This commit adds a new script `start.sh` that automates the process of processing raw data, building features, and training a model. The script uses Python scripts from the `src` directory to perform these tasks. The processed data is saved in the `data/processed` directory, the feature matrix is saved in the `data/features` directory, and the trained model is saved in the `models` directory.
The purpose of these changes is to streamline the data processing and model training workflow, making it easier to reproduce and iterate on the results.
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.
- Implement FeatureExtractor class in time_domain_features.py for calculating statistical features from dataset columns.
- Create build_features.py script to automate feature extraction from processed data and save results in a structured format.
- Adjust build_features.py to read processed data, utilize FeatureExtractor, and save feature matrix.
This update supports enhanced analysis capabilities within the thesis-project structure, allowing for more sophisticated data processing and model training stages.
Closes#1
The code changes add a new file `time_domain_features.py` that contains a `FeatureExtractor` class. This class calculates various time domain features for a given dataset. 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 class takes a file path as input and reads the data from a CSV file. It assumes the data to analyze is in the first column. The calculated features are stored in a dictionary.
The commit message suggests that the purpose of the changes is to add a new class for time domain feature extraction.
