Metadata-Version: 2.1
Name: cane
Version: 0.0.1.7.1
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
Author-email: luis.matos@dsi.uminho.pt
License: MIT
Description: # Cane - Categorical Attribute traNsformation Environment 
        CANE is a simpler but powerful preprocessing method for machine learning. 
        
        
        At the moment offers 3 preprocessing methods:
        
        --> The Percentage Categorical Pruned (PCP) merges all least frequent levels (summing up to "perc" percent) into a single level as presented in (https://doi.org/10.1109/IJCNN.2019.8851888), which, for example, can be "Others" category. It can be useful when dealing with several amounts of categorical information (e.g., city data). Also providing the dictionary with the transformations for each column.
        
        --> The Inverse Document Frequency (IDF) codifies the categorical levels into frequency values, where the closer to 0 means, the more frequent it is (https://ieeexplore.ieee.org/document/8710472). 
        
        --> Finally it also has implemented a simpler standard One-Hot-Encoding method.
        
        
        
        
        # Installation
        
        To install this package please run the following command
        
        ``` cmd
        pip install cane 
        
        ```
        
        # Suggestions and feedback
        Any feedback will be appreciated.
        For questions and other suggestions contact luis.matos@dsi.uminho.pt
        
        
        # Example
        ``` python
        import pandas as pd
        import cane
        import timeit
        x = [k for s in ([k] * n for k, n in [('a', 30000), ('b', 50000), ('c', 70000), ('d', 10000), ('e', 1000)]) for k in s]
        df = pd.DataFrame({f'x{i}' : x for i in range(1, 13)})
        
        dataPCP, dicionary = cane.pcp(df)  # uses the PCP method and only 1 core with perc == 0.05
        dataPCP, dicionary = cane.pcp(df, n_coresJob=2)  # uses the PCP method and only 2 cores
        dataPCP, dicionary = cane.pcp(df, n_coresJob=2,disableLoadBar = False)  # With Progress Bar
        
        dataIDF = cane.idf(df)  # uses the IDF method and only 1 core
        dataIDF = cane.idf(df, n_coresJob=2)  # uses the IDF method and only 2 core
        dataIDF = cane.idf(df, n_coresJob=2,disableLoadBar = False)  # With Progress Bar
        
        dataH = cane.one_hot(df)  # without a column prefixer
        dataH2 = cane.one_hot(df, column_prefix='column')  # it will use the original column name prefix
        # (useful for when dealing with id number columns)
        dataH3 = cane.one_hot(df, column_prefix='customColName')  # it will use a custom prefix defined by
        # the value of the column_prefix
        dataH4 = cane.one_hot(df, column_prefix='column', n_coresJob=2)  # it will use the original column name prefix
        # (useful for when dealing with id number columns)
        # with 2 cores
        
        dataH4 = cane.one_hot(df, column_prefix='column', n_coresJob=2
                              ,disableLoadBar = False)  # With Progress Bar Active!
        # with 2 cores
        
        #Time Measurement in 10 runs
        OT = timeit.timeit(lambda:cane.one_hot(df, column_prefix='column', n_coresJob=1),number = 10)
        IT = timeit.timeit(lambda:cane.idf(df),number = 10)
        PT = timeit.timeit(lambda:cane.pcp(df),number = 10)
        print("One-Hot Time:",OT)
        print("IDF Time:",IT)
        print("PCP Time:",PT)
        
        #Time Measurment in 10 runs (multicore)
        OTM = timeit.timeit(lambda:cane.one_hot(df, column_prefix='column', n_coresJob=1),number = 10)
        ITM = timeit.timeit(lambda:cane.idf(df,n_coresJob=2),number = 10)
        PTM = timeit.timeit(lambda:cane.pcp(df,n_coresJob=2),number = 10)
        print("One-Hot Time Multicore:",OTM)
        print("IDF Time Multicore:",ITM)
        print("PCP Time Multicore:",PTM)
        
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
