From 5c74b60840ee5f73eeb2b5852c3c62cffacc8caf Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 25 Jan 2023 14:34:53 -0700 Subject: [PATCH 01/74] re-added testing files inst/examples --- inst/examples/dev_15.tif | Bin 0 -> 238110 bytes inst/examples/elev_scottsburg.flt | Bin 0 -> 395016 bytes inst/examples/elev_scottsburg.flt.aux.xml | 6 ++++ inst/examples/elev_scottsburg.hdr | 7 +++++ inst/examples/elev_scottsburg.prj | 1 + inst/examples/elev_scottsburg.stx | 1 + inst/examples/elev_scottsburg.tif | Bin 0 -> 216346 bytes inst/examples/grad_15.tif | Bin 0 -> 215843 bytes inst/examples/initiation_points.cpg | 1 + inst/examples/initiation_points.dbf | Bin 0 -> 78866 bytes inst/examples/initiation_points.prj | 1 + inst/examples/initiation_points.shp | Bin 0 -> 2172 bytes inst/examples/initiation_points.shx | Bin 0 -> 692 bytes inst/examples/input_makegrids.txt | 12 ++++++++ inst/examples/makegrids_input_sample.txt | 11 +++++++ inst/examples/partial_input_sample.txt | 9 ++++++ inst/examples/pca_15m_48hr.tif | Bin 0 -> 217410 bytes inst/examples/plan_15.tif | Bin 0 -> 239866 bytes inst/examples/prof_15.tif | Bin 0 -> 240195 bytes inst/examples/scottsburg.R | 34 ++++++++++++++++++++++ 20 files changed, 83 insertions(+) create mode 100644 inst/examples/dev_15.tif create mode 100644 inst/examples/elev_scottsburg.flt create mode 100644 inst/examples/elev_scottsburg.flt.aux.xml create mode 100644 inst/examples/elev_scottsburg.hdr create mode 100644 inst/examples/elev_scottsburg.prj create mode 100644 inst/examples/elev_scottsburg.stx create mode 100644 inst/examples/elev_scottsburg.tif create mode 100644 inst/examples/grad_15.tif create mode 100644 inst/examples/initiation_points.cpg create mode 100644 inst/examples/initiation_points.dbf create mode 100644 inst/examples/initiation_points.prj create mode 100644 inst/examples/initiation_points.shp create mode 100644 inst/examples/initiation_points.shx create mode 100644 inst/examples/input_makegrids.txt create mode 100644 inst/examples/makegrids_input_sample.txt create mode 100644 inst/examples/partial_input_sample.txt create mode 100644 inst/examples/pca_15m_48hr.tif create mode 100644 inst/examples/plan_15.tif create mode 100644 inst/examples/prof_15.tif create mode 100644 inst/examples/scottsburg.R diff --git a/inst/examples/dev_15.tif b/inst/examples/dev_15.tif new file mode 100644 index 0000000000000000000000000000000000000000..9f96b8bd54e86e895cb68dacf685e789c55feaf0 GIT binary patch literal 238110 zcmc$_c~leG+Amy{sS+}Wp~8?#0*E+4zzG#FVG~0(Tpj`n3oP(mG$Mz&RbWplGDB5&4r_Zf(?zwBdcYWXb?tB0GveuLR z)UI8%YtO&^JinciG8te303ZSYkP3jL7DoPo|IrtaI^{pQjJ!wvk4`6b#((sGJtG8Z zz-%(?ATPZCOT5p2bOw13{!f}$q#p2J(!~51J@Y?0l}y7kae#k?01W_)WgOsN?+}yv z1q&6Bk$N(zw^}HGgw!iY-Oxw@^2z*9QUCxWbutRrsNG2Z1Au^xUr$E-0s+8n769~+ z62k_7U;zO9gDg7V699I31HezdWOe}n@VOWO%3%Qbo)jjjdy_(tQbfuSna-DtSCD!h zDeFk_C$DiI05FrXpOinyxOb#%C1sct11Udy0RT$o_l%Sg9{C)H{5~!K%;f{X6jEl9 za)69qM#?2Jo<)A+PO@n-NO?}m1oCmVK?OdGrvmn+RG>VM3hXPQ0^H41 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FILE = d:\data\AK\BirchCreek\bcon10 diff --git a/inst/examples/makegrids_input_sample.txt b/inst/examples/makegrids_input_sample.txt new file mode 100644 index 0000000..40d2a48 --- /dev/null +++ b/inst/examples/makegrids_input_sample.txt @@ -0,0 +1,11 @@ +# Input file for makeGrids +# Creating by input_file_utils.R +# On 2022-10-19 10:57:24 +DEM: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\examples\elev_scottsburg +SCRATCH DIRECTORY: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\scratch +LENGTH SCALE: 15 +GRID: GRADIENT, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/grad.flt +GRID: PLAN CURVATURE, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/plan.flt +GRID: PROFILE CURVATURE, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/prof.flt +GRID: NORMAL SLOPE CURVATURE, OUTPUT FILE = 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zvt*f@+FX)}M@dQw=n66vQZZEWcKN*xb>5lxoW2%s+W7WLo*>=n*F9SvJ+#{|esol3 z-|pD-?$;Z;o;W`k+?AESaP;Q?C^7#RasU7M zhLU`nNAp5U*LMgOwyyMDd#&?kLHNnL1&d1WPacTTtnpuW-RHA9vBS7EtZakV=~dn9 z0@q*fTK2`p^Y^zeE_;xDL!e6!+R(>3IFf(Kv@?AFhK^r%_HGW|*w=OQx%B#z-AndA znEdB}ehWLjpY<v#P2^q!^V4|8Hp8w Date: Wed, 25 Jan 2023 19:30:04 -0700 Subject: [PATCH 02/74] add option for linear regression. --- DESCRIPTION | 55 ++++++++++++++++++++++++------------------------ R/build_models.R | 10 ++++++--- 2 files changed, 35 insertions(+), 30 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 287f5b7..efa30ec 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,27 +1,28 @@ -Package: TerrainWorksUtils -Type: Package -Title: General utility functions for TerrainWorks -Version: 0.1.3 -Authors@R: - c( - person("Helen", "Miller", email = "helenthehuman@gmail.com", role = c("aut", "cre")), - person("Tate", "Brasel", role = "aut") - ) -Description: TerrainWorks resources for creating and assessing statistical models for predicting landslide susceptibility. -License: MIT + file LICENSE -Encoding: UTF-8 -LazyData: true -Imports: - methods, - randomForest, - ROCR, - stats, - terra (>= 1.5) -RoxygenNote: 7.2.3 -Suggests: - knitr, - covr, - testthat (>= 3.0.0), - rmarkdown -Config/testthat/edition: 3 -VignetteBuilder: knitr +Package: TerrainWorksUtils +Type: Package +Title: General utility functions for TerrainWorks +Version: 0.1.3 +Authors@R: + c( + person("Helen", "Miller", email = "helenthehuman@gmail.com", role = c("aut", "cre")), + person("Tate", "Brasel", role = "aut") + ) +Description: TerrainWorks resources for creating and assessing statistical models for predicting landslide susceptibility. +License: MIT + file LICENSE +Encoding: UTF-8 +LazyData: true +Imports: + methods, + randomForest, + caret, + ROCR, + stats, + terra (>= 1.5) +RoxygenNote: 7.2.3 +Suggests: + knitr, + covr, + testthat (>= 3.0.0), + rmarkdown +Config/testthat/edition: 3 +VignetteBuilder: knitr diff --git a/R/build_models.R b/R/build_models.R index 68349ee..588c18e 100644 --- a/R/build_models.R +++ b/R/build_models.R @@ -33,7 +33,8 @@ #' @return Nothing #' @export #' -build_k_fold_rf_model <- function(data, +build_k_fold_model <- function(data, + type, seed = NULL, ctrl_method = "repeatedcv", folds = 5, @@ -44,6 +45,10 @@ build_k_fold_rf_model <- function(data, stop("Must provide data.") } + if (!type %in% c("rf", "glm")) { + stop("Must specify model type. Current options are: rf, glm") + } + if (!is.data.frame(data) | is.null(data$class)) { stop("Data must be a data frame (or coercible) with a \"class\" column") } @@ -68,7 +73,7 @@ build_k_fold_rf_model <- function(data, data = data, preProcess = preprocess, trControl = ctrl, - method = "rf", + method = type, metric = "AUC")) print(model) @@ -80,7 +85,6 @@ build_k_fold_rf_model <- function(data, } - #' @title Build Random Forest Model #' #' @param data A dataframe containing columns with model inputs and a From c625b8f34515c99cef7774bf920a5cfc376c17a4 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 27 Jan 2023 13:42:33 -0700 Subject: [PATCH 03/74] add principal comp. markdown document --- analysis/pca_training_data.Rmd | 53 ++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 analysis/pca_training_data.Rmd diff --git a/analysis/pca_training_data.Rmd b/analysis/pca_training_data.Rmd new file mode 100644 index 0000000..9a40111 --- /dev/null +++ b/analysis/pca_training_data.Rmd @@ -0,0 +1,53 @@ +--- +title: "Principal component analysis on landslide training data" +author: Julia Lober +output: + html_notebook: + df_print: paged + html_document: + toc: true +params: + data_dir: + label: Data Directory + value: //Mac/Home/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: /sample_umpqua.Rdata + input: text +editor_options: + chunk_output_type: inline +--- + + +```{r setup, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +# Principal component analysis +A principal component analysis (PCA) allows us to reduce the dimensionality of data and visualize the variation in a dataset. It is usually an exploratory analysis rather than a key part of a quantitative analysis. Here, we do it to see where different covariates might be correlated and how the initiation points vs. non-inititation points are reflected in the overall variability. + +```{r} + +load(paste0(params$data_dir, params$training_data)) + +pc <- prcomp(sample_pnts[,2:7], + center = TRUE, + scale. = TRUE) + +plot(pc$x[sample_pnts$class == "neg",], pch = 19) +points(pc$x[sample_pnts$class == "pos",], col = "red", pch = 19) + +legend("topright", + legend = c("Negative points", "Initiation points"), + pch = 19, + col = c("black", "red"), + bty= "n") + + +``` + From 245dbf06f3f313ed6771d4d9163b589ffc0db9f5 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 31 Jan 2023 09:50:22 -0700 Subject: [PATCH 04/74] more pca plots --- analysis/pca_training_data.Rmd | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/analysis/pca_training_data.Rmd b/analysis/pca_training_data.Rmd index 9a40111..cad7011 100644 --- a/analysis/pca_training_data.Rmd +++ b/analysis/pca_training_data.Rmd @@ -39,15 +39,38 @@ pc <- prcomp(sample_pnts[,2:7], center = TRUE, scale. = TRUE) -plot(pc$x[sample_pnts$class == "neg",], pch = 19) +p <- plot(pc$x[sample_pnts$class == "neg",], pch = 19, + main = "Principal Components Analysis", font.main = 4) points(pc$x[sample_pnts$class == "pos",], col = "red", pch = 19) legend("topright", - legend = c("Negative points", "Initiation points"), + legend = c("Negative points", "Landslide initiation points"), pch = 19, col = c("black", "red"), bty= "n") +l.x <- cbind(pc$rotation[,1]) * 2 +l.y <- cbind(pc$rotation[,2]) * 2 +arrows(x0=0, x1=l.x, y0=0, y1=l.y, col="darkgreen", length=0.15, lwd=3) +text(l.x, l.y, labels=rownames(l.x) , col="darkgreen", pos=c(3, 1, 3, 1), offset=1, cex=1.2) + + ``` +The first two principal components are the ones that explain the most amount of variability in the data. This should be the axis across which we see the data as separated as it gets. We hope to see some separation of the different categories we are interested (in this case, landslide initiation points and negative points), indicating that the covariates we are looking at can explain the differences between these classes. + +For the example Umpqua dataset, there is a slight trend toward landslide initiation points being on the left side of the plot, but lots of overlap/noise in the plots still. + +```{r} +preset <- par(mfrow = c(1, 2)) + +barplot(pc$rotation[,1], main="PC 1 Loadings Plot", las=2, + ylim = c(-1, 1)) +barplot(pc$rotation[,2], main="PC 2 Loadings Plot", las=2, + ylim = c(-1, 1)) + +par(preset) + +``` +Looking at the loadings of each covariate on the principal components, we can try to grasp what each of the dimensions is trying to capture. For example, the first principal component is mostly from the partial contributing area calculations, while the second component is by far most affected by gradient. From 975a8f1f5fa5bb3947a445e2aed42fffba8d03eb Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 8 Feb 2023 13:49:48 -0700 Subject: [PATCH 05/74] added principal component analysis --- analysis/prcomp_training_data.qmd | 117 ++++++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 analysis/prcomp_training_data.qmd diff --git a/analysis/prcomp_training_data.qmd b/analysis/prcomp_training_data.qmd new file mode 100644 index 0000000..26ef90e --- /dev/null +++ b/analysis/prcomp_training_data.qmd @@ -0,0 +1,117 @@ +--- +title: "Principal component analysis on training data" +author: "Julia Lober" +format: html +editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: sample_umpqua.Rdata + input: text + points: + label: Training data points + value: DeanCr/DeanCr_Initiation_Points.shp + input: text + dem: + label: Training data dem + value: DeanCr/elev_deancr.flt + input: text +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(terra) +``` + +```{r} +#| echo: false +load(paste0(params$data_dir, params$training_data)) +pnts <- vect(paste0(params$data_dir, params$points)) +dem <- rast(paste0(params$data_dir, params$dem)) +``` + +## Looking at the data: + +Change the parameters in the .qmd document to use your own dataset for this markdown document. Right now, we are looking at the Dean Creek area of the Umpqua Basin. The covariates for model building are extracted from a LIDAR digital elevation model (DEM) using FORTRAN scripts. The points are from the DOGAMI landslide inventory. + +```{r} +#| echo: false +plot(dem) +points(pnts) +``` + +## Training Data + +A sampling strategy for selecting negative points gives us a data frame of training data with a random selection of non-landslide initiation points based on a buffer. There is a full description of the negative sampling strategy in a different document in this package. + +There are 8 columns that represent the covariates used for building the model. Gradient and mean curvature are the two physical parameters chosen based on the condition needed for landslide initiation. There are also 4 partial contributing area calculations for storms of different duration (6hr, 12hr, 24hr, and 48hr). + +```{r} +head(sample_pnts) +``` + +## Principal component analysis (PCA) + +I am using the built-in `prcomp()` function from the + +```{r} +pc <- prcomp(sample_pnts[,2:7], + center = TRUE, + scale. = TRUE) +``` + +Lets look at the results of the analysis. We can graph the data on the axis of the first two principal components. These are linear combinations of the covariates that we provide that should explain variability in the data. Visually, we should see the data as separated as it gets. We can graph the landslide initiation points and negative points in different colors to see whether these principal components can point to differences between our two classes. + +```{r} +#| echo: false +old_par <- par(xpd = TRUE, mar = c(2, 2, 2, 8)) + +p <- plot(pc$x[sample_pnts$class == "neg",], + pch = 20, + main = "Principal Components Analysis", + font.main = 2, + xlim = c(-6, 6), + ylim = c(-4, 4), + xlab = "Principal component 1", + ylab = "Principal component 2") +points(pc$x[sample_pnts$class == "pos",], col = "red", pch = 20) + +legend("topright", + inset = c(-.3, 0), + legend = c("Negative points", "Landslide \ninitiation points"), + pch = 20, + col = c("black", "red"), + bty= "n") + +l.x <- cbind(pc$rotation[,1]) * 2 +l.y <- cbind(pc$rotation[,2]) * 2 + +par(old_par) +``` + +We can look at the contributions of each covariate to each of the principal components. We see that the first principal component is a fairly equal combination of partial contributing areas and the second principal component is mostly contributed to by gradient. + +```{r} +preset <- par(mfrow = c(1, 2)) + +barplot(pc$rotation[,1], main="PC 1 Loadings Plot", las=2, + ylim = c(-1, 1)) +barplot(pc$rotation[,2], main="PC 2 Loadings Plot", las=2, + ylim = c(-1, 1)) + +par(preset) +# text(l.x, l.y, labels=rownames(l.x) , col="darkgreen", pos=c(3, 1, 3, 1), offset=1, cex=1.2) +# plot(arrows(x0=0, x1=l.x, y0=0, y1=l.y, col="darkgreen", length=0.15, lwd=3)) +``` From 71234b6705a81daec91da0b39a46585afe59f706 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 9 Feb 2023 11:40:51 -0700 Subject: [PATCH 06/74] start mlr3 workflow document --- analysis/mlr3_workflow.qmd | 118 +++++++++++++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) create mode 100644 analysis/mlr3_workflow.qmd diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd new file mode 100644 index 0000000..4318fc2 --- /dev/null +++ b/analysis/mlr3_workflow.qmd @@ -0,0 +1,118 @@ +--- +title: "mlr3 Workflow" +author: "Julia Lober" +format: html +editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: sample_umpqua.Rdata + input: text + points: + label: Training data points + value: DeanCr/DeanCr_Initiation_Points.shp + input: text + dem: + label: Training data dem + value: DeanCr/elev_deancr.flt + input: text +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r} +#| echo: false +load(paste0(params$data_dir, params$training_data)) +pnts <- vect(paste0(params$data_dir, params$points)) +dem <- rast(paste0(params$data_dir, params$dem)) +``` + +## Workflow for the mlr3 package + +This is a document where I work out how to use the `mlr3` package. According to Dan, this is package ecosystem has been taking over for `caret`. + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +# Packages for models - change these based on what type of model you build +library(glmnet) +``` + +Major points for the `mlr3` package include the different nomenclature. The data used to train a model is referred to a `task` and the model being trained is referred to as a `learner`. + +This is a general workflow for any type of model. Another major difference between `mlr3` and `caret` is the names of the model types. The `mlr3learners` package provides the following model types. + +```{r} +mlr_learners +``` + +## Step 1. Creating a task and a learner + +Our task leaves out the `id` column in the sample_pnts data frame, as it will not be relevant information for the model! Specifying the target says what we want the learner to predict - for us, this is the `class` which is positive for landslide initiation points. + +```{r} +task <- as_task_classif(x = sample_pnts[, 2:8], id = "landslide_init", target = "class") +head(task) +``` + +The learner is created based on what type of model we want to train. See above for a list of learners that are available (and keep in mind that you could load `mlr3extralearners` for an even bigger selection). We are interested in classification learners, since the input is categorized into two different classes. + +```{r} +learner <- lrn("classif.log_reg", predict_type = "prob") +print(learner) +``` + +As an experiment, we could train and predict using all of the data. This is bad practice in machine learning but can be an interesting baseline and will help me figure out the syntax. + +```{r} +learner$train(task) +print(learner$model) +``` + +For the generalized linear model (`glmnet`), we need to manually set lambda. This parameter cannot be tuned, so + +```{r} +predicted <- learner$predict(task) +print(predicted) +``` + +Next, we can take a general look at some performance measures. + +```{r} +head(as.data.table(mlr_measures)) +``` + +Print some of these metrics. + +`classif.acc` is the accuracy of the model and `classif.ce` is the error. These should always be inverses of each other. + +`classif.auc` is the area under the ROC curve. Since we are interested more in the probability of a yes (landslide initiation) than simply classification into binary classes, this measure is a bit more useful. + +```{r} +scores <- predicted$score(msrs(c("classif.acc", "classif.ce", "classif.auc"))) +print(scores) + +conf_matrix <- predicted$confusion +print(conf_matrix) +``` + +## Step 2. Add a resampling method + +For any machine learning + +```{r} + +``` From a2e7bee634c2900435ecd1679c6b6214c66f450c Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 10 Feb 2023 21:28:24 -0700 Subject: [PATCH 07/74] more mlr3 workflow --- analysis/mlr3_workflow.qmd | 44 +++++++++++++++++++++++++++++++++++--- 1 file changed, 41 insertions(+), 3 deletions(-) diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd index 4318fc2..f022ce0 100644 --- a/analysis/mlr3_workflow.qmd +++ b/analysis/mlr3_workflow.qmd @@ -47,6 +47,7 @@ library(mlr3) library(mlr3spatial) library(mlr3spatiotempcv) library(mlr3learners) +library(mlr3tuning) # Packages for models - change these based on what type of model you build library(glmnet) ``` @@ -70,8 +71,12 @@ head(task) The learner is created based on what type of model we want to train. See above for a list of learners that are available (and keep in mind that you could load `mlr3extralearners` for an even bigger selection). We are interested in classification learners, since the input is categorized into two different classes. +`classif.glmnet` is a linear regression strategy for classification problems. Notably, `lambda` is a hyperparameter that cannot be automatically optimized. A bandaid solution was to specify a value in the creation of the learner, which silences a warning at prediction time. I am currently reading about `mlr3tuning` and how to create a wrapper tuner object that allows manual specifications for hyperparameter optimization (HPO). + ```{r} -learner <- lrn("classif.log_reg", predict_type = "prob") +learner <- lrn("classif.glmnet", + lambda = 0.01, + predict_type = "prob") print(learner) ``` @@ -109,9 +114,42 @@ conf_matrix <- predicted$confusion print(conf_matrix) ``` -## Step 2. Add a resampling method +## Step 2. Tune and resample + +In order to train this model with some hyperparameter tuning, we need to go back and specify which hyperparameters need to be tuned in the learner. + +```{r} +learner <- lrn("classif.glmnet", + alpha = to_tune(1e-5, 1), + s = to_tune(1e-5, 1e5), + predict_type = "prob") + +tuner = tnr("grid_search", resolution = 5, batch_size = 5) + +resampling <- rsmp("cv", folds = 5) + +learner_at = auto_tuner( + method = tnr("grid_search", resolution = 5, batch_size = 5), + learner = learner, + resampling = resampling, + measure = msr("classif.auc"), +) +print(learner_at) +``` + +```{r} +learner_at$train(task) +``` + +```{r} +predicted <- learner_at$predict(task) +print(predicted$score(msrs(c("classif.auc", "classif.acc")))) +print(predicted$confusion) +``` + +## Step 3. Add a resampling method -For any machine learning +For any machine learning workflow, you want to including a resampling method that will train models on a couple different subsets of data as training and testing sets to reduce the role of chance and produce a more robust basis for evaluating the model's performance. ```{r} From 72ff1c1fb6afa4318f453806ed2aa686a4e714c3 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 16 Feb 2023 15:06:52 -0700 Subject: [PATCH 08/74] added benchmark, code to predict full raster --- analysis/mlr3_workflow.qmd | 55 +++++++++++++++++++++++++++++++++----- 1 file changed, 48 insertions(+), 7 deletions(-) diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd index f022ce0..318ec9d 100644 --- a/analysis/mlr3_workflow.qmd +++ b/analysis/mlr3_workflow.qmd @@ -48,8 +48,10 @@ library(mlr3spatial) library(mlr3spatiotempcv) library(mlr3learners) library(mlr3tuning) +library(mlr3viz) # Packages for models - change these based on what type of model you build library(glmnet) +library(ranger) ``` Major points for the `mlr3` package include the different nomenclature. The data used to train a model is referred to a `task` and the model being trained is referred to as a `learner`. @@ -74,8 +76,7 @@ The learner is created based on what type of model we want to train. See above f `classif.glmnet` is a linear regression strategy for classification problems. Notably, `lambda` is a hyperparameter that cannot be automatically optimized. A bandaid solution was to specify a value in the creation of the learner, which silences a warning at prediction time. I am currently reading about `mlr3tuning` and how to create a wrapper tuner object that allows manual specifications for hyperparameter optimization (HPO). ```{r} -learner <- lrn("classif.glmnet", - lambda = 0.01, +learner <- lrn("classif.log_reg", predict_type = "prob") print(learner) ``` @@ -87,11 +88,17 @@ learner$train(task) print(learner$model) ``` -For the generalized linear model (`glmnet`), we need to manually set lambda. This parameter cannot be tuned, so - ```{r} predicted <- learner$predict(task) -print(predicted) + +``` + +```{r} +tsk_predict = as_task_unsupervised(dem) + +# plan("multisession") # optional parallelization +pred = predict_spatial(tsk_predict, learner, format = "terra") +class(pred) ``` Next, we can take a general look at some performance measures. @@ -114,6 +121,32 @@ conf_matrix <- predicted$confusion print(conf_matrix) ``` +Resampling: + +```{r} +resampling <- rsmp("cv", folds = 5) + +learner_2 <- lrn("classif.ranger", + predict_type = "prob") +learner_3 <- lrn("classif.featureless", + predict_type = "prob") + +bench <- benchmark_grid(task = task, + learner = c(learner, learner_2, learner_3), + resampling = resampling) + +mark <- benchmark(bench) + +``` + +```{r} +autoplot(mark, measure = msr("classif.acc")) +``` + +```{r} +autoplot(mark, type = "roc") +``` + ## Step 2. Tune and resample In order to train this model with some hyperparameter tuning, we need to go back and specify which hyperparameters need to be tuned in the learner. @@ -121,7 +154,8 @@ In order to train this model with some hyperparameter tuning, we need to go back ```{r} learner <- lrn("classif.glmnet", alpha = to_tune(1e-5, 1), - s = to_tune(1e-5, 1e5), + lambda = 0.1, + # s = to_tune(1e-5, 1e5), predict_type = "prob") tuner = tnr("grid_search", resolution = 5, batch_size = 5) @@ -147,10 +181,17 @@ print(predicted$score(msrs(c("classif.auc", "classif.acc")))) print(predicted$confusion) ``` -## Step 3. Add a resampling method +## Step 3. Comparing spatiotemporal cross-validation vs normal cross-validation. For any machine learning workflow, you want to including a resampling method that will train models on a couple different subsets of data as training and testing sets to reduce the role of chance and produce a more robust basis for evaluating the model's performance. +I need data with x and y coordinates attached to do this. This is currently my standstill. + +```{r} +plot(dem) +points(pnts) +``` + ```{r} ``` From 8873c7f638a1c5941aafe9ca751a4dea8bc23274 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 22 Feb 2023 13:51:51 -0700 Subject: [PATCH 09/74] start resampling methods document --- analysis/resampling_methods.qmd | 109 ++++++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 analysis/resampling_methods.qmd diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd new file mode 100644 index 0000000..8180e81 --- /dev/null +++ b/analysis/resampling_methods.qmd @@ -0,0 +1,109 @@ +--- +title: "Spatial CV vs traditional CV" +author: "Julia Lober" +format: html +editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: sample_wilson.Rdata + input: text + points: + label: Training data points + value: DeanCr/DeanCr_Initiation_Points.shp + input: text + dem: + label: Training data dem + value: DeanCr/elev_deancr.flt + input: text +--- + +```{r} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r} +#| echo: false +load(paste0(params$data_dir, params$training_data)) +pnts <- vect(paste0(params$data_dir, params$points)) +dem <- rast(paste0(params$data_dir, params$dem)) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +# Packages for models - change these based on what type of model you build +library(glmnet) +library(ranger) +``` + +## Evaluate Spatial CV Methods vs Normal CV + +Data is loaded. First we need to look at the points on a map. + +```{r} +orig_p <- par(mar = c(5, 5, 0, 5)) + +plot(sample_pnts[, "x"], sample_pnts[, "y"], pch = 20) +sample_pnts[, "class"] <- as.factor(sample_pnts[, "class"]) + +par(orig_p) + +``` + +We want to train a logistic regression learner based on the landslide data points loaded into the data frame sample_pnts. + +```{r} +task <- as_task_classif_st(x = sample_pnts[, 2:10], + id = "landslide_init", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = 2992) + +learner <- lrn("classif.log_reg", + predict_type = "prob") +``` + +```{r} +#| echo: false +head(sample_pnts[, 2:10]) +``` + +We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. + +> Nevertheless, despite the random selection of initial cluster centers, repeated partitionings may in some cases be nearly identical. Also, k-means clustering may be less suitable for data sets with pre-existing clusters of points and/or with isolated, distant sample locations. When distinct clusters of points are present, as in multi-level sampling, it may be better to define clusters using a factor variable. ([mlr3spatiotempcv reference](https://arxiv.org/pdf/2110.12674.pdf)) + +```{r} +# Define the spatial resampling method. +rsmp_clus <- rsmp("repeated_spcv_coords", + folds = 5, repeats = 2) + +# rr_sp = resample(task = task, +# learner = learner, +# resampling = resampling_sp) + +autoplot(rsmp_clus, task, fold_id = c(1:5), size = 1) * + ggplot2::scale_y_continuous(breaks = seq(-3.97, -4, -0.01)) * + ggplot2::scale_x_continuous(breaks = seq(-79.06, -79.08, -0.02)) +``` + +I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). + +Other options we have are: + +1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this +2. From f99e9472f39f20a9e398ac987fb49a6375d77f26 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 23 Feb 2023 14:02:51 -0700 Subject: [PATCH 10/74] added blockcv example --- analysis/resampling_methods.qmd | 46 +++++++++++++++++++++++---------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 8180e81..5539be4 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -34,8 +34,8 @@ knitr::opts_chunk$set( ```{r} #| echo: false load(paste0(params$data_dir, params$training_data)) -pnts <- vect(paste0(params$data_dir, params$points)) -dem <- rast(paste0(params$data_dir, params$dem)) +pnts <- terra::vect(paste0(params$data_dir, params$points)) +dem <- terra::rast(paste0(params$data_dir, params$dem)) ``` ```{r load, include = FALSE} @@ -57,7 +57,8 @@ Data is loaded. First we need to look at the points on a map. ```{r} orig_p <- par(mar = c(5, 5, 0, 5)) -plot(sample_pnts[, "x"], sample_pnts[, "y"], pch = 20) +# plot(dem) +plot(x = sample_pnts[, "x"], y = sample_pnts[, "y"], pch = 20) sample_pnts[, "class"] <- as.factor(sample_pnts[, "class"]) par(orig_p) @@ -80,30 +81,47 @@ learner <- lrn("classif.log_reg", ```{r} #| echo: false -head(sample_pnts[, 2:10]) +head(sample_pnts) ``` -We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. +We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. This is one of the more straight-forward methods of spatial cross-validation: divide our data into groupings that are all near each other. > Nevertheless, despite the random selection of initial cluster centers, repeated partitionings may in some cases be nearly identical. Also, k-means clustering may be less suitable for data sets with pre-existing clusters of points and/or with isolated, distant sample locations. When distinct clusters of points are present, as in multi-level sampling, it may be better to define clusters using a factor variable. ([mlr3spatiotempcv reference](https://arxiv.org/pdf/2110.12674.pdf)) ```{r} -# Define the spatial resampling method. -rsmp_clus <- rsmp("repeated_spcv_coords", - folds = 5, repeats = 2) +set.seed(12345) +rsmp_clus <- rsmp("spcv_coords", + folds = 5) # rr_sp = resample(task = task, # learner = learner, # resampling = resampling_sp) -autoplot(rsmp_clus, task, fold_id = c(1:5), size = 1) * - ggplot2::scale_y_continuous(breaks = seq(-3.97, -4, -0.01)) * - ggplot2::scale_x_continuous(breaks = seq(-79.06, -79.08, -0.02)) +autoplot(rsmp_clus, task, fold_id = c(1:5)) * + ggplot2::scale_x_continuous(breaks = seq(124.0, 125.0, 0.4)) * + ggplot2::scale_y_continuous(breaks = seq(55.38, 55.48, 0.2)) ``` I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). -Other options we have are: +Other otions we have are ("method ="): -1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this -2. +1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). +2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. +3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). + +This is the block method. Equal-sized blocks are created and then grouped to create the number of splits. The more columns/rows are created, the closer this method would get to a non-spatial cross validation method, so this is an important factor for this method. + +This method is probably not as well suited to non-rectangular study areas. + +```{r} +set.seed(12345) +rsmp_block <- rsmp("spcv_block", + folds = 5, + cols = 4, + rows = 4) + +autoplot(rsmp_block, task, fold_id = c(1:5), size = 1, show_blocks = TRUE) +``` + +We have to wait on the temporal cross-validation methods for now, since we don't have the date in our current data frame (as of 2/23). From ea185b0ef77dc70f8cd20a21ebbecb9ab367f8a8 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 27 Feb 2023 13:26:13 -0700 Subject: [PATCH 11/74] added script for assembling model input dataframe --- R/create_model_dataframe.R | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 R/create_model_dataframe.R diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R new file mode 100644 index 0000000..319f833 --- /dev/null +++ b/R/create_model_dataframe.R @@ -0,0 +1,34 @@ +# define where to find the data and load it. +folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_2.27/out/" +load(paste0(folder, "ls_1996.Rdata")) +load(paste0(folder, "ls_2007.Rdata")) +load(paste0(folder, "ls_2011.Rdata")) +load(paste0(folder, "nonls_1996.Rdata")) +load(paste0(folder, "nonls_2007.Rdata")) +load(paste0(folder, "nonls_2011.Rdata")) + +# remove these lines when the bug is fixed. the $geo field in the 2011 and 2007 +# data frames is not stored as an integer value +ls_2011$geo <- ls_2011$geo$GeoClass +ls_2007$geo <- ls_2007$geo$GeoClass + +# add year labels to each selection of landslide points and non-ls points +ls_1996$year <- rep(1996, length(ls_1996[, 1])) +ls_2007$year <- rep(2007, length(ls_2007[, 1])) +ls_2011$year <- rep(2011, length(ls_2011[, 1])) +nonls_1996$year <- rep(1996, length(nonls_1996[, 1])) +nonls_2007$year <- rep(2007, length(nonls_2007[, 1])) +nonls_2011$year <- rep(2011, length(nonls_2011[, 1])) + +# combine into one data frame. +subset96 <- 1:length(ls_1996[, 1]) +subset07 <- 1:length(ls_2007[, 1]) +subset11 <- 1:length(ls_2011[, 1]) +training_data <- rbind(ls_1996, + ls_2007, + ls_2011, + nonls_1996[subset96, ], + nonls_2007[subset07, ], + nonls_2011[subset11, ]) + +training_data$class <- as.factor(training_data$class) From d3830bc052a302f5231666d0b037beb94153f408 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 27 Feb 2023 13:27:36 -0700 Subject: [PATCH 12/74] added some resampling methods for comparison --- analysis/resampling_methods.qmd | 90 ++++++++++++++++++++++++++------- 1 file changed, 71 insertions(+), 19 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 5539be4..085a9e5 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -10,15 +10,15 @@ params: input: text training_data: label: Training data .Rdata file - value: sample_wilson.Rdata + value: sample_wilson.Rdata input: text points: label: Training data points - value: DeanCr/DeanCr_Initiation_Points.shp + value: DeanCr/DeanCr_Initiation_Points.shp input: text dem: label: Training data dem - value: DeanCr/elev_deancr.flt + value: DeanCr/elev_deancr.flt input: text --- @@ -33,9 +33,12 @@ knitr::opts_chunk$set( ```{r} #| echo: false -load(paste0(params$data_dir, params$training_data)) -pnts <- terra::vect(paste0(params$data_dir, params$points)) -dem <- terra::rast(paste0(params$data_dir, params$dem)) +# load(paste0(params$data_dir, params$training_data)) +# pnts <- terra::vect(paste0(params$data_dir, params$points)) +# dem <- terra::rast(paste0(params$data_dir, params$dem)) +source("../R/create_model_dataframe.R") + +sample_pnts <- training_data ``` ```{r load, include = FALSE} @@ -50,7 +53,7 @@ library(glmnet) library(ranger) ``` -## Evaluate Spatial CV Methods vs Normal CV +## Evaluate Spatial CV Methods vs Normal CV Data is loaded. First we need to look at the points on a map. @@ -68,7 +71,7 @@ par(orig_p) We want to train a logistic regression learner based on the landslide data points loaded into the data frame sample_pnts. ```{r} -task <- as_task_classif_st(x = sample_pnts[, 2:10], +task <- as_task_classif_st(x = sample_pnts[, 2:14], id = "landslide_init", target = "class", positive = "pos", @@ -84,6 +87,20 @@ learner <- lrn("classif.log_reg", head(sample_pnts) ``` +### Non-spatial cross validation + +Non-spatial cross validation randomly selects points to split the training data into training and testing sets. The concern we have with this method is that in some cases, it will end up overestimating the performance of the model or underestimating error. We want to approximate model performance as closely as possible in order to assess how much confidence we can put in it. + +```{r} +set.seed(12345) +rsmp_nonsp <- rsmp("cv", + folds = 5) + +autoplot(rsmp_nonsp, task, fold_id = c(1:5)) +``` + +### K-means clustering spatial cross-validation + We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. This is one of the more straight-forward methods of spatial cross-validation: divide our data into groupings that are all near each other. > Nevertheless, despite the random selection of initial cluster centers, repeated partitionings may in some cases be nearly identical. Also, k-means clustering may be less suitable for data sets with pre-existing clusters of points and/or with isolated, distant sample locations. When distinct clusters of points are present, as in multi-level sampling, it may be better to define clusters using a factor variable. ([mlr3spatiotempcv reference](https://arxiv.org/pdf/2110.12674.pdf)) @@ -97,18 +114,12 @@ rsmp_clus <- rsmp("spcv_coords", # learner = learner, # resampling = resampling_sp) -autoplot(rsmp_clus, task, fold_id = c(1:5)) * - ggplot2::scale_x_continuous(breaks = seq(124.0, 125.0, 0.4)) * - ggplot2::scale_y_continuous(breaks = seq(55.38, 55.48, 0.2)) +autoplot(rsmp_clus, task, fold_id = c(1:5)) + # ggplot2::scale_x_continuous(breaks = seq(124.50, 124.62, 0.04)) * + # ggplot2::scale_y_continuous(breaks = seq(55.38, 55.48, 0.03)) ``` -I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). - -Other otions we have are ("method ="): - -1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). -2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. -3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). +### Block spatial cross-validation This is the block method. Equal-sized blocks are created and then grouped to create the number of splits. The more columns/rows are created, the closer this method would get to a non-spatial cross validation method, so this is an important factor for this method. @@ -124,4 +135,45 @@ rsmp_block <- rsmp("spcv_block", autoplot(rsmp_block, task, fold_id = c(1:5), size = 1, show_blocks = TRUE) ``` -We have to wait on the temporal cross-validation methods for now, since we don't have the date in our current data frame (as of 2/23). +### Temporal cross-validation + +Another concept for resampling is to design a strategy that is tailored to our specific dataset. We could do this to varying level of degrees, based on the predictors that are included in our data. + +One option is to simply divide the dataset into the three natural temporal divisions that we observe: (roughly) 1996, 2007, and 2011. First, we define the year column as a factor variable, then pass it to custom_cv as the split determination variable. However, this method produces drastically uneven folds - we have 213 observations from 1996, 137 form 2007, and only 47 from 2011. + +```{r} +set.seed(12345) +yr_splits <- as.factor(sample_pnts$year) + +rsmp_temp <- rsmp("custom_cv") +rsmp_temp$instantiate(task, f = yr_splits) + +autoplot(rsmp_temp, task, fold_id = c(1:3)) +``` + +Another proposal was to divide by both time and lithology. For example: + +> Train on 1996 sedimentary + volcaniclastic,  +> +> test on 1996 other rock types, 2007 sedimentary+volcaniclastic, 2007 other rock types, 2011 sed+volcaniclastic, 2011 other rock types. + +This method is based in our physical understanding of how landslides occur, and will help to decide how much we can extrapolate a model that is built using a certain subset of landslides to other lithologies. + +```{r} +set.seed(12345) +yr_lith <- (sample_pnts$year * sample_pnts$geo) +yr_lith_splits <- as.factor(yr_lith) + +rsmp_templith <- rsmp("custom_cv") +rsmp_templith$instantiate(task, f = yr_lith_splits) + +autoplot(rsmp_templith, task, fold_id = c(1:11)) +``` + +I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). + +Other otions we have are ("method ="): + +1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). +2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. +3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). From a3f7d6744fa462525740cb1095f0b98767ab4703 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 28 Feb 2023 11:24:58 -0700 Subject: [PATCH 13/74] added map of sample points. --- analysis/resampling_methods.qmd | 31 +++++++++++++++++++++++-------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 085a9e5..51bfedb 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -57,14 +57,31 @@ library(ranger) Data is loaded. First we need to look at the points on a map. +TODO: figure out how to add a map background to the + ```{r} -orig_p <- par(mar = c(5, 5, 0, 5)) +# orig_p <- par(mar = c(5, 5, 0, 5)) +points <- as.data.frame(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points <- vect(points, geom = c("V1", "y")) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") # plot(dem) -plot(x = sample_pnts[, "x"], y = sample_pnts[, "y"], pch = 20) -sample_pnts[, "class"] <- as.factor(sample_pnts[, "class"]) - -par(orig_p) +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 2, color = "red") + + geom_spatvector(data = points[points$class == "neg", ], + mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + show.legend = TRUE, size = 2, color = "black") + + scale_shape_manual(values = c(3, 1, 17)) + + labs(x = "Longitude", y = "Latitude", shape = "Study points") + +# par(orig_p) ``` @@ -174,6 +191,4 @@ I am going to ignore the leave-one-out methods for now since we don't have many Other otions we have are ("method ="): -1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). -2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. -3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). +1. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). From ae3d1b46b0955760e0a1911361cb2bbc1a22d5aa Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 28 Feb 2023 18:01:54 -0700 Subject: [PATCH 14/74] select dispersed negative sample points instead of first tenth of negative sample points --- R/create_model_dataframe.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R index 319f833..82f5365 100644 --- a/R/create_model_dataframe.R +++ b/R/create_model_dataframe.R @@ -21,9 +21,9 @@ nonls_2007$year <- rep(2007, length(nonls_2007[, 1])) nonls_2011$year <- rep(2011, length(nonls_2011[, 1])) # combine into one data frame. -subset96 <- 1:length(ls_1996[, 1]) -subset07 <- 1:length(ls_2007[, 1]) -subset11 <- 1:length(ls_2011[, 1]) +subset96 <- seq(1, length(nonls_1996[,1]), 10) # 1:length(ls_1996[, 1]) +subset07 <- seq(1, length(nonls_2007[,1]), 10) # 1:length(ls_2007[, 1]) +subset11 <- seq(1, length(nonls_2011[,1]), 10) # 1:length(ls_2011[, 1]) training_data <- rbind(ls_1996, ls_2007, ls_2011, From 85968a19d5f7663fd8571d4f93855e979297d39e Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 28 Feb 2023 18:04:15 -0700 Subject: [PATCH 15/74] refining map --- analysis/resampling_methods.qmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 51bfedb..3044f3a 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -74,10 +74,10 @@ ggplot() + color = "white", fill = "white") + geom_spatvector(data = points[points$class == "pos", ], mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), - show.legend = TRUE, size = 2, color = "red") + - geom_spatvector(data = points[points$class == "neg", ], - mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - show.legend = TRUE, size = 2, color = "black") + + show.legend = TRUE, size = 1, color = "red", alpha = 0.5) + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 0.5) + scale_shape_manual(values = c(3, 1, 17)) + labs(x = "Longitude", y = "Latitude", shape = "Study points") From 283111acbf0a933c4a2230c094a52441ae7d56e4 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 11:52:25 -0700 Subject: [PATCH 16/74] updating to new data location --- R/create_model_dataframe.