feyn


Namefeyn JSON
Version 2.0.4 PyPI version JSON
download
home_pagehttps://abzu.ai
SummaryFeyn is the high level Python interface to interact with an Abzu QLattice.
upload_time2021-07-07 10:30:30
maintainer
docs_urlNone
authorAbzu
requires_python
licenseCC BY-ND 4.0
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # Feyn

## Quick start

`Feyn` is available as Python3.6+ package [through `pip`](https://pypi.org/project/feyn/). You can install it with the following command:

```bash
richard@feyn:~$ pip3 install feyn
```

Once installed, go to your preferred `Python` environment and follow along with this example.

## Connect to a QLattice

If you're using the community edition of a `QLattice` then you can get connected to it by:

```python
import feyn

ql = feyn.connect_qlattice()
```

## Auto run

The quickest way to get started is to use the `auto_run` function on the `QLattice`. First we will make a classification problem with `feyn.datasets.make_classification`.


```python
from feyn.datasets import make_classification

train, test = make_classification()
models = ql.auto_run(train, output_name = 'y', kind = 'classification')
```

This returns a list of fitted models that are the best the `QLattice` has sampled, sorted by ascending loss.

## Evaluate

The model with the lowest loss is `models[0]`. We can evaluate that model with the `plot` function and it's ROC curve.

```python
best = models[0]
best.plot(train, test)
best.plot_roc_curve(test)
```


            

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    "description": "# Feyn\n\n## Quick start\n\n`Feyn` is available as Python3.6+ package [through `pip`](https://pypi.org/project/feyn/). You can install it with the following command:\n\n```bash\nrichard@feyn:~$ pip3 install feyn\n```\n\nOnce installed, go to your preferred `Python` environment and follow along with this example.\n\n## Connect to a QLattice\n\nIf you're using the community edition of a `QLattice` then you can get connected to it by:\n\n```python\nimport feyn\n\nql = feyn.connect_qlattice()\n```\n\n## Auto run\n\nThe quickest way to get started is to use the `auto_run` function on the `QLattice`. First we will make a classification problem with `feyn.datasets.make_classification`.\n\n\n```python\nfrom feyn.datasets import make_classification\n\ntrain, test = make_classification()\nmodels = ql.auto_run(train, output_name = 'y', kind = 'classification')\n```\n\nThis returns a list of fitted models that are the best the `QLattice` has sampled, sorted by ascending loss.\n\n## Evaluate\n\nThe model with the lowest loss is `models[0]`. We can evaluate that model with the `plot` function and it's ROC curve.\n\n```python\nbest = models[0]\nbest.plot(train, test)\nbest.plot_roc_curve(test)\n```\n\n",
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