add pred type and wait for guess params in method analysis.create_prediction_mltask

Author: Louis Pouillot

dataikuapi/dss/analysis.py

@@ -152,24 +152,24 @@ def set_definition(self, definition):
     # ML
     ########################################################
 
-    def create_prediction_ml_task(self, target_variable,
-                                   ml_backend_type = "PY_MEMORY",
-                                   guess_policy = "DEFAULT"):
-
-
+    def create_prediction_ml_task(self,
+                                  target_variable,
+                                  ml_backend_type="PY_MEMORY",
+                                  guess_policy="DEFAULT",
+                                  prediction_type=None,
+                                  wait_guess_complete=True):
         """Creates a new prediction task in this visual analysis lab
         for a dataset.
 
-
-        The returned ML task will be in 'guessing' state, i.e. analyzing
-        the input dataset to determine feature handling and algorithms.
-
-        You should wait for the guessing to be completed by calling
-        ``wait_guess_complete`` on the returned object before doing anything
-        else (in particular calling ``train`` or ``get_settings``)
-
+        :param string target_variable: Variable to predict
         :param string ml_backend_type: ML backend to use, one of PY_MEMORY, MLLIB or H2O
         :param string guess_policy: Policy to use for setting the default parameters.  Valid values are: DEFAULT, SIMPLE_FORMULA, DECISION_TREE, EXPLANATORY and PERFORMANCE
+        :param string prediction_type: The type of prediction problem this is. If not provided the prediction type will be guessed. Valid values are: BINARY_CLASSIFICATION, REGRESSION, MULTICLASS
+        :param boolean wait_guess_complete: if False, the returned ML task will be in 'guessing' state, i.e. analyzing the input dataset to determine feature handling and algorithms.
+                                            You should wait for the guessing to be completed by calling
+                                            ``wait_guess_complete`` on the returned object before doing anything
+                                            else (in particular calling ``train`` or ``get_settings``)
+        :return :class dataiku.dss.ml.DSSMLTask
         """
 
         obj = {
@@ -178,9 +178,14 @@ def create_prediction_ml_task(self, target_variable,
             "backendType": ml_backend_type,
             "guessPolicy":  guess_policy
         }
-
+        if prediction_type is not None:
+            obj["predictionType"] = prediction_type
         ref = self.client._perform_json("POST", "/projects/%s/lab/%s/models/" % (self.project_key, self.analysis_id), body=obj)
-        return DSSMLTask(self.client, self.project_key, self.analysis_id, ref["mlTaskId"])
+        mltask = DSSMLTask(self.client, self.project_key, self.analysis_id, ref["mlTaskId"])
+
+        if wait_guess_complete:
+            mltask.wait_guess_complete()
+        return mltask
 
     def create_clustering_ml_task(self,
                                    ml_backend_type = "PY_MEMORY",

dataikuapi/dss/ml.py

@@ -923,7 +923,7 @@ def redeploy_to_flow(self, model_id, recipe_name=None, saved_model_id=None, acti
 
     def start_guess(self,
                     prediction_type=None,
-                    wait_guess_complete=True):
+                    wait_guess_complete=False):
         """
         Guess the feature handling and the algorithms.
         :param string prediction_type: In case of a prediction problem the prediction type can be specify. Valid values are BINARY_CLASSIFICATION, REGRESSION, MULTICLASS.