Metadata-Version: 2.1
Name: cane
Version: 2.3.1
Summary: Cane - Categorical Attribute traNsformation Environment
Home-page: https://github.com/Metalkiler/Cane-Categorical-Attribute-traNsformation-Environment
Author: Luís Miguel Matos, João Azevedo, Arthur Matta, André Pilastri, Paulo Cortez, Rui Mendes
Author-email: luis.matos@dsi.uminho.pt
License: MIT
Description: # Cane - Categorical Attribute traNsformation Environment
        [![Downloads](https://pepy.tech/badge/cane)](https://pepy.tech/project/cane) [![Downloads](https://pepy.tech/badge/cane/month)](https://pepy.tech/project/cane) [![Downloads](https://pepy.tech/badge/cane/week)](https://pepy.tech/project/cane)
        
        CANE is a simpler but powerful preprocessing method for machine learning.
        
        At the moment offers some 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).
        
        An example of this can be viewed by the following pdf:
         
        <p><a href="https://github.com/Metalkiler/Cane-Categorical-Attribute-traNsformation-Environment/blob/master/cities.pdf">View PDF</a>.</p>
          
        
        Which the 1,000 highest frequency values (decreasing order) for the user city attribute for the TEST traffic data (which contains a total of 10,690 levels).
        For this attribute and when <img src="https://render.githubusercontent.com/render/math?math=P=10">, PCP selects only the most frequent 688 levels (dashed vertical line) merging the other 10,002 infrequent levels into the "Others" label.
        
        This method results in 689 binary inputs, which is much less than the 10690 binary inputs required by the standard one-hot transform (reduction of <img src="https://render.githubusercontent.com/render/math?math=\frac{10690-689}{10690}=94"> percentage points).
        
        --> 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>).
        
        --> 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)
        
        --> Use IDF with spark dataframes
        
        
        Future Function ideas:
        -- 
        MultiColumn scale (based on the implementation of IDF and PCP)
        
        Scaling of IDF values (normalized IDF) 
        
        
        
        
        # Installation
        
        To install this package please run the following command
        
        ``` cmd
        pip install cane
        ```
        
        # New
        Version 2.3:
        
        [x] - PCP with spark dataframes
        
        [x] - Improvements in the example file and readme
        
        [x] -  New Citation
        
        
        
        
        
        # 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!
        
        # Citation
        To cite this module please use:
        
        ```
        @article{MATOS2022100359,
        	author = {Lu{\'\i}s Miguel Matos and Jo{\~a}o Azevedo and Arthur Matta and Andr{\'e} Pilastri and Paulo Cortez and Rui Mendes},
        	doi = {https://doi.org/10.1016/j.simpa.2022.100359},
        	issn = {2665-9638},
        	journal = {Software Impacts},
        	keywords = {Data preprocessing, CANE, Python programming language, Machine learning},
        	pages = {100359},
        	title = {Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing},
        	url = {https://www.sciencedirect.com/science/article/pii/S2665963822000720},
        	year = {2022},
        	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S2665963822000720},
        	bdsk-url-2 = {https://doi.org/10.1016/j.simpa.2022.100359}}
        
        ```
        
        # Example
        
        ``` python
        import pandas as pd
        import cane
        import timeit
        import numpy as np
        x = [k for s in ([k] * n for k, n in [('a', 70000), ('b', 50000), ('c', 30000), ('d', 10000), ('e', 1000)]) for k in s]
        df = pd.DataFrame({f'x{i}' : x for i in range(1, 130)})
        
        dataPCP = cane.pcp(df)  # uses the PCP method and only 1 core with perc == 0.05 for all columns
        dataPCP = cane.pcp(df, n_coresJob=2)  # uses the PCP method and only 2 cores for all columns
        dataPCP = cane.pcp(df, n_coresJob=2,disableLoadBar = False)  # With Progress Bar for all columns
        dataPCP = cane.pcp(df, n_coresJob=2,disableLoadBar = False, columns_use = ["x1","x2"])  # With Progress Bar and specific columns
        
        
        
