various fix, estimparam release

.travis.yml

@@ -36,7 +36,7 @@ script:
   - cd vcpkg
   - CC=clang-7 && CXX=clang++-7 ./bootstrap-vcpkg.sh -useSystemBinaries -disableMetrics
   - ./vcpkg integrate install
-  - ./vcpkg install range-v3 catch2 hdf5[cpp] highfive cxxopts
+  - ./vcpkg install range-v3 catch2 hdf5[cpp] highfive cxxopts fmt boost-accumulators
   - ./vcpkg install eigen3 --head
   - cd ..
   - mkdir build

README.md

@@ -12,16 +12,17 @@ Libraries we use :
 - [Ranger : A Fast Implementation of Random Forests](https://github.com/imbs-hl/ranger) [@wright2015ranger], there tuned to make "online" calculations (not storing the whole forests in memory)
 - [Eigen : C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.](http://eigen.tuxfamily.org)
 
-# Usage
-```
+## Model Choice
+
+```text
  - ABC Random Forest/Model choice command line options
 Usage:
   ModelChoice [OPTION...]
 
   -h, --header arg        Header file (default: headerRF.txt)
   -r, --reftable arg      Reftable file (default: reftableRF.bin)
   -b, --statobs arg       Statobs file (default: statobsRF.txt)
-  -o, --output arg        Prefix output (default: onlineranger_out)
+  -o, --output arg        Prefix output (default: modelchoice_out)
   -n, --nref arg          Number of samples, 0 means all (default: 0)
   -m, --minnodesize arg   Minimal node size. 0 means 1 for classification or
                           5 for regression (default: 0)
@@ -34,8 +35,52 @@ Usage:
 ```
 
 Header, reftable and statobs files should be in the current directory.
-Three files are created : 
-- `onlineranger_out.ooberror` : OOB Error rate vs number of trees (line number is the number of trees)
-- `onlineranger_out.confusion` : OOB Confusion matrix of the classifier
-- `onlineranger_out.importance` : variables importance (sorted)
 
+Three files are created :
+
+
+- `modelchoice_out.ooberror` : OOB Error rate vs number of trees (line number is the number of trees)
+- `modelchoice_out.importance` : variables importance (sorted)
+- `modelchoice_out.predictions` : votes, prediction and posterior error rate
+- `modelchoice_out.confusion` : OOB Confusion matrix of the classifier
+
+## Parameter Estimation
+
+```text
+ - ABC Random Forest/Model parameter estimation command line options
+Usage:
+  ..\..\build\EstimParam.exe [OPTION...]
+
+  -h, --header arg        Header file (default: headerRF.txt)
+  -r, --reftable arg      Reftable file (default: reftableRF.bin)
+  -b, --statobs arg       Statobs file (default: statobsRF.txt)
+  -o, --output arg        Prefix output (default: estimparam_out)
+  -n, --nref arg          Number of samples, 0 means all (default: 0)
+  -m, --minnodesize arg   Minimal node size. 0 means 1 for classification or
+                          5 for regression (default: 0)
+  -t, --ntree arg         Number of trees (default: 500)
+  -j, --threads arg       Number of threads, 0 means all (default: 0)
+  -s, --seed arg          Seed, 0 means generated (default: 0)
+  -c, --noisecolumns arg  Number of noise columns (default: 5)
+  -p, --pls               Enable PLS (default: true)
+      --chosenscen arg    Chosen scenario (mandatory)
+      --ntrain arg        number of training samples (mandatory)
+      --ntest arg         number of testing samples (mandatory)
+      --parameter arg     name of the parameter of interest (mandatory)
+      --help              Print help
+```
+
+Header, reftable and statobs files should be in the current directory.
+
+Five files (or seven if pls activated) are created :
+
+- `estimparam_out.ooberror` : OOB MSE rate vs number of trees (line number is the number of trees)
+- `estimparam_out.importance` : variables importance (sorted)
+- `estimparam_out.predictions` : expectation, variance and 0.05, 0.5, 0.95 quantile for prediction
+- `estimparam_out.predweights` : weights of the prediction (for density plot)
+- `estimparam_out.teststats` : various statistics on test (MSE, NMSE, NMAE etc.)
+
+if pls enabled :
+
+- `estimparam_out.plsvar` : variance explained by number of components
+- `estimparam_out.plsweights` : variable weight in the first component (sorted by absolute value)