Saravanan666/pypi-org huggingface dataset:
Package: roc2.1.1
ÔËÐÐ°ì·¨£º»·¾³£ºpython2    
ÔËÐÐÇ°ÏÈ°²×°ROC,transformºÍconRoc,°²×°°ì·¨£º
pip install roc2.1.2


roc:
    ÊäÈë£ºÎÄ¼þ¼Ðtruth£¨±ê×¢½á¹ûÎÄ¼þ¼Ð£©,test£¨Ä£ÐÍÊä³öµÄ½á¹ûÎÄ¼þ¼Ð£©,result(Òª±£´æµÄtxtÎÄ±¾ÎÄµµÂ·¾¶£©£¬roc(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©
    python 
            from ROC import roc
            roc.roc("truth","test","result","roc")
    Êä³ö£ºrocºÍ·ÖÊýÕÛÏßÍ¼,½á¹ûµÄÎÄ±¾ÎÄµµ
    PS:result ºÍroc²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\result')


conRoc:
    ÊäÈë£ºÎÄ¼þ¼Ðpre_file(ÐèÒª»æÍ¼µÄtxtËùÔÚµÄÎÄ¼þ¼Ð£©,image(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©,titlestr(Í¼Æ¬±êÌâ£©
    python 
            from ROC import conRoc
            conRoc.conRoc("pre_file","image")
    Êä³ö£ºrocÕÛÏßÍ¼
    PS:image²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\image') 
 

txt2xml:
    ÊäÈë£ºtxt_file(txt¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©
    python
            from transform import txt2xml
            txt2xml.txt2xml("txt_file","xml_file")
    Êä³ö£ºxml_file(xml¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©ÎÄ¼þ¼ÐËµÃ÷£ºtransformÎÄ¼þ¼Ð£ºtxt2xml £º½«txt¸ñÊ½ÎÄ±¾×ª»»ÎªxmlÎÄ¼þ£¨Ã¿¸ötxtÎÄ¼þ×ª»»ÎªÒ»¸öxmlÎÄ¼þ£©
ÐèÒª×ª»»µÄËùÓÐtxtÎÄ¼þ·ÅÔÚtxt_fileÖÐ£¬×ª»»ºóµÄxmlÎÄ¼þ´æ·ÅÔÚxml_fileÖÐROCÎÄ¼þ¼Ð£ºroc: »æÖÆrocÇúÏßºÍ·ÖÊýÇúÏß²¢¼ÆËãacu¡£Êý¾ÝÀ´Ô´ÓÚdbÁÐ±íIOU: ¼ÆËãÁ½¾ØÐÎIOU,´«ÈëÊý¾ÝÎªÃ¿¸ö¾ØÐÎÁ½Ìõ¶Ô½ÇÏßµÄºá×Ý×ø±êReframe=[X1,Y1,X2,Y2],GTframe=[X1,Y1,X2,Y2]¡£read:¶ÁÈ¡xmlÎÄ¼þµÄËùÓÐ±ê×¢µÄ±êÇ©Êý¾Ý(xmin,ymin,xmax,ymax)¡£´«ÈëÊý¾ÝÎª´ý²âÊÔxmlÎÄµµµÄÔªËØ¶ÔÏó¡£Analyze_xml£º½âÎöxmlÎÄ¼þ²¢µÃ³öIOU±È¶ÔÖÐ±È¶Ô´íÎó(ÖØµþ±ÈÂÊÐ¡ÓÚ0.5£©ºÍ±È¶ÔÕýÈ·µÄ¸öÊý¡£
ÊäÈë£ºtruthÎª±ê×¼xmlÎÄ¼þËùÔÚµÄÎÄ¼þ¼Ð£¬testÎª´ý±È¶ÔµÄxmlÎÄ¼þËùÔÚµÄÎÄ¼þ¼Ð¡£conROCÎÄ¼þ¼Ð:conRoc:¸ù¾ÝÎÄ¼þ¼ÐÖÐµÄËùÓÐtxtÎÄ¼þÄÚÈÝ»æÖÆÇúÏßÍ¼£¨Ã¿¸öÎÄ¼þ¶ÔÓ¦Á½ÌõÍ¬É«µÄÕÛÏß£©
Package: roc2.1.2
ÔËÐÐ°ì·¨£º»·¾³£ºpython2    
ÔËÐÐÇ°ÏÈ°²×°ROC,transformºÍconRoc,°²×°°ì·¨£º
pip install roc2.1.2


