#!/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 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):
"""
Import MOJO Model
"""
algo = "generic"
supervised_learning = False
_options_ = {'model_extensions': ['h2o.model.extensions.Contributions'],
'requires_training_frame': False}
def __init__(self,
model_id=None, # type: Optional[Union[None, str, H2OEstimator]]
model_key=None, # type: Optional[Union[None, str, H2OFrame]]
path=None, # type: Optional[str]
):
"""
:param model_id: Destination id for this model; auto-generated if not specified.
Defaults to ``None``.
:type model_id: Union[None, str, H2OEstimator], optional
:param model_key: Key to the self-contained model archive already uploaded to H2O.
Defaults to ``None``.
:type model_key: Union[None, str, H2OFrame], optional
:param path: Path to file with self-contained model archive.
Defaults to ``None``.
:type path: str, optional
"""
super(H2OGenericEstimator, self).__init__()
self._parms = {}
self._id = self._parms['model_id'] = model_id
self.model_key = model_key
self.path = path
@property
def model_key(self):
"""
Key to the self-contained model archive already uploaded to H2O.
Type: ``Union[None, str, H2OFrame]``.
:examples:
>>> from h2o.estimators import H2OGenericEstimator, H2OXGBoostEstimator
>>> import tempfile
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> y = "IsDepDelayed"
>>> x = ["fYear","fMonth","Origin","Dest","Distance"]
>>> xgb = H2OXGBoostEstimator(ntrees=1, nfolds=3)
>>> xgb.train(x=x, y=y, training_frame=airlines)
>>> original_model_filename = tempfile.mkdtemp()
>>> original_model_filename = xgb.download_mojo(original_model_filename)
>>> key = h2o.lazy_import(original_model_filename)
>>> fr = h2o.get_frame(key[0])
>>> model = H2OGenericEstimator(model_key=fr)
>>> model.train()
>>> model.auc()
"""
return self._parms.get("model_key")
@model_key.setter
def model_key(self, model_key):
self._parms["model_key"] = H2OFrame._validate(model_key, 'model_key')
@property
def path(self):
"""
Path to file with self-contained model archive.
Type: ``str``.
:examples:
>>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator
>>> import tempfile
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> ifr = H2OIsolationForestEstimator(ntrees=1)
>>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines)
>>> generic_mojo_filename = tempfile.mkdtemp("zip","genericMojo")
>>> generic_mojo_filename = model.download_mojo(path=generic_mojo_filename)
>>> model = H2OGenericEstimator.from_file(generic_mojo_filename)
>>> model.model_performance()
"""
return self._parms.get("path")
@path.setter
def path(self, path):
assert_is_type(path, None, str)
self._parms["path"] = path
[docs] @staticmethod
def from_file(file=str, model_id=None):
"""
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
:param model_id: Model ID
:return: H2OGenericEstimator instance representing the generic model
:examples:
>>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator
>>> import tempfile
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> ifr = H2OIsolationForestEstimator(ntrees=1)
>>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines)
>>> original_model_filename = tempfile.mkdtemp()
>>> original_model_filename = ifr.download_mojo(original_model_filename)
>>> model = H2OGenericEstimator.from_file(original_model_filename)
>>> model.model_performance()
"""
model = H2OGenericEstimator(path=file, model_id=model_id)
model.train()
return model