https://github.com/tmadl/sklearn-random-bits-forest
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)
https://github.com/tmadl/sklearn-random-bits-forest
Last synced: 5 days ago
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Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)
- Host: GitHub
- URL: https://github.com/tmadl/sklearn-random-bits-forest
- Owner: tmadl
- Created: 2016-07-31T15:17:14.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-07-31T15:23:41.000Z (over 9 years ago)
- Last Synced: 2024-04-29T09:33:28.847Z (over 1 year ago)
- Language: Python
- Size: 883 KB
- Stars: 9
- Watchers: 3
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-python-data-science - sklearn-random-bits-forest - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).<img height="20" src="img/sklearn_big.png" alt="sklearn"> (Machine Learning / Random Forests)
- awesome-python-data-science - sklearn-random-bits-forest - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).<img height="20" src="img/sklearn_big.png" alt="sklearn"> (Machine Learning / Random Forests)
README
sklearn-compatible Random Bits Forest
===============
Scikit-learn compatible wrapper of the Random Bits Forest program written by
[Wang et al., 2016](http://www.nature.com/articles/srep30086), available as a binary
on [Sourceforge](https://sourceforge.net/projects/random-bits-forest/). All credits
belong to the authors. This is just some quick and dirty wrapper and testing code.
The authors present "*...a classification and regression algorithm called Random
Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness)
and random forest (for accuracy). It first generates and selects ~10,000 small
three-layer threshold random neural networks as basis by gradient boosting scheme.
These binary basis are then feed into a modified random forest algorithm to obtain
predictions. In conclusion, RBF is a novel framework that performs strongly especially
on data with large size.*"
Note: the executable supplied by the authors has been compiled for Linux, and for
CPUs supporting SSE instructions.
Usage
===============
Usage example of the Random Bits Forest:
```python
from uci_loader import *
from randombitsforest import RandomBitsForest
X, y = getdataset('diabetes')
from sklearn.ensemble.forest import RandomForestClassifier
classifier = RandomBitsForest()
classifier.fit(X[:len(y)/2], y[:len(y)/2])
p = classifier.predict(X[len(y)/2:])
print "Random Bits Forest Accuracy:", np.mean(p == y[len(y)/2:])
classifier = RandomForestClassifier(n_estimators=20)
classifier.fit(X[:len(y)/2], y[:len(y)/2])
print "Random Forest Accuracy:", np.mean(classifier.predict(X[len(y)/2:]) == y[len(y)/2:])
```
Usage example for the UCI comparison:
```python
from uci_comparison import compare_estimators
from sklearn.ensemble.forest import RandomForestClassifier, ExtraTreesClassifier
from randombitsforest import RandomBitsForest
estimators = {
'RandomForest': RandomForestClassifier(n_estimators=200),
'ExtraTrees': ExtraTreesClassifier(n_estimators=200),
'RandomBitsForest': RandomBitsForest(number_of_trees=200)
}
# optionally, pass a list of UCI dataset identifiers as the datasets parameter, e.g. datasets=['iris', 'diabetes']
# optionally, pass a dict of scoring functions as the metric parameter, e.g. metrics={'F1-score': f1_score}
compare_estimators(estimators)
"""
ExtraTrees F1score RandomBitsForest F1score RandomForest F1score
========================================================================================
breastcancer (n=683) 0.960 (SE=0.003) 0.954 (SE=0.003) *0.963 (SE=0.003)
breastw (n=699) *0.956 (SE=0.003) 0.951 (SE=0.003) 0.953 (SE=0.005)
creditg (n=1000) *0.372 (SE=0.005) 0.121 (SE=0.003) 0.371 (SE=0.005)
haberman (n=306) 0.317 (SE=0.015) *0.346 (SE=0.020) 0.305 (SE=0.016)
heart (n=270) 0.852 (SE=0.004) *0.854 (SE=0.004) 0.852 (SE=0.006)
ionosphere (n=351) 0.740 (SE=0.037) *0.741 (SE=0.037) 0.736 (SE=0.037)
labor (n=57) 0.246 (SE=0.016) 0.128 (SE=0.014) *0.361 (SE=0.018)
liverdisorders (n=345) 0.707 (SE=0.013) *0.723 (SE=0.013) 0.713 (SE=0.012)
tictactoe (n=958) 0.030 (SE=0.007) *0.336 (SE=0.040) 0.030 (SE=0.007)
vote (n=435) *0.658 (SE=0.012) 0.228 (SE=0.017) *0.658 (SE=0.012)
"""
```