import h2o
from .model_base import ModelBase
[docs]class H2OAutoEncoderModel(ModelBase):
[docs] def anomaly(self,test_data,per_feature=False):
"""Obtain the reconstruction error for the input test_data.
Parameters
----------
test_data : H2OFrame
The dataset upon which the reconstruction error is computed.
per_feature : bool
Whether to return the square reconstruction error per feature. Otherwise, return
the mean square error.
Returns
-------
Return the reconstruction error.
"""
if test_data is None or test_data.nrow == 0: raise ValueError("Must specify test data")
j = h2o.H2OConnection.post_json("Predictions/models/" + self.model_id + "/frames/" + test_data.frame_id, reconstruction_error=True, reconstruction_error_per_feature=per_feature)
return h2o.get_frame(j["model_metrics"][0]["predictions"]["frame_id"]["name"])