Table of Contents

1. About The Project
2. Structure of the Repository
3. Package Installation
4. Usage
5. Contributing
6. License
7. Contact

Provider Information

Source code has been tested solely for our own use cases, which might differ from yours. This project is actively maintained and contributing is endorsed.

About The Project

The mf4_ai_importer Python package has been developed with the specific purpose of importing .MF4 files and transforming them into feature and target dataframes, with the aim of supporting AI applications within the automotive industry.

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Structure of the repository

├── MF4_AI_IMPORTER/
├── src/
│   └── mf4_ai_importer/
│       ├── data.py
│       ├── main.py

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Package installation

1. Clone the source code:

   git clone <repository_url>

2. Install Python dependencies in your Python environment:

   python3 -m pip install -e .

3. Build a package and install it on the system:

   python3 -m pip install --upgrade build
   python3 -m build
   pip install dist/*.tar.gz

4. Verify if the installation was successful:

   pip list | grep mf4-ai-importer

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Usage

This program processes MF4 data files, applying specific features, targets, and blacklists based on provided command-line arguments. Below are the details of the command-line options you can use to run this program:

Command-line Arguments

• --filedir, -f (Required)

  • Description: Path to the directory containing feature files (feature.mf4) and target files (target.mf4).
  • Usage: --filedir <path_to_all_files_directory>

• --targetdir, -td (Optional)

  • Description: Path to the directory containing all target files target.mf4, including a targetname.txt file that lists all the target names. See an example in target_files.
  • Usage: --targetdir <path_to_target_directory>

• --targetname, -tn (Required)

  • Description: Name of the target (If targetdir exists, the targetname will be taken from the targetname.txt,in this case, it is A15; otherwise, it can be extracted from features.mf4 using the specified target name.).
  • Usage: --targetname <target_name>

• --bl, -b (Required)

  • Description: Path to the file containing signals to be blacklisted.
  • Usage: --bl <path_to_blacklist_file>

• --raster, -r (Required)

  • Description: Sampling value for data processing, specified as an integer.
  • Usage: --raster <sampling_value>

Function Description

The main() function processes MF4 data files to extract and prepare target and feature datasets, suitable for further analysis or machine learning applications. Here are the details of the outputs:

• df_target: This DataFrame contains the target data extracted based on the specified target parameter. It is crucial for supervised learning tasks where the target variable is predicted.

• df_features: This DataFrame holds the features extracted from the MF4 files that are relevant for modeling purposes.

Example

Here's an example of how you might use these arguments:

• Example 1:

  main --filedir ./sample_files --targetdir ./target_files --targetname A15 --bl ./sample_files/blacklist.csv --raster 5 
  

• Example 2:

  main --filedir ./sample_files  --targetname target1 --bl ./sample_files/blacklist.csv --raster 5 

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Contributing

The instructions on how to contribute can be found in the file CONTRIBUTING.md in this repository.

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License

The code is published under the MIT license. Further information on that can be found in the LICENSE.md file in this repository.

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