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+%global _empty_manifest_terminate_build 0
+Name:		python-pyabcranger
+Version:	0.0.69
+Release:	1
+Summary:	ABC random forests for model choice and parameter estimation, python wrapper
+License:	MIT License
+URL:		https://github.com/diyabc/abcranger
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/47/37/21ddd826ccf085c31879705a0b283dd3f16e0712ce8563d325e786b7ed7b/pyabcranger-0.0.69.tar.gz
+
+
+%description
+- <a href="#python" id="toc-python">Python</a>
+- <a href="#usage" id="toc-usage">Usage</a>
+- <a href="#model-choice" id="toc-model-choice">Model Choice</a>
+- <a href="#parameter-estimation" id="toc-parameter-estimation">Parameter
+  Estimation</a>
+- <a href="#various" id="toc-various">Various</a>
+- <a href="#todo" id="toc-todo">TODO</a>
+- <a href="#references" id="toc-references">References</a>
+<!-- pandoc -f markdown README-ORIG.md -t gfm -o README.md --citeproc -s --toc --toc-depth=1 --webtex -->
+[![PyPI](https://img.shields.io/pypi/v/pyabcranger.svg)](https://pypi.python.org/pypi/pyabcranger)
+[![abcranger-build](https://github.com/diyabc/abcranger/workflows/abcranger-build/badge.svg)](https://github.com/diyabc/abcranger/actions?query=workflow%3Aabcranger-build+branch%3Amaster)
+Random forests methodologies for :
+- ABC model choice ([Pudlo et al. 2015](#ref-pudlo2015reliable))
+- ABC Bayesian parameter inference ([Raynal et al.
+  2018](#ref-raynal2016abc))
+Libraries we use :
+- [Ranger](https://github.com/imbs-hl/ranger) ([Wright and Ziegler
+  2015](#ref-wright2015ranger)) : we use our own fork and have tuned
+  forests to do “online”[^1] computations (Growing trees AND making
+  predictions in the same pass, which removes the need of in-memory
+  storage of the whole forest)[^2].
+- [Eigen3](http://eigen.tuxfamily.org) ([Guennebaud, Jacob, et al.
+  2010](#ref-eigenweb))
+As a mention, we use our own implementation of LDA and PLS from
+([Friedman, Hastie, and Tibshirani 2001, 1:81,
+114](#ref-friedman2001elements)), PLS is optimized for univariate, see
+[5.1](#sec-plsalgo). For linear algebra optimization purposes on large
+reftables, the Linux version of binaries (standalone and python wheel)
+are statically linked with [Intel’s Math Kernel
+Library](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-programming-guide/top/api-based-programming/intel-oneapi-math-kernel-library-onemkl.html),
+in order to leverage multicore and SIMD extensions on modern cpus.
+There is one set of binaries, which contains a Macos/Linux/Windows (x64
+only) binary for each platform. There are available within the
+“[Releases](https://github.com/fradav/abcranger/releases)” tab, under
+“Assets” section (unfold it to see the list).
+This is pure command line binary, and they are no prerequisites or
+library dependencies in order to run it. Just download them and launch
+them from your terminal software of choice. The usual caveats with
+command line executable apply there : if you’re not proficient with the
+command line interface of your platform, please learn some basics or ask
+someone who might help you in those matters.
+The standalone is part of a specialized Population Genetics graphical
+interface [DIYABC-RF](https://diyabc.github.io/), presented in MER
+(Molecular Ecology Resources, Special Issue), ([Collin et al.
+2021](#ref-Collin_2021)).
+# Python
+## Installation
+``` bash
+pip install pyabcranger
+```
+## Notebooks examples
+- On a [toy example with
+  ![MA(q)](https://latex.codecogs.com/png.image?%5Cbg_black&space;MA%28q%29 "MA(q)")](https://github.com/diyabc/abcranger/blob/master/notebooks/Toy%20example%20MA(q).ipynb),
+  using ([Lintusaari et al. 2018](#ref-JMLR:v19:17-374)) as
+  graph-powered engine.
+- [Population genetics
+  demo](https://github.com/diyabc/abcranger/blob/master/notebooks/Population%20genetics%20Demo.ipynb),
+  data from ([Collin et al. 2021](#ref-Collin_2021)), available
+  [there](https://github.com/diyabc/diyabc/tree/master/diyabc-tests/MER/modelchoice/IndSeq)
+# Usage
+``` text
+ - ABC Random Forest - Model choice or parameter estimation command line options
+Usage:
+  -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 (modelchoice_out or estimparam_out by
+                          default)
+  -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, generated by default (default: 0)
+  -c, --noisecolumns arg  Number of noise columns (default: 5)
+      --nolinear          Disable LDA for model choice or PLS for parameter
+                          estimation
+      --plsmaxvar arg     Percentage of maximum explained Y-variance for
+                          retaining pls axis (default: 0.9)
+      --chosenscen arg    Chosen scenario (mandatory for parameter
+                          estimation)
+      --noob arg          number of oob testing samples (mandatory for
+                          parameter estimation)
+      --parameter arg     name of the parameter of interest (mandatory for
+                          parameter estimation)
+  -g, --groups arg        Groups of models
+      --help              Print help
+```
+- If you provide `--chosenscen`, `--parameter` and `--noob`, parameter
+  estimation mode is selected.
+- Otherwise by default it’s model choice mode.
+- Linear additions are LDA for model choice and PLS for parameter
+  estimation, “–nolinear” options disables them in both case.
+# Model Choice
+![Terminal model choice](./model_choice.gif)
+## Example
+Example :
+`abcranger -t 10000 -j 8`
+Header, reftable and statobs files should be in the current directory.
+## Groups
+With the option `-g` (or `--groups`), you may “group” your models in
+several groups splitted . For example if you have six models, labeled
+from 1 to 6 \`-g “1,2,3;4,5,6”
+## Generated files
+Four 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
+![Terminal estim param](./estim_param.gif)
+## Composite parameters
+When specifying the parameter (option `--parameter`), one may specify
+simple composite parameters as division, addition or multiplication of
+two existing parameters. like `t/N` or `T1+T2`.
+## A note about PLS heuristic
+The `--plsmaxvar` option (defaulting at 0.90) fixes the number of
+selected pls axes so that we get at least the specified percentage of
+maximum explained variance of the output. The explained variance of the
+output of the
+![m](https://latex.codecogs.com/png.image?%5Cbg_black&space;m "m") first
+axes is defined by the R-squared of the output:
+![Yvar^m = \frac{\sum\_{i=1}^{N}{(\hat{y}^{m}\_{i}-\bar{y})^2}}{\sum\_{i=1}^{N}{(y\_{i}-\hat{y})^2}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;Yvar%5Em%20%3D%20%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5E%7BN%7D%7B%28%5Chat%7By%7D%5E%7Bm%7D_%7Bi%7D-%5Cbar%7By%7D%29%5E2%7D%7D%7B%5Csum_%7Bi%3D1%7D%5E%7BN%7D%7B%28y_%7Bi%7D-%5Chat%7By%7D%29%5E2%7D%7D "Yvar^m = \frac{\sum_{i=1}^{N}{(\hat{y}^{m}_{i}-\bar{y})^2}}{\sum_{i=1}^{N}{(y_{i}-\hat{y})^2}}")
+where
+![\hat{y}^{m}](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Chat%7By%7D%5E%7Bm%7D "\hat{y}^{m}")
+is the output
+![Y](https://latex.codecogs.com/png.image?%5Cbg_black&space;Y "Y")
+scored by the pls for the
+![m](https://latex.codecogs.com/png.image?%5Cbg_black&space;m "m")th
+component. So, only the
+![n\_{comp}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D "n_{comp}")
+first axis are kept, and :
+![n\_{comp} = \underset{Yvar^m \leq{} 0.90\*Yvar^M, }{\operatorname{argmax}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D%20%3D%20%5Cunderset%7BYvar%5Em%20%5Cleq%7B%7D%200.90%2AYvar%5EM%2C%20%7D%7B%5Coperatorname%7Bargmax%7D%7D "n_{comp} = \underset{Yvar^m \leq{} 0.90*Yvar^M, }{\operatorname{argmax}}")
+Note that if you specify 0 as `--plsmaxvar`, an “elbow” heuristic is
+activiated where the following condition is tested for every computed
+axis :
+![\frac{Yvar^{k+1}+Yvar^{k}}{2} \geq 0.99(N-k)\left(Yvar^{k+1}-Yvar^ {k}\right)](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cfrac%7BYvar%5E%7Bk%2B1%7D%2BYvar%5E%7Bk%7D%7D%7B2%7D%20%5Cgeq%200.99%28N-k%29%5Cleft%28Yvar%5E%7Bk%2B1%7D-Yvar%5E%20%7Bk%7D%5Cright%29 "\frac{Yvar^{k+1}+Yvar^{k}}{2} \geq 0.99(N-k)\left(Yvar^{k+1}-Yvar^ {k}\right)")
+If this condition is true for a windows of previous axes, sized to 10%
+of the total possible axis, then we stop the PLS axis computation.
