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ryanhsin98 · Nov 2, 2020 · 9e14cf7 · 9e14cf7
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diff --git a/Py_FS.egg-info/PKG-INFO b/Py_FS.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: Py-FS
-Version: 0.0.5.11
+Version: 0.0.6.0
 Summary: A Python Package for Feature Selection
 Home-page: https://github.com/Ritam-Guha/Py_FS
 Author: Ritam Guha
@@ -69,12 +69,6 @@ Description: # Py_FS: A Python Package for Feature Selection
 
         ### Wrappers
 
-        #### BBA
-
-        #### CS
-
-        #### EO
-
         #### GA
 
         * Guha, R., Ghosh, M., Kapri, S., Shaw, S., Mutsuddi, S., Bhateja, V., & Sarkar, R. (2019). Deluge based Genetic Algorithm for feature selection. Evolutionary intelligence, 1-11.
@@ -95,40 +89,33 @@ Description: # Py_FS: A Python Package for Feature Selection
 
         #### GWO
 
-        #### HS
+        * Dhargupta, S., Ghosh, M., Mirjalili, S., & Sarkar, R. (2020). Selective opposition based grey wolf optimization. Expert Systems with Applications, 113389.
 
         #### MA
 
+        * T. Bhattacharyya, B. Chatterjee, P. K. Singh, J. H. Yoon, Z. W. Geem, R. Sarkar, “Mayfly in Harmony: A New Hybrid Meta-heuristic Feature Selection Algorithm”, IEEE Access, 2020.
+
         #### PSO
 
         * Ghosh, M., Guha, R., Singh, P. K., Bhateja, V., & Sarkar, R. (2019). A histogram based fuzzy ensemble technique for feature selection. Evolutionary Intelligence, 12(4), 713-724.
 
         * Ghosh, M., Guha, R., Alam, I., Lohariwal, P., Jalan, D., & Sarkar, R. (2019). Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection. Journal of Intelligent Systems, 29(1), 1598-1610.
 
-        #### RDA
-
-        #### SCA
-
         #### WOA
 
-        * Guha, R., Ghosh, M., Mutsuddi, S., Sarkar, R., & Mirjalili, S. (2020). Embedded chaotic whale survival algorithm for filter-wrapper feature selection. arXiv preprint arXiv:2005.04593.
+        * Guha, R., Ghosh, M., Mutsuddi, S. et al. Embedded chaotic whale survival algorithm for filter–wrapper feature selection. Soft Comput 24, 12821–12843 (2020). https://doi.org/10.1007/s00500-020-05183-1.
 
         ### Filters
 
         #### PCC
 
         * Guha, R., Ghosh, K. K., Bhowmik, S., & Sarkar, R. (2020, February). Mutually Informed Correlation Coefficient (MICC)-a New Filter Based Feature Selection Method. In 2020 IEEE Calcutta Conference (CALCON) (pp. 54-58). IEEE.
 
-        #### SCC
-
         #### MI
 
-        * Guha, R., Ghosh, K. K., Bhowmik, S., & Sarkar, R. (2020, February). Mutually Informed Correlation Coefficient (MICC)-a New Filter Based Feature Selection Method. In 2020 IEEE Calcutta Conference (CALCON) (pp. 54-58). IEEE.
-
-        #### Relief
-
+        * Guha, R., Ghosh, K. K., Bhowmik, S., & Sarkar, R. (2020, February). Mutually Informed Correlation Coefficient (MICC)-a New Filter Based Feature Selection Method. In 2020 IEEE Calcutta Conference (CALCON) (pp. 54-58). IEEE.  
 
-        ## 1. Wrapper-based Nature-inpsired Feature Selection
+        ## 1. Wrapper-based Nature-inspired Feature Selection
         Wrapper-based Nature-inspired methods are very popular feature selection approaches due to their efficiency and simplicity. These methods progress by introducing random set of candidate solutions (agents which are natural elements like particles, whales, bats etc.) and improving these solutions gradually by using guidance mechanisms of fitter agents. In order to calculate the fitness of the candidate solutions, wrappers require some learning algorithm (like classifiers) to calculate the worth of a solution at every iteration. This makes wrapper methods extremely reliable but computationally expensive as well.
 