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R index 82f5365..d106192 100644 --- a/R/create_model_dataframe.R +++ b/R/create_model_dataframe.R @@ -1,5 +1,5 @@ # define where to find the data and load it. -folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_2.27/out/" +folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/" load(paste0(folder, "ls_1996.Rdata")) load(paste0(folder, "ls_2007.Rdata")) load(paste0(folder, "ls_2011.Rdata")) From 8e4555f649008bbd1a7004cf9d05569e5ebb55bc Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 11:52:44 -0700 Subject: [PATCH 17/74] exploring feature selection and filtering methods --- analysis/feature_selection.qmd | 54 ++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 analysis/feature_selection.qmd diff --git a/analysis/feature_selection.qmd b/analysis/feature_selection.qmd new file mode 100644 index 0000000..7ddf4fe --- /dev/null +++ b/analysis/feature_selection.qmd @@ -0,0 +1,54 @@ +--- +title: "Landslide initiation feature selection" +author: Julia Lober +format: html +editor: visual +--- + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3verse) +``` + +## Feature selection for landslide points + +This document explores the impact of each predictor on a logistic regression model. We will look at a couple of different ways to do feature selection, and see if they offer consistent results or different options each time. + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +``` + +```{r} +names(sample_pnts) +``` + +```{r} +task = as_task_classif(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") +``` + +## Filtering + +Lets look at a few filtering methods. These calculate some measure of correlation between the target variable and the predictors, then assigns a threshold which must be met in order to be included in the final model. + +The benefit of these methods are a simpler method that does not involve building a bunch of models to test the impact of different features. The downside is that it is possible that the filter does not truly reflect the impact on the final model. + +```{r} +library(FSelectorRcpp) +info_filter = flt("information_gain") +info_filter$calculate(task) + +as.data.table(info_filter) +``` From aacfb87d06237a9598629953b8d20fe6fac96650 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 11:53:07 -0700 Subject: [PATCH 18/74] new workflow document --- analysis/modeling_landslide_initiation.qmd | 150 +++++++++++++++++++++ 1 file changed, 150 insertions(+) create mode 100644 analysis/modeling_landslide_initiation.qmd diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd new file mode 100644 index 0000000..a6b1ea0 --- /dev/null +++ b/analysis/modeling_landslide_initiation.qmd @@ -0,0 +1,150 @@ +--- +title: "Modeling Landslide Initiation" +autho: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +``` + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +``` + +## A look at the data. + +The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. + +The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. + +```{r} +#| echo: false +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. + +## Defining the modeling task and learner + +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. + +There are several steps we need to work through: + +1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. +2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. +3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. +4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. + +```{r} +set.seed(12345) +landslide_task <- as_task_classif_st(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +rf_learner <- lrn("classif.ranger", + predict_type = "prob") + +landslide_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3) +``` + +These are the first four folds of the clustering spatial resampling. + +```{r} +#| echo: false +autoplot(landslide_resamp, + landslide_task, + fold_id = c(1:4)) +``` + +## Feature selection + +We'll work this in in a bit. + +## Training the logistic regression model + +To train the logistic regression model, which has no hyperparameters that need to be tuned, we call the method resample. + +```{r} +build_time <- system.time(logreg_model <- resample(task= landslide_task, + learner = logreg_learner, + resampling = landslide_resamp)) + +build_time +``` + +And now we look at the results. + +```{r} +logreg_model +``` + +## Training the random forest model + +```{r} +build_time <- system.time(rf_model <- resample(task= landslide_task, + learner = rf_learner, + resampling = landslide_resamp)) +``` + +## Compare the two models + +```{r} +autoplot(c(logreg_model, rf_model), measure = msr("classif.auc")) +``` From 56b7f86b4b1bdc236943027ca16f6f09eff7d4fa Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 12:02:41 -0700 Subject: [PATCH 19/74] minor changes --- analysis/resampling_methods.qmd | 58 ++++++++++++++++++++++++++------- 1 file changed, 47 insertions(+), 11 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 3044f3a..ed1ceb4 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -37,7 +37,6 @@ knitr::opts_chunk$set( # pnts <- terra::vect(paste0(params$data_dir, params$points)) # dem <- terra::rast(paste0(params$data_dir, params$dem)) source("../R/create_model_dataframe.R") - sample_pnts <- training_data ``` @@ -62,22 +61,20 @@ TODO: figure out how to add a map background to the ```{r} # orig_p <- par(mar = c(5, 5, 0, 5)) points <- as.data.frame(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) -points <- vect(points, geom = c("V1", "y")) points$yr <- sample_pnts$year points$class <- sample_pnts$class -crs(points) <- "epsg:26910" +terra::crs(points) <- "epsg:26910" points <- project(points, "epsg:4326") -# plot(dem) ggplot() + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), color = "white", fill = "white") + geom_spatvector(data = points[points$class == "pos", ], mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), show.legend = TRUE, size = 1, color = "red", alpha = 0.5) + - # geom_spatvector(data = points[points$class == "neg", ], - # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - # show.legend = TRUE, size = 1 , color = "black", alpha = 0.5) + + geom_spatvector(data = points[points$class == "neg", ], + mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + show.legend = TRUE, size = 1 , color = "black", alpha = 0.5) + scale_shape_manual(values = c(3, 1, 17)) + labs(x = "Longitude", y = "Latitude", shape = "Study points") @@ -88,7 +85,7 @@ ggplot() + We want to train a logistic regression learner based on the landslide data points loaded into the data frame sample_pnts. ```{r} -task <- as_task_classif_st(x = sample_pnts[, 2:14], +task <- as_task_classif_st(x = sample_pnts[!(is.na(sample_pnts$dist_to_road)), 2:14], id = "landslide_init", target = "class", positive = "pos", @@ -156,11 +153,11 @@ autoplot(rsmp_block, task, fold_id = c(1:5), size = 1, show_blocks = TRUE) Another concept for resampling is to design a strategy that is tailored to our specific dataset. We could do this to varying level of degrees, based on the predictors that are included in our data. -One option is to simply divide the dataset into the three natural temporal divisions that we observe: (roughly) 1996, 2007, and 2011. First, we define the year column as a factor variable, then pass it to custom_cv as the split determination variable. However, this method produces drastically uneven folds - we have 213 observations from 1996, 137 form 2007, and only 47 from 2011. +One option is to simply divide the dataset into the three natural temporal divisions that we observe: (roughly) 1996, 2007, and 2011. First, we define the year column as a factor variable, then pass it to custom_cv as the split determination variable. However, this method produces drastically uneven folds - we have 213 observations from 1996, 137 from 2007, and only 47 from 2011. ```{r} set.seed(12345) -yr_splits <- as.factor(sample_pnts$year) +yr_splits <- as.factor(sample_pnts[!(is.na(sample_pnts$dist_to_road)),]$year) rsmp_temp <- rsmp("custom_cv") rsmp_temp$instantiate(task, f = yr_splits) @@ -178,7 +175,7 @@ This method is based in our physical understanding of how landslides occur, and ```{r} set.seed(12345) -yr_lith <- (sample_pnts$year * sample_pnts$geo) +yr_lith <- ((sample_pnts[!(is.na(sample_pnts$dist_to_road)),]$year) * sample_pnts[!(is.na(sample_pnts$dist_to_road)),]$geo) yr_lith_splits <- as.factor(yr_lith) rsmp_templith <- rsmp("custom_cv") @@ -192,3 +189,42 @@ I am going to ignore the leave-one-out methods for now since we don't have many Other otions we have are ("method ="): 1. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). + +## Evaluating the performance of each method. + +Now we need to get the performance metric calculated using each resampling method, so that we can compare them. This step is likely to take a bit of time. + +```{r} +methods = c(rsmp_block, rsmp_clus, rsmp_nonsp, rsmp_temp, rsmp_templith) +times = list() +model_perf = list() + +for (method in methods) { + build_time <- system.time(model <- resample(task= task, + learner = learner, + resampling = method)) + times <- append(times, build_time["elapsed"]) + model_perf <- append(model_perf, model$aggregate(measures = msr("classif.auc"))) +} + +``` + +```{r} +method_names = c("rsmp_block", "rsmp_clus", "rsmp_nonsp", "rsmp_temp", "rsmp_templith") + +to_plot <- c(model_perf[1]$classif.auc, + model_perf[2]$classif.auc, + model_perf[3]$classif.auc, + model_perf[4]$classif.auc, + model_perf[5]$classif.auc) +to_plot <- as.data.frame(to_plot) +to_plot$names <- method_names[1:5] + +ggplot(to_plot, aes(x = names, y = to_plot)) + + geom_bar(stat = "identity") + + coord_cartesian(ylim=c(0.8,1)) + + labs(y = "Area under the ROC curve", x = "Resampling method") + # scale_y_continuous(limits = c(0.5, 1)) +``` + +Based on literature, lets go with the clustering resampling method. From 8d9ad324ab77b8f3994a80d19f38e1db6953b847 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 8 Mar 2023 14:08:05 -0700 Subject: [PATCH 20/74] add forward feature selection --- analysis/feature_selection.qmd | 182 ++++++++++++++++++++++++++++++++- 1 file changed, 178 insertions(+), 4 deletions(-) diff --git a/analysis/feature_selection.qmd b/analysis/feature_selection.qmd index 7ddf4fe..84e9131 100644 --- a/analysis/feature_selection.qmd +++ b/analysis/feature_selection.qmd @@ -31,7 +31,7 @@ names(sample_pnts) ``` ```{r} -task = as_task_classif(x = sample_pnts, +task_orig <- as_task_classif_st(x = sample_pnts, id = "landslide_initiation", target = "class", positive = "pos", @@ -39,16 +39,190 @@ task = as_task_classif(x = sample_pnts, crs = "epsg:26910") ``` -## Filtering +We'll also look at adding a few manual predictors that might help us develop the relationship that we observe, or expect to observe based on our understanding of the physical processes. + +```{r} +sample_pnts_ext <- sample_pnts +sample_pnts_ext$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts_ext$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 + +task_ext <- as_task_classif_st(x = sample_pnts_ext, + id = "landslide_initiation_ext", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") +``` + +## Filtering Lets look at a few filtering methods. These calculate some measure of correlation between the target variable and the predictors, then assigns a threshold which must be met in order to be included in the final model. The benefit of these methods are a simpler method that does not involve building a bunch of models to test the impact of different features. The downside is that it is possible that the filter does not truly reflect the impact on the final model. ```{r} -library(FSelectorRcpp) +task <- task_orig +``` + +```{r} info_filter = flt("information_gain") info_filter$calculate(task) -as.data.table(info_filter) +res_info <- as.data.table(info_filter) +res_info$rank <- 1:length(res_info$feature) +``` + +```{r} +anova_filter = flt("anova") +anova_filter$calculate(task) + +res_anova <- as.data.table(anova_filter) +res_anova$rank <- 1:length(res_anova$feature) +``` + +```{r} +auc_filter = flt("auc") +auc_filter$calculate(task) + +res_auc <- as.data.table(auc_filter) +res_auc$rank <- 1:length(res_auc$feature) +``` + +```{r} +cmim_filter = flt("cmim") +cmim_filter$calculate(task) + +res_cmim <- as.data.table(cmim_filter) +res_cmim$rank <- 1:length(res_cmim$feature) +``` + +```{r} +mim_filter = flt("mim") +mim_filter$calculate(task) + +res_mim <- as.data.table(mim_filter) +res_mim$rank <- 1:length(res_mim$feature) +``` + +#### Compare the filters. + +```{r} +results <- merge(res_auc, res_anova, by = "feature", suffixes = c(".auc", ".anova")) +results <- merge(results, res_info, by = "feature", suffixes = c("", ".info")) +results <- merge(results, res_cmim, by = "feature", suffixes = c("", ".cmim")) +results <- merge(results, res_mim, by = "feature", suffixes = c("", ".mim")) +results$total <- rowSums(cbind(results$rank.auc, results$rank.anova, results$rank, results$rank.cmim, results$rank.mim)) + +barplot(height = results$total, names = results$feature, las = 2) + +``` + +## Basic forward feature selection + +This method tests the model created adding one feature each time until the model performance is no longer improving. We can use a variety of + +```{r} +lgr::get_logger("mlr3")$set_threshold("warn") +set.seed(12345) +instance = fselect( + fselector = fs("sequential"), + task = task, + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3), + measure = msr("classif.auc") +) +``` + +```{r} +dt <- as.data.table(instance$archive) +dt[batch_nr == 1, 1:14] + +``` + +The result we have here is likely a light over-estimation of modeling performance. The best way to assess performance is to do nested resampling - an outer resampling strategy that reserves a test set for estimating model performance, and an inner resampling strategy that is used for the feature selection process (or, hyperparameter tuning). I do implement this below. + +```{r} +instance$result +instance$result_feature_set +``` + +We can also use the feature selector as an auto_fselector, which inherits from the learner class and can be treated as such. This allows us to put it in a benchmark function and compare it to other learners. + +```{r} +fs_learner = auto_fselector( + fselector = fs("sequential"), + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2), + measure = msr("classif.auc") +) + +# Tried, did not improve training time (by much) and performed slightly worse. +fs_learner_rand = auto_fselector( + fselector = fs("random_search"), + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2), + measure = msr("classif.auc") +) +``` + +```{r} +lgr::get_logger("mlr3")$set_threshold("error") +bm_grid <- benchmark_grid( + task = task, + learner = list(fs_learner, lrn("classif.log_reg", predict_type = "prob")), + resampling = rsmp("spcv_coords", folds = 3) +) + +bm <- benchmark(bm_grid) +``` + +It seems that there is not really any significant improvement of the feature-selected model over the normal model. + +```{r} +aggr = bm$aggregate(msrs(c("classif.auc", "time_train"))) +as.data.frame(aggr)[, c("learner_id", "classif.auc", "time_train")] +autoplot(bm, measure = msr("classif.auc")) +``` + +```{r} +rf_learner = auto_fselector( + fselector = fs("sequential"), + learner = lrn("classif.ranger", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2), + measure = msr("classif.auc") +) + +outer_resample <- rsmp("repeated_spcv_coords", folds = 5, repeats = 5) +rr <- resample(task, rf_learner, outer_resample, store_models = TRUE) +``` + +```{r} +rr2 <- resample(task, lrn("classif.ranger", predict_type = "prob"), outer_resample, store_models = TRUE) + +rr2$score(measures = msr("classif.auc"))[, "classif.auc"] +``` + +Lets try using the Aikake Information Criterion for the forward feature selection. + +ERROR: this always results in an error that says there is no model stored in classif.log_reg(). I don't think that it is worth the time to try to figure this error out. + +```{r} +# lgr::get_logger("mlr3")$set_threshold("debug") +# aic_select = fselect( +# fselector = fs("sequential"), +# task = task, +# learner = lrn("classif.log_reg", predict_type = "prob"), +# resampling = rsmp("repeated_spcv_coords", +# folds = 3, +# repeats = 1), +# measure = msr("aic") +# ) ``` From e4eb84be30608c8eb0c29fbd9a41bb4c0756386b Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 10 Mar 2023 12:27:40 -0700 Subject: [PATCH 21/74] pre-pipeline save --- analysis/modeling_landslide_initiation.qmd | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd index a6b1ea0..ba81d68 100644 --- a/analysis/modeling_landslide_initiation.qmd +++ b/analysis/modeling_landslide_initiation.qmd @@ -37,7 +37,7 @@ source("../R/create_model_dataframe.R") sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] ``` -## A look at the data. +## A look at the data. The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. @@ -75,7 +75,7 @@ Note that the landslide initiation points tend to come in clusters and the years ## Defining the modeling task and learner -For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. There are several steps we need to work through: @@ -99,6 +99,8 @@ logreg_learner <- lrn("classif.log_reg", rf_learner <- lrn("classif.ranger", predict_type = "prob") + + landslide_resamp <- rsmp("repeated_spcv_coords", folds = 5, repeats = 3) @@ -113,11 +115,15 @@ autoplot(landslide_resamp, fold_id = c(1:4)) ``` -## Feature selection +### 1. Feature selection + +The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. -We'll work this in in a bit. +```{r} + +``` -## Training the logistic regression model +## Training the logistic regression model To train the logistic regression model, which has no hyperparameters that need to be tuned, we call the method resample. From 72cad7bb7bf109d1db96414cd0971c2861b9676a Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 13 Mar 2023 11:10:14 -0600 Subject: [PATCH 22/74] added pipeline for comparing models --- analysis/modeling_landslide_initiation.qmd | 78 ++++++++++++++-------- 1 file changed, 50 insertions(+), 28 deletions(-) diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd index ba81d68..3607ea5 100644 --- a/analysis/modeling_landslide_initiation.qmd +++ b/analysis/modeling_landslide_initiation.qmd @@ -21,6 +21,8 @@ library(mlr3spatiotempcv) library(mlr3learners) library(mlr3tuning) library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) # Packages for model-building library(glmnet) @@ -99,14 +101,12 @@ logreg_learner <- lrn("classif.log_reg", rf_learner <- lrn("classif.ranger", predict_type = "prob") - - -landslide_resamp <- rsmp("repeated_spcv_coords", - folds = 5, - repeats = 3) +spat_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3) ``` -These are the first four folds of the clustering spatial resampling. +We will design a pipeline to compare the different models. ```{r} #| echo: false @@ -120,37 +120,59 @@ autoplot(landslide_resamp, The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. ```{r} - +logreg_fselect <- auto_fselector( + fs("sequential"), + learner = logreg_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) + +rf_fselect <- auto_fselector( + fs("sequential"), + learner = rf_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) ``` -## Training the logistic regression model - -To train the logistic regression model, which has no hyperparameters that need to be tuned, we call the method resample. +## Make a pipeline ```{r} -build_time <- system.time(logreg_model <- resample(task= landslide_task, - learner = logreg_learner, - resampling = landslide_resamp)) -build_time +pipe <- + ppl("branch", list( + "anova" = po("filter", filter = flt("anova")), + "nothing" = po("nop")), prefix_branchops = "filt") %>>% + ppl("branch", list( + "lr" = logreg_learner, + "rf" = rf_learner, + "lr_fs" = logreg_fselect, + "rf_fs" = rf_fselect)) + +pipe$plot(html = FALSE) ``` -And now we look at the results. - ```{r} -logreg_model -``` +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), + ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 20L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 10), + search_space = param_set + +) -## Training the random forest model +tuner <- tnr("random_search") -```{r} -build_time <- system.time(rf_model <- resample(task= landslide_task, - learner = rf_learner, - resampling = landslide_resamp)) -``` +tuner$optimize(target) -## Compare the two models - -```{r} -autoplot(c(logreg_model, rf_model), measure = msr("classif.auc")) +# out <- mlr3::resample(task = landslide_task, +# learner = as_learner(pipe), +# resampling = spat_resamp) ``` From d59a2ab95aa708fdd90ccc3f2271f9cc86e05323 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 22 Mar 2023 16:19:44 -0600 Subject: [PATCH 23/74] added final model training steps --- analysis/comparing_landslide_models.qmd | 333 +++++++++++++++++++++ analysis/modeling_landslide_initiation.qmd | 178 ----------- 2 files changed, 333 insertions(+), 178 deletions(-) create mode 100644 analysis/comparing_landslide_models.qmd delete mode 100644 analysis/modeling_landslide_initiation.qmd diff --git a/analysis/comparing_landslide_models.qmd b/analysis/comparing_landslide_models.qmd new file mode 100644 index 0000000..2891b06 --- /dev/null +++ b/analysis/comparing_landslide_models.qmd @@ -0,0 +1,333 @@ +--- +title: "Modeling Landslide Initiation" +autho: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3pipelines) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +library(tidyterra) +``` + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +sample_pnts$geo <- as.factor(sample_pnts$geo) +sample_pnts <- subset(sample_pnts, select = -c(year)) + +sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 +``` + +## A look at the data. + +The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. + +The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. + +```{r} +#| echo: false +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. + +## Defining the modeling task and learner + +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. + +There are several steps we need to work through: + +1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. +2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. +3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. +4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. + +```{r} +set.seed(12345) +landslide_task <- as_task_classif_st(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +rf_learner <- lrn("classif.ranger", + predict_type = "prob") + +spat_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2) +``` + +We will design a pipeline to compare the different models. + +```{r} +#| echo: false +autoplot(spat_resamp, + landslide_task, + fold_id = c(1:4)) +``` + +### 1. Feature selection + +The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. + +```{r} +logreg_fselect <- auto_fselector( + fs("sequential"), + learner = logreg_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) + +rf_fselect <- auto_fselector( + fs("sequential"), + learner = rf_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) +``` + +## Make a pipeline + +```{r} + +pipe <- + po("encode") %>>% + ppl("branch", list( + "anova" = po("filter", filter = flt("anova")), + "nothing" = po("nop")), prefix_branchops = "filt") %>>% + ppl("branch", list( + "lr" = logreg_learner, + "rf" = rf_learner, + "lr_fs" = logreg_fselect, + "rf_fs" = rf_fselect)) + +pipe$plot(html = FALSE) +``` + +Now, we can define the parameters over which to evaluate the models and tune it. I use a random search to start just gathering an idea of the best ranges of parameters. Since we are comparing a handful of different modeling techniques, one of the major parameters to tune will be the model type, called "branch.selection" in the parameter set. + +I ran this initially with a filtering method as an option, but found the no matter the filtering, the forward feature selection works better. It is possible that combining filtering and forward feature would reduce run-time, but since we only have about 15 features to evaluate, I don't believe that the time save will be substantial enough for the possible performance trade-off. + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), + ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 20), + search_space = param_set + +) + +tuner <- tnr("random_search") + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 20), + search_space = param_set + +) + +tuner <- tnr("random_search") + +tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 5L), + ParamFct$new("branch.selection", levels = c("rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("none"), + search_space = param_set + +) + +tuner <- tnr("grid_search", resolution = 5, batch_size = 400) + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 10L), + ParamFct$new("branch.selection", levels = c("rf")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("none"), + search_space = param_set + +) + +tuner <- tnr("grid_search", resolution = 10, batch_size = 100) + +tuner$optimize(target) +``` + +This tuning process takes an average of 2 minutes \* number of evals to run. I have run it twice so far, and save the results in data files to minimize the number of times we want to re-run the code. + +Overall, the results have shown AUC values between 0.82 and 0.86 for all configurations of models. The logistic regression model with the forward feature selection performed significantly better than the logistic regression without feature selection, and slightly (though not significantly) better than the random forest models. However, logistic regression is much faster to train, especially when using a forward feature selection algorithm that involves training so many models. + +```{r} +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.13.pipelinetuneresult.Rdata") +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.rf_fs_mtry.Rdata") +``` + +```{r} +#| echo: false +to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) +names(to_plot) <- c("auc", "model_type") +to_plot$model_type <- as.factor(to_plot$model_type) + +ggplot(to_plot, aes(x = model_type, y = auc)) + + geom_boxplot(aes(col = model_type)) + + geom_jitter(aes(col = model_type), position = position_jitter(0.2)) +``` + +## Logistic regression model + +Based on the results above, we will work with a logistic regression model and use forward feature selection to select the subset of features that provide the most amount of information. + +Our pipeline includes a scaling step, to even out the influence of each predictor, and an encoding step, to translate the geology predictor from a factor to a one-hot strategy that is treated more logically by a numerical model. + +```{r} + +pl_fselect <- + po("scale") %>>% + po("encode") %>>% + po("learner", logreg_fselect) + +pl_fselect$train(landslide_task) +#pl_lrn$graph$pipeops$classif.log_reg.fselector$learner + +``` + +Now that we know the best set of features, we can train a model with the resampling method to get a more exact estimate of how well the final model will actually perform. To do this, we use a k-means clustering based spatial resampling method, with 8 folds (7 for training, 1 for testing) and 50 repeats, to get a measure of performance that we can be relatively confident in. + +Then, we look at the aggregate AUC measure for all 50\*8 models that were trained (400 models total). + +```{r} +feature_set <- c("age", "dist_to_road", "mean_curv", "pca_k1_48") +landslide_task$select(feature_set) + +pl_feature_eng <- + po("scale") %>>% + po("encode") + +pl_feature_eng$train(landslide_task) +landslide_task_eng <- pl_feature_eng$predict(landslide_task)$encode.output + +model <- mlr3::resample( + task = landslide_task_eng, + learner = logreg_learner, + resampling = rsmp("repeated_spcv_coords", + folds = 8, + repeats = 50) +) + +model$aggregate(msr("classif.auc")) + +``` + +Our final step is to train a model using all of the data. While this is generally frowned upon in machine learning, our inventory produces a relatively small data set for this project and we want to leverage all of the data available. + +TODO: look up coefficient interpretation. Does a bigger coefficient mean that the variable has a bigger influence? I think yes, if the data are scaled first. + +Now that I am using the scaled version of the data, I agree. The pca value has the largest coefficient, and is consistently the variable that is chosen first during the feature selection, indicating that it is a highly explanatory variable for our dataset. Mean curvature is next, which + +```{r} +logreg_learner$train(landslide_task_eng) + +logreg_learner$model +``` diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd deleted file mode 100644 index 3607ea5..0000000 --- a/analysis/modeling_landslide_initiation.qmd +++ /dev/null @@ -1,178 +0,0 @@ ---- -title: "Modeling Landslide Initiation" -autho: Julia Lober -format: html -editor: visual ---- - -```{r setup} -#| echo: false -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - error = FALSE -) -``` - -```{r load, include = FALSE} -library(mlr3) -library(mlr3spatial) -library(mlr3spatiotempcv) -library(mlr3learners) -library(mlr3tuning) -library(mlr3viz) -library(mlr3fselect) -library(mlr3filters) - -# Packages for model-building -library(glmnet) -library(ranger) - -# Packages for other things -library(terra) -library(ggplot2) -library(maps) -``` - -```{r} -source("../R/create_model_dataframe.R") -sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] -``` - -## A look at the data. - -The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. - -The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. - -```{r} -#| echo: false -head(sample_pnts) -``` - -```{r} -#| echo: false -points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) -points$yr <- sample_pnts$year -points$class <- sample_pnts$class -crs(points) <- "epsg:26910" -points <- project(points, "epsg:4326") - -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") - -ggplot() + - geom_polygon(data = oregon, mapping = aes(x = long, y = lat), - color = "white", fill = "white") + - # geom_spatvector(data = points[points$class == "neg", ], - # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + - geom_spatvector(data = points[points$class == "pos", ], - mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), - show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + - scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + - labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") -``` - -Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. - -## Defining the modeling task and learner - -For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. - -There are several steps we need to work through: - -1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. -2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. -3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. -4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. - -```{r} -set.seed(12345) -landslide_task <- as_task_classif_st(x = sample_pnts, - id = "landslide_initiation", - target = "class", - positive = "pos", - coordinate_names = c("x", "y"), - crs = "epsg:26910") - -logreg_learner <- lrn("classif.log_reg", - predict_type = "prob") - -rf_learner <- lrn("classif.ranger", - predict_type = "prob") - -spat_resamp <- rsmp("repeated_spcv_coords", - folds = 5, - repeats = 3) -``` - -We will design a pipeline to compare the different models. - -```{r} -#| echo: false -autoplot(landslide_resamp, - landslide_task, - fold_id = c(1:4)) -``` - -### 1. Feature selection - -The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. - -```{r} -logreg_fselect <- auto_fselector( - fs("sequential"), - learner = logreg_learner, - resampling = spat_resamp, - measure = msr("classif.auc")) - -rf_fselect <- auto_fselector( - fs("sequential"), - learner = rf_learner, - resampling = spat_resamp, - measure = msr("classif.auc")) -``` - -## Make a pipeline - -```{r} - -pipe <- - ppl("branch", list( - "anova" = po("filter", filter = flt("anova")), - "nothing" = po("nop")), prefix_branchops = "filt") %>>% - ppl("branch", list( - "lr" = logreg_learner, - "rf" = rf_learner, - "lr_fs" = logreg_fselect, - "rf_fs" = rf_fselect)) - -pipe$plot(html = FALSE) -``` - -```{r} -param_set <- ParamSet$new(list( - ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), - ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), - ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 20L), - ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) -)) - -target <- ti( - task = landslide_task, - learner = as_learner(pipe), - resampling = rsmp("spcv_coords", folds = 3), - measures = msr("classif.auc"), - terminator = trm("evals", n_evals = 10), - search_space = param_set - -) - -tuner <- tnr("random_search") - -tuner$optimize(target) - -# out <- mlr3::resample(task = landslide_task, -# learner = as_learner(pipe), -# resampling = spat_resamp) -``` From 286207b4727b323ca000a137cc15609e72acc61a Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 13:39:52 -0600 Subject: [PATCH 24/74] initiation model document first commit --- analysis/PFA_initiation_model.qmd | 122 ++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 analysis/PFA_initiation_model.qmd diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd new file mode 100644 index 0000000..4976139 --- /dev/null +++ b/analysis/PFA_initiation_model.qmd @@ -0,0 +1,122 @@ +--- +title: "Landslide initiation model" +author: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3pipelines) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +library(tidyterra) +``` + +## Landslide Initiation susceptibility model for the Private Forest Accord + +This document outlines the process for making the landslide initiation model for the Oregon Private Forest Accord project. We work with data generated from the PFA_Sampling_Multiple_DEMs document that is included in this package. This document outlines in more detail where the data for different predictors is sourced and the strategy for negative sampling. + +The primary focus of this document is to outline the model-building process and decisions that resulted in the final model used in combination with the landslide runout model to generate riparian buffers for the Private Forest Accord. Broadly speaking, there were 3 major decisions made in this process: + +1. A method for feature selection. + +2. The type of model built. + +3. A spatio-temporal resampling strategy. + +### About the data + +```{r} +#| echo: false +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +sample_pnts$geo <- as.factor(sample_pnts$geo) + +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +The data includes 767 landslide initiation points, with equal proportions of landslide points and non-landslide points. The data has obvious spatial distributions and is divided into 3 approximate year groups. Exact dates for the landslide record were not always available, so these should be viewed as groupings rather than divisions. + +### Feature Selection + +Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. + +```{r} +#| echo: false +load("/Users/julialober/Documents/terrainworks/code/sandbox/filter_results.Rdata") + +ggplot(results, aes(x = feature, y = c(rank.auc, rank.anova, rank, rank.cmim, rank.mim))) + + geom_bar(stat = "identity") +``` + +### Logistic Regression model + +A logistic regression model was chosen for two primary purposes: because it is simple to code into FORTRAN and because it performed just as well as the Random Forest model when exploring some different types of models. + +The first reason is particularly important, as this is how the model will be implemented for predicting. The data sets for predicting encompass all of Oregon and would take a very long time to put through the model in a high-level language like R. + +The figure below shows the measured performance across a couple of different types of model. Our performance measure for this comparison (and for feature selection) is the area under the ROC curve (or AUC). For a more detail on the comparisons that were performed between the Random Forest model and linear regression model, refer to the analysis/comparing_landslide_models document. + +```{r} +#| echo: false +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") + +to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) +names(to_plot) <- c("auc", "model_type") +to_plot$model_type <- as.factor(to_plot$model_type) + +ggplot(to_plot, aes(x = model_type, y = auc)) + + geom_boxplot(aes(col = model_type)) + + geom_jitter(aes(col = model_type), position = position_jitter(0.2)) + + scale_x_discrete(breaks = c("lr", "lr_fs", "rf", "rf_fs"), labels = c("LR", "LR with feature selection", "RF", "RF with feature selection")) + + xlab("Type of model") + + ylab("Performance (AUC)") + + theme(legend.position = "none") +``` From 55e2e9a5fd2011f0cfe239e8a2ce20534f812c5e Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 15:23:54 -0600 Subject: [PATCH 25/74] add feature distribution plots --- analysis/PFA_initiation_model.qmd | 63 ++++++++++++++++++++++++++++++- 1 file changed, 61 insertions(+), 2 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 4976139..0c53795 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -32,6 +32,7 @@ library(ranger) # Packages for other things library(terra) library(ggplot2) +library(ggpubr) library(maps) library(tidyterra) ``` @@ -55,8 +56,13 @@ The primary focus of this document is to outline the model-building process and source("../R/create_model_dataframe.R") sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] sample_pnts$geo <- as.factor(sample_pnts$geo) +sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 + +all_neg <- rbind(nonls_1996, nonls_2007, nonls_2011) +all_neg$gradient_sq <- all_neg$gradient ^ 2 +all_neg$grad_by_pca <- all_neg$gradient * all_neg$pca_k1_48 -head(sample_pnts) ``` ```{r} @@ -84,6 +90,59 @@ ggplot() + The data includes 767 landslide initiation points, with equal proportions of landslide points and non-landslide points. The data has obvious spatial distributions and is divided into 3 approximate year groups. Exact dates for the landslide record were not always available, so these should be viewed as groupings rather than divisions. +It is helpful to look at the how the distributions of predictors vary between our landslide points and our non-landslide points. This informs our expectations for which features will be the most important for the model and increases our confidence in the results of feature selection processes. + +```{r} +#| echo: false +neg_pts <- sample_pnts[(sample_pnts$class == "neg"), ] +pos_pts <- sample_pnts[(sample_pnts$class == "pos"), ] + +p1 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = gradient), color = "darkgray") + + geom_density(data = neg_pts, aes(x = gradient), color = "black") + + geom_density(data = pos_pts, aes(x = gradient), color = "red") + +p2 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = mean_curv), color = "darkgray") + + geom_density(data = neg_pts, aes(x = mean_curv), color = "black") + + geom_density(data = pos_pts, aes(x = mean_curv), color = "red") + +p3 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = pca_k1_48), color = "darkgray") + + geom_density(data = neg_pts, aes(x = pca_k1_48), color = "black") + + geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") + +p4 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = age), color = "darkgray") + + geom_density(data = neg_pts, aes(x = age), color = "black") + + geom_density(data = pos_pts, aes(x = age), color = "red") + + xlim(0, 300) + +p5 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = dist_to_road), color = "darkgray") + + geom_density(data = neg_pts, aes(x = dist_to_road), color = "black") + + geom_density(data = pos_pts, aes(x = dist_to_road), color = "red") + + xlim(0, 600) + +p6 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = grad_by_pca), color = "darkgray") + + geom_density(data = neg_pts, aes(x = grad_by_pca), color = "black") + + geom_density(data = pos_pts, aes(x = grad_by_pca), color = "red") + +p7 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = pca_k1_6), color = "darkgray") + + geom_density(data = neg_pts, aes(x = pca_k1_6), color = "black") + + geom_density(data = pos_pts, aes(x = pca_k1_6), color = "red") + +p8 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = total_accum), color = "darkgray") + + geom_density(data = neg_pts, aes(x = total_accum), color = "black") + + geom_density(data = pos_pts, aes(x = total_accum), color = "red") + + xlim(0, 150) + +ggarrange(p1, p2, p7, p3, p4, p5, p6, p8) +``` + ### Feature Selection Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. @@ -92,7 +151,7 @@ Choosing good features is an essential part of the machine learning process. A g #| echo: false load("/Users/julialober/Documents/terrainworks/code/sandbox/filter_results.Rdata") -ggplot(results, aes(x = feature, y = c(rank.auc, rank.anova, rank, rank.cmim, rank.mim))) + +ggplot(results, aes(x = feature, y = total)) + geom_bar(stat = "identity") ``` From 1e85f3bfeac5403e7ee6a79e5fba51a95a7400eb Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 15:43:19 -0600 Subject: [PATCH 26/74] add histograms to feature distribution plots --- analysis/PFA_initiation_model.qmd | 32 +++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 0c53795..5b8edcf 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -98,49 +98,77 @@ neg_pts <- sample_pnts[(sample_pnts$class == "neg"), ] pos_pts <- sample_pnts[(sample_pnts$class == "pos"), ] p1 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = gradient, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = gradient, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = gradient), color = "darkgray") + geom_density(data = neg_pts, aes(x = gradient), color = "black") + geom_density(data = pos_pts, aes(x = gradient), color = "red") p2 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = mean_curv, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = mean_curv, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = mean_curv), color = "darkgray") + geom_density(data = neg_pts, aes(x = mean_curv), color = "black") + geom_density(data = pos_pts, aes(x = mean_curv), color = "red") p3 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = pca_k1_48, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = pca_k1_48, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = pca_k1_48), color = "darkgray") + geom_density(data = neg_pts, aes(x = pca_k1_48), color = "black") + - geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") + geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") p4 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = age, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = age, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = age), color = "darkgray") + geom_density(data = neg_pts, aes(x = age), color = "black") + geom_density(data = pos_pts, aes(x = age), color = "red") + xlim(0, 300) p5 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = dist_to_road, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = dist_to_road, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = dist_to_road), color = "darkgray") + geom_density(data = neg_pts, aes(x = dist_to_road), color = "black") + geom_density(data = pos_pts, aes(x = dist_to_road), color = "red") + xlim(0, 600) p6 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = grad_by_pca, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = grad_by_pca, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = grad_by_pca), color = "darkgray") + geom_density(data = neg_pts, aes(x = grad_by_pca), color = "black") + geom_density(data = pos_pts, aes(x = grad_by_pca), color = "red") p7 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = pca_k1_6, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = pca_k1_6, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = pca_k1_6), color = "darkgray") + geom_density(data = neg_pts, aes(x = pca_k1_6), color = "black") + geom_density(data = pos_pts, aes(x = pca_k1_6), color = "red") p8 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = total_accum, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = total_accum, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = total_accum), color = "darkgray") + geom_density(data = neg_pts, aes(x = total_accum), color = "black") + geom_density(data = pos_pts, aes(x = total_accum), color = "red") + xlim(0, 150) -ggarrange(p1, p2, p7, p3, p4, p5, p6, p8) +ggarrange(p1, p2, p7, p3) +``` + +```{r} +ggarrange(p4, p5, p6, p8) +``` + +```{r} +p1 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = gradient), fill = "black", alpha = 0.7) + + geom_histogram(data = pos_pts, aes(x = gradient), fill = "red", alpha = 0.7) + +ggarrange(p1) ``` ### Feature Selection From e544004453be0f6c3e02783a157e57d1494598b7 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 15:51:25 -0600 Subject: [PATCH 27/74] added some text --- analysis/PFA_initiation_model.qmd | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 5b8edcf..db3f806 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -163,13 +163,9 @@ ggarrange(p1, p2, p7, p3) ggarrange(p4, p5, p6, p8) ``` -```{r} -p1 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = gradient), fill = "black", alpha = 0.7) + - geom_histogram(data = pos_pts, aes(x = gradient), fill = "red", alpha = 0.7) +These plots show us the indicate the features where the biggest differences are noticed between landslide points and non-landslide points. Notably, the difference in the distributions of gradient between landslide points and non-landslide points are quite similar. This is surprising, given our physical understanding of slope angle as a major factor in landslide initiation. It is important to note that the sampled non-landslide points are limited to the range of gradient values observed in the landslide inventory. That is, any point with too low or too high of a slope angle is assumed to have zero probability of landslide initiation and is left out of this study. -ggarrange(p1) -``` +Features with noticeably different distributions between landslide and non-landslide points include mean curvature and partial contributing area. ### Feature Selection From efceaa8e1768aab7e728f94e6ad041a3fb7355bd Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 13 Apr 2023 08:58:24 -0600 Subject: [PATCH 28/74] assembling data for prediction --- analysis/predicting_landslide_initiation.qmd | 189 +++++++++++++++++++ 1 file changed, 189 insertions(+) create mode 100644 analysis/predicting_landslide_initiation.qmd diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd new file mode 100644 index 0000000..e96925a --- /dev/null +++ b/analysis/predicting_landslide_initiation.qmd @@ -0,0 +1,189 @@ +--- +title: "Predicting landslide initiation" +author: Julia Lober +format: html +editor: visual +--- + +```{r load, include = FALSE} +library(terra) + +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3pipelines) +library(mlr3learners) +``` + +## Predicting landslide initiation + +We start by predicting on 1 small basin as part of 1 of the watershed designations of DOGAMI in Oregon. Find the data wherever it is located on your computer. + +```{r} +dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dem <- terra::rast(paste0(dir, "Umpqua/basin_1.flt")) +pts <- terra::vect(paste0(dir, "all_initiation_points.shp")) + +pts_local <- crop(pts, dem) + +plot(dem) +plot(pts_local, + add = TRUE) +``` + +### Calculating the predictors + +The predictors chosen by our model are: + +1. Partial contributing area with a hydraulic conductivity of 1 and storm duration of 48 hours. + +2. Mean curvature + +3. Distance to road + +4. Stand age + +We need to calculate the partial contributing area and mean curvature using the executables included in this package. There is also a function for calculating the distance to a road, and the stand age can be collected from available data. + +The model is trained on all of the available inventory points and stored in "logreg_learner". This happens in the comparing_landslide_models.qmd file. + +```{r} +scratch <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/scratch/" +scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" + +rasters <- c(paste0("GRADIENT,", scratch, "gradient"), + paste0("MEAN CURVATURE,", scratch, "mean_curv")) + +length_scale <- 15 + +rasters <- elev_deriv(rasters = rasters, + length_scale = length_scale, + dem = paste0(dir, "Umpqua/basin_1.flt"), + scratch_dir = scratch) +``` + +Now, calculate partial contributing area. The 48-hour duration was the best across all of the model investigating, so we only need to calculate that one. + +```{r} +rasters <- c(rasters, contributing_area(raster = paste0(scratch, "pca_k1_d48"), + dem = paste0(dir, "Umpqua/basin_1.flt"), + length_scale = 15, + k = 1, + d = 48, + scratch_dir = scratch)) +``` + +Stand age data comes from 2017. It has to be first reprojected into the UTM zone 10 CRS and then resampled to the same extent and resolution as the DEM. Then, we can extract the values and match them with the values in the topographical parameters DEM. + +```{r} +age_fn <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/age_dom_2017.tif" + +# \\Mac\Home\Documents\terrainworks\code\sandbox\data\downloaded_3.6\in +# "\\Mac\Home\Documents\terrainworks\code\sandbox\data\downloaded_3.6\in\age_dom_2017.tif" + +age_rast <- terra::rast(age_fn) + +age_proj <- project(age_rast, "epsg:26910") +age_resampled <- resample(age_proj, dem, 'near') +``` + +```{r} +plot(age_resampled) +``` + +Now that the scripts have been run, we can assemble the data from files (and never run the 2 hour script again). + +```{r} +scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" + +topo_files <- c(paste0("GRADIENT,", scratch, "gradient.flt"), + paste0("MEAN CURVATURE,", scratch, "mean_curv.flt")) + +topo_rast <- elev_deriv(rasters = topo_files) +pca_rast <- contributing_area(raster = paste0(scratch, "pca_k1_d48.flt")) + +rasters <- c(topo_rast, pca_rast) +``` + +### Success rate curves + +To evaluate the model using proportions, we use a success rate curve. The curve has the proportion of the total area on the x-axis, with the proportion of landslides on the y-axis. To generate this curve, we train a model on the sample points, then predict the entire DEM. The sampling to generate training points and model decisions have been made in a different document: we use a logistic regression learner trained on the mean curvature, log value of partial contributing area with a duration of 48 hours, and gradient. + +```{r} +folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/" +load(paste0(folder, "ls_1996.Rdata")) +load(paste0(folder, "ls_2007.Rdata")) +load(paste0(folder, "ls_2011.Rdata")) +load(paste0(folder, "nonls_1996.Rdata")) +load(paste0(folder, "nonls_2007.Rdata")) +load(paste0(folder, "nonls_2011.Rdata")) + +training_data <- rbind(ls_1996, + ls_2007, + ls_2011, + nonls_1996, + nonls_2007, + nonls_2011) + +training_data <- subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")) +training_data$pca_k1_48 <- log(training_data$pca_k1_48, base = 2) +training_data$class <- as.factor(training_data$class) +``` + +```{r} +landslide_task <- as_task_classif_st(x = training_data, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +pl_feature_eng <- + po("scale") %>>% + po("encode") + +pl_feature_eng$train(landslide_task) +landslide_task_eng <- pl_feature_eng$predict(landslide_task)$encode.output + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +logreg_learner$train(landslide_task_eng) +logreg_learner$model +``` + +Now, we are ready to predict the basin area. We need to extract the all the data points from the DEM and make sure the names match what is expected by the model (gradient, mean_curv, and pca_k1_48). + +```{r} +names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") +predicting_data <- as.data.frame(rasters, xy = TRUE) +``` + +The mlr3 package support spatial prediction with the predict_spatial function. This function accepts new data in raster format and outputs a raster, but I can't figure out if the function will support a probability output instead of a binary class output. + +```{r} +predictions <- predict_spatial(rasters, logreg_learner) +``` + +For now, I'll try predicting on a data frame. The downside of this strategy is the extra time needed to convert the DEM to a data frame, and the lack of space optimization that the spatial_predict function allows. + +```{r} +predicting_data$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) + +#pre process the data: take the log of the pca and center+scale the data +# predict_task <- as_task_classif_st(predicting_data) +predicting_data_eng <- pl_feature_eng$predict_newdata(predicting_data) + +predictions_df <- logreg_learner$predict_newdata(predicting_data_eng) +``` + +```{r} +plot(predictions) + +``` + +```{r} +predictions_dt <- as.data.table(predictions_df) +predictions_dt <- cbind(predictions_dt, predicting_data) + +``` From 2e3bb297c6c5c00ef4ba2e1384c00bfba455192d Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 14 Apr 2023 10:19:11 -0600 Subject: [PATCH 29/74] finished code to predict on small basin --- analysis/predicting_landslide_initiation.qmd | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index e96925a..19cb69b 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -168,13 +168,18 @@ predictions <- predict_spatial(rasters, logreg_learner) For now, I'll try predicting on a data frame. The downside of this strategy is the extra time needed to convert the DEM to a data frame, and the lack of space optimization that the spatial_predict function allows. ```{r} -predicting_data$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) +pd <- predicting_data +pd$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) -#pre process the data: take the log of the pca and center+scale the data -# predict_task <- as_task_classif_st(predicting_data) -predicting_data_eng <- pl_feature_eng$predict_newdata(predicting_data) +# manually pre process the data: take the log of the pca and center+scale the data +center_vals <- pl_feature_eng$pipeops$scale$state$center +scale_vals <- pl_feature_eng$pipeops$scale$state$scale -predictions_df <- logreg_learner$predict_newdata(predicting_data_eng) +pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] +pd$mean_curv <- (pd$mean_curv - center_vals[2]) / scale_vals[2] +pd$pca_k1_48 <- (pd$pca_k1_48 - center_vals[3]) / scale_vals[3] + +predictions_df <- logreg_learner$predict_newdata(pd) ``` ```{r} @@ -186,4 +191,5 @@ plot(predictions) predictions_dt <- as.data.table(predictions_df) predictions_dt <- cbind(predictions_dt, predicting_data) + ``` From f14d1ac4c23b38f6a999dc0a912818dbf6ea0020 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 19 Apr 2023 10:19:11 -0600 Subject: [PATCH 30/74] finish success rate curve and generate initial graphs --- analysis/predicting_landslide_initiation.qmd | 119 +++++++++++++++++-- 1 file changed, 112 insertions(+), 7 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 19cb69b..562bdef 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -6,13 +6,23 @@ editor: visual --- ```{r load, include = FALSE} +# for spatial data library(terra) +# for modeling library(mlr3) library(mlr3spatial) library(mlr3spatiotempcv) library(mlr3pipelines) library(mlr3learners) + +# for visualizations +library(ggplot2) +library(tidyterra) +library(viridis) + +# basics +library(data.table) ``` ## Predicting landslide initiation @@ -21,6 +31,7 @@ We start by predicting on 1 small basin as part of 1 of the watershed designatio ```{r} dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" dem <- terra::rast(paste0(dir, "Umpqua/basin_1.flt")) pts <- terra::vect(paste0(dir, "all_initiation_points.shp")) @@ -125,13 +136,15 @@ training_data <- rbind(ls_1996, nonls_2007, nonls_2011) -training_data <- subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")) -training_data$pca_k1_48 <- log(training_data$pca_k1_48, base = 2) -training_data$class <- as.factor(training_data$class) +train_subset <- subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")) +train_subset$pca_k1_48 <- log(train_subset$pca_k1_48, base = 2) +train_subset$class <- as.factor(train_subset$class) ``` ```{r} -landslide_task <- as_task_classif_st(x = training_data, +set.seed(12345) +# train_subset <- subset(train_subset, select = c("mean_curv", "pca_k1_48", "class", "x", "y")) +landslide_task <- as_task_classif_st(x = train_subset, id = "landslide_initiation", target = "class", positive = "pos", @@ -152,6 +165,29 @@ logreg_learner$train(landslide_task_eng) logreg_learner$model ``` +As a brief sanity check, we will compare a version of the model that sub-samples the negative points to create an even balance between the two classes as input. I am curious to see how these compare in a final output model. + +```{r} +source("../R/create_model_dataframe.R") +training_data <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] + +landslide_task_subsamp <- as_task_classif_st(x = subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")), + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +pl_feature_eng$train(landslide_task_subsamp) +task_subsamp <- pl_feature_eng$predict(landslide_task_subsamp)$encode.output + +logreg_learner2 <- lrn("classif.log_reg", + predict_type = "prob") + +logreg_learner2$train(task_subsamp) +logreg_learner2$model +``` + Now, we are ready to predict the basin area. We need to extract the all the data points from the DEM and make sure the names match what is expected by the model (gradient, mean_curv, and pca_k1_48). ```{r} @@ -165,6 +201,12 @@ The mlr3 package support spatial prediction with the predict_spatial function. T predictions <- predict_spatial(rasters, logreg_learner) ``` +```{r} +plot(predictions) +``` + +Note that this classifies most of the area as positive while only the river areas and hilltops are marked as negative. This indicates that a low threshold was chosen for class assignment when we compare to the probability map produced below. + For now, I'll try predicting on a data frame. The downside of this strategy is the extra time needed to convert the DEM to a data frame, and the lack of space optimization that the spatial_predict function allows. ```{r} @@ -179,17 +221,80 @@ pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] pd$mean_curv <- (pd$mean_curv - center_vals[2]) / scale_vals[2] pd$pca_k1_48 <- (pd$pca_k1_48 - center_vals[3]) / scale_vals[3] +# predict the basin using scaled and centered data. predictions_df <- logreg_learner$predict_newdata(pd) ``` +Convert the predictions outputted to a data table, then add the columns with useful information like the predictors and (x,y) coordinates. + ```{r} -plot(predictions) +predictions_dt <- as.data.table(predictions_df) +predictions_dt <- cbind(predictions_dt, predicting_data) +names(predictions_dt) +``` +We also convert it to a raster format for future use. + +```{r} +prob_rast <- (rast(subset(predictions_dt, select = c("x", "y", "prob.pos")))) ``` ```{r} -predictions_dt <- as.data.table(predictions_df) -predictions_dt <- cbind(predictions_dt, predicting_data) +ggplot(predictions_dt) + + geom_tile(aes(x = x, y = y, fill = prob.pos)) + + scale_fill_viridis(option = "A", direction = -1) + + geom_spatvector(data = pts_local) +``` + +### Success rate curve + +Our goal when looking at model performance will be to consider an analysis using proportions. +To calculate the success rate curve, we first calculate the proportion of the area that falls up to a given probability value and the proportion of the total probability that falls up to a given probability value for each DEM cell. The steps for this calculation will be spelled out in the code below. + +```{r} +proportions <- subset(predictions_dt, select = c("x", "y", "prob.pos")) +setorder(proportions, cols = "prob.pos") + +# calculate the proportion of total landslide probability for each DEM cell +proportions$prob.cumsum <- cumsum(proportions$prob.pos) +prob_sum <- max(proportions$prob.cumsum) +proportions$prob.prop <- proportions$prob.cumsum / prob_sum + +# calculate the proportion of total area for each DEM cell +proportions$area.cumsum <- seq(1, length(proportions$x)) +area_sum <- max(proportions$area.cumsum) +proportions$area.prop <- proportions$area.cumsum / area_sum + +setattr(proportions, "sorted", "prob.pos") +``` +First, I calculated cumulative sums for each row. Now, we define some much larger interval for binning this data, which will allow much easier plotting. Between 0 and the maximum value for probability, we look at 100 values of the predicted probability. We extract the nearest row for each of these values from the proportions data table, and now have all of the information we need for plotting. + +```{r} +bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 100)) +names(bins) <- c("probability") + +# match these probability bins with nearest rows in the proportions data table +binned_prop <- proportions[J(bins$probability), roll = "nearest"] +``` + +That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now, we can create the plots. + +The first plot that we look at shows the cumulative proportions of area, modeled landslides, and observed landslides at each probability mark. Ideally, we would want the modeled landslide curve to match up with the observed landslide curve. Since we are looking at a single basin right now, there is probably not enough information to really evaluate this correlation. + +```{r} +ggplot(cbind(bins, binned_prop)) + + geom_line(aes(x = probability, prob.prop)) + + geom_point(aes(x = probability, prob.prop)) + + geom_line(aes(x = probability, area.prop)) + + geom_point(aes(x = probability, area.prop)) +``` + +The main curve that we want to use to compare models is the success rate curve, or the proportion of the modeled landslides plotted against the proportion of area. Each point along the curve comes from one of the probability bins. One thing that this curve tells us is how specific the model is with the areas that it feels are landslide-prone. + +```{r} +ggplot(cbind(bins, binned_prop)) + + geom_line(aes(x = area.prop, y = prob.prop)) + + geom_point(aes(x = area.prop, y = prob.prop)) ``` From 92e55a83339679f67b7c4f0693adb6293a025811 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 19 Apr 2023 14:33:24 -0600 Subject: [PATCH 31/74] adding observed landslides to figures --- analysis/predicting_landslide_initiation.qmd | 27 +++++++++++++++----- 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 562bdef..ac6df59 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -279,16 +279,29 @@ names(bins) <- c("probability") binned_prop <- proportions[J(bins$probability), roll = "nearest"] ``` -That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now, we can create the plots. +That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now we need the observed landslide data. + +```{r} +pts_prob <- extract(prob_rast, pts_local, xy = TRUE) +pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] +setorder(pts_prob, cols = "prob.pos") + +total_pts <- length(pts_prob$ID) + +pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] +pts_cumul$ls.prop = seq(1, 9) / total_pts +``` The first plot that we look at shows the cumulative proportions of area, modeled landslides, and observed landslides at each probability mark. Ideally, we would want the modeled landslide curve to match up with the observed landslide curve. Since we are looking at a single basin right now, there is probably not enough information to really evaluate this correlation. ```{r} ggplot(cbind(bins, binned_prop)) + - geom_line(aes(x = probability, prob.prop)) + - geom_point(aes(x = probability, prob.prop)) + - geom_line(aes(x = probability, area.prop)) + - geom_point(aes(x = probability, area.prop)) + geom_line(aes(x = probability, y = prob.prop), color = "red") + + geom_point(aes(x = probability, y = prob.prop), color = "red") + + geom_line(aes(x = probability, y = area.prop), color = "lightblue") + + geom_point(aes(x = probability, y = area.prop), color = "lightblue") + + geom_line(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + + geom_point(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) ``` The main curve that we want to use to compare models is the success rate curve, or the proportion of the modeled landslides plotted against the proportion of area. Each point along the curve comes from one of the probability bins. One thing that this curve tells us is how specific the model is with the areas that it feels are landslide-prone. @@ -296,5 +309,7 @@ The main curve that we want to use to compare models is the success rate curve, ```{r} ggplot(cbind(bins, binned_prop)) + geom_line(aes(x = area.prop, y = prob.prop)) + - geom_point(aes(x = area.prop, y = prob.prop)) + geom_point(aes(x = area.prop, y = prob.prop)) + + geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop)) + + geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop)) ``` From 0a98b1f88fd261907045cd32d65e8f0117ca88c2 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 20 Apr 2023 12:17:24 -0600 Subject: [PATCH 32/74] added origin point to observed landslide curve --- analysis/predicting_landslide_initiation.qmd | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index ac6df59..275ad31 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -282,14 +282,22 @@ binned_prop <- proportions[J(bins$probability), roll = "nearest"] That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now we need the observed landslide data. ```{r} +# extract the probability values for the test points pts_prob <- extract(prob_rast, pts_local, xy = TRUE) pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] setorder(pts_prob, cols = "prob.pos") total_pts <- length(pts_prob$ID) +# get the cumulative area (already calculated) and calculate the cumulative landslides pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] pts_cumul$ls.prop = seq(1, 9) / total_pts + +# add the origin and a point at (1, 1) to make the graphs prettier +pts_cumul <- rbind(0, pts_cumul, fill = TRUE) +pts_cumul[1] <- 0 +pts_cumul <- rbind(pts_cumul, 0, fill = TRUE) +pts_cumul[length(pts_cumul$x)] <- 1 ``` The first plot that we look at shows the cumulative proportions of area, modeled landslides, and observed landslides at each probability mark. Ideally, we would want the modeled landslide curve to match up with the observed landslide curve. Since we are looking at a single basin right now, there is probably not enough information to really evaluate this correlation. @@ -308,8 +316,8 @@ The main curve that we want to use to compare models is the success rate curve, ```{r} ggplot(cbind(bins, binned_prop)) + - geom_line(aes(x = area.prop, y = prob.prop)) + - geom_point(aes(x = area.prop, y = prob.prop)) + - geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop)) + - geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop)) + geom_line(aes(x = area.prop, y = prob.prop), color = "red") + + geom_point(aes(x = area.prop, y = prob.prop), color = "red") + + geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") + + geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") ``` From 70120313bc7ddeae25afda1c9cdbd1629e6b85f2 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 21 Apr 2023 15:57:44 -0400 Subject: [PATCH 33/74] add loop for calculating success rate curve from multiple dems --- analysis/predicting_landslide_initiation.qmd | 104 ++++++++++++++++++- 1 file changed, 103 insertions(+), 1 deletion(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 275ad31..9a1de0c 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -23,6 +23,7 @@ library(viridis) # basics library(data.table) +library(tictoc) ``` ## Predicting landslide initiation @@ -272,7 +273,7 @@ setattr(proportions, "sorted", "prob.pos") First, I calculated cumulative sums for each row. Now, we define some much larger interval for binning this data, which will allow much easier plotting. Between 0 and the maximum value for probability, we look at 100 values of the predicted probability. We extract the nearest row for each of these values from the proportions data table, and now have all of the information we need for plotting. ```{r} -bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 100)) +bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 500)) names(bins) <- c("probability") # match these probability bins with nearest rows in the proportions data table @@ -321,3 +322,104 @@ ggplot(cbind(bins, binned_prop)) + geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") + geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") ``` + +## Combining multiple basins + +Really, we want to be able to generate this curve over many different basins. We start this process by assuming that you have already generated all of the elevation derivative and partial contributing area files that are needed for the model to predict on the basin (and that you know where the files are). This part of the program will use that information to predict the probability of initiation for each basin, then combines these into one success rate curve. + +Since each basin will have millions of DEM cells, we have to sub-sample the cumulative values down to a number of data points that is reasonable to manage. We already do this for plotting; for combining multiple DEMs, we will just need to make sure to use consistent probability values for sub-sampling, and then the cumulative values will be averaged based on how many DEM cells the cumulative proportion was calculated from. TODO: figure out the exact formulas for calculating these weighted averages. + +Updated average = ( (Past average \* past \# cells) + new value ) / new cell total + +OR -- we just sum the cumulative sums and the total number of cells/cumulative probability and then take the averages at the end, once the totals can actually be calculated. This is probably the simpler option and I will go this route unless for some reason this ends up not working. + +Finding the observed landslide information will probably be easier - we will want to keep track of which points are located within each DEM, mark those points, and then the final step will be the same calculations as above. Since there is a maximum of about 400 points, we don't need to worry about data size as much. + +I think this will be one large for loop that iterates once per basin. I may break it out into a function, or maybe break out the inside of the for loop into a function? I'll start without the function and change it later if needed, though. + +```{r} +# replace these with the respective file paths to your DEMs +dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +basin_list = c(paste0(dir, "Umpqua/basin_1/"), + paste0(dir, "Umpqua/basin_2/")) + # paste0(dir, "Umpqua/basin_3/"), + # paste0(dir, "Umpqua/basin_4/")) +``` + +A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. + +```{r} +model <- logreg_learner +bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 500)) +names(bins) <- c("probability") + +cumul_prop <- data.frame("area" = rep(0, 500), + "prob" = rep(0, 500)) + +for (basin in basin_list) { + + tic(msg = paste0("loop for ", basin)) + + # find and load the data files + topo_files <- c(paste0("GRADIENT,", basin, "gradient.flt"), + paste0("MEAN CURVATURE,", basin, "mean_curv.flt")) + topo_rast <- elev_deriv(rasters = topo_files) + pca_rast <- contributing_area(raster = paste0(basin, "pca_k1_d48.flt")) + rasters <- c(topo_rast, pca_rast) + + names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") + predicting_data <- as.data.frame(rasters, xy = TRUE) + + pd <- predicting_data + pd$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) + + # manually pre process the data: take the log of the pca and center+scale the data + center_vals <- pl_feature_eng$pipeops$scale$state$center + scale_vals <- pl_feature_eng$pipeops$scale$state$scale + + pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] + pd$mean_curv <- (pd$mean_curv - center_vals[2]) / scale_vals[2] + pd$pca_k1_48 <- (pd$pca_k1_48 - center_vals[3]) / scale_vals[3] + + # predict the basin using scaled and centered data. + predictions_df <- as.data.table(logreg_learner$predict_newdata(pd)) + predictions_df <- cbind(predictions_df, predicting_data) + + # head(predictions_df) + + proportions <- as.data.table(subset(predictions_df, select = c("x", "y", "prob.pos"))) + setorder(proportions, cols = "prob.pos") + + # calculate the cumulative sums of area and probabilities + proportions$prob.cumsum <- cumsum(proportions$prob.pos) + prob_sum <- max(proportions$prob.cumsum) + proportions$area.cumsum <- seq(1, length(proportions$x)) + area_sum <- max(proportions$area.cumsum) + + setattr(proportions, "sorted", "prob.pos") + + binned_prop <- proportions[J(bins$probability), roll = "nearest"] + cumul_prop$area <- cumul_prop$area + binned_prop$area.cumsum + cumul_prop$prob <- cumul_prop$prob + binned_prop$prob.cumsum + + toc() +} + + +total_area <- max(cumul_prop$area) +total_prob <- max(cumul_prop$prob) + +cumul_prop$area_prop <- cumul_prop$area / total_area +cumul_prop$prob_prop <- cumul_prop$prob / total_prob + +``` + +```{r} +ggplot(cbind(bins, cumul_prop)) + + geom_line(aes(x = probability, y = prob_prop), color = "red") + + geom_point(aes(x = probability, y = prob_prop), color = "red") + + geom_line(aes(x = probability, y = area_prop), color = "lightblue") + + geom_point(aes(x = probability, y = area_prop), color = "lightblue") + + geom_line(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + + geom_point(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) +``` From e3b1b193f636d7862574011e9459645d395854b7 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 1 May 2023 13:31:45 -0600 Subject: [PATCH 34/74] multiple basin success rate curve - added observed landslide curve --- analysis/predicting_landslide_initiation.qmd | 138 +++++++++++++++++-- 1 file changed, 123 insertions(+), 15 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 9a1de0c..03d7912 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -12,14 +12,14 @@ library(terra) # for modeling library(mlr3) library(mlr3spatial) -library(mlr3spatiotempcv) -library(mlr3pipelines) -library(mlr3learners) +# library(mlr3spatiotempcv) +# library(mlr3pipelines) +# library(mlr3learners) # for visualizations library(ggplot2) -library(tidyterra) -library(viridis) +# library(tidyterra) +# library(viridis) # basics library(data.table) @@ -292,7 +292,7 @@ total_pts <- length(pts_prob$ID) # get the cumulative area (already calculated) and calculate the cumulative landslides pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] -pts_cumul$ls.prop = seq(1, 9) / total_pts +pts_cumul$ls.prop = seq(1, length(pts_cumul$x)) / total_pts # add the origin and a point at (1, 1) to make the graphs prettier pts_cumul <- rbind(0, pts_cumul, fill = TRUE) @@ -339,32 +339,113 @@ I think this will be one large for loop that iterates once per basin. I may brea ```{r} # replace these with the respective file paths to your DEMs -dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" -basin_list = c(paste0(dir, "Umpqua/basin_1/"), - paste0(dir, "Umpqua/basin_2/")) +basin_list = c("Umpqua/basin_1", + "Umpqua/basin_2", + "Umpqua/basin_3", + "Umpqua/basin_4", + "Umpqua/basin_5", + "Umpqua/basin_6", + "Umpqua/basin_7", + "Umpqua/basin_8", + "Umpqua/basin_9", + "Umpqua/basin_10", + "Umpqua/basin_11", + "Umpqua/basin_12", + "Umpqua/basin_13", + "Umpqua/basin_14", + "Umpqua/basin_15", + "Umpqua/basin_16", + "Umpqua/basin_17") + +# basin_list = c(paste0(dir, "Umpqua/basin_1/"), +# paste0(dir, "Umpqua/basin_2/")) # paste0(dir, "Umpqua/basin_3/"), # paste0(dir, "Umpqua/basin_4/")) ``` -A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. +#### For calculating elevation derivatives: + +This code only needs to be run once. Eventually, I will remove it from this document. ```{r} +dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/predicting_input/" +dem_dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" + + + +for (basin in basin_list) { + rasters <- c(paste0("GRADIENT,", dir, basin, "/gradient"), + paste0("MEAN CURVATURE,", dir, basin, "/mean_curv")) + + print(rasters) + + length_scale <- 15 + + dir.create(paste0(dir, basin)) + + rasters <- elev_deriv(rasters = rasters, + length_scale = length_scale, + dem = paste0(dem_dir, basin, ".flt"), + scratch_dir = paste0(dir, basin)) +} +``` + +Short utility script for copying the partial contributing area files downloaded from sharefile into the folders structure that will be easy for me to use. + +```{r} +dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +pca_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/pca/" + +count <- 1 + +for (basin in basin_list) { + target_dir <- paste0(dir, basin, "/") + target_file <- paste0(pca_dir, "pca_6_", count) + + target_files <- list.files(pca_dir, paste0("^pca_6_", count, "\\."), full.names = TRUE) + file.copy(target_files, target_dir) + + count <- count + 1 + +} +``` + +A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. I am timing each iteration of the loop to get an idea of how long it will take to run. So far, we are hovering around 50 seconds for one basin. + +TODO: figure out how to crop the points and add the observed landslides to a data frame to compare. This was relatively simple above, so shouldn't be too hard to figure out. + +```{r} +dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" + model <- logreg_learner bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 500)) names(bins) <- c("probability") +count <- 1 + cumul_prop <- data.frame("area" = rep(0, 500), "prob" = rep(0, 500)) +pts_prop <- data.frame("x" = 0, + "y" = 0, + "prob.pos" = 0, + "prob.cumsum" = 0, + "area.cumsum" = 0, + "ls.prop" = 0) + for (basin in basin_list) { tic(msg = paste0("loop for ", basin)) # find and load the data files - topo_files <- c(paste0("GRADIENT,", basin, "gradient.flt"), - paste0("MEAN CURVATURE,", basin, "mean_curv.flt")) + topo_files <- c(paste0("GRADIENT,", dir, basin, "/gradient.flt"), + paste0("MEAN CURVATURE,", dir, basin, "/mean_curv.flt")) topo_rast <- elev_deriv(rasters = topo_files) - pca_rast <- contributing_area(raster = paste0(basin, "pca_k1_d48.flt")) + pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_", count, ".flt")) + count <- count + 1 rasters <- c(topo_rast, pca_rast) names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") @@ -402,6 +483,20 @@ for (basin in basin_list) { cumul_prop$area <- cumul_prop$area + binned_prop$area.cumsum cumul_prop$prob <- cumul_prop$prob + binned_prop$prob.cumsum + pts_local <- crop(pts, rasters) + # extract the probability values for the test points + pts_prob <- extract(prob_rast, pts_local, xy = TRUE) + pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] + setorder(pts_prob, "prob.pos") + + total_pts <- length((pts_prob$prob.pos)) + + # get the cumulative area (already calculated) and calculate the cumulative landslides + pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] + pts_cumul$ls.prop = seq(1, length(pts_cumul$x)) / total_pts + + pts_prop <- rbind(pts_prop, pts_cumul) + toc() } @@ -412,14 +507,27 @@ total_prob <- max(cumul_prop$prob) cumul_prop$area_prop <- cumul_prop$area / total_area cumul_prop$prob_prop <- cumul_prop$prob / total_prob +dt <- cbind(bins, cumul_prop)[J(pts_prop$prob.pos), roll = "nearest", on = "probability"] +pts_prop$area_prop <- dt$area_prop + ``` +With basins combined, we see a slightly different shape in the curves. The proportions of area and probability jump up greatly at smaller probabilities, indicating that + ```{r} ggplot(cbind(bins, cumul_prop)) + geom_line(aes(x = probability, y = prob_prop), color = "red") + geom_point(aes(x = probability, y = prob_prop), color = "red") + geom_line(aes(x = probability, y = area_prop), color = "lightblue") + geom_point(aes(x = probability, y = area_prop), color = "lightblue") + - geom_line(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + - geom_point(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + geom_line(data = pts_prop, aes(x = prob.pos, y = ls.prop)) + + geom_point(data = pts_prop, aes(x = prob.pos, y = ls.prop)) +``` + +```{r} +ggplot(cbind(bins, cumul_prop)) + + geom_line(aes(x = area_prop, y = prob_prop), color = "red") + + geom_point(aes(x = area_prop, y = prob_prop), color = "red") + + geom_line(data = pts_prop, aes(x = area_prop, y = ls.prop), color = "black") + + geom_point(data = pts_prop, aes(x = area_prop, y = ls.prop), color = "black") ``` From 810d3a002f37c5b4c3281b7eba818d8c72ef536b Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 25 Jan 2023 14:34:53 -0700 Subject: [PATCH 35/74] re-added testing files inst/examples --- inst/examples/dev_15.tif | Bin 0 -> 238110 bytes inst/examples/elev_scottsburg.flt | Bin 0 -> 395016 bytes inst/examples/elev_scottsburg.flt.aux.xml | 6 ++++ inst/examples/elev_scottsburg.hdr | 7 +++++ inst/examples/elev_scottsburg.prj | 1 + inst/examples/elev_scottsburg.stx | 1 + inst/examples/elev_scottsburg.tif | Bin 0 -> 216346 bytes inst/examples/grad_15.tif | Bin 0 -> 215843 bytes inst/examples/initiation_points.cpg | 1 + inst/examples/initiation_points.dbf | Bin 0 -> 78866 bytes inst/examples/initiation_points.prj | 1 + inst/examples/initiation_points.shp | Bin 0 -> 2172 bytes inst/examples/initiation_points.shx | Bin 0 -> 692 bytes inst/examples/input_makegrids.txt | 12 ++++++++ inst/examples/makegrids_input_sample.txt | 11 +++++++ inst/examples/partial_input_sample.txt | 9 ++++++ inst/examples/pca_15m_48hr.tif | Bin 0 -> 217410 bytes inst/examples/plan_15.tif | Bin 0 -> 239866 bytes inst/examples/prof_15.tif | Bin 0 -> 240195 bytes inst/examples/scottsburg.R | 34 ++++++++++++++++++++++ 20 files changed, 83 insertions(+) create mode 100644 inst/examples/dev_15.tif create mode 100644 inst/examples/elev_scottsburg.flt create mode 100644 inst/examples/elev_scottsburg.flt.aux.xml create mode 100644 inst/examples/elev_scottsburg.hdr create mode 100644 inst/examples/elev_scottsburg.prj create mode 100644 inst/examples/elev_scottsburg.stx create mode 100644 inst/examples/elev_scottsburg.tif create mode 100644 inst/examples/grad_15.tif create mode 100644 inst/examples/initiation_points.cpg create mode 100644 inst/examples/initiation_points.dbf create mode 100644 inst/examples/initiation_points.prj create mode 100644 inst/examples/initiation_points.shp create mode 100644 inst/examples/initiation_points.shx create mode 100644 inst/examples/input_makegrids.txt create mode 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FILE = d:\data\AK\BirchCreek\bcon10 diff --git a/inst/examples/makegrids_input_sample.txt b/inst/examples/makegrids_input_sample.txt new file mode 100644 index 0000000..40d2a48 --- /dev/null +++ b/inst/examples/makegrids_input_sample.txt @@ -0,0 +1,11 @@ +# Input file for makeGrids +# Creating by input_file_utils.R +# On 2022-10-19 10:57:24 +DEM: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\examples\elev_scottsburg +SCRATCH DIRECTORY: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\scratch +LENGTH SCALE: 15 +GRID: GRADIENT, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/grad.flt +GRID: PLAN CURVATURE, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/plan.flt +GRID: PROFILE CURVATURE, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/prof.flt +GRID: NORMAL SLOPE CURVATURE, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/norm.flt +GRID: TANGENTIAL CURVATURE, OUTPUT FILE = \\Mac/Home/Documents/terrainworks/code/TerrainWorksUtils/inst/scratch/tan.flt diff --git a/inst/examples/partial_input_sample.txt b/inst/examples/partial_input_sample.txt new file mode 100644 index 0000000..38804ea --- /dev/null +++ b/inst/examples/partial_input_sample.txt @@ -0,0 +1,9 @@ +# Input file for makeGrids +# Creating by input_file_utils.R +# On 2022-10-19 15:05:51 +DEM: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\examples\elev_scottsburg +SCRATCH DIRECTORY: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\scratch +LENGTH SCALE: 15 +DURATION: 5 +CONDUCTIVITY: 1 +OUTPUT RASTER: \\Mac\Home\Documents\terrainworks\code\TerrainWorksUtils\inst\scratch\pca.flt diff --git a/inst/examples/pca_15m_48hr.tif b/inst/examples/pca_15m_48hr.tif new file mode 100644 index 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zvt*f@+FX)}M@dQw=n66vQZZEWcKN*xb>5lxoW2%s+W7WLo*>=n*F9SvJ+#{|esol3 z-|pD-?$;Z;o;W`k+?AESaP;Q?C^7#RasU7M zhLU`nNAp5U*LMgOwyyMDd#&?kLHNnL1&d1WPacTTtnpuW-RHA9vBS7EtZakV=~dn9 z0@q*fTK2`p^Y^zeE_;xDL!e6!+R(>3IFf(Kv@?AFhK^r%_HGW|*w=OQx%B#z-AndA znEdB}ehWLjpY<v#P2^q!