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easyopt-0.2
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-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | easyopt-0.2 |
| نام | easyopt |
| نسخه کتابخانه | 0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Federico A. Galatolo |
| ایمیل نویسنده | federico.galatolo@ing.unipi.it |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/easyopt/ |
| مجوز | - |
# easyopt
`easyopt` is a **super simple** yet **super powerful** [optuna](https://optuna.org/)-based Hyperparameters Optimization Framework that requires **no coding**.
## Features
* YAML Configuration
* Distributed Parallel Optimization
* Experiments Monitoring and Crash Recovering
* Experiments Replicas
* Real Time Pruning
* A wide variety of sampling strategies
* Tree-structured Parzen Estimator
* CMA-ES
* Grid Search
* Random Search
* A wide variety of pruning strategies
* Asynchronous Successive Halving Pruning
* Hyperband Pruning
* Median Pruning
* Threshold Pruning
* A wide variety of DBMSs
* Redis
* SQLite
* PostgreSQL
* MySQL
* Oracle
* And many [more](https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine)
## Installation
To install `easyopt` just type:
```
pip install easyopt
```
## Example
`easyopt` expects that hyperparameters are passed using the command line arguments.
For example this problem has two hyperparameters `x` and `y`
```python
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--x", type=float, required=True)
parser.add_argument("--y", type=float, required=True)
args = parser.parse_args()
def objective(x, y):
return x**2 + y**2
F = objective(args.x ,args.y)
```
To integrate `easyopt` you just have to
* `import easyopt`
* Add `easyopt.objective(...)` to report the experiment objective function value
The above code becomes:
```python
import argparse
import easyopt
parser = argparse.ArgumentParser()
parser.add_argument("--x", type=float, required=True)
parser.add_argument("--y", type=float, required=True)
args = parser.parse_args()
def objective(x, y):
return x**2 + y**2
F = objective(args.x ,args.y)
easyopt.objective(F)
```
Next you have to create the `easyopt.yml` to define the problem search space, sampler, pruner, storage, etc.
```yaml
command: python problem.py {args}
storage: sqlite:////tmp/easyopt-toy-problem.db
sampler: TPESampler
parameters:
x:
distribution: uniform
low: -10
high: 10
y:
distribution: uniform
low: -10
high: 10
```
You can find the compete list of distributions [here](https://optuna.readthedocs.io/en/v1.4.0/reference/trial.html) (all the `suggest_*` functions)
Finally you have to create a study
```
easyopt create test-study
```
And run as many agents as you want
```
easyopt agent test-study
```
After a while the hyperparameter optimization will finish
```
Trial 0 finished with value: 90.0401543850028 and parameters: {'x': 5.552902529323713, 'y': 7.694506344453366}. Best is trial 0 with value: 90.0401543850028.
Trial 1 finished with value: 53.38635524683359 and parameters: {'x': 0.26609756303111, 'y': 7.301749607716118}. Best is trial 1 with value: 53.38635524683359.
Trial 2 finished with value: 64.41207387363161 and parameters: {'x': 7.706366704967074, 'y': 2.2414250115064167}. Best is trial 1 with value: 53.38635524683359.
...
...
Trial 53 finished with value: 0.5326245807950265 and parameters: {'x': -0.26584110075742917, 'y': 0.6796713102251005}. Best is trial 35 with value: 0.11134607529340049.
Trial 54 finished with value: 8.570230212116037 and parameters: {'x': 2.8425893061307295, 'y': 0.6999401751487438}. Best is trial 35 with value: 0.11134607529340049.
Trial 55 finished with value: 96.69479467451664 and parameters: {'x': -0.3606041968175481, 'y': -9.826736960342137}. Best is trial 35 with value: 0.11134607529340049.
```
## YAML Structure
The `YAML` configuration file is structured as follows
```yaml
command: <command>
storage: <storage>
sampler: <sampler>
pruner: <pruner>
direction: <direction>
replicas: <replicas>
parameters:
parameter-1:
distribution: <distribution>
<arg1>: <value1>
<arg2>: <value2>
...
...
```
* `command`: the command to execute to run the experiment.
* `{args}` will be expanded to `--parameter-1=value-1 --parameter-2=value-2`
* `{name}` will be expanded to the study name
* `storage`: the storage to use for the study. A full list of storages is available [here](https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine)
* `sampler`: the sampler to use. The full list of samplers is available [here](https://optuna.readthedocs.io/en/stable/reference/samplers.html)
* `pruner`: the pruner to use. The full list of pruners is available [here](https://optuna.readthedocs.io/en/stable/reference/pruners.html)
* `direction`: can be `minimize` or `maximize` (default: `minimize`)
* `replicas`: the number of replicas to run for the same experiment (the experiment result is the average). (default: `1`)
* `parameters`: the parameters to optimize
* for each parameter have to specify
* `distribution` the distribution to use. The full list of distributions is available [here](https://optuna.readthedocs.io/en/v1.4.0/reference/trial.html) (all the `suggest_*` functions)
* `arg`: `value`
* Arguments of the distribution. The arguments documentation is available [here](https://optuna.readthedocs.io/en/v1.4.0/reference/trial.html)
## CLI Interface
`easyopt` offer two CLI commands:
* `create` to create a study using the `easyopt.yml` file or the one specified with `--config`
* `agent <study_name>` to run the agent for `<study_name>`
## LIB Interface
When importing `easyopt` you can use three functions:
* `easyopt.objective(value)` to report the **final** objective function value of the experiment
* `easyopt.report(value)` to report the **current** objective function value of the experiment (used by the pruner)
* `easyopt.should_prune()` it returns `True` if the pruner thinks that the run should be pruned
## Examples
You can find some examples [here](https://github.com/galatolofederico/easyopt/tree/main/examples)
## Contributions and license
The code is released as Free Software under the [GNU/GPLv3](https://choosealicense.com/licenses/gpl-3.0/) license. Copying, adapting and republishing it is not only allowed but also encouraged.
For any further question feel free to reach me at [federico.galatolo@ing.unipi.it](mailto:federico.galatolo@ing.unipi.it) or on Telegram [@galatolo](https://t.me/galatolo)
نیازمندی
| مقدار | نام |
|---|---|
| ==2.9.1 | optuna |
| ==5.4.1 | PyYAML |
| ==0.4.4 | colorama |
نحوه نصب
نصب پکیج whl easyopt-0.2:
pip install easyopt-0.2.whl
نصب پکیج tar.gz easyopt-0.2:
pip install easyopt-0.2.tar.gz