Feature/dss10 api evaluate recipe creation (#162)

* Add egg-info to gitignore

* Add EvaluationRecipeCreator

* Fix typos and pep8

* Improve documentation of EvaluationRecipeCreator

* Fix documentation typo.

* Pep8 and removed redefinition of unused class

* Correct typo

* Revert "Pep8 and removed redefinition of unused class"

This reverts commit ee76fc3.

* Update naming

Co-authored-by: Adrien Lavoillotte <adrien.lavoillotte@dataiku.com>

Co-authored-by: Adrien Lavoillotte <adrien.lavoillotte@dataiku.com>

Author: Valentin Thorey

.gitignore

@@ -1,3 +1,4 @@
 *.pyc
 .idea
 *.iml
+egg-info

dataikuapi/dss/project.py

@@ -1333,6 +1333,8 @@ def new_recipe(self, type, name=None):
             return recipe.PrepareRecipeCreator(name, self)
         elif type == "prediction_scoring":
             return recipe.PredictionScoringRecipeCreator(name, self)
+        elif type == "evaluation":
+            return recipe.EvaluationRecipeCreator(name, self)
         elif type == "clustering_scoring":
             return recipe.ClusteringScoringRecipeCreator(name, self)
         elif type == "download":

dataikuapi/dss/recipe.py

@@ -89,6 +89,7 @@ def run(self, job_type="NON_RECURSIVE_FORCED_BUILD", partitions=None, wait=True,
             "COMPUTABLE_FOLDER": "MANAGED_FOLDER",
             "COMPUTABLE_SAVED_MODEL": "SAVED_MODEL",
             "COMPUTABLE_STREAMING_ENDPOINT": "STREAMING_ENDPOINT",
+            "COMPUTABLE_MODEL_EVALUATION_STORE": "MODEL_EVALUATION_STORE"
         }
         if first_output["type"] in object_type_map:
             jd = project.new_job(job_type)
@@ -1320,6 +1321,61 @@ def with_input_model(self, model_id):
         return self._with_input(model_id, self.project.project_key, "model")
 
 
+class EvaluationRecipeCreator(DSSRecipeCreator):
+    """
+    Builder for the creation of a new "Evaluate" recipe, from an
+    input dataset, with an input saved model identifier
+
+    .. code-block:: python
+
+        # Create a new prediction scoring recipe outputing to a new dataset
+
+        project = client.get_project("MYPROJECT")
+        builder = EvaluationRecipeCreator("my_scoring_recipe", project)
+        builder.with_input_model(saved_model_id)
+        builder.with_input("dataset_to_evaluate")
+
+        builder.with_output("output_scored")
+        builder.with_output_metrics("output_metrics")
+        builder.with_output_evaluation_store(evaluation_store_id)
+
+        new_recipe = builder.build()
+    
+    Outputs must exist. They can be created using the following:
+
+    .. code-block:: python
+
+        builder = project.new_managed_dataset("output_scored")
+        builder.with_store_into(connection)
+        dataset = builder.create()
+
+        builder = project.new_managed_dataset("output_scored")
+        builder.with_store_into(connection)
+        dataset = builder.create()
+
+        evaluation_store_id = project.create_model_evaluation_store("output_model_evaluation").id
+    """
+
+    def __init__(self, name, project):
+        DSSRecipeCreator.__init__(self, 'evaluation', name, project)
+
+    def with_input_model(self, model_id):
+        """Sets the input model"""
+        return self._with_input(model_id, self.project.project_key, "model")
+
+    def with_output(self, name):
+        """Sets the ouput dataset containing the scored input"""
+        return self._with_output(name, role="main")
+
+    def with_output_metrics(self, name):
+        """Sets the output dataset containing the metrics"""
+        return self._with_output(name, role="metrics")
+
+    def with_output_evaluation_store(self, name):
+        """Sets the output model evaluation store"""
+        return self._with_output(name, role="evaluationStore")
+
+
 class ClusteringScoringRecipeCreator(SingleOutputRecipeCreator):
     """
     Builder for the creation of a new "Clustering scoring" recipe, from an