84 lines
2.1 KiB
Markdown
84 lines
2.1 KiB
Markdown
# Sign Coordinate Regressor
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This project fine-tunes a sentence-transformer model to predict a latitude and
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longitude pair from text observed on signs at an intersection.
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The raw dataset is expected at `training_data_raw.csv`. It may include the
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pandas-exported index column; the preprocessing script only uses:
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- `intersection`
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- `text_on_sign_exact`
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- `latitude`
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- `longitude`
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## Workflow
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Prepare bootstrapped training rows:
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```bash
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source .venv/bin/activate
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python prepare_training_data.py --seed 1992 --bag-size 5 --samples-per-intersection 50
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```
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This writes `training.csv` with rows shaped like:
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```text
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intersection,sample_id,text,latitude,longitude,unique_sign_count,raw_sign_count
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```
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Each row is a deterministic bootstrap sample of sign texts from one
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intersection, joined into a single text field. This trains on "some signs seen
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at this coordinate" instead of one sign or every sign at that coordinate.
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Train the model:
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```bash
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python train.py
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```
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Evaluate and write predictions:
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```bash
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python eval.py
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```
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This writes `predictions.csv` and a map-style diagnostic plot:
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It also writes a coordinate calibration plot:
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Or run the full pipeline:
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```bash
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make
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```
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## Useful Options
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`prepare_training_data.py`:
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- `--seed`: deterministic bootstrap seed.
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- `--bag-size`: number of sign texts per sampled bag.
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- `--samples-per-intersection`: number of bags generated per intersection.
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`train.py`:
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- `--model-name`: sentence-transformers base model.
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- `--epochs`: training epochs.
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- `--batch-size`: batch size.
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- `--device`: device override such as `cpu`, `cuda`, or `mps`. Defaults to `cuda`.
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## Outputs
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- `training.csv`: prepared bootstrapped dataset.
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- `output/`: saved sentence-transformer encoder, coordinate head, and coordinate
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normalization metadata.
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- `predictions.csv`: evaluation rows with predicted coordinates and `error_km`.
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- `plots/prediction_map.png`: actual vs predicted coordinates with line segments
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showing the prediction error.
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- `plots/predicted_vs_actual.png`: predicted vs actual latitude and longitude
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scatter plots.
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