| Name | feyn JSON |
| Version |
3.2.0
JSON |
| download |
| home_page | |
| Summary | Feyn is a symbolic regression package named after Richard Feynman, that uses the QLattice as a simulator to generate models. |
| upload_time | 2024-02-26 14:46:13 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | CC BY-NC-ND 4.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Feyn
## Quick start
`Feyn` is available as Python3.8+ 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.
## Running a QLattice
If you're using the community edition of a `QLattice` then you can instantiate it by:
```python
import feyn
ql = feyn.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 Python3.8+ 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## Running a QLattice\n\nIf you're using the community edition of a `QLattice` then you can instantiate it by:\n\n```python\nimport feyn\n\nql = feyn.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```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",
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