| Name | gsppy JSON |
| Version |
2.3.0
JSON |
| download |
| home_page | None |
| Summary | GSP (Generalized Sequence Pattern) algorithm in Python |
| upload_time | 2025-01-05 19:41:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2025 Jackson Antonio do Prado Lima Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
gsp
data analysis
sequence mining
sequential patterns
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[]()
[](https://doi.org/10.5281/zenodo.3333987)
[](https://pypi.org/project/gsppy/)
[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)
[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)
[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)
[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)
[](https://codecov.io/github/jacksonpradolima/gsp-py)
# GSP-Py
**GSP-Py**: A Python-powered library to mine sequential patterns in large datasets, based on the robust **Generalized
Sequence Pattern (GSP)** algorithm. Ideal for market basket analysis, temporal mining, and user journey discovery.
> [!IMPORTANT]
> GSP-Py is compatible with Python 3.8 and later versions!
---
## 📚 Table of Contents
1. [🔍 What is GSP?](#what-is-gsp)
2. [🔧 Requirements](#requirements)
3. [🚀 Installation](#installation)
- [❖ Clone Repository](#option-1-clone-the-repository)
- [❖ Install via PyPI](#option-2-install-via-pip)
4. [🛠️ Developer Installation](#developer-installation)
5. [💡 Usage](#usage)
- [✅ Example: Analyzing Sales Data](#example-analyzing-sales-data)
- [📊 Explanation: Support and Results](#explanation-support-and-results)
6. [🌟 Planned Features](#planned-features)
7. [🤝 Contributing](#contributing)
8. [📝 License](#license)
9. [📖 Citation](#citation)
---
## 🔍 What is GSP?
The **Generalized Sequential Pattern (GSP)** algorithm is a sequential pattern mining technique based on **Apriori
principles**. Using support thresholds, GSP identifies frequent sequences of items in transaction datasets.
### Key Features:
- **Support-based pruning**: Only retains sequences that meet the minimum support threshold.
- **Candidate generation**: Iteratively generates candidate sequences of increasing length.
- **General-purpose**: Useful in retail, web analytics, social networks, temporal sequence mining, and more.
For example:
- In a shopping dataset, GSP can identify patterns like "Customers who buy bread and milk often purchase diapers next."
- In a website clickstream, GSP might find patterns like "Users visit A, then go to B, and later proceed to C."
---
## 🔧 Requirements
You will need Python installed on your system. On most Linux systems, you can install Python with:
```bash
sudo apt install python3
```
For package dependencies of GSP-Py, they will automatically be installed when using `pip`.
---
## 🚀 Installation
GSP-Py can be easily installed from either the **repository** or PyPI.
### Option 1: Clone the Repository
To manually clone the repository and set up the environment:
```bash
git clone https://github.com/jacksonpradolima/gsp-py.git
cd gsp-py
```
Refer to the [Developer Installation](#developer-installation) section and run:
```bash
rye sync
```
### Option 2: Install via `pip`
Alternatively, install GSP-Py from PyPI with:
```bash
pip install gsppy
```
---
## 🛠️ Developer Installation
This project uses [Rye](https://github.com/mitsuhiko/rye) for managing dependencies, running scripts, and setting up the environment. Follow these steps to install and set up Rye for this project:
#### 1. Install Rye
Run the following command to install Rye:
```bash
curl -sSf https://rye.astral.sh/get | bash
```
If the `~/.rye/bin` directory is not in your PATH, add the following line to your shell configuration file (e.g., `~/.bashrc`, `~/.zshrc`, etc.):
```bash
export PATH="$HOME/.rye/bin:$PATH"
```
Reload your shell configuration file:
```bash
source ~/.bashrc # or `source ~/.zshrc`
```
#### 2. Set Up the Project Environment
To configure the project environment and install its dependencies, run:
```bash
rye sync
```
#### 3. Use Rye Scripts
Once the environment is set up, you can run the following commands to simplify project tasks:
- Run tests (in parallel): `rye run test`
- Format code: `rye run format`
- Lint code: `rye run lint`
- Type-check: `rye run typecheck`
- Add new dependencies: `rye add <package-name>`
- Add new dependency to dev dependencies: `rye add --dev <package-name>`
#### Notes
- Rye automatically reads dependencies and scripts from the `pyproject.toml` file.
