Fix conda dependencies and serialization issues

Signed-off-by: Eduardo de Leon <eddeleon@microsoft.com>

python/interpret-core/interpret/glassbox/mlflow/__init__.py

@@ -12,11 +12,25 @@ def load_model(*args, **kwargs):
     return mlflow.pyfunc.load_model(*args, **kwargs)
 
 
+def _sanitize_explanation_data(data):  # TODO Explanations should have a to_json()
+    if isinstance(data, dict):
+        for key, val in data.items():
+            data[key] = _sanitize_explanation_data(data[key])
+            return data
+
+    elif isinstance(data, list):
+        return [_sanitize_explanation_data[x] for x in data]
+    else:
+        # numpy type conversion to python https://stackoverflow.com/questions/9452775 primitive
+        return data.item() if hasattr(data, "item") else data
+
+
 def _load_pyfunc(path):
     import cloudpickle as pickle
     with open(os.path.join(path, "model.pkl"), "rb") as f:
         return pickle.load(f)
 
+
 def _save_model(model, output_path):
     import cloudpickle as pickle
     if not os.path.exists(output_path):
@@ -25,7 +39,15 @@ def _save_model(model, output_path):
         pickle.dump(model, stream)
     try:
         with open(os.path.join(output_path, "global_explanation.json"), "w") as stream:
-            json.dump(model.explain_global().data(-1)["mli"], stream)
+            data = model.explain_global().data(-1)["mli"]
+            if isinstance(data, list):
+                data = data[0]
+                if "global" not in data["explanation_type"]:
+                    raise Exception("Invalid explanation, not global")
+            for key in data:
+                if isinstance(data[key], list):
+                    data[key] = [float(x) for x in data[key]]
+            json.dump(data, stream)
     except ValueError as e:
         raise Exception("Unsupported glassbox model type {}. Failed with error {}.".format(type(model), e))
 
@@ -34,14 +56,18 @@ def log_model(path, model):
         import mlflow.pyfunc
     except ImportError as e:
         raise Exception("Could not log_model to mlflow. Missing mlflow dependency, pip install mlflow to resolve the error: {}.".format(e))
+    import cloudpickle as pickle
 
     with TemporaryDirectory() as tempdir:
         _save_model(model, tempdir)
 
         conda_env = {"name": "mlflow-env",
                      "channels": ["defaults"],
-                     "dependencies": ["interpret=".format(interpret.version.__version__),
-                                      "cloudpickle==0.5.8"
+                     "dependencies": ["pip",
+                                      {"pip": [
+                                        "interpret=={}".format(interpret.version.__version__),
+                                        "cloudpickle=={}".format(pickle.__version__)]
+                                      }
                                      ]
                     }
         conda_path = os.path.join(tempdir, "conda.yaml")  # TODO Open issue and bug fix for dict support