# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pickle # nosec # pylint: disable=import_pickle
from .train import StatsModelsTrain
from .predict import StatsModelsPredict
try:
from statsmodels.base.distributed_estimation import DistributedModel
except ImportError:
DistributedModel = None
[docs]class MarsDistributedModel:
[docs] def __init__(
self,
factor=None,
num_partitions=None,
model_class=None,
init_kwds=None,
estimation_method=None,
estimation_kwds=None,
join_method=None,
join_kwds=None,
results_class=None,
results_kwds=None,
):
self._factor = factor
self._sm_model = DistributedModel(
num_partitions or 10,
model_class=model_class,
init_kwds=init_kwds,
estimation_method=estimation_method,
estimation_kwds=estimation_kwds,
join_method=join_method,
join_kwds=join_kwds,
results_class=results_class,
results_kwds=results_kwds,
)
def fit(self, endog, exog, session=None, **kwargs):
num_partitions = None if self._factor is not None else self._sm_model.partitions
run_kwargs = kwargs.pop("run_kwargs", dict())
op = StatsModelsTrain(
endog=endog,
exog=exog,
num_partitions=num_partitions,
factor=self._factor,
model_class=self._sm_model.model_class,
init_kwds=self._sm_model.init_kwds,
fit_kwds=kwargs,
estimation_method=self._sm_model.estimation_method,
estimation_kwds=self._sm_model.estimation_kwds,
join_method=self._sm_model.join_method,
join_kwds=self._sm_model.join_kwds,
results_class=self._sm_model.results_class,
results_kwds=self._sm_model.results_kwds,
)
result = (
op(exog, endog)
.execute(session=session, **run_kwargs)
.fetch(session=session)
)
return MarsResults(pickle.loads(result)) # nosec
[docs]class MarsResults:
[docs] def __init__(self, model):
self._model = model
@property
def model(self):
return self._model
def __getattr__(self, item):
if item == "_model":
raise AttributeError
return getattr(self._model, item)
def __mars_tokenize__(self):
return pickle.dumps(self.model)
def predict(self, exog, *args, **kwargs):
session = kwargs.pop("session", None)
run_kwargs = kwargs.pop("run_kwargs", dict())
op = StatsModelsPredict(
model_results=self, predict_args=args, predict_kwargs=kwargs
)
return op(exog).execute(session=session, **run_kwargs)