+In practice, we find this
+![n\_{heur}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bheur%7D "n_{heur}")
+close enough to the previous
+![n\_{comp}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D "n_{comp}")
+for 99%, but it isn’t guaranteed.
+## The signification of the `noob` parameter
+The median global/local statistics and confidence intervals (global)
+measures for parameter estimation need a number of OOB samples
+(`--noob`) to be reliable (typlially 30% of the size of the dataset is
+sufficient). Be aware than computing the whole set (i.e. assigning
+`--noob` the same than for `--nref`) for weights predictions ([Raynal et
+al. 2018](#ref-raynal2016abc)) could be very costly, memory and
+cpu-wise, if your dataset is large in number of samples, so it could be
+adviseable to compute them for only choose a subset of size `noob`.
+## Example (parameter estimation)
+Example (working with the dataset in `test/data`) :
+`abcranger -t 1000 -j 8 --parameter ra --chosenscen 1 --noob 50`
+Header, reftable and statobs files should be in the current directory.
+## Generated files (parameter estimation)
+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` : csv of the value/weights pairs of the
+  prediction (for density plot)
+- `estimparam_out.oobstats` : various statistics on oob (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)
+# Various
+## Partial Least Squares algorithm
+1.  ![X\_{0}=X ; y\_{0}=y](https://latex.codecogs.com/png.image?%5Cbg_black&space;X_%7B0%7D%3DX%20%3B%20y_%7B0%7D%3Dy "X_{0}=X ; y_{0}=y")
+2.  For
+    ![k=1,2,...,s](https://latex.codecogs.com/png.image?%5Cbg_black&space;k%3D1%2C2%2C...%2Cs "k=1,2,...,s")
+    1.  ![w\_{k}=\frac{X\_{k-1}^{T} y\_{k-1}}{y\_{k-1}^{T} y\_{k-1}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D%7By_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D "w_{k}=\frac{X_{k-1}^{T} y_{k-1}}{y_{k-1}^{T} y_{k-1}}")
+    2.  Normalize
+        ![w_k](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_k "w_k")
+        to
+        ![1](https://latex.codecogs.com/png.image?%5Cbg_black&space;1 "1")
+    3.  ![t\_{k}=\frac{X\_{k-1} w\_{k}}{w\_{k}^{T} w\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;t_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%20w_%7Bk%7D%7D%7Bw_%7Bk%7D%5E%7BT%7D%20w_%7Bk%7D%7D "t_{k}=\frac{X_{k-1} w_{k}}{w_{k}^{T} w_{k}}")
+    4.  ![p\_{k}=\frac{X\_{k-1}^{T} t\_{k}}{t\_{k}^{T} t\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;p_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20t_%7Bk%7D%7D%7Bt_%7Bk%7D%5E%7BT%7D%20t_%7Bk%7D%7D "p_{k}=\frac{X_{k-1}^{T} t_{k}}{t_{k}^{T} t_{k}}")
+    5.  ![X\_{k}=X\_{k-1}-t\_{k} p\_{k}^{T}](https://latex.codecogs.com/png.image?%5Cbg_black&space;X_%7Bk%7D%3DX_%7Bk-1%7D-t_%7Bk%7D%20p_%7Bk%7D%5E%7BT%7D "X_{k}=X_{k-1}-t_{k} p_{k}^{T}")
+    6.  ![q\_{k}=\frac{y\_{k-1}^{T} t\_{k}}{t\_{k}^{T} t\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;q_%7Bk%7D%3D%5Cfrac%7By_%7Bk-1%7D%5E%7BT%7D%20t_%7Bk%7D%7D%7Bt_%7Bk%7D%5E%7BT%7D%20t_%7Bk%7D%7D "q_{k}=\frac{y_{k-1}^{T} t_{k}}{t_{k}^{T} t_{k}}")
+    7.  ![u\_{k}=\frac{y\_{k-1}}{q\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;u_%7Bk%7D%3D%5Cfrac%7By_%7Bk-1%7D%7D%7Bq_%7Bk%7D%7D "u_{k}=\frac{y_{k-1}}{q_{k}}")
+    8.  ![y\_{k}=y\_{k-1}-q\_{k} t\_{k}](https://latex.codecogs.com/png.image?%5Cbg_black&space;y_%7Bk%7D%3Dy_%7Bk-1%7D-q_%7Bk%7D%20t_%7Bk%7D "y_{k}=y_{k-1}-q_{k} t_{k}")
+**Comment** When there isn’t any missing data, stages
+![2.1](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.1 "2.1")
+and
+![2.2](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.2 "2.2")
+could be replaced by
+![w\_{k}=\frac{X\_{k-1}^{T} y\_{k-1}}{\left\\\|X\_{k-1}^{T} y\_{k-1}\right\\\|}](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D%7B%5Cleft%5C%7CX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%5Cright%5C%7C%7D "w_{k}=\frac{X_{k-1}^{T} y_{k-1}}{\left\|X_{k-1}^{T} y_{k-1}\right\|}")
+and
+![2.3](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.3 "2.3")
+by
+![t\_{k}=X\_{k-1}w\_{k}](https://latex.codecogs.com/png.image?%5Cbg_black&space;t_%7Bk%7D%3DX_%7Bk-1%7Dw_%7Bk%7D "t_{k}=X_{k-1}w_{k}")
+To get
+![W](https://latex.codecogs.com/png.image?%5Cbg_black&space;W "W") so
+that
+![T=XW](https://latex.codecogs.com/png.image?%5Cbg_black&space;T%3DXW "T=XW")
+we compute :
+![\mathbf{W}=\mathbf{W}^{\*}\left(\widetilde{\mathbf{P}} \mathbf{W}^{\*}\right)^{-1}](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cmathbf%7BW%7D%3D%5Cmathbf%7BW%7D%5E%7B%2A%7D%5Cleft%28%5Cwidetilde%7B%5Cmathbf%7BP%7D%7D%20%5Cmathbf%7BW%7D%5E%7B%2A%7D%5Cright%29%5E%7B-1%7D "\mathbf{W}=\mathbf{W}^{*}\left(\widetilde{\mathbf{P}} \mathbf{W}^{*}\right)^{-1}")
+where
+![\widetilde{\mathbf{P}}\_{K \times p}=\mathbf{t}\left\[p\_{1}, \ldots, p\_{K}\right\]](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cwidetilde%7B%5Cmathbf%7BP%7D%7D_%7BK%20%5Ctimes%20p%7D%3D%5Cmathbf%7Bt%7D%5Cleft%5Bp_%7B1%7D%2C%20%5Cldots%2C%20p_%7BK%7D%5Cright%5D "\widetilde{\mathbf{P}}_{K \times p}=\mathbf{t}\left[p_{1}, \ldots, p_{K}\right]")
+where
+![\mathbf{W}^{\*}\_{p \times K} = \[w_1, \ldots, w_K\]](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cmathbf%7BW%7D%5E%7B%2A%7D_%7Bp%20%5Ctimes%20K%7D%20%3D%20%5Bw_1%2C%20%5Cldots%2C%20w_K%5D "\mathbf{W}^{*}_{p \times K} = [w_1, \ldots, w_K]")
+# TODO
+## Input/Output
+- [x] Integrate hdf5 (or exdir? msgpack?) routines to save/load
+  reftables/observed stats with associated metadata
+- [ ] Provide R code to save/load the data
+- [x] Provide Python code to save/load the data
+## C++ standalone
+- [x] Merge the two methodologies in a single executable with the
+  (almost) the same options
+- [ ] (Optional) Possibly move to another options parser (CLI?)
