Skip to content

Commit

Permalink
Add readme (#8)
Browse files Browse the repository at this point in the history 
* Add readme

* complete steps of the pipeline

* Update README.md

fix a grammer

---------

Co-authored-by: parisa-zahedi <p.zahedi@uu.nl>
Co-authored-by: parisa-zahedi <parisa.zahedi@gmail.com>
  • Loading branch information
@ShNadi @parisa-zahedi
3 people authored Apr 8, 2024
1 parent e5a02b3 commit 05a74ea
Showing 1 changed file with 222 additions and 38 deletions.
260 changes: 222 additions & 38 deletions README.md
View file
Original file line number Diff line number Diff line change
@@ -1,51 +1,235 @@
# re-python-package
# INTEREST

This template repository is created by the [UU Research Engineering team](https://utrechtuniversity.github.io/research-engineering/) and is aimed to provide a simple project template for python package development.
The code in this repository implements a pipeline to extract specific articles from a large corpus.

The template includes:
- Project directory structure
- Project configuration using `pyproject.toml`
- GitHub actions workflows for testing, linting, type checking and publishing on pypi
Currently, this tool is tailored for the [Delpher Kranten](https://www.delpher.nl/nl/kranten) corpus, but it can be adapted for other corpora as well.

Many other project templates exist, check for example this advanced [python template](https://github.com/NLeSC/python-template) by the NL eScience Center.
Articles can be filtered based on individual or multiple features such as title, year, decade, or a set of keywords. To select the most relevant articles, we utilize models such as tf-idf. These models are configurable and extendable.

## Dependencies
This template uses:
| Tool | Aim |
| --- | --- |
| setuptools | building |
| flake8, pylint | code linting |
| pytest | testing |
| pydocstyle | checking docstrings |
| mypy | type checking |
| sphinx | documentation generation |

If needed, most of these tools can be removed by simply removing the GitHub action that calls the tool, or by changing `pyproject.toml`
## Getting Started
Clone this repository to your working station to obtain examples and python scripts:
```
git clone https://github.com/UtrechtUniversity/historical-news-sentiment.git
```

## How to use
### Prerequisites
To install and run this project you need to have the following prerequisites installed.
```
- Python [>=3.9, <3.11]
```

### Step 1: Create new repository from this template
Click `Use this template` at the top of this page to create a new repository using this template
### Installation
#### Option 1 - Install interest package
To run the project, ensure to install the interest package that is part of this project.
```
pip install interest
```
#### Option 2 - Run from source code
If you want to run the scripts without installation you need to:

### Step 2: Change the name of your package in pyproject.toml
- Change the name of the folder `package-name` to the name of your package
- Open `pyproject.toml` and change `package-name` to the name of your package
- Also change the authors and optionally any other items that you want to change
- Install requirement
```commandline
pip install setuptools wheel
```
Change your current working directory to the location of your pyproject.toml file.
```
python -m build
pip install .
```
- Set PYTHONPATH environment:
On Linux and Mac OS, you might have to set the PYTHONPATH environment variable to point to this directory.

### Step 3: Change GitHub Actions workflow
- Open `.github/workflows/python-package.yml`
- Change `package-name` to the name of your package (line 21)
- Many actions are commented out, uncomment them when you want to start using them.
```commandline
export PYTHONPATH="current working directory/historical-news-sentiment:${PYTHONPATH}"
```
### Built with
These packages are automatically installed in the step above:
* [scikit-learn](https://scikit-learn.org/stable/)
* [SciPy](https://scipy.org)
* [NumPy](https://numpy.org)
* [spaCy](https://spacy.io)
* [pandas](https://pandas.pydata.org)

### Step 4: Replace this README file with your README
- You may use this [README template](https://github.com/UtrechtUniversity/rse-project-templates/blob/master/README-template.md)
## Usage
### 1. Preparation
#### Data Prepration
Before proceeding, ensure that you have the data prepared in the following format: The expected format is a set of JSON files compressed in the .gz format. Each JSON file contains metadata related to a newsletter, magazine, etc., as well as a list of article titles and their corresponding bodies. These files may be organized within different folders or sub-folders.
Below is a snapshot of the JSON file format:
```commandline
{
"newsletter_metadata": {
"title": "Newspaper title ..",
"language": "NL",
"date": "1878-04-29",
...
},
"articles": {
"1": {
"title": "title of article1 ",
"body": [
"paragraph 1 ....",
"paragraph 2...."
]
},
"2": {
"title": "title of article2",
"body": [
"text..."
]
}
}
}
```

