Add MOFA

conda/meta.yaml

@@ -38,6 +38,7 @@ requirements:
     - scikit-learn
     - psutil
     - pytest >=3.0.0
+    - scikit-optimize
 
 test:
   import:

docs/src/code/algo.rst

@@ -20,6 +20,7 @@ Algorithm modules
    algo/pbt
    algo/pb2
    algo/tpe
+   algo/mofa
    algo/nevergrad
    algo/parallel_strategy
    algo/registry

docs/src/code/algo/mofa.rst

@@ -0,0 +1,5 @@
+MOFA Algorithm
+===================
+
+.. automodule:: orion.algo.mofa.mofa
+   :members:

docs/src/conf.py

@@ -254,6 +254,7 @@
     "plotly": ("https://plotly.com/python-api-reference/", None),
     "numpy": ("https://numpy.org/doc/stable/", None),
     "torch": ("https://pytorch.org/docs/stable/", None),
+    "pandas": ("https://pandas.pydata.org/docs/", None)
 }
 
 

docs/src/user/algorithms.rst

@@ -543,6 +543,56 @@ Configuration
                      requires_dist, requires_type
 
 
+.. _mofa algorithm:
+
+MOFA
+-----
+
+The MOdular FActorial Design (`MOFA`_) algorithm is based on factorial design and factorial
+analysis methods to optmimize hyperparameters. It performs multiple iterations each of which
+starts with sampling hyperparameter trial values from an orthogonal latin hypercube to cover
+the search space well while de-correlating hyperparameters. Once all trials in an iteration
+are returned, MOFA performs factorial analysis to determine which hyperparameters should be
+fixed in value and which hyperparameters require further exploration. As the hyperparameters
+become fixed, the number of trials are reduced in subsequent iterations.
+
+.. _MOFA: https://arxiv.org/abs/2011.09545
+
+.. note::
+
+   MOFA requires Python v3.8 or greater and scipy v1.8 or greater.
+
+.. note::
+
+   Default values for the ``index``, ``n_levels``, and ``strength`` parameters are set
+   to the empirically obtained optimal values described in section 5.2 of the paper.
+   The ``strength`` parameter must be set to either ``1`` or ``2``.
+
+.. note::
+
+   The number of trials N for a single MOFA iteration is set to ``N = index * n_levels^strength``.
+   The ``--exp-max-trials`` should at least be a multiple of ``N``.
+
+Configuration
+~~~~~~~~~~~~~
+
+.. code-block:: yaml
+
+    experiment:
+        algorithms:
+            MOFA:
+               seed: null
+               index: 1
+               n_levels: 5
+               strength: 2
+               threshold: 0.1
+
+.. autoclass:: orion.algo.mofa.mofa.MOFA
+   :noindex:
+   :exclude-members: space, state_dict, set_state, suggest, observe, is_done, seed_rng,
+                     requires_dist, requires_type
+
+
 .. _nevergrad-algorithm:
 
 
@@ -554,7 +604,6 @@ a library of algorithms for hyperparameter search.
 
 .. _nevergrad: https://facebookresearch.github.io/nevergrad/
 
-
 .. code-block:: yaml
 
     experiment:

setup.py

@@ -15,6 +15,7 @@
 
 packages = [  # Packages must be sorted alphabetically to ease maintenance and merges.
     "orion.algo",
+    "orion.algo.mofa",
     "orion.algo.dehb",
     "orion.algo.pbt",
     "orion.analysis",
@@ -90,6 +91,7 @@
             "tpe = orion.algo.tpe:TPE",
             "EvolutionES = orion.algo.evolution_es:EvolutionES",
             "pbt = orion.algo.pbt.pbt:PBT",
+            "mofa = orion.algo.mofa.mofa:MOFA",
             "pb2 = orion.algo.pbt.pb2:PB2",
             "bohb = orion.algo.bohb:BOHB",
             "nevergrad = orion.algo.nevergradoptimizer:NevergradOptimizer",
@@ -130,6 +132,7 @@
         "psutil",
         "joblib",
         "pytest>=3.0.0",
+        "scikit-optimize",
     ],
     tests_require=tests_require,
     setup_requires=["setuptools", "appdirs", "pytest-runner"],
@@ -159,7 +162,7 @@
     "Programming Language :: Python",
     "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
-] + [("Programming Language :: Python :: %s" % x) for x in "3 3.7 3.8 3.9".split()]
+] + [("Programming Language :: Python :: %s" % x) for x in "3 3.8 3.9".split()]
 
 if __name__ == "__main__":
     setup(**setup_args)