Duplicate from CMU-OSPO/protostar-climate-submission-template

Co-authored-by: Alfredo Gonzalez-Espinoza <spiralizing@users.noreply.huggingface.co>

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README.md

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+---
+pretty_name: "Protostar Climate Challenge Submission: <Team Name>"
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+tags:
+- climate
+- forecasting
+- protostar-climate-2026
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: submission.csv
+---
+
+> 📋 **This is the submission template** for the [Protostar Challenge: Climate Risk Intelligence](https://huggingface.co/spaces/CMU-OSPO/protostar-challenge-climate-risk-intelligence). Copy it into your Hugging Face account, fill it in, and **delete this box** when you are done.
+>
+> 1. **Copy:** `hf download CMU-OSPO/protostar-climate-submission-template --repo-type dataset --local-dir my-submission`, then `hf upload <your-name>/<dataset-name> my-submission . --repo-type dataset` (or create a new dataset on the Hub and upload these files).
+> 2. **Fill in** the `predicted_value` column of `submission.csv`: the city, year and metric of all 5,880 rows (210 cities × 7 years × 4 metrics) are already there. Check it with `python code/check_submission.py`.
+> 3. **Replace** every `<...>` placeholder in this README, including `<Team Name>` in the header.
+> 4. **Add** your notebooks or scripts to `code/`, and the curated data they use to `data/`: together they should reproduce `submission.csv`. Fill in the data card in `data/README.md`. If you use an AI agent, start from `code/AGENTS.md`.
+> 5. **Make the dataset public** and paste its link in the *Submission* tab of the challenge Space.
+>
+> Keep the tag `protostar-climate-2026` in the header: it is how submissions are found.
+
+# <Team Name>: Protostar Climate Challenge Submission
+
+**Negative result:** no <!-- Write "yes" if your main finding is why reuse or reproduction did not work. -->
+
+Forecasts of yearly weather for 210 U.S. cities, 2019–2025, made as of the end of 2018 (no data that reveals 2019–2025 weather), for the [Protostar Challenge: Climate Risk Intelligence](https://huggingface.co/spaces/CMU-OSPO/protostar-challenge-climate-risk-intelligence).
+
+## Team
+
+| Name | Hugging Face username | University, Program |
+|---|---|---|
+| <Name> | <@username> | <e.g., Carnegie Mellon University, Heinz MSPPM> |
+
+## Method
+
+<!-- A few lines: what model you used, which features, and how you checked it (e.g., trained up to 2010, tested on 2011–2018). -->
+
+- **Model:** <e.g., 10-year average plus a linear trend, per city>
+- **Features:** <e.g., the last 30 years of each metric>
+- **How we checked it:** <e.g., practice run on 2011–2018: CDD MAE = ... °F-days>
+
+## AI Tools and Agents
+
+<!-- List every LLM or agent you used, or write "none". AI tools may write code and help with text; they may not produce forecast values (see the FAQ in the challenge's Data & submission tab). For ideas on documenting LLM and agent use: https://guides.library.cmu.edu/LLMDocumentationGuide -->
+
+- **Tools and models:** <none, or e.g., Claude Code (Claude Sonnet 5.5), ChatGPT (GPT-…)>
+- **What they did:** <e.g., wrote the data-loading code, ran the backtests, drafted the README>
+- **What a person reviewed:** <e.g., every download, all model code, the final numbers>
+- **Agent instructions file:** <e.g., code/AGENTS.md, or none>
+
+## Policy Idea
+
+<!-- Half a page at most. Answer the three questions however you like. -->
+
+1. **What risk did we notice?** <...>
+2. **What would we do about it, and who would do it?** <...>
+3. **Why is it sustainable?** <...>
+
+## Feedback to the Ecosystem
+
+<!-- What got in the way, and what would fix it: data, metadata, documentation, licensing, tools, infrastructure, policy. Be specific. -->
+
+- **What got in the way:** <...>
+- **What would fix it:** <...>
+
+## Files
+
+| File | Content |
+|---|---|
+| `submission.csv` | Our forecasts: `city_id,year,metric,predicted_value` |
+| `code/` | <Our notebooks or scripts> |
+| `data/` | The curated data our code uses; described in its data card, [`data/README.md`](data/README.md) |
+
+**Metric codes:** `annual_avg_temp` (°F), `total_precip_in` (inches), `hottest_day_temp` (°F), `cdd` (°F-days).
+**City IDs:** the station `ID` from the `city_info` table of the training dataset (e.g., Pittsburgh = `USW00094823`).
+
+## How to Reproduce
+
+<!-- The steps someone else would follow to recreate submission.csv. -->
+
+1. <e.g., install the packages in code/requirements.txt>
+2. <e.g., run code/forecast.ipynb, which reads data/ and writes submission.csv>
+
+## Data
+
+<!-- List every external source (name, link, years used) and say whether it was used for the forecasts or only for the analysis and policy idea. Forecast inputs: weather records up to 2018-12-31, and data from any year that does not respond to the weather. -->
+
+- **External data:** <none, or: name, link, years, forecast or analysis>
+- **Curated data:** see the data card in [`data/README.md`](data/README.md) (each file, its source, license and preparation).
+
+Training data: [`CMU-OSPO/protostar-climate-train-to-2018`](https://huggingface.co/datasets/CMU-OSPO/protostar-climate-train-to-2018), from:
+
+- Lai, Y., & Dzombak, D. A. (2019). Compiled historical daily temperature and precipitation data for selected 210 U.S. cities. Carnegie Mellon University. https://doi.org/10.1184/R1/7890488.v6
+- Lai, Y., & Dzombak, D. A. (2019). *Use of historical data to assess regional climate change.* Journal of Climate, 32(14), 4299–4320. https://doi.org/10.1175/JCLI-D-18-0630.1

