knitr::opts_chunk$set(echo = TRUE) temp <- globalenv() TempNames <- names(temp) for(nam in TempNames) { assign(x = nam, value = eval(temp[[nam]]), envir = .GlobalEnv) }
```{css, echo=FALSE} @import url('https://fonts.googleapis.com/css2?family=Yusei+Magic&display=swap');
body { padding: 0; margin: 0; font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 16px; line-height: 1.5; color: #000000; background: linear-gradient(180deg, #787989, #b7b8cf); }
a { color: #b1c5d8; text-decoration: none; } a:hover { text-decoration: underline; }
.page-header { color: #fff; text-align: center; background-color: #152399; background-image: linear-gradient(120deg,#000121,#152399); padding: 1.5rem 2rem; } .page-header :last-child { margin-bottom: 0.5rem; } @media screen and (max-width: 42em) { .page-header { padding: 1rem 1rem; } }
.project-name { margin-top: 0; margin-bottom: 0.1rem; font-size: 2rem; } @media screen and (max-width: 42em) { .project-name { font-size: 1.75rem; } }
.project-tagline { margin-bottom: 2rem; font-weight: normal; opacity: 0.7; font-size: 1.5rem; } @media screen and (max-width: 42em) { .project-tagline { font-size: 1.2rem; } }
.project-author, .project-date { font-weight: normal; opacity: 0.7; font-size: 1.2rem; } @media screen and (max-width: 42em) { .project-author, .project-date { font-size: 1rem; } }
.main-content, .toc { max-width: 64rem; padding: 2rem 4rem; margin: 0 auto; font-size: 1.1rem; }
.toc { padding-bottom: 0; } .toc .toc-box { padding: 55px; background: linear-gradient(90deg, #00052b, #00198a); border: solid 3px #000000; border-radius: 70px; color: white; box-shadow: 8px 5px 10px 0px #000000; } .toc .toc-box .toc-title { margin: 0 0 0.5rem; text-align: center; } .toc .toc-box > ul { margin: 0; padding-left: 1.5rem; } @media screen and (min-width: 42em) and (max-width: 64em) { .toc { padding: 2rem 2rem 0; } } @media screen and (max-width: 42em) { .toc { padding: 2rem 1rem 0; font-size: 1rem; } }
.main-content :first-child { margin-top: 0; } @media screen and (min-width: 42em) and (max-width: 64em) { .main-content { padding: 2rem; } } @media screen and (max-width: 42em) { .main-content { padding: 2rem 1rem; font-size: 1rem; } } .main-content img { max-width: 100%; } .main-content h1, .main-content h2, .main-content h3, .main-content h4, .main-content h5, .main-content h6 { margin-top: 2rem; margin-bottom: 1rem; font-weight: normal; color: #000000; } .main-content p { margin-bottom: 1em; } .main-content code { padding: 2px 4px; font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace; color: #2c2c2d; background-color: #f3f6fa; border-radius: 0.3rem; } .main-content pre { padding: 0.8rem; margin-top: 0; margin-bottom: 1rem; font: 1rem Consolas, "Liberation Mono", Menlo, Courier, monospace; color: #2c2c2d; word-wrap: normal; background-color: #e9edf9c9; border: solid 1px #001a35; border-radius: 0.3rem; line-height: 1.45; overflow: auto; } @media screen and (max-width: 42em) { .main-content pre { font-size: 0.9rem; } } .main-content pre > code { padding: 0; margin: 0; color: #2c2c2d; word-break: normal; white-space: pre; background: transparent; border: 0; } @media screen and (max-width: 42em) { .main-content pre > code { font-size: 0.9rem; } } .main-content pre code, .main-content pre tt { display: inline; max-width: initial; padding: 0; margin: 0; overflow: initial; line-height: inherit; word-wrap: normal; background-color: transparent; border: 0; } .main-content pre code:before, .main-content pre code:after, .main-content pre tt:before, .main-content pre tt:after { content: normal; } .main-content ul, .main-content ol { margin-top: 0; } .main-content blockquote { padding: 0 1rem; margin-left: 0; color: #2c2c2d; border-left: 0.3rem solid #dce6f0; font-size: 1.2rem; } .main-content blockquote > :first-child { margin-top: 0; } .main-content blockquote > :last-child { margin-bottom: 0; } @media screen and (max-width: 42em) { .main-content blockquote { font-size: 1.1rem; } } .main-content table { width: 100%; overflow: auto; word-break: normal; word-break: keep-all; -webkit-overflow-scrolling: touch; border-collapse: collapse; border-spacing: 0; margin: 1rem 0; } .main-content table th { font-weight: bold; background-color: #159957; color: #fff; } .main-content table th, .main-content table td { padding: 0.5rem 1rem; border-bottom: 1px solid #e9ebec; text-align: left; } .main-content table tr:nth-child(odd) { background-color: #f2f2f2; } .main-content dl { padding: 0; } .main-content dl dt { padding: 0; margin-top: 1rem; font-size: 1rem; font-weight: bold; } .main-content dl dd { padding: 0; margin-bottom: 1rem; } .main-content hr { height: 2px; padding: 0; margin: 1rem 0; background-color: #eff0f1; border: 0; }
