semvec/pypi-packages huggingface dataset:
resend-client-sdk-python 1.8.2
Python Client SDK Generated by Speakeasy
Requires Python: >=3.9

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   <p>Resend is the email platform for developers.</p>
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<!-- Start SDK Installation -->
## SDK Installation

```bash
pip install resend-client-sdk-python
```
<!-- End SDK Installation -->

## Authentication

To authenticate you need to add an Authorization header with the contents of the header being Bearer re_123456789 where re_123456789 is your API Key. First, you need to get an API key, which is available in the [Resend Dashboard](https://resend.com/login).

```bash
Authorization: Bearer re_123
```

## SDK Example Usage
```python
import resend
from resend.models import operations, shared

s = resend.Resend()
s.config_security(
    security=shared.Security(
        bearer_auth="Bearer YOUR_BEARER_TOKEN_HERE",
    )
)
   
req = operations.SendEmailRequest(
    request=shared.Email(
        from_="hello@resend.com",
        to="thefuture@yourcompany.com",
        subject="Welcome to Resend!",
        text="Hello, World!",
    ),
)
    
res = s.email.send_email(req)

if res.send_email_response is not None:
    # handle response
```

<!-- Start SDK Available Operations -->
## SDK Available Operations


### email

* `send_email` - Send an email
<!-- End SDK Available Operations -->

## Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release !

### SDK Generated by [Speakeasy](https://docs.speakeasyapi.dev/docs/using-speakeasy/client-sdks)
auto-tabnet 0.0.1
Automated implementation of Google TabNet.
Keywords: automl,tabular

# Auto-Tabnet
Auto-TabNet is the implementation of Google's TabNet model using dreamquark-ai's pytorch implementation along with hyperparameter search with Optuna.

Google's TabNet was proposed in 2019 with the idea of effectively using deep neural networks for tabular data.

TabNet is a complex model composed of a feature transformer, attentive transformer, and feature masking, that soft feature selection with controllable sparsity in end-to-end learning. The reason for the high performance of TabNet is that it focuses on the most important features that have been considered by the Attentive Transformer. The Attentive Transformer performs feature selection to select which model features to reason from at each step in the model, and a Feature Transformer processes feature into more useful representations and learn complex data patterns, which improve interpretability and help it learn more accurate models.



## Requirements

-
 ```sh
python 3.7 >
```

- 
```sh
pip (python package manager)
```


## Installation

With pip:

```sh
pip install auto-tabnet
```

## Source Code

If you want to use it locally within a pip virtualenv:

- Clone the repository

```sh
git clone https://github.com/Femme-js/auto-tabnet.git
```
- Create a pip virtual environment.

```sh
virtualenv env
```
- Install the dependencies from requirements.txt file.

```sh
pip install -r requirements.txt
```


## Usage

```sh
from auto_tabnet import AutoTabnetClassifier

clf = AutoTabnetClassifier(X, y, X_test)

```
To get the prediction on test data.

```sh
results = clf.predict()

```
To get the auc_roc_score:

```sh
results = clf.get_roc_auc_score()

```
To get the best hyperparamters tuned by optuna:

```sh
results = clf.get_best_params()

```

The targets on y_train should contain a unique type (e.g. they must all be strings or integers).