WLEdataloader is a flexible and efficient data loading framework designed to handle various datasets for wireless communication research. This framework provides tools to preprocess, load, and analyze data, making it easier for researchers to work with large datasets.

• Support for multiple datasets (e.g., Rutgers, Colorado)
• Data preprocessing and feature engineering
• Easy-to-use API for data loading and splitting
• Customizable data transformations
• Integration with popular machine learning libraries

You can install the WLEdataloader framework using pip:

pip install wledataloader

from wledataloader import ColoradoLoader
from wledataloader import JsiSigfoxLoader
from wledataloader import PktMetaLoader
from wledataloader import RutgersLoader
from wledataloader import IECWLE24Loader

• ColoradoLoader: Load data from Colorado dataset
• JsiSigfoxLoader: Load data from JSI Sigfox dataset
• PktMetaLoader: Load data from PktMeta dataset
• RutgersLoader: Load data from Rutgers dataset
• IECWLE24Loader: Load data from IECWLE24 dataset

data = ColoradoLoader(seed=0xDEADBEEF, printable=True, rate_list=[0.9, 0.1, 0.0], save_csv=True, rssi_process_type=1)

This initializes an instance of ColoradoLoader, automatically checking and managing cached data.

data.PrepareData(reload=False, extend_feature=True)

• reload (bool, default False): If True, reloads data from the source, ignoring cached files.
• extend_feature (bool, default True): If True, generates additional features for analysis or model training.

print(data._data_df.shape)

The ShowBasicAnalysis() method provides a quick overview of the dataset, including statistical summaries and visualizations.

data.ShowBasicAnalysis()

X, y = data.GetXY()

X_train, X_test, y_train, y_test = data.TrainTestSplit(test_size=0.2)

• test_size (float, default 0.2): The proportion of the dataset to include in the test split.

from wledataloader import ColoradoLoader

# Initialize loader
data = ColoradoLoader()

# Prepare dataset
data.PrepareData(reload=False, extend_feature=True)

# Perform basic analysis
data.ShowBasicAnalysis()

# Extract features and labels
X, y = data.GetXY()

# Split data into training and testing sets
X_train, X_test, y_train, y_test = data.TrainTestSplit(test_size=0.2)

# Display dataset shapes
print("Training set shape:", X_train.shape, y_train.shape)
print("Testing set shape:", X_test.shape, y_test.shape)

• The library may require an internet connection if the data is not cached locally.
• If missing data errors occur, try setting reload=True in PrepareData().

We welcome contributions to the WLEdataloader framework. If you have any suggestions, bug reports, or feature requests, please open an issue on the GitHub repository.

This project is licensed under the MIT License. See the LICENSE file for more details.

For any questions or inquiries, please contact the project maintainers at [email@example.com].