Updates to score code and model card example

examples/pzmm_generate_complete_model_card.ipynb

@@ -568,7 +568,8 @@
     "    df.columns = df.columns.str.replace(' ', '')\n",
     "    df.columns = df.columns.str.replace('-', '_')\n",
     "    df = df.drop(['Sex_Male'], axis=1)\n",
-    "    df = pd.concat([df, cat_vals], axis=1).drop('index', axis=1)\n",
+    "    if 'index' in df.columns or 'index' in cat_vals.columns:\n",
+    "        df = pd.concat([df, cat_vals], axis=1).drop('index', axis=1)\n",
     "    # For the model to score correctly, all OHE columns must exist\n",
     "    input_cols = [\n",
     "        \"Education_9th\", \"Education_10th\", \"Education_11th\", \"Education_12th\", \"Education_Assoc_voc\", \"Education_Assoc_acdm\", \"Education_Masters\", \"Education_Prof_school\",\n",
@@ -579,9 +580,20 @@
     "        'Relationship_Not_in_family', 'Relationship_Own_child', 'Relationship_Unmarried', 'Relationship_Wife', 'Relationship_Other_relative', 'WorkClass_Private',\n",
     "        'Education_Bachelors'\n",
     "    ]\n",
+    "    # OHE columns must be removed after data combination\n",
+    "    predictor_columns = ['Age', 'HoursPerWeek', 'WorkClass_Private', 'WorkClass_Self', 'WorkClass_Gov', \n",
+    "       'WorkClass_Other', 'Education_HS_grad', 'Education_Some_HS', 'Education_Assoc', 'Education_Some_college',\n",
+    "       'Education_Bachelors', 'Education_Adv_Degree', 'Education_No_HS', 'MartialStatus_Married_civ_spouse',\n",
+    "       'MartialStatus_Never_married', 'MartialStatus_Divorced', 'MartialStatus_Separated', 'MartialStatus_Widowed',\n",
+    "       'MartialStatus_Other', 'Relationship_Husband', 'Relationship_Not_in_family', 'Relationship_Own_child', 'Relationship_Unmarried',\n",
+    "       'Relationship_Wife', 'Relationship_Other_relative', 'Race_White', 'Race_Black', 'Race_Asian_Pac_Islander',\n",
+    "       'Race_Amer_Indian_Eskimo', 'Race_Other', 'Sex_Female']\n",
+    "\n",
     "    for col in input_cols:\n",
     "        if col not in df.columns:\n",
     "            df[col] = 0\n",
+    "    \n",
+    "\n",
     "    df[\"Education_Some_HS\"] = df[\"Education_9th\"] | df[\"Education_10th\"] | df[\"Education_11th\"] | df[\"Education_12th\"]\n",
     "    df[\"Education_Assoc\"] = df[\"Education_Assoc_voc\"] | df[\"Education_Assoc_acdm\"]\n",
     "    df[\"Education_Adv_Degree\"] = df[\"Education_Masters\"] | df[\"Education_Prof_school\"] | df[\"Education_Doctorate\"]\n",
@@ -593,6 +605,8 @@
     "\n",
     "    df[\"MartialStatus_Other\"] = df[\"MartialStatus_Married_spouse_absent\"] | df[\"MartialStatus_Married_AF_spouse\"]\n",
     "\n",
+    "    df = df[predictor_columns]\n",
+    "\n",
     "    return df"
    ]
   },

src/sasctl/pzmm/write_score_code.py

@@ -497,7 +497,7 @@ def _write_imports(
 import codecs
 
 binary_string = "<binary string>"
-model = pickle.load(codecs.decode(binary_string.encode(), "base64"))
+model = pickle.loads(codecs.decode(binary_string.encode(), "base64"))
             """
 
     def _viya35_model_load(
@@ -562,6 +562,26 @@ def _viya35_model_load(
                 f'{model_id}/{model_file_name}")))'
             )
         else:
+            if pickle_type.lower() == "pickle":
+                self.score_code += (
+                    f'model_path = Path("/models/resources/viya/{model_id}'
+                    f'")\nwith open(model_path / "{model_file_name}", '
+                    f"\"rb\") as pickle_model:\n{'':4}model = pd.read_pickle"
+                    "(pickle_model)\n\n"
+                )
+                """
+model_path = Path("/models/resources/viya/<UUID>")
+with open(model_path / "model.pickle", "rb") as pickle_model:
+    model = pd.read_pickle(pickle_model)
+
+                """
+                return (
+                    f"{'':8}model_path = Path(\"/models/resources/viya/{model_id}"
+                    f"\")\n{'':8}with open(model_path / \"{model_file_name}\", "
+                    f"\"rb\") as pickle_model:\n{'':12}model = pd.read_pickle"
+                    "(pickle_model)"
+                )
+
             self.score_code += (
                 f'model_path = Path("/models/resources/viya/{model_id}'
                 f'")\nwith open(model_path / "{model_file_name}", '
@@ -658,6 +678,23 @@ def _viya4_model_load(
                 f"safe_mode=True)\n"
             )
         else:
+            if pickle_type.lower() == "pickle":
+                self.score_code += (
+                    f"with open(Path(settings.pickle_path) / "
+                    f'"{model_file_name}", "rb") as pickle_model:\n'
+                    f"{'':4}model = pd.read_pickle(pickle_model)\n\n"
+                )
+                """
+    with open(Path(settings.pickle_path) / "model.pickle", "rb") as pickle_model:
+        model = pd.read_pickle(pickle_model)
+
+                """
+                return (
+                    f"{'':8}with open(Path(settings.pickle_path) / "
+                    f'"{model_file_name}", "rb") as pickle_model:\n'
+                    f"{'':12}model = pd.read_pickle(pickle_model)\n\n"
+                )
+
             self.score_code += (
                 f"with open(Path(settings.pickle_path) / "
                 f'"{model_file_name}", "rb") as pickle_model:\n'

tests/unit/test_write_score_code.py

@@ -118,8 +118,13 @@ def test_viya35_model_load():
     """
     sc = ScoreCode()
     load_text = sc._viya35_model_load("1234", "normal")
-    assert "pickle.load(pickle_model)" in sc.score_code
-    assert "pickle.load(pickle_model)" in load_text
+    assert "pd.read_pickle(pickle_model)" in sc.score_code
+    assert "pd.read_pickle(pickle_model)" in load_text
+
+    sc = ScoreCode()
+    load_text = sc._viya35_model_load("1234", "normal", pickle_type="dill")
+    assert "dill.load(pickle_model)" in sc.score_code
+    assert "dill.load(pickle_model)" in load_text
 
     sc = ScoreCode()
     mojo_text = sc._viya35_model_load("2345", "mojo", mojo_model=True)
@@ -142,8 +147,13 @@ def test_viya4_model_load():
     """
     sc = ScoreCode()
     load_text = sc._viya4_model_load("normal")
-    assert "pickle.load(pickle_model)" in sc.score_code
-    assert "pickle.load(pickle_model)" in load_text
+    assert "pd.read_pickle(pickle_model)" in sc.score_code
+    assert "pd.read_pickle(pickle_model)" in load_text
+
+    sc = ScoreCode()
+    load_text = sc._viya35_model_load("1234", "normal", pickle_type="dill")
+    assert "dill.load(pickle_model)" in sc.score_code
+    assert "dill.load(pickle_model)" in load_text
 
     sc = ScoreCode()
     mojo_text = sc._viya4_model_load("mojo", mojo_model=True)