# -*- encoding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
from h2o.utils.compatibility import * # NOQA
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.
:param H2OFrame test_data: The dataset upon which the reconstruction error is computed.
:param bool per_feature: Whether to return the square reconstruction error per feature.
Otherwise, return the mean square error.
:returns: the reconstruction error.
:examples:
>>> from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> predictors = list(range(0,784))
>>> resp = 784
>>> train = train[predictors]
>>> test = test[predictors]
>>> ae_model = H2OAutoEncoderEstimator(activation="Tanh",
... hidden=[2],
... l1=1e-5,
... ignore_const_cols=False,
... epochs=1)
>>> ae_model.train(x=predictors,training_frame=train)
>>> test_rec_error = ae_model.anomaly(test)
>>> test_rec_error
>>> test_rec_error_features = ae_model.anomaly(test, per_feature=True)
>>> test_rec_error_features
"""
if test_data is None or test_data.nrow == 0: raise ValueError("Must specify test data")
j = h2o.api("POST /3/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id),
data={"reconstruction_error": True, "reconstruction_error_per_feature": per_feature})
return h2o.get_frame(j["model_metrics"][0]["predictions"]["frame_id"]["name"])