^V4|8Hp8w Date: Wed, 25 Jan 2023 19:30:04 -0700 Subject: [PATCH 36/74] add option for linear regression. --- DESCRIPTION | 55 ++++++++++++++++++++++++------------------------ R/build_models.R | 10 ++++++--- 2 files changed, 35 insertions(+), 30 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 287f5b7..efa30ec 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,27 +1,28 @@ -Package: TerrainWorksUtils -Type: Package -Title: General utility functions for TerrainWorks -Version: 0.1.3 -Authors@R: - c( - person("Helen", "Miller", email = "helenthehuman@gmail.com", role = c("aut", "cre")), - person("Tate", "Brasel", role = "aut") - ) -Description: TerrainWorks resources for creating and assessing statistical models for predicting landslide susceptibility. -License: MIT + file LICENSE -Encoding: UTF-8 -LazyData: true -Imports: - methods, - randomForest, - ROCR, - stats, - terra (>= 1.5) -RoxygenNote: 7.2.3 -Suggests: - knitr, - covr, - testthat (>= 3.0.0), - rmarkdown -Config/testthat/edition: 3 -VignetteBuilder: knitr +Package: TerrainWorksUtils +Type: Package +Title: General utility functions for TerrainWorks +Version: 0.1.3 +Authors@R: + c( + person("Helen", "Miller", email = "helenthehuman@gmail.com", role = c("aut", "cre")), + person("Tate", "Brasel", role = "aut") + ) +Description: TerrainWorks resources for creating and assessing statistical models for predicting landslide susceptibility. +License: MIT + file LICENSE +Encoding: UTF-8 +LazyData: true +Imports: + methods, + randomForest, + caret, + ROCR, + stats, + terra (>= 1.5) +RoxygenNote: 7.2.3 +Suggests: + knitr, + covr, + testthat (>= 3.0.0), + rmarkdown +Config/testthat/edition: 3 +VignetteBuilder: knitr diff --git a/R/build_models.R b/R/build_models.R index 68349ee..588c18e 100644 --- a/R/build_models.R +++ b/R/build_models.R @@ -33,7 +33,8 @@ #' @return Nothing #' @export #' -build_k_fold_rf_model <- function(data, +build_k_fold_model <- function(data, + type, seed = NULL, ctrl_method = "repeatedcv", folds = 5, @@ -44,6 +45,10 @@ build_k_fold_rf_model <- function(data, stop("Must provide data.") } + if (!type %in% c("rf", "glm")) { + stop("Must specify model type. Current options are: rf, glm") + } + if (!is.data.frame(data) | is.null(data$class)) { stop("Data must be a data frame (or coercible) with a \"class\" column") } @@ -68,7 +73,7 @@ build_k_fold_rf_model <- function(data, data = data, preProcess = preprocess, trControl = ctrl, - method = "rf", + method = type, metric = "AUC")) print(model) @@ -80,7 +85,6 @@ build_k_fold_rf_model <- function(data, } - #' @title Build Random Forest Model #' #' @param data A dataframe containing columns with model inputs and a From 83df182c07c86fc0cb0cb070f769e0cf36c43b7c Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 27 Jan 2023 13:42:33 -0700 Subject: [PATCH 37/74] add principal comp. markdown document --- analysis/pca_training_data.Rmd | 53 ++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 analysis/pca_training_data.Rmd diff --git a/analysis/pca_training_data.Rmd b/analysis/pca_training_data.Rmd new file mode 100644 index 0000000..9a40111 --- /dev/null +++ b/analysis/pca_training_data.Rmd @@ -0,0 +1,53 @@ +--- +title: "Principal component analysis on landslide training data" +author: Julia Lober +output: + html_notebook: + df_print: paged + html_document: + toc: true +params: + data_dir: + label: Data Directory + value: //Mac/Home/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: /sample_umpqua.Rdata + input: text +editor_options: + chunk_output_type: inline +--- + + +```{r setup, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +# Principal component analysis +A principal component analysis (PCA) allows us to reduce the dimensionality of data and visualize the variation in a dataset. It is usually an exploratory analysis rather than a key part of a quantitative analysis. Here, we do it to see where different covariates might be correlated and how the initiation points vs. non-inititation points are reflected in the overall variability. + +```{r} + +load(paste0(params$data_dir, params$training_data)) + +pc <- prcomp(sample_pnts[,2:7], + center = TRUE, + scale. = TRUE) + +plot(pc$x[sample_pnts$class == "neg",], pch = 19) +points(pc$x[sample_pnts$class == "pos",], col = "red", pch = 19) + +legend("topright", + legend = c("Negative points", "Initiation points"), + pch = 19, + col = c("black", "red"), + bty= "n") + + +``` + From c5c37a1737bb83f462ea878f28c754a1e8ac0649 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 31 Jan 2023 09:50:22 -0700 Subject: [PATCH 38/74] more pca plots --- analysis/pca_training_data.Rmd | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/analysis/pca_training_data.Rmd b/analysis/pca_training_data.Rmd index 9a40111..cad7011 100644 --- a/analysis/pca_training_data.Rmd +++ b/analysis/pca_training_data.Rmd @@ -39,15 +39,38 @@ pc <- prcomp(sample_pnts[,2:7], center = TRUE, scale. = TRUE) -plot(pc$x[sample_pnts$class == "neg",], pch = 19) +p <- plot(pc$x[sample_pnts$class == "neg",], pch = 19, + main = "Principal Components Analysis", font.main = 4) points(pc$x[sample_pnts$class == "pos",], col = "red", pch = 19) legend("topright", - legend = c("Negative points", "Initiation points"), + legend = c("Negative points", "Landslide initiation points"), pch = 19, col = c("black", "red"), bty= "n") +l.x <- cbind(pc$rotation[,1]) * 2 +l.y <- cbind(pc$rotation[,2]) * 2 +arrows(x0=0, x1=l.x, y0=0, y1=l.y, col="darkgreen", length=0.15, lwd=3) +text(l.x, l.y, labels=rownames(l.x) , col="darkgreen", pos=c(3, 1, 3, 1), offset=1, cex=1.2) + + ``` +The first two principal components are the ones that explain the most amount of variability in the data. This should be the axis across which we see the data as separated as it gets. We hope to see some separation of the different categories we are interested (in this case, landslide initiation points and negative points), indicating that the covariates we are looking at can explain the differences between these classes. + +For the example Umpqua dataset, there is a slight trend toward landslide initiation points being on the left side of the plot, but lots of overlap/noise in the plots still. + +```{r} +preset <- par(mfrow = c(1, 2)) + +barplot(pc$rotation[,1], main="PC 1 Loadings Plot", las=2, + ylim = c(-1, 1)) +barplot(pc$rotation[,2], main="PC 2 Loadings Plot", las=2, + ylim = c(-1, 1)) + +par(preset) + +``` +Looking at the loadings of each covariate on the principal components, we can try to grasp what each of the dimensions is trying to capture. For example, the first principal component is mostly from the partial contributing area calculations, while the second component is by far most affected by gradient. From dda7aadf265aa08e7a42afe0507b949bdc989ca0 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 8 Feb 2023 13:49:48 -0700 Subject: [PATCH 39/74] added principal component analysis --- analysis/prcomp_training_data.qmd | 117 ++++++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 analysis/prcomp_training_data.qmd diff --git a/analysis/prcomp_training_data.qmd b/analysis/prcomp_training_data.qmd new file mode 100644 index 0000000..26ef90e --- /dev/null +++ b/analysis/prcomp_training_data.qmd @@ -0,0 +1,117 @@ +--- +title: "Principal component analysis on training data" +author: "Julia Lober" +format: html +editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: sample_umpqua.Rdata + input: text + points: + label: Training data points + value: DeanCr/DeanCr_Initiation_Points.shp + input: text + dem: + label: Training data dem + value: DeanCr/elev_deancr.flt + input: text +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(terra) +``` + +```{r} +#| echo: false +load(paste0(params$data_dir, params$training_data)) +pnts <- vect(paste0(params$data_dir, params$points)) +dem <- rast(paste0(params$data_dir, params$dem)) +``` + +## Looking at the data: + +Change the parameters in the .qmd document to use your own dataset for this markdown document. Right now, we are looking at the Dean Creek area of the Umpqua Basin. The covariates for model building are extracted from a LIDAR digital elevation model (DEM) using FORTRAN scripts. The points are from the DOGAMI landslide inventory. + +```{r} +#| echo: false +plot(dem) +points(pnts) +``` + +## Training Data + +A sampling strategy for selecting negative points gives us a data frame of training data with a random selection of non-landslide initiation points based on a buffer. There is a full description of the negative sampling strategy in a different document in this package. + +There are 8 columns that represent the covariates used for building the model. Gradient and mean curvature are the two physical parameters chosen based on the condition needed for landslide initiation. There are also 4 partial contributing area calculations for storms of different duration (6hr, 12hr, 24hr, and 48hr). + +```{r} +head(sample_pnts) +``` + +## Principal component analysis (PCA) + +I am using the built-in `prcomp()` function from the + +```{r} +pc <- prcomp(sample_pnts[,2:7], + center = TRUE, + scale. = TRUE) +``` + +Lets look at the results of the analysis. We can graph the data on the axis of the first two principal components. These are linear combinations of the covariates that we provide that should explain variability in the data. Visually, we should see the data as separated as it gets. We can graph the landslide initiation points and negative points in different colors to see whether these principal components can point to differences between our two classes. + +```{r} +#| echo: false +old_par <- par(xpd = TRUE, mar = c(2, 2, 2, 8)) + +p <- plot(pc$x[sample_pnts$class == "neg",], + pch = 20, + main = "Principal Components Analysis", + font.main = 2, + xlim = c(-6, 6), + ylim = c(-4, 4), + xlab = "Principal component 1", + ylab = "Principal component 2") +points(pc$x[sample_pnts$class == "pos",], col = "red", pch = 20) + +legend("topright", + inset = c(-.3, 0), + legend = c("Negative points", "Landslide \ninitiation points"), + pch = 20, + col = c("black", "red"), + bty= "n") + +l.x <- cbind(pc$rotation[,1]) * 2 +l.y <- cbind(pc$rotation[,2]) * 2 + +par(old_par) +``` + +We can look at the contributions of each covariate to each of the principal components. We see that the first principal component is a fairly equal combination of partial contributing areas and the second principal component is mostly contributed to by gradient. + +```{r} +preset <- par(mfrow = c(1, 2)) + +barplot(pc$rotation[,1], main="PC 1 Loadings Plot", las=2, + ylim = c(-1, 1)) +barplot(pc$rotation[,2], main="PC 2 Loadings Plot", las=2, + ylim = c(-1, 1)) + +par(preset) +# text(l.x, l.y, labels=rownames(l.x) , col="darkgreen", pos=c(3, 1, 3, 1), offset=1, cex=1.2) +# plot(arrows(x0=0, x1=l.x, y0=0, y1=l.y, col="darkgreen", length=0.15, lwd=3)) +``` From f785116d8ee175a6bff7154ffb6ec9f389cc53c9 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 9 Feb 2023 11:40:51 -0700 Subject: [PATCH 40/74] start mlr3 workflow document --- analysis/mlr3_workflow.qmd | 118 +++++++++++++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) create mode 100644 analysis/mlr3_workflow.qmd diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd new file mode 100644 index 0000000..4318fc2 --- /dev/null +++ b/analysis/mlr3_workflow.qmd @@ -0,0 +1,118 @@ +--- +title: "mlr3 Workflow" +author: "Julia Lober" +format: html +editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: sample_umpqua.Rdata + input: text + points: + label: Training data points + value: DeanCr/DeanCr_Initiation_Points.shp + input: text + dem: + label: Training data dem + value: DeanCr/elev_deancr.flt + input: text +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r} +#| echo: false +load(paste0(params$data_dir, params$training_data)) +pnts <- vect(paste0(params$data_dir, params$points)) +dem <- rast(paste0(params$data_dir, params$dem)) +``` + +## Workflow for the mlr3 package + +This is a document where I work out how to use the `mlr3` package. According to Dan, this is package ecosystem has been taking over for `caret`. + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +# Packages for models - change these based on what type of model you build +library(glmnet) +``` + +Major points for the `mlr3` package include the different nomenclature. The data used to train a model is referred to a `task` and the model being trained is referred to as a `learner`. + +This is a general workflow for any type of model. Another major difference between `mlr3` and `caret` is the names of the model types. The `mlr3learners` package provides the following model types. + +```{r} +mlr_learners +``` + +## Step 1. Creating a task and a learner + +Our task leaves out the `id` column in the sample_pnts data frame, as it will not be relevant information for the model! Specifying the target says what we want the learner to predict - for us, this is the `class` which is positive for landslide initiation points. + +```{r} +task <- as_task_classif(x = sample_pnts[, 2:8], id = "landslide_init", target = "class") +head(task) +``` + +The learner is created based on what type of model we want to train. See above for a list of learners that are available (and keep in mind that you could load `mlr3extralearners` for an even bigger selection). We are interested in classification learners, since the input is categorized into two different classes. + +```{r} +learner <- lrn("classif.log_reg", predict_type = "prob") +print(learner) +``` + +As an experiment, we could train and predict using all of the data. This is bad practice in machine learning but can be an interesting baseline and will help me figure out the syntax. + +```{r} +learner$train(task) +print(learner$model) +``` + +For the generalized linear model (`glmnet`), we need to manually set lambda. This parameter cannot be tuned, so + +```{r} +predicted <- learner$predict(task) +print(predicted) +``` + +Next, we can take a general look at some performance measures. + +```{r} +head(as.data.table(mlr_measures)) +``` + +Print some of these metrics. + +`classif.acc` is the accuracy of the model and `classif.ce` is the error. These should always be inverses of each other. + +`classif.auc` is the area under the ROC curve. Since we are interested more in the probability of a yes (landslide initiation) than simply classification into binary classes, this measure is a bit more useful. + +```{r} +scores <- predicted$score(msrs(c("classif.acc", "classif.ce", "classif.auc"))) +print(scores) + +conf_matrix <- predicted$confusion +print(conf_matrix) +``` + +## Step 2. Add a resampling method + +For any machine learning + +```{r} + +``` From dff16ea1aae39129b57e5e31f2dce65ebf3c4b25 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 10 Feb 2023 21:28:24 -0700 Subject: [PATCH 41/74] more mlr3 workflow --- analysis/mlr3_workflow.qmd | 44 +++++++++++++++++++++++++++++++++++--- 1 file changed, 41 insertions(+), 3 deletions(-) diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd index 4318fc2..f022ce0 100644 --- a/analysis/mlr3_workflow.qmd +++ b/analysis/mlr3_workflow.qmd @@ -47,6 +47,7 @@ library(mlr3) library(mlr3spatial) library(mlr3spatiotempcv) library(mlr3learners) +library(mlr3tuning) # Packages for models - change these based on what type of model you build library(glmnet) ``` @@ -70,8 +71,12 @@ head(task) The learner is created based on what type of model we want to train. See above for a list of learners that are available (and keep in mind that you could load `mlr3extralearners` for an even bigger selection). We are interested in classification learners, since the input is categorized into two different classes. +`classif.glmnet` is a linear regression strategy for classification problems. Notably, `lambda` is a hyperparameter that cannot be automatically optimized. A bandaid solution was to specify a value in the creation of the learner, which silences a warning at prediction time. I am currently reading about `mlr3tuning` and how to create a wrapper tuner object that allows manual specifications for hyperparameter optimization (HPO). + ```{r} -learner <- lrn("classif.log_reg", predict_type = "prob") +learner <- lrn("classif.glmnet", + lambda = 0.01, + predict_type = "prob") print(learner) ``` @@ -109,9 +114,42 @@ conf_matrix <- predicted$confusion print(conf_matrix) ``` -## Step 2. Add a resampling method +## Step 2. Tune and resample + +In order to train this model with some hyperparameter tuning, we need to go back and specify which hyperparameters need to be tuned in the learner. + +```{r} +learner <- lrn("classif.glmnet", + alpha = to_tune(1e-5, 1), + s = to_tune(1e-5, 1e5), + predict_type = "prob") + +tuner = tnr("grid_search", resolution = 5, batch_size = 5) + +resampling <- rsmp("cv", folds = 5) + +learner_at = auto_tuner( + method = tnr("grid_search", resolution = 5, batch_size = 5), + learner = learner, + resampling = resampling, + measure = msr("classif.auc"), +) +print(learner_at) +``` + +```{r} +learner_at$train(task) +``` + +```{r} +predicted <- learner_at$predict(task) +print(predicted$score(msrs(c("classif.auc", "classif.acc")))) +print(predicted$confusion) +``` + +## Step 3. Add a resampling method -For any machine learning +For any machine learning workflow, you want to including a resampling method that will train models on a couple different subsets of data as training and testing sets to reduce the role of chance and produce a more robust basis for evaluating the model's performance. ```{r} From d6128f89e1cf1b2ded7cb653f499f5d8a80b5563 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 16 Feb 2023 15:06:52 -0700 Subject: [PATCH 42/74] added benchmark, code to predict full raster --- analysis/mlr3_workflow.qmd | 55 +++++++++++++++++++++++++++++++++----- 1 file changed, 48 insertions(+), 7 deletions(-) diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd index f022ce0..318ec9d 100644 --- a/analysis/mlr3_workflow.qmd +++ b/analysis/mlr3_workflow.qmd @@ -48,8 +48,10 @@ library(mlr3spatial) library(mlr3spatiotempcv) library(mlr3learners) library(mlr3tuning) +library(mlr3viz) # Packages for models - change these based on what type of model you build library(glmnet) +library(ranger) ``` Major points for the `mlr3` package include the different nomenclature. The data used to train a model is referred to a `task` and the model being trained is referred to as a `learner`. @@ -74,8 +76,7 @@ The learner is created based on what type of model we want to train. See above f `classif.glmnet` is a linear regression strategy for classification problems. Notably, `lambda` is a hyperparameter that cannot be automatically optimized. A bandaid solution was to specify a value in the creation of the learner, which silences a warning at prediction time. I am currently reading about `mlr3tuning` and how to create a wrapper tuner object that allows manual specifications for hyperparameter optimization (HPO). ```{r} -learner <- lrn("classif.glmnet", - lambda = 0.01, +learner <- lrn("classif.log_reg", predict_type = "prob") print(learner) ``` @@ -87,11 +88,17 @@ learner$train(task) print(learner$model) ``` -For the generalized linear model (`glmnet`), we need to manually set lambda. This parameter cannot be tuned, so - ```{r} predicted <- learner$predict(task) -print(predicted) + +``` + +```{r} +tsk_predict = as_task_unsupervised(dem) + +# plan("multisession") # optional parallelization +pred = predict_spatial(tsk_predict, learner, format = "terra") +class(pred) ``` Next, we can take a general look at some performance measures. @@ -114,6 +121,32 @@ conf_matrix <- predicted$confusion print(conf_matrix) ``` +Resampling: + +```{r} +resampling <- rsmp("cv", folds = 5) + +learner_2 <- lrn("classif.ranger", + predict_type = "prob") +learner_3 <- lrn("classif.featureless", + predict_type = "prob") + +bench <- benchmark_grid(task = task, + learner = c(learner, learner_2, learner_3), + resampling = resampling) + +mark <- benchmark(bench) + +``` + +```{r} +autoplot(mark, measure = msr("classif.acc")) +``` + +```{r} +autoplot(mark, type = "roc") +``` + ## Step 2. Tune and resample In order to train this model with some hyperparameter tuning, we need to go back and specify which hyperparameters need to be tuned in the learner. @@ -121,7 +154,8 @@ In order to train this model with some hyperparameter tuning, we need to go back ```{r} learner <- lrn("classif.glmnet", alpha = to_tune(1e-5, 1), - s = to_tune(1e-5, 1e5), + lambda = 0.1, + # s = to_tune(1e-5, 1e5), predict_type = "prob") tuner = tnr("grid_search", resolution = 5, batch_size = 5) @@ -147,10 +181,17 @@ print(predicted$score(msrs(c("classif.auc", "classif.acc")))) print(predicted$confusion) ``` -## Step 3. Add a resampling method +## Step 3. Comparing spatiotemporal cross-validation vs normal cross-validation. For any machine learning workflow, you want to including a resampling method that will train models on a couple different subsets of data as training and testing sets to reduce the role of chance and produce a more robust basis for evaluating the model's performance. +I need data with x and y coordinates attached to do this. This is currently my standstill. + +```{r} +plot(dem) +points(pnts) +``` + ```{r} ``` From 26eba1ba5e7bfbbb47c6c0e0b4bf21f438347b30 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 22 Feb 2023 13:51:51 -0700 Subject: [PATCH 43/74] start resampling methods document --- analysis/resampling_methods.qmd | 109 ++++++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 analysis/resampling_methods.qmd diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd new file mode 100644 index 0000000..8180e81 --- /dev/null +++ b/analysis/resampling_methods.qmd @@ -0,0 +1,109 @@ +--- +title: "Spatial CV vs traditional CV" +author: "Julia Lober" +format: html +editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/ + input: text + training_data: + label: Training data .Rdata file + value: sample_wilson.Rdata + input: text + points: + label: Training data points + value: DeanCr/DeanCr_Initiation_Points.shp + input: text + dem: + label: Training data dem + value: DeanCr/elev_deancr.flt + input: text +--- + +```{r} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r} +#| echo: false +load(paste0(params$data_dir, params$training_data)) +pnts <- vect(paste0(params$data_dir, params$points)) +dem <- rast(paste0(params$data_dir, params$dem)) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +# Packages for models - change these based on what type of model you build +library(glmnet) +library(ranger) +``` + +## Evaluate Spatial CV Methods vs Normal CV + +Data is loaded. First we need to look at the points on a map. + +```{r} +orig_p <- par(mar = c(5, 5, 0, 5)) + +plot(sample_pnts[, "x"], sample_pnts[, "y"], pch = 20) +sample_pnts[, "class"] <- as.factor(sample_pnts[, "class"]) + +par(orig_p) + +``` + +We want to train a logistic regression learner based on the landslide data points loaded into the data frame sample_pnts. + +```{r} +task <- as_task_classif_st(x = sample_pnts[, 2:10], + id = "landslide_init", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = 2992) + +learner <- lrn("classif.log_reg", + predict_type = "prob") +``` + +```{r} +#| echo: false +head(sample_pnts[, 2:10]) +``` + +We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. + +> Nevertheless, despite the random selection of initial cluster centers, repeated partitionings may in some cases be nearly identical. Also, k-means clustering may be less suitable for data sets with pre-existing clusters of points and/or with isolated, distant sample locations. When distinct clusters of points are present, as in multi-level sampling, it may be better to define clusters using a factor variable. ([mlr3spatiotempcv reference](https://arxiv.org/pdf/2110.12674.pdf)) + +```{r} +# Define the spatial resampling method. +rsmp_clus <- rsmp("repeated_spcv_coords", + folds = 5, repeats = 2) + +# rr_sp = resample(task = task, +# learner = learner, +# resampling = resampling_sp) + +autoplot(rsmp_clus, task, fold_id = c(1:5), size = 1) * + ggplot2::scale_y_continuous(breaks = seq(-3.97, -4, -0.01)) * + ggplot2::scale_x_continuous(breaks = seq(-79.06, -79.08, -0.02)) +``` + +I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). + +Other options we have are: + +1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this +2. From b9f49eca7637eb2d872122d692a88ee3918a56e8 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 23 Feb 2023 14:02:51 -0700 Subject: [PATCH 44/74] added blockcv example --- analysis/resampling_methods.qmd | 46 +++++++++++++++++++++++---------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 8180e81..5539be4 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -34,8 +34,8 @@ knitr::opts_chunk$set( ```{r} #| echo: false load(paste0(params$data_dir, params$training_data)) -pnts <- vect(paste0(params$data_dir, params$points)) -dem <- rast(paste0(params$data_dir, params$dem)) +pnts <- terra::vect(paste0(params$data_dir, params$points)) +dem <- terra::rast(paste0(params$data_dir, params$dem)) ``` ```{r load, include = FALSE} @@ -57,7 +57,8 @@ Data is loaded. First we need to look at the points on a map. ```{r} orig_p <- par(mar = c(5, 5, 0, 5)) -plot(sample_pnts[, "x"], sample_pnts[, "y"], pch = 20) +# plot(dem) +plot(x = sample_pnts[, "x"], y = sample_pnts[, "y"], pch = 20) sample_pnts[, "class"] <- as.factor(sample_pnts[, "class"]) par(orig_p) @@ -80,30 +81,47 @@ learner <- lrn("classif.log_reg", ```{r} #| echo: false -head(sample_pnts[, 2:10]) +head(sample_pnts) ``` -We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. +We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. This is one of the more straight-forward methods of spatial cross-validation: divide our data into groupings that are all near each other. > Nevertheless, despite the random selection of initial cluster centers, repeated partitionings may in some cases be nearly identical. Also, k-means clustering may be less suitable for data sets with pre-existing clusters of points and/or with isolated, distant sample locations. When distinct clusters of points are present, as in multi-level sampling, it may be better to define clusters using a factor variable. ([mlr3spatiotempcv reference](https://arxiv.org/pdf/2110.12674.pdf)) ```{r} -# Define the spatial resampling method. -rsmp_clus <- rsmp("repeated_spcv_coords", - folds = 5, repeats = 2) +set.seed(12345) +rsmp_clus <- rsmp("spcv_coords", + folds = 5) # rr_sp = resample(task = task, # learner = learner, # resampling = resampling_sp) -autoplot(rsmp_clus, task, fold_id = c(1:5), size = 1) * - ggplot2::scale_y_continuous(breaks = seq(-3.97, -4, -0.01)) * - ggplot2::scale_x_continuous(breaks = seq(-79.06, -79.08, -0.02)) +autoplot(rsmp_clus, task, fold_id = c(1:5)) * + ggplot2::scale_x_continuous(breaks = seq(124.0, 125.0, 0.4)) * + ggplot2::scale_y_continuous(breaks = seq(55.38, 55.48, 0.2)) ``` I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). -Other options we have are: +Other otions we have are ("method ="): -1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this -2. +1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). +2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. +3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). + +This is the block method. Equal-sized blocks are created and then grouped to create the number of splits. The more columns/rows are created, the closer this method would get to a non-spatial cross validation method, so this is an important factor for this method. + +This method is probably not as well suited to non-rectangular study areas. + +```{r} +set.seed(12345) +rsmp_block <- rsmp("spcv_block", + folds = 5, + cols = 4, + rows = 4) + +autoplot(rsmp_block, task, fold_id = c(1:5), size = 1, show_blocks = TRUE) +``` + +We have to wait on the temporal cross-validation methods for now, since we don't have the date in our current data frame (as of 2/23). From 91700127ea21fe167bd743b8a1cba9600165e212 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 27 Feb 2023 13:26:13 -0700 Subject: [PATCH 45/74] added script for assembling model input dataframe --- R/create_model_dataframe.R | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 R/create_model_dataframe.R diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R new file mode 100644 index 0000000..319f833 --- /dev/null +++ b/R/create_model_dataframe.R @@ -0,0 +1,34 @@ +# define where to find the data and load it. +folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_2.27/out/" +load(paste0(folder, "ls_1996.Rdata")) +load(paste0(folder, "ls_2007.Rdata")) +load(paste0(folder, "ls_2011.Rdata")) +load(paste0(folder, "nonls_1996.Rdata")) +load(paste0(folder, "nonls_2007.Rdata")) +load(paste0(folder, "nonls_2011.Rdata")) + +# remove these lines when the bug is fixed. the $geo field in the 2011 and 2007 +# data frames is not stored as an integer value +ls_2011$geo <- ls_2011$geo$GeoClass +ls_2007$geo <- ls_2007$geo$GeoClass + +# add year labels to each selection of landslide points and non-ls points +ls_1996$year <- rep(1996, length(ls_1996[, 1])) +ls_2007$year <- rep(2007, length(ls_2007[, 1])) +ls_2011$year <- rep(2011, length(ls_2011[, 1])) +nonls_1996$year <- rep(1996, length(nonls_1996[, 1])) +nonls_2007$year <- rep(2007, length(nonls_2007[, 1])) +nonls_2011$year <- rep(2011, length(nonls_2011[, 1])) + +# combine into one data frame. +subset96 <- 1:length(ls_1996[, 1]) +subset07 <- 1:length(ls_2007[, 1]) +subset11 <- 1:length(ls_2011[, 1]) +training_data <- rbind(ls_1996, + ls_2007, + ls_2011, + nonls_1996[subset96, ], + nonls_2007[subset07, ], + nonls_2011[subset11, ]) + +training_data$class <- as.factor(training_data$class) From fb015b61733e428dd256ce03699236b5a835a952 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 27 Feb 2023 13:27:36 -0700 Subject: [PATCH 46/74] added some resampling methods for comparison --- analysis/resampling_methods.qmd | 90 ++++++++++++++++++++++++++------- 1 file changed, 71 insertions(+), 19 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 5539be4..085a9e5 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -10,15 +10,15 @@ params: input: text training_data: label: Training data .Rdata file - value: sample_wilson.Rdata + value: sample_wilson.Rdata input: text points: label: Training data points - value: DeanCr/DeanCr_Initiation_Points.shp + value: DeanCr/DeanCr_Initiation_Points.shp input: text dem: label: Training data dem - value: DeanCr/elev_deancr.flt + value: DeanCr/elev_deancr.flt input: text --- @@ -33,9 +33,12 @@ knitr::opts_chunk$set( ```{r} #| echo: false -load(paste0(params$data_dir, params$training_data)) -pnts <- terra::vect(paste0(params$data_dir, params$points)) -dem <- terra::rast(paste0(params$data_dir, params$dem)) +# load(paste0(params$data_dir, params$training_data)) +# pnts <- terra::vect(paste0(params$data_dir, params$points)) +# dem <- terra::rast(paste0(params$data_dir, params$dem)) +source("../R/create_model_dataframe.R") + +sample_pnts <- training_data ``` ```{r load, include = FALSE} @@ -50,7 +53,7 @@ library(glmnet) library(ranger) ``` -## Evaluate Spatial CV Methods vs Normal CV +## Evaluate Spatial CV Methods vs Normal CV Data is loaded. First we need to look at the points on a map. @@ -68,7 +71,7 @@ par(orig_p) We want to train a logistic regression learner based on the landslide data points loaded into the data frame sample_pnts. ```{r} -task <- as_task_classif_st(x = sample_pnts[, 2:10], +task <- as_task_classif_st(x = sample_pnts[, 2:14], id = "landslide_init", target = "class", positive = "pos", @@ -84,6 +87,20 @@ learner <- lrn("classif.log_reg", head(sample_pnts) ``` +### Non-spatial cross validation + +Non-spatial cross validation randomly selects points to split the training data into training and testing sets. The concern we have with this method is that in some cases, it will end up overestimating the performance of the model or underestimating error. We want to approximate model performance as closely as possible in order to assess how much confidence we can put in it. + +```{r} +set.seed(12345) +rsmp_nonsp <- rsmp("cv", + folds = 5) + +autoplot(rsmp_nonsp, task, fold_id = c(1:5)) +``` + +### K-means clustering spatial cross-validation + We can start running a cluster-based spatial CV method. This method does not consider the temporal distribution of the points, but uses a k-means clustering algorithm to split the data into the defined number of regions based on their x-y coordinates. This is one of the more straight-forward methods of spatial cross-validation: divide our data into groupings that are all near each other. > Nevertheless, despite the random selection of initial cluster centers, repeated partitionings may in some cases be nearly identical. Also, k-means clustering may be less suitable for data sets with pre-existing clusters of points and/or with isolated, distant sample locations. When distinct clusters of points are present, as in multi-level sampling, it may be better to define clusters using a factor variable. ([mlr3spatiotempcv reference](https://arxiv.org/pdf/2110.12674.pdf)) @@ -97,18 +114,12 @@ rsmp_clus <- rsmp("spcv_coords", # learner = learner, # resampling = resampling_sp) -autoplot(rsmp_clus, task, fold_id = c(1:5)) * - ggplot2::scale_x_continuous(breaks = seq(124.0, 125.0, 0.4)) * - ggplot2::scale_y_continuous(breaks = seq(55.38, 55.48, 0.2)) +autoplot(rsmp_clus, task, fold_id = c(1:5)) + # ggplot2::scale_x_continuous(breaks = seq(124.50, 124.62, 0.04)) * + # ggplot2::scale_y_continuous(breaks = seq(55.38, 55.48, 0.03)) ``` -I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). - -Other otions we have are ("method ="): - -1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). -2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. -3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). +### Block spatial cross-validation This is the block method. Equal-sized blocks are created and then grouped to create the number of splits. The more columns/rows are created, the closer this method would get to a non-spatial cross validation method, so this is an important factor for this method. @@ -124,4 +135,45 @@ rsmp_block <- rsmp("spcv_block", autoplot(rsmp_block, task, fold_id = c(1:5), size = 1, show_blocks = TRUE) ``` -We have to wait on the temporal cross-validation methods for now, since we don't have the date in our current data frame (as of 2/23). +### Temporal cross-validation + +Another concept for resampling is to design a strategy that is tailored to our specific dataset. We could do this to varying level of degrees, based on the predictors that are included in our data. + +One option is to simply divide the dataset into the three natural temporal divisions that we observe: (roughly) 1996, 2007, and 2011. First, we define the year column as a factor variable, then pass it to custom_cv as the split determination variable. However, this method produces drastically uneven folds - we have 213 observations from 1996, 137 form 2007, and only 47 from 2011. + +```{r} +set.seed(12345) +yr_splits <- as.factor(sample_pnts$year) + +rsmp_temp <- rsmp("custom_cv") +rsmp_temp$instantiate(task, f = yr_splits) + +autoplot(rsmp_temp, task, fold_id = c(1:3)) +``` + +Another proposal was to divide by both time and lithology. For example: + +> Train on 1996 sedimentary + volcaniclastic,  +> +> test on 1996 other rock types, 2007 sedimentary+volcaniclastic, 2007 other rock types, 2011 sed+volcaniclastic, 2011 other rock types. + +This method is based in our physical understanding of how landslides occur, and will help to decide how much we can extrapolate a model that is built using a certain subset of landslides to other lithologies. + +```{r} +set.seed(12345) +yr_lith <- (sample_pnts$year * sample_pnts$geo) +yr_lith_splits <- as.factor(yr_lith) + +rsmp_templith <- rsmp("custom_cv") +rsmp_templith$instantiate(task, f = yr_lith_splits) + +autoplot(rsmp_templith, task, fold_id = c(1:11)) +``` + +I am going to ignore the leave-one-out methods for now since we don't have many data points, and so leaving any of them out would likely have a big impact on the results that we observe (I would expect it to report lower performance than we actually observe). + +Other otions we have are ("method ="): + +1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). +2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. +3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). From c213602232019bf3bbd7055a73065f42018f3de1 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 28 Feb 2023 11:24:58 -0700 Subject: [PATCH 47/74] added map of sample points. --- analysis/resampling_methods.qmd | 31 +++++++++++++++++++++++-------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 085a9e5..51bfedb 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -57,14 +57,31 @@ library(ranger) Data is loaded. First we need to look at the points on a map. +TODO: figure out how to add a map background to the + ```{r} -orig_p <- par(mar = c(5, 5, 0, 5)) +# orig_p <- par(mar = c(5, 5, 0, 5)) +points <- as.data.frame(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points <- vect(points, geom = c("V1", "y")) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") # plot(dem) -plot(x = sample_pnts[, "x"], y = sample_pnts[, "y"], pch = 20) -sample_pnts[, "class"] <- as.factor(sample_pnts[, "class"]) - -par(orig_p) +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 2, color = "red") + + geom_spatvector(data = points[points$class == "neg", ], + mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + show.legend = TRUE, size = 2, color = "black") + + scale_shape_manual(values = c(3, 1, 17)) + + labs(x = "Longitude", y = "Latitude", shape = "Study points") + +# par(orig_p) ``` @@ -174,6 +191,4 @@ I am going to ignore the leave-one-out methods for now since we don't have many Other otions we have are ("method ="): -1. "custom_cv" - an mlr3-supported way to customize the resampling splits, using either a vector of a factor variable, or a feature of the input (e.g., altitude). I think that we may consider doing this but am not sure how we would divide (maybe we would want to consider combining a few methods?). -2. "spcv_block" - block spatial cross validation. The whole area is divided up geometrically into blocks and one or multiple of these blocks is included in each split. -3. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). +1. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). From e317c67e7f805ef757caa39dd3cf10b03dee5352 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 28 Feb 2023 18:01:54 -0700 Subject: [PATCH 48/74] select dispersed negative sample points instead of first tenth of negative sample points --- R/create_model_dataframe.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R index 319f833..82f5365 100644 --- a/R/create_model_dataframe.R +++ b/R/create_model_dataframe.R @@ -21,9 +21,9 @@ nonls_2007$year <- rep(2007, length(nonls_2007[, 1])) nonls_2011$year <- rep(2011, length(nonls_2011[, 1])) # combine into one data frame. -subset96 <- 1:length(ls_1996[, 1]) -subset07 <- 1:length(ls_2007[, 1]) -subset11 <- 1:length(ls_2011[, 1]) +subset96 <- seq(1, length(nonls_1996[,1]), 10) # 1:length(ls_1996[, 1]) +subset07 <- seq(1, length(nonls_2007[,1]), 10) # 1:length(ls_2007[, 1]) +subset11 <- seq(1, length(nonls_2011[,1]), 10) # 1:length(ls_2011[, 1]) training_data <- rbind(ls_1996, ls_2007, ls_2011, From c25418d2e69e557778aea830f94e6672adbfae02 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 28 Feb 2023 18:04:15 -0700 Subject: [PATCH 49/74] refining map --- analysis/resampling_methods.qmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 51bfedb..3044f3a 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -74,10 +74,10 @@ ggplot() + color = "white", fill = "white") + geom_spatvector(data = points[points$class == "pos", ], mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), - show.legend = TRUE, size = 2, color = "red") + - geom_spatvector(data = points[points$class == "neg", ], - mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - show.legend = TRUE, size = 2, color = "black") + + show.legend = TRUE, size = 1, color = "red", alpha = 0.5) + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 0.5) + scale_shape_manual(values = c(3, 1, 17)) + labs(x = "Longitude", y = "Latitude", shape = "Study points") From bca9dc6c18183fa6960dbad2fae4109dea853520 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 11:52:25 -0700 Subject: [PATCH 50/74] updating to new data location --- R/create_model_dataframe.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R index 82f5365..d106192 100644 --- a/R/create_model_dataframe.R +++ b/R/create_model_dataframe.R @@ -1,5 +1,5 @@ # define where to find the data and load it. -folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_2.27/out/" +folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/" load(paste0(folder, "ls_1996.Rdata")) load(paste0(folder, "ls_2007.Rdata")) load(paste0(folder, "ls_2011.Rdata")) From c920e0768fee9f0805f1d03cb328f35035ce2d90 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 11:52:44 -0700 Subject: [PATCH 51/74] exploring feature selection and filtering methods --- analysis/feature_selection.qmd | 54 ++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 analysis/feature_selection.qmd diff --git a/analysis/feature_selection.qmd b/analysis/feature_selection.qmd new file mode 100644 index 0000000..7ddf4fe --- /dev/null +++ b/analysis/feature_selection.qmd @@ -0,0 +1,54 @@ +--- +title: "Landslide initiation feature selection" +author: Julia Lober +format: html +editor: visual +--- + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3verse) +``` + +## Feature selection for landslide points + +This document explores the impact of each predictor on a logistic regression model. We will look at a couple of different ways to do feature selection, and see if they offer consistent results or different options each time. + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +``` + +```{r} +names(sample_pnts) +``` + +```{r} +task = as_task_classif(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") +``` + +## Filtering + +Lets look at a few filtering methods. These calculate some measure of correlation between the target variable and the predictors, then assigns a threshold which must be met in order to be included in the final model. + +The benefit of these methods are a simpler method that does not involve building a bunch of models to test the impact of different features. The downside is that it is possible that the filter does not truly reflect the impact on the final model. + +```{r} +library(FSelectorRcpp) +info_filter = flt("information_gain") +info_filter$calculate(task) + +as.data.table(info_filter) +``` From 02e782231f80110006a8d713e65354355bba9631 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 11:53:07 -0700 Subject: [PATCH 52/74] new workflow document --- analysis/modeling_landslide_initiation.qmd | 150 +++++++++++++++++++++ 1 file changed, 150 insertions(+) create mode 100644 analysis/modeling_landslide_initiation.qmd diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd new file mode 100644 index 0000000..a6b1ea0 --- /dev/null +++ b/analysis/modeling_landslide_initiation.qmd @@ -0,0 +1,150 @@ +--- +title: "Modeling Landslide Initiation" +autho: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +``` + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +``` + +## A look at the data. + +The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. + +The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. + +```{r} +#| echo: false +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. + +## Defining the modeling task and learner + +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. + +There are several steps we need to work through: + +1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. +2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. +3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. +4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. + +```{r} +set.seed(12345) +landslide_task <- as_task_classif_st(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +rf_learner <- lrn("classif.ranger", + predict_type = "prob") + +landslide_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3) +``` + +These are the first four folds of the clustering spatial resampling. + +```{r} +#| echo: false +autoplot(landslide_resamp, + landslide_task, + fold_id = c(1:4)) +``` + +## Feature selection + +We'll work this in in a bit. + +## Training the logistic regression model + +To train the logistic regression model, which has no hyperparameters that need to be tuned, we call the method resample. + +```{r} +build_time <- system.time(logreg_model <- resample(task= landslide_task, + learner = logreg_learner, + resampling = landslide_resamp)) + +build_time +``` + +And now we look at the results. + +```{r} +logreg_model +``` + +## Training the random forest model + +```{r} +build_time <- system.time(rf_model <- resample(task= landslide_task, + learner = rf_learner, + resampling = landslide_resamp)) +``` + +## Compare the two models + +```{r} +autoplot(c(logreg_model, rf_model), measure = msr("classif.auc")) +``` From 902c38e4be12e5dada9ec2552098f0cef3bfac6a Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 6 Mar 2023 12:02:41 -0700 Subject: [PATCH 53/74] minor changes --- analysis/resampling_methods.qmd | 58 ++++++++++++++++++++++++++------- 1 file changed, 47 insertions(+), 11 deletions(-) diff --git a/analysis/resampling_methods.qmd b/analysis/resampling_methods.qmd index 3044f3a..ed1ceb4 100644 --- a/analysis/resampling_methods.qmd +++ b/analysis/resampling_methods.qmd @@ -37,7 +37,6 @@ knitr::opts_chunk$set( # pnts <- terra::vect(paste0(params$data_dir, params$points)) # dem <- terra::rast(paste0(params$data_dir, params$dem)) source("../R/create_model_dataframe.R") - sample_pnts <- training_data ``` @@ -62,22 +61,20 @@ TODO: figure out how to add a map background to the ```{r} # orig_p <- par(mar = c(5, 5, 0, 5)) points <- as.data.frame(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) -points <- vect(points, geom = c("V1", "y")) points$yr <- sample_pnts$year points$class <- sample_pnts$class -crs(points) <- "epsg:26910" +terra::crs(points) <- "epsg:26910" points <- project(points, "epsg:4326") -# plot(dem) ggplot() + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), color = "white", fill = "white") + geom_spatvector(data = points[points$class == "pos", ], mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), show.legend = TRUE, size = 1, color = "red", alpha = 0.5) + - # geom_spatvector(data = points[points$class == "neg", ], - # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - # show.legend = TRUE, size = 1 , color = "black", alpha = 0.5) + + geom_spatvector(data = points[points$class == "neg", ], + mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + show.legend = TRUE, size = 1 , color = "black", alpha = 0.5) + scale_shape_manual(values = c(3, 1, 17)) + labs(x = "Longitude", y = "Latitude", shape = "Study points") @@ -88,7 +85,7 @@ ggplot() + We want to train a logistic regression learner based on the landslide data points loaded into the data frame sample_pnts. ```{r} -task <- as_task_classif_st(x = sample_pnts[, 2:14], +task <- as_task_classif_st(x = sample_pnts[!