Add templates for csv mc int#825
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Pull request overview
Adds CSV integer multicontext dataset preparation, sampling, demos, and roomba visualization utilities for training and generating tabular integer continuations.
Changes:
- Adds CSV integer dataset builder scripts, manifest/sample input, and documentation.
- Extends
sample.pyto accept CSV multicontext prompts and emit generated CSV outputs. - Adds demo/monitoring scripts plus a roomba grayscale/pose HTML viewer.
Reviewed changes
Copilot reviewed 13 out of 13 changed files in this pull request and generated 13 comments.
Show a summary per file
| File | Description |
|---|---|
sample.py |
Adds CSV prompt parsing, integer-range tokenizer support, and CSV sample output. |
demos/csv_mc_int_demo.sh |
Adds end-to-end CSV multicontext training/sampling demo. |
demos/csv_mc_cone_monitor.py |
Adds polling monitor that samples multiple futures and plots them. |
data/roomba/roomba_grayscale_viewer.html |
Adds browser viewer for roomba pose and grayscale pixel columns. |
data/csv_mc_int/run_roomba_dataset.sh |
Adds roomba conditioning + dataset generation wrapper. |
data/csv_mc_int/roomba_data_conditioning.py |
Adds roomba CSV integer conditioning script. |
data/csv_mc_int/README.md |
Documents CSV integer multicontext workflow. |
data/csv_mc_int/prepare_csv_integer_multicontext.py |
Adds generic CSV-to-multicontext dataset preparation tool. |
data/csv_mc_int/manifest.json |
Adds generated roomba multicontext manifest. |
data/csv_mc_int/input.csv |
Adds small roomba-style input CSV sample. |
data/csv_mc_int/get_dataset.sh |
Adds shell wrapper for dataset preparation. |
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| #!/usr/bin/env bash |
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| manifest = { | ||
| "tokenizer": "csv_integer_range_multicontext_manifest", | ||
| "source_csv": str(input_csv), | ||
| "has_header": bool(args.has_header), |
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| mapfile -t DATASETS < <(python3 - <<PY | ||
| import json | ||
| from pathlib import Path | ||
| manifest = json.loads(Path('data/$OUTPUT_ROOT/manifest.json').read_text()) | ||
| for dataset in manifest['multicontext_datasets']: |
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| "--top_k", | ||
| "1", | ||
| "--num_samples", | ||
| str(args.cone_width), |
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| <label> | ||
| Timestamp modulo | ||
| <input id="timestampModulo" type="number" min="0" step="1" value="1000" /> |
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| if not args.has_header: | ||
| cmd.append("--no-multicontext_csv_has_header") | ||
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| print("Running:", " ".join(cmd)) | ||
| subprocess.run(cmd, check=True) | ||
| sample_csvs = sorted(run_samples_dir.glob("*.csv")) | ||
| write_cone_plot(snapshot, sample_csvs, plot_path) |
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| { | |||
| "tokenizer": "csv_integer_range_multicontext_manifest", | |||
| "source_csv": "/home/kauna/nanogpt_csv_generic_mc_forecast/data/csv_mc_int/roomba_integer.csv", | |||
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| if has_header: | ||
| headers = [cell.strip() for cell in first] | ||
| if any(not header for header in headers): | ||
| raise ValueError("CSV prompt header cells must be non-empty") |
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| for idx, (header, context_name) in enumerate(zip(headers, context_names)): | ||
| for alias in column_aliases(header, context_name, idx): | ||
| alias_to_idx[alias] = idx |
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| ```bash | ||
| data/csv_mc_int/get_dataset.sh data/csv_mc_int/input.csv \ | ||
| --output_root csv_mc_int \ | ||
| --range time:0:100 \ | ||
| --range temp:0:100 \ | ||
| --range pressure:900:1100 |
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