Source code for h2o.model.autoencoder

AutoEncoder Models

from model_base import *
from metrics_base import *

[docs]class H2OAutoEncoderModel(ModelBase): """ Class for AutoEncoder models. """ def __init__(self, dest_key, model_json): super(H2OAutoEncoderModel, self).__init__(dest_key, model_json,H2OAutoEncoderModelMetrics)
[docs] def anomaly(self,test_data): """ Obtain the reconstruction error for the input test_data. :param test_data: The dataset upon which the reconstruction error is computed. :return: Return the reconstruction error. """ if not test_data: raise ValueError("Must specify test data") # cbind the test_data vecs together and produce a temp key test_data_key = H2OFrame.send_frame(test_data) # get the anomaly j = H2OConnection.post_json("Predictions/models/" + self._key + "/frames/" + test_data_key, reconstruction_error=True) # extract the frame data anomaly_frame_key = j["model_metrics"][0]["predictions"]["frame_id"]["name"] anomaly_frame_meta = h2o.frame(anomaly_frame_key)["frames"][0] # create vecs by extracting vec_keys, col length, and col names vec_ids = anomaly_frame_meta["vec_ids"] rows = anomaly_frame_meta["rows"] cols = [col["label"] for col in anomaly_frame_meta["columns"]] vecs = H2OVec.new_vecs(zip(cols, vec_ids), rows) # remove test_data shallow key h2o.removeFrameShallow(test_data_key) # return new H2OFrame object return H2OFrame(vecs=vecs)