(is.na(sample_pnts$dist_to_road)), 2:14], id = "landslide_init", target = "class", positive = "pos", @@ -156,11 +153,11 @@ autoplot(rsmp_block, task, fold_id = c(1:5), size = 1, show_blocks = TRUE) Another concept for resampling is to design a strategy that is tailored to our specific dataset. We could do this to varying level of degrees, based on the predictors that are included in our data. -One option is to simply divide the dataset into the three natural temporal divisions that we observe: (roughly) 1996, 2007, and 2011. First, we define the year column as a factor variable, then pass it to custom_cv as the split determination variable. However, this method produces drastically uneven folds - we have 213 observations from 1996, 137 form 2007, and only 47 from 2011. +One option is to simply divide the dataset into the three natural temporal divisions that we observe: (roughly) 1996, 2007, and 2011. First, we define the year column as a factor variable, then pass it to custom_cv as the split determination variable. However, this method produces drastically uneven folds - we have 213 observations from 1996, 137 from 2007, and only 47 from 2011. ```{r} set.seed(12345) -yr_splits <- as.factor(sample_pnts$year) +yr_splits <- as.factor(sample_pnts[!(is.na(sample_pnts$dist_to_road)),]$year) rsmp_temp <- rsmp("custom_cv") rsmp_temp$instantiate(task, f = yr_splits) @@ -178,7 +175,7 @@ This method is based in our physical understanding of how landslides occur, and ```{r} set.seed(12345) -yr_lith <- (sample_pnts$year * sample_pnts$geo) +yr_lith <- ((sample_pnts[!(is.na(sample_pnts$dist_to_road)),]$year) * sample_pnts[!(is.na(sample_pnts$dist_to_road)),]$geo) yr_lith_splits <- as.factor(yr_lith) rsmp_templith <- rsmp("custom_cv") @@ -192,3 +189,42 @@ I am going to ignore the leave-one-out methods for now since we don't have many Other otions we have are ("method ="): 1. "sptcv_cstf" - leave-location-out, leave-time-out, or leave-location-time-out methods. Control which method is being used by parameters "time =" and "space =". I believe this is the only way to do temporal cross-validation (aside from "custom_cv"). + +## Evaluating the performance of each method. + +Now we need to get the performance metric calculated using each resampling method, so that we can compare them. This step is likely to take a bit of time. + +```{r} +methods = c(rsmp_block, rsmp_clus, rsmp_nonsp, rsmp_temp, rsmp_templith) +times = list() +model_perf = list() + +for (method in methods) { + build_time <- system.time(model <- resample(task= task, + learner = learner, + resampling = method)) + times <- append(times, build_time["elapsed"]) + model_perf <- append(model_perf, model$aggregate(measures = msr("classif.auc"))) +} + +``` + +```{r} +method_names = c("rsmp_block", "rsmp_clus", "rsmp_nonsp", "rsmp_temp", "rsmp_templith") + +to_plot <- c(model_perf[1]$classif.auc, + model_perf[2]$classif.auc, + model_perf[3]$classif.auc, + model_perf[4]$classif.auc, + model_perf[5]$classif.auc) +to_plot <- as.data.frame(to_plot) +to_plot$names <- method_names[1:5] + +ggplot(to_plot, aes(x = names, y = to_plot)) + + geom_bar(stat = "identity") + + coord_cartesian(ylim=c(0.8,1)) + + labs(y = "Area under the ROC curve", x = "Resampling method") + # scale_y_continuous(limits = c(0.5, 1)) +``` + +Based on literature, lets go with the clustering resampling method. From 295f2d61167c62f67087740bc94dd212b8a3eb76 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 8 Mar 2023 14:08:05 -0700 Subject: [PATCH 54/74] add forward feature selection --- analysis/feature_selection.qmd | 182 ++++++++++++++++++++++++++++++++- 1 file changed, 178 insertions(+), 4 deletions(-) diff --git a/analysis/feature_selection.qmd b/analysis/feature_selection.qmd index 7ddf4fe..84e9131 100644 --- a/analysis/feature_selection.qmd +++ b/analysis/feature_selection.qmd @@ -31,7 +31,7 @@ names(sample_pnts) ``` ```{r} -task = as_task_classif(x = sample_pnts, +task_orig <- as_task_classif_st(x = sample_pnts, id = "landslide_initiation", target = "class", positive = "pos", @@ -39,16 +39,190 @@ task = as_task_classif(x = sample_pnts, crs = "epsg:26910") ``` -## Filtering +We'll also look at adding a few manual predictors that might help us develop the relationship that we observe, or expect to observe based on our understanding of the physical processes. + +```{r} +sample_pnts_ext <- sample_pnts +sample_pnts_ext$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts_ext$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 + +task_ext <- as_task_classif_st(x = sample_pnts_ext, + id = "landslide_initiation_ext", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") +``` + +## Filtering Lets look at a few filtering methods. These calculate some measure of correlation between the target variable and the predictors, then assigns a threshold which must be met in order to be included in the final model. The benefit of these methods are a simpler method that does not involve building a bunch of models to test the impact of different features. The downside is that it is possible that the filter does not truly reflect the impact on the final model. ```{r} -library(FSelectorRcpp) +task <- task_orig +``` + +```{r} info_filter = flt("information_gain") info_filter$calculate(task) -as.data.table(info_filter) +res_info <- as.data.table(info_filter) +res_info$rank <- 1:length(res_info$feature) +``` + +```{r} +anova_filter = flt("anova") +anova_filter$calculate(task) + +res_anova <- as.data.table(anova_filter) +res_anova$rank <- 1:length(res_anova$feature) +``` + +```{r} +auc_filter = flt("auc") +auc_filter$calculate(task) + +res_auc <- as.data.table(auc_filter) +res_auc$rank <- 1:length(res_auc$feature) +``` + +```{r} +cmim_filter = flt("cmim") +cmim_filter$calculate(task) + +res_cmim <- as.data.table(cmim_filter) +res_cmim$rank <- 1:length(res_cmim$feature) +``` + +```{r} +mim_filter = flt("mim") +mim_filter$calculate(task) + +res_mim <- as.data.table(mim_filter) +res_mim$rank <- 1:length(res_mim$feature) +``` + +#### Compare the filters. + +```{r} +results <- merge(res_auc, res_anova, by = "feature", suffixes = c(".auc", ".anova")) +results <- merge(results, res_info, by = "feature", suffixes = c("", ".info")) +results <- merge(results, res_cmim, by = "feature", suffixes = c("", ".cmim")) +results <- merge(results, res_mim, by = "feature", suffixes = c("", ".mim")) +results$total <- rowSums(cbind(results$rank.auc, results$rank.anova, results$rank, results$rank.cmim, results$rank.mim)) + +barplot(height = results$total, names = results$feature, las = 2) + +``` + +## Basic forward feature selection + +This method tests the model created adding one feature each time until the model performance is no longer improving. We can use a variety of + +```{r} +lgr::get_logger("mlr3")$set_threshold("warn") +set.seed(12345) +instance = fselect( + fselector = fs("sequential"), + task = task, + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3), + measure = msr("classif.auc") +) +``` + +```{r} +dt <- as.data.table(instance$archive) +dt[batch_nr == 1, 1:14] + +``` + +The result we have here is likely a light over-estimation of modeling performance. The best way to assess performance is to do nested resampling - an outer resampling strategy that reserves a test set for estimating model performance, and an inner resampling strategy that is used for the feature selection process (or, hyperparameter tuning). I do implement this below. + +```{r} +instance$result +instance$result_feature_set +``` + +We can also use the feature selector as an auto_fselector, which inherits from the learner class and can be treated as such. This allows us to put it in a benchmark function and compare it to other learners. + +```{r} +fs_learner = auto_fselector( + fselector = fs("sequential"), + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2), + measure = msr("classif.auc") +) + +# Tried, did not improve training time (by much) and performed slightly worse. +fs_learner_rand = auto_fselector( + fselector = fs("random_search"), + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2), + measure = msr("classif.auc") +) +``` + +```{r} +lgr::get_logger("mlr3")$set_threshold("error") +bm_grid <- benchmark_grid( + task = task, + learner = list(fs_learner, lrn("classif.log_reg", predict_type = "prob")), + resampling = rsmp("spcv_coords", folds = 3) +) + +bm <- benchmark(bm_grid) +``` + +It seems that there is not really any significant improvement of the feature-selected model over the normal model. + +```{r} +aggr = bm$aggregate(msrs(c("classif.auc", "time_train"))) +as.data.frame(aggr)[, c("learner_id", "classif.auc", "time_train")] +autoplot(bm, measure = msr("classif.auc")) +``` + +```{r} +rf_learner = auto_fselector( + fselector = fs("sequential"), + learner = lrn("classif.ranger", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2), + measure = msr("classif.auc") +) + +outer_resample <- rsmp("repeated_spcv_coords", folds = 5, repeats = 5) +rr <- resample(task, rf_learner, outer_resample, store_models = TRUE) +``` + +```{r} +rr2 <- resample(task, lrn("classif.ranger", predict_type = "prob"), outer_resample, store_models = TRUE) + +rr2$score(measures = msr("classif.auc"))[, "classif.auc"] +``` + +Lets try using the Aikake Information Criterion for the forward feature selection. + +ERROR: this always results in an error that says there is no model stored in classif.log_reg(). I don't think that it is worth the time to try to figure this error out. + +```{r} +# lgr::get_logger("mlr3")$set_threshold("debug") +# aic_select = fselect( +# fselector = fs("sequential"), +# task = task, +# learner = lrn("classif.log_reg", predict_type = "prob"), +# resampling = rsmp("repeated_spcv_coords", +# folds = 3, +# repeats = 1), +# measure = msr("aic") +# ) ``` From 35105cd343556ce166eeafbe06f262af0b8beefa Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 10 Mar 2023 12:27:40 -0700 Subject: [PATCH 55/74] pre-pipeline save --- analysis/modeling_landslide_initiation.qmd | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd index a6b1ea0..ba81d68 100644 --- a/analysis/modeling_landslide_initiation.qmd +++ b/analysis/modeling_landslide_initiation.qmd @@ -37,7 +37,7 @@ source("../R/create_model_dataframe.R") sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] ``` -## A look at the data. +## A look at the data. The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. @@ -75,7 +75,7 @@ Note that the landslide initiation points tend to come in clusters and the years ## Defining the modeling task and learner -For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. There are several steps we need to work through: @@ -99,6 +99,8 @@ logreg_learner <- lrn("classif.log_reg", rf_learner <- lrn("classif.ranger", predict_type = "prob") + + landslide_resamp <- rsmp("repeated_spcv_coords", folds = 5, repeats = 3) @@ -113,11 +115,15 @@ autoplot(landslide_resamp, fold_id = c(1:4)) ``` -## Feature selection +### 1. Feature selection + +The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. -We'll work this in in a bit. +```{r} + +``` -## Training the logistic regression model +## Training the logistic regression model To train the logistic regression model, which has no hyperparameters that need to be tuned, we call the method resample. From 5b66c0f1280295e5e6bd62b85fc71c16644f1670 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 13 Mar 2023 11:10:14 -0600 Subject: [PATCH 56/74] added pipeline for comparing models --- analysis/modeling_landslide_initiation.qmd | 78 ++++++++++++++-------- 1 file changed, 50 insertions(+), 28 deletions(-) diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd index ba81d68..3607ea5 100644 --- a/analysis/modeling_landslide_initiation.qmd +++ b/analysis/modeling_landslide_initiation.qmd @@ -21,6 +21,8 @@ library(mlr3spatiotempcv) library(mlr3learners) library(mlr3tuning) library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) # Packages for model-building library(glmnet) @@ -99,14 +101,12 @@ logreg_learner <- lrn("classif.log_reg", rf_learner <- lrn("classif.ranger", predict_type = "prob") - - -landslide_resamp <- rsmp("repeated_spcv_coords", - folds = 5, - repeats = 3) +spat_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3) ``` -These are the first four folds of the clustering spatial resampling. +We will design a pipeline to compare the different models. ```{r} #| echo: false @@ -120,37 +120,59 @@ autoplot(landslide_resamp, The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. ```{r} - +logreg_fselect <- auto_fselector( + fs("sequential"), + learner = logreg_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) + +rf_fselect <- auto_fselector( + fs("sequential"), + learner = rf_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) ``` -## Training the logistic regression model - -To train the logistic regression model, which has no hyperparameters that need to be tuned, we call the method resample. +## Make a pipeline ```{r} -build_time <- system.time(logreg_model <- resample(task= landslide_task, - learner = logreg_learner, - resampling = landslide_resamp)) -build_time +pipe <- + ppl("branch", list( + "anova" = po("filter", filter = flt("anova")), + "nothing" = po("nop")), prefix_branchops = "filt") %>>% + ppl("branch", list( + "lr" = logreg_learner, + "rf" = rf_learner, + "lr_fs" = logreg_fselect, + "rf_fs" = rf_fselect)) + +pipe$plot(html = FALSE) ``` -And now we look at the results. - ```{r} -logreg_model -``` +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), + ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 20L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 10), + search_space = param_set + +) -## Training the random forest model +tuner <- tnr("random_search") -```{r} -build_time <- system.time(rf_model <- resample(task= landslide_task, - learner = rf_learner, - resampling = landslide_resamp)) -``` +tuner$optimize(target) -## Compare the two models - -```{r} -autoplot(c(logreg_model, rf_model), measure = msr("classif.auc")) +# out <- mlr3::resample(task = landslide_task, +# learner = as_learner(pipe), +# resampling = spat_resamp) ``` From 0dd5898b6430397d55c03d994eceb6ce3313feaa Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 22 Mar 2023 16:19:44 -0600 Subject: [PATCH 57/74] added final model training steps --- analysis/comparing_landslide_models.qmd | 333 +++++++++++++++++++++ analysis/modeling_landslide_initiation.qmd | 178 ----------- 2 files changed, 333 insertions(+), 178 deletions(-) create mode 100644 analysis/comparing_landslide_models.qmd delete mode 100644 analysis/modeling_landslide_initiation.qmd diff --git a/analysis/comparing_landslide_models.qmd b/analysis/comparing_landslide_models.qmd new file mode 100644 index 0000000..2891b06 --- /dev/null +++ b/analysis/comparing_landslide_models.qmd @@ -0,0 +1,333 @@ +--- +title: "Modeling Landslide Initiation" +autho: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3pipelines) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +library(tidyterra) +``` + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +sample_pnts$geo <- as.factor(sample_pnts$geo) +sample_pnts <- subset(sample_pnts, select = -c(year)) + +sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 +``` + +## A look at the data. + +The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. + +The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. + +```{r} +#| echo: false +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. + +## Defining the modeling task and learner + +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. + +There are several steps we need to work through: + +1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. +2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. +3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. +4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. + +```{r} +set.seed(12345) +landslide_task <- as_task_classif_st(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +rf_learner <- lrn("classif.ranger", + predict_type = "prob") + +spat_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2) +``` + +We will design a pipeline to compare the different models. + +```{r} +#| echo: false +autoplot(spat_resamp, + landslide_task, + fold_id = c(1:4)) +``` + +### 1. Feature selection + +The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. + +```{r} +logreg_fselect <- auto_fselector( + fs("sequential"), + learner = logreg_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) + +rf_fselect <- auto_fselector( + fs("sequential"), + learner = rf_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) +``` + +## Make a pipeline + +```{r} + +pipe <- + po("encode") %>>% + ppl("branch", list( + "anova" = po("filter", filter = flt("anova")), + "nothing" = po("nop")), prefix_branchops = "filt") %>>% + ppl("branch", list( + "lr" = logreg_learner, + "rf" = rf_learner, + "lr_fs" = logreg_fselect, + "rf_fs" = rf_fselect)) + +pipe$plot(html = FALSE) +``` + +Now, we can define the parameters over which to evaluate the models and tune it. I use a random search to start just gathering an idea of the best ranges of parameters. Since we are comparing a handful of different modeling techniques, one of the major parameters to tune will be the model type, called "branch.selection" in the parameter set. + +I ran this initially with a filtering method as an option, but found the no matter the filtering, the forward feature selection works better. It is possible that combining filtering and forward feature would reduce run-time, but since we only have about 15 features to evaluate, I don't believe that the time save will be substantial enough for the possible performance trade-off. + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), + ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 20), + search_space = param_set + +) + +tuner <- tnr("random_search") + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 20), + search_space = param_set + +) + +tuner <- tnr("random_search") + +tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 5L), + ParamFct$new("branch.selection", levels = c("rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("none"), + search_space = param_set + +) + +tuner <- tnr("grid_search", resolution = 5, batch_size = 400) + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 10L), + ParamFct$new("branch.selection", levels = c("rf")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("none"), + search_space = param_set + +) + +tuner <- tnr("grid_search", resolution = 10, batch_size = 100) + +tuner$optimize(target) +``` + +This tuning process takes an average of 2 minutes \* number of evals to run. I have run it twice so far, and save the results in data files to minimize the number of times we want to re-run the code. + +Overall, the results have shown AUC values between 0.82 and 0.86 for all configurations of models. The logistic regression model with the forward feature selection performed significantly better than the logistic regression without feature selection, and slightly (though not significantly) better than the random forest models. However, logistic regression is much faster to train, especially when using a forward feature selection algorithm that involves training so many models. + +```{r} +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.13.pipelinetuneresult.Rdata") +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.rf_fs_mtry.Rdata") +``` + +```{r} +#| echo: false +to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) +names(to_plot) <- c("auc", "model_type") +to_plot$model_type <- as.factor(to_plot$model_type) + +ggplot(to_plot, aes(x = model_type, y = auc)) + + geom_boxplot(aes(col = model_type)) + + geom_jitter(aes(col = model_type), position = position_jitter(0.2)) +``` + +## Logistic regression model + +Based on the results above, we will work with a logistic regression model and use forward feature selection to select the subset of features that provide the most amount of information. + +Our pipeline includes a scaling step, to even out the influence of each predictor, and an encoding step, to translate the geology predictor from a factor to a one-hot strategy that is treated more logically by a numerical model. + +```{r} + +pl_fselect <- + po("scale") %>>% + po("encode") %>>% + po("learner", logreg_fselect) + +pl_fselect$train(landslide_task) +#pl_lrn$graph$pipeops$classif.log_reg.fselector$learner + +``` + +Now that we know the best set of features, we can train a model with the resampling method to get a more exact estimate of how well the final model will actually perform. To do this, we use a k-means clustering based spatial resampling method, with 8 folds (7 for training, 1 for testing) and 50 repeats, to get a measure of performance that we can be relatively confident in. + +Then, we look at the aggregate AUC measure for all 50\*8 models that were trained (400 models total). + +```{r} +feature_set <- c("age", "dist_to_road", "mean_curv", "pca_k1_48") +landslide_task$select(feature_set) + +pl_feature_eng <- + po("scale") %>>% + po("encode") + +pl_feature_eng$train(landslide_task) +landslide_task_eng <- pl_feature_eng$predict(landslide_task)$encode.output + +model <- mlr3::resample( + task = landslide_task_eng, + learner = logreg_learner, + resampling = rsmp("repeated_spcv_coords", + folds = 8, + repeats = 50) +) + +model$aggregate(msr("classif.auc")) + +``` + +Our final step is to train a model using all of the data. While this is generally frowned upon in machine learning, our inventory produces a relatively small data set for this project and we want to leverage all of the data available. + +TODO: look up coefficient interpretation. Does a bigger coefficient mean that the variable has a bigger influence? I think yes, if the data are scaled first. + +Now that I am using the scaled version of the data, I agree. The pca value has the largest coefficient, and is consistently the variable that is chosen first during the feature selection, indicating that it is a highly explanatory variable for our dataset. Mean curvature is next, which + +```{r} +logreg_learner$train(landslide_task_eng) + +logreg_learner$model +``` diff --git a/analysis/modeling_landslide_initiation.qmd b/analysis/modeling_landslide_initiation.qmd deleted file mode 100644 index 3607ea5..0000000 --- a/analysis/modeling_landslide_initiation.qmd +++ /dev/null @@ -1,178 +0,0 @@ ---- -title: "Modeling Landslide Initiation" -autho: Julia Lober -format: html -editor: visual ---- - -```{r setup} -#| echo: false -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - error = FALSE -) -``` - -```{r load, include = FALSE} -library(mlr3) -library(mlr3spatial) -library(mlr3spatiotempcv) -library(mlr3learners) -library(mlr3tuning) -library(mlr3viz) -library(mlr3fselect) -library(mlr3filters) - -# Packages for model-building -library(glmnet) -library(ranger) - -# Packages for other things -library(terra) -library(ggplot2) -library(maps) -``` - -```{r} -source("../R/create_model_dataframe.R") -sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] -``` - -## A look at the data. - -The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. - -The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. - -```{r} -#| echo: false -head(sample_pnts) -``` - -```{r} -#| echo: false -points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) -points$yr <- sample_pnts$year -points$class <- sample_pnts$class -crs(points) <- "epsg:26910" -points <- project(points, "epsg:4326") - -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") - -ggplot() + - geom_polygon(data = oregon, mapping = aes(x = long, y = lat), - color = "white", fill = "white") + - # geom_spatvector(data = points[points$class == "neg", ], - # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + - geom_spatvector(data = points[points$class == "pos", ], - mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), - show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + - scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + - labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") -``` - -Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. - -## Defining the modeling task and learner - -For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. - -There are several steps we need to work through: - -1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. -2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. -3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. -4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. - -```{r} -set.seed(12345) -landslide_task <- as_task_classif_st(x = sample_pnts, - id = "landslide_initiation", - target = "class", - positive = "pos", - coordinate_names = c("x", "y"), - crs = "epsg:26910") - -logreg_learner <- lrn("classif.log_reg", - predict_type = "prob") - -rf_learner <- lrn("classif.ranger", - predict_type = "prob") - -spat_resamp <- rsmp("repeated_spcv_coords", - folds = 5, - repeats = 3) -``` - -We will design a pipeline to compare the different models. - -```{r} -#| echo: false -autoplot(landslide_resamp, - landslide_task, - fold_id = c(1:4)) -``` - -### 1. Feature selection - -The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. - -```{r} -logreg_fselect <- auto_fselector( - fs("sequential"), - learner = logreg_learner, - resampling = spat_resamp, - measure = msr("classif.auc")) - -rf_fselect <- auto_fselector( - fs("sequential"), - learner = rf_learner, - resampling = spat_resamp, - measure = msr("classif.auc")) -``` - -## Make a pipeline - -```{r} - -pipe <- - ppl("branch", list( - "anova" = po("filter", filter = flt("anova")), - "nothing" = po("nop")), prefix_branchops = "filt") %>>% - ppl("branch", list( - "lr" = logreg_learner, - "rf" = rf_learner, - "lr_fs" = logreg_fselect, - "rf_fs" = rf_fselect)) - -pipe$plot(html = FALSE) -``` - -```{r} -param_set <- ParamSet$new(list( - ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), - ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), - ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 20L), - ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) -)) - -target <- ti( - task = landslide_task, - learner = as_learner(pipe), - resampling = rsmp("spcv_coords", folds = 3), - measures = msr("classif.auc"), - terminator = trm("evals", n_evals = 10), - search_space = param_set - -) - -tuner <- tnr("random_search") - -tuner$optimize(target) - -# out <- mlr3::resample(task = landslide_task, -# learner = as_learner(pipe), -# resampling = spat_resamp) -``` From d139967a05a78e9dcd47012a8f45e47d30cffcfc Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 13:39:52 -0600 Subject: [PATCH 58/74] initiation model document first commit --- analysis/PFA_initiation_model.qmd | 122 ++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 analysis/PFA_initiation_model.qmd diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd new file mode 100644 index 0000000..4976139 --- /dev/null +++ b/analysis/PFA_initiation_model.qmd @@ -0,0 +1,122 @@ +--- +title: "Landslide initiation model" +author: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3pipelines) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +library(tidyterra) +``` + +## Landslide Initiation susceptibility model for the Private Forest Accord + +This document outlines the process for making the landslide initiation model for the Oregon Private Forest Accord project. We work with data generated from the PFA_Sampling_Multiple_DEMs document that is included in this package. This document outlines in more detail where the data for different predictors is sourced and the strategy for negative sampling. + +The primary focus of this document is to outline the model-building process and decisions that resulted in the final model used in combination with the landslide runout model to generate riparian buffers for the Private Forest Accord. Broadly speaking, there were 3 major decisions made in this process: + +1. A method for feature selection. + +2. The type of model built. + +3. A spatio-temporal resampling strategy. + +### About the data + +```{r} +#| echo: false +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +sample_pnts$geo <- as.factor(sample_pnts$geo) + +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +The data includes 767 landslide initiation points, with equal proportions of landslide points and non-landslide points. The data has obvious spatial distributions and is divided into 3 approximate year groups. Exact dates for the landslide record were not always available, so these should be viewed as groupings rather than divisions. + +### Feature Selection + +Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. + +```{r} +#| echo: false +load("/Users/julialober/Documents/terrainworks/code/sandbox/filter_results.Rdata") + +ggplot(results, aes(x = feature, y = c(rank.auc, rank.anova, rank, rank.cmim, rank.mim))) + + geom_bar(stat = "identity") +``` + +### Logistic Regression model + +A logistic regression model was chosen for two primary purposes: because it is simple to code into FORTRAN and because it performed just as well as the Random Forest model when exploring some different types of models. + +The first reason is particularly important, as this is how the model will be implemented for predicting. The data sets for predicting encompass all of Oregon and would take a very long time to put through the model in a high-level language like R. + +The figure below shows the measured performance across a couple of different types of model. Our performance measure for this comparison (and for feature selection) is the area under the ROC curve (or AUC). For a more detail on the comparisons that were performed between the Random Forest model and linear regression model, refer to the analysis/comparing_landslide_models document. + +```{r} +#| echo: false +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") + +to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) +names(to_plot) <- c("auc", "model_type") +to_plot$model_type <- as.factor(to_plot$model_type) + +ggplot(to_plot, aes(x = model_type, y = auc)) + + geom_boxplot(aes(col = model_type)) + + geom_jitter(aes(col = model_type), position = position_jitter(0.2)) + + scale_x_discrete(breaks = c("lr", "lr_fs", "rf", "rf_fs"), labels = c("LR", "LR with feature selection", "RF", "RF with feature selection")) + + xlab("Type of model") + + ylab("Performance (AUC)") + + theme(legend.position = "none") +``` From 93d4f6fb89fc62bf4bae125d240ffdcbf035c14f Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 15:23:54 -0600 Subject: [PATCH 59/74] add feature distribution plots --- analysis/PFA_initiation_model.qmd | 63 ++++++++++++++++++++++++++++++- 1 file changed, 61 insertions(+), 2 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 4976139..0c53795 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -32,6 +32,7 @@ library(ranger) # Packages for other things library(terra) library(ggplot2) +library(ggpubr) library(maps) library(tidyterra) ``` @@ -55,8 +56,13 @@ The primary focus of this document is to outline the model-building process and source("../R/create_model_dataframe.R") sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] sample_pnts$geo <- as.factor(sample_pnts$geo) +sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 + +all_neg <- rbind(nonls_1996, nonls_2007, nonls_2011) +all_neg$gradient_sq <- all_neg$gradient ^ 2 +all_neg$grad_by_pca <- all_neg$gradient * all_neg$pca_k1_48 -head(sample_pnts) ``` ```{r} @@ -84,6 +90,59 @@ ggplot() + The data includes 767 landslide initiation points, with equal proportions of landslide points and non-landslide points. The data has obvious spatial distributions and is divided into 3 approximate year groups. Exact dates for the landslide record were not always available, so these should be viewed as groupings rather than divisions. +It is helpful to look at the how the distributions of predictors vary between our landslide points and our non-landslide points. This informs our expectations for which features will be the most important for the model and increases our confidence in the results of feature selection processes. + +```{r} +#| echo: false +neg_pts <- sample_pnts[(sample_pnts$class == "neg"), ] +pos_pts <- sample_pnts[(sample_pnts$class == "pos"), ] + +p1 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = gradient), color = "darkgray") + + geom_density(data = neg_pts, aes(x = gradient), color = "black") + + geom_density(data = pos_pts, aes(x = gradient), color = "red") + +p2 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = mean_curv), color = "darkgray") + + geom_density(data = neg_pts, aes(x = mean_curv), color = "black") + + geom_density(data = pos_pts, aes(x = mean_curv), color = "red") + +p3 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = pca_k1_48), color = "darkgray") + + geom_density(data = neg_pts, aes(x = pca_k1_48), color = "black") + + geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") + +p4 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = age), color = "darkgray") + + geom_density(data = neg_pts, aes(x = age), color = "black") + + geom_density(data = pos_pts, aes(x = age), color = "red") + + xlim(0, 300) + +p5 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = dist_to_road), color = "darkgray") + + geom_density(data = neg_pts, aes(x = dist_to_road), color = "black") + + geom_density(data = pos_pts, aes(x = dist_to_road), color = "red") + + xlim(0, 600) + +p6 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = grad_by_pca), color = "darkgray") + + geom_density(data = neg_pts, aes(x = grad_by_pca), color = "black") + + geom_density(data = pos_pts, aes(x = grad_by_pca), color = "red") + +p7 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = pca_k1_6), color = "darkgray") + + geom_density(data = neg_pts, aes(x = pca_k1_6), color = "black") + + geom_density(data = pos_pts, aes(x = pca_k1_6), color = "red") + +p8 <- ggplot(data = training_data) + + geom_density(data = all_neg, aes(x = total_accum), color = "darkgray") + + geom_density(data = neg_pts, aes(x = total_accum), color = "black") + + geom_density(data = pos_pts, aes(x = total_accum), color = "red") + + xlim(0, 150) + +ggarrange(p1, p2, p7, p3, p4, p5, p6, p8) +``` + ### Feature Selection Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. @@ -92,7 +151,7 @@ Choosing good features is an essential part of the machine learning process. A g #| echo: false load("/Users/julialober/Documents/terrainworks/code/sandbox/filter_results.Rdata") -ggplot(results, aes(x = feature, y = c(rank.auc, rank.anova, rank, rank.cmim, rank.mim))) + +ggplot(results, aes(x = feature, y = total)) + geom_bar(stat = "identity") ``` From 7c0434ae7414eb1ea5f72ce2c2fd24f9f7f9bdaf Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 15:43:19 -0600 Subject: [PATCH 60/74] add histograms to feature distribution plots --- analysis/PFA_initiation_model.qmd | 32 +++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 0c53795..5b8edcf 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -98,49 +98,77 @@ neg_pts <- sample_pnts[(sample_pnts$class == "neg"), ] pos_pts <- sample_pnts[(sample_pnts$class == "pos"), ] p1 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = gradient, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = gradient, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = gradient), color = "darkgray") + geom_density(data = neg_pts, aes(x = gradient), color = "black") + geom_density(data = pos_pts, aes(x = gradient), color = "red") p2 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = mean_curv, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = mean_curv, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = mean_curv), color = "darkgray") + geom_density(data = neg_pts, aes(x = mean_curv), color = "black") + geom_density(data = pos_pts, aes(x = mean_curv), color = "red") p3 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = pca_k1_48, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = pca_k1_48, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = pca_k1_48), color = "darkgray") + geom_density(data = neg_pts, aes(x = pca_k1_48), color = "black") + - geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") + geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") p4 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = age, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = age, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = age), color = "darkgray") + geom_density(data = neg_pts, aes(x = age), color = "black") + geom_density(data = pos_pts, aes(x = age), color = "red") + xlim(0, 300) p5 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = dist_to_road, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = dist_to_road, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = dist_to_road), color = "darkgray") + geom_density(data = neg_pts, aes(x = dist_to_road), color = "black") + geom_density(data = pos_pts, aes(x = dist_to_road), color = "red") + xlim(0, 600) p6 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = grad_by_pca, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = grad_by_pca, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = grad_by_pca), color = "darkgray") + geom_density(data = neg_pts, aes(x = grad_by_pca), color = "black") + geom_density(data = pos_pts, aes(x = grad_by_pca), color = "red") p7 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = pca_k1_6, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = pca_k1_6, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = pca_k1_6), color = "darkgray") + geom_density(data = neg_pts, aes(x = pca_k1_6), color = "black") + geom_density(data = pos_pts, aes(x = pca_k1_6), color = "red") p8 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = total_accum, y = ..density..), fill = "black", alpha = 0.4) + + geom_histogram(data = pos_pts, aes(x = total_accum, y = ..density..), fill = "red", alpha = 0.3) + geom_density(data = all_neg, aes(x = total_accum), color = "darkgray") + geom_density(data = neg_pts, aes(x = total_accum), color = "black") + geom_density(data = pos_pts, aes(x = total_accum), color = "red") + xlim(0, 150) -ggarrange(p1, p2, p7, p3, p4, p5, p6, p8) +ggarrange(p1, p2, p7, p3) +``` + +```{r} +ggarrange(p4, p5, p6, p8) +``` + +```{r} +p1 <- ggplot(data = training_data) + + geom_histogram(data = neg_pts, aes(x = gradient), fill = "black", alpha = 0.7) + + geom_histogram(data = pos_pts, aes(x = gradient), fill = "red", alpha = 0.7) + +ggarrange(p1) ``` ### Feature Selection From 91b667ea54268f1dc7a1f5a01d3434c62fa4b33e Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 5 Apr 2023 15:51:25 -0600 Subject: [PATCH 61/74] added some text --- analysis/PFA_initiation_model.qmd | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 5b8edcf..db3f806 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -163,13 +163,9 @@ ggarrange(p1, p2, p7, p3) ggarrange(p4, p5, p6, p8) ``` -```{r} -p1 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = gradient), fill = "black", alpha = 0.7) + - geom_histogram(data = pos_pts, aes(x = gradient), fill = "red", alpha = 0.7) +These plots show us the indicate the features where the biggest differences are noticed between landslide points and non-landslide points. Notably, the difference in the distributions of gradient between landslide points and non-landslide points are quite similar. This is surprising, given our physical understanding of slope angle as a major factor in landslide initiation. It is important to note that the sampled non-landslide points are limited to the range of gradient values observed in the landslide inventory. That is, any point with too low or too high of a slope angle is assumed to have zero probability of landslide initiation and is left out of this study. -ggarrange(p1) -``` +Features with noticeably different distributions between landslide and non-landslide points include mean curvature and partial contributing area. ### Feature Selection From f40358e01471eceb2b868f9c5672d45c6ccc7229 