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https://github.com/antononcube/wl-nlptemplateengine-paclet
Wolfram Language (aka Mathematica) paclet that fills in (code) templates using NLP techniques.
https://github.com/antononcube/wl-nlptemplateengine-paclet
chatgpt-api machine-learning nlp-machine-learning opeanai
Last synced: about 1 month ago
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Wolfram Language (aka Mathematica) paclet that fills in (code) templates using NLP techniques.
- Host: GitHub
- URL: https://github.com/antononcube/wl-nlptemplateengine-paclet
- Owner: antononcube
- License: artistic-2.0
- Created: 2023-04-13T01:14:18.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-29T02:26:10.000Z (5 months ago)
- Last Synced: 2024-08-29T19:16:25.041Z (5 months ago)
- Topics: chatgpt-api, machine-learning, nlp-machine-learning, opeanai
- Language: Mathematica
- Homepage: https://resources.wolframcloud.com/PacletRepository/resources/AntonAntonov/NLPTemplateEngine/
- Size: 2.06 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NLPTemplateEngine WL paclet
## In brief
Wolfram Language (aka Mathematica) paclet for code generation by filling in templates using NLP techniques.
The paclet has a template fill in function `Concretize` and a related classifier of natural language specifications.
A user can provide code templates to be filled in using CSV files.
The pre-loaded templates are used to produce Machine Learning (ML) computational workflow specifications for:
- Classification
- Latent Semantic Analysis
- Quantile Regression
- Recommendations
- Random tabular data generation
The implementations are in Python, R, and WL. Here is example of Python recommender creation spec:
-----
## Usage examples
### Classification (WL)
```mathematica
Concretize["Quantile regression with the dataset dfTemp; use 20 knots and the probabilities 0.1 and 0.9."]
```
```mathematica
Hold[qrObj =
QRMonUnit[dfTemp]⟹
QRMonEchoDataSummary[]⟹
QRMonQuantileRegression[20, {0.1, 0.9}, InterpolationOrder -> 3]⟹
QRMonPlot["DateListPlot" -> False, PlotTheme -> "Detailed"]⟹
QRMonErrorPlots["RelativeErrors" -> False, "DateListPlot" -> False, PlotTheme -> "Detailed"];
]
```
### Quantile Regression (WL)
```mathematica
Concretize["Quantile regression with the dataset dfTemp; use 20 knots and the probabilities 0.1 and 0.9."]
```
```mathematica
Hold[
qrObj = QRMonUnit[dfTmep]⟹
QRMonEchoDataSummary[]⟹
QRMonQuantileRegression[20, {0.1, 0.9}, InterpolationOrder -> 3]⟹
QRMonPlot["DateListPlot" -> False, PlotTheme -> "Detailed"]⟹
QRMonErrorPlots["RelativeErrors" -> False, "DateListPlot" -> False, PlotTheme -> "Detailed"];
]
```
### Recommendations (Python)
```mathematica
Concretize["Make a recommender over the data dfMovies; Give recommendations for the profile actor:Willis and year:1995.",
"TargetLanguage" -> "Python"]
```
```python
smrObj = (SparseMatrixRecommender()
.create_from_wide_form(data = dfMovies, item_column_name="id", columns=None, add_tag_types_to_column_names=True, tag_value_separator=":")
.apply_term_weight_functions("IDF", "None", "Cosine")
.recommend_by_profile(profile=["Willis", "year"], nrecs=12)
.join_across(data=dfMovies, on="id")
.echo_value())
```
-----
## Workflow
*TBD...*
-----
## Targets
*TBF...*
### Classification
### Latent Semantic Analysis
### Quantile Regression
### Recommendations
### Random tabular dataset
- Python,
- R,
- Raku
- WL,
[`RandomTabularDataset`](https://resources.wolframcloud.com/FunctionRepository/resources/RandomTabularDataset/)
------
## Bring your own templates
0. Load the paclet [NLP-Template-Engine](https://resources.wolframcloud.com/PacletRepository/resources/AntonAntonov/NLPTemplateEngine/).
```mathematica
Needs["AntonAntonov`NLPTemplateEngine`"]
```
1. Get the "training" templates data (from CSV file you have created or changed) for a new workflow
(["SendMail"](./TemplateData/dsQASParameters-SendMail.csv)):
```mathematica
dsSendMailTemplateEngineData = ResourceFunction["ImportCSVToDataset"][
"https://raw.githubusercontent.com/antononcube/NLP-Template-Engine/main/TemplateData/dsQASParameters-SendMail.csv"];
Dimensions[dsSendMailTemplateEngineData]
(* {43, 5} *)
```
2. Add the ingested data for the new workflow (from the CSV file) into the NLP-Template-Engine:
```mathematica
NLPTemplateEngineAddData[dsSendMailTemplateEngineData] // Keys
(* {"Questions", "Templates", "Defaults", "Shortcuts"} *)
```
3. Parse natural language specification with the newly ingested and onboarded workflow ("SendMail"):
```mathematica
Concretize["SendMail", "Send email to [email protected] with content RandomReal[343], and the subject this is a random real call.", PerformanceGoal -> "Speed"]
(* Hold[
SendMail[
Association["To" -> {"[email protected]"},
"Subject" -> "a random real call", "Body" -> RandomReal,
"AttachedFiles" -> None]]] *)
```
4. Experiment with running the generated code!
-----
## References
### Repositories
[AAr1] Anton Antonov
[NLP Template Engine](https://github.com/antononcube/NLP-Template-Engine),
(2021),
[GitHub/antononcube](https://github.com/antononcube).
### Videos
[AAv1] Anton Antonov
["NLP Template Engine, Part 1"](https://www.youtube.com/watch?v=a6PvmZnvF9I),
(2021),
[YouTube/AntonAntonov](https://www.youtube.com/@AAA4prediction).
[AAv2] Anton Antonov,
[Natural Language Processing Template Engine](https://www.youtube.com/watch?v=IrIW9dB5sRM),
(2022),
[Wolfram Technology Conference 2022](https://www.wolfram.com/events/technology-conference/2022/)
presentation.
[YouTube/WolframResearch](https://www.youtube.com/@WolframResearch).