- No need for `requirements.txt`, as Rye manages all dependencies!
## 💡 Usage
The library is designed to be easy to use and integrate with your own projects. Below is an example of how you can
configure and run GSP-Py.
### Example Input Data
The input to the algorithm is a sequence of transactions, where each transaction contains a sequence of items:
```python
transactions = [
['Bread', 'Milk'],
['Bread', 'Diaper', 'Beer', 'Eggs'],
['Milk', 'Diaper', 'Beer', 'Coke'],
['Bread', 'Milk', 'Diaper', 'Beer'],
['Bread', 'Milk', 'Diaper', 'Coke']
]
```
### Importing and Initializing the GSP Algorithm
Import the `GSP` class from the `gsppy` package and call the `search` method to find frequent patterns with a support
threshold (e.g., `0.3`):
```python
from gsppy.gsp import GSP
# Example transactions: customer purchases
transactions = [
['Bread', 'Milk'], # Transaction 1
['Bread', 'Diaper', 'Beer', 'Eggs'], # Transaction 2
['Milk', 'Diaper', 'Beer', 'Coke'], # Transaction 3
['Bread', 'Milk', 'Diaper', 'Beer'], # Transaction 4
['Bread', 'Milk', 'Diaper', 'Coke'] # Transaction 5
]
# Set minimum support threshold (30%)
min_support = 0.3
# Find frequent patterns
result = GSP(transactions).search(min_support)
# Output the results
print(result)
```
### Output
The algorithm will return a list of patterns with their corresponding support.
Sample Output:
```python
[
{('Bread',): 4, ('Milk',): 4, ('Diaper',): 4, ('Beer',): 3, ('Coke',): 2},
{('Bread', 'Milk'): 3, ('Milk', 'Diaper'): 3, ('Diaper', 'Beer'): 3},
{('Bread', 'Milk', 'Diaper'): 2, ('Milk', 'Diaper', 'Beer'): 2}
]
```
- The **first dictionary** contains single-item sequences with their frequencies (e.g., `('Bread',): 4` means "Bread"
appears in 4 transactions).
- The **second dictionary** contains 2-item sequential patterns (e.g., `('Bread', 'Milk'): 3` means the sequence "
Bread → Milk" appears in 3 transactions).
- The **third dictionary** contains 3-item sequential patterns (e.g., `('Bread', 'Milk', 'Diaper'): 2` means the
sequence "Bread → Milk → Diaper" appears in 2 transactions).
> [!NOTE]
> The **support** of a sequence is calculated as the fraction of transactions containing the sequence, e.g.,
`[Bread, Milk]` appears in 3 out of 5 transactions → Support = `3 / 5 = 0.6` (60%).
> This insight helps identify frequently occurring sequential patterns in datasets, such as shopping trends or user
> behavior.
> [!TIP]
> For more complex examples, find example scripts in the [`gsppy/tests`](gsppy/tests) folder.
---
## 🌟 Planned Features
We are actively working to improve GSP-Py. Here are some exciting features planned for future releases:
1. **Custom Filters for Candidate Pruning**:
- Enable users to define their own pruning logic during the mining process.
2. **Support for Preprocessing and Postprocessing**:
- Add hooks to allow users to transform datasets before mining and customize the output results.
3. **Support for Time-Constrained Pattern Mining**:
- Extend GSP-Py to handle temporal datasets by allowing users to define time constraints (e.g., maximum time gaps
between events, time windows) during the sequence mining process.
- Enable candidate pruning and support calculations based on these temporal constraints.
Want to contribute or suggest an
improvement? [Open a discussion or issue!](https://github.com/jacksonpradolima/gsp-py/issues)
---
## 🤝 Contributing
We welcome contributions from the community! If you'd like to help improve GSP-Py, read
our [CONTRIBUTING.md](CONTRIBUTING.md) guide to get started.