         Py_FS currently supports the following 12 wrapper-based FS methods:

diff --git a/Py_FS.egg-info/SOURCES.txt b/Py_FS.egg-info/SOURCES.txt
@@ -67,8 +67,4 @@ Py_FS/wrapper/nature_inspired/__pycache__/WOA.cpython-38.pyc
 Py_FS/wrapper/nature_inspired/__pycache__/__init__.cpython-38.pyc
 Py_FS/wrapper/nature_inspired/__pycache__/_transfer_functions.cpython-38.pyc
 Py_FS/wrapper/nature_inspired/__pycache__/_transformation_functions.cpython-38.pyc
-Py_FS/wrapper/nature_inspired/__pycache__/_utilities.cpython-38.pyc
-dist/Py_FS-0.0.5.10-py3-none-any.whl
-dist/Py_FS-0.0.5.10.tar.gz
-dist/Py_FS-0.0.5.9-py3-none-any.whl
-dist/Py_FS-0.0.5.9.tar.gz
+Py_FS/wrapper/nature_inspired/__pycache__/_utilities.cpython-38.pyc
diff --git a/Py_FS/evaluation/evaluate.py b/Py_FS/evaluation/evaluate.py
@@ -1,8 +1,8 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.evaluation._utilities import Metric
-# from _utilities import Metric
+# from Py_FS.evaluation._utilities import Metric
+from _utilities import Metric
 
 import numpy as np
 

diff --git a/Py_FS/filter/MI.py b/Py_FS/filter/MI.py
@@ -6,8 +6,8 @@
 """
 
 import numpy as np
-from Py_FS.filter._utilities import normalize, Result
-# from sklearn import datasets
+# from Py_FS.filter._utilities import normalize, Result
+from sklearn import datasets
 
 def MI(data, target):
     # function that assigns scores to features according to Mutual Information (MI)

diff --git a/Py_FS/filter/PCC.py b/Py_FS/filter/PCC.py
@@ -6,8 +6,8 @@
 """
 
 import numpy as np
-from Py_FS.filter._utilities import normalize, Result
-# from sklearn import datasets
+# from Py_FS.filter._utilities import normalize, Result
+from sklearn import datasets
 
 def PCC(data, target):
     # function that assigns scores to features according to Pearson's Correlation Coefficient (PCC)

diff --git a/Py_FS/filter/Relief.py b/Py_FS/filter/Relief.py
@@ -7,8 +7,8 @@
 
 from ReliefF import ReliefF
 import numpy as np
-from Py_FS.filter._utilities import normalize, Result
-# from sklearn import datasets
+# from Py_FS.filter._utilities import normalize, Result
+from sklearn import datasets
 
 def Relief(data, target):
     # function that assigns scores to features according to Relief algorithm

diff --git a/Py_FS/filter/SCC.py b/Py_FS/filter/SCC.py
@@ -6,8 +6,8 @@
 """
 
 import numpy as np
-from Py_FS.filter._utilities import normalize, Result
-# from sklearn import datasets
+# from Py_FS.filter._utilities import normalize, Result
+from sklearn import datasets
 
 def SCC(data, target):
     # function that assigns scores to features according to Spearman's Correlation Coefficient (SCC)

diff --git a/Py_FS/wrapper/nature_inspired/BBA.py b/Py_FS/wrapper/nature_inspired/BBA.py
@@ -14,10 +14,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 def BBA(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_function_shape='s', constantLoudness=True, save_conv_graph=False):
 

diff --git a/Py_FS/wrapper/nature_inspired/CS.py b/Py_FS/wrapper/nature_inspired/CS.py
@@ -15,10 +15,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function,sigmoid
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function,sigmoid
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 def CS (num_nests, max_iter, train_data, train_label, obj_function=compute_fitness, trans_function_shape='s', save_conv_graph=False):
 

diff --git a/Py_FS/wrapper/nature_inspired/EO.py b/Py_FS/wrapper/nature_inspired/EO.py
@@ -15,10 +15,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 def EO(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_func_shape='s', save_conv_graph=False):

diff --git a/Py_FS/wrapper/nature_inspired/GA.py b/Py_FS/wrapper/nature_inspired/GA.py
@@ -12,8 +12,8 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
 
 
 def GA(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, prob_cross=0.4, prob_mut=0.3, save_conv_graph=False):

diff --git a/Py_FS/wrapper/nature_inspired/GSA.py b/Py_FS/wrapper/nature_inspired/GSA.py
@@ -16,10 +16,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 def GSA(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_function_shape='s', save_conv_graph=False):

diff --git a/Py_FS/wrapper/nature_inspired/GWO.py b/Py_FS/wrapper/nature_inspired/GWO.py
@@ -15,10 +15,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 def GWO(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_func_shape='s', save_conv_graph=False):

diff --git a/Py_FS/wrapper/nature_inspired/HS.py b/Py_FS/wrapper/nature_inspired/HS.py
@@ -16,8 +16,8 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
 
 def HS(num_agents, max_iter, train_data, train_label, obj_function = compute_fitness, save_conv_graph = False):
 