```r if(!is.null(ModelObject)) { # DataSets TestData <- ModelObject[['TestData']] TrainData <- ModelObject[['TrainData']] # Meta info TargetColumnName <- ModelObject[['ArgsList']][['TargetColumnName']] PredictionColumnName <- 'Predict' if(is.null(FeatureColumnNames)) { FeatureColumnNames <- ModelObject[['ColNames']][[1L]] } if(is.null(DateColumnName) && !is.null(ModelObject[['ArgsList']][['PrimaryDateColumn']])) { DateColumnName <- ModelObject[['ArgsList']][['PrimaryDateColumn']] } else { DateColumnName <- NULL } }
if(!is.null(ModelObject)) { # Model MetaData ---- ## Model_MetaData_Parameters ---- ArgsList <- ModelObject[['ArgsList']] ## Model_MetaData_GridMetrics ---- GridMetrics <- ModelObject[['GridMetrics']] }
if(!is.null(ModelObject)) { # Evaluation Metrics ---- ## Model_Evaluation_Metrics (catboost check, h2o check) ---- Test_EvalMetrics <- ModelObject[['EvaluationMetrics']][['TestData']] Train_EvalMetrics <- ModelObject[['EvaluationMetrics']][['TrainData']] ## Model_VarImportanceTable ---- if(tolower(Algo) == 'catboost') { # Store data Test_Importance <- ModelObject[['VariableImportance']][['Test_Importance']] Validation_Importance <- ModelObject[['VariableImportance']][['Validation_Importance']] Train_Importance <- ModelObject[['VariableImportance']][['Train_Importance']] # Update Colnames if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = 'Importance', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Importance)) data.table::setnames(Validation_Importance, old = 'Importance', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Importance)) data.table::setnames(Train_Importance, old = 'Importance', new = 'Train_Importance', skip_absent = TRUE) # CatBoost only if(is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- NULL } else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE) All_Importance <- merge(All_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE) } else if(!is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Validation_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- Train_Importance } else if(is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- Validation_Importance } else if(!is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- Test_Importance } else { All_Importance <- NULL } } else { # Store data (xgb check, ) Test_Importance <- ModelObject[['VariableImportance']] Validation_Importance <- NULL Train_Importance <- NULL # Update Colnames if(Algo %in% c("xgboost","lightgbm")) { if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = names(Test_Importance)[2], new = 'Test_Importance', skip_absent = TRUE) } else { # h2o col 3 is scaled importance which is preferred if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = names(Test_Importance)[3], new = 'Test_Importance', skip_absent = TRUE) } # Non CatBoost only if(!is.null(Test_Importance)) { All_Importance <- Test_Importance } else { All_Importance <- NULL } } ## Model_IntImportanceTable ---- if(tolower(Algo) == 'catboost') { Test_Interaction <- ModelObject[['InteractionImportance']][['Test_Interaction']] Validation_Interaction <- ModelObject[['InteractionImportance']][['Validation_Interaction']] Train_Interaction <- ModelObject[['InteractionImportance']][['Train_Interaction']] # Update Colnames if(!is.null(Test_Interaction)) data.table::setnames(Test_Interaction, old = 'score', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Interaction)) data.table::setnames(Validation_Interaction, old = 'score', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Interaction)) data.table::setnames(Train_Interaction, old = 'score', new = 'Train_Importance', skip_absent = TRUE) # CatBoost only if(is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- NULL } else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE) All_Interaction <- merge(All_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Validation_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- Train_Interaction } else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- Validation_Interaction } else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- Test_Interaction } else { All_Interaction <- NULL } } else { All_Interaction <- NULL } }