roc:
    ÊäÈë£ºÎÄ¼þ¼Ðtruth£¨±ê×¢½á¹ûÎÄ¼þ¼Ð£©,test£¨Ä£ÐÍÊä³öµÄ½á¹ûÎÄ¼þ¼Ð£©,result(Òª±£´æµÄtxtÎÄ±¾ÎÄµµÂ·¾¶£©£¬roc(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©
    python 
            from ROC import roc
            roc.roc("truth","test","result","roc")
    Êä³ö£ºrocºÍ·ÖÊýÕÛÏßÍ¼,½á¹ûµÄÎÄ±¾ÎÄµµ
    PS:result ºÍroc²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\result')


conRoc:
    ÊäÈë£ºÎÄ¼þ¼Ðpre_file(ÐèÒª»æÍ¼µÄtxtËùÔÚµÄÎÄ¼þ¼Ð£©,image(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©,titlestr(Í¼Æ¬±êÌâ£©
    python 
            from ROC import conRoc
            conRoc.conRoc("pre_file","image","titlestr")
    Êä³ö£ºrocÕÛÏßÍ¼
    PS:image²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\image') 
 

txt2xml:
    ÊäÈë£ºtxt_file(txt¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©
    python
            from transform import txt2xml
            txt2xml.txt2xml("txt_file","xml_file")
    Êä³ö£ºxml_file(xml¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©ÎÄ¼þ¼ÐËµÃ÷£ºtransformÎÄ¼þ¼Ð£ºtxt2xml £º½«txt¸ñÊ½ÎÄ±¾×ª»»ÎªxmlÎÄ¼þ£¨Ã¿¸ötxtÎÄ¼þ×ª»»ÎªÒ»¸öxmlÎÄ¼þ£©
ÐèÒª×ª»»µÄËùÓÐtxtÎÄ¼þ·ÅÔÚtxt_fileÖÐ£¬×ª»»ºóµÄxmlÎÄ¼þ´æ·ÅÔÚxml_fileÖÐROCÎÄ¼þ¼Ð£ºroc: »æÖÆrocÇúÏßºÍ·ÖÊýÇúÏß²¢¼ÆËãacu¡£Êý¾ÝÀ´Ô´ÓÚdbÁÐ±íIOU: ¼ÆËãÁ½¾ØÐÎIOU,´«ÈëÊý¾ÝÎªÃ¿¸ö¾ØÐÎÁ½Ìõ¶Ô½ÇÏßµÄºá×Ý×ø±êReframe=[X1,Y1,X2,Y2],GTframe=[X1,Y1,X2,Y2]¡£read:¶ÁÈ¡xmlÎÄ¼þµÄËùÓÐ±ê×¢µÄ±êÇ©Êý¾Ý(xmin,ymin,xmax,ymax)¡£´«ÈëÊý¾ÝÎª´ý²âÊÔxmlÎÄµµµÄÔªËØ¶ÔÏó¡£Analyze_xml£º½âÎöxmlÎÄ¼þ²¢µÃ³öIOU±È¶ÔÖÐ±È¶Ô´íÎó(ÖØµþ±ÈÂÊÐ¡ÓÚ0.5£©ºÍ±È¶ÔÕýÈ·µÄ¸öÊý¡£
ÊäÈë£ºtruthÎª±ê×¼xmlÎÄ¼þËùÔÚµÄÎÄ¼þ¼Ð£¬testÎª´ý±È¶ÔµÄxmlÎÄ¼þËùÔÚµÄÎÄ¼þ¼Ð¡£conROCÎÄ¼þ¼Ð:conRoc:¸ù¾ÝÎÄ¼þ¼ÐÖÐµÄËùÓÐtxtÎÄ¼þÄÚÈÝ»æÖÆÇúÏßÍ¼£¨Ã¿¸öÎÄ¼þ¶ÔÓ¦Á½ÌõÍ¬É«µÄÕÛÏß£©
Package: roc2.1.3
ÔËÐÐ°ì·¨£º»·¾³£ºpython2    
ÔËÐÐÇ°ÏÈ°²×°ROC,transformºÍconRoc,°²×°°ì·¨£º
pip install roc2.1.2