Development dependencies (e.g., testing and linting tools) are automatically managed using Rye. To install
these dependencies and set up the environment, run:
```bash
rye sync
```
After syncing, you can run the following scripts using Rye for development tasks:
- Run tests (in parallel): `rye run test`
- Lint code: `rye run lint`
- Type-check: `rye run typecheck`
- Format code: `rye run format`
### General Steps:
1. Fork the repository.
2. Create a feature branch: `git checkout -b feature/my-feature`.
3. Commit your changes: `git commit -m "Add my feature."`
4. Push to your branch: `git push origin feature/my-feature`.
5. Submit a pull request to the main repository!
Looking for ideas? Check out our [Planned Features](#planned-features) section.
---
## 📝 License
This project is licensed under the terms of the **MIT License**. For more details, refer to the [LICENSE](LICENSE) file.
---
## 📖 Citation
If GSP-Py contributed to your research or project that led to a publication, we kindly ask that you cite it as follows:
```
@misc{pradolima_gsppy,
author = {Prado Lima, Jackson Antonio do},
title = {{GSP-Py - Generalized Sequence Pattern algorithm in Python}},
month = Dec,
year = 2025,
doi = {10.5281/zenodo.3333987},
url = {https://doi.org/10.5281/zenodo.3333987}
}
```
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"description": "[]()\n\n[](https://doi.org/10.5281/zenodo.3333987)\n\n[](https://pypi.org/project/gsppy/)\n[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)\n[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)\n[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)\n[](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)\n[](https://codecov.io/github/jacksonpradolima/gsp-py)\n\n# GSP-Py\n\n**GSP-Py**: A Python-powered library to mine sequential patterns in large datasets, based on the robust **Generalized\nSequence Pattern (GSP)** algorithm. Ideal for market basket analysis, temporal mining, and user journey discovery.\n\n> [!IMPORTANT]\n> GSP-Py is compatible with Python 3.8 and later versions!\n\n---\n\n## \ud83d\udcda Table of Contents\n\n1. [\ud83d\udd0d What is GSP?](#what-is-gsp)\n2. [\ud83d\udd27 Requirements](#requirements)\n3. [\ud83d\ude80 Installation](#installation)\n - [\u2756 Clone Repository](#option-1-clone-the-repository)\n - [\u2756 Install via PyPI](#option-2-install-via-pip)\n4. [\ud83d\udee0\ufe0f Developer Installation](#developer-installation)\n5. [\ud83d\udca1 Usage](#usage)\n - [\u2705 Example: Analyzing Sales Data](#example-analyzing-sales-data)\n - [\ud83d\udcca Explanation: Support and Results](#explanation-support-and-results)\n6. [\ud83c\udf1f Planned Features](#planned-features)\n7. [\ud83e\udd1d Contributing](#contributing)\n8. [\ud83d\udcdd License](#license)\n9. [\ud83d\udcd6 Citation](#citation)\n\n---\n\n## \ud83d\udd0d What is GSP?\n\nThe **Generalized Sequential Pattern (GSP)** algorithm is a sequential pattern mining technique based on **Apriori\nprinciples**. Using support thresholds, GSP identifies frequent sequences of items in transaction datasets.\n\n### Key Features:\n\n- **Support-based pruning**: Only retains sequences that meet the minimum support threshold.\n- **Candidate generation**: Iteratively generates candidate sequences of increasing length.\n- **General-purpose**: Useful in retail, web analytics, social networks, temporal sequence mining, and more.\n\nFor example:\n\n- In a shopping dataset, GSP can identify patterns like \"Customers who buy bread and milk often purchase diapers next.\"\n- In a website clickstream, GSP might find patterns like \"Users visit A, then go to B, and later proceed to C.