if(is.null(ModelObject)) { # DataSets if(is.null(TestData) && file.exists(file.path(SourcePath, paste0(ModelID, "_ValidationData.csv")))) { TestData <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_ValidationData.csv"))) } # Validate if(is.null(ValidationData) && file.exists(file.path(SourcePath, paste0(ModelID, "_ValData.csv")))) { ValidationDataData <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_ValData.csv"))) } # Train if(is.null(TrainData) && file.exists(file.path(SourcePath, paste0(ModelID, "_TrainData.csv")))) { TrainData <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_TrainData.csv"))) } # Meta info TargetColumnName <- TargetColumnName PredictionColumnName <- PredictionColumnName if(is.null(FeatureColumnNames) && !is.null(TestData)) { FeatureColumnNames <- names(TestData)[!names(TestData) %in% c(TargetColumnName, PredictionColumnName)] } if(is.null(FeatureColumnNames) && !is.null(ValidationData)) { FeatureColumnNames <- names(ValidationData)[!names(ValidationData) %in% c(TargetColumnName, PredictionColumnName)] } if(is.null(FeatureColumnNames) && !is.null(TrainData)) { FeatureColumnNames <- names(TrainData)[!names(TrainData) %in% c(TargetColumnName, PredictionColumnName)] } if(is.list(FeatureColumnNames) || data.table::is.data.table(FeatureColumnNames)) { FeatureColumnNames <- FeatureColumnNames[[1L]] } if(is.null(DateColumnName) && !is.null(ModelObject[['ArgsList']][['PrimaryDateColumn']])) { DateColumnName <- ModelObject[['ArgsList']][['PrimaryDateColumn']] } else { DateColumnName <- NULL } }
if(is.null(ModelObject)) { # Model MetaData ---- ## Model_MetaData_Parameters ---- if(!is.null(SourcePath) && !is.null(ModelID)) { if(file.exists(file.path(SourcePath, paste0(ModelID, "_ArgsList.Rdata")))) { load(file.path(SourcePath, paste0(ModelID, "_ArgsList.Rdata"))) } } else { ArgsList <- NULL } ## Model_MetaData_GridMetrics ---- if(!is.null(SourcePath) && !is.null(ModelID)) { if(file.exists(file.path(SourcePath, paste0(ModelID, "_GridMetrics.csv")))) { GridMetrics <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_GridMetrics.csv"))) } else { GridMetrics <- NULL } } else { GridMetrics <- NULL } }
if(is.null(ModelObject)) { # Evaluation Metrics ---- ## Model_Evaluation_Metrics ---- ### Test if(!is.null(TestData) && !is.null(TrainData) && !file.exists(file.path(SourcePath, paste0(ModelID, "_Test_EvaluationMetrics.csv")))) { Test_EvalMetrics <- AutoQuant:::RegressionMetrics( SaveModelObjects. = FALSE, data. = TrainData, ValidationData. = TestData, TrainOnFull. = TRUE, LossFunction. = 'mse', EvalMetric. = 'RMSE', TargetColumnName. = TargetColumnName, ModelID. = ModelID, model_path. = SourcePath, metadata_path. = SourcePath) } else if(!is.null(TestData) && is.null(TrainData) && !file.exists(file.path(SourcePath, paste0(ModelID, "_Test_EvaluationMetrics.csv")))) { Test_EvalMetrics <- AutoQuant:::RegressionMetrics( SaveModelObjects. = FALSE, data. = TestData, ValidationData. = TestData, TrainOnFull. = TRUE, LossFunction. = 'mse', EvalMetric. = 'RMSE', TargetColumnName. = TargetColumnName, ModelID. = ModelID, model_path. = SourcePath, metadata_path. = SourcePath) } else if(file.exists(file.path(SourcePath, paste0(ModelID, "_Test_EvaluationMetrics.csv")))) { Test_EvalMetrics <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Test_EvaluationMetrics.csv"))) } else { Test_EvalMetrics <- NULL } ### Train if(!is.null(TrainData) && !file.exists(file.path(SourcePath, paste0(ModelID, "_Train_EvaluationMetrics.csv")))) { Train_EvalMetrics <- AutoQuant:::RegressionMetrics( SaveModelObjects. = FALSE, data. = TrainData, ValidationData. = TrainData, TrainOnFull. = TRUE, LossFunction. = 'mse', EvalMetric. = 'RMSE', TargetColumnName. = TargetColumnName, ModelID. = ModelID, model_path. = SourcePath, metadata_path. = SourcePath) } else if(file.exists(file.path(SourcePath, paste0(ModelID, "_Train_EvaluationMetrics.csv")))) { Train_EvalMetrics <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Train_EvaluationMetrics.csv"))) } else { Train_EvalMetrics <- NULL } ## Model_VarImportanceTable ---- if(tolower(Algo) == 