roc:
    ÊäÈë£ºÎÄ¼þ¼Ðtruth£¨±ê×¢½á¹ûÎÄ¼þ¼Ð£©,test£¨Ä£ÐÍÊä³öµÄ½á¹ûÎÄ¼þ¼Ð£©,result(Òª±£´æµÄtxtÎÄ±¾ÎÄµµÂ·¾¶£©£¬roc(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©
    python 
            from ROC import roc
            roc.roc("truth","test","result","roc")
    Êä³ö£ºrocºÍ·ÖÊýÕÛÏßÍ¼,½á¹ûµÄÎÄ±¾ÎÄµµ
    PS:result ºÍroc²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\result')


drawpic:
    ÊäÈë£ºÎÄ¼þ¼Ðpre_file(ÐèÒª»æÍ¼µÄtxtËùÔÚµÄÎÄ¼þ¼Ð£©,image(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©,titlestr(Í¼Æ¬±êÌâ£©
    python 
            from ROC import conRoc
            conRoc.conRoc("pre_file","image","titlestr")
    Êä³ö£ºrocÕÛÏßÍ¼
    PS:image²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\image') 
 

txt2xml:
    ÊäÈë£ºtxt_file(txt¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©
    python
            from transform import txt2xml
            txt2xml.txt2xml("txt_file","xml_file")
    Êä³ö£ºxml_file(xml¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©  


xml2txt:
    ÊäÈë£ºxml_file(xml¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©
    python
            from transform import xml2txt
            xml2txt.xml2txt("xml_file","txt_file")
    Êä³ö£ºtxt_file(txt¸ñÊ½±ê×¢ÐÅÏ¢Ëù´æ·ÅÎÄ¼þ¼Ð£©


DataOfRoc:
    ÊäÈë£ºÎÄ¼þ¼Ðtruth£¨±ê×¢½á¹ûÎÄ¼þ¼Ð£©,test£¨Ä£ÐÍÊä³öµÄ½á¹ûÎÄ¼þ¼Ð£©
    python 
            from ROC import DataOfRoc
            DataOfRoc.DataOfRoc("truth","test")
    Êä³ö£ºtp£¨Îó¶Á¸öÊý£©,pos£¨Êµ¼ÊÕýÈ·¸öÊý£©,rate£¨ÕÙ»ØÂÊ£©
  

IOU£º
    ÊäÈë£ºReframe,GTframe(Á½¸öÁÐ±í£¬ÄÚÈÝÎª¾ØÐÎµÄ¶¨µã×ø±êXmin,Ymin,Xmax,Ymax)
    python
            from ROC import IOU
            IOU.IOU(Reframe,GTframe)
    Êä³ö£ºratio(Á½³¤·½ÐÎÃæ»ýÖØµþ±ÈÂÊ£©


scoreRoc£º
    ÊäÈë£ºlistdata(Êý¾ÝÁÐ±í:Ã¿×éÊý¾Ýwei:[ÊÇ·ñ´íÎó£¨1/0£©,ÊÇ·ñÕýÈ·£¨1/0£©,score]£©,result(Òª±£´æµÄtxtÎÄ±¾ÎÄµµÂ·¾¶£©£¬roc(Òª±£´æµÄÍ¼Æ¬Â·¾¶£©
    python 
            from ROC import scoreRoc
            scoreRoc.scoreRoc(listdata,"result","roc")
    Êä³ö£ºrocºÍ·ÖÊýÕÛÏßÍ¼,½á¹ûµÄÎÄ±¾ÎÄµµ
    PS:result ºÍroc²»´ø¸ñÊ½£¨Àý£º'D:\\python_work\\result')


