Source code for h2o.model.multinomial

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
[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()