https://github.com/sirbradflies/sibyl

https://github.com/sirbradflies/sibyl

ai keras ml pipeline

Last synced: 27 days ago
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• Host: GitHub
• URL: https://github.com/sirbradflies/sibyl
• Owner: sirbradflies
• Created: 2021-06-19T09:16:20.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-01-30T03:17:38.000Z (about 3 years ago)
• Last Synced: 2025-01-03T14:24:07.248Z (27 days ago)
• Topics: ai, keras, ml, pipeline
• Language: Python
• Size: 42 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Sibyl-AI

##### Provided under MIT License by Francesco Baldisserri

*Note: this library may be subtly broken or buggy. The code is released under

the MIT License – please take the following message to heart:*

> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS

FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR

COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER

IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN

CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

> 

## Benefits

- Sibyl is a simple wrapper of SciKit Learn Pipeline

- The default pipeline steps can be used as a simple AutoML tool

- The package includes Keras and Catboost serializable wrappers to be able to use these models with the Pipeline and save them as any other SKLearn estimator

- The default pipeline steps include an OmniEncoder which recognizes the feature types and already transforms them with the most appropriate encoding

## Getting Started

This README is documentation on the syntax of the Sibyl module in this repository. See function docstrings for full syntax details.  

**This module attempts to add features to SKLearn Pipeline module, but in order to use it

to its full potential, you should familiarize yourself with the official SKLearn documentation.**

- https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html

- You may manually install the project or use ```pip```:

```python

pip install sibyl-ai

#or

pip install git+git://github.com/sirbradflies/Sibyl.git

```

### Sibyl as AutoML

Sibyl framework can be used with its default steps as a simple AutoML pipeline.

The standard steps are:

1. ("omni", OmniEncoder()) (encoder for all features with simple heuristic to recognize continuous, discrete and categorial variables)

2. ("pca", PCA())

3. ("model", KerasDenseRegressor()) or ("model", KerasDenseClassifier()) depending on the predictor pipeline's task

The score used is r2 for the Regressor and accuracy for the Classifier

```python

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sibyl import predictor as pred

X, y = datasets.load_boston(return_X_y=True)  # No encoding needed since OmniEncoder recognizes discrete, continuous and categoricals

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

predrgr = pred.SibylRegressor()

predrgr.search(X_train, y_train)  # RandomizedGridSearchCV available but also fit method is available

print(f"Test score: {predrgr.score(X_test, y_test):.4f}")

```

### Sibyl for custom pipelines

The Sibyl framework can be also used to setup a custom pipeline that includes all features of the SKLearn pipeline but also embeds RandomizedGridSearch and Keras and CatBoost models that can be easily serialized and saved.

Here's a custom pipeline for MNIST digit classification

```python

from sklearn import datasets

from sklearn.decomposition import PCA

from sklearn.model_selection import train_test_split

from sibyl import predictor as pred

from sibyl.models import kerasmodels as kd

STEPS = [("preprocessing", PCA()),

         ("model", kd.KerasDenseClassifier(n_iter_no_change=1,

                                           epochs=1000))]

SEARCH_PARAMS = {"preprocessing__n_components": [None, 0.99, 0.90],

                 "model__units": [(64,), (64, 64), (64, 64, 64)],

                 "model__batch_norm": [True, False]}

X, y = datasets.fetch_openml('mnist_784', version=1, return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

predclf = pred.SibylClassifier(steps=STEPS)

predclf.search(X_train, y_train,

               params=SEARCH_PARAMS)  # RandomizedGridSearchCV available but also fit method is available

print(f"Test score: {predclf.score(X_test, y_test):.4f}")

```