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 13 Apr 2023 08:58:24 -0600 Subject: [PATCH 62/74] assembling data for prediction --- analysis/predicting_landslide_initiation.qmd | 189 +++++++++++++++++++ 1 file changed, 189 insertions(+) create mode 100644 analysis/predicting_landslide_initiation.qmd diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd new file mode 100644 index 0000000..e96925a --- /dev/null +++ b/analysis/predicting_landslide_initiation.qmd @@ -0,0 +1,189 @@ +--- +title: "Predicting landslide initiation" +author: Julia Lober +format: html +editor: visual +--- + +```{r load, include = FALSE} +library(terra) + +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3pipelines) +library(mlr3learners) +``` + +## Predicting landslide initiation + +We start by predicting on 1 small basin as part of 1 of the watershed designations of DOGAMI in Oregon. Find the data wherever it is located on your computer. + +```{r} +dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dem <- terra::rast(paste0(dir, "Umpqua/basin_1.flt")) +pts <- terra::vect(paste0(dir, "all_initiation_points.shp")) + +pts_local <- crop(pts, dem) + +plot(dem) +plot(pts_local, + add = TRUE) +``` + +### Calculating the predictors + +The predictors chosen by our model are: + +1. Partial contributing area with a hydraulic conductivity of 1 and storm duration of 48 hours. + +2. Mean curvature + +3. Distance to road + +4. Stand age + +We need to calculate the partial contributing area and mean curvature using the executables included in this package. There is also a function for calculating the distance to a road, and the stand age can be collected from available data. + +The model is trained on all of the available inventory points and stored in "logreg_learner". This happens in the comparing_landslide_models.qmd file. + +```{r} +scratch <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/scratch/" +scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" + +rasters <- c(paste0("GRADIENT,", scratch, "gradient"), + paste0("MEAN CURVATURE,", scratch, "mean_curv")) + +length_scale <- 15 + +rasters <- elev_deriv(rasters = rasters, + length_scale = length_scale, + dem = paste0(dir, "Umpqua/basin_1.flt"), + scratch_dir = scratch) +``` + +Now, calculate partial contributing area. The 48-hour duration was the best across all of the model investigating, so we only need to calculate that one. + +```{r} +rasters <- c(rasters, contributing_area(raster = paste0(scratch, "pca_k1_d48"), + dem = paste0(dir, "Umpqua/basin_1.flt"), + length_scale = 15, + k = 1, + d = 48, + scratch_dir = scratch)) +``` + +Stand age data comes from 2017. It has to be first reprojected into the UTM zone 10 CRS and then resampled to the same extent and resolution as the DEM. Then, we can extract the values and match them with the values in the topographical parameters DEM. + +```{r} +age_fn <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/age_dom_2017.tif" + +# \\Mac\Home\Documents\terrainworks\code\sandbox\data\downloaded_3.6\in +# "\\Mac\Home\Documents\terrainworks\code\sandbox\data\downloaded_3.6\in\age_dom_2017.tif" + +age_rast <- terra::rast(age_fn) + +age_proj <- project(age_rast, "epsg:26910") +age_resampled <- resample(age_proj, dem, 'near') +``` + +```{r} +plot(age_resampled) +``` + +Now that the scripts have been run, we can assemble the data from files (and never run the 2 hour script again). + +```{r} +scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" + +topo_files <- c(paste0("GRADIENT,", scratch, "gradient.flt"), + paste0("MEAN CURVATURE,", scratch, "mean_curv.flt")) + +topo_rast <- elev_deriv(rasters = topo_files) +pca_rast <- contributing_area(raster = paste0(scratch, "pca_k1_d48.flt")) + +rasters <- c(topo_rast, pca_rast) +``` + +### Success rate curves + +To evaluate the model using proportions, we use a success rate curve. The curve has the proportion of the total area on the x-axis, with the proportion of landslides on the y-axis. To generate this curve, we train a model on the sample points, then predict the entire DEM. The sampling to generate training points and model decisions have been made in a different document: we use a logistic regression learner trained on the mean curvature, log value of partial contributing area with a duration of 48 hours, and gradient. + +```{r} +folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/" +load(paste0(folder, "ls_1996.Rdata")) +load(paste0(folder, "ls_2007.Rdata")) +load(paste0(folder, "ls_2011.Rdata")) +load(paste0(folder, "nonls_1996.Rdata")) +load(paste0(folder, "nonls_2007.Rdata")) +load(paste0(folder, "nonls_2011.Rdata")) + +training_data <- rbind(ls_1996, + ls_2007, + ls_2011, + nonls_1996, + nonls_2007, + nonls_2011) + +training_data <- subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")) +training_data$pca_k1_48 <- log(training_data$pca_k1_48, base = 2) +training_data$class <- as.factor(training_data$class) +``` + +```{r} +landslide_task <- as_task_classif_st(x = training_data, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +pl_feature_eng <- + po("scale") %>>% + po("encode") + +pl_feature_eng$train(landslide_task) +landslide_task_eng <- pl_feature_eng$predict(landslide_task)$encode.output + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +logreg_learner$train(landslide_task_eng) +logreg_learner$model +``` + +Now, we are ready to predict the basin area. We need to extract the all the data points from the DEM and make sure the names match what is expected by the model (gradient, mean_curv, and pca_k1_48). + +```{r} +names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") +predicting_data <- as.data.frame(rasters, xy = TRUE) +``` + +The mlr3 package support spatial prediction with the predict_spatial function. This function accepts new data in raster format and outputs a raster, but I can't figure out if the function will support a probability output instead of a binary class output. + +```{r} +predictions <- predict_spatial(rasters, logreg_learner) +``` + +For now, I'll try predicting on a data frame. The downside of this strategy is the extra time needed to convert the DEM to a data frame, and the lack of space optimization that the spatial_predict function allows. + +```{r} +predicting_data$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) + +#pre process the data: take the log of the pca and center+scale the data +# predict_task <- as_task_classif_st(predicting_data) +predicting_data_eng <- pl_feature_eng$predict_newdata(predicting_data) + +predictions_df <- logreg_learner$predict_newdata(predicting_data_eng) +``` + +```{r} +plot(predictions) + +``` + +```{r} +predictions_dt <- as.data.table(predictions_df) +predictions_dt <- cbind(predictions_dt, predicting_data) + +``` From 0ac4875e311c784dd17a739d0607f65659369b25 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 14 Apr 2023 10:19:11 -0600 Subject: [PATCH 63/74] finished code to predict on small basin --- analysis/predicting_landslide_initiation.qmd | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index e96925a..19cb69b 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -168,13 +168,18 @@ predictions <- predict_spatial(rasters, logreg_learner) For now, I'll try predicting on a data frame. The downside of this strategy is the extra time needed to convert the DEM to a data frame, and the lack of space optimization that the spatial_predict function allows. ```{r} -predicting_data$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) +pd <- predicting_data +pd$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) -#pre process the data: take the log of the pca and center+scale the data -# predict_task <- as_task_classif_st(predicting_data) -predicting_data_eng <- pl_feature_eng$predict_newdata(predicting_data) +# manually pre process the data: take the log of the pca and center+scale the data +center_vals <- pl_feature_eng$pipeops$scale$state$center +scale_vals <- pl_feature_eng$pipeops$scale$state$scale -predictions_df <- logreg_learner$predict_newdata(predicting_data_eng) +pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] +pd$mean_curv <- (pd$mean_curv - center_vals[2]) / scale_vals[2] +pd$pca_k1_48 <- (pd$pca_k1_48 - center_vals[3]) / scale_vals[3] + +predictions_df <- logreg_learner$predict_newdata(pd) ``` ```{r} @@ -186,4 +191,5 @@ plot(predictions) predictions_dt <- as.data.table(predictions_df) predictions_dt <- cbind(predictions_dt, predicting_data) + ``` From 79e86e865b2f9260c522c3bb570e24c5a814cf1b Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 19 Apr 2023 10:19:11 -0600 Subject: [PATCH 64/74] finish success rate curve and generate initial graphs --- analysis/predicting_landslide_initiation.qmd | 119 +++++++++++++++++-- 1 file changed, 112 insertions(+), 7 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 19cb69b..562bdef 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -6,13 +6,23 @@ editor: visual --- ```{r load, include = FALSE} +# for spatial data library(terra) +# for modeling library(mlr3) library(mlr3spatial) library(mlr3spatiotempcv) library(mlr3pipelines) library(mlr3learners) + +# for visualizations +library(ggplot2) +library(tidyterra) +library(viridis) + +# basics +library(data.table) ``` ## Predicting landslide initiation @@ -21,6 +31,7 @@ We start by predicting on 1 small basin as part of 1 of the watershed designatio ```{r} dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" dem <- terra::rast(paste0(dir, "Umpqua/basin_1.flt")) pts <- terra::vect(paste0(dir, "all_initiation_points.shp")) @@ -125,13 +136,15 @@ training_data <- rbind(ls_1996, nonls_2007, nonls_2011) -training_data <- subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")) -training_data$pca_k1_48 <- log(training_data$pca_k1_48, base = 2) -training_data$class <- as.factor(training_data$class) +train_subset <- subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")) +train_subset$pca_k1_48 <- log(train_subset$pca_k1_48, base = 2) +train_subset$class <- as.factor(train_subset$class) ``` ```{r} -landslide_task <- as_task_classif_st(x = training_data, +set.seed(12345) +# train_subset <- subset(train_subset, select = c("mean_curv", "pca_k1_48", "class", "x", "y")) +landslide_task <- as_task_classif_st(x = train_subset, id = "landslide_initiation", target = "class", positive = "pos", @@ -152,6 +165,29 @@ logreg_learner$train(landslide_task_eng) logreg_learner$model ``` +As a brief sanity check, we will compare a version of the model that sub-samples the negative points to create an even balance between the two classes as input. I am curious to see how these compare in a final output model. + +```{r} +source("../R/create_model_dataframe.R") +training_data <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] + +landslide_task_subsamp <- as_task_classif_st(x = subset(training_data, select = c("gradient", "mean_curv", "pca_k1_48", "class", "x", "y")), + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +pl_feature_eng$train(landslide_task_subsamp) +task_subsamp <- pl_feature_eng$predict(landslide_task_subsamp)$encode.output + +logreg_learner2 <- lrn("classif.log_reg", + predict_type = "prob") + +logreg_learner2$train(task_subsamp) +logreg_learner2$model +``` + Now, we are ready to predict the basin area. We need to extract the all the data points from the DEM and make sure the names match what is expected by the model (gradient, mean_curv, and pca_k1_48). ```{r} @@ -165,6 +201,12 @@ The mlr3 package support spatial prediction with the predict_spatial function. T predictions <- predict_spatial(rasters, logreg_learner) ``` +```{r} +plot(predictions) +``` + +Note that this classifies most of the area as positive while only the river areas and hilltops are marked as negative. This indicates that a low threshold was chosen for class assignment when we compare to the probability map produced below. + For now, I'll try predicting on a data frame. The downside of this strategy is the extra time needed to convert the DEM to a data frame, and the lack of space optimization that the spatial_predict function allows. ```{r} @@ -179,17 +221,80 @@ pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] pd$mean_curv <- (pd$mean_curv - center_vals[2]) / scale_vals[2] pd$pca_k1_48 <- (pd$pca_k1_48 - center_vals[3]) / scale_vals[3] +# predict the basin using scaled and centered data. predictions_df <- logreg_learner$predict_newdata(pd) ``` +Convert the predictions outputted to a data table, then add the columns with useful information like the predictors and (x,y) coordinates. + ```{r} -plot(predictions) +predictions_dt <- as.data.table(predictions_df) +predictions_dt <- cbind(predictions_dt, predicting_data) +names(predictions_dt) +``` +We also convert it to a raster format for future use. + +```{r} +prob_rast <- (rast(subset(predictions_dt, select = c("x", "y", "prob.pos")))) ``` ```{r} -predictions_dt <- as.data.table(predictions_df) -predictions_dt <- cbind(predictions_dt, predicting_data) +ggplot(predictions_dt) + + geom_tile(aes(x = x, y = y, fill = prob.pos)) + + scale_fill_viridis(option = "A", direction = -1) + + geom_spatvector(data = pts_local) +``` + +### Success rate curve + +Our goal when looking at model performance will be to consider an analysis using proportions. +To calculate the success rate curve, we first calculate the proportion of the area that falls up to a given probability value and the proportion of the total probability that falls up to a given probability value for each DEM cell. The steps for this calculation will be spelled out in the code below. + +```{r} +proportions <- subset(predictions_dt, select = c("x", "y", "prob.pos")) +setorder(proportions, cols = "prob.pos") + +# calculate the proportion of total landslide probability for each DEM cell +proportions$prob.cumsum <- cumsum(proportions$prob.pos) +prob_sum <- max(proportions$prob.cumsum) +proportions$prob.prop <- proportions$prob.cumsum / prob_sum + +# calculate the proportion of total area for each DEM cell +proportions$area.cumsum <- seq(1, length(proportions$x)) +area_sum <- max(proportions$area.cumsum) +proportions$area.prop <- proportions$area.cumsum / area_sum + +setattr(proportions, "sorted", "prob.pos") +``` +First, I calculated cumulative sums for each row. Now, we define some much larger interval for binning this data, which will allow much easier plotting. Between 0 and the maximum value for probability, we look at 100 values of the predicted probability. We extract the nearest row for each of these values from the proportions data table, and now have all of the information we need for plotting. + +```{r} +bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 100)) +names(bins) <- c("probability") + +# match these probability bins with nearest rows in the proportions data table +binned_prop <- proportions[J(bins$probability), roll = "nearest"] +``` + +That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now, we can create the plots. + +The first plot that we look at shows the cumulative proportions of area, modeled landslides, and observed landslides at each probability mark. Ideally, we would want the modeled landslide curve to match up with the observed landslide curve. Since we are looking at a single basin right now, there is probably not enough information to really evaluate this correlation. + +```{r} +ggplot(cbind(bins, binned_prop)) + + geom_line(aes(x = probability, prob.prop)) + + geom_point(aes(x = probability, prob.prop)) + + geom_line(aes(x = probability, area.prop)) + + geom_point(aes(x = probability, area.prop)) +``` + +The main curve that we want to use to compare models is the success rate curve, or the proportion of the modeled landslides plotted against the proportion of area. Each point along the curve comes from one of the probability bins. One thing that this curve tells us is how specific the model is with the areas that it feels are landslide-prone. + +```{r} +ggplot(cbind(bins, binned_prop)) + + geom_line(aes(x = area.prop, y = prob.prop)) + + geom_point(aes(x = area.prop, y = prob.prop)) ``` From f48e1df223fb3d222665e0e256a3a9d47f00e73b Mon Sep 17 00:00:00 2001 From: jlober01 Date: Wed, 19 Apr 2023 14:33:24 -0600 Subject: [PATCH 65/74] adding observed landslides to figures --- analysis/predicting_landslide_initiation.qmd | 27 +++++++++++++++----- 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 562bdef..ac6df59 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -279,16 +279,29 @@ names(bins) <- c("probability") binned_prop <- proportions[J(bins$probability), roll = "nearest"] ``` -That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now, we can create the plots. +That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now we need the observed landslide data. + +```{r} +pts_prob <- extract(prob_rast, pts_local, xy = TRUE) +pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] +setorder(pts_prob, cols = "prob.pos") + +total_pts <- length(pts_prob$ID) + +pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] +pts_cumul$ls.prop = seq(1, 9) / total_pts +``` The first plot that we look at shows the cumulative proportions of area, modeled landslides, and observed landslides at each probability mark. Ideally, we would want the modeled landslide curve to match up with the observed landslide curve. Since we are looking at a single basin right now, there is probably not enough information to really evaluate this correlation. ```{r} ggplot(cbind(bins, binned_prop)) + - geom_line(aes(x = probability, prob.prop)) + - geom_point(aes(x = probability, prob.prop)) + - geom_line(aes(x = probability, area.prop)) + - geom_point(aes(x = probability, area.prop)) + geom_line(aes(x = probability, y = prob.prop), color = "red") + + geom_point(aes(x = probability, y = prob.prop), color = "red") + + geom_line(aes(x = probability, y = area.prop), color = "lightblue") + + geom_point(aes(x = probability, y = area.prop), color = "lightblue") + + geom_line(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + + geom_point(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) ``` The main curve that we want to use to compare models is the success rate curve, or the proportion of the modeled landslides plotted against the proportion of area. Each point along the curve comes from one of the probability bins. One thing that this curve tells us is how specific the model is with the areas that it feels are landslide-prone. @@ -296,5 +309,7 @@ The main curve that we want to use to compare models is the success rate curve, ```{r} ggplot(cbind(bins, binned_prop)) + geom_line(aes(x = area.prop, y = prob.prop)) + - geom_point(aes(x = area.prop, y = prob.prop)) + geom_point(aes(x = area.prop, y = prob.prop)) + + geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop)) + + geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop)) ``` From c34d9ff8689e22905f2b4e819f91a50099702b48 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 20 Apr 2023 12:17:24 -0600 Subject: [PATCH 66/74] added origin point to observed landslide curve --- analysis/predicting_landslide_initiation.qmd | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index ac6df59..275ad31 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -282,14 +282,22 @@ binned_prop <- proportions[J(bins$probability), roll = "nearest"] That one line of code should have created a new data frame with 100 rows and the nearest row from each of the cumulative sums above. Now we need the observed landslide data. ```{r} +# extract the probability values for the test points pts_prob <- extract(prob_rast, pts_local, xy = TRUE) pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] setorder(pts_prob, cols = "prob.pos") total_pts <- length(pts_prob$ID) +# get the cumulative area (already calculated) and calculate the cumulative landslides pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] pts_cumul$ls.prop = seq(1, 9) / total_pts + +# add the origin and a point at (1, 1) to make the graphs prettier +pts_cumul <- rbind(0, pts_cumul, fill = TRUE) +pts_cumul[1] <- 0 +pts_cumul <- rbind(pts_cumul, 0, fill = TRUE) +pts_cumul[length(pts_cumul$x)] <- 1 ``` The first plot that we look at shows the cumulative proportions of area, modeled landslides, and observed landslides at each probability mark. Ideally, we would want the modeled landslide curve to match up with the observed landslide curve. Since we are looking at a single basin right now, there is probably not enough information to really evaluate this correlation. @@ -308,8 +316,8 @@ The main curve that we want to use to compare models is the success rate curve, ```{r} ggplot(cbind(bins, binned_prop)) + - geom_line(aes(x = area.prop, y = prob.prop)) + - geom_point(aes(x = area.prop, y = prob.prop)) + - geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop)) + - geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop)) + geom_line(aes(x = area.prop, y = prob.prop), color = "red") + + geom_point(aes(x = area.prop, y = prob.prop), color = "red") + + geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") + + geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") ``` From 006ac4df05119c6c0ca637c4559350402bd180cc Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 21 Apr 2023 15:57:44 -0400 Subject: [PATCH 67/74] add loop for calculating success rate curve from multiple dems --- analysis/predicting_landslide_initiation.qmd | 104 ++++++++++++++++++- 1 file changed, 103 insertions(+), 1 deletion(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 275ad31..9a1de0c 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -23,6 +23,7 @@ library(viridis) # basics library(data.table) +library(tictoc) ``` ## Predicting landslide initiation @@ -272,7 +273,7 @@ setattr(proportions, "sorted", "prob.pos") First, I calculated cumulative sums for each row. Now, we define some much larger interval for binning this data, which will allow much easier plotting. Between 0 and the maximum value for probability, we look at 100 values of the predicted probability. We extract the nearest row for each of these values from the proportions data table, and now have all of the information we need for plotting. ```{r} -bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 100)) +bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 500)) names(bins) <- c("probability") # match these probability bins with nearest rows in the proportions data table @@ -321,3 +322,104 @@ ggplot(cbind(bins, binned_prop)) + geom_line(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") + geom_point(data = pts_cumul, aes(x = area.prop, y = ls.prop), color = "black") ``` + +## Combining multiple basins + +Really, we want to be able to generate this curve over many different basins. We start this process by assuming that you have already generated all of the elevation derivative and partial contributing area files that are needed for the model to predict on the basin (and that you know where the files are). This part of the program will use that information to predict the probability of initiation for each basin, then combines these into one success rate curve. + +Since each basin will have millions of DEM cells, we have to sub-sample the cumulative values down to a number of data points that is reasonable to manage. We already do this for plotting; for combining multiple DEMs, we will just need to make sure to use consistent probability values for sub-sampling, and then the cumulative values will be averaged based on how many DEM cells the cumulative proportion was calculated from. TODO: figure out the exact formulas for calculating these weighted averages. + +Updated average = ( (Past average \* past \# cells) + new value ) / new cell total + +OR -- we just sum the cumulative sums and the total number of cells/cumulative probability and then take the averages at the end, once the totals can actually be calculated. This is probably the simpler option and I will go this route unless for some reason this ends up not working. + +Finding the observed landslide information will probably be easier - we will want to keep track of which points are located within each DEM, mark those points, and then the final step will be the same calculations as above. Since there is a maximum of about 400 points, we don't need to worry about data size as much. + +I think this will be one large for loop that iterates once per basin. I may break it out into a function, or maybe break out the inside of the for loop into a function? I'll start without the function and change it later if needed, though. + +```{r} +# replace these with the respective file paths to your DEMs +dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +basin_list = c(paste0(dir, "Umpqua/basin_1/"), + paste0(dir, "Umpqua/basin_2/")) + # paste0(dir, "Umpqua/basin_3/"), + # paste0(dir, "Umpqua/basin_4/")) +``` + +A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. + +```{r} +model <- logreg_learner +bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 500)) +names(bins) <- c("probability") + +cumul_prop <- data.frame("area" = rep(0, 500), + "prob" = rep(0, 500)) + +for (basin in basin_list) { + + tic(msg = paste0("loop for ", basin)) + + # find and load the data files + topo_files <- c(paste0("GRADIENT,", basin, "gradient.flt"), + paste0("MEAN CURVATURE,", basin, "mean_curv.flt")) + topo_rast <- elev_deriv(rasters = topo_files) + pca_rast <- contributing_area(raster = paste0(basin, "pca_k1_d48.flt")) + rasters <- c(topo_rast, pca_rast) + + names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") + predicting_data <- as.data.frame(rasters, xy = TRUE) + + pd <- predicting_data + pd$pca_k1_48 <- log(predicting_data$pca_k1_48, base = 2) + + # manually pre process the data: take the log of the pca and center+scale the data + center_vals <- pl_feature_eng$pipeops$scale$state$center + scale_vals <- pl_feature_eng$pipeops$scale$state$scale + + pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] + pd$mean_curv <- (pd$mean_curv - center_vals[2]) / scale_vals[2] + pd$pca_k1_48 <- (pd$pca_k1_48 - center_vals[3]) / scale_vals[3] + + # predict the basin using scaled and centered data. + predictions_df <- as.data.table(logreg_learner$predict_newdata(pd)) + predictions_df <- cbind(predictions_df, predicting_data) + + # head(predictions_df) + + proportions <- as.data.table(subset(predictions_df, select = c("x", "y", "prob.pos"))) + setorder(proportions, cols = "prob.pos") + + # calculate the cumulative sums of area and probabilities + proportions$prob.cumsum <- cumsum(proportions$prob.pos) + prob_sum <- max(proportions$prob.cumsum) + proportions$area.cumsum <- seq(1, length(proportions$x)) + area_sum <- max(proportions$area.cumsum) + + setattr(proportions, "sorted", "prob.pos") + + binned_prop <- proportions[J(bins$probability), roll = "nearest"] + cumul_prop$area <- cumul_prop$area + binned_prop$area.cumsum + cumul_prop$prob <- cumul_prop$prob + binned_prop$prob.cumsum + + toc() +} + + +total_area <- max(cumul_prop$area) +total_prob <- max(cumul_prop$prob) + +cumul_prop$area_prop <- cumul_prop$area / total_area +cumul_prop$prob_prop <- cumul_prop$prob / total_prob + +``` + +```{r} +ggplot(cbind(bins, cumul_prop)) + + geom_line(aes(x = probability, y = prob_prop), color = "red") + + geom_point(aes(x = probability, y = prob_prop), color = "red") + + geom_line(aes(x = probability, y = area_prop), color = "lightblue") + + geom_point(aes(x = probability, y = area_prop), color = "lightblue") + + geom_line(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + + geom_point(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) +``` From 54ca87c4a9a214a37c63911a6d90c4784265b726 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 1 May 2023 13:31:45 -0600 Subject: [PATCH 68/74] multiple basin success rate curve - added observed landslide curve --- analysis/predicting_landslide_initiation.qmd | 138 +++++++++++++++++-- 1 file changed, 123 insertions(+), 15 deletions(-) diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 9a1de0c..03d7912 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -12,14 +12,14 @@ library(terra) # for modeling library(mlr3) library(mlr3spatial) -library(mlr3spatiotempcv) -library(mlr3pipelines) -library(mlr3learners) +# library(mlr3spatiotempcv) +# library(mlr3pipelines) +# library(mlr3learners) # for visualizations library(ggplot2) -library(tidyterra) -library(viridis) +# library(tidyterra) +# library(viridis) # basics library(data.table) @@ -292,7 +292,7 @@ total_pts <- length(pts_prob$ID) # get the cumulative area (already calculated) and calculate the cumulative landslides pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] -pts_cumul$ls.prop = seq(1, 9) / total_pts +pts_cumul$ls.prop = seq(1, length(pts_cumul$x)) / total_pts # add the origin and a point at (1, 1) to make the graphs prettier pts_cumul <- rbind(0, pts_cumul, fill = TRUE) @@ -339,32 +339,113 @@ I think this will be one large for loop that iterates once per basin. I may brea ```{r} # replace these with the respective file paths to your DEMs -dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" -basin_list = c(paste0(dir, "Umpqua/basin_1/"), - paste0(dir, "Umpqua/basin_2/")) +basin_list = c("Umpqua/basin_1", + "Umpqua/basin_2", + "Umpqua/basin_3", + "Umpqua/basin_4", + "Umpqua/basin_5", + "Umpqua/basin_6", + "Umpqua/basin_7", + "Umpqua/basin_8", + "Umpqua/basin_9", + "Umpqua/basin_10", + "Umpqua/basin_11", + "Umpqua/basin_12", + "Umpqua/basin_13", + "Umpqua/basin_14", + "Umpqua/basin_15", + "Umpqua/basin_16", + "Umpqua/basin_17") + +# basin_list = c(paste0(dir, "Umpqua/basin_1/"), +# paste0(dir, "Umpqua/basin_2/")) # paste0(dir, "Umpqua/basin_3/"), # paste0(dir, "Umpqua/basin_4/")) ``` -A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. +#### For calculating elevation derivatives: + +This code only needs to be run once. Eventually, I will remove it from this document. ```{r} +dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/predicting_input/" +dem_dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" + + + +for (basin in basin_list) { + rasters <- c(paste0("GRADIENT,", dir, basin, "/gradient"), + paste0("MEAN CURVATURE,", dir, basin, "/mean_curv")) + + print(rasters) + + length_scale <- 15 + + dir.create(paste0(dir, basin)) + + rasters <- elev_deriv(rasters = rasters, + length_scale = length_scale, + dem = paste0(dem_dir, basin, ".flt"), + scratch_dir = paste0(dir, basin)) +} +``` + +Short utility script for copying the partial contributing area files downloaded from sharefile into the folders structure that will be easy for me to use. + +```{r} +dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +pca_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/pca/" + +count <- 1 + +for (basin in basin_list) { + target_dir <- paste0(dir, basin, "/") + target_file <- paste0(pca_dir, "pca_6_", count) + + target_files <- list.files(pca_dir, paste0("^pca_6_", count, "\\."), full.names = TRUE) + file.copy(target_files, target_dir) + + count <- count + 1 + +} +``` + +A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. I am timing each iteration of the loop to get an idea of how long it will take to run. So far, we are hovering around 50 seconds for one basin. + +TODO: figure out how to crop the points and add the observed landslides to a data frame to compare. This was relatively simple above, so shouldn't be too hard to figure out. + +```{r} +dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" + model <- logreg_learner bins <- as.data.table(seq(0, max(proportions$prob.pos), length.out = 500)) names(bins) <- c("probability") +count <- 1 + cumul_prop <- data.frame("area" = rep(0, 500), "prob" = rep(0, 500)) +pts_prop <- data.frame("x" = 0, + "y" = 0, + "prob.pos" = 0, + "prob.cumsum" = 0, + "area.cumsum" = 0, + "ls.prop" = 0) + for (basin in basin_list) { tic(msg = paste0("loop for ", basin)) # find and load the data files - topo_files <- c(paste0("GRADIENT,", basin, "gradient.flt"), - paste0("MEAN CURVATURE,", basin, "mean_curv.flt")) + topo_files <- c(paste0("GRADIENT,", dir, basin, "/gradient.flt"), + paste0("MEAN CURVATURE,", dir, basin, "/mean_curv.flt")) topo_rast <- elev_deriv(rasters = topo_files) - pca_rast <- contributing_area(raster = paste0(basin, "pca_k1_d48.flt")) + pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_", count, ".flt")) + count <- count + 1 rasters <- c(topo_rast, pca_rast) names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") @@ -402,6 +483,20 @@ for (basin in basin_list) { cumul_prop$area <- cumul_prop$area + binned_prop$area.cumsum cumul_prop$prob <- cumul_prop$prob + binned_prop$prob.cumsum + pts_local <- crop(pts, rasters) + # extract the probability values for the test points + pts_prob <- extract(prob_rast, pts_local, xy = TRUE) + pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] + setorder(pts_prob, "prob.pos") + + total_pts <- length((pts_prob$prob.pos)) + + # get the cumulative area (already calculated) and calculate the cumulative landslides + pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] + pts_cumul$ls.prop = seq(1, length(pts_cumul$x)) / total_pts + + pts_prop <- rbind(pts_prop, pts_cumul) + toc() } @@ -412,14 +507,27 @@ total_prob <- max(cumul_prop$prob) cumul_prop$area_prop <- cumul_prop$area / total_area cumul_prop$prob_prop <- cumul_prop$prob / total_prob +dt <- cbind(bins, cumul_prop)[J(pts_prop$prob.pos), roll = "nearest", on = "probability"] +pts_prop$area_prop <- dt$area_prop + ``` +With basins combined, we see a slightly different shape in the curves. The proportions of area and probability jump up greatly at smaller probabilities, indicating that + ```{r} ggplot(cbind(bins, cumul_prop)) + geom_line(aes(x = probability, y = prob_prop), color = "red") + geom_point(aes(x = probability, y = prob_prop), color = "red") + geom_line(aes(x = probability, y = area_prop), color = "lightblue") + geom_point(aes(x = probability, y = area_prop), color = "lightblue") + - geom_line(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + - geom_point(data = pts_cumul, aes(x = prob.pos, y = ls.prop)) + geom_line(data = pts_prop, aes(x = prob.pos, y = ls.prop)) + + geom_point(data = pts_prop, aes(x = prob.pos, y = ls.prop)) +``` + +```{r} +ggplot(cbind(bins, cumul_prop)) + + geom_line(aes(x = area_prop, y = prob_prop), color = "red") + + geom_point(aes(x = area_prop, y = prob_prop), color = "red") + + geom_line(data = pts_prop, aes(x = area_prop, y = ls.prop), color = "black") + + geom_point(data = pts_prop, aes(x = area_prop, y = ls.prop), color = "black") ``` From ead2fd1745a1671b6a83506fd64dfb22ff922e8b Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 5 May 2023 11:52:46 -0600 Subject: [PATCH 69/74] working on initiation documentation --- analysis/PFA_initiation_model.qmd | 301 +++++++++++++++++++----------- 1 file changed, 194 insertions(+), 107 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index db3f806..08392a3 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -1,8 +1,18 @@ --- -title: "Landslide initiation model" -author: Julia Lober -format: html +title: "PFA Landslide Initiation Model" +author: "Julia Lober" +format: + html: + toc: true + number-section: true + highlight-style: github +date: May 1, 2023 editor: visual +params: + data_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/ + input: text --- ```{r setup} @@ -15,6 +25,7 @@ knitr::opts_chunk$set( ``` ```{r load, include = FALSE} +# Modeling packages library(mlr3) library(mlr3spatial) library(mlr3spatiotempcv) @@ -32,16 +43,16 @@ library(ranger) # Packages for other things library(terra) library(ggplot2) +library(viridis) +library(patchwork) library(ggpubr) library(maps) library(tidyterra) ``` -## Landslide Initiation susceptibility model for the Private Forest Accord +# Landslide initiation susceptibility model for the Private Forest Accord -This document outlines the process for making the landslide initiation model for the Oregon Private Forest Accord project. We work with data generated from the PFA_Sampling_Multiple_DEMs document that is included in this package. This document outlines in more detail where the data for different predictors is sourced and the strategy for negative sampling. - -The primary focus of this document is to outline the model-building process and decisions that resulted in the final model used in combination with the landslide runout model to generate riparian buffers for the Private Forest Accord. Broadly speaking, there were 3 major decisions made in this process: +This document outlines the process for making the landslide initiation model for the Oregon Private Forest Accord project. We work with data generated from the PFA_Sampling_Multiple_DEMs document that is included in this package. The primary focus of this document is to show the model-building process and decisions that resulted in the final model used in combination with the landslide runout model to generate riparian buffers for the Private Forest Accord. Broadly speaking, there were 3 major decisions made in this process: 1. A method for feature selection. @@ -49,19 +60,39 @@ The primary focus of this document is to outline the model-building process and 3. A spatio-temporal resampling strategy. -### About the data +## The data + +Locate the data files and assemble into one data frame for model training. We use the natural log the partial contributing area values, as it shows a much more symmetrical distribution, which should be a better input for the model. ```{r} -#| echo: false -source("../R/create_model_dataframe.R") -sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +# Load all of the data +list_files <- c("ls_1996.Rdata", "ls_2007.Rdata", "ls_2011.Rdata", + "nonls_1996.Rdata", "nonls_2007.Rdata", "nonls_2011.Rdata") +capture.output(lapply(paste0(params$data_dir, list_files), load, .GlobalEnv), file = 'NUL') +ls_2011$geo <- ls_2011$geo$GeoClass +ls_2007$geo <- ls_2007$geo$GeoClass + +# Concatenate into one data frame +data <- rbind(transform(ls_1996, year = "1996"), + transform(ls_2007, year = "2007"), + transform(ls_2011, year = "2011"), + transform(nonls_1996, year = "1996"), + transform(nonls_2007, year = "2007"), + transform(nonls_2011, year = "2011")) + +sample_pnts <- data[!