\"\n\n---\n\n## \ud83d\udd27 Requirements\n\nYou will need Python installed on your system. On most Linux systems, you can install Python with:\n\n```bash\nsudo apt install python3\n```\n\nFor package dependencies of GSP-Py, they will automatically be installed when using `pip`.\n\n---\n\n## \ud83d\ude80 Installation\n\nGSP-Py can be easily installed from either the **repository** or PyPI.\n\n### Option 1: Clone the Repository\n\nTo manually clone the repository and set up the environment:\n\n```bash\ngit clone https://github.com/jacksonpradolima/gsp-py.git\ncd gsp-py\n```\n\nRefer to the [Developer Installation](#developer-installation) section and run:\n\n```bash\nrye sync\n```\n\n### Option 2: Install via `pip`\n\nAlternatively, install GSP-Py from PyPI with:\n\n```bash\npip install gsppy\n```\n\n---\n\n## \ud83d\udee0\ufe0f Developer Installation\n\nThis project uses [Rye](https://github.com/mitsuhiko/rye) for managing dependencies, running scripts, and setting up the environment. Follow these steps to install and set up Rye for this project:\n\n#### 1. Install Rye\nRun the following command to install Rye:\n\n```bash\ncurl -sSf https://rye.astral.sh/get | bash\n```\n\nIf the `~/.rye/bin` directory is not in your PATH, add the following line to your shell configuration file (e.g., `~/.bashrc`, `~/.zshrc`, etc.):\n\n```bash\nexport PATH=\"$HOME/.rye/bin:$PATH\"\n```\n\nReload your shell configuration file:\n\n```bash\nsource ~/.bashrc # or `source ~/.zshrc`\n```\n\n#### 2. Set Up the Project Environment\nTo configure the project environment and install its dependencies, run:\n\n```bash\nrye sync\n```\n\n#### 3. Use Rye Scripts\nOnce the environment is set up, you can run the following commands to simplify project tasks:\n\n- Run tests (in parallel): `rye run test`\n- Format code: `rye run format`\n- Lint code: `rye run lint`\n- Type-check: `rye run typecheck`\n- Add new dependencies: `rye add <package-name>`\n - Add new dependency to dev dependencies: `rye add --dev <package-name>`\n\n#### Notes\n- Rye automatically reads dependencies and scripts from the `pyproject.toml` file.\n- No need for `requirements.txt`, as Rye manages all dependencies!\n\n## \ud83d\udca1 Usage\n\nThe library is designed to be easy to use and integrate with your own projects. Below is an example of how you can\nconfigure and run GSP-Py.\n\n### Example Input Data\n\nThe input to the algorithm is a sequence of transactions, where each transaction contains a sequence of items:\n\n```python\ntransactions = [\n ['Bread', 'Milk'],\n ['Bread', 'Diaper', 'Beer', 'Eggs'],\n ['Milk', 'Diaper', 'Beer', 'Coke'],\n ['Bread', 'Milk', 'Diaper', 'Beer'],\n ['Bread', 'Milk', 'Diaper', 'Coke']\n]\n```\n\n### Importing and Initializing the GSP Algorithm\n\nImport the `GSP` class from the `gsppy` package and call the `search` method to find frequent patterns with a support\nthreshold (e.g., `0.3`):\n\n```python\nfrom gsppy.gsp import GSP\n\n# Example transactions: customer purchases\ntransactions = [\n ['Bread', 'Milk'], # Transaction 1\n ['Bread', 'Diaper', 'Beer', 'Eggs'], # Transaction 2\n ['Milk', 'Diaper', 'Beer', 'Coke'], # Transaction 3\n ['Bread', 'Milk', 'Diaper', 'Beer'], # Transaction 4\n ['Bread', 'Milk', 'Diaper', 'Coke'] # Transaction 5\n]\n\n# Set minimum support threshold (30%)\nmin_support = 0.3\n\n# Find frequent patterns\nresult = GSP(transactions).search(min_support)\n\n# Output the results\nprint(result)\n```\n\n### Output\n\nThe algorithm will return a list of patterns with their corresponding support.