nms£º
    ÊäÈë£º
    python
            from nms import nms
            nms.nms()
    Êä³ö£ºÎÄ¼þ¼ÐËµÃ÷£ºtransformÎÄ¼þ¼Ð£ºtxt2xml £º½«txt¸ñÊ½ÎÄ±¾×ª»»ÎªxmlÎÄ¼þ£¨Ã¿¸ötxtÎÄ¼þ×ª»»ÎªÒ»¸öxmlÎÄ¼þ£©
ÐèÒª×ª»»µÄËùÓÐtxtÎÄ¼þ·ÅÔÚtxt_fileÖÐ£¬×ª»»ºóµÄxmlÎÄ¼þ´æ·ÅÔÚxml_fileÖÐxml2txt £º½«xml¸ñÊ½ÎÄ±¾×ª»»ÎªtxtÎÄ¼þ£¨Ã¿¸öxmlÎÄ¼þ×ª»»ÎªÒ»¸ötxtÎÄ¼þ£©
ÐèÒª×ª»»µÄËùÓÐxmlÎÄ¼þ·ÅÔÚxml_fileÖÐ£¬×ª»»ºóµÄtxtÎÄ¼þ´æ·ÅÔÚtxt_fileÖÐROCÎÄ¼þ¼Ð£ºroc: »æÖÆrocÇúÏßºÍ·ÖÊýÇúÏß²¢¼ÆËãacu¡£Êý¾ÝÀ´Ô´ÓÚdbÁÐ±íIOU: ¼ÆËãÁ½¾ØÐÎIOU,´«ÈëÊý¾ÝÎªÃ¿¸ö¾ØÐÎÁ½Ìõ¶Ô½ÇÏßµÄºá×Ý×ø±êReframe=[X1,Y1,X2,Y2],GTframe=[X1,Y1,X2,Y2]¡£read:¶ÁÈ¡xmlÎÄ¼þµÄËùÓÐ±ê×¢µÄ±êÇ©Êý¾Ý(xmin,ymin,xmax,ymax)¡£´«ÈëÊý¾ÝÎª´ý²âÊÔxmlÎÄµµµÄÔªËØ¶ÔÏó¡£Analyze£º½âÎöxmlÎÄ¼þ²¢µÃ³öIOU±È¶ÔÖÐ±È¶Ô´íÎó(ÖØµþ±ÈÂÊÐ¡ÓÚ0.5£©ºÍ±È¶ÔÕýÈ·µÄ¸öÊý¡£
ÊäÈë£ºtruthÎª±ê×¼xmlÎÄ¼þËùÔÚµÄÎÄ¼þ¼Ð£¬testÎª´ý±È¶ÔµÄxmlÎÄ¼þËùÔÚµÄÎÄ¼þ¼Ð¡£scoreRoc£º¸ù¾ÝÁÐ±íÊý¾Ý£¬»æÖÆrocÇúÏß
ÁÐ±íÃ¿×éÊý¾ÝÎª[wrong,right,score] wrongÓërightÎªÎó¶ÁºÍÕýÈ·µÄ±êÊ¶£¬ÖµÎª1/0£¬scoreÀ´Ô´ÓÚxmlÎÄ¼þDataOfRoc:±È½ÏÁ½¸öÎÄ¼þ¼ÐÖÐµÄxmlÎÄ¼þ£¬²¢¼ÆËã³öÎó¶Á¸öÊý£¬Êµ¼ÊÕýÈ·¸öÊý£¬ºÍÕÙ»ØÂÊdrawpic:¸ù¾ÝÁ½¸öÎÄ¼þ¼ÐÖÐtxtÎÄ¼þ»­rocÇúÏßÍ¼¡£Ã¿¸ötxtÎÄ¼þ¶ÔÓ¦ÑÕÉ«ÏàÍ¬µÄÁ½ÌõÇúÏß£¨ÕÙ»ØÂÊÇúÏßºÍ·ÖÊýÇúÏß£©
Package: roc2.1.4
Operation method £ºBofore Operation install "ROC,transform,nms"£º
pip install roc2.1.4


roc:
    input£ºtruth£¨folder with Annotation results£©,test£¨folder with Model output£©,stdscore(standard score to Judge right or wrong £©£¬result(path of txt file to besaved£©£¬roc(path of image to besaved£©
    python 
            from ROC import roc
            roc.roc("truth","test",stdscore£¬"result","roc")
    output£ºroc and score image,txt file
    PS:'result' and 'roc' not have format £¨such as£º'D:\\python_work\\result')


drampic:
    input:pre_file(folder with txt files£©,image(path of image to besaved£©,titlestr(title of image£©
    python 
            from ROC import conRoc
            conRoc.conRoc("pre_file","image","titlestr")
    output£ºroc image 
    PS:'image' not have format £¨such as£º'D:\\python_work\\image') 


