https://github.com/landerlini/hopaas_client

Hyperparameter Optimization as a Service (Client)
https://github.com/landerlini/hopaas_client

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Hyperparameter Optimization as a Service (Client)

• Host: GitHub
• URL: https://github.com/landerlini/hopaas_client
• Owner: landerlini
• License: mit
• Created: 2022-07-08T16:07:10.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-12T10:17:32.000Z (almost 2 years ago)
• Last Synced: 2024-07-03T23:44:06.564Z (4 months ago)
• Language: Python
• Size: 43 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
[![DOI](https://zenodo.org/badge/511953143.svg)](https://zenodo.org/badge/latestdoi/511953143)

# Hopaas Python Client: `hopaas_client`

Hopaas is a service to handle parameter optimization as a service, 

based on RESTful APIs.

`hopaas_client` provides a Python front-end to ease the access to the service, 

embedding the requests in objects and calls for a most Pythonic experience. 

### Install

`hopaas_client` is not released, yet. Still, it can be installed from source as :

```bash

pip install git+https://github.com/landerlini/hopaas_client.git

```

### Configuration 

The first time you run `hopaas_client` it will prompt you for the server address 

and port to be used, and the API token associated to your own account. 

Sometimes (for example for batch jobs) this can be annoying. 

In order to configure `hopaas_client` manually you can create the file `.hopaasrc`

```bash

vim `python -c "import hopaas_client; import os.path; print(os.path.dirname(hopaas_client.__file__))"`/.hopaasrc

```

The configuration file should look like:

```ini

[server]

address = 

port = <80 or 443>

[auth]

api_token = 

```

### Minimal example

The following code snippet uses `hopaas` via `hopaas_client` to optimize a BDT.

```python

from sklearn.ensemble import GradientBoostingClassifier as GBDT

from sklearn.datasets import make_classification

# Load the hopaas frontend

import hopaas_client as hpc

# Create the dataset and split it into classification and validation samples

X, y = make_classification(n_samples=10000)

X_train, X_test = X[::2], X[1::2]

y_train, y_test = y[::2], y[1::2]

# Create the study

study = hpc.Study('Iris GBDT v7', properties=dict(

    n_estimators=hpc.suggestions.Int(2,20),

    learning_rate=0.1, 

    max_depth=hpc.suggestions.Int(2,5)

  ),

  sampler=hpc.samplers.TPESampler(),

  pruner=hpc.pruners.MedianPruner()

)

# Iterate over a given number of trials

for iTrial in range(20):

  # Requests a trial to the hopaas server

  with study.trial() as trial:

    # Instanciate a BDT with the parameters suggested by hopaas 

    bdt = GBDT(

        n_estimators=trial.n_estimators, 

        learning_rate=trial.learning_rate,

        max_depth=trial.max_depth

        )

    

    # Train the bdt

    bdt.fit(X_train, y_train)

    # Update the trial with the score of the BDT providing a loss 

    # that hopaas will try to minimize

    trial.loss = 1.-bdt.score(X_test, y_test)

```

## Licence

`hopaas_client` is made available under MIT licence. 

The backend will be released under GPL 3 at a more advanced stage of the development.