From 60c3233c6a0e7d2bc485639de194a02788f5a1c0 Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 10:04:53 +0000 Subject: [PATCH 1/9] Revert "eval=FALSE in c15" This reverts commit 80872c3e50d00f2d7ee819aeb5eee0116eae17e8. --- 15-eco.Rmd | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/15-eco.Rmd b/15-eco.Rmd index ef74c296e..4b37e40c5 100644 --- a/15-eco.Rmd +++ b/15-eco.Rmd @@ -505,27 +505,22 @@ autotuner_rf = mlr3tuning::auto_tuner( Calling the `train()`-method of the `AutoTuner`-object finally runs the hyperparameter\index{hyperparameter} tuning, and will find the optimal hyperparameter\index{hyperparameter} combination for the specified parameters. -```{r 15-eco-24, eval=FALSE, cache=TRUE, cache.lazy=FALSE} +```{r 15-eco-24, eval=TRUE, cache=TRUE, cache.lazy=FALSE} # hyperparameter tuning set.seed(08012024) -autotuner_rf$train(task) +autotuner_rf$train(task, store_backends = TRUE) ``` -```{r 15-eco-25, cache=TRUE, cache.lazy=FALSE, eval=FALSE, echo=FALSE} +```{r 15-eco-25, eval=TRUE, echo=FALSE} saveRDS(autotuner_rf, "extdata/15-tune.rds") ``` -```{r 15-eco-26, echo=FALSE, eval=FALSE} +```{r 15-eco-26, echo=FALSE} autotuner_rf = readRDS("extdata/15-tune.rds") ``` - - -```{r tuning-result, eval=FALSE} +```{r tuning-result, cache=TRUE, cache.lazy=FALSE} autotuner_rf$tuning_result -#> mtry sample.fraction min.node.size learner_param_vals x_domain regr.rmse -#> -#> 1: 4 0.878 7 0.368 ``` ### Predictive mapping @@ -533,7 +528,7 @@ autotuner_rf$tuning_result The tuned hyperparameters\index{hyperparameter} can now be used for the prediction. To do so, we only need to run the `predict` method of our fitted `AutoTuner` object. -```{r 15-eco-27, cache=TRUE, cache.lazy=FALSE, warning=FALSE, eval=FALSE} +```{r 15-eco-27, cache=TRUE, cache.lazy=FALSE, warning=FALSE} # predicting using the best hyperparameter combination autotuner_rf$predict(task) ``` From 6e8c945cbf58950f806d5d6c7fa16455ea486d44 Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 10:04:59 +0000 Subject: [PATCH 2/9] Revert "store_backends" This reverts commit e6f74bd6821ad81df2741fcc98c723c0f882d881. --- 15-eco.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/15-eco.Rmd b/15-eco.Rmd index 4b37e40c5..163e6b6cc 100644 --- a/15-eco.Rmd +++ b/15-eco.Rmd @@ -508,7 +508,7 @@ Calling the `train()`-method of the `AutoTuner`-object finally runs the hyperpar ```{r 15-eco-24, eval=TRUE, cache=TRUE, cache.lazy=FALSE} # hyperparameter tuning set.seed(08012024) -autotuner_rf$train(task, store_backends = TRUE) +autotuner_rf$train(task) ``` ```{r 15-eco-25, eval=TRUE, echo=FALSE} From 5b55b7b7dfdd4bd4913467fad6384ee8f111e484 Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 10:05:01 +0000 Subject: [PATCH 3/9] Revert "Remove cache from chunk 15-eco-26" This reverts commit 7217843359486ea89bbc2a1511351215728264a7. --- 15-eco.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/15-eco.Rmd b/15-eco.Rmd index 163e6b6cc..bccb4d8c8 100644 --- a/15-eco.Rmd +++ b/15-eco.Rmd @@ -511,7 +511,7 @@ set.seed(08012024) autotuner_rf$train(task) ``` -```{r 15-eco-25, eval=TRUE, echo=FALSE} +```{r 15-eco-25, cache=TRUE, cache.lazy=FALSE, eval=TRUE, echo=FALSE} saveRDS(autotuner_rf, "extdata/15-tune.rds") ``` From 457adad87f1718080d425c9666d71cf4af652cff Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 10:05:03 +0000 Subject: [PATCH 4/9] Revert "Try evaluating train function" This reverts commit 6302bc7cf5a3d46d5460fa12cf039d5ba0cb3a4d. --- 15-eco.