def load_model_callback(self, model_path, stream, version):
self._logger.info(self.get_info())
try:
if self._run_language == RunLanguage.PYTHON:
from datarobot_drum.drum.language_predictors.python_predictor.python_predictor import (
PythonPredictor,
)
self._predictor = PythonPredictor()
elif self._run_language == RunLanguage.JAVA:
from datarobot_drum.drum.language_predictors.java_predictor.java_predictor import (
JavaPredictor,
)
self._predictor = JavaPredictor()
elif self._run_language == RunLanguage.R:
from datarobot_drum.drum.language_predictors.r_predictor.r_predictor import (
RPredictor,
)
self._predictor = RPredictor()
self._predictor.configure(self._params)
except Exception as e:
self._error_response = {"message": "ERROR: {}".format(e)}
def configure(self, params):
super(GenericPredictorComponent, self).configure(params)
self._run_language = RunLanguage(params.get("run_language"))
self._target_type = TargetType(params[TARGET_TYPE_ARG_KEYWORD])
if self._run_language == RunLanguage.PYTHON:
from datarobot_drum.drum.language_predictors.python_predictor.python_predictor import (
PythonPredictor, )
self._predictor = PythonPredictor()
elif self._run_language == RunLanguage.JAVA:
from datarobot_drum.drum.language_predictors.java_predictor.java_predictor import (
JavaPredictor, )
self._predictor = JavaPredictor()
elif self._run_language == RunLanguage.R:
# this import is here, because RPredictor imports rpy library,
# which is not installed for Java and Python cases.
from datarobot_drum.drum.language_predictors.r_predictor.r_predictor import RPredictor
self._predictor = RPredictor()
else:
raise DrumCommonException(
"Prediction server doesn't support language: {} ".format(
self._run_language))
self._predictor.configure(params)
def configure(self, params):
super(PredictionServer, self).configure(params)
self._show_perf = self._params.get("show_perf")
self._run_language = RunLanguage(params.get("run_language"))
self._target_type = params[TARGET_TYPE_ARG_KEYWORD]
self._stats_collector = StatsCollector(
disable_instance=not self._show_perf)
self._stats_collector.register_report("run_predictor_total", "finish",
StatsOperation.SUB, "start")
self._memory_monitor = MemoryMonitor(monitor_current_process=True)
if self._run_language == RunLanguage.PYTHON:
from datarobot_drum.drum.language_predictors.python_predictor.python_predictor import (
PythonPredictor, )
self._predictor = PythonPredictor()
elif self._run_language == RunLanguage.JAVA:
from datarobot_drum.drum.language_predictors.java_predictor.java_predictor import (
JavaPredictor, )
self._predictor = JavaPredictor()
elif self._run_language == RunLanguage.R:
# this import is here, because RPredictor imports rpy library,
# which is not installed for Java and Python cases.
from datarobot_drum.drum.language_predictors.r_predictor.r_predictor import RPredictor
self._predictor = RPredictor()
else:
raise DrumCommonException(
"Prediction server doesn't support language: {} ".format(
self._run_language))
self._predictor.configure(params)
def test_add_to_one(self):
positive_label = "poslabel"
negative_label = "neglabel"
for predictor in [PythonPredictor(), RPredictor(), JavaPredictor()]:
predictor._target_type = TargetType.BINARY
df_good = pd.DataFrame(
{positive_label: [0.1, 0.2, 0.3], negative_label: [0.9, 0.8, 0.7]}
)
predictor.validate_predictions(df_good)
df_bad = pd.DataFrame({positive_label: [1, 1, 1], negative_label: [-1, 0, 0]})
with pytest.raises(ValueError):
predictor.validate_predictions(df_bad)
