Merge pull request #4 from VicentePerezSoloviev/devel

change package name

EDApy.egg-info/PKG-INFO → EDAspy.egg-info/PKG-INFO

@@ -1,12 +1,13 @@
 Metadata-Version: 2.1
-Name: EDApy
-Version: 0.0.1
+Name: EDAspy
+Version: 0.1.0
 Summary: This is a package where some estimation of distribution algorithms are implemented.
-Home-page: https://github.com/VicentePerezSoloviev/EDApy
+Home-page: https://github.com/VicentePerezSoloviev/EDAspy
 Author: Vicente P. Soloviev
 Author-email: vicente.perez.soloviev@gmail.com
-License: UNKNOWN
-Description: # EDApy
+License: LGPL-2.1
+Download-URL: https://github.com/VicentePerezSoloviev/EDAspy/archive/0.1.0.tar.gz
+Description: # EDAspy
 
         ## Description
 
@@ -22,7 +23,7 @@ Description: # EDApy
         #### Binary univariate EDA
         It can be used as a simple example of EDA, or to use it for feature selection. The cost function to optimize is the metric of the model. An example is shown.
         ```python
-        from EDApy.optimization.univariate import EDA_discrete as EDAd
+        from EDAspy.optimization.univariate import EDA_discrete as EDAd
         import pandas as pd
 
         def check_solution_in_model(dictionary):
@@ -51,7 +52,7 @@ Description: # EDApy
 
         This EDA is used when some continuous parameters must be optimized. 
         ```python
-        from EDApy.optimization.univariate import EDA_continuous as EDAc
+        from EDAspy.optimization.univariate import EDA_continuous as EDAc
         import pandas as pd
         import numpy as np
 
@@ -89,7 +90,7 @@ Description: # EDApy
         The optimizer will find the optimum values of the non-evidence-variables based on the value of the evidences. This is widely used in problems where dependencies among variables must be considered.
 
         ```python
-        from EDApy.optimization.multivariate import EDA_multivariate as EDAm
+        from EDAspy.optimization.multivariate import EDA_multivariate as EDAm
         import pandas as pd
 
         blacklist = pd.DataFrame(columns=['from', 'to'])
@@ -115,12 +116,47 @@ Description: # EDApy
 
         In this case, the output is the self class that can be saved as a pickle in order to explore the attributes. One of the attributes is the optimum structure of the optimum generation, from which the structure can be plotted and observe the dependencies among the variables. The function to plot the structure is the following:
         ```python
-        from EDApy.optimization.multivariate import print_structure
+        from EDAspy.optimization.multivariate import print_structure
         print_structure(structure=structure, var2optimize=['param2', 'param3', 'param4'], evidences=['param1', 'param5'])
         ```
 
         ![Structure praph plot](/structure.PNG "Structure of the optimum generation found by the EDA")
 
+        #### Another Continuous multivariate EDA approach
+
+        In this EDA approach, new individuals are sampled from a multivariate normal distribution. Evidences are not allowed in the optimizer. If desired, the previous approach should be used.
+        The EDA is initialized, as in the univariate continuous EDA, with univariate mus and sigma for the variables. In the execution, a multivariate gaussian is built to sample from it. As it is multivariate, correlation among variables is considered.
+
+        ```python
+        import pandas as pd
+        from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+
+
+        def cost_function(dictionary):
+            suma = dictionary['param1'] + dictionary['param2']
+            if suma < 0:
+                return 999999999
+            return suma
+
+        mus = pd.DataFrame(columns=['param1', 'param2'])
+        mus.loc[0] = [10, 8]
+
+        sigma = pd.DataFrame(columns=['param1', 'param2'])
+        sigma.loc[0] = 5
+
+        EDAmulti = EDAmg(SIZE_GEN=40, MAX_ITER=1000, DEAD_ITER=50, ALPHA=0.6, aim='minimize',
+                                             cost_function=cost_function, mus=mus, sigma=sigma)
+
+        bestcost, params, history = EDAmulti.run(output=True)
+        print(bestcost)
+        print(params)
+        print(history)
+        ```
+
+        The cost function to optimize is the minimization of two parameter sum. Both parameters are continuous, and to be initialized two pandas dataframes are needed: one with mus and another with sigmas (diagonal of the covariance matrix)
+
+        The EDA returns the best cost, the combination and the history of costs if wanted to be plotted.
+
         ## Getting started
 
         #### Prerequisites
@@ -129,12 +165,14 @@ Description: # EDApy
 
         #### Installing
         ```
-        pip install git+https://github.com/vicenteperezsoloviev/EDApy.git#egg=EDApy
+        pip install git+https://github.com/vicenteperezsoloviev/EDAspy.git#egg=EDApy
         ```
 