+## External interfaces
+- [ ] R package
+- [x] Python package
+## Documentation
+- [ ] Code documentation
+- [ ] Document the build
+## Continuous integration
+- [x] Linux CI build with intel/MKL optimizations
+- [x] osX CI build
+- [x] Windows CI build
+## Long/Mid term TODO
+- methodologies parameters auto-tuning
+  - auto-discovering the optimal number of trees by monitoring OOB error
+  - auto-limiting number of threads by available memory
+- Streamline the two methodologies (model choice and then parameters
+  estimation)
+- Write our own tree/rf implementation with better storage efficiency
+  than ranger
+- Make functional tests for the two methodologies
+- Possible to use mondrian forests for online batches ? See
+  ([Lakshminarayanan, Roy, and Teh
+  2014](#ref-lakshminarayanan2014mondrian))
+# References
+This have been the subject of a proceedings in [JOBIM
+2020](https://jobim2020.sciencesconf.org/),
+[PDF](https://hal.archives-ouvertes.fr/hal-02910067v2) and
+[video](https://relaiswebcasting.mediasite.com/mediasite/Play/8ddb4e40fc88422481f1494cf6af2bb71d?catalog=e534823f0c954836bf85bfa80af2290921)
+(in french), ([Collin et al. 2020](#ref-collin:hal-02910067)).
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Collin_2021" class="csl-entry">
+Collin, François-David, Ghislain Durif, Louis Raynal, Eric Lombaert,
+Mathieu Gautier, Renaud Vitalis, Jean-Michel Marin, and Arnaud Estoup.
+2021. “Extending Approximate Bayesian Computation with Supervised
+Machine Learning to Infer Demographic History from Genetic Polymorphisms
+Using DIYABC Random Forest.” *Molecular Ecology Resources* 21 (8):
+2598–2613. https://doi.org/<https://doi.org/10.1111/1755-0998.13413>.
+</div>
+<div id="ref-collin:hal-02910067" class="csl-entry">
+Collin, François-David, Arnaud Estoup, Jean-Michel Marin, and Louis
+Raynal. 2020. “<span class="nocase">Bringing ABC inference to the
+machine learning realm : AbcRanger, an optimized random forests library
+for ABC</span>.” In *JOBIM 2020*, 2020:66. JOBIM. Montpellier, France.
+<https://hal.archives-ouvertes.fr/hal-02910067>.
+</div>
+<div id="ref-friedman2001elements" class="csl-entry">
+Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. *The
+Elements of Statistical Learning*. Vol. 1. 10. Springer series in
+statistics New York, NY, USA:
+</div>
+<div id="ref-eigenweb" class="csl-entry">
+Guennebaud, Gaël, Benoît Jacob, et al. 2010. “Eigen V3.”
+http://eigen.tuxfamily.org.
+</div>
+<div id="ref-lakshminarayanan2014mondrian" class="csl-entry">
+Lakshminarayanan, Balaji, Daniel M Roy, and Yee Whye Teh. 2014.
+“Mondrian Forests: Efficient Online Random Forests.” In *Advances in
+Neural Information Processing Systems*, 3140–48.
+</div>
+<div id="ref-JMLR:v19:17-374" class="csl-entry">
+Lintusaari, Jarno, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén,
+Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka
+Corander, and Samuel Kaski. 2018. “ELFI: Engine for Likelihood-Free
+Inference.” *Journal of Machine Learning Research* 19 (16): 1–7.
+<http://jmlr.org/papers/v19/17-374.html>.
+</div>
+<div id="ref-pudlo2015reliable" class="csl-entry">
+Pudlo, Pierre, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet,
+Mathieu Gautier, and Christian P Robert. 2015. “Reliable ABC Model
+Choice via Random Forests.” *Bioinformatics* 32 (6): 859–66.
+</div>
+<div id="ref-raynal2016abc" class="csl-entry">
+Raynal, Louis, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet,
+Christian P Robert, and Arnaud Estoup. 2018. “<span class="nocase">ABC
+random forests for Bayesian parameter inference</span>.”
+*Bioinformatics* 35 (10): 1720–28.
+<https://doi.org/10.1093/bioinformatics/bty867>.
+</div>
+<div id="ref-wright2015ranger" class="csl-entry">
+Wright, Marvin N, and Andreas Ziegler. 2015. “Ranger: A Fast
+Implementation of Random Forests for High Dimensional Data in c++ and
+r.” *arXiv Preprint arXiv:1508.04409*.
+</div>
+</div>
+[^1]: The term “online” there and in the code has not the usual meaning
+    it has, as coined in “online machine learning”. We still need the
+    entire training data set at once. Our implementation is an “online”
+    one not by the sequential order of the input data, but by the
+    sequential order of computation of the trees in random forests,
+    sequentially computed and then discarded.
+[^2]: We only use the C++ Core of ranger, which is under [MIT
+    License](https://raw.githubusercontent.com/imbs-hl/ranger/master/cpp_version/COPYING),
+    same as ours.
+
+%package -n python3-pyabcranger
+Summary:	ABC random forests for model choice and parameter estimation, python wrapper
+Provides:	python-pyabcranger
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-pyabcranger
+- <a href="#python" id="toc-python">Python</a>
+- <a href="#usage" id="toc-usage">Usage</a>
+- <a href="#model-choice" id="toc-model-choice">Model Choice</a>
+- <a href="#parameter-estimation" id="toc-parameter-estimation">Parameter
+  Estimation</a>
+- <a href="#various" id="toc-various">Various</a>
+- <a href="#todo" id="toc-todo">TODO</a>
+- <a href="#references" id="toc-references">References</a>
+<!-- pandoc -f markdown README-ORIG.md -t gfm -o README.md --citeproc -s --toc --toc-depth=1 --webtex -->
+[![PyPI](https://img.shields.io/pypi/v/pyabcranger.svg)](https://pypi.python.org/pypi/pyabcranger)
+[![abcranger-build](https://github.com/diyabc/abcranger/workflows/abcranger-build/badge.svg)](https://github.com/diyabc/abcranger/actions?query=workflow%3Aabcranger-build+branch%3Amaster)
+Random forests methodologies for :
+- ABC model choice ([Pudlo et al. 2015](#ref-pudlo2015reliable))
+- ABC Bayesian parameter inference ([Raynal et al.
+  2018](#ref-raynal2016abc))
+Libraries we use :
+- [Ranger](https://github.com/imbs-hl/ranger) ([Wright and Ziegler
+  2015](#ref-wright2015ranger)) : we use our own fork and have tuned
+  forests to do “online”[^1] computations (Growing trees AND making
+  predictions in the same pass, which removes the need of in-memory
+  storage of the whole forest)[^2].
+- [Eigen3](http://eigen.tuxfamily.org) ([Guennebaud, Jacob, et al.
+  2010](#ref-eigenweb))
+As a mention, we use our own implementation of LDA and PLS from
+([Friedman, Hastie, and Tibshirani 2001, 1:81,
+114](#ref-friedman2001elements)), PLS is optimized for univariate, see
+[5.1](#sec-plsalgo). For linear algebra optimization purposes on large
+reftables, the Linux version of binaries (standalone and python wheel)
+are statically linked with [Intel’s Math Kernel
+Library](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-programming-guide/top/api-based-programming/intel-oneapi-math-kernel-library-onemkl.html),
+in order to leverage multicore and SIMD extensions on modern cpus.
+There is one set of binaries, which contains a Macos/Linux/Windows (x64
+only) binary for each platform. There are available within the
+“[Releases](https://github.com/fradav/abcranger/releases)” tab, under
+“Assets” section (unfold it to see the list).
