# SPMF
Python Wrapper for [SPMF Java library](http://www.philippe-fournier-viger.com/spmf).
## Information
This module contains python wrappers for pattern mining algorithms implemented in SPMF Java library. Each algorithm is implemented as a standalone Python class with fully descriptive and tested APIs. It also provides native support for Pandas dataframes.
Why? If you're in a Python pipeline, it might be cumbersome to use Java as an intermediate step. Using `spmf-wrapper` you can stay in your pipeline as though Java is never used at all.
## Installation
[`pip install spmf-wrapper`](https://pypi.org/project/spmf-wrapper/)
A Java Runtime Environment is required to run this wrapper. If an existing installation is not detected, JRE v21 is automatically installed using `install-jdk` python module at `$HOME/.jre/jdk-21.0.2+13-jre`. If you prefer to install Java Runtime manually, follow instructions [`here`](https://www.java.com/en/download/help/download_options.html). Test installation by running the following command on the terminal:
```
> java -version
java version "1.8.0_391"
Java(TM) SE Runtime Environment (build 1.8.0_391-b13)
Java HotSpot(TM) 64-Bit Server VM (build 25.391-b13, mixed mode)
```
## Usage
Example:
```python
from spmf import EMMA
emma = EMMA(min_support=2, max_window=2, timestamp_present=True, transform=True)
output = emma.run_pandas(input_df)
```
Input:
| | Time points | Itemset
| ---- | ------ | -------
| 0 | 1 | a
| 1 | 2 | a
| 2 | 3 | a
| 3 | 3 | b
| 4 | 6 | a
| 5 | 7 | a
| 6 | 7 | b
| 7 | 8 | c
| 8 | 9 | b
| 9 | 11 | d
Output:
| | Frequent episode | Support
| --- | ---------------- | -------
|0 | a | 5
|1 | b | 3
|2 | a b | 2
|3 | a-> a | 3
|4 | a -> b | 2
|5 | a -> a b | 2
See [examples]('https://github.com/AakashVasudevan/Py-SPMF/tree/main/examples') for more details.
For a detailed explanation of the algorithm and parameters, refer to the corresponding webpage in the SPMF [documentation](http://www.philippe-fournier-viger.com/spmf/index.php?link=documentation.php).
## Implementation Checklist
### Sequential Pattern Mining
| Algorithm| Type | Implemented
| -------- | ------- | ---------
| PrefixSpan | Frequent Sequential Pattern | ✓
| GSP | Frequent Sequential Pattern |
| SPADE | Frequent Sequential Pattern | ✓
| CM-SPADE | Frequent Sequential Pattern | ✓
| SPAM | Frequent Sequential Pattern | ✓
| CM-SPAM | Frequent Sequential Pattern |
| FAST | Frequent Sequential Pattern |
| LAPIN | Frequent Sequential Pattern |
| ClaSP | Frequent Closed Sequential Pattern | ✓
| CM-ClaSP | Frequent Closed Sequential Pattern | ✓
| CloFAST | Frequent Closed Sequential Pattern |
| CloSpan | Frequent Closed Sequential Pattern |
| BIDE+ | Frequent Closed Sequential Pattern |
| Post Processing SPAM or PrefixSpan | Frequent Closed Sequential Pattern |
| MaxSP | Frequent Maximal Sequential Pattern |
| VMSP | Frequent Maximal Sequential Pattern | ✓
| FEAT | Frequent Sequential Generator Pattern |
| FSGP | Frequent Sequential Generator Pattern |
| VGEN | Frequent Sequential Generator Pattern | ✓
| NOSEP | Non-overlapping Sequential Pattern | ✓
| GoKrimp | Compressing Sequential Pattern |
| TKS | Top-k Frequent Sequential Pattern | ✓
| TSP | Top-k Frequent Sequential Pattern |
### Episode Mining
| Algorithm| Type | Implemented
| -------- | ------- | ---------
| EMMA | Frequent Episode | ✓
| AFEM | Frequent Episode | ✓
| MINEPI | Frequent Episode |
| MINEPI+ | Frequent Episode | ✓
| TKE | Top-k Frequent Episodes | ✓
| MaxFEM | Maximal Frequent Episodes | ✓
| POERM | Episode Rules |
| POERM-ALL | Episode Rules |
| POERMH | Episode Rules |
| NONEPI | Episode Rules | ✓
| TKE-Rules | Episode Rules | ✓
| AFEM-Rules | Episode Rules | ✓
| EMMA-Rules | Epsiode Rules | ✓
| MINEPI+-Rules | Episode Rules |
| HUE-SPAN | High Utility Episodes |
| US-SPAN | High Utility Episodes |
| TUP | Top-K High Utility Episodes |
## Bibliography
```
Fournier-Viger, P., Lin, C.W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H. T. (2016).
The SPMF Open-Source Data Mining Library Version 2.
Proc. 19th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2016) Part III, Springer LNCS 9853, pp. 36-40.
