Heart Disease Prediction (openai#538)

# Thank you for contributing an eval! ♥️

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Note that even if the criteria are met, that does not guarantee the PR
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__PLEASE READ THIS__:

In order for a PR to be merged, it must fail on GPT-4. We are aware that
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## Eval details 📑
### Eval name
Heart Disease Prediction

### Eval description

This eval tests the models ability to correctly predict the probability
of a patient to have heart disease. The dataset is constructed from the
[Heart Failure Prediction
Dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction)
on Kaggle. The data includes the patient's age, sex, and a number of
medical signals relevant to the diagnosis of heart disease.

The data is provided under the Open Database License (ODbL). 

```
fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Mar 31, 2023] from https://www.kaggle.com/fedesoriano/heart-failure-prediction.
```

### What makes this a useful eval?

This assesses the model's ability to correctly predict adverse medical
events. Correctly predicting heart disease shows the model's capability
for a strong understanding of medicine. The GPT-3.5-turbo models
currently receives an accuracy of 0.778.

<img width="1250" alt="Screenshot 2023-03-31 at 2 24 13 PM"
src="https://user-images.githubusercontent.com/9121162/229234376-9cdd1315-5df0-48bf-9328-ac31aabec3cc.png">

## Criteria for a good eval ✅

Below are some of the criteria we look for in a good eval. In general,
we are seeking cases where the model does not do a good job despite
being capable of generating a good response (note that there are some
things large language models cannot do, so those would not make good
evals).

Your eval should be:

- [x] Thematically consistent: The eval should be thematically
consistent. We'd like to see a number of prompts all demonstrating some
particular failure mode. For example, we can create an eval on cases
where the model fails to reason about the physical world.
- [x] Contains failures where a human can do the task, but either GPT-4
or GPT-3.5-Turbo could not.
- [x] Includes good signal around what is the right behavior. This means
either a correct answer for `Basic` evals or the `Fact` Model-graded
eval, or an exhaustive rubric for evaluating answers for the `Criteria`
Model-graded eval.
- [x] Include at least 100 high quality examples (it is okay to only
contribute 5-10 meaningful examples and have us test them with GPT-4
before adding all 100)

If there is anything else that makes your eval worth including, please
document it below.

### Unique eval value

> Insert what makes your eval high quality that was not mentioned above.
(Not required)

As far as I can tell, this is the only eval so far related to making
medical diagnoses. To make sure it was a high quality eval, I tried to
find a dataset with a lot of observations and created by doctors with
the relevant expertise.

## Eval structure 🏗️

Your eval should
- [x] Check that your data is in `evals/registry/data/{name}`
- [x] Check that your yaml is registered at
`evals/registry/evals/{name}.yaml`
- [x] Ensure you have the right to use the data you submit via this eval

(For now, we will only be approving evals that use one of the existing
eval classes. You may still write custom eval classes for your own
cases, and we may consider merging them in the future.)

## Final checklist 👀

### Submission agreement

By contributing to Evals, you are agreeing to make your evaluation logic
and data under the same MIT license as this repository. You must have
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right to use this data in future service improvements to our product.
Contributions to OpenAI Evals will be subject to our usual Usage
Policies (https://platform.openai.com/docs/usage-policies).

- [x] I agree that my submission will be made available under an MIT
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### Limited availability acknowledgement

We know that you might be excited to contribute to OpenAI's mission,
help improve our models, and gain access to GPT-4. However, due to the
requirements mentioned above and high volume of submissions, we will not
be able to accept all submissions and thus not grant everyone who opens
a PR GPT-4 access. We know this is disappointing, but we hope to set the
right expectation before you open this PR.

- [x] I understand that opening a PR, even if it meets the requirements
above, does not guarantee the PR will be merged nor GPT-4 access
granted.

### Submit eval

- [x] I have filled out all required fields in the evals PR form
- [x] (Ignore if not submitting code) I have run `pip install
pre-commit; pre-commit install` and have verified that `black`, `isort`,
and `autoflake` are running when I commit and push

Failure to fill out all required fields will result in the PR being
closed.

