R/ChannelAttribution.R

Defines functions auto_markov_model transition_matrix markov_model choose_order heuristic_models .onAttach

Documented in auto_markov_model choose_order heuristic_models markov_model transition_matrix

# ChannelAttribution: Markov model for online multi-channel attribution
# Copyright (C) 2015 - 2020  Davide Altomare and David Loris <http://www.channelattribution.net>

# ChannelAttribution is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# ChannelAttribution is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.

# You should have received a copy of the GNU General Public License
# along with ChannelAttribution.  If not, see <http://www.gnu.org/licenses/>.

.v=packageVersion("ChannelAttribution")
 
.onAttach = function(libname, pkgname) {

 packageStartupMessage(paste0("ChannelAttribution ",.v))
 packageStartupMessage("Visit www.channelattribution.net for more information about ChannelAttribution")
 packageStartupMessage("Function makov_model_mp has been renamed markov_model")

}

heuristic_models=function(Data, var_path, var_conv, var_value=NULL, sep=">"){

 if(!("data.frame"%in%class(Data)|"data.table"%in%class(Data))){
  print("Data must be a data.frame or a data.table")
 } 
 
 if(is.character(var_path)){
	if(!var_path%in%names(Data)){
	 print("var_path must be a column of Data")
	}
 }else{
  print("var_path must be a string")
 }
 if(is.character(var_conv)){
	if(!var_conv%in%names(Data)){
	 print("var_conv must be a column of Data")
	}
 }else{
  print("var_conv must be a string")
 }
 
 if(!is.null(var_value)){
  if(!var_value%in%names(Data)){
   print("var_value must be a column of Data")
  }
 }

 if(length(sep)>1){stop("Separator must have length 1")}

 if(is.null(var_value)){var_value="0"}

 res=.Call("heuristic_models_cpp", Data, var_path, var_conv, var_value, sep)
 
 return(as.data.frame(res)) 

}	

choose_order=function(Data, var_path, var_conv, var_null, max_order=10, sep=">", ncore=1, roc_npt=100, plot=TRUE){
 
 if(!("data.frame"%in%class(Data)|"data.table"%in%class(Data))){
  print("Data must be a data.frame or a data.table")
 } 
 
 if(is.character(var_path)){
	if(!var_path%in%names(Data)){
	 print("var_path must be a column of Data")
	}
 }else{
  print("var_path must be a string")
 }
 
 if(is.character(var_conv)){
	if(!var_conv%in%names(Data)){
	 print("var_conv must be a column of Data")
	}
 }else{
  print("var_conv must be a string")
 }
  
 if(!is.null(var_null)){
  if(!var_null%in%names(Data)){
   print("var_null must be a column of Data")
  }
 }

 if(length(sep)>1){stop("sep must have length 1")}
 if(ncore<1){stop("ncore must be >= 1")}
 if(roc_npt<10){stop("roc_npt must be >= 10")}
 if(!plot%in%c(0,1)){stop("plot must be FALSE or TRUE")}

 res=.Call("choose_order_cpp", Data, var_path, var_conv, var_null, max_order, sep, ncore, roc_npt)
 
 ck=res$auc$order[res$auc$order!=0]
 res$auc$order=res$auc$order[ck]
 res$auc$auc=res$auc$auc[ck]
 res$auc$pauc=res$auc$pauc[ck]
 
 best_order=res$auc$order[res$auc$pauc==max(res$auc$pauc)]
 
 if(best_order==max_order){
  print(paste0("Suggested order not found. Try increasing max_order."))
 }else{
  print(paste0("Suggested order: ", res$auc$order[res$auc$pauc==max(res$auc$pauc)]))
 }
 
 if(plot=="TRUE"){
  plot(res$auc$order,res$auc$pauc,type="l",xlab="order",ylab="penalized auc",main="PENALIZED AUC")
 }
 
 res[['suggested_order']]=best_order
 
 return(res)
 
}

markov_model=function(Data, var_path, var_conv, var_value=NULL, var_null=NULL, order=1, nsim_start=1e5, max_step=NULL, out_more=FALSE, sep=">", ncore=1, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5, verbose=TRUE){
 
 
 if(!("data.frame"%in%class(Data)|"data.table"%in%class(Data))){
  print("Data must be a data.frame or a data.table")
 } 
 
 if(is.character(var_path)){
	if(!var_path%in%names(Data)){
	 print("var_path must be a column of Data")
	}
 }else{
  print("var_path must be a string")
 }
 
 if(is.character(var_conv)){
	if(!var_conv%in%names(Data)){
	 print("var_conv must be a column of Data")
	}
 }else{
  print("var_conv must be a string")
 }
 
 if(!is.null(var_value)){
  if(!var_value%in%names(Data)){
   print("var_value must be a column of Data")
  }
 }
 
 if(!is.null(var_null)){
  if(!var_null%in%names(Data)){
   print("var_null must be a column of Data")
  }
 }
 
 if(order<1){stop("order must be >= 1")}
 if(nsim_start<1){stop("nsim_start must be >= 1")}
 if(!is.null(max_step)){if(max_step<1){stop("max_step must be >= 1")}}
 if(!out_more%in%c(0,1)){stop("out_more must be FALSE or TRUE")}
 if(length(sep)>1){stop("sep must have length 1")}
 if(ncore<1){stop("ncore must be >= 1")}
 if(nfold<1){stop("nfold must be >= 1")}
 if(seed<0){stop("seed must be >= 0")}
 if(conv_par<0){stop("conv_par must be > 0")}
 if(rate_step_sim<0){stop("rate_step_sim must be > 0")}
 if(!verbose%in%c(0,1)){stop("verbose must be FALSE or TRUE")}
 
