Makefile and scripts cleanup; lazar, R and tensorflow tables

1 files changed, 11 insertions, 1 deletions

diff --git a/mutagenicity.md b/mutagenicity.md
index 418c2d1..9012ce5 100644
--- a/mutagenicity.md
+++ b/mutagenicity.md
@@ -476,6 +476,16 @@ Results
 
 Crossvalidation results are summarized in the following tables: @tbl:lazar shows `lazar` results with MolPrint2D and PaDEL descriptors, @tbl:R summarizes R results and @tbl:tensorflow Tensorflow results.
 
+
+```{#tbl:lazar .table file="tables/lazar-summary.csv" caption="Summary of lazar crossvalidation results"}
+```
+
+```{#tbl:R .table file="tables/r-summary.csv" caption="Summary of R crossvalidation results"}
+```
+
+```{#tbl:tensorflow .table file="tables/tensorflow-summary.csv" caption="Summary of tensorflow crossvalidation results"}
+```
+
 @fig:roc depicts the position of all crossvalidation results in receiver operating characteristic (ROC) space.
 
 Confusion matrices for all models are available from the git repository http://git.in-silico.ch/mutagenicity-paper/10-fold-crossvalidations/confusion-matrices/, individual predictions can be found in 
@@ -483,6 +493,7 @@ http://git.in-silico.ch/mutagenicity-paper/10-fold-crossvalidations/predictions/
 
 The most accurate crossvalidation predictions have been obtained with `lazar` models with MolPrint2D descriptors ({{lazar-high-confidence.acc}} for predictions with high confidence, {{lazar-all.acc}} for all predictions). Models utilizing PaDEL descriptors have generally lower accuracies ranging from TODO to TODO. Sensitivity and specificity is generally well balanced with the exception of `lazar`-PaDEL (low sensitivity) and R deep learning (low specificity) models.
 
+<!--
 | |R-RF | R-SVM | R-DL | TF | TF-FS | L | L-HC | L-P | L-P-HC|
 |-|-----|-------|------|----|-------|---|------|------|--------|
 |Accuracy|{{R-RF.acc}}|{{R-SVM.acc}}|{{R-DL.acc}}|{{tensorflow-all.acc}}|{{tensorflow-selected.acc}}|{{lazar-all.acc}}|{{lazar-high-confidence.acc}}|{{lazar-padel-all.acc}}|{{lazar-padel-high-confidence.acc}}|
@@ -496,7 +507,6 @@ The most accurate crossvalidation predictions have been obtained with `lazar` mo
 
 ![ROC plot of crossvalidation results. *R-RF*: R Random Forests, *R-SVM*: R Support Vector Machines, *R-DL*: R Deep Learning, *TF*: Tensorflow without feature selection, *TF-FS*: Tensorflow with feature selection, *L*: lazar, *L-HC*: lazar high confidence predictions, *L-P*: lazar with PaDEL descriptors, *L-P-HC*: lazar PaDEL high confidence predictions (overlaps with L-P)](figures/roc.png){#fig:roc}
 
-<!--
 R Models
 --------