Simple implementation for vector parameters #191

CHANGELOG.md

@@ -1,6 +1,11 @@
-## 3.x
+## 3.5.1
+
+### General
+
+- Use of vector parameters now documented (PR #191; inspired by @lukashergt, thanks!)
 
 ### Cosmology
+
 - Added DESI 1yr BAO data and SN from Pantheon Plus, DESY5 and Union3 (thanks DESI team, @adematti, @rubind, @WillMatt4 and @rodri981)
 
 ## 3.5 – 2024-02-16
@@ -145,15 +150,6 @@
 
 - Documented uses of `Model` class in general contexts (previously only cosmo)
 - `Model` methods to compute log-probabilities and derived parameters now have an `as_dict` keyword (default `False`), for more informative return value.
-- Added `--minimize` flag to `cobaya-run` for quick minimization (replaces sampler, uses previous output).
-- Add `COBAYA_USE_FILE_LOCKING` environment variable to allow disabling of file locks. Warning not to use `--test` with MPI.
-- Installation of external packages is now version-aware for some packages; added `--upgrade` option to `cobaya-install`, off by default to preserve possible user changes.
-- Introduced `cobaya.component.ComponentNotFoundError` to handle cases in which internal or external components cannot be found.
-- In Linux terminals, added `COBAYA_COLOR` environment variable to get colourful output, useful e.g. for debugging, but *not* recommended for output to a file (e.g. running in a cluster).
-
-### PolyChord
-
-- Updated to v1.20.1: adds `nfail` to control failed initialisation, `synchronous` to choose sync/async parallelisation, variable number of live points, and the possibility to use an internal maximiser. Merges #232 (thanks @williamjameshandley).
 
 ### Cosmological likelihoods and theory codes
 

cobaya/__init__.py

@@ -15,7 +15,7 @@
 
 
 __author__ = "Jesus Torrado and Antony Lewis"
-__version__ = "3.5"
+__version__ = "3.5.1"
 __obsolete__ = False
 __year__ = "2024"
 __url__ = "https://cobaya.readthedocs.io"

cobaya/collection.py

@@ -458,8 +458,18 @@ def _cache_add_row(self, pos: int, values: Union[Sequence[float], np.ndarray],
                 self._cache[pos, self._icol[name]] = -2 * value
             self._cache[pos, self._icol[OutPar.chi2]] = -2 * logposterior.loglike
         if len(logposterior.derived):
-            for name, value in zip(self.derived_params, logposterior.derived):
-                self._cache[pos, self._icol[name]] = value
+            try:
+                for name, value in zip(self.derived_params, logposterior.derived):
+                    self._cache[pos, self._icol[name]] = value
+            except ValueError:
+                raise LoggedError(
+                    self.log, "Was expecting float for derived parameter %r, but "
+                              "got %r (type %r) instead. If you have defined this "
+                              "parameter manually (e.g. with a 'lambda') either make "
+                              "sure that it returns a number (or nan), or set "
+                              "'derived: False' for this parameter, so that its value"
+                              " is not stored in the sample.",
+                    name, value, type(value).__class__)
 
     def _cache_dump(self):
         """

cobaya/prior.py

@@ -297,6 +297,43 @@
           value: "lambda logx: np.exp(logx)"
 
 
+Vector parameters
+-----------------
+
+If your theory/likelihood takes an array of parameters as an argument, you can define the
+individual components' priors separately, and then combine them into an array of any shape
+using a  dynamically defined parameter.
+
+Notice that the individual components must have
+``drop: True`` in order not to be passed down the pipeline, and the dynamically-defined
+vector must carry ``derived: False``, since only numbers, not arrays, can be saved as
+derived parameters.
+
+You can see an example below, defining a Gaussian likelihood on the sum of two parameters,
+which are taken as components of a single array-like argument:
+
+
+.. code:: Python
+
+   from scipy import stats
+
+
+   def gauss_band(x_vector):
+       return stats.norm.logpdf(
+           x_vector[0] + x_vector[1], loc=1, scale=0.02)
+
+   # NB: don't forget the 'drop: True' in the individual parameters
+   #     and 'derived: False' in the vector parameters
+
+   info = {
+       "likelihood": {"band": gauss_band},
+       "params": {
+           "x": {"prior": {"min": 0, "max": 2}, "proposal": 0.1, "drop": True},
+           "y": {"prior": {"min": 0, "max": 2}, "proposal": 0.1, "drop": True},
+           "x_vector": {"value": lambda x, y: [x, y], "derived": False}},
+       "sampler": {"mcmc": None}}
+
+
 .. _prior_inheritance:
 
 Changing and redefining parameters; inheritance

cobaya/samplers/minimize/minimize.py

@@ -267,7 +267,7 @@ def minuslogp_transf(x):
             self.log.debug("Starting point: %r", initial_point)
             self._affine_transform_baseline = initial_point
             initial_point = self.affine_transform(initial_point)
-            np.testing.assert_allclose(initial_point, np.zeros(initial_point.shape))
+            assert np.allclose(initial_point, 0.)
             bounds = np.array(
                 [self.affine_transform(self._bounds[:, i]) for i in range(2)]).T
             try:
@@ -444,7 +444,7 @@ def products(self):
                 "X0": self._affine_transform_baseline}
 
     def getdist_point_text(self, params, weight=None, minuslogpost=None):
-        """Creates the multi-line string containing the minumum in GetDist format."""
+        """Creates the multi-line string containing the minimum in GetDist format."""
         lines = []
         if weight is not None:
             lines.append('  weight    = %s' % weight)

cobaya/theories/classy/classy.py

@@ -95,14 +95,23 @@
 sense to add it.
 
 
+String-vector parameters
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+At the time of writing, the CLASS Python interface takes some vector-like parameters
+as string in which different components are separater by a space. To be able to set priors
+or fixed values on each components, see `this trick
+<https://github.com/CobayaSampler/cobaya/issues/110#issuecomment-652333489>`_, and don't
+forget the ``derived: False`` in the vector parameter (thanks to Lukas Hergt).
+
 .. _classy_modify:
 
 Modifying CLASS
 ^^^^^^^^^^^^^^^
 
 If you modify CLASS and add new variables, make sure that the variables you create are
 exposed in the Python interface
-(`instructions here <https://github.com/lesgourg/class_public/wiki/Python-wrapper>`__).
+(`instructions here <https://github.com/lesgourg/class_public/wiki/Python-wrapper>`_).
 If you follow those instructions you do not need to make any additional modification in
 Cobaya.