Merge pull request #30 from jMetal/develop

Minor release 0.5.1
jMetal · Sep 4, 2018 · 95835ec · 95835ec
2 parents 4bf1d8a + 4ec8db1
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diff --git a/.travis.yml b/.travis.yml
@@ -2,16 +2,12 @@ language: python
 python: '3.6'
 install: pip install -r requirements.txt
 script: python -m unittest discover
-branches:
-  only:
-  - master
-  - "/.*/"
 deploy:
   provider: pypi
   skip_cleanup: true
-  user: ajnebro
+  user: benhid
   distributions: sdist bdist_wheel
   password:
-    secure: 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
+    secure: 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
   on:
     branch: master
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
diff --git a/README.md b/README.md
@@ -14,7 +14,6 @@
 - [Installation](#installation)
 - [Usage](#usage)
 - [Features](#features)
-- [Contributing](#contributing)
 - [License](#license)
 
 ## Installation
@@ -33,18 +32,22 @@ $ pip install jmetalpy
 Examples of configuring and running all the included algorithms are located [in the docs](https://jmetalpy.readthedocs.io/en/latest/examples.html).
 
 ## Features
-The current release of jMetalPy (v0.5.0) contains the following components:
-
-* Algorithms: random search, NSGA-II, SMPSO, SMPSO/RP
-* Problems: ZDT1-6, DTLZ1-2, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
-* Encodings: real, binary
-* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random)
-* Quality indicators: hypervolume
-* Density estimator: crowding distance
-* Graphics: 2D/3D plotting in real-time
-
-## Contributing
-Please read [CONTRIBUTING](CONTRIBUTING.md) for details on how to contribute to the project.
+The current release of jMetalPy (v0.5.1) contains the following components:
+
+* Algorithms: random search, NSGA-II, SMPSO, SMPSO/RP.
+* Benchmark problems: ZDT1-6, DTLZ1-2, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
+* Encodings: real, binary.
+* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
+* Quality indicators: hypervolume.
+* Density estimator: crowding distance.
+* Graphics: Pareto front plotting (2 or more objectives).
+* Laboratory: Experiment class for performing studies.
+
+<p align="center">
+  <br/>
+  <img src=docs/source/visualization.png alt="Visualization">
+  <br/>
+</p>
 
 ## License
 This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.
diff --git a/docs/source/NSGAII-ZDT1.png b/docs/source/NSGAII-ZDT1.png
diff --git a/docs/source/about.rst b/docs/source/about.rst
@@ -7,4 +7,5 @@ References
 --------------------------------
 
 1. J.J. Durillo, A.J. Nebro jMetal: a Java Framework for Multi-Objective Optimization. Advances in Engineering Software 42 (2011) 760-771.
-2. A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
+2. A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
+3. Nebro A.J. et al. (2018) Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation. In: Auger A., Fonseca C., Lourenço N., Machado P., Paquete L., Whitley D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science, vol 11101. Springer, Cham
diff --git a/docs/source/api/jmetal.util.rst b/docs/source/api/jmetal.util.rst
@@ -1,14 +1,6 @@
 Utils
 ===================
 
-Front file
------------------------------
-
-.. automodule:: jmetal.util.front_file
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 Graphic
 --------------------------
 

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -19,9 +19,10 @@
 # http://blog.rtwilson.com/how-to-make-your-sphinx-documentation-compile-with-readthedocs-when-youre-using-numpy-and-scipy/
 import mock
 
-MOCK_MODULES = ['numpy', 'dask', 'dask.distributed', 'tqdm', 'bokeh', 'bokeh.embed', 'bokeh.front', 'bokeh.client',
-                'bokeh.io', 'bokeh.layouts','bokeh.models','bokeh.plotting', 'bokeh.resources',
-                'mpl_toolkits', 'mpl_toolkits.mplot3d', 'matplotlib', 'matplotlib.pyplot', 'matplotlib.axes']
+MOCK_MODULES = ['numpy', 'pandas',
+                'tqdm',
+                'plotly', 'plotly.offline',
+                'matplotlib', 'matplotlib.pyplot', 'mpl_toolkits', 'mpl_toolkits.mplot3d']
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = mock.Mock()
 

diff --git a/docs/source/examples.rst b/docs/source/examples.rst
@@ -1,15 +1,16 @@
-Examples
-==============
+Getting started
+==================
 
 .. toctree::
    :maxdepth: 2
    :caption: Algorithms:
 
-   runner/nsgaii
-   runner/smpso
+   examples/nsgaii
+   examples/smpso
 
 .. toctree::
    :maxdepth: 2
    :caption: Further configuration:
 
