Package 'deepdive' reference manual

Title:	Deep Learning for General Purpose
Description:	Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network.
Authors:	Rajesh Balakrishnan
Maintainer:	Rajesh Balakirshnan <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.4
Built:	2024-11-24 04:54:40 UTC
Source:	https://github.com/rajeshb24/deepdive

Build or train bagged deeptree or deepnet of multiple architecture

Description

Build or train bagged deeptree or deepnet of multiple architecture.Based on error choice either select best model or average multiple model with random variable cut,data cut and architechture

Usage

deepforest(
  x,
  y,
  networkCount = 3,
  layerChoice = c(2:3),
  unitsChoice = c(4:10),
  cutVarSizePercent = 0.6,
  cutDataSizePercent = 0.6,
  activation = c("sigmoid", "sigmoid"),
  reluLeak = 0,
  modelType = "regress",
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  treeLeaves = NA,
  treeMinSplitPercent = 0.3,
  treeMinSplitCount = 100,
  treeCp = 0.01,
  errorCover = 0.2,
  treeAugment = TRUE,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = TRUE
)
deepforest(
  x,
  y,
  networkCount = 3,
  layerChoice = c(2:3),
  unitsChoice = c(4:10),
  cutVarSizePercent = 0.6,
  cutDataSizePercent = 0.6,
  activation = c("sigmoid", "sigmoid"),
  reluLeak = 0,
  modelType = "regress",
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  treeLeaves = NA,
  treeMinSplitPercent = 0.3,
  treeMinSplitCount = 100,
  treeCp = 0.01,
  errorCover = 0.2,
  treeAugment = TRUE,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = TRUE
)

Arguments

`x`	a data frame with input variables
`y`	a data frame with ouptut variable
`networkCount`	Integer, Number of deepnet or deeptree to build
`layerChoice`	vector, different layer choices
`unitsChoice`	vector , number of units choice
`cutVarSizePercent`	ratio, percentage of variable to for each network
`cutDataSizePercent`	ratio, percentage of data to for each network
`activation`	choose from "sigmoid","relu","sin","cos","none".Activations will be randomly chosen from chosen. Default is relu and sin
`reluLeak`	numeric. Applicable when activation is "relu". Specify value between 0 any number close to zero below 1. Eg: 0.01,0.001 etc
`modelType`	one of "regress","binary","multiClass". "regress" for regression will create a linear single unit output layer. "binary" will create a single unit sigmoid activated layer. "multiClass" will create layer with units corresponding to number of output classes with softmax activation.
`iterations`	integer. This indicates number of iteratios or epochs in backpropagtion .The default value is 500.
`eta`	numeric.Hyperparameter,sets the Learning rate for backpropagation. Eta determines the convergence ability and speed of convergence.
`seed`	numeric. Set seed with this parameter. Incase of sin activation sometimes changing seed can yeild better results. Default is 2
`gradientClip`	numeric. Hyperparameter numeric value which limits gradient size for weight update operation in backpropagation. Default is 0.8 . It can take any postive value.
`regularisePar`	numeric. L2 Regularisation Parameter .
`optimiser`	one of "gradientDescent","momentum","rmsProp","adam". Default value "adam"
`parMomentum`	numeric. Applicable for optimiser "mometum" and "adam"
`inputSizeImpact`	numeric. Adjusts the gradient size by factor of percentage of rows in input. For very small data set setting this to 0 could yeild faster result. Default is 1.
`parRmsPropZeroAdjust`	numeric. Applicable for optimiser "rmsProp" and "adam"
`parRmsProp`	numeric.Applicable for optimiser "rmsProp" and "adam"
`treeLeaves`	vector.Optional , leaves numbers from externally trained tree model can be supplied here. If supplied then model will not build a explicit tree and just fit a neural network to mentioned leaves.
`treeMinSplitPercent`	numeric. This parameter controls depth of tree setting min split count for leaf subdivision as percentage of observations. Final minimum split will be chosen as max of count calculted with treeMinSplitPercent and treeMinSplitCount. Default 0.3. Range 0 to 1.
`treeMinSplitCount`	numeric. This parameter controls depth of tree setting min split count.Final minimum split will be chosen as max of count calculted with treeMinSplitPercent and treeMinSplitCount. Default 30
`treeCp`	complexity parameter. `rpart.control`
`errorCover`	Ratio. Deault is 0.2 i.e all models within 20 percent error of best model will be selected.
`treeAugment`	logical. If True fits deeptree and if False fits deepnet. Default is T
`printItrSize`	numeric. Number of iterations after which progress message should be shown. Default value 100 and for iterations below 100 atleast 5 messages will be seen
`showProgress`	logical. True will show progress and F will not show progress
`stopError`	Numeric. Rmse at which iterations can be stopped. Default is 0.01, can be set as NA in case all iterations needs to run.
`miniBatchSize`	integer. Set the mini batch size for mini batch gradient
`useBatchProgress`	logical. Applicable for miniBatch , setting T will use show rmse in Batch and F will show error on full dataset. For large dataset set T