diff --git a/Py_FS/wrapper/nature_inspired/MA.py b/Py_FS/wrapper/nature_inspired/MA.py
@@ -15,10 +15,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function,sigmoid
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function,sigmoid
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 def MA(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_function_shape='s',  prob_mut=0.2,  save_conv_graph=False):
 

diff --git a/Py_FS/wrapper/nature_inspired/PSO.py b/Py_FS/wrapper/nature_inspired/PSO.py
@@ -16,10 +16,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 def PSO(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_func_shape='s', save_conv_graph=False):

diff --git a/Py_FS/wrapper/nature_inspired/RDA.py b/Py_FS/wrapper/nature_inspired/RDA.py
@@ -16,10 +16,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 def RDA(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_function_shape='s', save_conv_graph=False):

diff --git a/Py_FS/wrapper/nature_inspired/SCA.py b/Py_FS/wrapper/nature_inspired/SCA.py
@@ -13,10 +13,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 

diff --git a/Py_FS/wrapper/nature_inspired/WOA.py b/Py_FS/wrapper/nature_inspired/WOA.py
@@ -15,10 +15,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn import datasets
 
-from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
-# from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
-# from _transfer_functions import get_trans_function
+# from Py_FS.wrapper.nature_inspired._utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+# from Py_FS.wrapper.nature_inspired._transfer_functions import get_trans_function
+from _utilities import Solution, Data, initialize, sort_agents, display, compute_fitness, compute_accuracy
+from _transfer_functions import get_trans_function
 
 
 def WOA(num_agents, max_iter, train_data, train_label, obj_function=compute_fitness, trans_function_shape='s', save_conv_graph=False):

diff --git a/README.md b/README.md
@@ -61,12 +61,6 @@ This toolbox has been developed by a team of students from [Computer Science and
 
 ### Wrappers
 
-#### BBA
-
-#### CS
-
-#### EO
-
 #### GA
 
 * Guha, R., Ghosh, M., Kapri, S., Shaw, S., Mutsuddi, S., Bhateja, V., & Sarkar, R. (2019). Deluge based Genetic Algorithm for feature selection. Evolutionary intelligence, 1-11.
@@ -87,7 +81,7 @@ This toolbox has been developed by a team of students from [Computer Science and
 
 #### GWO
 
-#### HS
+* Dhargupta, S., Ghosh, M., Mirjalili, S., & Sarkar, R. (2020). Selective opposition based grey wolf optimization. Expert Systems with Applications, 113389.
 
 #### MA
 
@@ -99,10 +93,6 @@ This toolbox has been developed by a team of students from [Computer Science and
 
 * Ghosh, M., Guha, R., Alam, I., Lohariwal, P., Jalan, D., & Sarkar, R. (2019). Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection. Journal of Intelligent Systems, 29(1), 1598-1610.
 
-#### RDA
-
-#### SCA
-
 #### WOA
 
 * Guha, R., Ghosh, M., Mutsuddi, S. et al. Embedded chaotic whale survival algorithm for filter–wrapper feature selection. Soft Comput 24, 12821–12843 (2020). https://doi.org/10.1007/s00500-020-05183-1.
@@ -113,14 +103,9 @@ This toolbox has been developed by a team of students from [Computer Science and
 
 * Guha, R., Ghosh, K. K., Bhowmik, S., & Sarkar, R. (2020, February). Mutually Informed Correlation Coefficient (MICC)-a New Filter Based Feature Selection Method. In 2020 IEEE Calcutta Conference (CALCON) (pp. 54-58). IEEE.
 
-#### SCC
-
 #### MI
 
-* Guha, R., Ghosh, K. K., Bhowmik, S., & Sarkar, R. (2020, February). Mutually Informed Correlation Coefficient (MICC)-a New Filter Based Feature Selection Method. In 2020 IEEE Calcutta Conference (CALCON) (pp. 54-58). IEEE.
-
-#### Relief
-
+* Guha, R., Ghosh, K. K., Bhowmik, S., & Sarkar, R. (2020, February). Mutually Informed Correlation Coefficient (MICC)-a New Filter Based Feature Selection Method. In 2020 IEEE Calcutta Conference (CALCON) (pp. 54-58). IEEE.  
 
 ## 1. Wrapper-based Nature-inspired Feature Selection
 Wrapper-based Nature-inspired methods are very popular feature selection approaches due to their efficiency and simplicity. These methods progress by introducing random set of candidate solutions (agents which are natural elements like particles, whales, bats etc.) and improving these solutions gradually by using guidance mechanisms of fitter agents. In order to calculate the fitness of the candidate solutions, wrappers require some learning algorithm (like classifiers) to calculate the worth of a solution at every iteration. This makes wrapper methods extremely reliable but computationally expensive as well.