+This is pure command line binary, and they are no prerequisites or
+library dependencies in order to run it. Just download them and launch
+them from your terminal software of choice. The usual caveats with
+command line executable apply there : if you’re not proficient with the
+command line interface of your platform, please learn some basics or ask
+someone who might help you in those matters.
+The standalone is part of a specialized Population Genetics graphical
+interface [DIYABC-RF](https://diyabc.github.io/), presented in MER
+(Molecular Ecology Resources, Special Issue), ([Collin et al.
+2021](#ref-Collin_2021)).
+# Python
+## Installation
+``` bash
+pip install pyabcranger
+```
+## Notebooks examples
+- On a [toy example with
+  ![MA(q)](https://latex.codecogs.com/png.image?%5Cbg_black&space;MA%28q%29 "MA(q)")](https://github.com/diyabc/abcranger/blob/master/notebooks/Toy%20example%20MA(q).ipynb),
+  using ([Lintusaari et al. 2018](#ref-JMLR:v19:17-374)) as
+  graph-powered engine.
+- [Population genetics
+  demo](https://github.com/diyabc/abcranger/blob/master/notebooks/Population%20genetics%20Demo.ipynb),
+  data from ([Collin et al. 2021](#ref-Collin_2021)), available
+  [there](https://github.com/diyabc/diyabc/tree/master/diyabc-tests/MER/modelchoice/IndSeq)
+# Usage
+``` text
+ - ABC Random Forest - Model choice or parameter estimation command line options
+Usage:
+  -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 (modelchoice_out or estimparam_out by
+                          default)
+  -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, generated by default (default: 0)
+  -c, --noisecolumns arg  Number of noise columns (default: 5)
+      --nolinear          Disable LDA for model choice or PLS for parameter
+                          estimation
+      --plsmaxvar arg     Percentage of maximum explained Y-variance for
+                          retaining pls axis (default: 0.9)
+      --chosenscen arg    Chosen scenario (mandatory for parameter
+                          estimation)
+      --noob arg          number of oob testing samples (mandatory for
+                          parameter estimation)
+      --parameter arg     name of the parameter of interest (mandatory for
+                          parameter estimation)
+  -g, --groups arg        Groups of models
+      --help              Print help
+```
+- If you provide `--chosenscen`, `--parameter` and `--noob`, parameter
+  estimation mode is selected.
+- Otherwise by default it’s model choice mode.
+- Linear additions are LDA for model choice and PLS for parameter
+  estimation, “–nolinear” options disables them in both case.
+# Model Choice
+![Terminal model choice](./model_choice.gif)
+## Example
+Example :
+`abcranger -t 10000 -j 8`
+Header, reftable and statobs files should be in the current directory.
+## Groups
+With the option `-g` (or `--groups`), you may “group” your models in
+several groups splitted . For example if you have six models, labeled
+from 1 to 6 \`-g “1,2,3;4,5,6”
+## Generated files
+Four 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
+![Terminal estim param](./estim_param.gif)
+## Composite parameters
+When specifying the parameter (option `--parameter`), one may specify
+simple composite parameters as division, addition or multiplication of
+two existing parameters. like `t/N` or `T1+T2`.
+## A note about PLS heuristic
+The `--plsmaxvar` option (defaulting at 0.90) fixes the number of
+selected pls axes so that we get at least the specified percentage of
+maximum explained variance of the output. The explained variance of the
+output of the
+![m](https://latex.codecogs.com/png.image?%5Cbg_black&space;m "m") first
+axes is defined by the R-squared of the output:
+![Yvar^m = \frac{\sum\_{i=1}^{N}{(\hat{y}^{m}\_{i}-\bar{y})^2}}{\sum\_{i=1}^{N}{(y\_{i}-\hat{y})^2}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;Yvar%5Em%20%3D%20%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5E%7BN%7D%7B%28%5Chat%7By%7D%5E%7Bm%7D_%7Bi%7D-%5Cbar%7By%7D%29%5E2%7D%7D%7B%5Csum_%7Bi%3D1%7D%5E%7BN%7D%7B%28y_%7Bi%7D-%5Chat%7By%7D%29%5E2%7D%7D "Yvar^m = \frac{\sum_{i=1}^{N}{(\hat{y}^{m}_{i}-\bar{y})^2}}{\sum_{i=1}^{N}{(y_{i}-\hat{y})^2}}")
+where
+![\hat{y}^{m}](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Chat%7By%7D%5E%7Bm%7D "\hat{y}^{m}")
+is the output
+![Y](https://latex.codecogs.com/png.image?%5Cbg_black&space;Y "Y")
+scored by the pls for the
+![m](https://latex.codecogs.com/png.image?%5Cbg_black&space;m "m")th
+component. So, only the
+![n\_{comp}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D "n_{comp}")
+first axis are kept, and :
+![n\_{comp} = \underset{Yvar^m \leq{} 0.90\*Yvar^M, }{\operatorname{argmax}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D%20%3D%20%5Cunderset%7BYvar%5Em%20%5Cleq%7B%7D%200.90%2AYvar%5EM%2C%20%7D%7B%5Coperatorname%7Bargmax%7D%7D "n_{comp} = \underset{Yvar^m \leq{} 0.90*Yvar^M, }{\operatorname{argmax}}")
+Note that if you specify 0 as `--plsmaxvar`, an “elbow” heuristic is
+activiated where the following condition is tested for every computed
+axis :
+![\frac{Yvar^{k+1}+Yvar^{k}}{2} \geq 0.99(N-k)\left(Yvar^{k+1}-Yvar^ {k}\right)](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cfrac%7BYvar%5E%7Bk%2B1%7D%2BYvar%5E%7Bk%7D%7D%7B2%7D%20%5Cgeq%200.99%28N-k%29%5Cleft%28Yvar%5E%7Bk%2B1%7D-Yvar%5E%20%7Bk%7D%5Cright%29 "\frac{Yvar^{k+1}+Yvar^{k}}{2} \geq 0.99(N-k)\left(Yvar^{k+1}-Yvar^ {k}\right)")
+If this condition is true for a windows of previous axes, sized to 10%
+of the total possible axis, then we stop the PLS axis computation.
+In practice, we find this
+![n\_{heur}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bheur%7D "n_{heur}")
+close enough to the previous
+![n\_{comp}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D "n_{comp}")
+for 99%, but it isn’t guaranteed.
+## The signification of the `noob` parameter
+The median global/local statistics and confidence intervals (global)
+measures for parameter estimation need a number of OOB samples
+(`--noob`) to be reliable (typlially 30% of the size of the dataset is
+sufficient). Be aware than computing the whole set (i.e. assigning
+`--noob` the same than for `--nref`) for weights predictions ([Raynal et
+al. 2018](#ref-raynal2016abc)) could be very costly, memory and
+cpu-wise, if your dataset is large in number of samples, so it could be
+adviseable to compute them for only choose a subset of size `noob`.
+## Example (parameter estimation)
+Example (working with the dataset in `test/data`) :
+`abcranger -t 1000 -j 8 --parameter ra --chosenscen 1 --noob 50`
+Header, reftable and statobs files should be in the current directory.