Value

returns model object which can be passed into predict.deepforest

Examples


require(deepdive)

x<-data.frame(x1=runif(10),x2=runif(10))
y<-data.frame(y=10*x$x1+20*x$x2+20)

mdeepf<-deepforest(x,y,
                  networkCount=2,
                  layerChoice=c(2:3),
                  unitsChoice=c(4:10),
                  cutVarSizePercent=0.6,
                  cutDataSizePercent=0.6,
                  activation = c('relu',"sin"),
                  reluLeak=0.01,
                  modelType ='regress',
                  iterations = 10,
                  eta = 10 ^-2,
                  seed=2,
                  gradientClip=0.8,
                  regularisePar=0,
                  optimiser="adam",
                  parMomentum=0.9,
                  inputSizeImpact=1,
                  parRmsPropZeroAdjust=10^-8,
                  parRmsProp=0.9999,
                  treeLeaves=NA,
                  treeMinSplitPercent=0.3,
                  treeMinSplitCount=100,
                  treeCp=0.01 ,
                  errorCover=0.2,
                  treeAugment=TRUE,
                  printItrSize=100,
                  showProgress=TRUE,
                  stopError=0.01,
                  miniBatchSize=64,
                  useBatchProgress=TRUE)
require(deepdive)

x<-data.frame(x1=runif(10),x2=runif(10))
y<-data.frame(y=10*x$x1+20*x$x2+20)

mdeepf<-deepforest(x,y,
                  networkCount=2,
                  layerChoice=c(2:3),
                  unitsChoice=c(4:10),
                  cutVarSizePercent=0.6,
                  cutDataSizePercent=0.6,
                  activation = c('relu',"sin"),
                  reluLeak=0.01,
                  modelType ='regress',
                  iterations = 10,
                  eta = 10 ^-2,
                  seed=2,
                  gradientClip=0.8,
                  regularisePar=0,
                  optimiser="adam",
                  parMomentum=0.9,
                  inputSizeImpact=1,
                  parRmsPropZeroAdjust=10^-8,
                  parRmsProp=0.9999,
                  treeLeaves=NA,
                  treeMinSplitPercent=0.3,
                  treeMinSplitCount=100,
                  treeCp=0.01 ,
                  errorCover=0.2,
                  treeAugment=TRUE,
                  printItrSize=100,
                  showProgress=TRUE,
                  stopError=0.01,
                  miniBatchSize=64,
                  useBatchProgress=TRUE)

Build and train an Artificial Neural Network of any size

Description

Build and train Artifical Neural Network of any depth in a single line code. Choose the hyperparameters to improve the accuracy or generalisation of model.

Usage

deepnet(
  x,
  y,
  hiddenLayerUnits = c(2, 2),
  activation = c("sigmoid", "relu"),
  reluLeak = 0,
  modelType = c("regress"),
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = FALSE,
  ignoreNAerror = FALSE,
  normalise = TRUE
)
deepnet(
  x,
  y,
  hiddenLayerUnits = c(2, 2),
  activation = c("sigmoid", "relu"),
  reluLeak = 0,
  modelType = c("regress"),
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = FALSE,
  ignoreNAerror = FALSE,
  normalise = TRUE
)