code/AGENTS.md

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+<!-- Claude Code users: copy this file to CLAUDE.md, or put @code/AGENTS.md in your CLAUDE.md. -->
+
+# Instructions for AI agents
+
+This repository is a submission to the Protostar Challenge: Climate Risk Intelligence. Follow these rules in every task.
+
+## Data rule (never break it)
+
+- Forecasts are made as of 2018-12-31. Weather data used as a forecast input must be dated on or before 2018-12-31.
+- Do not download, query or use weather observations dated 2019-01-01 or later, from any source (e.g., NOAA, Meteostat, Open-Meteo, ERA5, weather websites), nor 2019–2025 data driven by the weather (e.g., streamflow, air quality, energy demand).
+- Set an end date of 2018-12-31 or earlier on every weather download.
+- Never write `predicted_value` from memory or from a web search: every value must come from the code's model.
+- If a task seems to need weather data after 2018, stop and ask a team member.
+- Data that does not respond to the weather (e.g., census, land cover, elevation) may come from any year; record its source in `data/README.md`.
+
+## Working rules
+
+- Training data: `CMU-OSPO/protostar-climate-train-to-2018` (Hugging Face).
+- Code goes in `code/`, curated data in `data/` (described in `data/README.md`).
+- After changing `submission.csv`, run `python code/check_submission.py`.
+- The code must reproduce `submission.csv` without an agent (scripts or notebooks that run top to bottom).
+- Never write tokens or credentials into any file.
+- Summarize what you did in the README's "AI Tools and Agents" section.
+
+## Team notes
+
+<!-- Add your team's own instructions below. -->

code/README.md

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+# Code
+
+Put the notebooks or scripts that produce `submission.csv` here, for example:
+
+- `forecast.ipynb`: loads the training data, fits the model, writes `submission.csv`
+- `requirements.txt`: the packages it needs
+
+`AGENTS.md` (from the template): instructions for AI agents, including the data rule. Keep it if you use an agent.
+
+`check_submission.py` (from the template) checks `submission.csv` before you submit: `python code/check_submission.py`.
+
+This dataset is a git repository, so you can push code like any other file. Replace this note with a short description of your files.

