https://github.com/kahsolt/youroqnet
Official implementaion of YouroQNet, a toyish quantum text classifier implemented with pyVQNet and pyQPanda
https://github.com/kahsolt/youroqnet
contest-solution quantum-classifier text-classification vqnet
Last synced: 11 months ago
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Official implementaion of YouroQNet, a toyish quantum text classifier implemented with pyVQNet and pyQPanda
- Host: GitHub
- URL: https://github.com/kahsolt/youroqnet
- Owner: Kahsolt
- License: mit
- Created: 2023-05-29T02:20:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-14T14:30:40.000Z (over 2 years ago)
- Last Synced: 2025-01-03T16:28:33.876Z (about 1 year ago)
- Topics: contest-solution, quantum-classifier, text-classification, vqnet
- Language: Python
- Homepage:
- Size: 7.03 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YouroQNet: Quantum Text Classification with Context Memory
Official implementaion of YouroQNet, a toyish quantum text classifier implemented with pyVQNet and pyQPanda
----
This repo contains code for the final problem of the OriginQ's [2nd CCF "Pilot Cup" contest](https://contest.originqc.com.cn/contest/4/contest:introduction) (Professional Group - Quantum Machine Learning Track).
> Oh yes yes child, we've run a hard time struggling.
The final total score is **79.2**, and ranking unknown; but why the fuck you owe me a point of 0.8?? π±
And, code repo for the qualifying stage is here: [第δΊε±βεΈεζ―βεθ΅](https://github.com/Kahsolt/CCF-2nd-Pilot-Cup-first-stage)
### Quickstart
βͺ install
- `conda create -n q python==3.8` (pyvqnet requires Python 3.8)
- `conda activate q`
- `pip install -r requirements.txt`
βͺ for contest problem (π **Follow this to reproduce our contest results!!**)
- `python answer.py` for preprocess & train (β VERY VERY SLOW!!)
- `python check.py` for evaluate
βͺ for quick peek of YouroQNet components
- `python vis_tokenizer.py` for adaptive k-gram tokeinzer interactive demo
- `python vis_youroqnet.py` for YouroQNet interactive demo
- `run_quantum_toy.cmd` (π run the toy version out of box before all)
βͺ for full development
- download the full dataset [simplifyweibo_4_moods](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/simplifyweibo_4_moods/intro.ipynb), unzip `simplifyweibo_4_moods.csv` to `data` folder
- `pip install -r requirements_dev.txt` for extra dependencies
- `pushd repo & init_repos.cmd & popd` for extra git repos
- fasttext==0.9.2 requires numpy<1.24 (things might changed)
- `start_shell.cmd` to enter deveolp run command env
- `start_shell.cmd py` to get a ipy console that quick refering to `pyvqnet`'s fucking undocumented-documentation with `help()`
- `mk_preprocess.cmd` for making clean datasets, stats, plots & vocabs etc... (~7 minutes)
- `python vis_project.py` to see 3d data projection (you will understand what the fuck this dataset is πΏ)
- `run_baseline.cmd` to run classic models
- `run_quantum.cmd` to run quantum models
β The training sometimes might fail due to ill random parameter initialization, when trainset loss not tends to decay or quickly go overfit, just kill it & retry π
βͺ core idea & contributions
- adaptive k-gram tokenizer (see [mk_vocab.py](mk_vocab.py), interactivate demo [vis_tokenizer.py](vis_tokenizer.py))
- YouroQNet for text clf (see [run_quantum.py](run_quantum.py), interactivate demo [vis_youroqnet.py](vis_youroqnet.py))
- theoretical analysis of why & how QNN works (see [vis_qc_apriori.py](vis_qc_apriori.py))
βΉ See our PPT [YouroQNet.pdf](ref/YouroQNet.pdf) for more conceptual understanding π
#### Dataset
A subset from [simplifyweibo_4_moods](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/simplifyweibo_4_moods/intro.ipynb): `1600` samples for train, `400` samples for test. Class label names: `0 - joy`, `1 - angry`, `2 - hate`, `3 - sad`, however is not very semantically corresponding in the datasets :(
β File naming rule: `train.csv` is train set, `test.csv` is valid set, and the generated `valid.csv` might be the real test set for this contest. **We use csv filename to refer to each split in the code**
### Todo List
- [x] data exploration
- [x] guess the target test set (`valid.txt`)
- [x] vocab & freq stats
- [x] pca & cluster
- [ ] data relabel (?)