Arguments

`x`	a data frame with input variables
`y`	a data frame with ouptut variable
`hiddenLayerUnits`	a numeric vector, length of vector indicates number of hidden layers and each element in vector indicates corresponding hidden units Eg: c(6,4) for two layers, one with 6 hiiden units and other with 4 hidden units. Note: Output layer is automatically created.
`activation`	one of "sigmoid","relu","sin","cos","none". The default is "sigmoid". Choose a activation per hidden layer
`reluLeak`	numeric. Applicable when activation is "relu". Specify value between 0 any number close to zero below 1. Eg: 0.01,0.001 etc
`modelType`	one of "regress","binary","multiClass". "regress" for regression will create a linear single unit output layer. "binary" will create a single unit sigmoid activated layer. "multiClass" will create layer with units corresponding to number of output classes with softmax activation.
`iterations`	integer. This indicates number of iteratios or epochs in backpropagtion .The default value is 500.
`eta`	numeric.Hyperparameter,sets the Learning rate for backpropagation. Eta determines the convergence ability and speed of convergence.
`seed`	numeric. Set seed with this parameter. Incase of sin activation sometimes changing seed can yeild better results. Default is 2
`gradientClip`	numeric. Hyperparameter numeric value which limits gradient size for weight update operation in backpropagation. Default is 0.8 . It can take any postive value.
`regularisePar`	numeric. L2 Regularisation Parameter .
`optimiser`	one of "gradientDescent","momentum","rmsProp","adam". Default value "adam"
`parMomentum`	numeric. Applicable for optimiser "mometum" and "adam"
`inputSizeImpact`	numeric. Adjusts the gradient size by factor of percentage of rows in input. For very small data set setting this to 0 could yeild faster result. Default is 1.
`parRmsPropZeroAdjust`	numeric. Applicable for optimiser "rmsProp" and "adam"
`parRmsProp`	numeric.Applicable for optimiser "rmsProp" and "adam"
`printItrSize`	numeric. Number of iterations after which progress message should be shown. Default value 100 and for iterations below 100 atleast 5 messages will be seen
`showProgress`	logical. True will show progress and F will not show progress
`stopError`	Numeric. Rmse at which iterations can be stopped. Default is 0.01, can be set as NA in case all iterations needs to run.
`miniBatchSize`	integer. Set the mini batch size for mini batch gradient
`useBatchProgress`	logical. Applicable for miniBatch , setting T will use show rmse in Batch and F will show error on full dataset. For large dataset set T
`ignoreNAerror`	logical. Set T if iteration needs to be stopped when predictions become NA
`normalise`	logical. Set F if normalisation not required.Default T

Value

returns model object which can be passed into predict.deepnet

Examples

require(deepdive)

x <- data.frame(x1 = runif(10),x2 = runif(10))
y<- data.frame(y=20*x$x1 +30*x$x2+10)

#train
modelnet<-deepnet(x,y,c(2,2),
activation = c('relu',"sigmoid"),
reluLeak = 0.01,
modelType = "regress",
iterations =5,
eta=0.8,
optimiser="adam")

#predict
predDeepNet<-predict.deepnet(modelnet,newData=x)

#evaluate
sqrt(mean((predDeepNet$ypred-y$y)^2))


require(deepdive)

x <- data.frame(x1 = runif(10),x2 = runif(10))
y<- data.frame(y=20*x$x1 +30*x$x2+10)

#train
modelnet<-deepnet(x,y,c(2,2),
activation = c('relu',"sigmoid"),
reluLeak = 0.01,
modelType = "regress",
iterations =5,
eta=0.8,
optimiser="adam")

#predict
predDeepNet<-predict.deepnet(modelnet,newData=x)

#evaluate
sqrt(mean((predDeepNet$ypred-y$y)^2))

Descision Tree augmented by Artificial Neural Network

Description

This models divides the input space by fitting a tree followed by artificial neural network to each of leaf. Decision tree model is built using rpart package and neural network using deepdive.Feature of stacking predictions from other models is also made available.

