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Learner training
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@fouodo
fouodo committed Jul 17, 2024
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79 changes: 66 additions & 13 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -118,22 +118,22 @@ print(same_param_varsel)

```{r varsel_object, include=TRUE, eval=TRUE}
varsel_ge <- VarSel$new(id = "varsel_geneexpr",
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_ge)
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_ge)
varsel_pr <- VarSel$new(id = "varsel_geneexpr",
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_pr)
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_pr)
varsel_me <- VarSel$new(id = "varsel_geneexpr",
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_me)
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_me)
```

- Perform variable selection on our training study.
Expand All @@ -143,4 +143,57 @@ var_sel_res <- train_study$varSelection()
print(var_sel_res)
```

For each layer the variable selection results show which variable have been selected.
For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire study. However, users can opt to conduct variable selection on individual layers if desired.

#### B) Training

We can now train our study using the subset of selected variables. Users can choose to set up layer-specific learners, but for illustration, we will use the same learner for all layers.

- Set up the same leaner parameters.

```{r lrner_param, include=TRUE, eval=TRUE}
same_param <- ParamLrner$new(id = "ParamRanger",
param_list = list(probability = TRUE,
mtry = 1),
hyperparam_list = list(num.trees = 1000))
```

- Set up learners for each layer. We will use a weighted sum for the meta-analysis.

```{r lrner, include=TRUE, eval=TRUE}
lrner_ge <- Lrner$new(id = "ranger",
package = "ranger",
lrn_fct = "ranger",
param = same_param,
train_layer = tl_ge)
lrner_pr <- Lrner$new(id = "ranger",
package = "ranger",
lrn_fct = "ranger",
param = same_param,
train_layer = tl_pr)
lrner_me <- Lrner$new(id = "ranger",
package = "ranger",
lrn_fct = "ranger",
param = same_param,
train_layer = tl_me)
lrner_meta <- Lrner$new(id = "weighted",
lrn_fct = "weightedMeanLearner",
param = ParamLrner$new(id = "ParamWeighted",
param_list = list(),
hyperparam_list = list()),
train_layer = tl_meta)
```

- Train the study with the selected variables.

```{r lrner_train, include=TRUE, eval=TRUE}
# Retrieve the target variable for resampling reasons. Resampling will be used by
# fuseMLR to generate meta data.
disease <- train_study$getTargetValues()$disease
trained_study <- train_study$train(resampling_method = "caret::createFolds",
resampling_arg = list(y = disease,
k = 2),
use_var_sel = TRUE)
# Let us now check the status of our study.
print(trained_study)
```
170 changes: 116 additions & 54 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -199,22 +199,22 @@ print(same_param_varsel)

``` r
varsel_ge <- VarSel$new(id = "varsel_geneexpr",
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_ge)
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_ge)

varsel_pr <- VarSel$new(id = "varsel_geneexpr",
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_pr)
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_pr)