        #dicionary with the transformed data
        dataPCP = cane.pcp(df) 
        dicionary = cane.PCPDictionary(dataset = dataPCP, columnsUse = dataPCP.columns,
                                      targetColumn = None) #no target feature to avoid going into dictionary
        print(dicionary)
        
        dataIDF = cane.idf(df)  # uses the IDF method and only 1 core for all columns 
        dataIDF = cane.idf(df, n_coresJob=2)  # uses the IDF method and only 2 core for all columns
        dataIDF = cane.idf(df, n_coresJob=2,disableLoadBar = False)  # With Progress Bar for all columns
        dataIDF = cane.idf(df, n_coresJob=2,disableLoadBar = False, columns_use = ["x1","x2"]) # specific columns
        dataIDF = cane.idf_multicolumn(df, columns_use = ["x1","x2"])  # aplication of specific multicolumn setting IDF
        
        idfDicionary = cane.idfDictionary(Original = df, Transformed = dataIDF, columns_use = ["x1","x2"]) #following the example above of the 2 columns
                                        
                                        
        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
        
        dataH4 = cane.one_hot(df, column_prefix='column', n_coresJob=2
                              ,disableLoadBar = False,columns_use = ["x1","x2"])  # With Progress Bar specific columns!
        
        
        
        #specific example with multicolumn
        x2 = [k for s in ([k] * n for k, n in [('a', 50),
                                               ('b', 10),
                                               ('c', 20),
                                               ('d', 15), 
                                               ('e', 5)]) for k in s]
        
        x3 = [k for s in ([k] * n for k, n in [('a', 40),
                                               ('b', 20),
                                               ('c', 1),
                                               ('d', 1), 
                                               ('e', 38)]) for k in s]
        df2 = pd.concat([pd.DataFrame({f'x{i}' : x2 for i in range(1, 3)}),pd.DataFrame({f'y{i}' : x3 for i in range(1, 3)})], axis=1)
        dataPCP = cane.pcp(df2, n_coresJob=2,disableLoadBar = False)
        print("normal PCP \n",dataPCP)
        dataPCP2 = cane.pcp_multicolumn(df2, columns_use = ["x1","y1"])  # aplication of specific multicolumn setting PCP
        print("multicolumn PCP \n",dataPCP2)
        
        dataIDF = cane.idf(df2, n_coresJob=2,disableLoadBar = False, columns_use = ["x1","y1"]) # specific columns
        print("normal idf \n",dataIDF)
        dataIDF2 = cane.idf_multicolumn(df2, columns_use = ["x1","y1"])  # aplication of specific multicolumn setting IDF
        print("multicolumn idf \n",dataIDF2)
        
        
        
        #Time Measurement in 10 runs
        print("Time Measurement in 10 runs (unicore)")
        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 Measurement in 10 runs (multicore)
        print("Time Measurement in 10 runs (multicore)")
        OTM = timeit.timeit(lambda:cane.one_hot(df, column_prefix='column', n_coresJob=10),number = 10)
        ITM = timeit.timeit(lambda:cane.idf(df,n_coresJob=10),number = 10)
        PTM = timeit.timeit(lambda:cane.pcp(df,n_coresJob=10),number = 10)
        print("One-Hot Time Multicore:",OTM)
        print("IDF Time Multicore:",ITM)
        print("PCP Time Multicore:",PTM)
        
        
        
        
        
        # IDF with pyspark configs
        import cane
        from pyspark.sql import SparkSession
        #Create PySpark SparkSession
        spark = SparkSession.builder.getOrCreate()
        #Create PySpark DataFrame from Pandas
        sparkDF=spark.createDataFrame(df)
        cols = sparkDF.columns
        DFIDF, idf = cane.spark_idf_multicolumn(sparkDF, cols)
        print(DFIDF.show(20))
        dataIDF = cane.idf(df)
        #check if it is correct:
        print(dataIDF.equals(DFIDF.toPandas())) #equals means correct for both pandas version and original
        
        
        #PCP with pyspark configs
        import cane
        from pyspark.sql import SparkSession
        #Create PySpark SparkSession
        spark = SparkSession.builder.getOrCreate()
        #Create PySpark DataFrame from Pandas
        sparkDF=spark.createDataFrame(df)
        cols = sparkDF.columns
        DFPCP, pcp = cane.spark_pcp(sparkDF, cols, 0.05, "Others")
        DFPCP.show(20)
        #check if it is correct:
        dataPCP = cane.pcp(df)
        print(dataPCP.equals(DFPCP.toPandas())) #equals means correct for both pandas version and original
        ```
        
        # 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") # 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
        
        
        
        #####################Custom Function Example#######################
        
        #This will be an example file you of your custom function (e.g., "functions.py")
        import pandas as pd
        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_function])
        
        
        
        ### This is will be your main script
        
        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)
            
        
        ```
        
        
        
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
