Meta Machine Learning

Meta machine learning is specific algorithms, like complicated hierarchical algorithm, hyperparameters optimization parameters. Also there is Factory which allows you to train set of models and compare them very simply.

Factory

Factory Examples

  • Prepare dataset
    >>> from sklearn import datasets
>>> import pandas, numpy
>>> from rep.utils import train_test_split
>>> from sklearn.metrics import roc_auc_score
>>> # iris data
>>> iris = datasets.load_iris()
>>> data = pandas.DataFrame(iris.data, columns=['a', 'b', 'c', 'd'])
>>> labels = iris.target
>>> # Take just two classes instead of three
>>> data = data[labels != 2]
>>> labels = labels[labels != 2]
>>> train_data, test_data, train_labels, test_labels = train_test_split(data, labels, train_size=0.7)
  • Train factory of classifiers
    >>> from rep.metaml import ClassifiersFactory
>>> from rep.estimators import TMVAClassifier, SklearnClassifier, XGBoostClassifier
>>> from sklearn.ensemble import GradientBoostingClassifier
>>> factory = ClassifiersFactory()
>>> estimators
>>> factory.add_classifier('tmva', TMVAClassifier(method='kBDT', NTrees=100, Shrinkage=0.1, nCuts=-1, BoostType='Grad', features=['a', 'b']))
>>> factory.add_classifier('ada', GradientBoostingClassifier())
>>> factory['xgb'] = XGBoostClassifier(features=['a', 'b'])
>>> factory.fit(train_data, train_labels)
model ef           was trained in 0.22 seconds
model tmva         was trained in 2.47 seconds
model ada          was trained in 0.02 seconds
model xgb          was trained in 0.01 seconds
Totally spent 2.71 seconds on training
>>> pred = factory.predict_proba(test_data)
data was predicted by tmva         in 0.02 seconds
data was predicted by ada          in 0.00 seconds
data was predicted by xgb          in 0.00 seconds
Totally spent 0.05 seconds on prediction
>>> print pred
OrderedDict([('tmva', array([[  9.98732217e-01,   1.26778255e-03], [  9.99649503e-01,   3.50497149e-04], ..])),
             ('ada', array([[  9.99705117e-01,   2.94883265e-04], [  9.99705117e-01,   2.94883265e-04], ..])),
             ('xgb', array([[  9.91589248e-01,   8.41078255e-03], ..], dtype=float32))])
>>> for key in pred:
>>>    print key, roc_auc_score(test_labels, pred[key][:, 1])
tmva 0.933035714286
ada 1.0
xgb 0.995535714286

Folding

Stacking