SciProgPackage 1.2.0

Demo library

In order to install the package pip install it with in a python terminal.

In order to import the library simply on the top of the file import it as follows:

from SciProgPackage.ScientificProgramming import SciProg as sp

In order to run the different functions:

sp.NAMEOFTHEFUNCTION(attributes)

As an example:

dat = np.arange(1,11)

discrete_dat, cutoff = sp.atributeDiscretizeEF(dat, 3)

---

Finally in order to run all the possible tests here is a code to test it:

# -*- coding: utf-8 -*-

import random

import numpy as np

from sklearn.metrics import roc_auc_score

from sklearn.metrics import roc_curve

import matplotlib.pyplot as plt

import collections

import math

import scipy 

from numpy import genfromtxt

import pandas as pd



from SciProgPackage.ScientificProgramming import SciProg as sp







##TESTS

#TEST

print("TEST1")

print(" ")

print("atributeDiscretizeEF")

print("data:")

dat = np.arange(1,11)

print(dat)

print("RESULT:-------------------")

discrete_dat, cutoff = sp.atributeDiscretizeEF(dat, 3)

print("discrete_dat: ", discrete_dat)

print("cutoff: ", cutoff)

print("--------------------------")

print(" ")





print("TEST2")

print(" ")

#TEST

print("datasetDiscretizeEF")

print("data:")

data=np.random.randint(10,size=(10,10))

print(data)

print("RESULT:-------------------")

print(sp.datasetDiscretizeEF(data,5))

print("--------------------------")

print(" ")



print("TEST3")

print(" ")

#TEST

print("atributeDiscretizeEW")

print("data:")

dat = np.arange(1,11)

print(dat)

discrete_dat, cutoff = sp.atributeDiscretizeEW(dat, 3)

print("RESULT:-------------------")

print("discrete_dat: ", discrete_dat)

print("cutoff: ", cutoff)

print("--------------------------")

print(" ")



print("TEST4")

print(" ")

#TEST

data=np.random.rand(10,10)

print("datasetDiscretizeEW")

print("dat: ",data)

print("RESULT:-------------------")

print(sp.datasetDiscretizeEW(data,5))

print("--------------------------")

print(" ")



print("TEST5") 

print(" ")

print("variance")

#TEST

print("data")

numberCol=np.random.rand(10)

print(numberCol)

print("RESULT:-------------------")

print(sp.variance(numberCol))

print("--------------------------")

print(" ")

 

print("TEST6")

print(" ")

print("auc")

print("data")

#TEST

numberCol=np.random.rand(10)

numberCol

boolCol=np.random.randint(0,2,size=10)

boolCol

print(numberCol)

print(boolCol)

result=sp.auc(numberCol,boolCol)

print("RESULT:-------------------")

print(result)

print("--------------------------")

print(" ")

 

print("TEST7")

print(" ")

print("datasetEntropy")

 #TEST

numberCol=np.random.rand(10)

boolCol=np.random.randint(0,2,size=10)

data=np.column_stack((numberCol,boolCol))

print("data")

print(data)

print("RESULT:-------------------")

val=sp.datasetEntropy(data)

print(val) 

print("--------------------------")

print(" ")



print("TEST8")

print(" ")

print("variableNormalization")

print("data:")

print(np.array([1,2,3,4,5,5,65,4,3]))

 #TEST

print("RESULT:-------------------")

data=sp.variableNormalization(np.array([1,2,3,4,5,5,65,4,3]))

print(data) 

print("--------------------------")

print(" ")





print("TEST9")

print(" ")

print("variableEstandarization")

print("data:")

print(np.array([1,2,3,4,5,5,65,4,3]))

 #TEST

print("RESULT:-------------------")

data=sp.variableEstandarization(np.array([1,2,3,4,5,5,65,4,3]))

print(data)

print("--------------------------")

print(" ")



print("TEST10")

print(" ")

print("datasetNormalization")

#TEST

data=np.random.rand(10,10)

a=np.array([1,2,3,4,5,5,65,4,3])

b=np.array([3,2,6,4,99,5,25,42,1])

data=np.column_stack((a,b))

print("data:")

print(data)

print("RESULT:-------------------")

norm=sp.datasetNormalization(data.astype(float))

print(norm) 

print("--------------------------")

print(" ")



print("TEST11")

print(" ")

print("datasetEstandarization")

 #TEST

data=np.random.rand(10,10)

a=np.array([1,2,3,4,5,5,65,4,3])

b=np.array([3,2,6,4,99,5,25,42,1])

data=np.column_stack((a,b))

print("data:")

print(data)

print("RESULT:-------------------")

norm=sp.datasetEstandarization(data.astype(float))

print(norm) 

print("--------------------------")

print(" ")



print("TEST12")

print(" ")

print("filterDataset")

 #TEST

data=np.random.rand(10,10)

a=np.array([1,2,3,4,5,5,65,4,3])

b=np.array([1,2,3,4,5,56,65,4,3])

c=np.array([3,2,6,4,99,5,25,42,1])

data=np.column_stack((a,b,c))

print("data:")

print(data)

print("RESULT:-------------------")

val=sp.filterDataset(np.array(data.astype(float)),10000,"variance")

print(val)

print("--------------------------")

print(" ")



print("TEST13")

print(" ")

print("atributesCorrelation")

 #TEST

data=np.random.rand(10,10)

a=np.array([1,2,3,4,5,5,65,4,3])

b=np.array([3,2,6,4,99,5,25,42,1])

b=np.array([3,4,4,4,9,5,25,42,1])

data=np.column_stack((a,b,c))

print("data:")

print(data)

print("RESULT:-------------------")

norm=sp.atributesCorrelation(data.astype(float))

print(norm) 

print("--------------------------")

print(" ")



print("TEST14")

print(" ")

print("plotAUC")

 #TEST

numberCol=np.random.rand(10)

boolCol=np.random.randint(0,2,size=10)

print("data:")

print(numberCol)

print("data:")

print(boolCol)

print("RESULT:-------------------")

result=sp.plotAUC(numberCol,boolCol)

print(result)

print("--------------------------")

print(" ")



print("TEST15") 

print(" ")

print("plotMutualInformation")

data=np.random.rand(10,2)

print("data:")

print(data)

print("RESULT:-------------------")

print(sp.plotMutualInformation(data))

print("--------------------------")

print(" ")



print("TEST16")

print(" ")

print("datasetRead")

print("RESULT:-------------------")

data=sp.datasetRead('/content/myData.csv') 

print(data)

print("--------------------------")





print("TEST17")

print(" ")

print("writeDatasetCSV")

data=np.random.rand(10,2)

print("data:")

print(data)

print("RESULT:-------------------")

print(sp.writeDatasetCSV(data,'newData.csv'))

print("--------------------------")