IOU:
    input£ºReframe,GTframe£¨two lists with information of Rectangle £¬[Xmin,Ymin,Xmax,Ymax])
    python 
            from ROC import IOU
            IOU.IOU(Reframe,GTframe)
    output£ºratio(Rectangle area overlap rate £©   


DataOfRoc:
    input£ºtruth£¨folder with Annotation results£©,test£¨folder with Model output£©
    python 
            from ROC import DataOfRoc
            DataOfRoc.DataOfRoc("truth","test")
    output£ºtp(wrong num£©,pos£¨Total correct number £©,rate£¨recall rate£©


scoreRoc:
    input£ºlistdata(list[1/0,1/0,score]),image(path of image to besaved£©,titlestr(title of image£©
    python 
            from ROC import scoreRoc
            scoreRoc.scoreRoc(listdata,"image","titlestr")
    output£ºroc image
    PS:'image' not have format£¨such as£º'D:\\python_work\\image')


txt2xml:
    input£ºtxt_file(folder with txt format Annotation information£©
    python
            from transform import txt2xml
            txt2xml.txt2xml("txt_file","xml_file")
    output£ºxml_file(folder with xml format Annotation information£©  


xml2txt:
    input£ºxml_file(folder with xml format Annotation information£©
    python
            from transform import xml2txt
            xml2txt.xml2txt("xml_file","txt_file")
    output£ºtxt_file(folder with txt format Annotation information£©



nms:
    input£º
    python
            from nms import nms
            nms.nms()
    output£º
Package: roc2.1.5
Operation method £ºBofore Operation install "ROC,transform,nms"£º
pip install roc2.1.5


roc:
    input£ºtruth£¨folder with Annotation results£©,test£¨folder with Model output£©,stdscore(standard score to Judge right or wrong £©£¬result(path of txt file to besaved£©£¬roc(path of image to besaved£©
    python 
            from ROC import roc
            roc.roc("truth","test",stdscore£¬"result","roc")
    output£ºroc and score image,txt file
    PS:'result' and 'roc' not have format £¨such as£º'D:\\python_work\\result')


drawpic:
    input:pre_file(folder with txt files£©,image(path of image to besaved£©,titlestr(title of image£©
    python 
            from ROC import drawpic
            drawpic.drawpic("pre_file","image","titlestr")
    output£ºroc image 
    PS:'image' not have format £¨such as£º'D:\\python_work\\image') 


IOU:
    input£ºReframe,GTframe£¨two lists with information of Rectangle £¬[Xmin,Ymin,Xmax,Ymax])
    python 
            from ROC import IOU
            IOU.IOU(Reframe,GTframe)
    output£ºratio(Rectangle area overlap rate £©   


DataOfRoc:
    input£ºtruth£¨folder with Annotation results£©,test£¨folder with Model output£©
    python 
            from ROC import DataOfRoc
            DataOfRoc.DataOfRoc("truth","test")
    output£ºtp(wrong num£©,pos£¨Total correct number £©,rate£¨recall rate£©


scoreRoc:
    input£ºlistdata(list[1/0,1/0,score]),image(path of image to besaved£©,titlestr(title of image£©
    python 
            from ROC import scoreRoc
            scoreRoc.scoreRoc(listdata,"image","titlestr")
    output£ºroc image
    PS:'image' not have format£¨such as£º'D:\\python_work\\image')


txt2xml:
    input£ºtxt_file(folder with txt format Annotation information£©
    python
            from transform import txt2xml
            txt2xml.txt2xml("txt_file","xml_file")
    output£ºxml_file(folder with xml format Annotation information£©  


xml2txt:
    input£ºxml_file(folder with xml format Annotation information£©
    python
            from transform import xml2txt
            xml2txt.xml2txt("xml_file","txt_file")
    output£ºtxt_file(folder with txt format Annotation information£©



nms:
    input£º
    python
            from nms import nms
            nms.nms()
    output£º
Package: roc2.1.6
Operation method £ºBofore Operation install "ROC,transform,nms"£º
pip install roc2.1.6


roc:
    input£ºtruth£¨folder with Annotation results£©,test£¨folder with Model output£©,stdscore(standard score to Judge right or wrong £©£¬result(path of txt file to besaved£©£¬roc(path of image to besaved£©
    python 
            from ROC import roc
            roc.roc("truth","test",stdscore£¬"result","roc")
    output£ºroc and score image,txt file
    PS:'result' and 'roc' not have format £¨such as£º'D:\\python_work\\result')


drawpic:
    input:pre_file(folder with txt files£©,image(path of image to besaved£©,titlestr(title of image£©
    python 
            from ROC import drawpic
            drawpic.drawpic("pre_file","image","titlestr")
    output£ºroc image 
    PS:'image' not have format £¨such as£º'D:\\python_work\\image') 