+## Generated files (parameter estimation)
+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` : csv of the value/weights pairs of the
+  prediction (for density plot)
+- `estimparam_out.oobstats` : various statistics on oob (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)
+# Various
+## Partial Least Squares algorithm
+1.  ![X\_{0}=X ; y\_{0}=y](https://latex.codecogs.com/png.image?%5Cbg_black&space;X_%7B0%7D%3DX%20%3B%20y_%7B0%7D%3Dy "X_{0}=X ; y_{0}=y")
+2.  For
+    ![k=1,2,...,s](https://latex.codecogs.com/png.image?%5Cbg_black&space;k%3D1%2C2%2C...%2Cs "k=1,2,...,s")
+    1.  ![w\_{k}=\frac{X\_{k-1}^{T} y\_{k-1}}{y\_{k-1}^{T} y\_{k-1}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D%7By_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D "w_{k}=\frac{X_{k-1}^{T} y_{k-1}}{y_{k-1}^{T} y_{k-1}}")
+    2.  Normalize
+        ![w_k](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_k "w_k")
+        to
+        ![1](https://latex.codecogs.com/png.image?%5Cbg_black&space;1 "1")
+    3.  ![t\_{k}=\frac{X\_{k-1} w\_{k}}{w\_{k}^{T} w\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;t_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%20w_%7Bk%7D%7D%7Bw_%7Bk%7D%5E%7BT%7D%20w_%7Bk%7D%7D "t_{k}=\frac{X_{k-1} w_{k}}{w_{k}^{T} w_{k}}")
+    4.  ![p\_{k}=\frac{X\_{k-1}^{T} t\_{k}}{t\_{k}^{T} t\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;p_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20t_%7Bk%7D%7D%7Bt_%7Bk%7D%5E%7BT%7D%20t_%7Bk%7D%7D "p_{k}=\frac{X_{k-1}^{T} t_{k}}{t_{k}^{T} t_{k}}")
+    5.  ![X\_{k}=X\_{k-1}-t\_{k} p\_{k}^{T}](https://latex.codecogs.com/png.image?%5Cbg_black&space;X_%7Bk%7D%3DX_%7Bk-1%7D-t_%7Bk%7D%20p_%7Bk%7D%5E%7BT%7D "X_{k}=X_{k-1}-t_{k} p_{k}^{T}")
+    6.  ![q\_{k}=\frac{y\_{k-1}^{T} t\_{k}}{t\_{k}^{T} t\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;q_%7Bk%7D%3D%5Cfrac%7By_%7Bk-1%7D%5E%7BT%7D%20t_%7Bk%7D%7D%7Bt_%7Bk%7D%5E%7BT%7D%20t_%7Bk%7D%7D "q_{k}=\frac{y_{k-1}^{T} t_{k}}{t_{k}^{T} t_{k}}")
+    7.  ![u\_{k}=\frac{y\_{k-1}}{q\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;u_%7Bk%7D%3D%5Cfrac%7By_%7Bk-1%7D%7D%7Bq_%7Bk%7D%7D "u_{k}=\frac{y_{k-1}}{q_{k}}")
+    8.  ![y\_{k}=y\_{k-1}-q\_{k} t\_{k}](https://latex.codecogs.com/png.image?%5Cbg_black&space;y_%7Bk%7D%3Dy_%7Bk-1%7D-q_%7Bk%7D%20t_%7Bk%7D "y_{k}=y_{k-1}-q_{k} t_{k}")
+**Comment** When there isn’t any missing data, stages
+![2.1](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.1 "2.1")
+and
+![2.2](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.2 "2.2")
+could be replaced by
+![w\_{k}=\frac{X\_{k-1}^{T} y\_{k-1}}{\left\\\|X\_{k-1}^{T} y\_{k-1}\right\\\|}](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D%7B%5Cleft%5C%7CX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%5Cright%5C%7C%7D "w_{k}=\frac{X_{k-1}^{T} y_{k-1}}{\left\|X_{k-1}^{T} y_{k-1}\right\|}")
+and
+![2.3](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.3 "2.3")
+by
+![t\_{k}=X\_{k-1}w\_{k}](https://latex.codecogs.com/png.image?%5Cbg_black&space;t_%7Bk%7D%3DX_%7Bk-1%7Dw_%7Bk%7D "t_{k}=X_{k-1}w_{k}")
+To get
+![W](https://latex.codecogs.com/png.image?%5Cbg_black&space;W "W") so
+that
+![T=XW](https://latex.codecogs.com/png.image?%5Cbg_black&space;T%3DXW "T=XW")
+we compute :
+![\mathbf{W}=\mathbf{W}^{\*}\left(\widetilde{\mathbf{P}} \mathbf{W}^{\*}\right)^{-1}](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cmathbf%7BW%7D%3D%5Cmathbf%7BW%7D%5E%7B%2A%7D%5Cleft%28%5Cwidetilde%7B%5Cmathbf%7BP%7D%7D%20%5Cmathbf%7BW%7D%5E%7B%2A%7D%5Cright%29%5E%7B-1%7D "\mathbf{W}=\mathbf{W}^{*}\left(\widetilde{\mathbf{P}} \mathbf{W}^{*}\right)^{-1}")
+where
+![\widetilde{\mathbf{P}}\_{K \times p}=\mathbf{t}\left\[p\_{1}, \ldots, p\_{K}\right\]](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cwidetilde%7B%5Cmathbf%7BP%7D%7D_%7BK%20%5Ctimes%20p%7D%3D%5Cmathbf%7Bt%7D%5Cleft%5Bp_%7B1%7D%2C%20%5Cldots%2C%20p_%7BK%7D%5Cright%5D "\widetilde{\mathbf{P}}_{K \times p}=\mathbf{t}\left[p_{1}, \ldots, p_{K}\right]")
+where
+![\mathbf{W}^{\*}\_{p \times K} = \[w_1, \ldots, w_K\]](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cmathbf%7BW%7D%5E%7B%2A%7D_%7Bp%20%5Ctimes%20K%7D%20%3D%20%5Bw_1%2C%20%5Cldots%2C%20w_K%5D "\mathbf{W}^{*}_{p \times K} = [w_1, \ldots, w_K]")
+# TODO
+## Input/Output
+- [x] Integrate hdf5 (or exdir? msgpack?) routines to save/load
+  reftables/observed stats with associated metadata
+- [ ] Provide R code to save/load the data
+- [x] Provide Python code to save/load the data
+## C++ standalone
+- [x] Merge the two methodologies in a single executable with the
+  (almost) the same options
+- [ ] (Optional) Possibly move to another options parser (CLI?)
+## External interfaces
+- [ ] R package
+- [x] Python package
+## Documentation
+- [ ] Code documentation
+- [ ] Document the build
+## Continuous integration
+- [x] Linux CI build with intel/MKL optimizations
+- [x] osX CI build
+- [x] Windows CI build
+## Long/Mid term TODO
+- methodologies parameters auto-tuning
+  - auto-discovering the optimal number of trees by monitoring OOB error
+  - auto-limiting number of threads by available memory
+- Streamline the two methodologies (model choice and then parameters
+  estimation)
+- Write our own tree/rf implementation with better storage efficiency
+  than ranger
+- Make functional tests for the two methodologies
+- Possible to use mondrian forests for online batches ? See
+  ([Lakshminarayanan, Roy, and Teh
+  2014](#ref-lakshminarayanan2014mondrian))
+# References
+This have been the subject of a proceedings in [JOBIM
+2020](https://jobim2020.sciencesconf.org/),
+[PDF](https://hal.archives-ouvertes.fr/hal-02910067v2) and
+[video](https://relaiswebcasting.mediasite.com/mediasite/Play/8ddb4e40fc88422481f1494cf6af2bb71d?catalog=e534823f0c954836bf85bfa80af2290921)
+(in french), ([Collin et al. 2020](#ref-collin:hal-02910067)).
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Collin_2021" class="csl-entry">
+Collin, François-David, Ghislain Durif, Louis Raynal, Eric Lombaert,
+Mathieu Gautier, Renaud Vitalis, Jean-Michel Marin, and Arnaud Estoup.
+2021. “Extending Approximate Bayesian Computation with Supervised
+Machine Learning to Infer Demographic History from Genetic Polymorphisms
+Using DIYABC Random Forest.” *Molecular Ecology Resources* 21 (8):
+2598–2613. https://doi.org/<https://doi.org/10.1111/1755-0998.13413>.
+</div>
+<div id="ref-collin:hal-02910067" class="csl-entry">
+Collin, François-David, Arnaud Estoup, Jean-Michel Marin, and Louis
+Raynal. 2020. “<span class="nocase">Bringing ABC inference to the
+machine learning realm : AbcRanger, an optimized random forests library
+for ABC</span>.” In *JOBIM 2020*, 2020:66. JOBIM. Montpellier, France.
+<https://hal.archives-ouvertes.fr/hal-02910067>.
+</div>
+<div id="ref-friedman2001elements" class="csl-entry">
+Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. *The
+Elements of Statistical Learning*. Vol. 1. 10. Springer series in
+statistics New York, NY, USA:
+</div>
+<div id="ref-eigenweb" class="csl-entry">
+Guennebaud, Gaël, Benoît Jacob, et al. 2010. “Eigen V3.”