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/15-eco.Rmd b/15-eco.Rmd index bccb4d8c8..2d57a7ad9 100644 --- a/15-eco.Rmd +++ b/15-eco.Rmd @@ -505,13 +505,13 @@ autotuner_rf = mlr3tuning::auto_tuner( Calling the `train()`-method of the `AutoTuner`-object finally runs the hyperparameter\index{hyperparameter} tuning, and will find the optimal hyperparameter\index{hyperparameter} combination for the specified parameters. -```{r 15-eco-24, eval=TRUE, cache=TRUE, cache.lazy=FALSE} +```{r 15-eco-24, eval=FALSE, cache=TRUE, cache.lazy=FALSE} # hyperparameter tuning set.seed(08012024) autotuner_rf$train(task) ``` -```{r 15-eco-25, cache=TRUE, cache.lazy=FALSE, eval=TRUE, echo=FALSE} +```{r 15-eco-25, cache=TRUE, cache.lazy=FALSE, eval=FALSE, echo=FALSE} saveRDS(autotuner_rf, "extdata/15-tune.rds") ``` From 6441e8109643b9fe0c91a4e7fd059747bbc2b3af Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 10:05:04 +0000 Subject: [PATCH 5/9] Revert "Update 15-tune.rds, fix actions (hopefully)" This reverts commit 737cbfcd0be08fbe2bfb0161dc9b08ce2fa00ad4. --- 15-eco.Rmd | 2 +- extdata/15-tune.rds | Bin 366979 -> 216306 bytes 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/15-eco.Rmd b/15-eco.Rmd index 2d57a7ad9..07758e904 100644 --- a/15-eco.Rmd +++ b/15-eco.Rmd @@ -515,7 +515,7 @@ autotuner_rf$train(task) saveRDS(autotuner_rf, "extdata/15-tune.rds") ``` -```{r 15-eco-26, echo=FALSE} +```{r 15-eco-26, echo=FALSE, cache=TRUE, cache.lazy=FALSE} autotuner_rf = readRDS("extdata/15-tune.rds") ``` diff --git a/extdata/15-tune.rds b/extdata/15-tune.rds index 0662c577340f773403d78d1b0626a95519f366c3..54735b0f23d24b06f046ed47cf4234eb7727f21e 100644 GIT binary patch literal 216306 zcmbTdbyOX}w=M`F!QI_SaF>GzC%9|y0KqM|yKC^^?(VJ!cXv6#-5utT`)2*#eQWNl zS#$o_RkiEeU-jv(?%KU;H%T}w#J?ZZF+Yq4rr2H5r29U#A7pn>pN1m6JFPy9Kx+;V z6*(j?C4d}W>6qNmCL}~3M^QAjinRSM3RXi1vx;u;#b%^!C4H^A*nQ~k%|)?I;Jo?iU)h*lHvkr?2gH!qM*aR$^ z!67>OP@P9(<0Ubq`<0G@WwFw|J*-UCo 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zijwdWxj#LZz<$J;vLSbvO6GHAxkZteQ$sjJXk*{VJ-1z^0c>`VUB+@jpL6U)&suA@ zdkM6uE0fu^Y!qjfxo8b;J&)T~TkPwxS}K_xd?3FC+2YJ>0{Ps@8q6+pGPBeB=hpIc zt-@?^;unjv8M0+_-~w$d>v=tE=ySvZ^5lJ`leb&e0L6T!Rx!6aS>+a&Oe<4tpBp_$5lO(+XiM@V*_pEE<^R-k@_#x_ zI_95|^beAb(w6%2viYXcGu69|<5QRr41Q9nzV=h)Y2^%mUVSa~W0z|$R9{QCyWstz z!rc6v-c@c6>T+5>cQ!z`z?J%=id5?J6+tU2AO&Jg$|%Wd>NYN8q)m#+UMI7G5&^N@bjlhBRQw&`EUKd znJt{BD-!1%;rSJX=Q+Yt83nMf2H=T&oFf9k^I3R!{P29PNj$&iQa^teuzt#lt`rwB l`$hm+>6ai^s|W|aY5O6 Date: Wed, 25 Sep 2024 10:05:06 +0000 Subject: [PATCH 6/9] Revert "Do not evaluate failing mlr3::lrn chunk (#1111 hotfix)" This reverts commit b40278062824f078fc2b2cb34e92d817123a2060. --- 12-spatial-cv.