PR changes

Author: pmmayne

dataikuapi/dss/modelcomparison.py

@@ -1,4 +1,5 @@
 from dataikuapi.dss.discussion import DSSObjectDiscussions
+import re
 
 
 class DSSModelComparison(object):
@@ -36,6 +37,36 @@ def get_object_discussions(self):
         """
         return DSSObjectDiscussions(self.client, self.project_key, "MODEL_COMPARISON", self.mec_id)
 
+    def get_evaluation_like_from_full_id(self, full_id):
+        """
+        Retrieves a Saved Model from the flow, a Lab Model from an Analysis or a Model Evaluation from a Model Evaluation Store) using its full id.
+
+        :param string full_id: the full id of the item to retrieve
+
+        :returns: A handle on the Saved Model, the Model Evaluation or the Lab Model
+        :rtype: :class:`dataikuapi.dss.savedmodel.DSSSavedModel`
+        :rtype: :class:`dataikuapi.dss.modelevaluationstore.DSSModelEvaluation`
+        :rtype: :class:`dataikuapi.dss.ml.DSSTrainedPredictionModelDetails`
+        """
+
+        saved_model_pattern = re.compile("^S-(\\w+)-(\\w+)-(\\w+)(?:-part-(\\w+)-(v?\\d+))?$\\Z")
+        analysis_model_pattern = re.compile("^A-(\\w+)-(\\w+)-(\\w+)-(s[0-9]+)-(pp[0-9]+(?:-part-(\\w+)|-base)?)-(m[0-9]+)$\\Z")
+        model_evaluation_pattern = re.compile("^ME-(\\w+)-(\\w+)-(\\w+)$\\Z")
+
+        if saved_model_pattern.match(full_id):
+            return self.project.get_saved_model(full_id)
+        elif model_evaluation_pattern.match(full_id):
+            mes_id = full_id.split('-')[2]
+            evaluation_id = full_id.split('-')[3]
+            mes = self.project.get_model_evaluation_store(mes_id)
+            return mes.get_model_evaluation(evaluation_id)
+        elif analysis_model_pattern.match(full_id):
+            analysis_id = full_id.split('-')[2]
+            task_id = full_id.split('-')[3]
+            return self.project.get_ml_task(analysis_id, task_id).get_trained_model_details(full_id)
+
+        raise ValueError("{} is not a valid full model id or full model evaluation id.".format(full_id))
+
     ########################################################
     # Deletion
     ########################################################

dataikuapi/dss/project.py

@@ -1,5 +1,4 @@
 import time, warnings, sys, os.path as osp
-import re
 from .dataset import DSSDataset, DSSDatasetListItem, DSSManagedDatasetCreationHelper
 from .modelcomparison import DSSModelComparison
 from .jupyternotebook import DSSJupyterNotebook, DSSJupyterNotebookListItem
@@ -866,36 +865,6 @@ def create_model_comparison(self, name, prediction_type):
         mec_id = res['id']
         return DSSModelComparison(self.client, self.project_key, mec_id)
 
-    def get_from_full_id(self, full_id):
-        """
-        Retrieves a Saved Model from the flow, a Lab Model from an Analysis or a Model Evaluation from a Model Evaluation Store) using its full id.
-
-        :param string full_id: the full id of the item to retrieve
-
-        :returns: A handle on the Saved Model, the Model Evaluation or the Lab Model
-        :rtype: :class:`dataikuapi.dss.savedmodel.DSSSavedModel`
-        :rtype: :class:`dataikuapi.dss.modelevaluationstore.DSSModelEvaluation`
-        :rtype: :class:`dataikuapi.dss.ml.DSSTrainedPredictionModelDetails`
-        """
-
-        saved_model_pattern = re.compile("^S-(\\w+)-(\\w+)-(\\w+)(?:-part-(\\w+)-(v?\\d+))?$\\Z")
-        analysis_model_pattern = re.compile("^A-(\\w+)-(\\w+)-(\\w+)-(s[0-9]+)-(pp[0-9]+(?:-part-(\\w+)|-base)?)-(m[0-9]+)$\\Z")
-        model_evaluation_pattern = re.compile("^ME-(\\w+)-(\\w+)-(\\w+)$\\Z")
-
-        if saved_model_pattern.match(full_id):
-            return self.get_saved_model(full_id)
-        elif model_evaluation_pattern.match(full_id):
-            mes_id = full_id.split('-')[2]
-            evaluation_id = full_id.split('-')[3]
-            mes = self.get_model_evaluation_store(mes_id)
-            return mes.get_model_evaluation(evaluation_id)
-        elif analysis_model_pattern.match(full_id):
-            analysis_id = full_id.split('-')[2]
-            task_id = full_id.split('-')[3]
-            return self.get_ml_task(analysis_id, task_id).get_trained_model_details(full_id)
-
-        raise ValueError("{} is not a valid full model id or full model evaluation id.".format(full_id))
-
     ########################################################
     # Jobs
     ########################################################

dataikuapi/dss/recipe.py

@@ -1331,7 +1331,7 @@ class EvaluationRecipeCreator(DSSRecipeCreator):
         # Create a new evaluation recipe outputing to a new dataset, to a metrics dataset and/or to a model evaluation store
 
         project = client.get_project("MYPROJECT")
-        builder = EvaluationRecipeCreator("my_evaluation_recipe", project)
+        builder = project.new_recipe("evaluation")
         builder.with_input_model(saved_model_id)
         builder.with_input("dataset_to_evaluate")
 
