Version 2.0.3

New Version documentation!

README.md

@@ -21,51 +21,45 @@ This method results in 689 binary inputs, which is much less than the 10690 bina
 
 --> Implementation of a simpler One-Hot-Encoding method.
 
+--> Minmax and Standard scaler (based on sklearn functions) with column selection and multicore support. Also, it is possible to apply these transformations to specific columns only instead of the full dataset (follow the example). However it only works with numerical data (e.g., MSE, decision scores)
 
+--> You can also provide a custom scaler version of your own! (check example)
 
-# Alpha-Release
---> Minmax and Standard scaler (based on sklearn functions) with column selection and multicore support
 
-It is possible to apply these transformations to specific columns only instead of the full dataset (follow the example).
 
-New Feature :
 
-[x] - Introduced a scaler function implementation based from skelearn package but allowing to choose each columns you want to use and multiprocessing function. Also you can provide a custom function of your own! (check Example)
+Future Function ideas:
+-- 
+MultiColumn scale (based on the implementation of IDF and PCP)
+Scaling of IDF values (normalized IDF) 
 
 
 
 
-Future Function:
-
-[Future] - MultiColumn scale (based on the implementation of IDF and PCP)
-
-
-
-
 # Installation
 
-## Stable Version
 To install this package please run the following command
 
 ``` cmd
 pip install cane
 ```
 
-## Alpha Version
-To install this package please run the following command
+# New
+Version 2.0.3:
+
+[x] - Introduced a scaler function implementation based from skelearn package but allowing to choose each columns you want to use and multiprocessing function. 
+
+[x] - Also you can provide a custom function of your own! (check Example)
 
-``` cmd
-pip install cane==2.0.3b1
-```
 
-# New
-Version 2.0.2 - This version has the requirements updated
 
 # Suggestions and feedback
 
 Any feedback will be appreciated.
 For questions and other suggestions contact luis.matos@dsi.uminho.pt
+Found any bugs? Post Them on the github page of the project! (https://github.com/Metalkiler/Cane-Categorical-Attribute-traNsformation-Environment)
 
+Thanks for the support!
 
 # Example
 
@@ -160,15 +154,19 @@ print("PCP Time Multicore:",PTM)
 
 ```
 
-# Alpha Release Example
+# Scaler Example with cane
+
+These examples present the usage of cane with the standard methods (standard scaler e min max scaler).
+Also, it is presented how to implement a custom scaler function of your own with cane!
 ``` python
 #New Scaler Function 
 
 
 
 dfNumbers = pd.DataFrame(np.random.randint(0,100000,size=(100000, 12)), columns=list('ABCDEFGHIJKL'))
-cane.scale_data(dfNumbers, n_cores = 3, scaleFunc="min_max")
-cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="min_max")
+cane.scale_data(dfNumbers, n_cores = 3, scaleFunc="min_max") # all columns using 3 cores
+cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="min_max") # scale specific columns
+cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="std") #standard Scaler
 
 
 
@@ -180,7 +178,7 @@ import numpy as np
 import cane 
 
 def customFunc(val):
-       return pd.DataFrame([round((i - 1) / 3, 2) for i in val],columns=[val.name + "_custom_scalled_min_max"])
+       return pd.DataFrame([round((i - 1) / 3, 2) for i in val],columns=[val.name + "_custom_scalled_function])
 
 
 

cane/README.md

@@ -21,51 +21,45 @@ This method results in 689 binary inputs, which is much less than the 10690 bina
 
 --> Implementation of a simpler One-Hot-Encoding method.
 
+--> Minmax and Standard scaler (based on sklearn functions) with column selection and multicore support. Also, it is possible to apply these transformations to specific columns only instead of the full dataset (follow the example). However it only works with numerical data (e.g., MSE, decision scores)
 
+--> You can also provide a custom scaler version of your own! (check example)
 
-# Alpha-Release
---> Minmax and Standard scaler (based on sklearn functions) with column selection and multicore support
 
-It is possible to apply these transformations to specific columns only instead of the full dataset (follow the example).
 