 if(nrow(Data[which(Data[var_conv]!=0),])==0){stop("Data must have at least one converting path")}

 if(is.null(var_value)){var_value="0"}
 if(is.null(var_null)){var_null="0"}
 if(is.null(max_step)){max_step=0}
 if(!is.null(seed)){set.seed(seed)}
 
 res=.Call("markov_model_cpp", Data, var_path, var_conv, var_value, var_null, order, nsim_start, max_step, out_more, sep, ncore, nfold, seed, conv_par, rate_step_sim,verbose)
 
 if(out_more==FALSE){
  return(as.data.frame(res)) 
 }else{
  return(list(result=as.data.frame(res$result),transition_matrix=as.data.frame(res$transition_matrix),removal_effects=as.data.frame(res$removal_effects)))
 }

}
 
 
transition_matrix=function(Data, var_path, var_conv, var_null, order=1, sep=">", flg_equal=TRUE){
 
 if(!("data.frame"%in%class(Data)|"data.table"%in%class(Data))){
  print("Data must be a data.frame or a data.table")
 } 
 
 if(is.character(var_path)){
	if(!var_path%in%names(Data)){
	 print("var_path must be a column of Data")
	}
 }else{
  print("var_path must be a string")
 }
 if(is.character(var_conv)){
	if(!var_conv%in%names(Data)){
	 print("var_conv must be a column of Data")
	}
 }else{
  print("var_conv must be a string")
 }
  
 if(!is.null(var_null)){
  if(!var_null%in%names(Data)){
   print("var_null must be a column of Data")
  }
 }
 
 if(order<1){stop("order must be >= 1")}
 if(length(sep)>1){stop("sep must have length 1")}
 if(!flg_equal%in%c(0,1)){stop("flg_equal must be FALSE or TRUE")}

 if(is.null(var_null)){var_null="0"}

 res=.Call("transition_matrix_cpp", Data, var_path, var_conv, var_null, order, sep, flg_equal)
 
 return(list(channels=data.frame(id=1:length(res$channels),channel_name=res$channels),transition_matrix=as.data.frame(res$transition_matrix)))
  
}
 
 
auto_markov_model=function(Data, var_path, var_conv, var_null, var_value=NULL, max_order=10, roc_npt=100, plot=FALSE, nsim_start=1e5, max_step=NULL, out_more=FALSE, sep=">", ncore=1, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5, verbose=TRUE){
 
 if(!("data.frame"%in%class(Data)|"data.table"%in%class(Data))){
  print("Data must be a data.frame or a data.table")
 } 
 
 if(is.character(var_path)){
	if(!var_path%in%names(Data)){
	 print("var_path must be a column of Data")
	}
 }else{
  print("var_path must be a string")
 }
 if(is.character(var_conv)){
	if(!var_conv%in%names(Data)){
	 print("var_conv must be a column of Data")
	}
 }else{
  print("var_conv must be a string")
 }
 
 if(!is.null(var_value)){
  if(!var_value%in%names(Data)){
   print("var_value must be a column of Data")
  }
 }
 
 if(!is.null(var_null)){
  if(!var_null%in%names(Data)){
   print("var_null must be a column of Data")
  }
 }
 
 if(max_order<1){stop("max_order must be >= 1")}
 if(roc_npt<10){stop("roc_npt must be >= 10")}
 if(!plot%in%c(0,1)){stop("plot must be FALSE or TRUE")}
 if(nsim_start<1){stop("nsim_start must be >= 1")}
 if(!is.null(max_step)){if(max_step<1){stop("max_step must be >= 1")}}
 if(!out_more%in%c(0,1)){stop("out_more must be FALSE or TRUE")}
 if(length(sep)>1){stop("sep must have length 1")}
 if(ncore<1){stop("ncore must be >= 1")}
 if(nfold<1){stop("nfold must be >= 1")}
 if(seed<0){stop("seed must be >= 0")}
 if(conv_par<0){stop("conv_par must be > 0")}
 if(rate_step_sim<0){stop("rate_step_sim must be > 0")}
 if(!verbose%in%c(0,1)){stop("verbose must be FALSE or TRUE")}
 
 order=choose_order(Data, var_path, var_conv, var_null, max_order=max_order, sep=sep, ncore=ncore, roc_npt=roc_npt, plot=plot)
 order=order[['suggested_order']]
 
 res=markov_model(Data, var_path, var_conv, var_value=var_value, var_null=var_null, order=order, nsim_start=nsim_start, max_step=max_step, out_more=out_more, sep=sep, ncore=ncore, nfold=nfold, seed=seed, conv_par=conv_par, rate_step_sim=rate_step_sim, verbose=verbose)

 if(out_more==FALSE){
  return(as.data.frame(res)) 
 }else{
  return(list(result=as.data.frame(res$result),transition_matrix=as.data.frame(res$transition_matrix),removal_effects=as.data.frame(res$removal_effects)))
 }

}

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ChannelAttribution documentation built on July 27, 2020, 9:06 a.m.