varsel_me <- VarSel$new(id = "varsel_geneexpr",
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_me)
package = "Boruta",
varsel_fct = "Boruta",
param = same_param_varsel,
train_layer = tl_me)
```

- Perform variable selection on our training study.
Expand All @@ -227,49 +227,111 @@ print(var_sel_res)
## Layer variable
## 1 geneexpr ACACA
## 2 geneexpr BAP1
## 3 geneexpr CDH3
## 4 geneexpr CHEK2
## 5 geneexpr EIF4E
## 6 geneexpr MAP2K1
## 7 geneexpr MAPK14
## 8 geneexpr PCNA
## 9 geneexpr YWHAE
## 10 geneexpr YWHAZ
## 11 proteinexpr Bap1.c.4
## 12 proteinexpr Bid
## 13 proteinexpr Cyclin_E2
## 14 proteinexpr P.Cadherin
## 15 proteinexpr Chk1
## 16 proteinexpr Chk1_pS345
## 17 proteinexpr EGFR
## 18 proteinexpr EGFR_pY1173
## 19 proteinexpr HER3_pY1289
## 20 proteinexpr MIG.6
## 21 proteinexpr ETS.1
## 22 proteinexpr MEK1_pS217_S221
## 23 proteinexpr p38_MAPK
## 24 proteinexpr c.Met_pY1235
## 25 proteinexpr N.Ras
## 26 proteinexpr PCNA
## 27 proteinexpr PEA15_pS116
## 28 proteinexpr PKC.delta_pS664
## 29 proteinexpr Rad50
## 30 proteinexpr C.Raf_pS338
## 31 proteinexpr p70S6K
## 32 proteinexpr p70S6K_pT389
## 33 proteinexpr Smad4
## 34 proteinexpr STAT3_pY705
## 35 proteinexpr 14.3.3_epsilon
## 36 methylation cg20139214
## 37 methylation cg18457775
## 38 methylation cg01306510
## 39 methylation cg02412050
## 3 geneexpr CHEK2
## 4 geneexpr EIF4E
## 5 geneexpr MAP2K1
## 6 geneexpr MAPK14
## 7 geneexpr PCNA
## 8 geneexpr YWHAE
## 9 proteinexpr Bap1.c.4
## 10 proteinexpr Bid
## 11 proteinexpr Cyclin_E2
## 12 proteinexpr P.Cadherin
## 13 proteinexpr Chk1
## 14 proteinexpr Chk1_pS345
## 15 proteinexpr EGFR
## 16 proteinexpr EGFR_pY1173
## 17 proteinexpr HER3_pY1289
## 18 proteinexpr MIG.6
## 19 proteinexpr ETS.1
## 20 proteinexpr MEK1_pS217_S221
## 21 proteinexpr p38_MAPK
## 22 proteinexpr c.Met_pY1235
## 23 proteinexpr N.Ras
## 24 proteinexpr PCNA
## 25 proteinexpr PEA15_pS116
## 26 proteinexpr PKC.delta_pS664
## 27 proteinexpr Rad50
## 28 proteinexpr C.Raf_pS338
## 29 proteinexpr p70S6K
## 30 proteinexpr p70S6K_pT389
## 31 proteinexpr Smad4
## 32 proteinexpr STAT3_pY705
## 33 proteinexpr 14.3.3_epsilon
## 34 methylation cg20139214
## 35 methylation cg18457775
## 36 methylation cg09637363
## 37 methylation cg01306510
## 38 methylation cg02412050
## 39 methylation cg25984124
## 40 methylation cg07566050
## 41 methylation cg02630105
## 42 methylation cg20849549
## 43 methylation cg00547829
## 44 methylation cg25539131
## 45 methylation cg07064406
## 43 methylation cg25539131
## 44 methylation cg07064406

For each layer the variable selection results show which variable have
been selected.
For each layer, the variable selection results show the chosen
variables. In this example, we perform variable selection on the entire
study. However, users can opt to conduct variable selection on
individual layers if desired.

#### B) Training

We can now train our study using the subset of selected variables. Users
can choose to set up layer-specific learners, but for illustration, we
will use the same learner for all layers.

- Set up the same leaner parameters.

``` r
same_param <- ParamLrner$new(id = "ParamRanger",
param_list = list(probability = TRUE,
mtry = 1),
hyperparam_list = list(num.trees = 1000))
```

- Set up learners for each layer. We will use a weighted sum for the
meta-analysis.

``` r
lrner_ge <- Lrner$new(id = "ranger",
package = "ranger",
lrn_fct = "ranger",
param = same_param,
train_layer = tl_ge)
lrner_pr <- Lrner$new(id = "ranger",
package = "ranger",
lrn_fct = "ranger",
param = same_param,
train_layer = tl_pr)
lrner_me <- Lrner$new(id = "ranger",
package = "ranger",
lrn_fct = "ranger",
param = same_param,
train_layer = tl_me)
lrner_meta <- Lrner$new(id = "weighted",
lrn_fct = "weightedMeanLearner",
param = ParamLrner$new(id = "ParamWeighted",
param_list = list(),
hyperparam_list = list()),
train_layer = tl_meta)
```

- Train the study with the selected variables.

``` r
# Retrieve the target variable for resampling reasons. Resampling will be used by
# fuseMLR to generate meta data.
disease <- train_study$getTargetValues()$disease
trained_study <- train_study$train(resampling_method = "caret::createFolds",
resampling_arg = list(y = disease,
k = 2),
use_var_sel = TRUE)
# Let us now check the status of our study.
print(trained_study)
```

## TrainStudy : train_study
## Status : Trained
## Number of layers: 4
## Layers trained : 4

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