-New Feature :
 
-[x] - Introduced a scaler function implementation based from skelearn package but allowing to choose each columns you want to use and multiprocessing function. Also you can provide a custom function of your own! (check Example)
+Future Function ideas:
+-- 
+MultiColumn scale (based on the implementation of IDF and PCP)
+Scaling of IDF values (normalized IDF) 
 
 
 
 
-Future Function:
-
-[Future] - MultiColumn scale (based on the implementation of IDF and PCP)
-
-
-
-
 # Installation
 
-## Stable Version
 To install this package please run the following command
 
 ``` cmd
 pip install cane
 ```
 
-## Alpha Version
-To install this package please run the following command
+# New
+Version 2.0.3:
+
+[x] - Introduced a scaler function implementation based from skelearn package but allowing to choose each columns you want to use and multiprocessing function. 
+
+[x] - Also you can provide a custom function of your own! (check Example)
 
-``` cmd
-pip install cane==2.0.3b1
-```
 
-# New
-Version 2.0.2 - This version has the requirements updated
 
 # Suggestions and feedback
 
 Any feedback will be appreciated.
 For questions and other suggestions contact luis.matos@dsi.uminho.pt
+Found any bugs? Post Them on the github page of the project! (https://github.com/Metalkiler/Cane-Categorical-Attribute-traNsformation-Environment)
 
+Thanks for the support!
 
 # Example
 
@@ -160,15 +154,19 @@ print("PCP Time Multicore:",PTM)
 
 ```
 
-# Alpha Release Example
+# Scaler Example with cane
+
+These examples present the usage of cane with the standard methods (standard scaler e min max scaler).
+Also, it is presented how to implement a custom scaler function of your own with cane!
 ``` python
 #New Scaler Function 
 
 
 
 dfNumbers = pd.DataFrame(np.random.randint(0,100000,size=(100000, 12)), columns=list('ABCDEFGHIJKL'))
-cane.scale_data(dfNumbers, n_cores = 3, scaleFunc="min_max")
-cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="min_max")
+cane.scale_data(dfNumbers, n_cores = 3, scaleFunc="min_max") # all columns using 3 cores
+cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="min_max") # scale specific columns
+cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="std") #standard Scaler
 
 
 
@@ -180,7 +178,7 @@ import numpy as np
 import cane 
 
 def customFunc(val):
-       return pd.DataFrame([round((i - 1) / 3, 2) for i in val],columns=[val.name + "_custom_scalled_min_max"])
+       return pd.DataFrame([round((i - 1) / 3, 2) for i in val],columns=[val.name + "_custom_scalled_function])
 
 
 

cane/build/lib/cane/__init__.py

@@ -320,6 +320,10 @@ def scale_data(df, column=[], n_cores=1, scaleFunc="", customfunc=None):
     if scaleFunc == 'custom':
         assert (callable(customfunc)), "Please provide a function for the custom function you want to use"
 
+    if column is not None:
+        assert all(flag in df.columns for flag in
+                   column), "Use columns specific to the dataset given the columns provided are not found " \
+                            + ' '.join([j for j in column])
     valArgs = []
     if len(column) == 0:
         columns = df.columns.values
@@ -330,7 +334,7 @@ def scale_data(df, column=[], n_cores=1, scaleFunc="", customfunc=None):
         columns = df.columns.values
         for i in column:
             valArgs.append(df[i])
-        diff = list(set(columns) - set(column))
+        diff = columns
     if scaleFunc == "min_max":
         func = partial(scale_single_min_max)
     elif scaleFunc == "std":
@@ -361,4 +365,4 @@ def scale_single_std(val):
 
 
 def __version__():
-    print("2.0.3a3")
+    print("2.0.3")

cane/cane.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: cane
-Version: 2.0.3a3
+Version: 2.0.3.1
 Summary: Cane - Categorical Attribute traNsformation Environment
 Home-page: https://github.com/Metalkiler/Cane-Categorical-Attribute-traNsformation-Environment
 Author: Luís Miguel Matos, Paulo Cortez, Rui Mendes
@@ -29,42 +29,45 @@ Description: # Cane - Categorical Attribute traNsformation Environment
 
         --> Implementation of a simpler One-Hot-Encoding method.
 
-        --> Minmax and Standard scaler (based on sklearn functions) with column selection and multicore support
+        --> Minmax and Standard scaler (based on sklearn functions) with column selection and multicore support. Also, it is possible to apply these transformations to specific columns only instead of the full dataset (follow the example). However it only works with numerical data (e.g., MSE, decision scores)
 
-        It is possible to apply these transformations to specific columns only instead of the full dataset (follow the example).
+        --> You can also provide a custom scaler version of your own! (check example)
 
-        New Feature :
 
-        [x] - Introduced a scaler function implementation based from skelearn package but allowing to choose each columns you want to use and multiprocessing function. Also you can provide a custom function of your own! (check Example)
 
 
+        Future Function ideas:
+        -- 
+        MultiColumn scale (based on the implementation of IDF and PCP)
+        Scaling of IDF values (normalized IDF) 
 