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/12-spatial-cv.Rmd b/12-spatial-cv.Rmd index 2788b5a3d..79c5ea545 100644 --- a/12-spatial-cv.Rmd +++ b/12-spatial-cv.Rmd @@ -501,7 +501,7 @@ To allow for non-linear relationships, we use the popular radial basis function Setting the `type` argument to `"C-svc"` makes sure that `ksvm()` is solving a classification task. To make sure that the tuning does not stop because of one failing model, we additionally define a fallback learner (for more information please refer to https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-fallback). -```{r 12-spatial-cv-24, eval=FALSE} +```{r 12-spatial-cv-24} lrn_ksvm = mlr3::lrn("classif.ksvm", predict_type = "prob", kernel = "rbfdot", type = "C-svc") lrn_ksvm$fallback = lrn("classif.featureless", predict_type = "prob") @@ -510,7 +510,7 @@ lrn_ksvm$fallback = lrn("classif.featureless", predict_type = "prob") The next stage is to specify a resampling strategy. Again we will use a 100-repeated 5-fold spatial CV\index{cross-validation!spatial CV}. -```{r 12-spatial-cv-25, eval=FALSE} +```{r 12-spatial-cv-25} # performance estimation level perf_level = mlr3::rsmp("repeated_spcv_coords", folds = 5, repeats = 100) ``` From 9710a814ecf262c839b48eed26c3a9f498e48fd6 Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 10:24:08 +0000 Subject: [PATCH 7/9] set eval=TRUE and save 15-tune.rds anew --- 12-spatial-cv.Rmd | 12 ++++++------ extdata/15-tune.rds | Bin 216306 -> 210998 bytes 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/12-spatial-cv.Rmd b/12-spatial-cv.Rmd index 79c5ea545..fbcc4e9f8 100644 --- a/12-spatial-cv.Rmd +++ b/12-spatial-cv.Rmd @@ -297,7 +297,7 @@ For spatial CV, we need to provide a few extra arguments. The `coordinate_names` argument expects the names of the coordinate columns (see Section \@ref(intro-cv) and Figure \@ref(fig:partitioning)). Additionally, we should indicate the used CRS (`crs`) and decide if we want to use the coordinates as predictors in the modeling (`coords_as_features`). -```{r 12-spatial-cv-11, eval=FALSE} +```{r 12-spatial-cv-11, eval=TRUE} # 1. create task task = mlr3spatiotempcv::as_task_classif_st( mlr3::as_data_backend(lsl), @@ -358,7 +358,7 @@ This yields all learners able to model two-class problems (landslide yes or no). We opt for the binomial classification\index{classification} method used in Section \@ref(conventional-model) and implemented as `classif.log_reg` in **mlr3learners**. Additionally, we need to specify the `predict.type` which determines the type of the prediction with `prob` resulting in the predicted probability for landslide occurrence between 0 and 1 (this corresponds to `type = response` in `predict.glm()`). -```{r 12-spatial-cv-13, eval=FALSE} +```{r 12-spatial-cv-13, eval=TRUE} # 2. specify learner learner = mlr3::lrn("classif.log_reg", predict_type = "prob") ``` @@ -402,7 +402,7 @@ We will use a 100-repeated 5-fold spatial CV\index{cross-validation!spatial CV}: In the meantime, its functionality was integrated into the **mlr3** ecosystem which is the reason why we are using **mlr3** [@schratz_hyperparameter_2019]. The **tidymodels** framework is another umbrella-package for streamlined modeling in R; however, it only recently integrated support for spatial cross validation via **spatialsample** which so far only supports one spatial resampling method. -```{r 12-spatial-cv-18, eval=FALSE} +```{r 12-spatial-cv-18, eval=TRUE} # 3. specify resampling resampling = mlr3::rsmp("repeated_spcv_coords", folds = 5, repeats = 100) ``` @@ -489,7 +489,7 @@ Before defining spatial tuning, we will set up the **mlr3**\index{mlr3 (package) The classification\index{classification} task remains the same, hence