Usage

deeptree(
  x,
  y,
  hiddenLayerUnits = c(2, 2),
  activation = c("sigmoid", "sigmoid"),
  reluLeak = 0,
  modelType = "regress",
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  treeLeaves = NA,
  treeMinSplitPercent = 0.3,
  treeMinSplitCount = 30,
  treeCp = 0.01,
  stackPred = NA,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = TRUE,
  ignoreNAerror = FALSE
)
deeptree(
  x,
  y,
  hiddenLayerUnits = c(2, 2),
  activation = c("sigmoid", "sigmoid"),
  reluLeak = 0,
  modelType = "regress",
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  treeLeaves = NA,
  treeMinSplitPercent = 0.3,
  treeMinSplitCount = 30,
  treeCp = 0.01,
  stackPred = NA,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = TRUE,
  ignoreNAerror = FALSE
)

Arguments

`x`	a data frame with input variables
`y`	a data frame with ouptut variable
`hiddenLayerUnits`	a numeric vector, length of vector indicates number of hidden layers and each element in vector indicates corresponding hidden units Eg: c(6,4) for two layers, one with 6 hiiden units and other with 4 hidden units. Note: Output layer is automatically created.
`activation`	one of "sigmoid","relu","sin","cos","none". The default is "sigmoid". Choose a activation per hidden layer
`reluLeak`	numeric. Applicable when activation is "relu". Specify value between 0 any number close to zero below 1. Eg: 0.01,0.001 etc
`modelType`	one of "regress","binary","multiClass". "regress" for regression will create a linear single unit output layer. "binary" will create a single unit sigmoid activated layer. "multiClass" will create layer with units corresponding to number of output classes with softmax activation.
`iterations`	integer. This indicates number of iteratios or epochs in backpropagtion .The default value is 500.
`eta`	numeric.Hyperparameter,sets the Learning rate for backpropagation. Eta determines the convergence ability and speed of convergence.
`seed`	numeric. Set seed with this parameter. Incase of sin activation sometimes changing seed can yeild better results. Default is 2
`gradientClip`	numeric. Hyperparameter numeric value which limits gradient size for weight update operation in backpropagation. Default is 0.8 . It can take any postive value.
`regularisePar`	numeric. L2 Regularisation Parameter .
`optimiser`	one of "gradientDescent","momentum","rmsProp","adam". Default value "adam"
`parMomentum`	numeric. Applicable for optimiser "mometum" and "adam"
`inputSizeImpact`	numeric. Adjusts the gradient size by factor of percentage of rows in input. For very small data set setting this to 0 could yeild faster result. Default is 1.
`parRmsPropZeroAdjust`	numeric. Applicable for optimiser "rmsProp" and "adam"
`parRmsProp`	numeric.Applicable for optimiser "rmsProp" and "adam"
`treeLeaves`	vector.Optional , leaves numbers from externally trained tree model can be supplied here. If supplied then model will not build a explicit tree and just fit a neural network to mentioned leaves.
`treeMinSplitPercent`	numeric. This parameter controls depth of tree setting min split count for leaf subdivision as percentage of observations. Final minimum split will be chosen as max of count calculted with treeMinSplitPercent and treeMinSplitCount. Default 0.3. Range 0 to 1.
`treeMinSplitCount`	numeric. This parameter controls depth of tree setting min split count.Final minimum split will be chosen as max of count calculted with treeMinSplitPercent and treeMinSplitCount. Default 30
`treeCp`	complexity parameter. `rpart.control`
`stackPred`	vector.Predictions from buildnet or other models can be supplied here. If for certain leaf stackPrep accuracy is better then stackpred predictions will be chosen.
`printItrSize`	numeric. Number of iterations after which progress message should be shown. Default value 100 and for iterations below 100 atleast 5 messages will be seen
`showProgress`	logical. True will show progress and F will not show progress
`stopError`	Numeric. Rmse at which iterations can be stopped. Default is 0.01, can be set as NA in case all iterations needs to run.
`miniBatchSize`	integer. Set the mini batch size for mini batch gradient
`useBatchProgress`	logical. Applicable for miniBatch , setting T will use show rmse in Batch and F will show error on full dataset. For large dataset set T
`ignoreNAerror`	logical. Set T if iteration needs to be stopped when predictions become NA