```
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"description": "# SPMF\nPython Wrapper for [SPMF Java library](http://www.philippe-fournier-viger.com/spmf).\n\n## Information\nThis module contains python wrappers for pattern mining algorithms implemented in SPMF Java library. Each algorithm is implemented as a standalone Python class with fully descriptive and tested APIs. It also provides native support for Pandas dataframes.\n\nWhy? If you're in a Python pipeline, it might be cumbersome to use Java as an intermediate step. Using `spmf-wrapper` you can stay in your pipeline as though Java is never used at all.\n\n## Installation\n[`pip install spmf-wrapper`](https://pypi.org/project/spmf-wrapper/)\n\nA Java Runtime Environment is required to run this wrapper. If an existing installation is not detected, JRE v21 is automatically installed using `install-jdk` python module at `$HOME/.jre/jdk-21.0.2+13-jre`. If you prefer to install Java Runtime manually, follow instructions [`here`](https://www.java.com/en/download/help/download_options.html). Test installation by running the following command on the terminal:\n\n```\n> java -version\njava version \"1.8.0_391\"\nJava(TM) SE Runtime Environment (build 1.8.0_391-b13)\nJava HotSpot(TM) 64-Bit Server VM (build 25.391-b13, mixed mode)\n```\n\n## Usage\nExample:\n```python\nfrom spmf import EMMA\n\nemma = EMMA(min_support=2, max_window=2, timestamp_present=True, transform=True)\noutput = emma.run_pandas(input_df)\n```\n\nInput:\n\n| | Time points | Itemset\n| ---- | ------ | -------\n| 0\t| 1\t| a\n| 1\t| 2\t| a\n| 2\t| 3\t| a\n| 3\t| 3\t| b\n| 4\t| 6\t| a\n| 5\t| 7\t| a\n| 6\t| 7\t| b\n| 7\t| 8\t| c\n| 8\t| 9\t| b\n| 9\t| 11 | d\n\nOutput:\n\n|\t| Frequent episode | Support\n| --- | ---------------- | -------\n|0 |\ta |\t5\n|1 |\tb\t| 3\n|2 |\ta b\t| 2\n|3\t| a-> a |\t3\n|4\t| a -> b |\t2\n|5\t| a -> a b | 2\n\nSee [examples]('https://github.com/AakashVasudevan/Py-SPMF/tree/main/examples') for more details.\n\nFor a detailed explanation of the algorithm and parameters, refer to the corresponding webpage in the SPMF [documentation](http://www.philippe-fournier-viger.com/spmf/index.php?link=documentation.php).\n\n## Implementation Checklist\n\n### Sequential Pattern Mining\n\n| Algorithm| Type | Implemented\n| -------- | ------- | ---------\n| PrefixSpan | Frequent Sequential Pattern | ✓\n| GSP | Frequent Sequential Pattern |\n| SPADE | Frequent Sequential Pattern | ✓\n| CM-SPADE | Frequent Sequential Pattern | ✓\n| SPAM | Frequent Sequential Pattern | ✓\n| CM-SPAM | Frequent Sequential Pattern |\n| FAST | Frequent Sequential Pattern |\n| LAPIN | Frequent Sequential Pattern |\n| ClaSP | Frequent Closed Sequential Pattern | ✓\n| CM-ClaSP | Frequent Closed Sequential Pattern | ✓\n| CloFAST | Frequent Closed Sequential Pattern |\n| CloSpan | Frequent Closed Sequential Pattern |\n| BIDE+ | Frequent Closed Sequential Pattern |\n| Post Processing SPAM or PrefixSpan | Frequent Closed Sequential Pattern |\n| MaxSP | Frequent Maximal Sequential Pattern |\n| VMSP | Frequent Maximal Sequential Pattern | ✓\n| FEAT | Frequent Sequential Generator Pattern |\n| FSGP | Frequent Sequential Generator Pattern |\n| VGEN | Frequent Sequential Generator Pattern | ✓\n| NOSEP | Non-overlapping Sequential Pattern | ✓\n| GoKrimp | Compressing Sequential Pattern |\n| TKS | Top-k Frequent Sequential Pattern | ✓\n| TSP | Top-k Frequent Sequential Pattern |\n\n### Episode Mining\n\n| Algorithm| Type | Implemented\n| -------- | ------- | ---------\n| EMMA | Frequent Episode | ✓\n| AFEM | Frequent Episode | ✓\n| MINEPI | Frequent Episode |\n| MINEPI+ | Frequent Episode | ✓\n| TKE | Top-k Frequent Episodes | ✓\n| MaxFEM | Maximal Frequent Episodes | ✓\n| POERM | Episode Rules |\n| POERM-ALL | Episode Rules |\n| POERMH | Episode Rules |\n| NONEPI | Episode Rules | ✓\n| TKE-Rules | Episode Rules | ✓\n| AFEM-Rules | Episode Rules | ✓\n| EMMA-Rules | Epsiode Rules | ✓\n| MINEPI+-Rules | Episode Rules |\n| HUE-SPAN | High Utility Episodes |\n| US-SPAN | High Utility Episodes |\n| TUP | Top-K High Utility Episodes |\n\n\n## Bibliography\n```\nFournier-Viger, P., Lin, C.W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H. T. (2016).\nThe SPMF Open-Source Data Mining Library Version 2.\nProc. 19th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2016) Part III, Springer LNCS 9853, pp. 36-40.\n```\n",
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