Error message brush up

Author: gustavocidornelas

unboxapi/__init__.py

@@ -356,8 +356,10 @@ def add_model(
             raise UnboxValidationError(
                 "`task_type` must be either TaskType.TabularClassification or TaskType.TextClassification. \n"
             )
-        if model_type not in []:
-            pass
+        if model_type not in [model_framework for model_framework in ModelType]:
+            raise UnboxValidationError(
+                "`model_type` must be one of the supported ModelTypes. Check out our API reference for a full list https://reference.unbox.ai/reference/api/unboxapi.ModelType.html. \n"
+            )
         model_schema = ModelSchema()
         try:
             model_schema.load(
@@ -386,14 +388,14 @@ def add_model(
         ):
             raise UnboxResourceError(
                 f"The file path `{requirements_txt_file}` specified on `requirements_txt_file` does not"
-                " contain a file with the requirements ."
+                " contain a file with the requirements. \n"
             )
 
         # Setup script
         if setup_script and not os.path.isfile(os.path.expanduser(setup_script)):
             raise UnboxResourceError(
                 f"The file path `{setup_script}` specified on `setup_script` does not"
-                " contain a file with the bash script with commands required before model loading."
+                " contain a file with the bash script with commands required before model loading. \n"
             )
 
         # Dependent dir
@@ -467,7 +469,7 @@ def add_model(
                     mitigation="Make sure to specify the additional kwargs needed for the model type.",
                 )
 
-        # ----------------- Resource-schema consistency validations ---------------- #
+        # ------------------ Resource-schema consistency validations ----------------- #
         # Feature validations
         if task_type in [TaskType.TabularClassification, TaskType.TabularRegression]:
             try:
@@ -483,7 +485,7 @@ def add_model(
                     if feature not in headers
                 ]
                 raise UnboxDatasetInconsistencyError(
-                    f"The features {features_not_in_dataset} specified as `feature_names` are not on the dataset. \n"
+                    f"The features {features_not_in_dataset} specified in `feature_names` are not on the dataset. \n"
                 )
 
             required_fields = [
@@ -497,7 +499,6 @@ def add_model(
                         message=f"TabularClassification task with `{field}` missing. \n",
                         mitigation=f"Make sure to specify `{field}` for tabular classification tasks.",
                     )
-        # --------------------- Subscription plan validations ---------------------- #
 
         with TempDirectory() as dir:
             bento_service = create_template_model(
@@ -775,13 +776,13 @@ def add_dataset(
                 + " preprocessing steps expected by your model.",
             )
 
-        # ----------------- Resource-schema consistency validations ---------------- #
+        # ------------------ Resource-schema consistency validations ----------------- #
         # Label column validations
         try:
             headers.index(label_column_name)
         except ValueError:
             raise UnboxDatasetInconsistencyError(
-                f"The column {label_column_name} specified as `label_column_name` is not on the dataset. \n"
+                f"The column `{label_column_name}` specified as `label_column_name` is not on the dataset. \n"
             )
 
         dataset_classes = list(df[label_column_name].unique())
@@ -801,14 +802,14 @@ def add_dataset(
         except ValueError:
             if text_column_name:
                 raise UnboxDatasetInconsistencyError(
-                    f"The column {text_column_name} specified as `text_column_name` is not on the dataset. \n"
+                    f"The column `{text_column_name}` specified as `text_column_name` is not on the dataset. \n"
                 )
             else:
                 features_not_in_dataset = [
                     feature for feature in feature_names if feature not in headers
                 ]
                 raise UnboxDatasetInconsistencyError(
-                    f"The features {features_not_in_dataset} specified as `feature_names` are not on the dataset. \n"
+                    f"The features {features_not_in_dataset} specified in `feature_names` are not on the dataset. \n"
                 )
 
         # Tag column validation
@@ -820,7 +821,7 @@ def add_dataset(
                 f"The column `{tag_column_name}` specified as `tag_column_name` is not on the dataset. \n"
             )
 