+Keywords: EDA,estimation,bayesian,evolutionary,algorithm,optimization
 Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
 Classifier: Programming Language :: Python :: 3
-Classifier: License :: OSI Approved :: MIT License
+Classifier: License :: OSI Approved :: LGPL-2.1
 Classifier: Operating System :: OS Independent
 Requires-Python: >=3.6
 Description-Content-Type: text/markdown

EDAspy.egg-info/SOURCES.txt

@@ -0,0 +1,18 @@
+README.md
+setup.py
+EDAspy/__init__.py
+EDAspy.egg-info/PKG-INFO
+EDAspy.egg-info/SOURCES.txt
+EDAspy.egg-info/dependency_links.txt
+EDAspy.egg-info/top_level.txt
+EDAspy/optimization/__init__.py
+EDAspy/optimization/multivariate/EDA_multivariate.py
+EDAspy/optimization/multivariate/EDA_multivariate_gaussian.py
+EDAspy/optimization/multivariate/__BayesianNetwork.py
+EDAspy/optimization/multivariate/__clustering.py
+EDAspy/optimization/multivariate/__init__.py
+EDAspy/optimization/multivariate/__matrix.py
+EDAspy/optimization/univariate/__init__.py
+EDAspy/optimization/univariate/continuous.py
+EDAspy/optimization/univariate/discrete.py
+tests/__init__.py

EDAspy.egg-info/top_level.txt

@@ -0,0 +1,2 @@
+EDAspy
+tests

EDApy/optimization/multivariate/__init__.py → EDAspy/optimization/multivariate/__init__.py

@@ -22,6 +22,7 @@ def check_package(installed_pack, package):
     else:
         return False
 
+
 if sys.version_info[0] < 3:
     raise Exception("Python version should be greater than 3")
 

README.md

@@ -1,4 +1,4 @@
-# EDApy
+# EDAspy
 
 ## Description
 
@@ -14,7 +14,7 @@ Three EDAs have been implemented:
 #### Binary univariate EDA
 It can be used as a simple example of EDA, or to use it for feature selection. The cost function to optimize is the metric of the model. An example is shown.
 ```python
-from EDApy.optimization.univariate import EDA_discrete as EDAd
+from EDAspy.optimization.univariate import EDA_discrete as EDAd
 import pandas as pd
 
 def check_solution_in_model(dictionary):
@@ -43,7 +43,7 @@ Vector probabilities are usually initialized to 0.5 to start from an equilibrium
 
 This EDA is used when some continuous parameters must be optimized. 
 ```python
-from EDApy.optimization.univariate import EDA_continuous as EDAc
+from EDAspy.optimization.univariate import EDA_continuous as EDAc
 import pandas as pd
 import numpy as np
 
@@ -81,7 +81,7 @@ In this case, dependencies among the variables are considered and managed with a
 The optimizer will find the optimum values of the non-evidence-variables based on the value of the evidences. This is widely used in problems where dependencies among variables must be considered.
 
 ```python
-from EDApy.optimization.multivariate import EDA_multivariate as EDAm
+from EDAspy.optimization.multivariate import EDA_multivariate as EDAm
 import pandas as pd
 
 blacklist = pd.DataFrame(columns=['from', 'to'])
@@ -107,7 +107,7 @@ Due to the evidences, to help the structure learning algorithm to find the arcs,
 
 In this case, the output is the self class that can be saved as a pickle in order to explore the attributes. One of the attributes is the optimum structure of the optimum generation, from which the structure can be plotted and observe the dependencies among the variables. The function to plot the structure is the following:
 ```python
-from EDApy.optimization.multivariate import print_structure
+from EDAspy.optimization.multivariate import print_structure
 print_structure(structure=structure, var2optimize=['param2', 'param3', 'param4'], evidences=['param1', 'param5'])
 ```
 
@@ -120,7 +120,7 @@ The EDA is initialized, as in the univariate continuous EDA, with univariate mus
 
 ```python
 import pandas as pd
-from EDApy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
+from EDAspy.optimization.multivariate import EDA_multivariate_gaussian as EDAmg
 
 
 def cost_function(dictionary):
@@ -151,10 +151,10 @@ The EDA returns the best cost, the combination and the history of costs if wante
 ## Getting started
 
 #### Prerequisites
-R must be installed to use the multivariate EDA, with installed libraries c("bnlearn", "dbnR", "data.table")
+R must be installed to use the multivariate EDA with Bayesian networks, with the following installed libraries: c("bnlearn", "dbnR", "data.table")
 To manage R from python, rpy2 package must also be installed.
 
 #### Installing
 ```
-pip install git+https://github.com/vicenteperezsoloviev/EDApy.git#egg=EDApy
+pip install git+https://github.com/vicenteperezsoloviev/EDAspy.git#egg=EDAspy
 ```