Metadata-Version: 2.1
Name: sberpm
Version: 3.4.0
Summary: Library that is intended to operate with various process mining tasks.
Author: Sberbank Process Mining Team
Author-email: spm.ml.team@gmail.com
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
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![](https://github.com/SPM-MLTeam/process-mining-spm/blob/main/imgs/Logo.png?raw=true)

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### [Documentation](https://process-mining-spm.readthedocs.io/en/latest/)  |  [Installation](#installation) | [Quick start](#quick-start)
# SberProcessMining (SberPM) – Process Mining Python framework
SberPM is an open-source Python library for conducting a comprehensive analysis of business processes with the use of process mining and machine learning techniques. By implementing this tool, objective and deep insights into the process on all levels can be revealed. These insights are then used to detect problems such as bottlenecks and deviations and identify potential opportunities for process improvement and optimization.

Authors: Sber Process Mining Team.

# Installation

To install SberPM framework on your machine from PyPI:
```bash
pip install sberpm
```
To install from sources:
```bash
git clone https://github.com/SberProcessMining/Sber_Process_Mining.git

cd Sber_Process_Mining
pip install .
```

Additionally, you have to install graphviz executables and add the path to the executables to PATH variable:  
https://graphviz.org/download/

# Quick start

There are some steps for quick start for your process log analysis:
* Create a DataHolder object:
```python
from sberpm import SuccessInputs, DurationUnits

path = "example_data.xlsx"
data_holder = DataHolder(
    data=path,
    col_case="id",
    col_stage="action",
    col_start_time="start_time",
    col_end_time="end_time",
    col_user="user_id",
    col_text="text",
    success_inputs=SuccessInputs(entries={"Подписание документов ", "Принято"}),
)

data_holder.data.head()
```
* Apply AutoInsights:
```python
from sberpm.autoinsights import AutoInsights

auto_insights = AutoInsights(data_holder, successful_stage="Принято")
auto_insights.apply()
```

# License
This project is released under the [MIT License](https://github.com/SPM-MLTeam/process-mining-spm/blob/main/LICENSE).
