Merge pull request #17 from gugarosa/dev

Merging up dev to master for newest stable release.

.pylintrc

@@ -149,7 +149,10 @@ disable=print-statement,
         no-self-use,
         duplicate-code,
         abstract-method,
-        super-with-arguments
+        super-with-arguments,
+        import-error,
+        unnecessary-pass,
+        redefined-builtin
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option
@@ -571,7 +574,7 @@ valid-metaclass-classmethod-first-arg=cls
 max-args=10
 
 # Maximum number of attributes for a class (see R0902).
-max-attributes=20
+max-attributes=25
 
 # Maximum number of boolean expressions in an if statement (see R0916).
 max-bool-expr=5

README.md

@@ -1,7 +1,7 @@
 # Opytimizer: A Nature-Inspired Python Optimizer
 
 [![Latest release](https://img.shields.io/github/release/gugarosa/opytimizer.svg)](https://github.com/gugarosa/opytimizer/releases)
-[![DOI](https://zenodo.org/badge/109152650.svg)](https://zenodo.org/badge/latestdoi/109152650)
+[![DOI](http://img.shields.io/badge/DOI-10.5281/zenodo.4594294-006DB9.svg)](https://doi.org/10.5281/zenodo.4594294)
 [![Build status](https://img.shields.io/travis/com/gugarosa/opytimizer/master.svg)](https://github.com/gugarosa/opytimizer/releases)
 [![Open issues](https://img.shields.io/github/issues/gugarosa/opytimizer.svg)](https://github.com/gugarosa/opytimizer/issues)
 [![License](https://img.shields.io/github/license/gugarosa/opytimizer.svg)](https://github.com/gugarosa/opytimizer/blob/master/LICENSE)
@@ -65,6 +65,7 @@ Opytimizer is based on the following structure, and you should pay attention to
         - optimizer
         - space
     - functions
+        - constrained
         - weighted
     - math
         - distribution
@@ -86,8 +87,8 @@ Opytimizer is based on the following structure, and you should pay attention to
         - search
         - tree
     - utils
-        - constants
-        - decorator
+        - callback
+        - constant
         - exception
         - history
         - logging
@@ -106,19 +107,19 @@ Instead of using raw and straightforward functions, why not try this module? Com
 
 ### Math
 
-Just because we are computing stuff, it does not means that we do not need math. Math is the mathematical package, containing low-level math implementations. From random numbers to distributions generation, you can find your needs on this module.
+Just because we are computing stuff does not means that we do not need math. Math is the mathematical package containing low-level math implementations. From random numbers to distribution generation, you can find your needs on this module.
 
 ### Optimizers
 
-This is why we are called Opytimizer. This is the heart of the heuristics, where you can find a large number of meta-heuristics, optimization techniques, anything that can be called as an optimizer. Please take a look on the [available optimizers](https://github.com/gugarosa/opytimizer/wiki/Types-of-Optimizers).
+This is why we are called Opytimizer. This is the heart of heuristics, where you can find a large number of meta-heuristics, optimization techniques, anything that can be called an optimizer. Please take a look at the [available optimizers](https://github.com/gugarosa/opytimizer/wiki/Types-of-Optimizers).
 
 ### Spaces
 
 One can see the space as the place that agents will update their positions and evaluate a fitness function. However, the newest approaches may consider a different type of space. Thinking about that, we are glad to support diverse space implementations.
 
 ### Utils
 
-This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use as you wish than re-implementing the same thing over and over again.
+This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use as you wish than re-implementing the same thing repeatedly.
 
 ### Visualization
 
@@ -128,7 +129,7 @@ Everyone needs images and plots to help visualize what is happening, correct? Th
 
 ## Installation
 
-We believe that everything has to be easy. Not tricky or daunting, Opytimizer will be the one-to-go package that you will need, from the very first installation to the daily-tasks implementing needs. If you may just run the following under your most preferred Python environment (raw, conda, virtualenv, whatever):
+We believe that everything has to be easy. Not tricky or daunting, Opytimizer will be the one-to-go package that you will need, from the first installation to the daily tasks implementing needs. If you may just run the following under your most preferred Python environment (raw, conda, virtualenv, whatever):
 
 ```bash
 pip install opytimizer
@@ -148,15 +149,15 @@ Note that sometimes, there is a need for additional implementation. If needed, f
 
 ### Ubuntu
 
-No specific additional commands needed.
+No specific additional commands are needed.
 
 ### Windows
 
-No specific additional commands needed.
+No specific additional commands are needed.
 
 ### MacOS
 
-No specific additional commands needed.
+No specific additional commands are needed.
 
 ---
 
@@ -168,25 +169,24 @@ Take a look at a quick working example of Opytimizer. Note that we are not passi
 import numpy as np
 
 from opytimizer import Opytimizer
-from opytimizer.core.function import Function
-from opytimizer.optimizers.swarm.pso import PSO
-from opytimizer.spaces.search import SearchSpace
+from opytimizer.core import Function
+from opytimizer.optimizers.swarm import PSO
+from opytimizer.spaces import SearchSpace
 
 def sphere(x):
   return np.sum(x ** 2)
 
 n_agents = 20
 n_variables = 2
-n_iterations = 1000
-lower_bound = (-10, -10)
-upper_bound = (10, 10)
+lower_bound = [-10, -10]
+upper_bound = [10, 10]
 
-s = SearchSpace(n_agents, n_iterations, n_variables, lower_bound, upper_bound)
-p = PSO()
-f = Function(sphere)
+space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
+optimizer = PSO()
+function = Function(sphere)
 
-o = Opytimizer(s, p, f)
-o.start()
+opt = Opytimizer(space, optimizer, function)
+opt.start(n_iterations=1000)
 ```
 