(is.na(data$dist_to_road)), 2:length(data)] sample_pnts$geo <- as.factor(sample_pnts$geo) -sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 -sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 +sample_pnts$class <- as.factor(sample_pnts$class) + +# Take the natural log of the PCA values for a more uniform distribution +sample_pnts$ln_pca_k1_48 <- log(sample_pnts$pca_k1_48) +sample_pnts$ln_pca_k1_24 <- log(sample_pnts$pca_k1_24) +sample_pnts$ln_pca_k1_12 <- log(sample_pnts$pca_k1_12) +sample_pnts$ln_pca_k1_6 <- log(sample_pnts$pca_k1_6) -all_neg <- rbind(nonls_1996, nonls_2007, nonls_2011) -all_neg$gradient_sq <- all_neg$gradient ^ 2 -all_neg$grad_by_pca <- all_neg$gradient * all_neg$pca_k1_48 +# Add a few transformed parameters to look at +sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$ln_pca_k1_48 ``` @@ -78,128 +109,184 @@ load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdat ggplot() + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), color = "white", fill = "white") + - # geom_spatvector(data = points[points$class == "neg", ], - # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + geom_spatvector(data = points[points$class == "pos", ], - mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), - show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + - scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + - labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") + mapping = aes(color = as.factor(points[points$class == "pos", ]$yr), shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, alpha = 0.6) + + scale_color_manual(name = "Landslide points (count)", values = viridis(4)[1:3], labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + scale_shape_manual(name = "Landslide points (count)",values = c(1, 4, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude") ``` -The data includes 767 landslide initiation points, with equal proportions of landslide points and non-landslide points. The data has obvious spatial distributions and is divided into 3 approximate year groups. Exact dates for the landslide record were not always available, so these should be viewed as groupings rather than divisions. +The data includes 397 landslide initiation points from the inventory assembled by the Oregon Department of Geology and Mineral Resources, and combine data points from a couple different storm studies. There are 3 broad temporal groupings roughly corresponding to the years 1996, 2007, and 2011. The majority of the points come from the 1996 storm study. + +The reader should refer for PFA_Sampling_Multiple_DEMs for details on how the negative points are selected. We use an imbalanced sample with 10x the number of non-landslide points as landslide points. The benefit of using more non-landslide points is a more accurate representation of the non-landslide area. An imbalanced sample for our linear regression model is likely to change the magnitude of the probabilities outputted but shouldn't change the marginal probabilities. Since much of our model analysis will be a comparison of proportions, where probabilities are integrated over total probabilities and area to produce a value for number of landslides, a uniform change in probabilities shouldn't impact our observations too much. + +### Covariates + +Candidate covariates were chosen based on our understanding of the physical properties that control landslide initiation. These include physical elevation derivatives calculated based on lidar DEMs, partial contributing area for a given storm duration, total contributing area, stand age, distance to a road, and geological sediment type. Although we look at models that include stand age and distance to a road, these covariates should actually be left out of the final model as they will change over time. + +The elevation derivatives that we consider are mean curvature and gradient (or slope angle). These were selected on the basis of the stability model which includes those as inputs for determining when a slope will fail. Steeper slopes are more likely to slide, as are more convergent slopes(?). Partial contributing area is calculated based on a hydraulic conductivity for the soil (which we assign a value of 1) and the storm duration. We looked at a variety of storm durations, since that reflects the conditions that we are actually interested in. It is helpful to look at the how the distributions of predictors vary between our landslide points and our non-landslide points. This informs our expectations for which features will be the most important for the model and increases our confidence in the results of feature selection processes. ```{r} #| echo: false -neg_pts <- sample_pnts[(sample_pnts$class == "neg"), ] -pos_pts <- sample_pnts[(sample_pnts$class == "pos"), ] - -p1 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = gradient, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = gradient, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = gradient), color = "darkgray") + - geom_density(data = neg_pts, aes(x = gradient), color = "black") + - geom_density(data = pos_pts, aes(x = gradient), color = "red") - -p2 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = mean_curv, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = mean_curv, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = mean_curv), color = "darkgray") + - geom_density(data = neg_pts, aes(x = mean_curv), color = "black") + - geom_density(data = pos_pts, aes(x = mean_curv), color = "red") - -p3 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = pca_k1_48, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = pca_k1_48, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = pca_k1_48), color = "darkgray") + - geom_density(data = neg_pts, aes(x = pca_k1_48), color = "black") + - geom_density(data = pos_pts, aes(x = pca_k1_48), color = "red") - -p4 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = age, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = age, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = age), color = "darkgray") + - geom_density(data = neg_pts, aes(x = age), color = "black") + - geom_density(data = pos_pts, aes(x = age), color = "red") + + +p1 <- ggplot(data = sample_pnts) + + geom_density(aes(x = gradient, fill = class), alpha = 0.4, color = NA) + + labs(x = "Gradient", y = "") + + # scale_color_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + +p2 <- ggplot(data = sample_pnts) + + geom_density(aes(x = mean_curv, fill = class), alpha = 0.4, color = NA) + + labs(x = "Mean curvature", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + +p3 <- ggplot(data = sample_pnts) + + geom_density(aes(x = ln_pca_k1_48, fill = class), alpha = 0.4, color = NA) + + labs(x = "ln(PCA), 48 hr duration", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + +p4 <- ggplot(data = sample_pnts) + + geom_density(aes(x = age, fill = class), alpha = 0.4, color = NA) + + labs(x = "Stand age", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + xlim(0, 300) -p5 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = dist_to_road, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = dist_to_road, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = dist_to_road), color = "darkgray") + - geom_density(data = neg_pts, aes(x = dist_to_road), color = "black") + - geom_density(data = pos_pts, aes(x = dist_to_road), color = "red") + +p5 <- ggplot(data = sample_pnts) + + geom_density(aes(x = dist_to_road, fill = class), alpha = 0.4, color = NA) + + labs(x = "Distance to a road", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + xlim(0, 600) -p6 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = grad_by_pca, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = grad_by_pca, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = grad_by_pca), color = "darkgray") + - geom_density(data = neg_pts, aes(x = grad_by_pca), color = "black") + - geom_density(data = pos_pts, aes(x = grad_by_pca), color = "red") - -p7 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = pca_k1_6, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = pca_k1_6, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = pca_k1_6), color = "darkgray") + - geom_density(data = neg_pts, aes(x = pca_k1_6), color = "black") + - geom_density(data = pos_pts, aes(x = pca_k1_6), color = "red") - -p8 <- ggplot(data = training_data) + - geom_histogram(data = neg_pts, aes(x = total_accum, y = ..density..), fill = "black", alpha = 0.4) + - geom_histogram(data = pos_pts, aes(x = total_accum, y = ..density..), fill = "red", alpha = 0.3) + - geom_density(data = all_neg, aes(x = total_accum), color = "darkgray") + - geom_density(data = neg_pts, aes(x = total_accum), color = "black") + - geom_density(data = pos_pts, aes(x = total_accum), color = "red") + +p6 <- ggplot(data = sample_pnts) + + geom_density(aes(x = grad_by_pca, fill = class), alpha = 0.4, color = NA) + + labs(x = "Gradient * ln(PCA), 48 hr duration", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + +p7 <- ggplot(data = sample_pnts) + + geom_density(aes(x = ln_pca_k1_6, fill = class), alpha = 0.4, color = NA) + + labs(x = "ln(PCA), 6 hr duration", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + + +p8 <- ggplot(data = sample_pnts) + + geom_density(aes(x = total_accum, fill = class), alpha = 0.4, color = NA) + + labs(x = "Total accumulation area", y = "") + + scale_fill_manual(values = c("black", "red"), labels = c("Non-landslide points", "Landslide points"), name = "") + xlim(0, 150) -ggarrange(p1, p2, p7, p3) +# ggarrange(p1, p2, p7, p3, common.legend = TRUE) +combo1 <- p1 + p2 + p7 + p3 + + plot_layout(guides = "collect") & + theme(legend.position = "top") +combo1 ``` ```{r} -ggarrange(p4, p5, p6, p8) +#| echo: false +combo2 <- p4 + p5 + p6 + p8 + + plot_layout(guides = "collect") & + theme(legend.position = "top") +combo2 ``` -These plots show us the indicate the features where the biggest differences are noticed between landslide points and non-landslide points. Notably, the difference in the distributions of gradient between landslide points and non-landslide points are quite similar. This is surprising, given our physical understanding of slope angle as a major factor in landslide initiation. It is important to note that the sampled non-landslide points are limited to the range of gradient values observed in the landslide inventory. That is, any point with too low or too high of a slope angle is assumed to have zero probability of landslide initiation and is left out of this study. +These plots show us the indicate the features where the biggest differences are noticed between landslide points and non-landslide points. Notably, the distributions of gradient between landslide and non-landslide points do not vary as much as some of the other covariate candidates. This is surprising, given our physical understanding of slope angle as a major factor in landslide initiation. It is important to note that the sampled non-landslide points are limited to the range of gradient values observed in the landslide inventory. That is, any point with too low or too high of a slope angle is assumed to have zero probability of landslide initiation and is left out of this study. This filtering method is called an analysis mask and the details on what this looks like can be found in the PCA_Sampling_Multiple_DEMs document. Features with noticeably different distributions between landslide and non-landslide points include mean curvature and partial contributing area. -### Feature Selection +## Logistic Regression model -Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. +A logistic regression model was primarily because it is simple to code into FORTRAN, where the bulk of the data will be run through, and because it performed just as well as a Random Forest model during preliminary comparisons. + +A logistic regression model is a type of linear classifier. The classifier finds coefficients for the covariates that minimizes some error value or maximizes accuracy (this measure can be specified). We are not interested in a binary classification for the issue of landslide initiation, so we look at the probability that a given point is classified in the positive class; in other words, the probability landslide initiation at a location. + +We are using the `mlr3` ecosystem for modeling in R. This package is generally considered the newest development in machine learning modeling in R - it is more nimble than some other modeling packages and is being updated and expanded frequently. It operates using an object-oriented design. `Tasks` are used to establish what data you are training and testing with, as well as what the prediction goal is. We specify our task as a spatio-temporal task, which allows us to use a spatial resampling method. + +For training, we leave out the un-transformed version of partial contributing area ```{r} -#| echo: false -load("/Users/julialober/Documents/terrainworks/code/sandbox/filter_results.Rdata") +# Df[,!(names(Df) %in% rows_to_select)] + +training_cols <- c("x", "y", "gradient", "mean_curv", "dist_to_road", "total_accum", "geo", "age", "class", "ln_pca_k1_48", "ln_pca_k1_24", "ln_pca_k1_12", "ln_pca_k1_6", "gradient_sq", "grad_by_pca") + +training_cols_less <- !(training_cols %in% c("age", "dist_to_road")) -ggplot(results, aes(x = feature, y = total)) + - geom_bar(stat = "identity") +ls_task <- as_task_classif_st( + x = sample_pnts[, training_cols], + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910" +) + +ls_task_less <- as_task_classif_st( + x = sample_pnts[, training_cols_less], + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910" +) ``` -### Logistic Regression model +### Data preprocessing -A logistic regression model was chosen for two primary purposes: because it is simple to code into FORTRAN and because it performed just as well as the Random Forest model when exploring some different types of models. +We also define a few data pre-processing steps in order to center and scale the data, which usually makes machine learning models perform better. Scaling helps avoid any artificial inflating of importance on variables whose values are higher than others. -The first reason is particularly important, as this is how the model will be implemented for predicting. The data sets for predicting encompass all of Oregon and would take a very long time to put through the model in a high-level language like R. +## Spatial resampling -The figure below shows the measured performance across a couple of different types of model. Our performance measure for this comparison (and for feature selection) is the area under the ROC curve (or AUC). For a more detail on the comparisons that were performed between the Random Forest model and linear regression model, refer to the analysis/comparing_landslide_models document. +**A common problem among modeling problems with a spatio-temporal aspect is that traditional resampling methods can lead to overfitting of models and overoptimistic estimations of how the model will perform when shown new data.** With traditional resampling methods for training and test set splits, both sets are likely to have representation across the spatio-temporal spread of the data. Due to the nature of spatio-temporal data, new data is often outside the domain of the training data (either temporally or spatially), which means that we are training a model in one domain and then using it on data in a slightly different domain. Spatio-temporal resampling methods prevent this by assigning training and test sets based on spatial or temporal groupings, testing the model on a section of data that it hasn't seen before. This helps to prevent models from being overfit to the specific spatial or temporal patterns that we see in the data. + +There are many strategies for spatio-temporal resampling strategies, including k-means clustering, block cross-validation, or temporal validation strategies. The K-means clustering strategy is a good choice because it creates relatively even clusters even with uneven distributions of points, as we see in our data set. ```{r} -#| echo: false -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") - -to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) -names(to_plot) <- c("auc", "model_type") -to_plot$model_type <- as.factor(to_plot$model_type) - -ggplot(to_plot, aes(x = model_type, y = auc)) + - geom_boxplot(aes(col = model_type)) + - geom_jitter(aes(col = model_type), position = position_jitter(0.2)) + - scale_x_discrete(breaks = c("lr", "lr_fs", "rf", "rf_fs"), labels = c("LR", "LR with feature selection", "RF", "RF with feature selection")) + - xlab("Type of model") + - ylab("Performance (AUC)") + - theme(legend.position = "none") +set.seed(12345) +rsmp_nonsp <- rsmp("cv", + folds = 5) + +autoplot(rsmp_nonsp, ls_task, fold_id = c(1:5), pch = 20, alpha = 0.5) + + plot_layout(widths = 1) ``` + +```{r} +set.seed(12345) +rsmp_clus <- rsmp("spcv_coords", + folds = 5) +autoplot(rsmp_clus, ls_task, fold_id = c(1:5), pch = 20, alpha = 0.5) + + plot_layout(widths = 1) +``` + +Here we see the difference between non-spatial resampling and spatial resampling. + +Divisions based on year, or a combination of lithology and year were also considered, but were deemed impractical because of the imbalanced splits that they produced. + +## Feature Selection + +Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. + +Ideally, we could do an exhaustive search of all possible combinations of features, which leads to 2\^n possible combinations of features. With our 13 features, this is already 8192 possibly configurations. When you add a layer of cross validation resampling (we use 5 folds and 2 repetitions), the runtime of this task rapidly grows, we approach the 10,000s to 100,000s of models to evaluate. + +Using the AUC allows us to work in the `mlr3` ecosystem, but ultimately, we want to evaluate the model based on the proportions and generate a success-rate curve. So, we use a combination of filtering methods, feature selection, and success-rate curves to determine which features produce the best model. + +Instead, we do a forward stepping feature selection process, called `sequential` in `mlr3fselect`. First, a model is built with a single candidate predictor. The predictor from the single-variable model that performs the best is then chosen, and a two-variable model is trained with that variable and the remaining variable. These steps are repeated until there are no more variables left to add. The model with the highest measure is then chosen (here, AUC). Generally, the best model will turn out to be one of the models that does not include all of the predictors. We create an instance of the feature selector and define our model-type (`learner`), resampling method, evaluation measure, and give it the data (`task`). + +```{r} +run <- TRUE + +if (run) { + instance <- fselect( + fselector = fs("sequential"), + task = ls_task, + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3), + measure = msr("classif.auc") + ) +} +``` + +For our task, the best model use age, distance to a road, partial contributing area (48 hr), and mean curvature. One possible consideration is that this is From 2c0b90ea7301c69eda9a338205e8dd0f3de393e3 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Fri, 5 May 2023 18:11:47 -0600 Subject: [PATCH 70/74] updates to documentation files --- analysis/PFA_initiation_model.qmd | 299 ++++++++++++++++++- analysis/feature_selection.qmd | 2 +- analysis/predicting_landslide_initiation.qmd | 43 ++- 3 files changed, 310 insertions(+), 34 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 08392a3..4b938ed 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -13,6 +13,14 @@ params: label: Data Directory value: /Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/ input: text + pred_dir: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/ + input: text + init_pts: + label: Data Directory + value: /Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/all_initiation_points.shp + input: text --- ```{r setup} @@ -232,13 +240,9 @@ ls_task_less <- as_task_classif_st( ) ``` -### Data preprocessing - -We also define a few data pre-processing steps in order to center and scale the data, which usually makes machine learning models perform better. Scaling helps avoid any artificial inflating of importance on variables whose values are higher than others. - ## Spatial resampling -**A common problem among modeling problems with a spatio-temporal aspect is that traditional resampling methods can lead to overfitting of models and overoptimistic estimations of how the model will perform when shown new data.** With traditional resampling methods for training and test set splits, both sets are likely to have representation across the spatio-temporal spread of the data. Due to the nature of spatio-temporal data, new data is often outside the domain of the training data (either temporally or spatially), which means that we are training a model in one domain and then using it on data in a slightly different domain. Spatio-temporal resampling methods prevent this by assigning training and test sets based on spatial or temporal groupings, testing the model on a section of data that it hasn't seen before. This helps to prevent models from being overfit to the specific spatial or temporal patterns that we see in the data. +A common problem among modeling problems with a spatio-temporal aspect is that traditional resampling methods can lead to overfitting of models and overoptimistic estimations of how the model will perform when shown new data. With traditional resampling methods for training and test set splits, both sets are likely to have representation across the spatio-temporal spread of the data. Due to the nature of spatio-temporal data, new data is often outside the domain of the training data (either temporally or spatially), which means that we are training a model in one domain and then using it on data in a slightly different domain. Spatio-temporal resampling methods prevent this by assigning training and test sets based on spatial or temporal groupings, testing the model on a section of data that it hasn't seen before. This helps to prevent models from being overfit to the specific spatial or temporal patterns that we see in the data. There are many strategies for spatio-temporal resampling strategies, including k-means clustering, block cross-validation, or temporal validation strategies. The K-means clustering strategy is a good choice because it creates relatively even clusters even with uneven distributions of points, as we see in our data set. @@ -265,6 +269,8 @@ Divisions based on year, or a combination of lithology and year were also consid ## Feature Selection +### Step-forward feature selection + Choosing good features is an essential part of the machine learning process. A good model should use features that are correlated to the output class and independent from each other. The other main goal of feature selection is to choose the smallest possible set of features that produces a good model, which both reduces the runtime of training models and the risk of over-fitting. The definition of a "good model" can be difficult to define, but for our purposes, we use the area under the ROC curve (AUC) as a measure of model performance. A higher AUC indicates a better model, so the feature selection processes will try to maximize this value. Ideally, we could do an exhaustive search of all possible combinations of features, which leads to 2\^n possible combinations of features. With our 13 features, this is already 8192 possibly configurations. When you add a layer of cross validation resampling (we use 5 folds and 2 repetitions), the runtime of this task rapidly grows, we approach the 10,000s to 100,000s of models to evaluate. @@ -274,7 +280,7 @@ Using the AUC allows us to work in the `mlr3` ecosystem, but ultimately, we want Instead, we do a forward stepping feature selection process, called `sequential` in `mlr3fselect`. First, a model is built with a single candidate predictor. The predictor from the single-variable model that performs the best is then chosen, and a two-variable model is trained with that variable and the remaining variable. These steps are repeated until there are no more variables left to add. The model with the highest measure is then chosen (here, AUC). Generally, the best model will turn out to be one of the models that does not include all of the predictors. We create an instance of the feature selector and define our model-type (`learner`), resampling method, evaluation measure, and give it the data (`task`). ```{r} -run <- TRUE +run <- FALSE if (run) { instance <- fselect( @@ -289,4 +295,283 @@ if (run) { } ``` -For our task, the best model use age, distance to a road, partial contributing area (48 hr), and mean curvature. One possible consideration is that this is +For our task, the best model use age, distance to a road, partial contributing area (48 hr), and mean curvature. + +Let's do the same thing, but leave out stand age and distance to road. We are interested in how these variables affect the landslide initiation probability, but don't want to include them in the final output for + +```{r} +run <- FALSE + +if (run) { + instance <- fselect( + fselector = fs("sequential"), + task = ls_task_less, + learner = lrn("classif.log_reg", predict_type = "prob"), + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 3), + measure = msr("classif.auc") + ) +} +``` + +### Feature filtering + +Another way of evaluating feature importance is through a filter. Filters use a correlation measure (of which there are many to choose from) to evaluate which features are most tied to the output class. A major difference between filters and other feature selection algorithms is that these methods do evaluate which features perform best in a model; they only look at the correlation between two variables. Filters are often used when the number of features is large enough that a feature selection algorithm is impractical to run in a reasonable amount of time. Here, we use it to gather more information about which features may be the most useful. + +```{r} +info_filter <- flt("information_gain") +info_filter$calculate(ls_task) +res_info <- as.data.table(info_filter) +res_info$rank <- 1:length(res_info$feature) + +cmim_filter <- flt("cmim") +cmim_filter$calculate(ls_task) +res_cmim <- as.data.table(cmim_filter) +res_cmim$rank <- 1:length(res_cmim$feature) + +jmi_filter <- flt("jmi") +jmi_filter$calculate(ls_task) +res_jmi <- as.data.table(jmi_filter) +res_jmi$rank <- 1:length(res_jmi$feature) + +relief_filter <- flt("relief") +relief_filter$calculate(ls_task) +res_relief <- as.data.table(relief_filter) +res_relief$rank <- 1:length(res_relief$feature) +``` + +The output of these filters has been scaled and combined in the figure below. Each different method showed relatively similar ordering of the variables. Of note is that the longer durations for the partial contributing areas are consistently higher + +```{r} +#| echo: false +filter_res <- res_info +filter_res$InfoGain <- (res_info$score / max(res_info$score)) +filter_res$CondMIM <- (res_cmim$score / max(res_cmim$score)) +filter_res$JMI <- (res_jmi$score / max(res_jmi$score)) +filter_res$Relief <- (res_relief$score / max(res_relief$score)) + +filter_res.m <- melt(filter_res[, c("feature", "InfoGain", "CondMIM", "JMI", "Relief")]) + +ggplot(filter_res.m) + + geom_bar(aes(x = reorder(feature, -value), y = value, fill = variable), stat = "identity", position = "stack") + + scale_fill_manual(values = viridis(5)[1:4], labels = c("Information Gain", "Minimal Conditional \nMutual Information \nMaximization", "Joint Mutual Information", "Relief"), name = "Filter name") + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, face = "bold"), + axis.text.y = element_blank(), + axis.ticks.y = element_blank()) + + labs(x = "", y = "Score") + + scale_x_discrete(labels = c("ln(pca), 48 hrs", "mean curvature", "ln(pca), 24 hrs", "total accumulation", "gradient * pca, 48 hrs", "ln(pca), 12 hrs", "ln(pca), 6 hrs", "gradient^2", "gradient", "distance to a road", "geology", "stand age")) + + facet_grid(variable ~ .) + + theme(legend.position = "none") + +``` + +## Training the model + +From the feature selection processes, we choose a handful of possible feature selection algorithms and do a more in-depth analysis using success-rate curve as the metric for model performance. This involves training a model and then using the model to predict on DEMs. + +We start with a model that uses gradient, 48-hour partial contributing area, and the product of the two. + +```{r} +set.seed(12345) +ls_task$select(c("ln_pca_k1_48", "grad_by_pca", "gradient")) + +# create a pipeline for data preprocessing +scale_features <- + po("scale") %>>% + po("encode") +scale_features$train(ls_task) +ls_task_eng <- scale_features$predict(ls_task)$encode.output + +# define the type of model +ls_model <- lrn("classif.log_reg", + predict_type = "prob") + +model <- mlr3::resample( + task = ls_task_eng, + learner = ls_model, + resampling = rsmp("repeated_spcv_coords", + folds = 5, + repeats = 50) +) + +model$aggregate(msr("classif.auc")) +``` + +First, we run a whole bunch of spatial resampling iterations to get a confident estimate of how well the model will perform. For this estimation, we are only able to look at measures that can be tested in the framework of `mlr3`. We look at area under the ROC curve (AUC). + +Our final model is trained on all of the data, to maximize the use of our landslide inventory. + +```{r} +ls_model$train(ls_task_eng) +ls_model$model +``` + +## Predicting + +### Single basin + +The predicting directory must have the elevation derivative files for the required predictors. + +```{r} +pred_dir <- params$pred_dir + +# find and load the data files +topo_files <- c(paste0("GRADIENT,", pred_dir, "Umpqua/basin_1/gradient.flt")) +topo_rast <- elev_deriv(rasters = topo_files) +pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_1.flt")) +rasters <- c(topo_rast, pca_rast) +names(rasters) <- c("gradient", "pca_k1_48") + +# convert to a data frame and add the feature interactions. +pd <- as.data.frame(rasters, xy = TRUE) +pd$grad_by_pca <- pd$gradient * pd$pca_k1_48 +pd$ln_pca_k1_48 <- log(pd$pca_k1_48) + +# manually pre process the data: take the log of the pca and center+scale the data +center_vals <- scale_features$pipeops$scale$state$center +scale_vals <- scale_features$pipeops$scale$state$scale + +pd$grad_by_pca <- (pd$grad_by_pca - center_vals[1]) / scale_vals[1] +pd$gradient <- (pd$gradient - center_vals[2]) / scale_vals[2] +pd$ln_pca_k1_48 <- (pd$ln_pca_k1_48 - center_vals[3]) / scale_vals[3] + +pd <- subset(pd, select = c("x", "y", "grad_by_pca", "gradient", "ln_pca_k1_48")) +``` + +```{r} +# predict the basin using scaled and centered data. +predictions_df <- as.data.table(ls_model$predict_newdata(pd)) +predictions_df <- cbind(pd, predictions_df) +``` + +Here we predict on a sub-basin of the larger Umpqua watershed. There are 9 landslide initiation points located inside this basin. + +```{r} +pts <- terra::vect(params$init_pts) +pts_local <- crop(pts, rasters) + +ggplot(predictions_dt) + + geom_tile(aes(x = x, y = y, fill = prob.pos)) + + scale_fill_viridis(option = "A", direction = -1) + + geom_spatvector(data = pts_local) +``` + +### Multiple basin + +To generate a success rate curve, we combine multiple basins. The success rate curve is the proportions of landslide predicted over the proportion of the predicted area. A smaller area under the success rate curve indicates that the model was more specific with the areas that it deemed to have a high probability of landslide initiation. Looking at the model performance in this way limits the + +```{r} +basin_list = c("Umpqua/basin_1", + "Umpqua/basin_2", + "Umpqua/basin_3", + "Umpqua/basin_4", + "Umpqua/basin_5", + "Umpqua/basin_6", + "Umpqua/basin_7", + "Umpqua/basin_8", + "Umpqua/basin_9", + "Umpqua/basin_10", + "Umpqua/basin_11", + "Umpqua/basin_12", + "Umpqua/basin_13", + "Umpqua/basin_14", + "Umpqua/basin_15", + "Umpqua/basin_16", + "Umpqua/basin_17") +``` + +```{r} +model <- ls_model +bins <- as.data.table(seq(0, 1, length.out = 500)) +names(bins) <- c("probability") + +count <- 1 +cumul_prop <- data.frame("area" = rep(0, 500), + "prob" = rep(0, 500)) +pts_prop <- data.frame("x" = 0, + "y" = 0, + "prob.pos" = 0, + "prob.cumsum" = 0, + "area.cumsum" = 0, + "ls.prop" = 0) + +for (basin in basin_list[1:5]) { + + tic(msg = paste0("loop for ", basin)) + + # find and load the data files + topo_files <- c(paste0("GRADIENT,", pred_dir, basin, "/gradient.flt")) + topo_rast <- elev_deriv(rasters = topo_files) + pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_", count, ".flt")) + count <- count + 1 + rasters <- c(topo_rast, pca_rast) + names(rasters) <- c("gradient", "pca_k1_48") + + predicting_data <- as.data.frame(rasters, xy = TRUE) + predicting_data$ln_pca_k1_48 <- log(predicting_data$pca_k1_48) + predicting_data$grad_by_pca <- predicting_data$gradient * predicting_data$pca_k1_48 + + pd <- predicting_data + pd <- subset(pd, select = c("x", "y", "grad_by_pca", "gradient", "ln_pca_k1_48")) + + # manually pre process the data: take the log of the pca and center+scale the data + center_vals <- pl_feature_eng$pipeops$scale$state$center + scale_vals <- pl_feature_eng$pipeops$scale$state$scale + + pd$grad_by_pca <- (pd$grad_by_pca - center_vals[1]) / scale_vals[1] + pd$gradient <- (pd$gradient - center_vals[2]) / scale_vals[2] + pd$ln_pca_k1_48 <- (pd$ln_pca_k1_48 - center_vals[3]) / scale_vals[3] + + # predict the basin using scaled and centered data. + predictions_df <- as.data.table(model$predict_newdata(pd)) + predictions_df <- cbind(predictions_df, pd) + + proportions <- as.data.table(subset(predictions_df, select = c("x", "y", "prob.pos"))) + setorder(proportions, cols = "prob.pos") + + # calculate the cumulative sums of area and probabilities + proportions$prob.cumsum <- cumsum(proportions$prob.pos) + prob_sum <- max(proportions$prob.cumsum) + proportions$area.cumsum <- seq(1, length(proportions$x)) + area_sum <- max(proportions$area.cumsum) + + setattr(proportions, "sorted", "prob.pos") + + binned_prop <- proportions[J(bins$probability), roll = "nearest"] + cumul_prop$area <- cumul_prop$area + binned_prop$area.cumsum + cumul_prop$prob <- cumul_prop$prob + binned_prop$prob.cumsum + + pts_local <- crop(pts, rasters) + # extract the probability values for the test points + pts_prob <- extract(prob_rast, pts_local, xy = TRUE) + pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] + setorder(pts_prob, "prob.pos") + + total_pts <- length((pts_prob$prob.pos)) + + # get the cumulative area (already calculated) and calculate the cumulative landslides + pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] + pts_cumul$ls.prop = seq(1, length(pts_cumul$x)) / total_pts + + pts_prop <- rbind(pts_prop, pts_cumul) + + toc() +} + + +total_area <- max(cumul_prop$area) +total_prob <- max(cumul_prop$prob) + +cumul_prop$area_prop <- cumul_prop$area / total_area +cumul_prop$prob_prop <- cumul_prop$prob / total_prob + +dt <- cbind(bins, cumul_prop)[J(pts_prop$prob.pos), roll = "nearest", on = "probability"] +pts_prop$area_prop <- dt$area_prop +``` + +```{r} +ggplot(cbind(bins, cumul_prop)) + + geom_line(aes(x = area_prop, y = prob_prop), color = "red") + + geom_point(aes(x = area_prop, y = prob_prop), color = "red") +``` diff --git a/analysis/feature_selection.qmd b/analysis/feature_selection.qmd index 84e9131..8bbe04a 100644 --- a/analysis/feature_selection.qmd +++ b/analysis/feature_selection.qmd @@ -61,7 +61,7 @@ Lets look at a few filtering methods. These calculate some measure of correlatio The benefit of these methods are a simpler method that does not involve building a bunch of models to test the impact of different features. The downside is that it is possible that the filter does not truly reflect the impact on the final model. ```{r} -task <- task_orig +task <- task_ext ``` ```{r} diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 03d7912..9e8271b 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -26,6 +26,10 @@ library(data.table) library(tictoc) ``` +```{r setup} +library(TerrainWorksUtils) +``` + ## Predicting landslide initiation We start by predicting on 1 small basin as part of 1 of the watershed designations of DOGAMI in Oregon. Find the data wherever it is located on your computer. @@ -60,6 +64,8 @@ We need to calculate the partial contributing area and mean curvature using the The model is trained on all of the available inventory points and stored in "logreg_learner". This happens in the comparing_landslide_models.qmd file. ```{r} +calculate <- FALSE + scratch <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/scratch/" scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" @@ -68,39 +74,26 @@ rasters <- c(paste0("GRADIENT,", scratch, "gradient"), length_scale <- 15 -rasters <- elev_deriv(rasters = rasters, - length_scale = length_scale, - dem = paste0(dir, "Umpqua/basin_1.flt"), +if (calculate) { + rasters <- elev_deriv(rasters = rasters, + length_scale = length_scale, + dem = paste0(dir, "Umpqua/basin_1.flt"), scratch_dir = scratch) +} ``` Now, calculate partial contributing area. The 48-hour duration was the best across all of the model investigating, so we only need to calculate that one. ```{r} -rasters <- c(rasters, contributing_area(raster = paste0(scratch, "pca_k1_d48"), +if (calculate) { + rasters <- c(rasters, contributing_area(raster = paste0(scratch, "pca_k1_d48"), dem = paste0(dir, "Umpqua/basin_1.flt"), length_scale = 15, k = 1, d = 48, scratch_dir = scratch)) -``` - -Stand age data comes from 2017. It has to be first reprojected into the UTM zone 10 CRS and then resampled to the same extent and resolution as the DEM. Then, we can extract the values and match them with the values in the topographical parameters DEM. - -```{r} -age_fn <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/age_dom_2017.tif" - -# \\Mac\Home\Documents\terrainworks\code\sandbox\data\downloaded_3.6\in -# "\\Mac\Home\Documents\terrainworks\code\sandbox\data\downloaded_3.6\in\age_dom_2017.tif" - -age_rast <- terra::rast(age_fn) - -age_proj <- project(age_rast, "epsg:26910") -age_resampled <- resample(age_proj, dem, 'near') -``` +} -```{r} -plot(age_resampled) ``` Now that the scripts have been run, we can assemble the data from files (and never run the 2 hour script again). @@ -363,7 +356,7 @@ basin_list = c("Umpqua/basin_1", # paste0(dir, "Umpqua/basin_4/")) ``` -#### For calculating elevation derivatives: +#### For calculating elevation derivatives: This code only needs to be run once. Eventually, I will remove it from this document. @@ -512,7 +505,7 @@ pts_prop$area_prop <- dt$area_prop ``` -With basins combined, we see a slightly different shape in the curves. The proportions of area and probability jump up greatly at smaller probabilities, indicating that +With basins combined, we see a slightly different shape in the curves. The proportions of area and probability jump up greatly at smaller probabilities. ```{r} ggplot(cbind(bins, cumul_prop)) + @@ -527,7 +520,5 @@ ggplot(cbind(bins, cumul_prop)) + ```{r} ggplot(cbind(bins, cumul_prop)) + geom_line(aes(x = area_prop, y = prob_prop), color = "red") + - geom_point(aes(x = area_prop, y = prob_prop), color = "red") + - geom_line(data = pts_prop, aes(x = area_prop, y = ls.prop), color = "black") + - geom_point(data = pts_prop, aes(x = area_prop, y = ls.prop), color = "black") + geom_point(aes(x = area_prop, y = prob_prop), color = "red") ``` From fb697bd0efbb64c8c7e298f6cca4bc2665c7cf2b Mon Sep 17 00:00:00 2001 From: jlober01 Date: Tue, 9 May 2023 09:45:47 -0600 Subject: [PATCH 71/74] update for success rate curve with all basins --- analysis/PFA_initiation_model.qmd | 85 ++++++++------------ analysis/predicting_landslide_initiation.qmd | 61 ++++++++------ 2 files changed, 69 insertions(+), 77 deletions(-) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index 4b938ed..967ef4f 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -56,6 +56,10 @@ library(patchwork) library(ggpubr) library(maps) library(tidyterra) +library(data.table) +library(tictoc) + +library(TerrainWorksUtils) ``` # Landslide initiation susceptibility model for the Private Forest Accord @@ -212,11 +216,7 @@ A logistic regression model is a type of linear classifier. The classifier finds We are using the `mlr3` ecosystem for modeling in R. This package is generally considered the newest development in machine learning modeling in R - it is more nimble than some other modeling packages and is being updated and expanded frequently. It operates using an object-oriented design. `Tasks` are used to establish what data you are training and testing with, as well as what the prediction goal is. We specify our task as a spatio-temporal task, which allows us to use a spatial resampling method. -For training, we leave out the un-transformed version of partial contributing area - ```{r} -# Df[,!(names(Df) %in% rows_to_select)] - training_cols <- c("x", "y", "gradient", "mean_curv", "dist_to_road", "total_accum", "geo", "age", "class", "ln_pca_k1_48", "ln_pca_k1_24", "ln_pca_k1_12", "ln_pca_k1_6", "gradient_sq", "grad_by_pca") training_cols_less <- !(training_cols %in% c("age", "dist_to_road")) @@ -240,7 +240,7 @@ ls_task_less <- as_task_classif_st( ) ``` -## Spatial resampling +## Spatial resampling A common problem among modeling problems with a spatio-temporal aspect is that traditional resampling methods can lead to overfitting of models and overoptimistic estimations of how the model will perform when shown new data. With traditional resampling methods for training and test set splits, both sets are likely to have representation across the spatio-temporal spread of the data. Due to the nature of spatio-temporal data, new data is often outside the domain of the training data (either temporally or spatially), which means that we are training a model in one domain and then using it on data in a slightly different domain. Spatio-temporal resampling methods prevent this by assigning training and test sets based on spatial or temporal groupings, testing the model on a section of data that it hasn't seen before. This helps to prevent models from being overfit to the specific spatial or temporal patterns that we see in the data. @@ -297,7 +297,7 @@ if (run) { For our task, the best model use age, distance to a road, partial contributing area (48 hr), and mean curvature. -Let's do the same thing, but leave out stand age and distance to road. We are interested in how these variables affect the landslide initiation probability, but don't want to include them in the final output for +Let's do the same thing, but leave out stand age and distance to road. We are interested in how these variables affect the landslide initiation probability, but don't want to include them in the final model that is linked with the runout model. ```{r} run <- FALSE @@ -341,7 +341,7 @@ res_relief <- as.data.table(relief_filter) res_relief$rank <- 1:length(res_relief$feature) ``` -The output of these filters has been scaled and combined in the figure below. Each different method showed relatively similar ordering of the variables. Of note is that the longer durations for the partial contributing areas are consistently higher +The output of these filters has been scaled and combined in the figure below. Each different method showed relatively similar ordering of the variables. Of note is that the longer durations for the partial contributing areas are consistently given better scores than shorter durations. ```{r} #| echo: false @@ -388,10 +388,10 @@ ls_model <- lrn("classif.log_reg", predict_type = "prob") model <- mlr3::resample( - task = ls_task_eng, - learner = ls_model, - resampling = rsmp("repeated_spcv_coords", - folds = 5, + task = ls_task_eng, + learner = ls_model, + resampling = rsmp("repeated_spcv_coords", + folds = 5, repeats = 50) ) @@ -407,7 +407,7 @@ ls_model$train(ls_task_eng) ls_model$model ``` -## Predicting +## Predicting ### Single basin @@ -419,7 +419,7 @@ pred_dir <- params$pred_dir # find and load the data files topo_files <- c(paste0("GRADIENT,", pred_dir, "Umpqua/basin_1/gradient.flt")) topo_rast <- elev_deriv(rasters = topo_files) -pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_1.flt")) +pca_rast <- contributing_area(raster = paste0(pred_dir, "Umpqua/basin_1/pca_6_1.flt")) rasters <- c(topo_rast, pca_rast) names(rasters) <- c("gradient", "pca_k1_48") @@ -445,13 +445,13 @@ predictions_df <- as.data.table(ls_model$predict_newdata(pd)) predictions_df <- cbind(pd, predictions_df) ``` -Here we predict on a sub-basin of the larger Umpqua watershed. There are 9 landslide initiation points located inside this basin. +Here we predict on a sub-basin of the larger Umpqua watershed. ```{r} pts <- terra::vect(params$init_pts) pts_local <- crop(pts, rasters) -ggplot(predictions_dt) + +ggplot(predictions_df) + geom_tile(aes(x = x, y = y, fill = prob.pos)) + scale_fill_viridis(option = "A", direction = -1) + geom_spatvector(data = pts_local) @@ -459,26 +459,10 @@ ggplot(predictions_dt) + ### Multiple basin -To generate a success rate curve, we combine multiple basins. The success rate curve is the proportions of landslide predicted over the proportion of the predicted area. A smaller area under the success rate curve indicates that the model was more specific with the areas that it deemed to have a high probability of landslide initiation. Looking at the model performance in this way limits the +To generate a success rate curve, we combine multiple basins. The success rate curve is the proportions of landslide predicted over the proportion of the predicted area. A smaller area under the success rate curve indicates that the model was more specific with the areas that it deemed to have a high probability of landslide initiation. ```{r} -basin_list = c("Umpqua/basin_1", - "Umpqua/basin_2", - "Umpqua/basin_3", - "Umpqua/basin_4", - "Umpqua/basin_5", - "Umpqua/basin_6", - "Umpqua/basin_7", - "Umpqua/basin_8", - "Umpqua/basin_9", - "Umpqua/basin_10", - "Umpqua/basin_11", - "Umpqua/basin_12", - "Umpqua/basin_13", - "Umpqua/basin_14", - "Umpqua/basin_15", - "Umpqua/basin_16", - "Umpqua/basin_17") +basin_list <- list.files(dem_dir, "^basin_[0-9]{1,3}\\.flt", recursive = TRUE) ``` ```{r} @@ -496,14 +480,19 @@ pts_prop <- data.frame("x" = 0, "area.cumsum" = 0, "ls.prop" = 0) -for (basin in basin_list[1:5]) { +for (basin in basin_list) { + + basin <- sub("\\..