### Step 5: Change the license file
- Open `LICENSE`, change the copyright holder when required (line 3)
- Or replace the entire license file if another license applies
In our use case, the harvested KB data is in XML format. We have provided the following script to transform the original data into the expected format.
```
from interest.preprocessor.parser import XMLExtractor
### Step 6: Add a citation file
- Create a citation file for your repository using [cffinit](https://citation-file-format.github.io/cff-initializer-javascript/#/)
extractor = XMLExtractor(Path(input_dir), Path(output_dir))
extractor.extract_xml_string()
```

### Step 7: Publising on Pypi (optional/later)
For publishing the package on Pypi you need to create [API tokens](https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries).
Navigate to scripts folder and run:
```
python3 convert_input_files.py --input_dir path/to/raw/xml/data --output_dir path/to/converted/json/compressed/output
```
#### Customize input-file

In order to define a corpus with a new data format you should:

- add a new input_file_type to [INPUT_FILE_TYPES](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/filter/__init__.py)
- implement a class that inherits from [input_file.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/filter/input_file.py).
This class is customized to read a new data format. In our case-study we defined [delpher_kranten.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/filter/delpher_kranten.py).


### 2. Filtering
In this step, you may select articles based on a filter or a collection of filters. Articles can be filtered by title, year, decade, or a set of keywords defined in the ```config.json``` file.
```commandline
"filters": [
{
"type": "TitleFilter",
"title": "example"
},
{
"type": "YearFilter",
"year": 2022
},
{
"type": "DecadeFilter",
"decade": 1960
},
{
"type": "KeywordsFilter",
"keywords": ["sustainability", "green"]
}
]
}
```
run the following to filter the articles:
```commandline
python3 scripts/step1_filter_articles.py --input-dir "path/to/converted/json/compressed/output/" --output-dir "output_filter/" --input-type "delpher_kranten" --glob "*.gz"
```
In our case, input-type is "delpher_kranten", and input data is a set of compresed json files with ```.gz``` extension.

The output of this script is a JSON file for each selected article in the following format:
```commandline
{
"file_path": "output/transfered_data/00/KRANTEN_KBPERS01_000002100.json.gz",
"article_id": "5",
"Date": "1878-04-29",
"Title": "Opregte Haarlemsche Courant"
}
```
### 3. Categorization by timestamp
The output files generated in the previous step are categorized based on a specified [period-type](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/temporal_categorization/__init__.py),
such as ```year``` or ```decade```. This categorization is essential for subsequent steps, especially if you intend to apply tf-idf or other models to specific periods. In our case, we applied tf-idf per decade.

```commandline
python3 scripts/step2_categorize_by_timestamp.py --input-dir "output_filter/" --glob "*.json" --period-type "decade" --output-dir "output_timestamped/"
```
The output consists of a .csv file for each period, such as one file per decade, containing the ```file_path``` and ```article_id``` of selected articles.

### 4. Select final articles
This step is applicable when articles are filtered (in step 2) using a set of keywords.
By utilizing tf-idf, the most relevant articles related to the specified topic (defined by the provided keywords) are selected.

Before applying tf-idf, articles containing any of the specified keywords in their title are selected.

From the rest of articles, to choose the most relevant ones, you can specify one of the following criteria in [config.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/config.json):

- Threshold for the tf-idf score value
- Maximum number of selected articles with the top scores

```commandline
"article_selector":
{
"type": "threshold",
"value": "0.02"
},
OR
"article_selector":
{
"type": "num_articles",
"value": "200"
},
```

The following script, add a new column, ```selected``` to the .csv files from the previous step.
```commandline
python3 scripts/3_select_final_articles.py --input_dir "output/output_timestamped/"
```

### 5. Generate output
As the final step of the pipeline, the text of the selected articles is saved in a .csv file, which can be used for manual labeling. The user has the option to choose whether the text should be divided into paragraphs.
This feature can be set in [config.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/config.json).
```commandline
"output_unit": "paragraph"
OR
"output_unit": "text"
```

```commandline
python3 scripts/step4_generate_output.py --input_dir "output/output_timestamped/” --output-dir “output/output_results/“ --glob “*.csv”
```
## About the Project
**Date**: February 2024

**Researcher(s)**:

Pim Huijnen (p.huijnen@uu.nl)

**Research Software Engineer(s)**:

- Parisa Zahedi (p.zahedi@uu.nl)
- Shiva Nadi (s.nadi@uu.nl)
- Matty Vermet (m.s.vermet@uu.nl)


### License

The code in this project is released under [MIT license](LICENSE).

## Contributing

Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

To contribute:

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## Contact

Pim Huijnen - p.huijnen@uu.nl

Project Link: [https://github.com/UtrechtUniversity/historical-news-sentiment](https://github.com/UtrechtUniversity/historical-news-sentiment)

0 comments on commit 05a74ea

Please sign in to comment.