-   runner/observer
+   examples/observer
+   examples/experiment
diff --git a/docs/source/examples/experiment.rst b/docs/source/examples/experiment.rst
@@ -0,0 +1,43 @@
+Experiments
+========================
+
+This is an example of an experimental study based on solving two problems of the ZDT family with two versions of the same algorithm (NSGAII).
+The hypervolume indicator is used for performance assessment.
+
+.. code-block:: python
+
+   # Configure experiment
+   problem_list = [ZDT1(), ZDT2()]
+   algorithm_list = []
+
+   for problem in problem_list:
+      algorithm_list.append(
+         ('NSGAII_A',
+          NSGAII(
+             problem=problem,
+             population_size=100,
+             max_evaluations=25000,
+             mutation=NullMutation(),
+             crossover=SBX(probability=1.0, distribution_index=20),
+             selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
+          ))
+      )
+      algorithm_list.append(
+         ('NSGAII_B',
+          NSGAII(
+             problem=problem,
+             population_size=100,
+             max_evaluations=25000,
+             mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20),
+             crossover=SBX(probability=1.0, distribution_index=20),
+             selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
+          ))
+      )
+
+   study = Experiment(algorithm_list, n_runs=2)
+   study.run()
+
+   # Compute quality indicators
+   metric_list = [HyperVolume(reference_point=[1, 1])]
+
+   print(study.compute_metrics(metric_list))
diff --git a/docs/source/examples/nsgaii.rst b/docs/source/examples/nsgaii.rst
diff --git a/docs/source/runner/observer.rst → docs/source/examples/observer.rst b/docs/source/runner/observer.rst → docs/source/examples/observer.rst
diff --git a/docs/source/examples/smpso.rst b/docs/source/examples/smpso.rst
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -34,26 +34,16 @@ Via Github:
     $ pip install -r requirements.txt
     $ python setup.py install
 
-Basic usage
------------
-
-.. code-block:: python
-
-    problem = ZDT1()
-
-    algorithm = NSGAII(
-        problem=problem,
-        population_size=100,
-        max_evaluations=25000,
-        mutation=Polynomial(probability=1.0/problem.number_of_variables, distribution_index=20),
-        crossover=SBX(probability=1.0, distribution_index=20),
-        selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
-    )
-
-    algorithm.run()
-    front = algorithm.get_result()
+Features
+------------------------
+The current release of jMetalPy (v0.5.1) contains the following components:
 
-    pareto_front = ScatterMatplotlib(plot_title='NSGAII for ZDT1', number_of_objectives=problem.number_of_objectives)
-    pareto_front.plot(front, reference=problem.get_reference_front(), output='NSGAII-ZDT1', show=False)
+* Algorithms: random search, NSGA-II, SMPSO, SMPSO/RP
+* Benchmark problems: ZDT1-6, DTLZ1-2, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
+* Encodings: real, binary
+* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random)
+* Quality indicators: hypervolume
+* Density estimator: crowding distance
+* Graphics: Pareto front plotting (2 or more objectives)
+* Laboratory: Experiment class for performing studies.
 
-.. image:: NSGAII-ZDT1.png
diff --git a/docs/source/runner/nsgaii.rst b/docs/source/runner/nsgaii.rst
diff --git a/docs/source/runner/smpso.rst b/docs/source/runner/smpso.rst
diff --git a/docs/source/visualization.png b/docs/source/visualization.png
diff --git a/examples/experiment/NSGAII for ZDT1-2.py b/examples/experiment/NSGAII for ZDT1-2.py
@@ -0,0 +1,42 @@
+from jmetal.algorithm import NSGAII
+from jmetal.component.comparator import RankingAndCrowdingDistanceComparator
+from jmetal.operator import NullMutation, SBX, BinaryTournamentSelection, Polynomial
+from jmetal.problem import ZDT1, ZDT2
+from jmetal.component.quality_indicator import HyperVolume
+from jmetal.util.laboratory import Experiment
+
+# Configure experiment
+problem_list = [ZDT1(), ZDT2()]
+algorithm_list = []
+
+for problem in problem_list:
+    algorithm_list.append(
+        ('NSGAII_A',
+         NSGAII(
+             problem=problem,
+             population_size=100,
+             max_evaluations=25000,
+             mutation=NullMutation(),
+             crossover=SBX(probability=1.0, distribution_index=20),
+             selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
+         ))
+    )
+    algorithm_list.append(
+        ('NSGAII_B',
+         NSGAII(
+             problem=problem,
+             population_size=100,
+             max_evaluations=25000,
+             mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20),
+             crossover=SBX(probability=1.0, distribution_index=20),
+             selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
+         ))
+    )
+
+study = Experiment(algorithm_list, n_runs=2)
+study.run()
+
+# Compute quality indicators
+metric_list = [HyperVolume(reference_point=[1, 1])]
+
+print(study.compute_metrics(metric_list))