+http://eigen.tuxfamily.org.
+</div>
+<div id="ref-lakshminarayanan2014mondrian" class="csl-entry">
+Lakshminarayanan, Balaji, Daniel M Roy, and Yee Whye Teh. 2014.
+“Mondrian Forests: Efficient Online Random Forests.” In *Advances in
+Neural Information Processing Systems*, 3140–48.
+</div>
+<div id="ref-JMLR:v19:17-374" class="csl-entry">
+Lintusaari, Jarno, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén,
+Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka
+Corander, and Samuel Kaski. 2018. “ELFI: Engine for Likelihood-Free
+Inference.” *Journal of Machine Learning Research* 19 (16): 1–7.
+<http://jmlr.org/papers/v19/17-374.html>.
+</div>
+<div id="ref-pudlo2015reliable" class="csl-entry">
+Pudlo, Pierre, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet,
+Mathieu Gautier, and Christian P Robert. 2015. “Reliable ABC Model
+Choice via Random Forests.” *Bioinformatics* 32 (6): 859–66.
+</div>
+<div id="ref-raynal2016abc" class="csl-entry">
+Raynal, Louis, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet,
+Christian P Robert, and Arnaud Estoup. 2018. “<span class="nocase">ABC
+random forests for Bayesian parameter inference</span>.”
+*Bioinformatics* 35 (10): 1720–28.
+<https://doi.org/10.1093/bioinformatics/bty867>.
+</div>
+<div id="ref-wright2015ranger" class="csl-entry">
+Wright, Marvin N, and Andreas Ziegler. 2015. “Ranger: A Fast
+Implementation of Random Forests for High Dimensional Data in c++ and
+r.” *arXiv Preprint arXiv:1508.04409*.
+</div>
+</div>
+[^1]: The term “online” there and in the code has not the usual meaning
+    it has, as coined in “online machine learning”. We still need the
+    entire training data set at once. Our implementation is an “online”
+    one not by the sequential order of the input data, but by the
+    sequential order of computation of the trees in random forests,
+    sequentially computed and then discarded.
+[^2]: We only use the C++ Core of ranger, which is under [MIT
+    License](https://raw.githubusercontent.com/imbs-hl/ranger/master/cpp_version/COPYING),
+    same as ours.
+
+%package help
+Summary:	Development documents and examples for pyabcranger
+Provides:	python3-pyabcranger-doc
+%description help
+- <a href="#python" id="toc-python">Python</a>
+- <a href="#usage" id="toc-usage">Usage</a>
+- <a href="#model-choice" id="toc-model-choice">Model Choice</a>
+- <a href="#parameter-estimation" id="toc-parameter-estimation">Parameter
+  Estimation</a>
+- <a href="#various" id="toc-various">Various</a>
+- <a href="#todo" id="toc-todo">TODO</a>
+- <a href="#references" id="toc-references">References</a>
+<!-- pandoc -f markdown README-ORIG.md -t gfm -o README.md --citeproc -s --toc --toc-depth=1 --webtex -->
+[![PyPI](https://img.shields.io/pypi/v/pyabcranger.svg)](https://pypi.python.org/pypi/pyabcranger)
+[![abcranger-build](https://github.com/diyabc/abcranger/workflows/abcranger-build/badge.svg)](https://github.com/diyabc/abcranger/actions?query=workflow%3Aabcranger-build+branch%3Amaster)
+Random forests methodologies for :
+- ABC model choice ([Pudlo et al. 2015](#ref-pudlo2015reliable))
+- ABC Bayesian parameter inference ([Raynal et al.
+  2018](#ref-raynal2016abc))
+Libraries we use :
+- [Ranger](https://github.com/imbs-hl/ranger) ([Wright and Ziegler
+  2015](#ref-wright2015ranger)) : we use our own fork and have tuned
+  forests to do “online”[^1] computations (Growing trees AND making
+  predictions in the same pass, which removes the need of in-memory
+  storage of the whole forest)[^2].
+- [Eigen3](http://eigen.tuxfamily.org) ([Guennebaud, Jacob, et al.
+  2010](#ref-eigenweb))
+As a mention, we use our own implementation of LDA and PLS from
+([Friedman, Hastie, and Tibshirani 2001, 1:81,
+114](#ref-friedman2001elements)), PLS is optimized for univariate, see
+[5.1](#sec-plsalgo). For linear algebra optimization purposes on large
+reftables, the Linux version of binaries (standalone and python wheel)
+are statically linked with [Intel’s Math Kernel
+Library](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-programming-guide/top/api-based-programming/intel-oneapi-math-kernel-library-onemkl.html),
+in order to leverage multicore and SIMD extensions on modern cpus.
+There is one set of binaries, which contains a Macos/Linux/Windows (x64
+only) binary for each platform. There are available within the
+“[Releases](https://github.com/fradav/abcranger/releases)” tab, under
+“Assets” section (unfold it to see the list).
+This is pure command line binary, and they are no prerequisites or
+library dependencies in order to run it. Just download them and launch
+them from your terminal software of choice. The usual caveats with
+command line executable apply there : if you’re not proficient with the
+command line interface of your platform, please learn some basics or ask
+someone who might help you in those matters.
+The standalone is part of a specialized Population Genetics graphical
+interface [DIYABC-RF](https://diyabc.github.io/), presented in MER
+(Molecular Ecology Resources, Special Issue), ([Collin et al.
+2021](#ref-Collin_2021)).
+# Python
+## Installation
+``` bash
+pip install pyabcranger
+```
+## Notebooks examples
+- On a [toy example with
+  ![MA(q)](https://latex.codecogs.com/png.image?%5Cbg_black&space;MA%28q%29 "MA(q)")](https://github.com/diyabc/abcranger/blob/master/notebooks/Toy%20example%20MA(q).ipynb),
+  using ([Lintusaari et al. 2018](#ref-JMLR:v19:17-374)) as
+  graph-powered engine.
+- [Population genetics
+  demo](https://github.com/diyabc/abcranger/blob/master/notebooks/Population%20genetics%20Demo.ipynb),
+  data from ([Collin et al. 2021](#ref-Collin_2021)), available
+  [there](https://github.com/diyabc/diyabc/tree/master/diyabc-tests/MER/modelchoice/IndSeq)
+# Usage
+``` text
+ - ABC Random Forest - Model choice or parameter estimation command line options
+Usage:
+  -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 (modelchoice_out or estimparam_out by
+                          default)
+  -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, generated by default (default: 0)
+  -c, --noisecolumns arg  Number of noise columns (default: 5)
+      --nolinear          Disable LDA for model choice or PLS for parameter
+                          estimation
+      --plsmaxvar arg     Percentage of maximum explained Y-variance for
+                          retaining pls axis (default: 0.9)
+      --chosenscen arg    Chosen scenario (mandatory for parameter
+                          estimation)
+      --noob arg          number of oob testing samples (mandatory for
+                          parameter estimation)
+      --parameter arg     name of the parameter of interest (mandatory for
+                          parameter estimation)
+  -g, --groups arg        Groups of models
+      --help              Print help
+```
+- If you provide `--chosenscen`, `--parameter` and `--noob`, parameter
+  estimation mode is selected.
+- Otherwise by default it’s model choice mode.
+- Linear additions are LDA for model choice and PLS for parameter
+  estimation, “–nolinear” options disables them in both case.
+# Model Choice
+![Terminal model choice](./model_choice.gif)
+## Example
+Example :
+`abcranger -t 10000 -j 8`
+Header, reftable and statobs files should be in the current directory.
+## Groups
+With the option `-g` (or `--groups`), you may “group” your models in
+several groups splitted . For example if you have six models, labeled
+from 1 to 6 \`-g “1,2,3;4,5,6”
+## Generated files
+Four 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
+![Terminal estim param](./estim_param.gif)
+## Composite parameters
+When specifying the parameter (option `--parameter`), one may specify
+simple composite parameters as division, addition or multiplication of
+two existing parameters. like `t/N` or `T1+T2`.
