Source code for h2o.model.autoencoder

# -*- 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"])