def fit(self, fr, **fit_params):
res = []
for step in self.steps:
res.append(step[1].to_rest(step[0]))
res = "[" + ",".join([_quoted(r.replace('"', "'")) for r in res]) + "]"
j = H2OConnection.post_json(url_suffix="Assembly", steps=res, frame=fr.frame_id, _rest_version=99)
self.id = j["assembly"]["name"]
return get_frame(j["result"]["name"])
def fit(self, fr, **fit_params):
res = []
for step in self.steps:
res.append(step[1].to_rest(step[0]))
res = "[" + ",".join([_quoted(r.replace('"', "'")) for r in res]) + "]"
j = H2OConnection.post_json(url_suffix="Assembly",
steps=res,
frame=fr.frame_id,
_rest_version=99)
self.id = j["assembly"]["name"]
return get_frame(j["result"]["name"])
def model_performance(self, test_data=None):
"""
Compute the binary classifier model metrics on `test_data`
:param test_data: An H2OFrame
:return: A H2OBinomialMetrics object; prints model metrics summary
"""
if not test_data:
raise ValueError("Missing`test_data`.")
if not isinstance(test_data, H2OFrame):
raise ValueError("`test_data` must be of type H2OFrame. Got: " +
type(test_data))
fr_key = H2OFrame.send_frame(test_data)
url_suffix = "ModelMetrics/models/" + self._key + "/frames/" + fr_key
res = H2OConnection.post_json(url_suffix=url_suffix)
raw_metrics = res["model_metrics"][0]
return H2OBinomialModelMetrics(raw_metrics)