-        # --------------------- Subscription plan validations ---------------------- #
+        # ----------------------- Subscription plan validations ---------------------- #
         if row_count > self.subscription_plan["datasetSize"]:
             raise UnboxSubscriptionPlanException(
                 f"The dataset your are trying to upload contains {row_count} rows, which exceeds your plan's"

unboxapi/exceptions.py

@@ -56,7 +56,7 @@ def __init__(self, message, context=None, mitigation=None):
         if not context:
             context = "You have reached your subscription plan's limits. \n"
         if not mitigation:
-            mitigation = "To upgrade your plan, visit our website https://unbox.ai"
+            mitigation = "To upgrade your plan, visit https://unbox.ai"
         super().__init__(context + message + mitigation)
 
 

unboxapi/schemas.py

@@ -5,6 +5,7 @@
     ValidationError,
     validates_schema,
 )
+from .models import ModelType
 
 
 class ProjectSchema(Schema):
@@ -80,7 +81,7 @@ def validates_label_column_not_in_feature_names(self, data, **kwargs):
         """Validates whether the label column name is not on the feature names list"""
         if data["label_column_name"] in data["feature_names"]:
             raise ValidationError(
-                f"The `label_column_name` {data['label_column_name']} must not be in `feature_names`."
+                f"The `label_column_name` `{data['label_column_name']}` must not be in `feature_names`."
             )
 
     @validates_schema
@@ -91,10 +92,12 @@ def validates_task_type_and_data_column(self, data, **kwargs):
             and "feature_names" not in data
         ):
             raise ValidationError(
-                "Must specify `feature_names` for TabularClassification."
+                "Must specify `feature_names` for TabularClassification `task_type`. \n"
             )
         elif data["task_type"] == "text-classification" and "text_column" not in data:
-            raise ValidationError("Must specify `text_column` for TextClassification.")
+            raise ValidationError(
+                "Must specify `text_column` for TextClassification. `task_type` \n"
+            )
 
 
 class ModelSchema(Schema):
@@ -128,8 +131,8 @@ class ModelSchema(Schema):
             "invalid": "`model_type` is not valid. Make sure you are importing ModelType correctly."
         },
         validate=validate.OneOf(
-            ["SklearnModelArtifact", "hugging-face"],
-            error=f"The `model_type` must be one of either ModelType.x or ModelType.y.",
+            [model_framework.value for model_framework in ModelType],
+            error=f"The `model_type` must be one of the supported frameworks. Check out our API reference for a full list https://reference.unbox.ai/reference/api/unboxapi.ModelType.html.\n ",
         ),
     )
     class_names = fields.List(
@@ -159,25 +162,25 @@ class ModelSchema(Schema):
     @validates_schema
     def validate_custom_model_code(self, data, **kwargs):
         """Validates the model type when `custom_code` is specified"""
-        if data["model_type"] == "custom" and data["custom_model_code"] is None:
+        if data["model_type"] == "Custom" and data["custom_model_code"] is None:
             raise ValidationError(
-                "`model_type` must be ModelType.custom if specifying `custom_model_code`."
+                "Must specify `custom_model_code` when using ModelType.custom. \n"
             )
-        elif data["custom_model_code"] is not None and data["model_type"] != "custom":
+        elif data["custom_model_code"] is not None and data["model_type"] != "Custom":
             raise ValidationError(
-                "Must specify `custom_model_code` when using ModelType.custom"
+                "`model_type` must be ModelType.custom if specifying `custom_model_code`. \n"
             )
 
     @validates_schema
     def validate_custom_model_dependent_dir(self, data, **kwargs):
-        if data["model_type"] == "custom" and data["dependent_dir"] is None:
+        if data["model_type"] == "Custom" and data["dependent_dir"] is None:
             raise ValidationError(
-                "Must specify `dependent_dir` when using ModelType.custom"
+                "Must specify `dependent_dir` when using ModelType.custom. \n"
             )
 
     @validates_schema
     def validate_custom_model_requirements(self, data, **kwargs):
-        if data["model_type"] == "custom" and data["requirements_txt_file"] is None:
+        if data["model_type"] == "Custom" and data["requirements_txt_file"] is None:
             raise ValidationError(
-                "Must specify `requirements_txt_file` when using ModelType.custom"
+                "Must specify `requirements_txt_file` when using ModelType.custom. \n"
             )