\n\nSample Output:\n\n```python\n[\n {('Bread',): 4, ('Milk',): 4, ('Diaper',): 4, ('Beer',): 3, ('Coke',): 2},\n {('Bread', 'Milk'): 3, ('Milk', 'Diaper'): 3, ('Diaper', 'Beer'): 3},\n {('Bread', 'Milk', 'Diaper'): 2, ('Milk', 'Diaper', 'Beer'): 2}\n]\n```\n\n- The **first dictionary** contains single-item sequences with their frequencies (e.g., `('Bread',): 4` means \"Bread\"\n appears in 4 transactions).\n- The **second dictionary** contains 2-item sequential patterns (e.g., `('Bread', 'Milk'): 3` means the sequence \"\n Bread \u2192 Milk\" appears in 3 transactions).\n- The **third dictionary** contains 3-item sequential patterns (e.g., `('Bread', 'Milk', 'Diaper'): 2` means the\n sequence \"Bread \u2192 Milk \u2192 Diaper\" appears in 2 transactions).\n\n> [!NOTE]\n> The **support** of a sequence is calculated as the fraction of transactions containing the sequence, e.g.,\n`[Bread, Milk]` appears in 3 out of 5 transactions \u2192 Support = `3 / 5 = 0.6` (60%).\n> This insight helps identify frequently occurring sequential patterns in datasets, such as shopping trends or user\n> behavior.\n\n\n> [!TIP]\n> For more complex examples, find example scripts in the [`gsppy/tests`](gsppy/tests) folder.\n\n---\n\n## \ud83c\udf1f Planned Features\n\nWe are actively working to improve GSP-Py. Here are some exciting features planned for future releases:\n\n1. **Custom Filters for Candidate Pruning**:\n - Enable users to define their own pruning logic during the mining process.\n\n2. **Support for Preprocessing and Postprocessing**:\n - Add hooks to allow users to transform datasets before mining and customize the output results.\n\n3. **Support for Time-Constrained Pattern Mining**:\n - Extend GSP-Py to handle temporal datasets by allowing users to define time constraints (e.g., maximum time gaps\n between events, time windows) during the sequence mining process.\n - Enable candidate pruning and support calculations based on these temporal constraints.\n\nWant to contribute or suggest an\nimprovement? [Open a discussion or issue!](https://github.com/jacksonpradolima/gsp-py/issues)\n\n---\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions from the community! If you'd like to help improve GSP-Py, read\nour [CONTRIBUTING.md](CONTRIBUTING.md) guide to get started.\n\nDevelopment dependencies (e.g., testing and linting tools) are automatically managed using Rye. To install\nthese dependencies and set up the environment, run:\n\n```bash\nrye sync\n```\n\nAfter syncing, you can run the following scripts using Rye for development tasks:\n\n- Run tests (in parallel): `rye run test`\n- Lint code: `rye run lint`\n- Type-check: `rye run typecheck`\n- Format code: `rye run format`\n\n### General Steps:\n\n1. Fork the repository.\n2. Create a feature branch: `git checkout -b feature/my-feature`.\n3. Commit your changes: `git commit -m \"Add my feature.\"`\n4. Push to your branch: `git push origin feature/my-feature`.\n5. Submit a pull request to the main repository!\n\nLooking for ideas? Check out our [Planned Features](#planned-features) section.\n\n---\n\n## \ud83d\udcdd License\n\nThis project is licensed under the terms of the **MIT License**. For more details, refer to the [LICENSE](LICENSE) file.\n\n---\n\n## \ud83d\udcd6 Citation\n\nIf GSP-Py contributed to your research or project that led to a publication, we kindly ask that you cite it as follows:\n\n```\n@misc{pradolima_gsppy,\n author = {Prado Lima, Jackson Antonio do},\n title = {{GSP-Py - Generalized Sequence Pattern algorithm in Python}},\n month = Dec,\n year = 2025,\n doi = {10.5281/zenodo.3333987},\n url = {https://doi.org/10.5281/zenodo.3333987}\n}\n```\n",
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