 ---

docs/api/opytimizer.core.rst

@@ -1,7 +1,7 @@
 opytimizer.core
 =========================
 
-Core is the core. Essentially, it is the parent of everything. You should find parent classes defining the basic of our structure. They should provide variables and methods that will help to construct other modules.
+Core is the core. Essentially, it is the parent of everything. You should find parent classes defining the basis of our structure. They should provide variables and methods that will help to construct other modules.
 
 .. toctree::
     opytimizer.core.agent

docs/api/opytimizer.functions.constrained.rst

@@ -0,0 +1,7 @@
+opytimizer.functions.constrained
+=================================
+
+.. automodule:: opytimizer.functions.constrained
+    :members:
+    :private-members:
+    :special-members:

docs/api/opytimizer.functions.rst

@@ -1,12 +1,12 @@
 opytimizer.functions
 =========================
 
-Instead of using raw and simple functions, why not try this module? Compose high-level abstract functions or even new function-based ideas in order to solve your problems. Note that for now, we will only support multi-objective function strategies.
+Instead of using raw and straightforward functions, why not try this module? Compose high-level abstract functions or even new function-based ideas in order to solve your problems. Note that for now, we will only support multi-objective function strategies.
 
 .. toctree::
+    opytimizer.functions.constrained
     opytimizer.functions.weighted
 
-
 .. automodule:: opytimizer.functions
    :members:
    :show-inheritance:

docs/api/opytimizer.math.rst

@@ -1,7 +1,7 @@
 opytimizer.math
 =========================
 
-Just because we are computing stuff, it does not means that we do not need math. Math is the mathematical package, containing low level math implementations. From random numbers to distributions generation, you can find your needs on this module.
+Just because we are computing stuff does not means that we do not need math. Math is the mathematical package containing low-level math implementations. From random numbers to distribution generation, you can find your needs on this module.
 
 .. toctree::
     opytimizer.math.distribution

docs/api/opytimizer.optimizers.rst

@@ -1,7 +1,7 @@
 opytimizer.optimizers
 ======================
 
-This is why we are called Opytimizer. This is the heart of the heuristics, where you can find a broad number of meta-heuristics, optimization techniques, anything that can be called as an optimizer. Investigate over any module for more information.
+This is why we are called Opytimizer. This is the heart of heuristics, where you can find a large number of meta-heuristics, optimization techniques, anything that can be called an optimizer. Please take a look at the [available optimizers](https://github.com/gugarosa/opytimizer/wiki/Types-of-Optimizers).
 
 .. toctree::
     opytimizer.optimizers.boolean

docs/api/opytimizer.rst

@@ -1,5 +1,5 @@
 opytimizer
-=========================
+===========
 
 .. autoclass:: opytimizer.Opytimizer
    :members:

docs/api/opytimizer.spaces.rst

@@ -1,7 +1,7 @@
 opytimizer.spaces
 =========================
 
-One can see the space as the place that agents will update their positions and evaluate a fitness function. However, newest approaches may consider a different type of space. Thinking on that, we are glad to support diverse space implementations.
+One can see the space as the place that agents will update their positions and evaluate a fitness function. However, the newest approaches may consider a different type of space. Thinking about that, we are glad to support diverse space implementations.
 
 .. toctree::
     opytimizer.spaces.boolean

docs/api/opytimizer.utils.decorator.rst → docs/api/opytimizer.utils.callback.rst

@@ -1,7 +1,7 @@
-opytimizer.utils.decorator
+opytimizer.utils.callback
 ===========================
 
-.. automodule:: opytimizer.utils.decorator
+.. automodule:: opytimizer.utils.callback
     :members:
     :private-members:
     :special-members:

docs/api/opytimizer.utils.constant.rst

@@ -0,0 +1,7 @@
+opytimizer.utils.constant
+==========================
+
+.. automodule:: opytimizer.utils.constant
+    :members:
+    :private-members:
+    :special-members:

docs/api/opytimizer.utils.rst

@@ -1,11 +1,11 @@
 opytimizer.utils
 =========================
 
-This is an utilities package. Common things shared across the application should be implemented here. It is better to implement once and use as you wish than re-implementing the same thing over and over again.
+This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use as you wish than re-implementing the same thing repeatedly.
 
