https://github.com/clamytoe/covid-risk-classifier
How likely are you to die if you take the shot?
https://github.com/clamytoe/covid-risk-classifier
Last synced: about 1 year ago
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How likely are you to die if you take the shot?
- Host: GitHub
- URL: https://github.com/clamytoe/covid-risk-classifier
- Owner: clamytoe
- License: mit
- Created: 2022-11-03T17:45:57.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-16T21:11:24.000Z (over 3 years ago)
- Last Synced: 2025-04-08T06:30:24.880Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 3.4 MB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Covid Risk Classifier
```bash
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```
## What does this do?
This classifier will predict the likelyhood of someone dying from taking the covid vaccination.
The dataset was collected from the [Vaccine Adverse Event Reporting System (VAERS)](https://vaers.hhs.gov/), which is an arm of the [U.S. Department of Health & Human Services (HHS)](https://www.hhs.gov/).
If you are interested in the actual raw data, the datasets can be downloaded from the VAERS Data Sets: [https://vaers.hhs.gov/data/datasets.html](https://vaers.hhs.gov/data/datasets.html).
These are the two datasets that were used:
* 2022VAERSDATA.csv
* 2022VAERSVAX.csv
## Running the model
The fastest way for you to run this model is to simply get the docker image [clamytoe/covid_risk_classifier](https://hub.docker.com/r/clamytoe/covid_risk_classifier) and run it locally.
### Download the image
```docker
docker pull clamytoe/covid_risk_classifier
```
### Start the service
```docker
docker run -it --rm -p 3000:3000 clamytoe/covid_risk_classifier serve --production
```
### Using the service
You can use the API by simply opening a browser to
If you scroll down a bit until you get to the **Service APIs** section and click on the **POST** drop down.
Once you've clicked on it to expand it, you will see a **Try it out** button.
Clicking on it will expose a text area with a schema of a valid input.
Enter your own values to try it out.
Due take note that at the top of the page, a description of each value is provided along with acceptable values.
Here is a sample for you to try:
```json
{
"state": "tx",
"age_yrs": 52,
"sex": "f",
"disable": 0,
"other_meds": 1,
"cur_ill": 0,
"history": 0,
"prior_vax": 0,
"ofc_visit": 0,
"allergies": 0,
"vax_name": "moderna",
"vax_dose_series": 1
}
```
Once you've entered your values, just click on the **Execute** button and scroll down a bit until you get to the *Server response* section.
In this case, we see that the user should be cautious if planning to take it.
## Sending multiple requests
If entering each patient's data takes too long, you can do something like what I'm doing with my `predict.py` script.
*predict.py:*
```python
import json
import requests
from service import CovidRisk
patients: list[CovidRisk] = [
{
"state": "mi",
"age_yrs": 85,
"sex": "f",
"disable": 0,
"other_meds": 0,
"cur_ill": 0,
"history": 0,
"prior_vax": 0,
"ofc_visit": 0,
"allergies": 0,
"vax_name": "pfizer\\biontech",
"vax_dose_series": 2,
},
...
{
"state": "tx",
"age_yrs": 52,
"sex": "f",
"disable": 0,
"other_meds": 1,
"cur_ill": 0,
"history": 0,
"prior_vax": 0,
"ofc_visit": 0,
"allergies": 0,
"vax_name": "moderna",
"vax_dose_series": 1,
},
]
def test_service(data: List[CovidRisk]):
for patient in data:
patient = json.dumps(patient)
response = requests.post(
"http://localhost:3000/classify",
headers={"content-type": "application/json"},
data=patient,
).json()
print(response["status"], response["proba"])
if __name__ == "__main__":
test_service(patients)
```
## Logging
I've enabled logging for the service.
In order to be able to keep the logs you will have to create a local mount point or a docker volume.
That's beyong the scope of this project, so I will leave that up to you to implement on your own.
### Log file location
* If you run the service locally, it is created in your project's root directory as `covid_risk_classifier.log`.
* If you run it from a docker image, it is created in `/home/bentoml/bento/src` with the same name.
*sample log entries:*
```log
cat covid_risk_classifier.log
...
2022-11-04T23:01:51.342665+0000 INFO Processing: state='tx' age_yrs=53 sex='f' disable=False other_meds=False cur_ill=False history=False prior_vax=False ofc_visit=False allergies=False vax_name='pfizer\\biontech' vax_dose_series=1
2022-11-04T23:01:51.347936+0000 INFO Probability: 0.19510182738304138
2022-11-04T23:01:51.348160+0000 INFO Prediction: {'status': 'SAFE', 'proba': 0.19510183}
2022-11-04T23:01:51.351920+0000 INFO Processing: state='ca' age_yrs=3 sex='m' disable=False other_meds=False cur_ill=False history=False prior_vax=False ofc_visit=False allergies=False vax_name='moderna' vax_dose_series=1
2022-11-04T23:01:51.357594+0000 INFO Probability: 0.003593571949750185
2022-11-04T23:01:51.357773+0000 INFO Prediction: {'status': 'SAFE', 'proba': 0.003593572}
2022-11-04T23:01:51.361099+0000 INFO Processing: state='tx' age_yrs=52 sex='f' disable=False other_meds=True cur_ill=False history=False prior_vax=False ofc_visit=False allergies=False vax_name='moderna' vax_dose_series=1
2022-11-04T23:01:51.366562+0000 INFO Probability: 0.13526801764965057
2022-11-04T23:01:51.366764+0000 INFO Prediction: {'status': 'SAFE', 'proba': 0.13526802}
```
## Short demo of it running on AWS
[](https://youtu.be/DiqvK98TG68)
## Further reading
If you want to dig in more and take a look at what it takes to create your own classifier, then a look at the [HOW-TO.md](HOW-TO.md) file.