Merge pull request #330 from elfi-dev/dev

Release v.0.7.6

CHANGELOG.rst

@@ -1,6 +1,11 @@
 Changelog
 =========
 
+0.7.6 (2020-08-29)
+------------------
+- Fix incompatibility with scipy>1.5 in bo.utils.stochastic_optimization
+- Minor improvements to documentation
+
 0.7.5 (2019-12-18)
 ------------------
 - Improved the appearance of figures produced by `plot_gp` and added the option

CONTRIBUTING.rst

@@ -96,7 +96,7 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
     $ make lint
     $ make test
 
-   You may run `make test-notslow` instead of `make test` *as long as your proposed changes are unrelated to BOLFI*.
+   You may run ``make test-notslow`` instead of ``make test`` *as long as your proposed changes are unrelated to BOLFI*.
 
    Also make sure that the docstrings of your code are formatted properly::
 

README.md

@@ -1,4 +1,4 @@
-**Version 0.7.5 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.7.6 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
 
 **NOTE:** For the time being NetworkX 2 is incompatible with ELFI.
 

elfi/__init__.py

@@ -26,4 +26,4 @@
 __email__ = 'elfi-support@hiit.fi'
 
 # make sure __version_ is on the last non-empty line (read by setup.py)
-__version__ = '0.7.5'
+__version__ = '0.7.6'

elfi/methods/bo/acquisition.py

@@ -511,9 +511,11 @@ def _evaluate_logpdf(theta):
                 return -np.inf
             return np.log(val_pdf)
 
+        batch_theta = np.zeros(shape=len(gp.bounds))
+
         # Obtaining the RandMaxVar acquisition.
         for i in range(self._limit_faulty_init + 1):
-            if i > self._limit_faulty_init:
+            if i == self._limit_faulty_init:
                 raise SystemExit("Unable to find a suitable initial point.")
 
             # Proposing the initial point.

elfi/methods/bo/utils.py

@@ -28,8 +28,12 @@ def stochastic_optimization(fun, bounds, maxiter=1000, polish=True, seed=0):
     tuple of the found coordinates of minimum and the corresponding value.
 
     """
+    def fun_1d(x):
+        return fun(x).ravel()
+
     result = differential_evolution(
-        func=fun, bounds=bounds, maxiter=maxiter, polish=polish, init='latinhypercube', seed=seed)
+        func=fun_1d, bounds=bounds, maxiter=maxiter,
+        polish=polish, init='latinhypercube', seed=seed)
     return result.x, result.fun
 
 

elfi/methods/parameter_inference.py

@@ -463,7 +463,7 @@ def set_objective(self, n_samples, threshold=None, quantile=None, n_sim=None):
             In between (0,1). Define the threshold as the p-quantile of all the
             simulations. n_sim = n_samples/quantile.
         n_sim : int
-            Total number of simulations. The threshold will be the n_samples smallest
+            Total number of simulations. The threshold will be the n_samples-th smallest
             discrepancy among n_sim simulations.
 
         """