- Updated paths in the STFT notebook to reflect new data files.
- Improved plotting aesthetics for combined plots and added grid lines.
- Introduced a 3D spectrogram visualization for better data representation.
- Refactored model training function to include error handling and model export functionality.
- Adjusted model training calls to include export paths for saved models. Closes#90
- Added additional markdown cells for better documentation and clarity in the notebook.
* wip: add function to create stratified train-test split from STFT data
* feat(src): implement working function for dataset B to create ready data from STFT files stft_files and add setup.py for package configuration
* feat(notebook): Update variable names for clarity, remove unused imports, and streamline data processing. Implement data concatenation using pandas concat for efficiency. Add validation steps for Dataset B and improve model training consistency across sensors.
* fix(.gitignore): add rule to ignore egg-info directories and ensure proper formatting
* docs(README): add instructions for running stft.ipynb notebook
* feat(notebook): Add evaluation metrics and confusion matrix visualizations for model predictions on Dataset B. Remove commented-out code and integrate data preparation using create_ready_data function.
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Co-authored-by: nuluh <dam.ar@outlook.com>
Implement extraction of 'labels' from directory names and append as a new column in the dataframe during feature extraction. Adapted from the existing `build_features.py` script to enhance data usability in supervised learning models within the Jupyter notebook environment.
Closes#10
This commit adds a new file, `.vscode/launch.json`, which contains the configuration for launching the Python debugger. The configuration includes the necessary attributes such as the debugger type, request type, program file, console type, and command-line arguments. This configuration allows developers to easily debug Python files in the integrated terminal.
This commit adds code to the `03_feature_extraction.ipynb` notebook to print time-domain features. The features include mean, max, peak, peak-to-peak, RMS, variance, standard deviation, power, crest factor, form factor, pulse indicator, margin, kurtosis, and skewness. The features are calculated using the `FeatureExtractor` class and displayed in a pandas DataFrame.
Introduce a new testing script that generates mockup data and applies the FeatureExtractor class to calculate and display features. This test script assists in verifying the functionality of the feature extraction methods with controlled input data.
