semvec/pypi-packages huggingface dataset:
aimstack 2.0.0
Version control for AI
Requires Python: >=3.5.2


# aim
#### Version control for AI

See the docs [here](https://docs.aimhub.io).

## Development

### Requirements

* Python 3

We suggest to use [virtual
environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) for managing local dependencies.

To start development first install all dependencies:

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

### Project Structure

```
├── aim  <----------------  main project code
│   ├── cli <-------------  command line interface
│   ├── engine <----------  business logic
│   ├── sdk <-------------  Python SDK
│   ├── artifacts <-------  managing tracked data
│   └── version_control <-  managing files and code
├── examples <------------  example usages of aim SDK
└── tests
```

### Code Style
We follow [pep8](https://www.python.org/dev/peps/pep-0008/) style guide for python code. We use [autopep8](https://pypi.org/project/autopep8/) and [pycodestyle](https://pypi.org/project/pycodestyle/) to enable checking and formatting Python code. 

To check code styles, run `pycodestyle .` in the root folder. 

To auto format, run `autopep8 --in-place --recursive --aggressive --aggressive .` in the root folder.
aim-stack 2.0.0
Version control for AI
Requires Python: >=3.5.2


# aim
#### Version control for AI

See the docs [here](https://docs.aimhub.io).

## Development

### Requirements

* Python 3

We suggest to use [virtual
environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) for managing local dependencies.

To start development first install all dependencies:

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

### Project Structure

```
├── aim  <----------------  main project code
│   ├── cli <-------------  command line interface
│   ├── engine <----------  business logic
│   ├── sdk <-------------  Python SDK
│   ├── artifacts <-------  managing tracked data
│   └── version_control <-  managing files and code
├── examples <------------  example usages of aim SDK
└── tests
```

### Code Style
We follow [pep8](https://www.python.org/dev/peps/pep-0008/) style guide for python code. We use [autopep8](https://pypi.org/project/autopep8/) and [pycodestyle](https://pypi.org/project/pycodestyle/) to enable checking and formatting Python code. 

To check code styles, run `pycodestyle .` in the root folder. 

To auto format, run `autopep8 --in-place --recursive --aggressive --aggressive .` in the root folder.
atml 0.0.1
Automation Toolkit for Machine Learning

# atml

atml tries to address the following problem: **Given a new data set, is there any toolkit that could help me quickly and conveniently come up with a good baseline model?**

# try it out

* Install from PyPi

```python
pip install atml
```

* Load the data set or use your own one

```python
import pandas as pd

df = pd.read_csv("./test/data/binary_data.csv")
X = df.drop('Survived', axis=1)

label_columns = ['Survived']
y = df[label_columns]
```

* Instantiate and run with AtmlController with default

```python
from atml import AtmlController

atml_c = AtmlController(with_default=True)
atml_c.run(X, y)
```

* Register a new Model and Hyper parameter space for tuning
```python
from atml import AtmlOrchestrator

atml_o = AtmlOrchestrator(with_default=True)

from sklearn.svm import SVC
sp = [
    {"property": "kernel", "type": "choice", "value": ["linear", "rbf"]},
    {"property": "gamma", "type": "choice", "value": ["scale", "auto"]}     
]
atml_o.auto_learning_socket.register(SVC(), sp)

atml_o.run(X, y)
'''