IOU:
    input£ºReframe,GTframe£¨two lists with information of Rectangle £¬[Xmin,Ymin,Xmax,Ymax])
    python 
            from ROC import IOU
            IOU.IOU(Reframe,GTframe)
    output£ºratio(Rectangle area overlap rate £©   


DataOfRoc:
    input£ºtruth£¨folder with Annotation results£©,test£¨folder with Model output£©
    python 
            from ROC import DataOfRoc
            DataOfRoc.DataOfRoc("truth","test")
    output£ºtp(wrong num£©,pos£¨Total correct number £©,rate£¨recall rate£©


scoreRoc:
    input£ºlistdata(list[1/0,1/0,score]),image(path of image to besaved£©,titlestr(title of image£©
    python 
            from ROC import scoreRoc
            scoreRoc.scoreRoc(listdata,"image","titlestr")
    output£ºroc image
    PS:'image' not have format£¨such as£º'D:\\python_work\\image')


txt2xml:
    input£ºtxt_file(folder with txt format Annotation information£©
    python
            from transform import txt2xml
            txt2xml.txt2xml("txt_file","xml_file")
    output£ºxml_file(folder with xml format Annotation information£©  


xml2txt:
    input£ºxml_file(folder with xml format Annotation information£©
    python
            from transform import xml2txt
            xml2txt.xml2txt("xml_file","txt_file")
    output£ºtxt_file(folder with txt format Annotation information£©



