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| # From https://github.com/zjp-CN/mdbook-template | ||
| # Simple workflow for deploying static content to GitHub Pages | ||
| name: Deploy mdBook | ||
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| on: | ||
| # Runs on pushes targeting the default branch | ||
| push: | ||
| branches: [ "main" ] | ||
| branches: ["main"] | ||
| workflow_run: | ||
| workflows: ["Mdbook build"] | ||
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| jobs: | ||
| build: | ||
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| runs-on: ubuntu-latest | ||
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| steps: | ||
| - uses: actions/checkout@v2 | ||
| - uses: XAMPPRocky/[email protected] | ||
| - uses: actions/checkout@v3 | ||
| - run: rustup toolchain update nightly && rustup default nightly | ||
| - run: cargo install mdbook | ||
| - name: Build mdbook | ||
| run: | | ||
| cd doc | ||
| mdbook build | ||
| - name: Deploy | ||
| uses: peaceiris/actions-gh-pages@v3 | ||
| with: | ||
| token: ${{ secrets.GITHUB_TOKEN }} | ||
| publish_branch: gh-pages | ||
| github_token: ${{ secrets.GITHUB_TOKEN }} | ||
| publish_dir: book/ | ||
| force_orphan: true |
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|  | ||
| [](https://codecov.io/gh/AlexandreDubray/schlandals) | ||
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| **The code is still in early development and should not be considered stable** | ||
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| # schlandals | ||
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| Schlandals is a projected weighted model counter which targets inference in probabilistic models. | ||
| Current known probability queries that can be solved with the solver include | ||
| - Computing the probability of some target variables (given some evidences) in a Bayesian network | ||
| Schlandals is a state-of-the-art _Projected Weighted Model Counter_ specialized for probabilistic inference over discrete probability distributions. | ||
| Currently, there are known modelization for the following problems | ||
| - Computing the marginal probabilities of a variable in a Bayesian Network | ||
| - Computing the probability that two nodes are connected in a probabilistic graph | ||
| - A mapping from [ProbLog](https://github.com/ML-KULeuven/problog) programs to Schlandals | ||
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| # Problem specification | ||
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| A model counter is a program that counts the number of satisfying assignments of a boolean formula F. | ||
| In its projected version, the models are reduced to a subset of the variables (which we call *probabilistic*) and if the problem is weighted, each model has a weight | ||
| and the counter returns the sum of the model's weight. | ||
| In addition to that, we impose two more constraints | ||
| - All clauses in the formula F are Horn clauses (of the form `I => h` with `I` a conjuction of literals) | ||
| - The probabilistic variables are partioned into *distributions* such that the weights in each distribution sum up to 1 | ||
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| Schlandals takes as input a file using a modified version of the [DIMACS](https://mccompetition.org/assets/files/2021/competition2021.pdf) format | ||
| ``` | ||
| c Lines starting with `c` alone are comments. | ||
| c The first line must be the head in the form of "p cnf <number variable> <number clauses> | ||
| p cnf 16 11 | ||
| c The distribution are defined by lines starting with "c p distribution" | ||
| c Each value in a distribution is assigned to a variable, starting from 1. Hence, the first N variables | ||
| c are used for the distributions. | ||
| c p distribution 0.2 0.8 | ||
| c p distribution 0.3 0.7 | ||
| c p distribution 0.4 0.6 | ||
| c p distribution 0.1 0.9 | ||
| c p distribution 0.5 0.5 | ||
| c Finally the clauses are encoded as in DIMACS. | ||
| 11 -1 0 | ||
| 12 -2 0 | ||
| 13 -11 -3 0 | ||
| 13 -12 -5 0 | ||
| 14 -11 -4 0 | ||
| 14 -12 -6 0 | ||
| 15 -13 -7 0 | ||
| 15 -14 -9 0 | ||
| 16 -13 -8 0 | ||
| 16 -14 -10 0 | ||
| -12 0 | ||
| ``` | ||
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| # Usage | ||
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| To use the solver you must have the Rust toolchain installed. Once this is done you can clone this repository and build the solver with the following commands | ||
| ``` | ||
| git clone [email protected]:aia-uclouvain/schlandals.git && cd schlandals && cargo build --release | ||
| ``` | ||
| Once the command has been built, the binary is located in `<SCHLANDALS_DIR>/target/release/schlandals` (with `<SCHLANDALS_DIR>` the directory in which you cloned the repo). | ||
| If you want to be able to run Schlandals for anywhere, you can add `<SCHLANDALS_DIR>/target/release` to your `$PATH`. | ||
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| The binary's CLI arguments are organized by commands. Three commands are currently supported: `search` to solve the problem by a DPLL search, `compile` to compile the input into an arithmetic circuit and `read-compiled` to evaluate a previously compiled input. | ||
| These are explained next, after a quick note on the heuristics supported by the solver (for the `search` and `compile` sub-command) | ||
| ### Heuristics | ||
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| Schlandals comes with various heuristic that can be used during the search/compilation. | ||
| The current heuristics are based on the implication graph of the input. In such graph, there is one node per clause and a link between clause C1 (I1 => h1) and C2 (I2 => h2) if the h1 is in I2. | ||
| The available heuristics are | ||
| - `min-in-degree`: Select a distribution from the clause with the lowest in degree. | ||
| - `min-out-degree`: Select a distribution from the clause with the lowest out degree. | ||
| - `max-degree`: Select a distribution from the clause with the highest degree. | ||
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| ### Search | ||
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| `schlandals search -i <input> -b <branching heuristic> [-s -m <memory limit]` launch the search based solver. | ||
| The `i` is a path to a valid input file, `b` is a valid branching heuristic. | ||
| The optional `s` flag tells if stats must be recorded or not and `m` can be used to provide a memory limit. | ||
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| ### Compilation | ||
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| `schlandals compile -i <input> -b <branching heuristic> [--fdac <outfile> --dotfile <dotfile>]` can be used to compile the input problem as an arithmetic circuit. | ||
| The circuit can be stored in the file given by the `fdac` argument and a DOT visualization can be saved in the argument provided by the `dotfile` argument. | ||
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| ### Reading a compiled file | ||
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| A previously compiled file can be read using `schlandals read-compiled -i <fdac file> [--dotfile <dotfile]`. | ||
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| # Citing | ||
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| To cite Schlandals in your work, use the following | ||
| - Computing the probability of [ProbLog](https://github.com/ML-KULeuven/problog) programs | ||
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| For more information on how to use Schlandals and its mechanics, check [the documentation](https://aia-uclouvain.github.io/schlandals/doc). | ||
| You can cite Schlandals using the following bibtex entry | ||
| ``` | ||
| @InProceedings{dubray_et_al:LIPIcs.CP.2023.15, | ||
| author = {Dubray, Alexandre and Schaus, Pierre and Nijssen, Siegfried}, | ||
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@@ -105,4 +28,4 @@ To cite Schlandals in your work, use the following | |
| doi = {10.4230/LIPIcs.CP.2023.15}, | ||
| annote = {Keywords: Model Counting, Bayesian Networks, Probabilistic Networks} | ||
| } | ||
| ``` | ||
| ``` | ||