Merge branch 'development'

Conflicts:
	.travis.yml
	README.md

.project

@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>SMAC3</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.python.pydev.PyDevBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.python.pydev.pythonNature</nature>
+	</natures>
+</projectDescription>

.pydevproject

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<?eclipse-pydev version="1.0"?><pydev_project>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.7</pydev_property>
+</pydev_project>

.travis.yml

@@ -45,7 +45,7 @@ before_install:
   - conda update --yes conda
 
 install:
-  - conda install --yes python=$TRAVIS_PYTHON_VERSION pip numpy>=1.6.1 scipy>=0.13.1 six nose matplotlib setuptools Cython
+  - conda install --yes python=$TRAVIS_PYTHON_VERSION pip numpy>=1.6.1 scipy>=0.13.1 six nose matplotlib setuptools Cython mock
   - pip install pep8 python-coveralls
   - pip install coverage
   - pip install psutil

README.md

@@ -8,16 +8,13 @@ A stable release of SMAC (v2) in Java can be found [here](http://www.cs.ubc.ca/l
 Status for master branch:
 
 [![Build Status](https://travis-ci.org/automl/SMAC3.svg?branch=master)](https://travis-ci.org/automl/SMAC3)
-[![Code Health](https://landscape.io/github/automl/SMAC3/master/landscape.png)](https://landscape.io/github/automl/SMAC3/master)
 [![Coverage Status](https://coveralls.io/repos/automl/auto-sklearn/badge.svg?branch=master&service=github)](https://coveralls.io/github/automl/SMAC3?branch=master)
 
 Status for development branch
 
 [![Build Status](https://travis-ci.org/automl/SMAC3.svg?branch=development)](https://travis-ci.org/automl/SMAC3)
-[![Code Health](https://landscape.io/github/automl/SMAC3/development/landscape.png)](https://landscape.io/github/automl/SMAC3/development)
 [![Coverage Status](https://coveralls.io/repos/automl/SMAC3/badge.svg?branch=development&service=github)](https://coveralls.io/github/automl/SMAC3?branch=development)
 
-
 #OVERVIEW
 
 SMAC is a tool for algorithm configuration 
@@ -46,18 +43,15 @@ Its [Random Forest](https://bitbucket.org/aadfreiburg/random_forest_run) is writ
 # License
 
 This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU Affero General Public License as
-published by the Free Software Foundation, either version 3 of the
-License, or (at your option) any later version.
+it under the terms of the 3-clause BSD license (please see the LICENSE file).
 
 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-GNU Affero General Public License for more details.
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 
-You should have received a copy of the GNU Affero General Public License
+You should have received a copy of the 3-clause BSD license 
 along with this program (see LICENSE file). 
-If not, see <http://www.gnu.org/licenses/>.
+If not, see <https://opensource.org/licenses/BSD-3-Clause>.
 
 # USAGE
 

changelog.md

@@ -0,0 +1,14 @@
+# 0.1.0
+
+* Moved to github instead of bitbucket
+* ADD further unit tests
+* CHANGE Stats object instead of static class
+* CHANGE requirement of ConfigSpace package to 0.2.0
+* FIX intensify runs at least two challengers
+* FIX intensify skips incumbent as challenger
+* FIX Function TAE runner passes random seed to target function
+* FIX parsing of emtpy lines in scenario file
+
+# 0.0.1
+
+* initial release

examples/rf.py

@@ -83,10 +83,7 @@ def rfr(cfg):
 
 # cv folds
 kf = KFold(X.shape[0], n_folds=4)
-
-# register function to be optimize
-taf = ExecuteTAFunc(rfr)
-
+
 # build Configuration Space which defines all parameters and their ranges
 # to illustrate different parameter types,
 # we use continuous, integer and categorical parameters
@@ -116,27 +113,31 @@ def rfr(cfg):
 max_num_nodes = UniformIntegerHyperparameter("max_num_nodes", 100, 100000, default=1000)
 cs.add_hyperparameter(max_num_nodes)
 
-# example call of the function
-# it returns: Status, Cost, Runtime, Additional Infos
-def_value = taf.run(cs.get_default_configuration())[1]
-print("Default Value: %.2f" % (def_value))
-
+# SMAC scenario oject
 scenario = Scenario({"run_obj": "quality",  # we optimize quality (alternative runtime)
                      "runcount-limit": 400,  # at most 200 function evaluations
                      "cs": cs, # configuration space
                      "deterministic": "true" 
                      })
+stats = Stats(scenario)
 
-# necessary to use stats options related to scenario information
-Stats.scenario = scenario
+# register function to be optimize
+taf = ExecuteTAFunc(rfr, stats=stats)
+
+# example call of the function
+# it returns: Status, Cost, Runtime, Additional Infos
+def_value = taf.run(cs.get_default_configuration())[1]
+print("Default Value: %.2f" % (def_value))
 