nms:
    input£º
    python
            from nms import nms
            nms.nms()
    output£º
Package: roc-aggregator
roc-aggregatorAggregates multiple Receiver Operating Characteristic (ROC) curves
obtained from different sources into one global ROC. Additionally, it’s
also possible to calculate the aggregated precision-recall (PR) curve.MotivationThe ROC and the AUC (Area Under the Curve) can be essential metrics when
evaluating a model. In situations where there is a parallelization of
the model development, such as federated learning, it becomes relevant
to obtain precise measures for these metrics. These approaches usually
produce partial results that require aggregation methods to get the
complete picture. The ROCaggregator appears in this context, allowing to
compute the precise ROC curve from the partial results.UsageInstall the package using one of the following options:pip3:pip3 install roc-aggregatorthis repositorypip3 install .Example:A complete example of the usage of the roc-aggregator can be foundhere.Obtain the global ROC curve from different sources by providing the false positive rate (fpr), true positive rate (tpr), thresholds (thresh), the total number of negative samples, and the total number of samples from each source:fromroc_aggregatorimportroc_curvefpr_1=[0,0,0,0,0.002,...]# false positive rate values for each thresholdtpr_1=[0,0.004,0.008,0.012,0.016,...]# true positive rate values for each thresholdthresh_1=[0.9994038,0.9986345,0.99847864,0.99575908,0.99567612,...]# thresholds usednegative_count_1=np.count_nonzero(y1==0)# count the number of negative labelstotal_count_1=len(y1)# total number of labels...# ROCfpr,tpr,thresh_stack=roc_curve([fpr_1,fpr_2,...],[tpr_1,tpr_2,...],[thresh_1,thresh_2,...],[negative_count_1,negative_count_2,...],[total_count_1,total_count_2,...])# Precision-Recallpre,recall,thresh_stack=precision_recall_curve([fpr_1,fpr_2,...],[tpr_1,tpr_2,...],[thresh_1,thresh_2,...],[negative_count_1,negative_count_2,...],[total_count_1,total_count_2,...])Calculate the AUC using numpy:importnumpyasnpnp.trapz(tpr,fpr)Visualizationimportmatplotlib.pyplotaspltplt.style.use('seaborn')plt.plot(fpr,tpr,color=color,label=label,linestyle=linestyle)plt.legend()plt.title('ROC curve')plt.xlabel('False Positive Rate')plt.ylabel('True Positive rate')plt.show()TestingUnit tests are available at/roc-aggregator/tests.
Install the dependencies required and run the tests usingpytestorpython3 setup.py test.
Package: roc-auc-pairwise
RocAuc Pairwise Loss/ObjectiveThis is gpu implementation of rocauc pairwise objectives for gradient boosting:
$$L = \sum_{i, j} (\hat P_{ij}\log{P_{ij}} + (1 - \hat P_{ij})\log{(1 - P_{ij})}) |\Delta_{AUC_{ij}}|$$
This package could be used to solve classification problems with relative small numbers of objects, where you need to improve rocauc score.Also there is cpu multithread implementation of this losses.Losses that are implemented in this packageSigmoid pairwise loss.(GPU or CPU implementations)
$$L = \sum_{i, j}\hat P_{ij}\log{P_{ij}} + (1 - \hat P_{ij})\log{(1 - P_{ij})}$$RocAuc Pairwise Losswith approximate auc computation. (GPU or CPU implementations)
$$L = \sum_{i, j} (\hat P_{ij}\log{P_{ij}} + (1 - \hat P_{ij})\log{(1 - P_{ij})})|\Delta_{AUC^{approx}_{ij}}|$$RocAuc Pairwise Loss Exact(GPU or CPU implementations) with exact auc computation. This could be more compute intensive, but this loss might be helpfull for first boosting rounds (if you are using gradient boosting)
$$L = \sum_{i, j} (\hat P_{ij}\log{P_{ij}} + (1 - \hat P_{ij})\log{(1 - P_{ij})})|\Delta_{AUC^{exact}_{ij}}|$$RocAuc Pairwise Loss Exact Smoothened(GPU or CPU implementations). This loss allows you to incorporate information about equal instances. Because $\Delta_{AUC_{ij}} = 0$ if $y_i = y_j$. So we just add small $\epsilon > 0$ in equation.
$$L = \sum_{i, j} (\hat P_{ij}\log{P_{ij}} + (1 - \hat P_{ij})\log{(1 - P_{ij})})(\epsilon + |\Delta_{AUC^{exact}_{ij}}|)$$PerformanceIntel Core i5 10600KF (12 threads), Nvidia RTX 3060For more informationYou can seeGitHub repository.Or you can use example notebook onGoogle ColabOr you can use example notebook onKaggleReferences[1] Sean J. Welleck, Efficient AUC Optimization for Information Ranking Applications, IBM USA (2016)[2] Burges, C.J.: From ranknet to lambdarank to lambdamart: An overview. Learning (2010)[3] Calders, T., Jaroszewicz, S.: Efficient auc optimization for classification. Knowledge
Discovery in Databases. (2007)
Package: rocc
rocc - Rate-of-change check for time seriesfrom rocc import Threshold, rocc