 .. toctree::
-    opytimizer.utils.constants
-    opytimizer.utils.decorator
+    opytimizer.utils.callback
+    opytimizer.utils.constant
     opytimizer.utils.exception
     opytimizer.utils.history
     opytimizer.utils.logging

docs/api/opytimizer.visualization.rst

@@ -1,7 +1,7 @@
 opytimizer.visualization
 =========================
 
-Everyone needs images and plots to help visualize what is happening, correct? This package will provide every visual-related method for you. Check a specific variable convergence, your fitness function convergence, plot benchmark function surfaces and much more!
+Everyone needs images and plots to help visualize what is happening, correct? This package will provide every visual-related method for you. Check a specific variable convergence, your fitness function convergence, plot benchmark function surfaces, and much more!
 
 .. toctree::
     opytimizer.visualization.convergence

examples/applications/additional_features/create_optimization_checkpoints.py

@@ -0,0 +1,31 @@
+import numpy as np
+from opytimark.markers.n_dimensional import Sphere
+
+from opytimizer import Opytimizer
+from opytimizer.core import Function
+from opytimizer.optimizers.swarm import PSO
+from opytimizer.spaces import SearchSpace
+from opytimizer.utils.callback import CheckpointCallback
+
+# Random seed for experimental consistency
+np.random.seed(0)
+
+# Number of agents and decision variables
+n_agents = 20
+n_variables = 2
+
+# Lower and upper bounds (has to be the same size as `n_variables`)
+lower_bound = [-10, -10]
+upper_bound = [10, 10]
+
+# Creates the space, optimizer and function
+space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
+optimizer = PSO()
+function = Function(Sphere())
+
+# Bundles every piece into Opytimizer class
+opt = Opytimizer(space, optimizer, function, save_agents=False)
+
+# Runs the optimization task
+# CheckpointCallback will snapshot the optimization every `frequency` iterations
+opt.start(n_iterations=10, callbacks=[CheckpointCallback(frequency=1)])

examples/applications/additional_features/multiple_optimization_runnings.py

@@ -0,0 +1,33 @@
+import numpy as np
+from opytimark.markers.n_dimensional import Sphere
+
+from opytimizer import Opytimizer
+from opytimizer.core import Function
+from opytimizer.optimizers.swarm import PSO
+from opytimizer.spaces import SearchSpace
+
+# Random seed for experimental consistency
+np.random.seed(0)
+
+# Number of agents and decision variables
+n_agents = 20
+n_variables = 2
+
+# Lower and upper bounds (has to be the same size as `n_variables`)
+lower_bound = [-10, -10]
+upper_bound = [10, 10]
+
+# Creates the space, optimizer and function
+space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
+optimizer = PSO()
+function = Function(Sphere())
+
+# Bundles every piece into Opytimizer class
+opt = Opytimizer(space, optimizer, function, save_agents=False)
+
+# Runs the optimization task
+# Every call on `start` will the continue the optimization for `n_iterations`
+# Note that the following lines achieves the same results as a 100-iteration running
+opt.start(n_iterations=50)
+opt.start(n_iterations=25)
+opt.start(n_iterations=25)

examples/applications/additional_features/resume_optimization_from_file.py

@@ -0,0 +1,38 @@
+import numpy as np
+from opytimark.markers.n_dimensional import Sphere
+
+from opytimizer import Opytimizer
+from opytimizer.core import Function
+from opytimizer.optimizers.swarm import PSO
+from opytimizer.spaces import SearchSpace
+from opytimizer.utils.callback import CheckpointCallback
+
+# Random seed for experimental consistency
+np.random.seed(0)
+
+# Number of agents and decision variables
+n_agents = 20
+n_variables = 2
+
+# Lower and upper bounds (has to be the same size as `n_variables`)
+lower_bound = [-10, -10]
+upper_bound = [10, 10]
+
+# Creates the space, optimizer and function
+space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
+optimizer = PSO()
+function = Function(Sphere())
+
+# Bundles every piece into Opytimizer class
+opt = Opytimizer(space, optimizer, function, save_agents=False)
+
+# Runs the optimization task
+opt.start(n_iterations=10, callbacks=[CheckpointCallback(frequency=10)])
+
+# Deletes the optimization objecs
+del opt
+
+# Loads the task from file and resumes it
+# Note that the following lines achieves the same results as a 35-iteration running
+opt = Opytimizer.load('iter_10_checkpoint.pkl')
+opt.start(n_iterations=25)