we can simply reuse the `task` object created in Section \@ref(glm). Learners implementing SVM can be found using the `list_mlr3learners()` command of the **mlr3extralearners**. -```{r 12-spatial-cv-23, eval=FALSE, echo=FALSE} +```{r 12-spatial-cv-23, eval=TRUE, echo=TRUE} mlr3_learners = mlr3extralearners::list_mlr3learners() mlr3_learners |> dplyr::filter(class == "classif" & grepl("svm", id)) |> @@ -531,7 +531,7 @@ The random selection of values C and Sigma is additionally restricted to a prede The range of the tuning space was chosen with values recommended in the literature [@schratz_hyperparameter_2019]. To find the optimal hyperparameter combination, we fit 50 models (`terminator` object in the code chunk below) in each of these subfolds with randomly selected values for the hyperparameters C and Sigma. -```{r 12-spatial-cv-26, eval=FALSE} +```{r 12-spatial-cv-26, eval=TRUE} # five spatially disjoint partitions tune_level = mlr3::rsmp("spcv_coords", folds = 5) # define the outer limits of the randomly selected hyperparameters @@ -546,7 +546,7 @@ tuner = mlr3tuning::tnr("random_search") The next stage is to modify the learner `lrn_ksvm` in accordance with all the characteristics defining the hyperparameter tuning with `auto_tuner()`. -```{r 12-spatial-cv-27, eval=FALSE} +```{r 12-spatial-cv-27, eval=TRUE} at_ksvm = mlr3tuning::auto_tuner( learner = lrn_ksvm, resampling = tune_level, diff --git a/extdata/15-tune.rds b/extdata/15-tune.rds index 54735b0f23d24b06f046ed47cf4234eb7727f21e..718cff88d9d5246a80e5aa5239d62ce7b184c37a 100644 GIT binary patch literal 210998 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file changed, 3 insertions(+), 1 deletion(-) diff --git a/12-spatial-cv.Rmd b/12-spatial-cv.Rmd index fbcc4e9f8..bb35c488d 100644 --- a/12-spatial-cv.Rmd +++ b/12-spatial-cv.Rmd @@ -504,7 +504,9 @@ To make sure that the tuning does not stop because of one failing model, we addi ```{r 12-spatial-cv-24} lrn_ksvm = mlr3::lrn("classif.ksvm", predict_type = "prob", kernel = "rbfdot", type = "C-svc") -lrn_ksvm$fallback = lrn("classif.featureless", predict_type = "prob") +lrn_ksvm$encapsulate(method = "try", + fallback = lrn("classif.featureless", + predict_type = "prob")) ``` The next stage is to specify a resampling strategy. From bd60a0531b83cba47856c58e894472aabd694bb5 Mon Sep 17 00:00:00 2001 From: jannes-m Date: Wed, 25 Sep 2024 12:32:39 +0000 Subject: [PATCH 9/9] rebuild the autotuner --- 15-eco.Rmd | 2 +- extdata/15-tune.rds | Bin 210998 -> 180878 bytes 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/15-eco.Rmd b/15-eco.Rmd index 07758e904..af07ad5e7 100644 --- a/15-eco.Rmd +++ b/15-eco.Rmd @@ -507,7 +507,7 @@ Calling the `train()`-method of the `AutoTuner`-object finally runs the hyperpar ```{r 15-eco-24, eval=FALSE, cache=TRUE, cache.lazy=FALSE} # hyperparameter tuning -set.seed(08012024) +set.seed(24092024) autotuner_rf$train(task) ``` diff --git a/extdata/15-tune.rds b/extdata/15-tune.rds index 718cff88d9d5246a80e5aa5239d62ce7b184c37a..b3b592461ed4e8864819f6a0e68633f328b7728e 100644 GIT binary patch literal 180878 zcmbrlbx<9_w>64ua1ZY8!6mr6yL)ig;O-7Vg1fsr0S@l&?(PuYA;0_GcfYFp{(4lM zJ-t`2UfoR1O!wYBLl_AI^6wA)j1SryeQ?n&cQFWYMhJtNSVQ5204yRP0xnELNCGje 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