cane 0.0.1.7.4

Cane - Categorical Attribute traNsformation Environment

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).

--> 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

pip install cane 

Suggestions and feedback

Any feedback will be appreciated. For questions and other suggestions contact [e-mail]

Example

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, 130)})

dataPCP = cane.pcp(df)  # uses the PCP method and only 1 core with perc == 0.05
dataPCP = cane.pcp(df, n_coresJob=2)  # uses the PCP method and only 2 cores
dataPCP = cane.pcp(df, n_coresJob=2,disableLoadBar = False)  # With Progress Bar

#dicionary with the transformed data

dicionary = cane.dic_pcp(dataPCP)
print(dicionary)

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
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)