'catboost') { if(file.exists(file.path(SourcePath, paste0(ModelID, "_Test_Importance_VariableImportance.csv")))) { Test_Importance <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Test_Importance_VariableImportance.csv"))) } else { Test_Importance <- NULL } if(file.exists(file.path(SourcePath, paste0(ModelID, "_Validation_Importance_VariableImportance.csv")))) { Validation_Importance <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Validation_Importance_VariableImportance.csv"))) } else { Validation_Importance <- NULL } if(file.exists(file.path(SourcePath, paste0(ModelID, "_Train_Importance_VariableImportance.csv")))) { Train_Importance <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Train_Importance_VariableImportance.csv"))) } else { Train_Importance <- NULL } # Update Colnames if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = 'Importance', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Importance)) data.table::setnames(Validation_Importance, old = 'Importance', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Importance)) data.table::setnames(Train_Importance, old = 'Importance', new = 'Train_Importance', skip_absent = TRUE) # CatBoost only if(is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- NULL } else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE) All_Importance <- merge(All_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(!is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Validation_Importance, by = 'Variable', all = TRUE) } else if(!is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Test_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(is.null(Test_Importance) && !is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- merge(Validation_Importance, Train_Importance, by = 'Variable', all = TRUE) } else if(is.null(Test_Importance) && is.null(Validation_Importance) && !is.null(Train_Importance)) { All_Importance <- Train_Importance } else if(is.null(Test_Importance) && !is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- Validation_Importance } else if(!is.null(Test_Importance) && is.null(Validation_Importance) && is.null(Train_Importance)) { All_Importance <- Test_Importance } else { All_Importance <- NULL } } else { # Encoding-based Models + Generic Connector if(is.null(Test_Importance_dt)) { Test_Importance <- NULL } else { Test_Importance <- Test_Importance_dt } if(is.null(Validation_Importance_dt)) { Validation_Importance <- NULL } else { Validation_Importance <- Validation_Importance_dt } if(is.null(Train_Importance_dt)) { Train_Importance <- NULL } else { Train_Importance <- Train_Importance_dt } # Update Colnames if(!is.null(Test_Importance)) data.table::setnames(Test_Importance, old = 'Importance', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Importance)) data.table::setnames(Validation_Importance, old = 'Importance', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Importance)) data.table::setnames(Train_Importance, old = 'Importance', new = 'Train_Importance', skip_absent = TRUE) } ## Model_IntImportanceTable ---- if(tolower(Algo) == 'catboost') { if(file.exists(file.path(SourcePath, paste0(ModelID, "_Test_Interaction_Interaction.csv")))) { Test_Interaction <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Test_Interaction_Interaction.csv"))) } else { Test_Interaction <- NULL } if(file.exists(file.path(SourcePath, paste0(ModelID, "_Validation_Interaction_Interaction.csv")))) { Validation_Interaction <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Validation_Interaction_Interaction.csv"))) } else { Validation_Interaction <- NULL } if(file.exists(file.path(SourcePath, paste0(ModelID, "_Train_Interaction_Interaction.csv")))) { Train_Interaction <- data.table::fread(file = file.path(SourcePath, paste0(ModelID, "_Train_Interaction_Interaction.csv"))) } else { Train_Interaction <- NULL } # Update Colnames if(!is.null(Test_Interaction)) data.table::setnames(Test_Interaction, old = 'score', new = 'Test_Importance', skip_absent = TRUE) if(!is.null(Validation_Interaction)) data.table::setnames(Validation_Interaction, old = 'score', new = 'Validation_Importance', skip_absent = TRUE) if(!is.null(Train_Interaction)) data.table::setnames(Train_Interaction, old = 'score', new = 'Train_Importance', skip_absent = TRUE) # CatBoost only