+## A note about PLS heuristic
+The `--plsmaxvar` option (defaulting at 0.90) fixes the number of
+selected pls axes so that we get at least the specified percentage of
+maximum explained variance of the output. The explained variance of the
+output of the
+![m](https://latex.codecogs.com/png.image?%5Cbg_black&space;m "m") first
+axes is defined by the R-squared of the output:
+![Yvar^m = \frac{\sum\_{i=1}^{N}{(\hat{y}^{m}\_{i}-\bar{y})^2}}{\sum\_{i=1}^{N}{(y\_{i}-\hat{y})^2}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;Yvar%5Em%20%3D%20%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5E%7BN%7D%7B%28%5Chat%7By%7D%5E%7Bm%7D_%7Bi%7D-%5Cbar%7By%7D%29%5E2%7D%7D%7B%5Csum_%7Bi%3D1%7D%5E%7BN%7D%7B%28y_%7Bi%7D-%5Chat%7By%7D%29%5E2%7D%7D "Yvar^m = \frac{\sum_{i=1}^{N}{(\hat{y}^{m}_{i}-\bar{y})^2}}{\sum_{i=1}^{N}{(y_{i}-\hat{y})^2}}")
+where
+![\hat{y}^{m}](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Chat%7By%7D%5E%7Bm%7D "\hat{y}^{m}")
+is the output
+![Y](https://latex.codecogs.com/png.image?%5Cbg_black&space;Y "Y")
+scored by the pls for the
+![m](https://latex.codecogs.com/png.image?%5Cbg_black&space;m "m")th
+component. So, only the
+![n\_{comp}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D "n_{comp}")
+first axis are kept, and :
+![n\_{comp} = \underset{Yvar^m \leq{} 0.90\*Yvar^M, }{\operatorname{argmax}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D%20%3D%20%5Cunderset%7BYvar%5Em%20%5Cleq%7B%7D%200.90%2AYvar%5EM%2C%20%7D%7B%5Coperatorname%7Bargmax%7D%7D "n_{comp} = \underset{Yvar^m \leq{} 0.90*Yvar^M, }{\operatorname{argmax}}")
+Note that if you specify 0 as `--plsmaxvar`, an “elbow” heuristic is
+activiated where the following condition is tested for every computed
+axis :
+![\frac{Yvar^{k+1}+Yvar^{k}}{2} \geq 0.99(N-k)\left(Yvar^{k+1}-Yvar^ {k}\right)](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cfrac%7BYvar%5E%7Bk%2B1%7D%2BYvar%5E%7Bk%7D%7D%7B2%7D%20%5Cgeq%200.99%28N-k%29%5Cleft%28Yvar%5E%7Bk%2B1%7D-Yvar%5E%20%7Bk%7D%5Cright%29 "\frac{Yvar^{k+1}+Yvar^{k}}{2} \geq 0.99(N-k)\left(Yvar^{k+1}-Yvar^ {k}\right)")
+If this condition is true for a windows of previous axes, sized to 10%
+of the total possible axis, then we stop the PLS axis computation.
+In practice, we find this
+![n\_{heur}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bheur%7D "n_{heur}")
+close enough to the previous
+![n\_{comp}](https://latex.codecogs.com/png.image?%5Cbg_black&space;n_%7Bcomp%7D "n_{comp}")
+for 99%, but it isn’t guaranteed.
+## The signification of the `noob` parameter
+The median global/local statistics and confidence intervals (global)
+measures for parameter estimation need a number of OOB samples
+(`--noob`) to be reliable (typlially 30% of the size of the dataset is
+sufficient). Be aware than computing the whole set (i.e. assigning
+`--noob` the same than for `--nref`) for weights predictions ([Raynal et
+al. 2018](#ref-raynal2016abc)) could be very costly, memory and
+cpu-wise, if your dataset is large in number of samples, so it could be
+adviseable to compute them for only choose a subset of size `noob`.
+## Example (parameter estimation)
+Example (working with the dataset in `test/data`) :
+`abcranger -t 1000 -j 8 --parameter ra --chosenscen 1 --noob 50`
+Header, reftable and statobs files should be in the current directory.
+## Generated files (parameter estimation)
+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` : csv of the value/weights pairs of the
+  prediction (for density plot)
+- `estimparam_out.oobstats` : various statistics on oob (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)
+# Various
+## Partial Least Squares algorithm
+1.  ![X\_{0}=X ; y\_{0}=y](https://latex.codecogs.com/png.image?%5Cbg_black&space;X_%7B0%7D%3DX%20%3B%20y_%7B0%7D%3Dy "X_{0}=X ; y_{0}=y")
+2.  For
+    ![k=1,2,...,s](https://latex.codecogs.com/png.image?%5Cbg_black&space;k%3D1%2C2%2C...%2Cs "k=1,2,...,s")
+    1.  ![w\_{k}=\frac{X\_{k-1}^{T} y\_{k-1}}{y\_{k-1}^{T} y\_{k-1}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D%7By_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D "w_{k}=\frac{X_{k-1}^{T} y_{k-1}}{y_{k-1}^{T} y_{k-1}}")
+    2.  Normalize
+        ![w_k](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_k "w_k")
+        to
+        ![1](https://latex.codecogs.com/png.image?%5Cbg_black&space;1 "1")
+    3.  ![t\_{k}=\frac{X\_{k-1} w\_{k}}{w\_{k}^{T} w\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;t_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%20w_%7Bk%7D%7D%7Bw_%7Bk%7D%5E%7BT%7D%20w_%7Bk%7D%7D "t_{k}=\frac{X_{k-1} w_{k}}{w_{k}^{T} w_{k}}")
+    4.  ![p\_{k}=\frac{X\_{k-1}^{T} t\_{k}}{t\_{k}^{T} t\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;p_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20t_%7Bk%7D%7D%7Bt_%7Bk%7D%5E%7BT%7D%20t_%7Bk%7D%7D "p_{k}=\frac{X_{k-1}^{T} t_{k}}{t_{k}^{T} t_{k}}")
+    5.  ![X\_{k}=X\_{k-1}-t\_{k} p\_{k}^{T}](https://latex.codecogs.com/png.image?%5Cbg_black&space;X_%7Bk%7D%3DX_%7Bk-1%7D-t_%7Bk%7D%20p_%7Bk%7D%5E%7BT%7D "X_{k}=X_{k-1}-t_{k} p_{k}^{T}")
+    6.  ![q\_{k}=\frac{y\_{k-1}^{T} t\_{k}}{t\_{k}^{T} t\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;q_%7Bk%7D%3D%5Cfrac%7By_%7Bk-1%7D%5E%7BT%7D%20t_%7Bk%7D%7D%7Bt_%7Bk%7D%5E%7BT%7D%20t_%7Bk%7D%7D "q_{k}=\frac{y_{k-1}^{T} t_{k}}{t_{k}^{T} t_{k}}")
+    7.  ![u\_{k}=\frac{y\_{k-1}}{q\_{k}}](https://latex.codecogs.com/png.image?%5Cbg_black&space;u_%7Bk%7D%3D%5Cfrac%7By_%7Bk-1%7D%7D%7Bq_%7Bk%7D%7D "u_{k}=\frac{y_{k-1}}{q_{k}}")
+    8.  ![y\_{k}=y\_{k-1}-q\_{k} t\_{k}](https://latex.codecogs.com/png.image?%5Cbg_black&space;y_%7Bk%7D%3Dy_%7Bk-1%7D-q_%7Bk%7D%20t_%7Bk%7D "y_{k}=y_{k-1}-q_{k} t_{k}")
+**Comment** When there isn’t any missing data, stages
+![2.1](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.1 "2.1")
+and
+![2.2](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.2 "2.2")
+could be replaced by
+![w\_{k}=\frac{X\_{k-1}^{T} y\_{k-1}}{\left\\\|X\_{k-1}^{T} y\_{k-1}\right\\\|}](https://latex.codecogs.com/png.image?%5Cbg_black&space;w_%7Bk%7D%3D%5Cfrac%7BX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%7D%7B%5Cleft%5C%7CX_%7Bk-1%7D%5E%7BT%7D%20y_%7Bk-1%7D%5Cright%5C%7C%7D "w_{k}=\frac{X_{k-1}^{T} y_{k-1}}{\left\|X_{k-1}^{T} y_{k-1}\right\|}")
+and
+![2.3](https://latex.codecogs.com/png.image?%5Cbg_black&space;2.3 "2.3")
+by
+![t\_{k}=X\_{k-1}w\_{k}](https://latex.codecogs.com/png.image?%5Cbg_black&space;t_%7Bk%7D%3DX_%7Bk-1%7Dw_%7Bk%7D "t_{k}=X_{k-1}w_{k}")
