"""
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)))
j = H2OConnection.post_json("Predictions/models/" + self._id + "/frames/" + data._id)
return j["model_metrics"][0]["cm"]["table"]
[docs] def hit_ratio_table(self, train=False, valid=False):
"""
Retrieve the Hit Ratios
:param train: Return the hit ratios for training data.
:param valid: Return the hit ratios for the validation data.
:return: The hit ratio table (H2OTwoDimTable).
"""
tm = ModelBase._get_metrics(self, *ModelBase._train_or_valid(train, valid))
if tm is None: return None
return tm.hit_ratio_table()