if(is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- NULL } else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE) All_Interaction <- merge(All_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(!is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Validation_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Test_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- merge(Validation_Interaction, Train_Interaction, by = c('Features1','Features2'), all = TRUE) data.table::setorderv(x = All_Interaction, cols = names(All_Interaction)[3L], order = -1) } else if(is.null(Test_Interaction) && is.null(Validation_Interaction) && !is.null(Train_Interaction)) { All_Interaction <- Train_Interaction } else if(is.null(Test_Interaction) && !is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- Validation_Interaction } else if(!is.null(Test_Interaction) && is.null(Validation_Interaction) && is.null(Train_Interaction)) { All_Interaction <- Test_Interaction } else { All_Interaction <- NULL } } else { All_Interaction <- NULL } }
# Evaluation Plots ---- ## EvaluationPlots_ResidualHistogram ---- ### Test Test_ResidualHistogram <- AutoPlots::Plot.Residuals.Histogram( dt = TestData, AggMethod = "mean", SampleSize = 100000, XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 20, Height = "600px", Width = "975px", Title = "Residuals Histogram", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = PredictionColumnName, EchartsTheme = "wef", TimeLine = FALSE, TextColor = "white", title.fontSize = 22, title.fontWeight = "bold", title.textShadowColor = "#63aeff", title.textShadowBlur = 3, title.textShadowOffsetY = 1, title.textShadowOffsetX = -1, xaxis.fontSize = 14, yaxis.fontSize = 14, Debug = FALSE) ### Train if(length(TrainData) > 0L) { Train_ResidualHistogram <- AutoPlots::Plot.Residuals.Histogram( dt = TrainData, AggMethod = "mean", SampleSize = 100000, XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 20, Height = "600px", Width = "975px", Title = "Residuals Histogram", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = 'Predict', EchartsTheme = "wef", TimeLine = FALSE, TextColor = "white", title.fontSize = 22, title.fontWeight = "bold", title.textShadowColor = "#63aeff", title.textShadowBlur = 3, title.textShadowOffsetY = 1, title.textShadowOffsetX = -1, xaxis.fontSize = 14, yaxis.fontSize = 14, Debug = FALSE) } ## EvaluationPlots_CalibrationPlot ---- ### Test Test_EvaluationPlot <- AutoPlots::Plot.Calibration.Line( dt = TestData, AggMethod = "mean", XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 21, Height = "600px", Width = "975px", Title = "Calibration Line Plot", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "Predict", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) ### Train if(!is.null(TrainData)) { Train_EvaluationPlot <- AutoPlots::Plot.Calibration.Line( dt = TrainData, AggMethod = "mean", XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 21, Height = "600px", Width = "975px", Title = "Calibration Line Plot", Title.YAxis = TargetColumnName, Title.XAxis = "Predict", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } ## EvaluationPlots_CalibrationBoxPlot ---- ### Test Test_EvaluationBoxPlot <- AutoPlots::Plot.Calibration.Box( dt = TestData, SampleSize = 100000L, AggMethod = "mean", XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 21, Height = "600px", Width = "975px", Title = "Calibration Plot", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "Predict", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) ### Train if(!is.null(TrainData)) { Train_EvaluationBoxPlot <- AutoPlots::Plot.Calibration.Box( dt = TrainData, SampleSize = 100000L, AggMethod = "mean", XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, NumberBins = 21, Height = "600px", Width = "975px", Title = "Calibration Plot", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = "Predict", EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } ## EvaluationPlots_ResidualsScatterPlot (depends on ) ---- Test_ScatterPlot <- AutoPlots::Plot.Residuals.Scatter( dt = TestData, AggMethod = "mean", SampleSize = 100000, XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, Height = "600px", Width = "975px", Title = "Calibration Plot", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) if(length(TrainData) > 0L) { Train_ScatterPlot <- AutoPlots::Plot.Residuals.Scatter( dt = TrainData, AggMethod = "mean", SampleSize = 100000, XVar = PredictionColumnName, YVar = TargetColumnName, GroupVar = NULL, YVarTrans = "Identity", XVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, Height = "600px", Width = "975px", Title = "Residuals ScatterPlot", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) }
# Model Interpretation ---- ## Model_Evaluation_Metrics_NumericVariables ---- ### TestData ---- # Plots to Add and Remove # Starting batch of plots Test_ParDepPlots <- list() # Add Plots if(!is.null(TestData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TestData[[g]])) { # Add Test_ParDepPlots[[g]] <- AutoPlots::Plot.PartialDependence.Line( dt = TestData, XVar = g, YVar = TargetColumnName, ZVar = 'Predict', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } } } ### TrainData ---- # Plots to Add and Remove # Starting batch of plots Train_ParDepPlots <- list() # ModelObject[['PlotList']][['Train_ParDepPlots']] # Add Plots if(!is.null(TrainData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TrainData[[g]])) { # Add Train_ParDepPlots[[g]] <- AutoPlots::Plot.PartialDependence.Line( dt = TrainData, XVar = g, YVar = TargetColumnName, ZVar = 'Predict', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } } } ## Model_Evaluation_Metrics_NumericVariables_Box ---- ### Test Data ---- # Starting batch of plots Test_ParDepBoxPlots <- list() # Add Plots if(!is.null(TestData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TestData[[g]])) { # Add Test_ParDepBoxPlots[[g]] <- AutoPlots::Plot.PartialDependence.Box( dt = TestData, XVar = g, YVar = TargetColumnName, ZVar = 'Predict', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } } } ### Train Data ---- # Starting batch of plots Train_ParDepBoxPlots <- list() # Add Plots if(!is.null(TrainData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(is.numeric(TrainData[[g]])) { # Add Train_ParDepBoxPlots[[g]] <- AutoPlots::Plot.PartialDependence.Box( dt = TrainData, XVar = g, YVar = TargetColumnName, ZVar = 'Predict', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } } } ## Model_Evaluation_Metrics_CategoricalVariables ---- ### Test Data ---- # Starting batch of plots Test_ParDepCatPlots <- list() # Add Plots if(!is.null(TestData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(!is.numeric(TestData[[g]])) { Test_ParDepCatPlots[[g]] <- AutoPlots::Plot.PartialDependence.HeatMap( dt = TestData, XVar = g, YVar = TargetColumnName, ZVar = 'Predict', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } } } ### Train Data ---- # Starting batch of plots Train_ParDepCatPlots <- list() # Add Plots if(!is.null(TrainData) && !is.null(FeatureColumnNames)) { for(g in FeatureColumnNames) { if(!is.numeric(TrainData[[g]])) { Train_ParDepCatPlots[[g]] <- AutoPlots::Plot.PartialDependence.HeatMap( dt = TrainData, XVar = g, YVar = TargetColumnName, ZVar = 'Predict', YVarTrans = "Identity", XVarTrans = "Identity", ZVarTrans = "Identity", FacetRows = 1, FacetCols = 1, FacetLevels = NULL, GroupVar = NULL, NumberBins = 20, AggMethod = "mean", Height = "600px", Width = "975px", Title = "Partial Dependence Line", ShowLabels = FALSE, Title.YAxis = TargetColumnName, Title.XAxis = g, EchartsTheme = "wef", TimeLine = TRUE, TextColor = "white", Debug = FALSE) } } }
The two main goals with this document are to provide a wide range of output to investigate high level performance and insights and to deliver a high quality report design layout to increase user experience. The metrics provided are intended to be semi-comprehensive. One can always dig deeper into results to gain further insights. In light of that, the results are intended to provide one what they need to come to a reasonable conclusion about their model or to find the area where they need to dig a deeper.
This section contains statistics and variable importance measures to help the user understand model performance at a high level. If the user chose to include Train Data results then these can be used to compare against the Test Data results to identify over / under fitting of models.
This section contains visualizations that span the range of predicted values and the associated accuracies across that range. The predicted values range are broken up into every 5th percentile to provide a solid range for evaluation.