### Eval JSON data 

Since we are using Git LFS, we are asking eval submitters to add in as
many Eval Samples (at least 5) from their contribution here:

<details>
  <summary>View evals in JSON</summary>

  ### Eval
  ```jsonl
{"input": [{"role": "system", "content": "You are an AI tasked with
predicting whether patients are likely to have heart disease. You will
be given a description of the patient with relevant medical signals.
Respond with only a 1 to signify if the patient is likely to have heart
disease, or a 0 if the patient is not likely to have heart disease. Do
not respond with any text or disclaimers, only respond with either 1 or
0."}, {"role": "user", "content": "Age: 40 years, Sex: Male, Chest pain
type: Atypical Angina, Resting blood pressure: 140 mm Hg, Serum
cholesterol: 289 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG
results: Normal, Max heart rate achieved: 172, Exercise induced angina:
No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"}
{"input": [{"role": "system", "content": "You are an AI tasked with
predicting whether patients are likely to have heart disease. You will
be given a description of the patient with relevant medical signals.
Respond with only a 1 to signify if the patient is likely to have heart
disease, or a 0 if the patient is not likely to have heart disease. Do
not respond with any text or disclaimers, only respond with either 1 or
0."}, {"role": "user", "content": "Age: 49 years, Sex: Female, Chest
pain type: Non-Anginal Pain, Resting blood pressure: 160 mm Hg, Serum
cholesterol: 180 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG
results: Normal, Max heart rate achieved: 156, Exercise induced angina:
No, Oldpeak: 1, ST Slope: Flat"}], "ideal": "1"}
{"input": [{"role": "system", "content": "You are an AI tasked with
predicting whether patients are likely to have heart disease. You will
be given a description of the patient with relevant medical signals.
Respond with only a 1 to signify if the patient is likely to have heart
disease, or a 0 if the patient is not likely to have heart disease. Do
not respond with any text or disclaimers, only respond with either 1 or
0."}, {"role": "user", "content": "Age: 37 years, Sex: Male, Chest pain
type: Atypical Angina, Resting blood pressure: 130 mm Hg, Serum
cholesterol: 283 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG
results: ST-T wave abnormality, Max heart rate achieved: 98, Exercise
induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"}
{"input": [{"role": "system", "content": "You are an AI tasked with
predicting whether patients are likely to have heart disease. You will
be given a description of the patient with relevant medical signals.
Respond with only a 1 to signify if the patient is likely to have heart
disease, or a 0 if the patient is not likely to have heart disease. Do
not respond with any text or disclaimers, only respond with either 1 or
0."}, {"role": "user", "content": "Age: 48 years, Sex: Female, Chest
pain type: Asymptomatic, Resting blood pressure: 138 mm Hg, Serum
cholesterol: 214 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG
results: Normal, Max heart rate achieved: 108, Exercise induced angina:
Yes, Oldpeak: 1.5, ST Slope: Flat"}], "ideal": "1"}
{"input": [{"role": "system", "content": "You are an AI tasked with
predicting whether patients are likely to have heart disease. You will
be given a description of the patient with relevant medical signals.
Respond with only a 1 to signify if the patient is likely to have heart
disease, or a 0 if the patient is not likely to have heart disease. Do
not respond with any text or disclaimers, only respond with either 1 or
0."}, {"role": "user", "content": "Age: 54 years, Sex: Male, Chest pain
type: Non-Anginal Pain, Resting blood pressure: 150 mm Hg, Serum
cholesterol: 195 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG
results: Normal, Max heart rate achieved: 122, Exercise induced angina:
No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"}
  ```
</details>

evals/registry/evals/heart-disease.yaml

@@ -0,0 +1,8 @@
+heart-disease:
+ id: heart-disease.v0
+ description: Test model's capability of predicting the presence of heart disease.
+ metrics: [accuracy]
+heart-disease.v0:
+ class: evals.elsuite.basic.match:Match
+ args:
+ samples_jsonl: heart-disease/samples.jsonl