"""
Multinomial Models
"""
from . import H2OFrame
from . import H2OConnection
from metrics_base import *
[docs]class H2OMultinomialModel(ModelBase):
def __init__(self, dest_key, model_json):
super(H2OMultinomialModel, self).__init__(dest_key, model_json,H2OMultinomialModelMetrics)
[docs] def confusion_matrix(self, data):
"""
Returns a confusion matrix based of H2O's default prediction threshold for a dataset
"""
if not isinstance(data, H2OFrame): raise ValueError("data argument must be of type H2OFrame, but got {0}"
.format(type(data)))
test_data_key = H2OFrame.send_frame(data)
# get the predictions
# this job call is blocking
j = H2OConnection.post_json("Predictions/models/" + self._key + "/frames/" + test_data_key)
# retrieve the confusion matrix
cm = j["model_metrics"][0]["cm"]["table"]
return cm