@@ -1344,21 +1344,20 @@ class EvaluationRecipeCreator(DSSRecipeCreator):
         # Access the settings
 
         er_settings = new_recipe.get_settings()
-        json_payload = er_settings.get_json_payload()
+        payload = er_settings.obj_payload
 
         # Change the settings
 
-        json_payload['dontComputePerformance'] = True
-        json_payload['outputProbabilities'] = False
-        json_payload['metrics'] = ["precision", "recall", "auc", "f1", "costMatrixGain"]
+        payload['dontComputePerformance'] = True
+        payload['outputProbabilities'] = False
+        payload['metrics'] = ["precision", "recall", "auc", "f1", "costMatrixGain"]
 
         # Manage evaluation labels
 
-        json_payload['labels'] = [dict(key="label_1", value="value_1"), dict(key="label_2", value="value_2")]
+        payload['labels'] = [dict(key="label_1", value="value_1"), dict(key="label_2", value="value_2")]
 
         # Save the settings and run the recipe
 
-        er_settings.set_json_payload(json_payload)
         er_settings.save()
 
         new_recipe.run()
@@ -1408,7 +1407,7 @@ class StandaloneEvaluationRecipeCreator(DSSRecipeCreator):
         # Create a new standalone evaluation of a scored dataset
 
         project = client.get_project("MYPROJECT")
-        builder = StandaloneEvaluationRecipeCreator("my_standalone_evaluation_recipe", project)
+        builder = project.new_recipe("standalone_evaluation")
         builder.with_input("scored_dataset_to_evaluate")
         builder.with_output_evaluation_store(evaluation_store_id)
 
@@ -1417,20 +1416,20 @@ class StandaloneEvaluationRecipeCreator(DSSRecipeCreator):
         # Modify the model parameters in the SER settings
 
         ser_settings = new_recipe.get_settings()
-        ser_json_payload = ser_settings.get_json_payload()
+        payload = ser_settings.obj_payload
 
-        ser_json_payload['predictionType'] = "BINARY_CLASSIFICATION"
-        ser_json_payload['targetVariable'] = "Survived"
-        ser_json_payload['predictionVariable'] = "prediction"
-        ser_json_payload['isProbaAware'] = True
-        ser_json_payload['dontComputePerformance'] = False
+        payload['predictionType'] = "BINARY_CLASSIFICATION"
+        payload['targetVariable'] = "Survived"
+        payload['predictionVariable'] = "prediction"
+        payload['isProbaAware'] = True
+        payload['dontComputePerformance'] = False
 
         # For a classification model with probabilities, the 'probas' section can be filled with the mapping of the class and the probability column
         # e.g. for a binary classification model with 2 columns: proba_0 and proba_1
 
         class_0 = dict(key=0, value="proba_0")
         class_1 = dict(key=1, value="proba_1")
-        ser_payload['probas'] = [class_0, class_1]
+        payload['probas'] = [class_0, class_1]
 
         # Change the 'features' settings for this standalone evaluation
         # e.g. reject the features that you do not want to use in the evaluation
@@ -1439,18 +1438,15 @@ class StandaloneEvaluationRecipeCreator(DSSRecipeCreator):
         feature_ticket = dict(name="Ticket", role="REJECT", type="TEXT")
         feature_cabin = dict(name="Cabin", role="REJECT", type="TEXT")
 
-        ser_payload['features'] = [feature_passengerid, feature_ticket, feature_cabin]
+        payload['features'] = [feature_passengerid, feature_ticket, feature_cabin]
 
         # To set the cost matrix properly, access the 'metricParams' section of the payload and set the cost matrix weights:
 
-        ser_payload['metricParams'] = dict(costMatrixWeights=dict(tpGain=0.4, fpGain=-1.0, tnGain=0.2, fnGain=-0.5))
+        payload['metricParams'] = dict(costMatrixWeights=dict(tpGain=0.4, fpGain=-1.0, tnGain=0.2, fnGain=-0.5))
 
-        # Add the modified json payload to the recipe settings and save the recipe
+        # Save the recipe and run the recipe
         # Note that with this method, all the settings that were not explicitly set are instead set to their default value.
 
-        ser_settings = new_recipe.get_settings()
-
-        ser_settings.set_json_payload(ser_payload)
         ser_settings.save()
 
         new_recipe.run()