result = rocc(
   timeseries=a_htimeseries_object,
   thresholds=(
      Threshold("10min", 10),
      Threshold("20min", 15),
      Threshold("H", 40),
   ),
   symmetric=True,
   flag="MYFLAG",
)timeseriesis aHTimeseriesobject.thresholdsis, obviously,
a list of thresholds.Thresholdis a named tuple whose items aredelta_t(a pandas interval specification) andallowed_diff(a
floating point number).The function checks whether there exist intervals during which the value
of the time series changes by more than the specified threshold. The
offending records are flagged with the specifiedflag.It returns a list of strings describing where the thresholds have been
exceeded.IfflagisNoneor the empty string, then the offending records
are not flagged, and the only result is the returned value.Here is an example time series:2020-10-06 14:30    24.0
2020-10-06 14:40    25.0
2020-10-06 14:50    36.0 *
2020-10-06 15:01    51.0
2020-10-06 15:21    55.0
2020-10-06 15:31    65.0
2020-10-06 15:41    75.0 *
2020-10-06 15:51    70.0After runningrocc()with thethresholdsspecified in the
example above, the records marked with a star will be flagged. The
record14:50will be flagged because in the preceding 10-minute
interval the value increases by 11, which is more than 10. The record15:41will be flagged because in the preceding 20-minute interval
the value increases by 20, which is more than 15. The record15:01will be unflagged; although there’s a large difference since14:40,
this is 21 minutes, not 20, so the 20-minute threshold of 15 does not
apply; likewise, there’s a difference of 15 from14:50, which does
not exceed the 20-minute threshold of 15, and while it does exceed the
10-minute threshold of 10, it’s 11 minutes, not 10. There’s also not any
difference larger than 40 within an hour anywhere.The return value in this example will be a list of two strings:"2020-10-06T14:50  +11.0 in 10min (> 10.0)"
"2020-10-06T15:41  +20.0 in 20min (> 15.0)"The return value should only be used for consumption by humans; it is
subject to change.IfsymmetricisTrue, it is the absolute value of the change
that matters, not its direction. In this case,allowed_diffmust be
positive. IfsymmetricisFalse(the default), only rates larger
than positiveallow_diffor rates smaller than negativeallow_diffare flagged.History3.0.1 (2023-12-20)Compatible with htimeseries 6.3.0.0 (2022-12-01)Compatible with htimeseries 4, which uses aware timestamps.2.0.0 (2020-02-11)rocc() now returns a list of strings with descriptions of where the
thresholds have been exceeded.The default flag is now to not add any flags.A bug has been fixed where it would crash if input time series was
empty.1.0.0 (2020-11-06)Initial release
Package: rocca
Rocca: Simple Machine LearningRocca adalah library Python yang berfokus pada penyediaan implementasi berbagai algoritma machine learning, termasuk metode ensemble, clustering, dan association rule mining.FiturAlgoritma ensemble: Bagging, Random Forest, XGBoostClustering: K-MeansAssociation Rule Mining: AprioriDecision Tree: Standar Decision Tree dan Regression TreeTujuanRocca dirancang untuk menyederhanakan proses pembelajaran dan eksplorasi dalam pembelajaran mesin. Dengan kode yang sederhana dan mudah dipahami, Rocca bertujuan untuk menjadi alat bantu yang efektif bagi mereka yang baru memulai atau sedang belajar tentang pembelajaran mesin.InstalasiUntuk menginstal Rocca, cukup gunakan pip:pipinstallroccaPenggunaanBerikut adalah beberapa contoh penggunaan library Rocca:from rocca.clustering import KMeans

model = KMeans(k=3)
model.fit(X)
labels = model.get_cluster_labels()Source CodeAkses source codenya diGitHub.
Package: rocco
ROCCO: [R]obust [O]pen [C]hromatin Detection via [C]onvex [O]ptimizationROCCO is a scalable consensus peak calling algorithm for open chromatin count signals in large sample sizes.FeaturesConsideration of enrichment and spatial characteristicsof open chromatin signals to capture the full extent of peaks;Mathematically tractable modelthat permits performance and efficiency guarantees.Efficient for large numbers of sampleswith an asymptotic time complexity independent of sample size;No arbitrary thresholdson the minimum number of supporting samples/replicates;No required training dataor a heuristically determined set of initial candidate peak regions;Paper/CitationIf using ROCCO in your research, please cite theoriginal paperinBioinformaticsNolan H Hamilton, Terrence S Furey, ROCCO: a robust method for detection of open chromatin via convex optimization,
 Bioinformatics, Volume 39, Issue 12, December 2023DOI:10.1093/bioinformatics/btad725DocumentationDocumentation and example usage are available athttps://nolan-h-hamilton.github.io/ROCCO/Installationpip install roccoInputROCCO acceptsBAMalignments orBigWigcoverage tracks and a genome sizes file as input. ROCCO has been tested primarily using ATAC-seq data but may be suitable for DNase-seq experiments as well.OutputABEDfile containing peak regions and scores.Minimal ExampleRun ROCCO on the test data included with this repository (BigWig files).rocco -i tests/data/*.bw --genome_file tests/test_hg38.sizesDefault output BED file named asrocco_peaks_[timestamp].bed. Because no--chrom_param_fileis supplied,
the default genome-wide budget (0.035), gamma (1.0), etc. are used for all chromosomes. See theDocumentationfor additional examples and details.Testing ROCCOcd tests
pytest -v -rPA -l -k "regular" test_rocco.pyVersion HistoryPrevious releases can be found athttps://github.com/nolan-h-hamilton/ROCCO/tagsAdditional dependencies for optional features:'mosek': Commercial grade solver. Users can instantly obtain a free academic license or generous trial commericial license athttps://www.mosek.com/products/academic-licenses/.'ortools': includes the first-order solver, PDLP.'pytest': allows local execution of the Tests workflow.