#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_python.py
# Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
#
from __future__ import absolute_import, division, print_function, unicode_literals
from h2o.estimators.estimator_base import H2OEstimator
from h2o.exceptions import H2OValueError
from h2o.frame import H2OFrame
from h2o.utils.typechecks import assert_is_type, Enum, numeric
[docs]class H2OGenericEstimator(H2OEstimator):
"""
Generic Model
"""
algo = "generic"
def __init__(self, **kwargs):
super(H2OGenericEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "model_key"}
if "Lambda" in kwargs: kwargs["lambda_"] = kwargs.pop("Lambda")
for pname, pvalue in kwargs.items():
if pname == 'model_id':
self._id = pvalue
self._parms["model_id"] = pvalue
elif pname in names_list:
# Using setattr(...) will invoke type-checking of the arguments
setattr(self, pname, pvalue)
else:
raise H2OValueError("Unknown parameter %s = %r" % (pname, pvalue))
@property
def model_key(self):
"""
Key to the self-contained model archive already uploaded to H2O.
Type: ``H2OFrame``.
"""
return self._parms.get("model_key")
@model_key.setter
def model_key(self, model_key):
assert_is_type(model_key, None, H2OFrame)
self._parms["model_key"] = model_key
def _requires_training_frame(self):
"""
Determines if Generic model requires a training frame.
:return: False.
"""
return False
[docs] @staticmethod
def from_file(file=str):
"""
Creates new Generic model by loading existing embedded model into library, e.g. from H2O MOJO.
The imported model must be supported by H2O.
:param file: A string containing path to the file to create the model from
:return: H2OGenericEstimator instance representing the generic model
"""
from h2o import lazy_import, get_frame
model_key = lazy_import(file)
model_bytes_frame = get_frame(model_key[0])
model = H2OGenericEstimator(model_key = model_bytes_frame)
model.train()
return model