code/check_submission.py

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+"""Check submission.csv before you submit: the same checks the challenge Space runs.
+
+Usage: python code/check_submission.py [path/to/submission.csv]
+Needs pandas and huggingface_hub (to read the list of cities from the training dataset).
+"""
+
+import sys
+
+import pandas as pd
+
+TRAIN = "hf://datasets/CMU-OSPO/protostar-climate-train-to-2018/city_info"
+COLUMNS = ["city_id", "year", "metric", "predicted_value"]
+YEARS = set(range(2019, 2026))
+METRICS = {"annual_avg_temp", "total_precip_in", "hottest_day_temp", "cdd"}
+
+path = sys.argv[1] if len(sys.argv) > 1 else "submission.csv"
+cities = set(pd.read_parquet(TRAIN)["ID"])
+df = pd.read_csv(path, dtype={"city_id": str, "metric": str})
+
+if list(df.columns) != COLUMNS:
+    sys.exit(f"Columns must be exactly: {','.join(COLUMNS)} (found: {','.join(df.columns)})")
+
+year = pd.to_numeric(df["year"], errors="coerce")
+value = pd.to_numeric(df["predicted_value"], errors="coerce")
+checks = {
+    "unknown city_id": ~df["city_id"].isin(cities),
+    "year outside 2019-2025": ~year.isin(YEARS),
+    "unknown metric": ~df["metric"].isin(METRICS),
+    "missing or non-numeric predicted_value": value.isna() | value.abs().eq(float("inf")),
+    "duplicate row": df.duplicated(["city_id", "year", "metric"]),
+}
+problems = [f"{mask.sum()} rows with {what}, e.g. line {mask.idxmax() + 2}" for what, mask in checks.items() if mask.any()]
+expected = len(cities) * len(YEARS) * len(METRICS)
+if not problems and len(df) != expected:
+    problems.append(f"{len(df)} rows; expected {expected}")
+
+if problems:
+    sys.exit("Not ready:\n- " + "\n- ".join(problems))
+print(f"OK: {path} has all {expected} forecasts and can be scored.")

data/README.md

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+# Data card: `data/`
+
+<!-- The curated data your code (`code/`) reads, as you prepared it. Together, code and data should reproduce `submission.csv`.
+Fill in this card for every file here, and delete the example rows and these comments.
+If a source is too large, or its license does not allow copying, do not upload it: list it below with its exact version and how to download it. -->
+
+## Files
+
+| File | What it holds | Source | License | Years covered | How we prepared it | Used for |
+|---|---|---|---|---|---|---|
+| <`annual_valid.csv`> | <One row per city and year: the four metrics> | <[Challenge dataset](https://huggingface.co/datasets/CMU-OSPO/protostar-climate-train-to-2018), `annual_indices`> | <CC0-1.0> | <1989–2018> | <Kept years with at most 10 missing days> | <Forecast input> |
+| <`city_tree_canopy.csv`> | <Tree canopy share per city> | <NLCD 2016, link> | <Public domain> | <2016> | <Averaged within 5 km of each station; joined by `city_id`> | <Forecast input / analysis only> |
+
+## Not uploaded (download instead)
+
+<!-- Delete this section if every file is uploaded. -->
+
+| Source | Exact version | How to download | Why not uploaded |
+|---|---|---|---|
+| <Name, link> | <Version, date or DOI> | <Command or steps> | <Too large / license> |
+
+## How the files fit together
+
+- **Join key:** <e.g., `city_id` = the station `ID` in the challenge `city_info` table>
+- **Units:** <e.g., °F, inches, °F-days; any conversions you made>
+- **Known limitations:** <e.g., missing years, estimated values, coverage gaps>
+
+## Data rule check
+
+<!-- Forecast inputs: weather records up to 2018-12-31, and data from any year that does not respond to the weather.
+2019–2025 data that responds to the weather may be used only for the analysis and policy idea (see the FAQ in the challenge Space). -->
+
+- [ ] Every file marked "Forecast input" is weather data up to 2018-12-31, or data that does not respond to the weather.
+- [ ] Files with 2019–2025 data that responds to the weather are marked "Analysis only" and are not read by the forecasting code.