*","", basin) tic(msg = paste0("loop for ", basin)) # find and load the data files topo_files <- c(paste0("GRADIENT,", pred_dir, basin, "/gradient.flt")) topo_rast <- elev_deriv(rasters = topo_files) - pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_", count, ".flt")) + + pca_file <- list.files(paste0(pred_dir, basin), "^pca_6_[0-9]{1,3}\\.flt", full.names = TRUE) + print(pca_file) + pca_rast <- contributing_area(raster = pca_file) count <- count + 1 rasters <- c(topo_rast, pca_rast) names(rasters) <- c("gradient", "pca_k1_48") @@ -516,8 +505,8 @@ for (basin in basin_list[1:5]) { pd <- subset(pd, select = c("x", "y", "grad_by_pca", "gradient", "ln_pca_k1_48")) # manually pre process the data: take the log of the pca and center+scale the data - center_vals <- pl_feature_eng$pipeops$scale$state$center - scale_vals <- pl_feature_eng$pipeops$scale$state$scale + center_vals <- scale_features$pipeops$scale$state$center + scale_vals <- scale_features$pipeops$scale$state$scale pd$grad_by_pca <- (pd$grad_by_pca - center_vals[1]) / scale_vals[1] pd$gradient <- (pd$gradient - center_vals[2]) / scale_vals[2] @@ -542,20 +531,6 @@ for (basin in basin_list[1:5]) { cumul_prop$area <- cumul_prop$area + binned_prop$area.cumsum cumul_prop$prob <- cumul_prop$prob + binned_prop$prob.cumsum - pts_local <- crop(pts, rasters) - # extract the probability values for the test points - pts_prob <- extract(prob_rast, pts_local, xy = TRUE) - pts_prob <- pts_prob[!is.na(pts_prob$prob.pos), ] - setorder(pts_prob, "prob.pos") - - total_pts <- length((pts_prob$prob.pos)) - - # get the cumulative area (already calculated) and calculate the cumulative landslides - pts_cumul <- proportions[J(pts_prob$prob.pos), roll = "nearest"] - pts_cumul$ls.prop = seq(1, length(pts_cumul$x)) / total_pts - - pts_prop <- rbind(pts_prop, pts_cumul) - toc() } @@ -573,5 +548,11 @@ pts_prop$area_prop <- dt$area_prop ```{r} ggplot(cbind(bins, cumul_prop)) + geom_line(aes(x = area_prop, y = prob_prop), color = "red") + - geom_point(aes(x = area_prop, y = prob_prop), color = "red") + geom_point(aes(x = area_prop, y = prob_prop), color = "red") + + scale_x_continuous(limits = c(0, 1), expand = c(0, 0)) + + scale_y_continuous(limits = c(0, 1), expand = c(0, 0)) + + labs(x = "Proportion of area", y = "Proportion of total probability") + + theme(panel.grid = element_blank(), + panel.background = element_rect(fill = "white", linewidth = 1, color = "black",), + panel.border = element_rect(linewidth = 1, color = NA, fill = NA)) ``` diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 9e8271b..1b90bd3 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -12,21 +12,18 @@ library(terra) # for modeling library(mlr3) library(mlr3spatial) -# library(mlr3spatiotempcv) -# library(mlr3pipelines) -# library(mlr3learners) +library(mlr3spatiotempcv) +library(mlr3pipelines) +library(mlr3learners) # for visualizations library(ggplot2) -# library(tidyterra) -# library(viridis) +library(tidyterra) +library(viridis) # basics library(data.table) library(tictoc) -``` - -```{r setup} library(TerrainWorksUtils) ``` @@ -350,6 +347,8 @@ basin_list = c("Umpqua/basin_1", "Umpqua/basin_16", "Umpqua/basin_17") +basin_list <- list.files(dem_dir, paste0("^basin_[0-9]{1,3}\\.flt"), recursive = TRUE) + # basin_list = c(paste0(dir, "Umpqua/basin_1/"), # paste0(dir, "Umpqua/basin_2/")) # paste0(dir, "Umpqua/basin_3/"), @@ -366,43 +365,53 @@ dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_inp dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/predicting_input/" dem_dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" - - -for (basin in basin_list) { - rasters <- c(paste0("GRADIENT,", dir, basin, "/gradient"), - paste0("MEAN CURVATURE,", dir, basin, "/mean_curv")) +if (calculate) { + for (basin in basin_list) { + + # create directories for basins and sub-basins if needed. + if (!dir.exists(paste0(dir, sub("/.*","", basin)))) { + dir.create(paste0(dir, sub("/.*","", basin))) + } + if (!dir.exists(paste0(dir, sub("\\..*","", basin)))) { + dir.create(paste0(dir, sub("\\..*","", basin))) + } + + # define the elevation derivatives that we want to be calculated + rasters <- c(paste0("GRADIENT,", dir, sub("\\..*","", basin), "/gradient"), + paste0("MEAN CURVATURE,", dir, sub("\\..*","", basin), "/mean_curv")) print(rasters) length_scale <- 15 - dir.create(paste0(dir, basin)) - rasters <- elev_deriv(rasters = rasters, length_scale = length_scale, - dem = paste0(dem_dir, basin, ".flt"), - scratch_dir = paste0(dir, basin)) + dem = paste0(dem_dir, basin), + scratch_dir = paste0(dir, sub("\\..*","", basin))) } +} + ``` -Short utility script for copying the partial contributing area files downloaded from sharefile into the folders structure that will be easy for me to use. +Short utility script for copying the partial contributing area files downloaded from sharefile into the folders structure that will be easy for me to use. I have the basins listed in a different order, so I have to inspect the input file for the correct number suffix that will correspond to the correct pca file. ```{r} dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" pca_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/pca/" -count <- 1 +# the pca files are given numbered in the order that they appear in this input file +input_order <- read.delim("/Users/julialober/Documents/input_list.txt") +names(input_order) <- c("list") for (basin in basin_list) { - target_dir <- paste0(dir, basin, "/") - target_file <- paste0(pca_dir, "pca_6_", count) - - target_files <- list.files(pca_dir, paste0("^pca_6_", count, "\\."), full.names = TRUE) - file.copy(target_files, target_dir) + target_dir <- paste0(dir, sub("\\..*","", basin), "/") - count <- count + 1 + # find the row number where the basin name matches + num <- which(grepl(sub("\\..*","", basin), input_order$list, ignore.case = TRUE)) + target_files <- list.files(pca_dir, paste0("^pca_6_", num, "\\."), full.names = TRUE) + file.copy(target_files, target_dir) } ``` @@ -431,6 +440,8 @@ pts_prop <- data.frame("x" = 0, for (basin in basin_list) { + basin <- sub("\\..*","", basin) + tic(msg = paste0("loop for ", basin)) # find and load the data files From cacb0a43261c496ea7d9d907fb6c1d29e4e9ec44 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Mon, 15 May 2023 10:01:11 -0600 Subject: [PATCH 72/74] add oregon_map file to inst/extdata --- inst/extdata/oregon_map.Rdata | Bin 0 -> 2248 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 inst/extdata/oregon_map.Rdata diff --git a/inst/extdata/oregon_map.Rdata b/inst/extdata/oregon_map.Rdata new file mode 100644 index 0000000000000000000000000000000000000000..3649c46add4190f52940c7770279936149c37c8d GIT binary patch literal 2248 zcmV;(2sig1iwFP!000001MQl9RFq{J#@~TK9Z(Y#2t*q|L3#a1@dLkN{Fp)6a4Qip zn;B3LO%w%5P*iF&a~0D{E!$O>Hf6KS4{Fx}oo)5Bdt7Cu1dRnJ$*@INOhFXIy?m~D z>AWm|?4HvD_day9fT1JZ8pQ21iNo zz|cv0$pGz?k(rv7u>_GlA6lS0xazQu%gM(d4(k}RN$AMtB%zTfX%E}#6gsqoaf42C zd|(aj{((lJA$5|_;0%uU4dHm76AsmQXd&&0os5fU=6v}G>kO_lsp={9hwo2P^*1Vk zIwGI>MviCvsAA@e+Q9L#B^)0=g8t|i*~g@%w9Pu!n`U60I3Lc7TS}c?w}(2&s2*>p zv7uB;AnQ$*na?!dRsXtE)z4TR`xqCe+0Q#5!zmEEemsl||BDD~bHJCU4=ldvPD_U45uMxGsB71IK+QhjJH_sC%$ zy^Zni?{dCdol&K_49;`o`Dxdkrmgd49yghOsaPHFvTYA|p4(d4pRJYW|BveOw?sOr z*Lj@p<``A>9b0~fjtuS_6r$SxlzQNex{I)zUzmiAk{~oXR zw)%}TIesIK{_E#xH*95{n;{OBeQgTudT++p&8MxL=g!ltQ(eRUs@~^#RUG{nRxnRx zA;-^o(|`5^ZRNT-6TtkZBDkMltmVApe`lRfpQL^=nf@b(8F%D z9{daQ9oRyZweZt6tlnY^0u6LvFx%q_+bEN6dVwHy!KPBrdhT+lA| z*KHf~8ivxZ@5VUSea(7%oA6s3=L)~|>Ne{4d#RUOL?71Lu8hBUoOac0j-UUl=)?L| zGW}&O^q(3f>RV4l({_yF{L&c4t^H7})4Jvc$J27Dw(0DL&sE9woHbb>csIfb6dV}3`0QRrujn7?Q? 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The success rate curve is the proportions of landslide predicted over the proportion of the predicted area. A smaller area under the success rate curve indicates that the model was more specific with the areas that it deemed to have a high probability of landslide initiation. ```{r} -basin_list <- list.files(dem_dir, "^basin_[0-9]{1,3}\\.flt", recursive = TRUE) +basin_list <- list.files(params$dem_dir, paste0("^basin_[1-9]{1,3}\\.flt"), recursive = TRUE) +# basin_list <- list.dirs(pred_dir, recursive = TRUE, full.names = FALSE) +predict = TRUE ``` ```{r} @@ -480,37 +488,42 @@ pts_prop <- data.frame("x" = 0, "area.cumsum" = 0, "ls.prop" = 0) -for (basin in basin_list) { +if (predict) { + for (basin in basin_list) { basin <- sub("\\..*","", basin) tic(msg = paste0("loop for ", basin)) # find and load the data files - topo_files <- c(paste0("GRADIENT,", pred_dir, basin, "/gradient.flt")) + topo_files <- c(paste0("GRADIENT,", pred_dir, basin, "/gradient.flt"), + paste0("MEAN CURVATURE,", pred_dir, basin, "/mean_curv.flt")) + print(topo_files) topo_rast <- elev_deriv(rasters = topo_files) pca_file <- list.files(paste0(pred_dir, basin), "^pca_6_[0-9]{1,3}\\.flt", full.names = TRUE) - print(pca_file) pca_rast <- contributing_area(raster = pca_file) count <- count + 1 rasters <- c(topo_rast, pca_rast) - names(rasters) <- c("gradient", "pca_k1_48") + names(rasters) <- c("gradient", "mean_curv", "pca_k1_48") predicting_data <- as.data.frame(rasters, xy = TRUE) predicting_data$ln_pca_k1_48 <- log(predicting_data$pca_k1_48) - predicting_data$grad_by_pca <- predicting_data$gradient * predicting_data$pca_k1_48 + # predicting_data$grad_by_pca <- predicting_data$gradient * predicting_data$pca_k1_48 pd <- predicting_data - pd <- subset(pd, select = c("x", "y", "grad_by_pca", "gradient", "ln_pca_k1_48")) + # pd <- subset(pd, select = c("x", "y", "grad_by_pca", "gradient", "ln_pca_k1_48")) + pd <- subset(pd, select = c("x", "y", "mean_curv", "gradient", "ln_pca_k1_48")) + # manually pre process the data: take the log of the pca and center+scale the data center_vals <- scale_features$pipeops$scale$state$center scale_vals <- scale_features$pipeops$scale$state$scale - pd$grad_by_pca <- (pd$grad_by_pca - center_vals[1]) / scale_vals[1] - pd$gradient <- (pd$gradient - center_vals[2]) / scale_vals[2] - pd$ln_pca_k1_48 <- (pd$ln_pca_k1_48 - center_vals[3]) / scale_vals[3] + # pd$grad_by_pca <- (pd$grad_by_pca - center_vals[1]) / scale_vals[1] + pd$gradient <- (pd$gradient - center_vals[1]) / scale_vals[1] + pd$ln_pca_k1_48 <- (pd$ln_pca_k1_48 - center_vals[2]) / scale_vals[2] + pd$mean_curv <- (pd$mean_curv - center_vals[3]) / scale_vals[3] # predict the basin using scaled and centered data. predictions_df <- as.data.table(model$predict_newdata(pd)) @@ -533,7 +546,7 @@ for (basin in basin_list) { toc() } - +} total_area <- max(cumul_prop$area) total_prob <- max(cumul_prop$prob) @@ -546,12 +559,18 @@ pts_prop$area_prop <- dt$area_prop ``` ```{r} -ggplot(cbind(bins, cumul_prop)) + +load(paste0(pred_dir, "grad_pca_gbyp.Rdata")) +success_curve_2 <- cbind(bins, cumul_prop) + +ggplot(success_curve) + geom_line(aes(x = area_prop, y = prob_prop), color = "red") + geom_point(aes(x = area_prop, y = prob_prop), color = "red") + + geom_line(data = success_curve_2, aes(x = area_prop, y = prob_prop), color = "blue") + + geom_point(data = success_curve_2, aes(x = area_prop, y = prob_prop), color = "blue") + scale_x_continuous(limits = c(0, 1), expand = c(0, 0)) + scale_y_continuous(limits = c(0, 1), expand = c(0, 0)) + labs(x = "Proportion of area", y = "Proportion of total probability") + + theme(legend.position = "right") + theme(panel.grid = element_blank(), panel.background = element_rect(fill = "white", linewidth = 1, color = "black",), panel.border = element_rect(linewidth = 1, color = NA, fill = NA)) From 35a6f4f78fbf2535496c143290d5f883076d8a75 Mon Sep 17 00:00:00 2001 From: jlober01 Date: Thu, 1 Jun 2023 11:02:33 -0600 Subject: [PATCH 74/74] saving some minor file changes --- R/create_model_dataframe.R | 1 + analysis/PFA_initiation_model.qmd | 35 + analysis/comparing_landslide_models.qmd | 688 ++++++++++--------- analysis/mlr3_workflow.qmd | 19 +- analysis/predicting_landslide_initiation.qmd | 71 +- 5 files changed, 437 insertions(+), 377 deletions(-) diff --git a/R/create_model_dataframe.R b/R/create_model_dataframe.R index d106192..01b1af9 100644 --- a/R/create_model_dataframe.R +++ b/R/create_model_dataframe.R @@ -1,4 +1,5 @@ # define where to find the data and load it. +folder <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/" folder <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/out/" load(paste0(folder, "ls_1996.Rdata")) load(paste0(folder, "ls_2007.Rdata")) diff --git a/analysis/PFA_initiation_model.qmd b/analysis/PFA_initiation_model.qmd index d7932e3..4ece060 100644 --- a/analysis/PFA_initiation_model.qmd +++ b/analysis/PFA_initiation_model.qmd @@ -47,6 +47,7 @@ library(mlr3viz) library(mlr3fselect) library(mlr3filters) library(mlr3pipelines) +library(iml) # Packages for model-building library(glmnet) @@ -411,6 +412,40 @@ ls_model$train(ls_task_eng) ls_model$model ``` +### Feature Importance + +```{r} +# set up the data frame + +# define a machine-learning task +task <- as_task_classif_st( + x = sample_pnts[, training_cols], + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910" +) + +# define the model to apply +log_reg <- lrn("classif.log_reg", + predict_type = "prob") +log_reg$train(task) # train (calibrate) the model on the entire data set + +# set up a data frame with just the features +x = sample_pnts[, training_cols] + +# create a new instance of an iml Predictor object for this model +model = iml::Predictor$new(log_reg, data = x, y = sample_pnts$class) + +# Calculate importance for all features +importance = iml::FeatureImp$new(model, loss = "ce") # "ce" is a measure of classification error +num_features = c("Gradient", "Tan_Curv", "Norm_Curv", "age", "geo") +importance$plot(features = num_features) + + labs(title = "Permutation Feature Importance", + subtitle = "For predicting landslide initiation") +``` + ## Predicting ### Single basin diff --git a/analysis/comparing_landslide_models.qmd b/analysis/comparing_landslide_models.qmd index 2891b06..43cbe79 100644 --- a/analysis/comparing_landslide_models.qmd +++ b/analysis/comparing_landslide_models.qmd @@ -1,333 +1,355 @@ ---- -title: "Modeling Landslide Initiation" -autho: Julia Lober -format: html -editor: visual ---- - -```{r setup} -#| echo: false -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - error = FALSE -) -``` - -```{r load, include = FALSE} -library(mlr3) -library(mlr3spatial) -library(mlr3spatiotempcv) -library(mlr3learners) -library(mlr3tuning) -library(mlr3viz) -library(mlr3fselect) -library(mlr3filters) -library(mlr3pipelines) - -# Packages for model-building -library(glmnet) -library(ranger) - -# Packages for other things -library(terra) -library(ggplot2) -library(maps) -library(tidyterra) -``` - -```{r} -source("../R/create_model_dataframe.R") -sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] -sample_pnts$geo <- as.factor(sample_pnts$geo) -sample_pnts <- subset(sample_pnts, select = -c(year)) - -sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 -sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 -``` - -## A look at the data. - -The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. - -The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. - -```{r} -#| echo: false -head(sample_pnts) -``` - -```{r} -#| echo: false -points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) -points$yr <- sample_pnts$year -points$class <- sample_pnts$class -crs(points) <- "epsg:26910" -points <- project(points, "epsg:4326") - -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") - -ggplot() + - geom_polygon(data = oregon, mapping = aes(x = long, y = lat), - color = "white", fill = "white") + - # geom_spatvector(data = points[points$class == "neg", ], - # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), - # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + - geom_spatvector(data = points[points$class == "pos", ], - mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), - show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + - scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + - labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") -``` - -Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. - -## Defining the modeling task and learner - -For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. - -There are several steps we need to work through: - -1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. -2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. -3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. -4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. - -```{r} -set.seed(12345) -landslide_task <- as_task_classif_st(x = sample_pnts, - id = "landslide_initiation", - target = "class", - positive = "pos", - coordinate_names = c("x", "y"), - crs = "epsg:26910") - -logreg_learner <- lrn("classif.log_reg", - predict_type = "prob") - -rf_learner <- lrn("classif.ranger", - predict_type = "prob") - -spat_resamp <- rsmp("repeated_spcv_coords", - folds = 5, - repeats = 2) -``` - -We will design a pipeline to compare the different models. - -```{r} -#| echo: false -autoplot(spat_resamp, - landslide_task, - fold_id = c(1:4)) -``` - -### 1. Feature selection - -The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. - -```{r} -logreg_fselect <- auto_fselector( - fs("sequential"), - learner = logreg_learner, - resampling = spat_resamp, - measure = msr("classif.auc")) - -rf_fselect <- auto_fselector( - fs("sequential"), - learner = rf_learner, - resampling = spat_resamp, - measure = msr("classif.auc")) -``` - -## Make a pipeline - -```{r} - -pipe <- - po("encode") %>>% - ppl("branch", list( - "anova" = po("filter", filter = flt("anova")), - "nothing" = po("nop")), prefix_branchops = "filt") %>>% - ppl("branch", list( - "lr" = logreg_learner, - "rf" = rf_learner, - "lr_fs" = logreg_fselect, - "rf_fs" = rf_fselect)) - -pipe$plot(html = FALSE) -``` - -Now, we can define the parameters over which to evaluate the models and tune it. I use a random search to start just gathering an idea of the best ranges of parameters. Since we are comparing a handful of different modeling techniques, one of the major parameters to tune will be the model type, called "branch.selection" in the parameter set. - -I ran this initially with a filtering method as an option, but found the no matter the filtering, the forward feature selection works better. It is possible that combining filtering and forward feature would reduce run-time, but since we only have about 15 features to evaluate, I don't believe that the time save will be substantial enough for the possible performance trade-off. - -```{r} -param_set <- ParamSet$new(list( - ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), - ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), - ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), - ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) -)) - -target <- ti( - task = landslide_task, - learner = as_learner(pipe), - resampling = rsmp("spcv_coords", folds = 3), - measures = msr("classif.auc"), - terminator = trm("evals", n_evals = 20), - search_space = param_set - -) - -tuner <- tnr("random_search") - -# tuner$optimize(target) -``` - -```{r} -param_set <- ParamSet$new(list( - ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), - ParamFct$new("filtbranch.selection", levels = c("nothing")), - ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), - ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs")) -)) - -target <- ti( - task = landslide_task, - learner = as_learner(pipe), - resampling = rsmp("spcv_coords", folds = 3), - measures = msr("classif.auc"), - terminator = trm("evals", n_evals = 20), - search_space = param_set - -) - -tuner <- tnr("random_search") - -tuner$optimize(target) -``` - -```{r} -param_set <- ParamSet$new(list( - ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), - ParamFct$new("filtbranch.selection", levels = c("nothing")), - ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 5L), - ParamFct$new("branch.selection", levels = c("rf_fs")) -)) - -target <- ti( - task = landslide_task, - learner = as_learner(pipe), - resampling = rsmp("spcv_coords", folds = 3), - measures = msr("classif.auc"), - terminator = trm("none"), - search_space = param_set - -) - -tuner <- tnr("grid_search", resolution = 5, batch_size = 400) - -# tuner$optimize(target) -``` - -```{r} -param_set <- ParamSet$new(list( - ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), - ParamFct$new("filtbranch.selection", levels = c("nothing")), - ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 10L), - ParamFct$new("branch.selection", levels = c("rf")) -)) - -target <- ti( - task = landslide_task, - learner = as_learner(pipe), - resampling = rsmp("spcv_coords", folds = 3), - measures = msr("classif.auc"), - terminator = trm("none"), - search_space = param_set - -) - -tuner <- tnr("grid_search", resolution = 10, batch_size = 100) - -tuner$optimize(target) -``` - -This tuning process takes an average of 2 minutes \* number of evals to run. I have run it twice so far, and save the results in data files to minimize the number of times we want to re-run the code. - -Overall, the results have shown AUC values between 0.82 and 0.86 for all configurations of models. The logistic regression model with the forward feature selection performed significantly better than the logistic regression without feature selection, and slightly (though not significantly) better than the random forest models. However, logistic regression is much faster to train, especially when using a forward feature selection algorithm that involves training so many models. - -```{r} -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.13.pipelinetuneresult.Rdata") -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") -load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.rf_fs_mtry.Rdata") -``` - -```{r} -#| echo: false -to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) -names(to_plot) <- c("auc", "model_type") -to_plot$model_type <- as.factor(to_plot$model_type) - -ggplot(to_plot, aes(x = model_type, y = auc)) + - geom_boxplot(aes(col = model_type)) + - geom_jitter(aes(col = model_type), position = position_jitter(0.2)) -``` - -## Logistic regression model - -Based on the results above, we will work with a logistic regression model and use forward feature selection to select the subset of features that provide the most amount of information. - -Our pipeline includes a scaling step, to even out the influence of each predictor, and an encoding step, to translate the geology predictor from a factor to a one-hot strategy that is treated more logically by a numerical model. - -```{r} - -pl_fselect <- - po("scale") %>>% - po("encode") %>>% - po("learner", logreg_fselect) - -pl_fselect$train(landslide_task) -#pl_lrn$graph$pipeops$classif.log_reg.fselector$learner - -``` - -Now that we know the best set of features, we can train a model with the resampling method to get a more exact estimate of how well the final model will actually perform. To do this, we use a k-means clustering based spatial resampling method, with 8 folds (7 for training, 1 for testing) and 50 repeats, to get a measure of performance that we can be relatively confident in. - -Then, we look at the aggregate AUC measure for all 50\*8 models that were trained (400 models total). - -```{r} -feature_set <- c("age", "dist_to_road", "mean_curv", "pca_k1_48") -landslide_task$select(feature_set) - -pl_feature_eng <- - po("scale") %>>% - po("encode") - -pl_feature_eng$train(landslide_task) -landslide_task_eng <- pl_feature_eng$predict(landslide_task)$encode.output - -model <- mlr3::resample( - task = landslide_task_eng, - learner = logreg_learner, - resampling = rsmp("repeated_spcv_coords", - folds = 8, - repeats = 50) -) - -model$aggregate(msr("classif.auc")) - -``` - -Our final step is to train a model using all of the data. While this is generally frowned upon in machine learning, our inventory produces a relatively small data set for this project and we want to leverage all of the data available. - -TODO: look up coefficient interpretation. Does a bigger coefficient mean that the variable has a bigger influence? I think yes, if the data are scaled first. - -Now that I am using the scaled version of the data, I agree. The pca value has the largest coefficient, and is consistently the variable that is chosen first during the feature selection, indicating that it is a highly explanatory variable for our dataset. Mean curvature is next, which - -```{r} -logreg_learner$train(landslide_task_eng) - -logreg_learner$model -``` +--- +title: "Modeling Landslide Initiation" +autho: Julia Lober +format: html +editor: visual +--- + +```{r setup} +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + error = FALSE +) +``` + +```{r load, include = FALSE} +library(mlr3) +library(mlr3spatial) +library(mlr3spatiotempcv) +library(mlr3learners) +library(mlr3tuning) +library(mlr3viz) +library(mlr3fselect) +library(mlr3filters) +library(mlr3pipelines) + +# Packages for model-building +library(glmnet) +library(ranger) + +# Packages for other things +library(terra) +library(ggplot2) +library(maps) +library(tidyterra) +``` + +```{r} +source("../R/create_model_dataframe.R") +sample_pnts <- training_data[!(is.na(training_data$dist_to_road)), 2:length(training_data)] +sample_pnts$geo <- as.factor(sample_pnts$geo) +# sample_pnts <- subset(sample_pnts, select = -c(year)) + +sample_pnts$gradient_sq <- sample_pnts$gradient ^ 2 +sample_pnts$grad_by_pca <- sample_pnts$gradient * sample_pnts$pca_k1_48 +``` + +## A look at the data. + +The first step is to look at the data we are using to built the model. We are working with data files that have been assembled by the PFA_Sampling_Multiple_DEMs documents included in this package. This package produces .Rdata files that contain data frames of landslide points divided by the approximate year in which they occurred and non-landslide points sampled from the surrounding area divided in the same way. + +The data frames contain a variety of predictors selected based on the physical parameters that we know control landslides. These predictors have various sources of data which will be explained later. + +```{r} +#| echo: false +head(sample_pnts) +``` + +```{r} +#| echo: false +points <- vect(cbind(sample_pnts[, "x"], y = sample_pnts[, "y"])) +points$yr <- sample_pnts$year +points$class <- sample_pnts$class +crs(points) <- "epsg:26910" +points <- project(points, "epsg:4326") + +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/oregon_map.Rdata") + +ggplot() + + geom_polygon(data = oregon, mapping = aes(x = long, y = lat), + color = "white", fill = "white") + + # geom_spatvector(data = points[points$class == "neg", ], + # mapping = aes(shape = as.factor(points[points$class == "neg", ]$yr)), + # show.legend = TRUE, size = 1 , color = "black", alpha = 1) + + geom_spatvector(data = points[points$class == "pos", ], + mapping = aes(shape = as.factor(points[points$class == "pos", ]$yr)), + show.legend = TRUE, size = 1, color = "red", alpha = 0.4) + + scale_shape_manual(values = c(3, 1, 17), labels = c("1996 (213)", "2007 (137)", "2011 (47)")) + + labs(x = "Longitude", y = "Latitude", shape = "Study points (count)") +``` + +Note that the landslide initiation points tend to come in clusters and the years 1996 and 2007 have much many more points located than 2011. Negative landslide points are randomly selected within a buffer area near the points. There are other documents that explain the steps and decisions to assemble this data set, so I will not go into it any further. + +## Defining the modeling task and learner + +For `mlr3`, we need to define the task, the type of model (called a learner), and the resampling method that we want to use. The task is our landslide initiation data set and the learner (for now) is a logistic regression model. If we want to add a different type of model, this will be done in a different document. + +There are several steps we need to work through: + +1. Feature selection - Many options exist for feature selection methods that vary slightly in the way that they attribute value to different predictors and iterate through subsets. We explore a small selection in the feature_selection.qmd document, select a basic method from there. +2. Tuning hyperparameters - This step is only necessary if we decide to use a model that has hyperparameters, like a random forest model. Otherwise, we will ignore it. +3. Estimating performance - Here, we use a spatial cross-validation resampling method to assess the model performance while minimizing bias. By using spatial cross-validation, we keep testing and training sets closer to independent inputs, which should give us a more realistic guess of how accurate the model will on new data. +4. Generating a proportion raster - Once we have the best model option as assessed from the previous steps, we can input all of the DEMs to produce a probability raster, which we will then convert to a proportion raster. + +```{r} +set.seed(12345) +sample_pnts <- subset(sample_pnts, select = -c(year)) + +landslide_task <- as_task_classif_st(x = sample_pnts, + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +rf_learner <- lrn("classif.ranger", + predict_type = "prob") + +spat_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2) +``` + +We will design a pipeline to compare the different models. + +```{r} +#| echo: false +autoplot(spat_resamp, + landslide_task, + fold_id = c(1:4)) +``` + +### 1. Feature selection + +The rule in machine learning is to create the best model possible with the fewest features needed. This not only makes the model quicker to train, but also makes it easier to predict (smaller data inputs) and also reduces the risk of over-fitting. + +```{r} +logreg_fselect <- auto_fselector( + fs("sequential"), + learner = logreg_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) + +rf_fselect <- auto_fselector( + fs("sequential"), + learner = rf_learner, + resampling = spat_resamp, + measure = msr("classif.auc")) +``` + +## Make a pipeline + +```{r} + +pipe <- + po("encode") %>>% + ppl("branch", list( + "anova" = po("filter", filter = flt("anova")), + "nothing" = po("nop")), prefix_branchops = "filt") %>>% + ppl("branch", list( + "lr" = logreg_learner, + "rf" = rf_learner, + "lr_fs" = logreg_fselect, + "rf_fs" = rf_fselect)) + +pipe$plot(html = FALSE) +``` + +Now, we can define the parameters over which to evaluate the models and tune it. I use a random search to start just gathering an idea of the best ranges of parameters. Since we are comparing a handful of different modeling techniques, one of the major parameters to tune will be the model type, called "branch.selection" in the parameter set. + +I ran this initially with a filtering method as an option, but found the no matter the filtering, the forward feature selection works better. It is possible that combining filtering and forward feature would reduce run-time, but since we only have about 15 features to evaluate, I don't believe that the time save will be substantial enough for the possible performance trade-off. + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 1), + ParamFct$new("filtbranch.selection", levels = c("anova", "nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs", "rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 20), + search_space = param_set + +) + +tuner <- tnr("random_search") + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 11L), + ParamFct$new("branch.selection", levels = c("lr", "rf", "lr_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("evals", n_evals = 20), + search_space = param_set + +) + +tuner <- tnr("random_search") + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 5L), + ParamFct$new("branch.selection", levels = c("rf_fs")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("none"), + search_space = param_set + +) + +tuner <- tnr("grid_search", resolution = 5, batch_size = 400) + +# tuner$optimize(target) +``` + +```{r} +param_set <- ParamSet$new(list( + ParamDbl$new("anova.filter.frac", lower = 0.3, upper = 0.3), + ParamFct$new("filtbranch.selection", levels = c("nothing")), + ParamInt$new("classif.ranger.mtry", lower = 1L, upper = 10L), + ParamFct$new("branch.selection", levels = c("rf")) +)) + +target <- ti( + task = landslide_task, + learner = as_learner(pipe), + resampling = rsmp("spcv_coords", folds = 3), + measures = msr("classif.auc"), + terminator = trm("none"), + search_space = param_set + +) + +tuner <- tnr("grid_search", resolution = 10, batch_size = 100) + +# tuner$optimize(target) +``` + +This tuning process takes an average of 2 minutes \* number of evals to run. I have run it twice so far, and save the results in data files to minimize the number of times we want to re-run the code. + +Overall, the results have shown AUC values between 0.82 and 0.86 for all configurations of models. The logistic regression model with the forward feature selection performed significantly better than the logistic regression without feature selection, and slightly (though not significantly) better than the random forest models. However, logistic regression is much faster to train, especially when using a forward feature selection algorithm that involves training so many models. + +```{r} +# load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.13.pipelinetuneresult.Rdata") +load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.pipelinetuneresult.Rdata") +# load("/Users/julialober/Documents/terrainworks/code/sandbox/data/3.14.rf_fs_mtry.Rdata") +``` + +```{r} +#| echo: false +to_plot <- cbind(as.data.frame(target_archive$classif.auc), (as.data.frame(target_archive$branch.selection))) +names(to_plot) <- c("auc", "model_type") +to_plot$model_type <- as.factor(to_plot$model_type) + +ggplot(to_plot, aes(x = model_type, y = auc)) + + geom_boxplot(aes(col = model_type)) + + geom_jitter(aes(col = model_type), position = position_jitter(0.2)) +``` + +## Logistic regression model + +Based on the results above, we will work with a logistic regression model and use forward feature selection to select the subset of features that provide the most amount of information. + +Our pipeline includes a scaling step, to even out the influence of each predictor, and an encoding step, to translate the geology predictor from a factor to a one-hot strategy that is treated more logically by a numerical model. + +```{r} +logreg_learner <- lrn("classif.log_reg", + predict_type = "prob") + +spat_resamp <- rsmp("repeated_spcv_coords", + folds = 5, + repeats = 2) + +logreg_fselect <- auto_fselector( + fs("sequential"), + learner = logreg_learner, + resampling = spat_resamp, + measure = msr("classif.bbrier")) + +landslide_task_noage <- as_task_classif_st(x = subset(sample_pnts, select = -c(age, dist_to_road)), + id = "landslide_initiation", + target = "class", + positive = "pos", + coordinate_names = c("x", "y"), + crs = "epsg:26910") + + +pl_fselect <- + po("scale") %>>% + po("encode") %>>% + po("learner", logreg_fselect) + +pl_fselect$train(landslide_task_noage) +#pl_lrn$graph$pipeops$classif.log_reg.fselector$learner + +``` + +Now that we know the best set of features, we can train a model with the resampling method to get a more exact estimate of how well the final model will actually perform. To do this, we use a k-means clustering based spatial resampling method, with 8 folds (7 for training, 1 for testing) and 50 repeats, to get a measure of performance that we can be relatively confident in. + +Then, we look at the aggregate AUC measure for all 50\*8 models that were trained (400 models total). + +```{r} +feature_set <- c("age", "dist_to_road", "mean_curv", "pca_k1_48") +# feature_set <- c("mean_curv", "pca_k1_48") +landslide_task$select(feature_set) + +pl_feature_eng <- + po("scale") %>>% + po("encode") + +pl_feature_eng$train(landslide_task) +landslide_task_eng <- pl_feature_eng$predict(landslide_task)$encode.output + +model <- mlr3::resample( + task = landslide_task_eng, + learner = logreg_learner, + resampling = rsmp("repeated_spcv_coords", + folds = 8, + repeats = 50) +) + +model$aggregate(msr("classif.auc")) + +``` + +Our final step is to train a model using all of the data. While this is generally frowned upon in machine learning, our inventory produces a relatively small data set for this project and we want to leverage all of the data available. + +TODO: look up coefficient interpretation. Does a bigger coefficient mean that the variable has a bigger influence? I think yes, if the data are scaled first. + +Now that I am using the scaled version of the data, I agree. The pca value has the largest coefficient, and is consistently the variable that is chosen first during the feature selection, indicating that it is a highly explanatory variable for our dataset. + +```{r} +logreg_learner$train(landslide_task_eng) +logreg_learner$model +``` diff --git a/analysis/mlr3_workflow.qmd b/analysis/mlr3_workflow.qmd index 318ec9d..ac7afa9 100644 --- a/analysis/mlr3_workflow.qmd +++ b/analysis/mlr3_workflow.qmd @@ -10,7 +10,7 @@ params: input: text training_data: label: Training data .Rdata file - value: sample_umpqua.Rdata + value: sample_wilson.Rdata input: text points: label: Training data points @@ -33,14 +33,16 @@ knitr::opts_chunk$set( ```{r} #| echo: false -load(paste0(params$data_dir, params$training_data)) -pnts <- vect(paste0(params$data_dir, params$points)) -dem <- rast(paste0(params$data_dir, params$dem)) +# load(paste0(params$data_dir, params$training_data)) +# pnts <- vect(paste0(params$data_dir, params$points)) +# dem <- rast(paste0(params$data_dir, params$dem)) +source("../R/create_model_dataframe.R") +sample_pnts <- training_data ``` ## Workflow for the mlr3 package -This is a document where I work out how to use the `mlr3` package. According to Dan, this is package ecosystem has been taking over for `caret`. +This is a document where I work out how to use the `mlr3` package. This package ecosystem is more modern and agile than the `caret` package, although because it is newer it has a smaller user base and a little bit less online resources. ```{r load, include = FALSE} library(mlr3) @@ -49,7 +51,8 @@ library(mlr3spatiotempcv) library(mlr3learners) library(mlr3tuning) library(mlr3viz) -# Packages for models - change these based on what type of model you build + +# Packages for model-building library(glmnet) library(ranger) ``` @@ -67,7 +70,7 @@ mlr_learners Our task leaves out the `id` column in the sample_pnts data frame, as it will not be relevant information for the model! Specifying the target says what we want the learner to predict - for us, this is the `class` which is positive for landslide initiation points. ```{r} -task <- as_task_classif(x = sample_pnts[, 2:8], id = "landslide_init", target = "class") +task <- as_task_classif(x = sample_pnts[, 2:10], id = "landslide_init", target = "class") head(task) ``` @@ -181,7 +184,7 @@ print(predicted$score(msrs(c("classif.auc", "classif.acc")))) print(predicted$confusion) ``` -## Step 3. Comparing spatiotemporal cross-validation vs normal cross-validation. +## Step 3. Comparing spatiotemporal cross-validation vs normal cross-validation. For any machine learning workflow, you want to including a resampling method that will train models on a couple different subsets of data as training and testing sets to reduce the role of chance and produce a more robust basis for evaluating the model's performance. diff --git a/analysis/predicting_landslide_initiation.qmd b/analysis/predicting_landslide_initiation.qmd index 1b90bd3..d45aaad 100644 --- a/analysis/predicting_landslide_initiation.qmd +++ b/analysis/predicting_landslide_initiation.qmd @@ -33,7 +33,7 @@ We start by predicting on 1 small basin as part of 1 of the watershed designatio ```{r} dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" -dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +# dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" dem <- terra::rast(paste0(dir, "Umpqua/basin_1.flt")) pts <- terra::vect(paste0(dir, "all_initiation_points.shp")) @@ -61,10 +61,10 @@ We need to calculate the partial contributing area and mean curvature using the The model is trained on all of the available inventory points and stored in "logreg_learner". This happens in the comparing_landslide_models.qmd file. ```{r} -calculate <- FALSE +calculate <- TRUE scratch <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/scratch/" -scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" +# scratch <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/scratch/" rasters <- c(paste0("GRADIENT,", scratch, "gradient"), paste0("MEAN CURVATURE,", scratch, "mean_curv")) @@ -329,30 +329,14 @@ I think this will be one large for loop that iterates once per basin. I may brea ```{r} # replace these with the respective file paths to your DEMs -basin_list = c("Umpqua/basin_1", - "Umpqua/basin_2", - "Umpqua/basin_3", - "Umpqua/basin_4", - "Umpqua/basin_5", - "Umpqua/basin_6", - "Umpqua/basin_7", - "Umpqua/basin_8", - "Umpqua/basin_9", - "Umpqua/basin_10", - "Umpqua/basin_11", - "Umpqua/basin_12", - "Umpqua/basin_13", - "Umpqua/basin_14", - "Umpqua/basin_15", - "Umpqua/basin_16", - "Umpqua/basin_17") - -basin_list <- list.files(dem_dir, paste0("^basin_[0-9]{1,3}\\.flt"), recursive = TRUE) - -# basin_list = c(paste0(dir, "Umpqua/basin_1/"), -# paste0(dir, "Umpqua/basin_2/")) - # paste0(dir, "Umpqua/basin_3/"), - # paste0(dir, "Umpqua/basin_4/")) +dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" +dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" +# dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/predicting_input/" +# dem_dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" + +calculate <- FALSE + +basin_list <- list.files(dem_dir, paste0("^basin_[0-9]{1,4}\\.flt"), recursive = TRUE) ``` #### For calculating elevation derivatives: @@ -360,21 +344,17 @@ basin_list <- list.files(dem_dir, paste0("^basin_[0-9]{1,3}\\.flt"), recursive = This code only needs to be run once. Eventually, I will remove it from this document. ```{r} -dem_dir <- "/Users/julialober/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" -dir = "/Users/julialober/Documents/terrainworks/code/sandbox/data/predicting_input/" -dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/predicting_input/" -dem_dir <- "//Mac/Home/Documents/terrainworks/code/sandbox/data/downloaded_3.6/in/" if (calculate) { for (basin in basin_list) { # create directories for basins and sub-basins if needed. - if (!dir.exists(paste0(dir, sub("/.*","", basin)))) { + # if (!dir.exists(paste0(dir, sub("/.*","", basin)))) { dir.create(paste0(dir, sub("/.*","", basin))) - } - if (!dir.exists(paste0(dir, sub("\\..*","", basin)))) { + # } + # if (!dir.exists(paste0(dir, sub("\\..*","", basin)))) { dir.create(paste0(dir, sub("\\..*","", basin))) - } + # } # define the elevation derivatives that we want to be calculated rasters <- c(paste0("GRADIENT,", dir, sub("\\..*","", basin), "/gradient"), @@ -415,6 +395,22 @@ for (basin in basin_list) { } ``` +A loop to figure out which basins are missing from the basin_list. These basins were included in the partial contributing area files that I got from Dan but for some reason are not included in the original input file for generating the training data that I downloaded. It looks like it is the McKenzie basin that is missing. + +```{r} +for (line in input_order$list) { + ans <- regexpr("/[A-Za-z]{5,30}/[A-Za-z]?basin_[0-9]{1,3}", line) + basin <- substr(line, ans[1] + 1, ans[1] + attr(ans, "match.length") - 1) + + # find the row number where the basin name matches + found <- which(grepl(basin, basin_list, ignore.case = TRUE), arr.ind = FALSE) + if (length(found) == 0) { + print(basin) + } + +} +``` + A note: this code assumes we already have a trained model to use for predictions. The learner we are using here comes from the code above. I am timing each iteration of the loop to get an idea of how long it will take to run. So far, we are hovering around 50 seconds for one basin. TODO: figure out how to crop the points and add the observed landslides to a data frame to compare. This was relatively simple above, so shouldn't be too hard to figure out. @@ -448,7 +444,10 @@ for (basin in basin_list) { topo_files <- c(paste0("GRADIENT,", dir, basin, "/gradient.flt"), paste0("MEAN CURVATURE,", dir, basin, "/mean_curv.flt")) topo_rast <- elev_deriv(rasters = topo_files) - pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_", count, ".flt")) + print(basin) + # print(count) + num <- which(grepl(sub("\\..*","", basin), input_order$list, ignore.case = TRUE)) + pca_rast <- contributing_area(raster = paste0(dir, basin, "/pca_6_", num, ".flt")) count <- count + 1 rasters <- c(topo_rast, pca_rast)

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