def update(self):
# Handle to storage for model parameters
params = self._parameters
print "Starting to update Decision tree regressor!"
# Make sure all my meta data is ready to go
params.validateMeta()
observation_vectors = []
truth_vectors = []
# Make sure my model data is ready to go
self._model_data.validate()
self._model_data.validateViews(self.getMetaData("db_views"))
# Check my model data
observation_vectors = self._model_data.getMetaData(
"observation_vectors")
truth_vectors = self._model_data.getMetaData("truth_vectors")
params.setMetaData("db_views", [])
# Houston we are go
dtr = DecisionTreeRegressor()
dtr.criterion = params.getMetaData("criterion")
dtr.splitter = params.getMetaData("splitter")
max_features = params.getMetaData("max_features")
if (max_features != None):
dtr.max_features = max_features
max_depth = params.getMetaData("max_depth")
if (max_depth != None):
dtr.max_depth = max_depth
dtr.min_samples_split = params.getMetaData("min_samples_split")
dtr.min_samples_leaf = params.getMetaData("min_samples_leaf")
max_leaf_nodes = params.getMetaData("max_leaf_nodes")
if (max_leaf_nodes != None):
dtr.max_leaf_nodes = max_leaf_nodes
dtr.random_state = params.getMetaData("random_state")
dtr.presort = params.getMetaData("presort")
# Evaluation mode loads several model artifacts from storage and sets them as inputs
dtr.fit(observation_vectors, truth_vectors)
params.setBinaryData("dtr_model", "application/pickle",
pickle.dumps(dtr))
self.finalize()
