making target optional for use of folktexts for encoding data only (n…

…o predictions)

folktexts/acs/acs_dataset.py

@@ -154,7 +154,7 @@ def _parse_task_data(cls, full_df: pd.DataFrame, task: ACSTaskMetadata) -> pd.Da
             parsed_df = full_df
 
         # Threshold the target column if necessary
-        if task.target_threshold is not None and task.get_target() not in parsed_df.columns:
+        if task.target is not None and task.target_threshold is not None and task.get_target() not in parsed_df.columns:
             parsed_df[task.get_target()] = task.target_threshold.apply_to_column_data(parsed_df[task.target])
 
         return parsed_df

folktexts/acs/acs_tasks.py

@@ -43,7 +43,7 @@ def make_task(
         cls,
         name: str,
         features: list[str],
-        target: str,
+        target: str = None,
         sensitive_attribute: str = None,
         target_threshold: Threshold = None,
         multiple_choice_qa: MultipleChoiceQA = None,
@@ -53,7 +53,11 @@ def make_task(
     ) -> ACSTaskMetadata:
         # Validate columns mappings exist
         if not all(col in acs_columns_map for col in (features + [target])):
-            raise ValueError("Not all columns have mappings to text descriptions.")
+            missing_cols = {col for col in (features + [target]) if col not in acs_columns_map}
+            raise ValueError(
+                f"Not all columns have mappings to textual descriptions. "
+                f"Missing columns: {missing_cols}."
+            )
 
         # Resolve target column name
         target_col_name = (

folktexts/dataset.py

@@ -61,11 +61,8 @@ def __init__(
                 f"Invalid `task` type: {type(self._task)}. "
                 f"Expected `TaskMetadata`.")
 
-        if not all(col in self.data.columns for col in (task.features + [task.get_target()])):
-            raise ValueError(
-                f"The following task columns were not found in the dataset: "
-                f"{list(set(task.features + [task.get_target()]) - set(self.data.columns))};"
-            )
+        # Validate data for this task
+        self._check_task_columns_are_in_df(task, data)
 
         self._test_size = test_size
         self._val_size = val_size or 0
@@ -86,17 +83,27 @@ def __init__(
         if subsampling is not None:
             self.subsample(subsampling)
 
+    @staticmethod
+    def _check_task_columns_are_in_df(task: TaskMetadata, df: pd.DataFrame, raise_: bool = True) -> bool:
+        available_cols = df.columns
+        required_cols = task.features + ([task.get_target()] if task.target else [])
+
+        if raise_ and not all(col in available_cols for col in required_cols):
+            raise ValueError(
+                f"The following required task columns were not found in the dataset: "
+                f"{list(set(required_cols) - set(available_cols))};"
+            )
+
+        return all(col in available_cols for col in required_cols)
+
     @property
     def data(self) -> pd.DataFrame:
         return self._data
 
     @data.setter
     def data(self, new_data) -> pd.DataFrame:
         # Check if task columns are in the data
-        if not all(col in new_data.columns for col in (self.task.features + [self.task.get_target()])):
-            raise ValueError(
-                f"Task columns not found in dataset: "
-                f"features={self.task.features}, target={self.task.get_target()}")
+        self._check_task_columns_are_in_df(self.task, new_data)
 
         # Update data
         self._data = new_data
@@ -120,10 +127,7 @@ def task(self) -> TaskMetadata:
     @task.setter
     def task(self, new_task: TaskMetadata):
         # Check if task columns are in the data
-        if not all(col in self.data.columns for col in (new_task.features + [new_task.get_target()])):
-            raise ValueError(
-                f"Task columns not found in dataset: "
-                f"features={new_task.features}, target={new_task.get_target()}")
+        self._check_task_columns_are_in_df(new_task, self.data)
 
         self._task = new_task
 

folktexts/task.py

@@ -60,8 +60,17 @@ def __post_init__(self):
         # Add this task to the class-level dictionary
         TaskMetadata._tasks[self.name] = self
 
+        # Check target is provided
+        if self.target is None:
+            logging.warning(
+                f"No target column provided for task '{self.name}'. "
+                f"Will not be able to generate predictions or use task Q&A prompts. "
+                f"Will still be able to generate row descriptions."
+            )
+            return
+
         # If no question is explicitly provided, use the question from the target column
-        if self.multiple_choice_qa is None and self.direct_numeric_qa is None:
+        if self.multiple_choice_qa is None and self.direct_numeric_qa is None and self.target is not None:
             logging.warning(
                 f"No question was explicitly provided for task '{self.name}'. "
                 f"Inferring from target column's default question ({self.get_target()}).")
@@ -85,6 +94,10 @@ def __hash__(self) -> int:
 
     def get_target(self) -> str:
         """Resolves the name of the target column depending on `self.target_threshold`."""
+        if self.target is None:
+            logging.critical(f"No target column provided for task {self.name}.")
+            return None
+
         if self.target_threshold is None:
             return self.target
         else:
@@ -160,7 +173,7 @@ def question(self) -> QAInterface:
             q = self.multiple_choice_qa
 
         if q is None:
-            raise ValueError(f"Invalid Q&A interface configured for task {self.name}.")
+            logging.critical(f"No Q&A interface provided for task {self.name}.")
         return q
 
     def get_row_description(self, row: pd.Series) -> str:

pyproject.toml

@@ -32,7 +32,7 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
 ]
 
-version = "0.0.23"
+version = "0.0.24"
 requires-python = ">=3.8"
 dynamic = [
     "readme",