These contains visualizations intended to open up the black box of your algorithm. When one inspects coefficients from a regression model, the insights they gain are two-fold: they get an understanding about statistics significance, and they gain an understanding of the variable's effect on the target variable. However, not all relationships are linear and sometimes the user doesn't specifiy an appropriate model structure to fully capture the nature of the relationship, which can lead to incorrect conclusions about statistical signifance along with incorrect conclusions about the nature of the relationship. These visualizations provide a way to understand what the exact nature of those relationships are and if the user chooses, they can attempt to fit the relationship better with an appropriate statistical model to gain a better understanding of statistical significance.
Expand
Evaluation Metrics Tables
Model Metrics Tables
TestData
Performance Metrics
if(!is.null(Test_EvalMetrics)) {
reactable::reactable(
width = 1075,
data = Test_EvalMetrics,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Test_EvalMetrics is NULL')
}
TrainData + ValidationData
Performance Metrics
if(!is.null(Train_EvalMetrics)) {
reactable::reactable(
width = 1075,
data = Train_EvalMetrics,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = FALSE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Train_EvalMetrics is NULL')
}
Variable Importance Table
if(!is.null(All_Importance)) {
data.table::setorderv(x = All_Importance, cols = names(All_Importance)[2L], order = -1L, na.last = TRUE)
reactable::reactable(
width = 1075,
data = All_Importance,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = TRUE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print("No importance data was provided")
}
Interaction Importance Tables
if(exists("All_Interaction") && !is.null(All_Interaction)) {
data.table::setorderv(x = All_Interaction, cols = names(All_Importance)[3L], order = -1L, na.last = TRUE)
reactable::reactable(
width = 1075,
data = All_Interaction,
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = TRUE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print('Interaction importance is only available with CatBoost')
}
Expand
Variable Importance Plots
Expand
if(!is.null(Test_Importance)) {
if("Train_Importance" %in% names(Test_Importance)) data.table::setnames(Test_Importance, 'Train_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Test_Importance)) data.table::setnames(Test_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Test_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
} else {
print("Test_Importance is NULL")
}
if(!is.null(Validation_Importance)) {
if("Validation_Importance" %in% names(Validation_Importance)) data.table::setnames(Validation_Importance, 'Validation_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Validation_Importance)) data.table::setnames(Validation_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Validation_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
} else {
print("Validation_Importance is only provided for with CatBoost")
}
if(!is.null(Train_Importance)) {
if("Train_Importance" %in% names(Train_Importance)) data.table::setnames(Train_Importance, 'Train_Importance', 'Importance', skip_absent = TRUE)
if("Test_Importance" %in% names(Train_Importance)) data.table::setnames(Train_Importance, "Test_Importance", "Importance", skip_absent = TRUE)
AutoPlots::Plot.VariableImportance(
dt = Train_Importance,
XVar = "Importance",
YVar = "Variable",
GroupVar = NULL,
YVarTrans = "Identity",
XVarTrans = "Identity",
FacetRows = 1,
FacetCols = 1,
FacetLevels = NULL,
AggMethod = "mean",
Height = "600px",
Width = "975px",
Title = "Variable Importance Plot",
ShowLabels = FALSE,
Title.YAxis = NULL,
Title.XAxis = NULL,
EchartsTheme = "wef",
TimeLine = FALSE,
TextColor = "white",
title.fontSize = 22,
title.fontWeight = "bold",
title.textShadowColor = "#63aeff",
title.textShadowBlur = 3,
title.textShadowOffsetY = 1,
title.textShadowOffsetX = -1,
