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from . import trees
from . import net
from . import bayes
def convert(estimator, kind=None, method='pymodule', dtype='float'):
"""Main entrypoint for converting a model"""
if kind is None:
kind = type(estimator).__name__
# Use name instead of instance to avoid hard dependency on the libraries
if kind in ['RandomForestClassifier', 'ExtraTreesClassifier', 'DecisionTreeClassifier']:
return trees.Wrapper(estimator, method, dtype=dtype)
elif kind == 'MLPClassifier':
return net.convert_sklearn_mlp(estimator, method)
elif kind == 'Sequential':
return net.convert_keras(estimator, method)
elif kind == 'GaussianNB':
return bayes.Wrapper(estimator, method)
else:
raise ValueError("Unknown model type: '{}'".format(kind))
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