The Candidate Elimination algorithm is a conceptual learning algorithm that incrementally refines the version space boundary. This implementation focuses on bringing this algorithm into the MeTTa relational programming environment.
- The algorithm refines the version space boundary using observed training examples.
- It maintains two sets of hypotheses:
- G: The most general hypotheses.
- S: The most specific hypotheses.
- Each training example is processed to refine the
GandSsets, eliminating inconsistent hypotheses.
- Build Hyperon Experimental.
# Clone the repository
git clone https://github.com/logicmoo/vspace-metta
# Change to the cloned directory
cd vspace-metta
chmod +x INSTALL.sh #incase Git lost +x n the file
# Answer the questions and follow the directions
./INSTALL.sh
This script automates the setup of SWI-Prolog and its associated Python packages on your system. Here's a brief overview of the main components it installs and updates:
- Checks if SWI-Prolog is already installed.
- Installs or Updates SWI-Prolog to ensure version 9.1 or higher is present.
- Verifies if Python's package manager
pipis installed. - Installs pip if it's not found, allowing Python packages to be managed.
- Installs Janus: A Python package that interfaces with SWI-Prolog.
- Installs PySWIP: Another Python package that provides integration with SWI-Prolog.
- Updates or Installs specific SWI-Prolog packages (
predicate_streams,logicmoo_utils, anddictoo) as needed.
- Requires sudo access for certain operations, such as installing SWI-Prolog and pip.
- Modifies system package sources to include the SWI-Prolog development repository.
- Installs and updates packages using the system's package manager and Python's pip.
Note: Running this script will modify your system's software configuration, including adding repositories and installing new packages. Ensure you understand these changes and have the necessary permissions before proceeding.
For testing/viewing to see how we see the flybase as MeTTa. Or to load it into a Rust Space instead of VSpace.
# takes around 8 minutes
./scripts/convert_to_metta.sh ./data/ftp.flybase.org/releases/FB2023_04/precomputed_files/
# to get an atoms count (should be at least 56 million)
find ./data/ftp.flybase.org/releases/FB2023_04/precomputed_files/ -type f -name "*.metta" -exec wc -l {} +
To build a docker image containing MeTTaLog readily available run the following command
docker build -t mettalog .You may then enter a corresponding containter with the following command
docker run --rm -it --entrypoint bash mettalogOnce inside the container you may enter the MeTTaLog REPL with the following command
MeTTa --replor run a metta script as follows
MeTTa myprg.mettaBeware that the container will be removed after leaving it. If you
wish to keep it, then remove the --rm flag from the command line
used to enter the container.
Docker has a rich functionality set. In particular it allows you to copy files back and forth between the host and the container. For more information about Docker you may refer to its manuals and its reference documentation.
- Run in MeTTaLog without Rust installed:
./MeTTa
metta &self +> !(ensure-loaded! whole_flybase)
metta &self +> !(match &flybase (gene_map_table $Dmel $abo FBgn0000018 $C $D $E) (gene_map_table $Dmel $abo FBgn0000018 $C $D $E))
!(match &flybase (gene_map_table $Dmel $Abo FBgn0000018 $C $D $E) (gene_map_table $Dmel $Abo FBgn0000018 $C $D $E))
NDet Result(1):
(gene_map_table Dmel abo FBgn0000018 2-44 32C1-32C1 2L:10973443..10975293(-1))
$E = 2L:10973443..10975293(-1)
$D = 32C1-32C1
$C = 2-44
$Abo = abo
$Dmel = Dmel
; Execution took 0.000279 secs. (279.20 microseconds)
More Solutions? key=(;)
NDet Result(2):
(gene_map_table Dmel abo FBgn0000018 2-44 32C1-32C1 2L:10973443..10975293(-1))
$E = 2L:10973443..10975293(-1)
$D = 32C1-32C1
$C = 2-44
$Abo = abo
$Dmel = Dmel
; Execution took 0.002 secs. (1.53 milliseconds)
More Solutions? key=(;)
Last Result(3):
(gene_map_table $_311278 $_311500 FBgn0000018 $_311724 $_311928 $_312132)
