semvec/pypi-packages huggingface dataset:
deeppipe-api 0.1.4
DeepPipe efficiently optimizes Machine Learning Pipelines using meta-learning.
Requires Python: ==3.9.*

# DeepPipe: Deep Pipeline Embeddings for AutoML

*DeepPipe* efficiently optimizes Machine Learning Pipelines using meta-learning. For detailed information, refer to our [paper](https://arxiv.org/abs/2305.14009) *Deep Pipeline Embeddings for AutoML* accepted at KDD 2023. Additionally, you can visit our [blog-post](https://releaunifreiburg.github.io/deepppipe/) to have a friendly insight on how our method works.

<p align="center">
  <img src="figures/DeepPipe_architecture.png" alt="DeepPipe Architecture" width="400px">
</p>


## Installation

We present an API for optimizing pipelines in scikit-learn based on the TensorOboe search space. You can use it to search for accurate pipelines or for benchmarking your Machine Learning model on tabular data. 

```bash
conda create -n deeppipe_env python==3.9
pip install deeppipe_api
```

## Getting started

We present an example using an OpenML dataset. However, it works with any tabular data typed as pandas dataframe.


```python
from deeppipe_api.deeppipe import load_data, openml, DeepPipe

task_id = 37
task = openml.tasks.get_task(task_id)
X_train, X_test, y_train, y_test = load_data(task, fold=0)
deep_pipe = DeepPipe(n_iters = 50,  #bo iterations
                    time_limit = 3600 #in seconds
                    )
deep_pipe.fit(X_train, y_train)
y_pred = deep_pipe.predict(X_test)

#Test
score = deep_pipe.score(X_test, y_test)
print("Test acc.:", score)

#print best pipeline
print(deep_pipe.model)
```


### Ensemble of Pipelines

It is possible to ensemble the best pipelines, by using a greedy approach. 


```python
from deeppipe_api.deeppipe import load_data, openml, DeepPipe

task = openml.tasks.get_task(task_id=37)
X_train, X_test, y_train, y_test = load_data(task, fold=0)
deep_pipe = DeepPipe(n_iters = 50,  #bo iterations
                    time_limit = 3600, #in seconds
                    create_ensemble = False,
                    ensemble_size = 10,
                    )
deep_pipe.fit(X_train, y_train)
y_pred = deep_pipe.predict(X_test)
score = deep_pipe.score(X_test, y_test)
print("Test acc.:", score) 
```

## Advanced Usage

For meta-training *DeepPipe* or testing other search spaces, you can refer to the folder `src/deeppipe_api/experiments/`.


## Our Paper

If you use this repository/package, please cite our paper:

```
@article{arango2023deep,
  title={Deep Pipeline Embeddings for AutoML},
  author={Arango, Sebastian Pineda and Grabocka, Josif},
  journal={arXiv preprint arXiv:2305.14009},
  year={2023}
}

```
pymelo 0.0.2
Wrapper for DiscordPomeloAPI

# pymelo
Wrapper for [DiscordPomeloAPI](https://github.com/Lixqa/DiscordPomelo)

# Install
Python 3 or higher is required.    
```cmd
# Linux/OS X
$ python -m pip install -U pymelo

# Windows
> py -3 -m pip install -U pymelo
```    

# Example
Check if the username 'nemupy' is available.    
```py
import pymelo


check = pymelo.Check("nemupy")

if check.status == 2:
    print(f"The username '{check.username}' is available.")
elif check.status == 3:
    print(f"The username '{check.username}' is not available.")
else:
    print(f"An error occurred while checking the username '{check.username}' availability.")

# The username 'nemupy' is not available.
```

# Response
- `username` Your checked username.
- `status` See [DiscordPomeloAPI](https://github.com/Lixqa/DiscordPomelo/blob/main/README.md#Status).
- `attempt` How many attempts it took until a valid response was returned by discord.