- [ ] data filtering
- [x] punctuation sanitize
- [ ] stop words removal
- [x] too short / long sententce
- [x] feature extraction
- [x] tf-idf (syntaxical)
- [x] fasttext embedding (sematical)
- [x] adaptive tokenizer
- [x] baseline models
- [x] sklearn
- [x] vqnet-classical
- [ ] quantum models
- [ ] quantum embedding
- [ ] model route on different length
- [x] multi to binary clf
- [ ] contrastive learning
- [ ] learn the difference
### Project layout
```python
# meterials
ref/ # thesis for dev
Question-ML.png # problem sheet
YouroQNet.pdf # solution PPT (YouroQNet)
init_thesis.cmd # thesis donwloader
repo/ # git repos for research
init_repos.cmd # git repo cloner
update_repos.cmd
data/ # dataset
simplifyweibo_4_moods.csv # raw dataset (manually download)
train|test.csv # context dataset
*_cleaned.csv
*_tokenized.txt
cc.zh.300.bin # FastText pretrained word embedding (auto downloaded)
log/ # outputs
/ # aka. vocab
/ # sklearn models
/ # vqnet/torch models
tmp/ # generated intermediate results for debug
# contest related
answer.py # run script for preprocessing & training
check.py # run script for evalution
# preprocessors
mk_*.py
mk_preprocess.cmd # run script for mk_*.py
# models
run_baseline_*.py # classical experiments
run_baseline.cmd # run script for run_baseline_*.py
run_quantum.py # quantum experiments
run_quantum.cmd # run script for run_quantum.py
run_quantum_toy.cmd # toy QNN for debug and verify
# misc
vis_*.py # intercative demos or debug scaffolds
utils.py # common utils
start_shell.cmd # develop env entry
# doc & lic
README.md
TECH.md # techincal & theoretical stuff
requirements_*.txt
LICESE
```
βΉ For the contest, only these files are submitted: `answer.py`, `mk_vocab.py`, `run_quantum.py`, `utils.py`, `README.md`; it should be enough to run all quantum parts π
### References
- FastText:
- Enriching Word Vectors with Subword Information: [https://arxiv.org/abs/1607.04606](https://arxiv.org/abs/1607.04606)
- Bag of Tricks for Efficient Text Classification: [https://arxiv.org/abs/1607.01759](https://arxiv.org/abs/1607.01759)
- repo: [https://github.com/facebookresearch/fastText](https://github.com/facebookresearch/fastText)
- QNN for text-clf:
- QNLP-DisCoCat: [https://arxiv.org/abs/2102.12846](https://arxiv.org/abs/2102.12846)
- QSANN: [https://arxiv.org/abs/2205.05625](https://arxiv.org/abs/2205.05625)
- OriginQ: [https://originqc.com.cn/index.html](https://originqc.com.cn/index.html)
- QPanda: [https://github.com/OriginQ/QPanda-2](https://github.com/OriginQ/QPanda-2)
- pyQPanda: [https://pyqpanda-toturial.readthedocs.io/zh/latest/](https://pyqpanda-toturial.readthedocs.io/zh/latest/)
- pyVQNet: [https://vqnet20-tutorial.readthedocs.io/en/main/index.html](https://vqnet20-tutorial.readthedocs.io/en/main/index.html)
- QCNN related:
- tensorflow-quantum impl: [https://www.tensorflow.org/quantum/tutorials/qcnn](https://www.tensorflow.org/quantum/tutorials/qcnn)
- pytorch + qiskit impl: [https://github.com/YPadawan/qiskit-hackathon](https://github.com/YPadawan/qiskit-hackathon)
- pytorch + pennylane impl: [https://github.com/christorange/QC-CNN](https://github.com/christorange/QC-CNN)
- Tiny-Q: [https://github.com/Kahsolt/Tiny-Q](https://github.com/Kahsolt/Tiny-Q)
=> find thesis of related work in [ref/init_thesis.cmd](ref/init_thesis.cmd)
=> find implementations of related work in [repo/init_repos.cmd](repo/init_repos.cmd)
### Citation
If you find this work useful, please give a star β and cite~ π
```
@misc{kahsolt2023,
author = {Kahsolt},
title = {YouroQNet: Quantum Text Classification with Context Memory},
howpublished = {\url{https://github.com/Kahsolt/YouroQNet}}
month = {May},
year = {2023}
}
```
----
by Armit
2023/05/03