """For the love of good don't use this:
+    it's insanely poorly conditioned, the worst numerical code I have ever written
+    and it's slow as molasses. Use the analytic solution instead.
+
+    Maybe remove
+    """
+    )
+    args = len(a_prior)
+    p = dirichlet(a_post).pdf
+    q = dirichlet(a_prior).pdf
+    (info, _) = nquad(relative_entropy(p, q), [make_range for _ in range(args - 1)], opts={"epsabs": 1e-8})
+    # info = naive_monte_carlo_integral(relative_entropy(p,q), len(a_post))
+    return info
+'''
+
 
 def analytic_solution(a_post, a_prior):
     """
@@ -57,26 +76,8 @@ def analytic_solution(a_post, a_prior):
     return info
 
 
-def infogain(a_post, a_prior):
-    raise (
-        """For the love of good don't use this:
-    it's insanely poorly conditioned, the worst numerical code I have ever written
-    and it's slow as molasses. Use the analytic solution instead.
-
-    Maybe remove
-    """
-    )
-    args = len(a_prior)
-    p = dirichlet(a_post).pdf
-    q = dirichlet(a_prior).pdf
-    (info, _) = nquad(relative_entropy(p, q), [make_range for _ in range(args - 1)], opts={"epsabs": 1e-8})
-    # info = naive_monte_carlo_integral(relative_entropy(p,q), len(a_post))
-    return info
-
-
 def uniform_expected_infogain(a_prior):
     mean_weight = dirichlet.mean(a_prior)
-    print("weight", mean_weight)
     results = []
     for i, w in enumerate(mean_weight):
         a_post = a_prior.copy()