semvec/pypi-packages huggingface dataset:
DetaMetrics 0.0.7
SDK for DetaMetrics TensorBoard alternative.
Requires Python: >=3.7

# DetaMetricsSDK

A Python SDK for interacting with my TensorBoard alternative that runs on [Deta Space](https://deta.space).

- [DetaMetricsSDK](#detametricssdk)
  - [Installation](#installation)
  - [Usage](#usage)
    - [TensorFlow Callback](#tensorflow-callback)
    - [Base API](#base-api)

---

## Installation

```powershell
pip install detametrics
```

## Usage

> All API classes need an URL ID, which can be gotten from the app UI.

> They also need a Deta Space App API key, which can be gotten from...
> - Click the 3 dots on the app on your Deta Space Canvas
> - Click **"Keys"**
> - Add an API Key, and input it in the box on the App UI and in your Python code as seen below

### TensorFlow Callback
Requires Tensorflow to be installed.

```python
from detametrics.tf import DetaMetricsTFCallback

# use in your keras model
model = ...
model.fit(
  ..., # other params here
  callbacks=[DetaMetricsTFCallback("MY_URL_ID", "MY_API_KEY")]
)
```

### Base API
Get your URL id from the UI on the [Deta Space app](https://deta.space/discovery/@sam-the-programmer/detametrics).

```python
from detametrics import DetaMetrics

metrics = DetaMetrics("MY_URL_ID", "MY_API_KEY")
metrics.clear() # WARNING: clears all past metrics

metrics.set("GraphName", "LineName", 0.1)
metrics.set("GraphName", "LineName", 123.45)
metrics.set("GraphName", "LineName", 78.9)
```
survival-datasets 0.1.5
Data loader for common datasets in Survival Analysis.
Keywords: Survival Analysis,Datasets

# survival-datasets

A simple data loader for the most common datasets in Survival Analysis. Currently the following are included: 
* Veterans Lung Cancer (https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.load_veterans_lung_cancer.html)
* German Breast Cancer Study Group (GBSG2) (https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.load_gbsg2.html)
* AIDS dataset (https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.load_aids.html)
* NHANES (https://shap.readthedocs.io/en/latest/generated/shap.datasets.nhanesi.html)
* SUPPORT Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (from DeepSurv paper, https://arxiv.org/abs/1606.00931)
* METABRIC The Molecular Taxonomy of Breast Cancer International Consortium (from DeepSurv paper, https://arxiv.org/abs/1606.00931)
* WHAS500 Worcester Heart Attack Study (https://scikit-survival.readthedocs.io/en/stable/api/datasets.html)
* FLCHAIN (https://scikit-survival.readthedocs.io/en/stable/api/datasets.html)
* SEER (from Kaggle, https://www.kaggle.com/code/jnegrini/breast-cancer-dataset)

## Requirements

* Python 3.8 or later
* scikit-survival 0.17.2 or later
* pandas 1.4.3 or later
* numpy 1.22.4 or later
* shap 0.41 or later
* pyarrow 11.0 or later

## Installation

Simply install via pip:
```
pip install survival-datasets
```

## Examples

Import the datasets module from the package and load your dataset of choice:
```
from survdata import datasets

if __name__ == "__main__":
    X, y = datasets.load_seer_dataset()
```