Value

returns model object which can be passed into predict.deeptree

Examples


require(deepdive)

x <- data.frame(x1 = runif(10),x2 = runif(10))

y<- data.frame(y=20*x$x1 +30* x$x2 +10)

deepTreeMod<-deeptree(x,
y,
hiddenLayerUnits=c(4,4),
activation = c('relu',"sin"),
reluLeak=0.01,
modelType ='regress',
iterations = 1000,
eta = 0.4,
seed=2,
gradientClip=0.8,
regularisePar=0,
optimiser="adam",
parMomentum=0.9,
inputSizeImpact=1,
parRmsPropZeroAdjust=10^-8,
parRmsProp=0.9999,
treeLeaves=NA,
treeMinSplitPercent=0.4,
treeMinSplitCount=100,
stackPred =NA,
stopError=4,
miniBatchSize=64,
useBatchProgress=TRUE,
ignoreNAerror=FALSE)

require(deepdive)

x <- data.frame(x1 = runif(10),x2 = runif(10))

y<- data.frame(y=20*x$x1 +30* x$x2 +10)

deepTreeMod<-deeptree(x,
y,
hiddenLayerUnits=c(4,4),
activation = c('relu',"sin"),
reluLeak=0.01,
modelType ='regress',
iterations = 1000,
eta = 0.4,
seed=2,
gradientClip=0.8,
regularisePar=0,
optimiser="adam",
parMomentum=0.9,
inputSizeImpact=1,
parRmsPropZeroAdjust=10^-8,
parRmsProp=0.9999,
treeLeaves=NA,
treeMinSplitPercent=0.4,
treeMinSplitCount=100,
stackPred =NA,
stopError=4,
miniBatchSize=64,
useBatchProgress=TRUE,
ignoreNAerror=FALSE)

Predict Function for DeepForest

Description

Predict Function for DeepForest

Usage

## S3 method for class 'deepforest'
predict(object, newData, ...)
## S3 method for class 'deepforest'
predict(object, newData, ...)

Arguments

`object`	deepforest model object
`newData`	pass dataframe for prediction
`...`	further arguments passed to or from other methods.

Value

returns predictions vector or dataframe

Predict Function for Deepnet

Description

Predict Function for Deepnet

Usage

## S3 method for class 'deepnet'
predict(object, newData, ...)
## S3 method for class 'deepnet'
predict(object, newData, ...)

Arguments

`object`	deepnet model object
`newData`	pass dataframe for prediction
`...`	further arguments passed to or from other methods.

Value

returns predictions vector or dataframe

Predict Function for Deeptree

Description

Predict Function for Deeptree

Usage

## S3 method for class 'deeptree'
predict(object, newData, treeLeaves = NA, stackPred = NA, ...)
## S3 method for class 'deeptree'
predict(object, newData, treeLeaves = NA, stackPred = NA, ...)

Arguments

`object`	deeptree model object
`newData`	pass dataframe for prediction
`treeLeaves`	Pass vector with tree leaves if fit outside deeptree. default NA.
`stackPred`	Pass stackPred of prediction data if it was passed in deeptree
`...`	further arguments passed to or from other methods.

Value

returns predictions vector or dataframe

Variable importance for models in this library

Description

Variable importance for models in this library

Usage

variableImportance(model, x, y, showPlot = T, seed = 2)
variableImportance(model, x, y, showPlot = T, seed = 2)

Arguments

`model`	Model object
`x`	a data frame with input variables
`y`	a data frame with ouptut variable
`showPlot`	logical. True will show importance plot. Default True
`seed`	Set seed with this parameter. Incase of sin activation sometimes changing seed can yeild better results. Default is 2

Value

returns variable importance data frame

Package 'deepdive'

Help Index

Build or train bagged deeptree or deepnet of multiple architecture

Description

Usage

Arguments

Value

Examples

Build and train an Artificial Neural Network of any size

Description

Usage

Arguments

Value

Examples

Descision Tree augmented by Artificial Neural Network

Description

Usage

Arguments

Value

Examples

Predict Function for DeepForest

Description

Usage

Arguments

Value

Predict Function for Deepnet

Description

Usage

Arguments

Value

Predict Function for Deeptree

Description

Usage

Arguments

Value

Variable importance for models in this library

Description

Usage

Arguments

Value