 # Optimize
 smbo = SMBO(scenario=scenario, rng=np.random.RandomState(42),
-            tae_runner=taf)
-smbo.run(max_iters=999)
-
-Stats.print_stats()
-print("Final Incumbent: %s" % (smbo.incumbent))
+            tae_runner=taf, stats=stats)
+try:
+
+    smbo.run(max_iters=999)
+finally:
+    smbo.stats.print_stats()
+    print("Final Incumbent: %s" % (smbo.incumbent))
 
 inc_value = taf.run(smbo.incumbent)[1]
 print("Optimized Value: %.2f" % (inc_value))

examples/rosenbrock.py

@@ -45,9 +45,6 @@ def rosenbrock_4d(cfg):
 logger = logging.getLogger("Optimizer") # Enable to show Debug outputs
 logger.parent.level = 20 #info level:20; debug:10
 
-# register function to be optimize
-taf = ExecuteTAFunc(rosenbrock_4d)
-
 # build Configuration Space which defines all parameters and their ranges
 # to illustrate different parameter types, 
 # we use continuous, integer and categorical parameters
@@ -62,26 +59,30 @@ def rosenbrock_4d(cfg):
 x4 = UniformIntegerHyperparameter("x4", -5, 5, default=5)
 cs.add_hyperparameter(x4)
 
-# example call of the function
-# it returns: Status, Cost, Runtime, Additional Infos
-def_value = taf.run( cs.get_default_configuration() )[1]
-print("Default Value: %.2f" %(def_value))
-
+# SMAC scenario object
 scenario = Scenario({"run_obj":"quality", # we optimize quality (alternative runtime)
                      "runcount-limit": 200, # at most 200 function evaluations
                      "cs": cs, # configuration space
                      "deterministic": "true"
                      })
+stats = Stats(scenario)
 
-# necessary to use stats options related to scenario information        
-Stats.scenario = scenario
+# register function to be optimize
+taf = ExecuteTAFunc(rosenbrock_4d, stats)
+
+# example call of the function
+# it returns: Status, Cost, Runtime, Additional Infos
+def_value = taf.run( cs.get_default_configuration() )[1]
+print("Default Value: %.2f" %(def_value))
 
 # Optimize
 smbo = SMBO(scenario=scenario, tae_runner=taf,
+            stats=stats,
             rng=np.random.RandomState(42))
-smbo.run(max_iters=999)
-
-Stats.print_stats()
+try:
+    smbo.run(max_iters=999)
+finally:
+    smbo.stats.print_stats()
 print("Final Incumbent: %s" %(smbo.incumbent))
 
 inc_value = taf.run( smbo.incumbent )[1]

requirements.txt

@@ -4,5 +4,5 @@ scipy>=0.13.1
 six
 Cython
 pynisher>=0.4.1
-ConfigSpace
+ConfigSpace>=0.2.0
 pyrfr

scripts/smac

@@ -7,7 +7,7 @@ import inspect
 cmd_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile( inspect.currentframe() ))[0]))
 cmd_folder = os.path.realpath(os.path.join(cmd_folder, ".."))
 if cmd_folder not in sys.path:
-    sys.path.append(cmd_folder)
+    sys.path.insert(0,cmd_folder)
 
 from smac.smac_cli import SMAC
 

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md

setup.py

@@ -11,12 +11,12 @@
 
 setuptools.setup(
     name="smac",
-    version="0.0.1",
+    version="0.1.0",
     author="Marius Lindauer, Matthias Feurer, Katharina Eggensperger, Aaron Klein, Stefan Falkner and Frank Hutter",
     author_email="[email protected]",
-    description=("SMAC3, a python implementation of 'Sequential Model-based "
+    description=("SMAC3, a Python implementation of 'Sequential Model-based "
                  "Algorithm Configuration'."),
-    license="GPLv3",
+    license="3-clause BSD",
     keywords="machine learning algorithm configuration hyperparameter "
              "optimization tuning",
     url="",

smac/epm/base_imputor.py

@@ -3,7 +3,7 @@
 
 __author__ = "Katharina Eggensperger"
 __copyright__ = "Copyright 2015, ML4AAD"
-__license__ = "AGPLv3"
+__license__ = "3-clause BSD"
 __maintainer__ = "Katharina Eggensperger"
 __email__ = "[email protected]"
 __version__ = "0.0.1"

smac/epm/random_epm.py

@@ -6,7 +6,7 @@
 
 __author__ = "Katharina Eggensperger"
 __copyright__ = "Copyright 2015, ML4AAD"
-__license__ = "AGPLv3"
+__license__ = "3-clause BSD"
 __maintainer__ = "Katharina Eggensperger"
 __email__ = "[email protected]"
 __version__ = "0.0.1"

smac/epm/rf_with_instances.py

@@ -6,7 +6,7 @@
 
 __author__ = "Aaron Klein"
 __copyright__ = "Copyright 2015, ML4AAD"
-__license__ = "AGPLv3"
+__license__ = "3-clause BSD"
 __maintainer__ = "Aaron Klein"
 __email__ = "[email protected]"
 __version__ = "0.0.1"

smac/epm/rfr_imputator.py

@@ -9,7 +9,7 @@
 
 __author__ = "Katharina Eggensperger"
 __copyright__ = "Copyright 2015, ML4AAD"
-__license__ = "AGPLv3"
+__license__ = "3-clause BSD"
 __maintainer__ = "Katharina Eggensperger"
 __email__ = "[email protected]"
 __version__ = "0.0.1"