latent-semantic-analysis 0.1.0
Pipeline for training LSA models using Scikit-Learn.
Requires Python: >=3.6

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### Latent Semantic Analysis
Pipeline for training **LSA** models using Scikit-Learn.

### Usage
Instead of writing custom code for latent semantic analysis, you just need:
1. install pipeline:
```shell script
pip install latent-semantic-analysis
```
2. run pipeline:
- either in **terminal**:
```shell script
lsa-train --path_to_config config.yaml
```
- or in **python**:
```python3
import latent_semantic_analysis

latent_semantic_analysis.train(path_to_config="config.yaml")
```

**NOTE**: more about config file [here](https://github.com/dayyass/latent-semantic-analysis/tree/main#config).

No data preparation is needed, only a **csv** file with raw text column (with arbitrary name).

#### Config
The user interface consists of only one files:
- [**config.yaml**](https://github.com/dayyass/latent-semantic-analysis/blob/main/config.yaml) - general configuration with sklearn **TF-IDF** and **SVD** parameters

Change **config.yaml** to create the desired configuration and train LSA model with the following command:
- **terminal**:
```shell script
lsa-train --path_to_config config.yaml
```
- **python**:
```python3
import latent_semantic_analysis

latent_semantic_analysis.train(path_to_config="config.yaml")
```

Default **config.yaml**:
```yaml
seed: 42
path_to_save_folder: models

# data
data:
  data_path: data/data.csv
  sep: ','
  text_column: text

# tf-idf
tf-idf:
  lowercase: true
  ngram_range: (1, 1)
  max_df: 1.0
  min_df: 1

# svd
svd:
  n_components: 10
  algorithm: arpack
```

**NOTE**: `tf-idf` and `svd` are sklearn [**TfidfVectorizer**](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=tfidf#sklearn.feature_extraction.text.TfidfVectorizer) and [**TruncatedSVD**](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) parameters correspondingly, so you can parameterize instances of these classes however you want.

#### Output
After training the model, the pipeline will return the following files:
- `model.joblib` - sklearn pipeline with LSA (TF-IDF and SVD steps)
- `config.yaml` - config that was used to train the model
- `logging.txt` - logging file
- `doc2topic.json` - document embeddings
- `term2topic.json` - term embeddings

### Requirements
Python >= 3.6

### Citation
If you use **latent-semantic-analysis** in a scientific publication, we would appreciate references to the following BibTex entry:
```bibtex
@misc{dayyass2021lsa,
    author       = {El-Ayyass, Dani},
    title        = {Pipeline for training LSA models},
    howpublished = {\url{https://github.com/dayyass/latent-semantic-analysis}},
    year         = {2021}
}
```