metta &self +> !(, (fbgn_fbtr_fbpp_expanded! $GeneID $TranscriptType $TranscriptID $GeneSymbol $GeneFullName $AnnotationID $28 $29 $30 $31 $32) (dmel_unique_protein_isoforms! $ProteinID $ProteinSymbol $TranscriptSymbol $33) (dmel_paralogs! $ParalogGeneID $ProteinSymbol $34 $35 $36 $37 $38 $39 $40 $41 $42) (gene_map_table! $MapTableID $OrganismAbbreviation $ParalogGeneID $RecombinationLoc $CytogeneticLoc $SequenceLoc) (synonym! $SynonymID $MapTableID $CurrentSymbol $CurrentFullName $43 $44))
- Run in Rust MeTTA:
export PYTHONPATH=metta_vspace:$PYTHONPATH metta 1-VSpaceTest.metta @h metta@&self +> !(ensure-loaded! whole_flybase) metta@&self +> !(, (fbgn_fbtr_fbpp_expanded! $GeneID $TranscriptType $TranscriptID $GeneSymbol $GeneFullName $AnnotationID $28 $29 $30 $31 $32) (dmel_unique_protein_isoforms! $ProteinID $ProteinSymbol $TranscriptSymbol $33) (dmel_paralogs! $ParalogGeneID $ProteinSymbol $34 $35 $36 $37 $38 $39 $40 $41 $42) (gene_map_table! $MapTableID $OrganismAbbreviation $ParalogGeneID $RecombinationLoc $CytogeneticLoc $SequenceLoc) (synonym! $SynonymID $MapTableID $CurrentSymbol $CurrentFullName $43 $44))
- Launch vspace-metta Jupyter notebook:
./scripts/start_jupyter.sh
- Run long huge tests of vspace-metta
./scripts/test_in_metta.sh
metta +> !(test_custom_v_space)
; (add-atom &vspace_8 a) ; (add-atom &vspace_8 b) ; (atom-count &vspace_8) Pass Test:(Values same: 2 == 2) Pass Test:(Values same: Test Space Payload Attrib == Test Space Payload Attrib) ; (get-atoms &vspace_8) Pass Test:( [a, b] == [a, b] ) ; (add-atom &vspace_9 a) ; (add-atom &vspace_9 b) ; (add-atom &vspace_9 c) ; (remove-atom &vspace_9 b) Pass Test:(remove_atom on a present atom should return true) ; (remove-atom &vspace_9 bogus) Pass Test:(remove_atom on a missing atom should return false) ; (get-atoms &vspace_9) Pass Test:( [a, c] == [a, c] ) ; (add-atom &vspace_10 a) ; (add-atom &vspace_10 b) ; (add-atom &vspace_10 c) ; (atom-replace &vspace_10 b d) ; (add-atom &vspace_10 d) Pass Test:(Expression is true: True) ; (get-atoms &vspace_10) Pass Test:( [a, c, d] == [a, d, c] ) ; (add-atom &vspace_11 (A B)) ; (add-atom &vspace_11 (C D)) ; (add-atom &vspace_11 (A E)) ; (match &vspace_11 ($_105354) (A $_105354)) ; RES: (metta-iter-bind &vspace_11 (A B) (B)) ; RES: (metta-iter-bind &vspace_11 (A E) (E)) Pass Test:( [ { $xx <- B }, { $xx <- E } ] == [{xx: B}, {xx: E}] ) ; (add-atom &vspace_12 (A B)) Pass Test:(Values same: CSpace == CSpace) ; (match &vspace_12 ($_117600) (A $_117600)) ; RES: (metta-iter-bind &vspace_12 (A B) (B)) Pass Test:( [ { $v <- B } ] == [{v: B}] ) ; (add-atom &vspace_12 (big-space None)) ; (add-atom &vspace_13 (A B)) ; (match &vspace_13 ($_129826) (A $_129826)) ; RES: (metta-iter-bind &vspace_13 (A B) (B)) ; (match &vspace_13 ($_135548) (: B $_135548)) Pass Test:(Values same: [[B]] == [[B]])
## :bulb: Expected Features
- **Relational Programming**: Utilize MeTTa's declarative paradigm to represent knowledge as relations and query this knowledge base.
- **Flybase Integration**: Test learning while interacting with Flybase data using MeTTa.
## :dart: Advantages of Version Space
- **Incremental Learning**: Suitable for handling one example at a time, refining the G-set and S-set without revisiting old data.
- **Consistency Maintenance**: Maintains all hypotheses consistent with observed data.
- **Transparent Learning**: The G-set and S-set provide an interpretable view of the learned content.
## :raised_hands: Acknowledgments
- Thanks to the developers and contributors of [Hyperon Experimental MeTTa](https://github.com/trueagi-io/hyperon-experimental) and [PySWIP](https://github.com/yuce/pyswip).
- Appreciation to [Flybase](https://flybase.org) for their genetic data that aids in learning and testing.
## :phone: Support
- For support, queries, or feature suggestions, kindly open an issue on our [GitHub repository](https://github.com/logicmoo/vspace-metta/issues).
## :scroll: License
- vspace-metta is distributed under the LGPL License.
## metta_interp.PL
[MeTTaLog (In Prolog)](https://github.com/logicmoo/vspace-metta/blob/main/MeTTaLog.md)