 
-        Future Function:
-
-        [Future] - MultiColumn scale (based on the implementation of IDF and PCP)
-
-
 
 
         # Installation
 
-        ## Stable Version
         To install this package please run the following command
 
         ``` cmd
         pip install cane
-
-
         ```
+
         # New
-        Version 2.0.2 - This version has the requirements updated
+        Version 2.0.3:
+
+        [x] - Introduced a scaler function implementation based from skelearn package but allowing to choose each columns you want to use and multiprocessing function. 
+
+        [x] - Also you can provide a custom function of your own! (check Example)
+
+
 
         # Suggestions and feedback
 
         Any feedback will be appreciated.
         For questions and other suggestions contact luis.matos@dsi.uminho.pt
+        Found any bugs? Post Them on the github page of the project! (https://github.com/Metalkiler/Cane-Categorical-Attribute-traNsformation-Environment)
 
+        Thanks for the support!
 
         # Example
 
@@ -155,13 +158,23 @@ Description: # Cane - Categorical Attribute traNsformation Environment
         print("IDF Time Multicore:",ITM)
         print("PCP Time Multicore:",PTM)
 
+
+
+        ```
+
+        # Scaler Example with cane
+
+        These examples present the usage of cane with the standard methods (standard scaler e min max scaler).
+        Also, it is presented how to implement a custom scaler function of your own with cane!
+        ``` python
         #New Scaler Function 
 
 
 
         dfNumbers = pd.DataFrame(np.random.randint(0,100000,size=(100000, 12)), columns=list('ABCDEFGHIJKL'))
-        cane.scale_data(dfNumbers, n_cores = 3, scaleFunc="min_max")
-        cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="min_max")
+        cane.scale_data(dfNumbers, n_cores = 3, scaleFunc="min_max") # all columns using 3 cores
+        cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="min_max") # scale specific columns
+        cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="std") #standard Scaler
 
 
 
@@ -173,7 +186,7 @@ Description: # Cane - Categorical Attribute traNsformation Environment
         import cane 
 
         def customFunc(val):
-               return pd.DataFrame([round((i - 1) / 3, 2) for i in val],columns=[val.name + "_custom_scalled_min_max"])
+               return pd.DataFrame([round((i - 1) / 3, 2) for i in val],columns=[val.name + "_custom_scalled_function])
 
 
 
@@ -182,6 +195,7 @@ Description: # Cane - Categorical Attribute traNsformation Environment
         from functions import *
         # with a custom function to apply to data:
         if __name__ == "__main__":
+            dfNumbers = pd.DataFrame(np.random.randint(0,100000,size=(100000, 12)), columns=list('ABCDEFGHIJKL'))
             cane.scale_data(dfNumbers, column=["A","B"], n_cores = 3, scaleFunc="custom", customfunc = customFunc)
 
 

cane/cane/__init__.py

@@ -334,7 +334,7 @@ def scale_data(df, column=[], n_cores=1, scaleFunc="", customfunc=None):
         columns = df.columns.values
         for i in column:
             valArgs.append(df[i])
-        diff = list(set(columns) - set(column))
+        diff = columns
     if scaleFunc == "min_max":
         func = partial(scale_single_min_max)
     elif scaleFunc == "std":
@@ -365,4 +365,4 @@ def scale_single_std(val):
 
 
 def __version__():
-    print("2.0.3b1")
+    print("2.0.3")

cane/setup.py

@@ -8,7 +8,7 @@
 
 
 setuptools.setup(name='cane',
-                 version='2.0.3a3',
+                 version='2.0.3.1',
                  description='Cane - Categorical Attribute traNsformation Environment',
                  author='Luís Miguel Matos, Paulo Cortez, Rui Mendes',
                  license='MIT',

cane/tests/MultiCoreScale.py

@@ -7,7 +7,8 @@
 
 from cane import scale_data, scale_single_min_max, scale_single_std
 
-class TestPCP(unittest.TestCase):
+
+class Testscalers(unittest.TestCase):
 
     @staticmethod
     def create_series():
@@ -36,7 +37,6 @@ def create_expected():
                               columns=["A_scalled_std"])
         return xA_new
 
-
     @staticmethod
     def create_expected_min_max():
         """
@@ -56,6 +56,7 @@ def create_expected_min_max():
         xA_new = pd.DataFrame([0.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.5, 1.0, 0.5],
                               columns=["A_scalled_min_max"])
         return xA_new
+
     def teststd(self):
         df = self.create_series()
         a = scale_single_std(df["A"])