xaxis.fontSize = 14,
yaxis.fontSize = 14,
Debug = FALSE)
} else {
print("Train_Importance is only provided for with CatBoost")
}
Expand
if(!is.null(Test_ResidualHistogram)) {
eval(Test_ResidualHistogram)
} else {
print('Test_ResidualHistogram is NULL or TestData is NULL')
}
if(!is.null(Train_ResidualHistogram)) {
eval(Train_ResidualHistogram)
} else {
print('Train_ResidualHistogram is NULL or TrainData is NULL')
}
Expand
if(!is.null(Test_EvaluationPlot)) {
eval(Test_EvaluationPlot)
} else {
print('Test_EvaluationPlot is NULL or TestData is NULL')
}
if(!is.null(Train_EvaluationPlot)) {
eval(Train_EvaluationPlot)
} else {
print('Test_EvaluationPlot is NULL or TrainData is NULL')
}
Expand
if(!is.null(Test_EvaluationBoxPlot)) {
eval(Test_EvaluationBoxPlot)
} else {
print('Test_EvaluationBoxPlot is NULL or TestData is NULL')
}
if(!is.null(Train_EvaluationBoxPlot)) {
eval(Train_EvaluationBoxPlot)
} else {
print('Train_EvaluationBoxPlot is NULL or TrainData is NULL')
}
Expand
if(!is.null(Test_ScatterPlot)) {
eval(Test_ScatterPlot)
} else {
print('Test_ScatterPlot is NULL or TestData is NULL')
}
if(!is.null(Train_ScatterPlot)) {
eval(Train_ScatterPlot)
} else {
print('Train_ScatterPlot is NULL or TrainData is NULL')
}
Expand
Partial Dependence Plots: Numeric-Features
Expand
Partial Dependence Line Plots
Expand
TestData
Partital Dependence Line Plots
options(warn = -1)
if(!is.null(Test_ParDepPlots) && length(Test_ParDepPlots) > 0) {
echarts4r::e_arrange(Test_ParDepPlots)
} else {
print('Test_ParDepPlots is NULL and TestData is NULL')
}
options(warn = 1)
TrainData + ValidationData
Partital Dependence Line Plots
options(warn = -1)
if(!is.null(Train_ParDepPlots) && length(Train_ParDepPlots) > 0) {
echarts4r::e_arrange(Train_ParDepPlots)
} else {
print('Train_ParDepPlots is NULL and TrainData is NULL')
}
options(warn = 1)
Partial Dependence Box Plots
Expand
TestData
Partital Dependence Box Plots
options(warn = -1)
if(!is.null(Test_ParDepBoxPlots) && length(Test_ParDepBoxPlots) > 0) {
echarts4r::e_arrange(Test_ParDepBoxPlots)
} else {
print('Test_ParDepBoxPlots is NULL and TestData is NULL')
}
options(warn = 1)
TrainData + ValidationData
Partital Dependence Box Plots
options(warn = -1)
if(!is.null(Train_ParDepBoxPlots) && length(Train_ParDepBoxPlots) > 0) {
echarts4r::e_arrange(Train_ParDepBoxPlots)
} else {
print('Train_ParDepBoxPlots is NULL and TestData is NULL')
}
options(warn = 1)
Expand
TestData
Partital Dependence Bar Plots
options(warn = -1)
if(!is.null(Test_ParDepCatPlots) && length(Test_ParDepCatPlots) > 0) {
echarts4r::e_arrange(Test_ParDepCatPlots)
} else {
print('Test_ParDepCatPlots is NULL and TestData is NULL')
}
options(warn = 1)
Partital Dependence Bar Plots
options(warn = -1)
if(!is.null(Train_ParDepCatPlots) && length(Train_ParDepCatPlots) > 0) {
echarts4r::e_arrange(Train_ParDepCatPlots)
} else {
print('Train_ParDepCatPlots is NULL and TrainData is NULL')
}
options(warn = 1)
Expand
Parameters and Settings
Model Parameters
if(!is.null(ArgsList)) {
for(nam in names(ArgsList)) print(paste0(nam, ": ", ArgsList[[nam]]))
} else {
txt <- paste0(ModelID, "_ArgsList.Rdata")
print(paste0('ArgsList is NULL'))
}
Grid Tuning Metrics
Grid Tuning Metrics
if(!is.null(GridMetrics)) {
reactable::reactable(
width = 1075,
data = GridMetrics[order(-MetricValue)],
compact = TRUE,
defaultPageSize = 10,
wrap = FALSE,
filterable = TRUE,
fullWidth = TRUE,
highlight = TRUE,
pagination = TRUE,
resizable = TRUE,
searchable = TRUE,
selection = "multiple",
showPagination = TRUE,
showSortable = TRUE,
showSortIcon = TRUE,
sortable = TRUE,
striped = TRUE,
theme = reactable::reactableTheme(
color = 'black',
backgroundColor = "#4f4f4f26",
borderColor = "#dfe2e5",
stripedColor = "#4f4f4f8f",
highlightColor = "#8989898f",
cellPadding = "8px 12px",
style = list(
fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif"
),
searchInputStyle = list(width = "100%")
)
)
} else {
print("GridTuning was not conducted")
}
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