+To get
+![W](https://latex.codecogs.com/png.image?%5Cbg_black&space;W "W") so
+that
+![T=XW](https://latex.codecogs.com/png.image?%5Cbg_black&space;T%3DXW "T=XW")
+we compute :
+![\mathbf{W}=\mathbf{W}^{\*}\left(\widetilde{\mathbf{P}} \mathbf{W}^{\*}\right)^{-1}](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cmathbf%7BW%7D%3D%5Cmathbf%7BW%7D%5E%7B%2A%7D%5Cleft%28%5Cwidetilde%7B%5Cmathbf%7BP%7D%7D%20%5Cmathbf%7BW%7D%5E%7B%2A%7D%5Cright%29%5E%7B-1%7D "\mathbf{W}=\mathbf{W}^{*}\left(\widetilde{\mathbf{P}} \mathbf{W}^{*}\right)^{-1}")
+where
+![\widetilde{\mathbf{P}}\_{K \times p}=\mathbf{t}\left\[p\_{1}, \ldots, p\_{K}\right\]](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cwidetilde%7B%5Cmathbf%7BP%7D%7D_%7BK%20%5Ctimes%20p%7D%3D%5Cmathbf%7Bt%7D%5Cleft%5Bp_%7B1%7D%2C%20%5Cldots%2C%20p_%7BK%7D%5Cright%5D "\widetilde{\mathbf{P}}_{K \times p}=\mathbf{t}\left[p_{1}, \ldots, p_{K}\right]")
+where
+![\mathbf{W}^{\*}\_{p \times K} = \[w_1, \ldots, w_K\]](https://latex.codecogs.com/png.image?%5Cbg_black&space;%5Cmathbf%7BW%7D%5E%7B%2A%7D_%7Bp%20%5Ctimes%20K%7D%20%3D%20%5Bw_1%2C%20%5Cldots%2C%20w_K%5D "\mathbf{W}^{*}_{p \times K} = [w_1, \ldots, w_K]")
+# TODO
+## Input/Output
+- [x] Integrate hdf5 (or exdir? msgpack?) routines to save/load
+  reftables/observed stats with associated metadata
+- [ ] Provide R code to save/load the data
+- [x] Provide Python code to save/load the data
+## C++ standalone
+- [x] Merge the two methodologies in a single executable with the
+  (almost) the same options
+- [ ] (Optional) Possibly move to another options parser (CLI?)
+## External interfaces
+- [ ] R package
+- [x] Python package
+## Documentation
+- [ ] Code documentation
+- [ ] Document the build
+## Continuous integration
+- [x] Linux CI build with intel/MKL optimizations
+- [x] osX CI build
+- [x] Windows CI build
+## Long/Mid term TODO
+- methodologies parameters auto-tuning
+  - auto-discovering the optimal number of trees by monitoring OOB error
+  - auto-limiting number of threads by available memory
+- Streamline the two methodologies (model choice and then parameters
+  estimation)
+- Write our own tree/rf implementation with better storage efficiency
+  than ranger
+- Make functional tests for the two methodologies
+- Possible to use mondrian forests for online batches ? See
+  ([Lakshminarayanan, Roy, and Teh
+  2014](#ref-lakshminarayanan2014mondrian))
+# References
+This have been the subject of a proceedings in [JOBIM
+2020](https://jobim2020.sciencesconf.org/),
+[PDF](https://hal.archives-ouvertes.fr/hal-02910067v2) and
+[video](https://relaiswebcasting.mediasite.com/mediasite/Play/8ddb4e40fc88422481f1494cf6af2bb71d?catalog=e534823f0c954836bf85bfa80af2290921)
+(in french), ([Collin et al. 2020](#ref-collin:hal-02910067)).
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Collin_2021" class="csl-entry">
+Collin, François-David, Ghislain Durif, Louis Raynal, Eric Lombaert,
+Mathieu Gautier, Renaud Vitalis, Jean-Michel Marin, and Arnaud Estoup.
+2021. “Extending Approximate Bayesian Computation with Supervised
+Machine Learning to Infer Demographic History from Genetic Polymorphisms
+Using DIYABC Random Forest.” *Molecular Ecology Resources* 21 (8):
+2598–2613. https://doi.org/<https://doi.org/10.1111/1755-0998.13413>.
+</div>
+<div id="ref-collin:hal-02910067" class="csl-entry">
+Collin, François-David, Arnaud Estoup, Jean-Michel Marin, and Louis
+Raynal. 2020. “<span class="nocase">Bringing ABC inference to the
+machine learning realm : AbcRanger, an optimized random forests library
+for ABC</span>.” In *JOBIM 2020*, 2020:66. JOBIM. Montpellier, France.
+<https://hal.archives-ouvertes.fr/hal-02910067>.
+</div>
+<div id="ref-friedman2001elements" class="csl-entry">
+Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. *The
+Elements of Statistical Learning*. Vol. 1. 10. Springer series in
+statistics New York, NY, USA:
+</div>
+<div id="ref-eigenweb" class="csl-entry">
+Guennebaud, Gaël, Benoît Jacob, et al. 2010. “Eigen V3.”
+http://eigen.tuxfamily.org.
+</div>
+<div id="ref-lakshminarayanan2014mondrian" class="csl-entry">
+Lakshminarayanan, Balaji, Daniel M Roy, and Yee Whye Teh. 2014.
+“Mondrian Forests: Efficient Online Random Forests.” In *Advances in
+Neural Information Processing Systems*, 3140–48.
+</div>
+<div id="ref-JMLR:v19:17-374" class="csl-entry">
+Lintusaari, Jarno, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén,
+Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka
+Corander, and Samuel Kaski. 2018. “ELFI: Engine for Likelihood-Free
+Inference.” *Journal of Machine Learning Research* 19 (16): 1–7.
+<http://jmlr.org/papers/v19/17-374.html>.
+</div>
+<div id="ref-pudlo2015reliable" class="csl-entry">
+Pudlo, Pierre, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet,
+Mathieu Gautier, and Christian P Robert. 2015. “Reliable ABC Model
+Choice via Random Forests.” *Bioinformatics* 32 (6): 859–66.
+</div>
+<div id="ref-raynal2016abc" class="csl-entry">
+Raynal, Louis, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet,
+Christian P Robert, and Arnaud Estoup. 2018. “<span class="nocase">ABC
+random forests for Bayesian parameter inference</span>.”
+*Bioinformatics* 35 (10): 1720–28.
+<https://doi.org/10.1093/bioinformatics/bty867>.
+</div>
+<div id="ref-wright2015ranger" class="csl-entry">
+Wright, Marvin N, and Andreas Ziegler. 2015. “Ranger: A Fast
+Implementation of Random Forests for High Dimensional Data in c++ and
+r.” *arXiv Preprint arXiv:1508.04409*.
+</div>
+</div>
+[^1]: The term “online” there and in the code has not the usual meaning
+    it has, as coined in “online machine learning”. We still need the
+    entire training data set at once. Our implementation is an “online”
+    one not by the sequential order of the input data, but by the
+    sequential order of computation of the trees in random forests,
+    sequentially computed and then discarded.
+[^2]: We only use the C++ Core of ranger, which is under [MIT
+    License](https://raw.githubusercontent.com/imbs-hl/ranger/master/cpp_version/COPYING),
+    same as ours.
+
+%prep
+%autosetup -n pyabcranger-0.0.69
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-pyabcranger -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.69-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..1d3f1ef
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d753357721d022bbe691c3f19aa6a7c0  pyabcranger-0.0.69.tar.gz