Improve task api code quality

src/datasets/tasks/base.py

@@ -1,10 +1,14 @@
 import abc
+import dataclasses
 from dataclasses import dataclass
-from typing import ClassVar, Dict
+from typing import ClassVar, Dict, Type, TypeVar
 
 from ..features import Features
 
 
+T = TypeVar("T", bound="TaskTemplate")
+
+
 @dataclass(frozen=True)
 class TaskTemplate(abc.ABC):
     task: ClassVar[str]
@@ -18,9 +22,9 @@ def features(self) -> Features:
     @property
     @abc.abstractmethod
     def column_mapping(self) -> Dict[str, str]:
-        return NotImplemented
+        raise NotImplementedError
 
     @classmethod
-    @abc.abstractmethod
-    def from_dict(cls, template_dict: dict) -> "TaskTemplate":
-        return NotImplemented
+    def from_dict(cls: Type[T], template_dict: dict) -> T:
+        field_names = set(f.name for f in dataclasses.fields(cls))
+        return cls(**{k: v for k, v in template_dict.items() if k in field_names})

src/datasets/tasks/question_answering.py

@@ -1,15 +1,15 @@
 from dataclasses import dataclass
-from typing import Dict
+from typing import ClassVar, Dict
 
 from ..features import Features, Sequence, Value
 from .base import TaskTemplate
 
 
 @dataclass(frozen=True)
 class QuestionAnswering(TaskTemplate):
-    task = "question-answering"
-    input_schema = Features({"question": Value("string"), "context": Value("string")})
-    label_schema = Features(
+    task: ClassVar[str] = "question-answering"
+    input_schema: ClassVar[Features] = Features({"question": Value("string"), "context": Value("string")})
+    label_schema: ClassVar[Features] = Features(
         {
             "answers": Sequence(
                 {
@@ -23,19 +23,6 @@ class QuestionAnswering(TaskTemplate):
     context_column: str = "context"
     answers_column: str = "answers"
 
-    def __post_init__(self):
-        object.__setattr__(self, "question_column", self.question_column)
-        object.__setattr__(self, "context_column", self.context_column)
-        object.__setattr__(self, "answers_column", self.answers_column)
-
     @property
     def column_mapping(self) -> Dict[str, str]:
         return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
-
-    @classmethod
-    def from_dict(cls, template_dict: dict) -> "QuestionAnswering":
-        return cls(
-            question_column=template_dict["question_column"],
-            context_column=template_dict["context_column"],
-            answers_column=template_dict["answers_column"],
-        )

src/datasets/tasks/text_classification.py

@@ -1,30 +1,40 @@
 from dataclasses import dataclass
-from typing import Dict, List
+from typing import ClassVar, Dict, List
 
 from ..features import ClassLabel, Features, Value
 from .base import TaskTemplate
 
 
+class FeaturesWithLazyClassLabel:
+    def __init__(self, features, label_column="labels"):
+        assert label_column in features, f"Key '{label_column}' missing in features {features}"
+        self._features = features
+        self._label_column = label_column
+
+    def __get__(self, obj, objtype=None):
+        if obj is None:
+            return self._features
+
+        assert hasattr(obj, self._label_column), f"Object has no attribute '{self._label_column}'"
+        features = self._features.copy()
+        features["labels"] = ClassLabel(names=getattr(obj, self._label_column))
+        return features
+
+
 @dataclass(frozen=True)
 class TextClassification(TaskTemplate):
-    task = "text-classification"
-    input_schema = Features({"text": Value("string")})
-    # TODO(lewtun): Since we update this in __post_init__ do we need to set a default? We'll need it for __init__ so
-    # investigate if there's a more elegant approach.
-    label_schema = Features({"labels": ClassLabel})
+    task: ClassVar[str] = "text-classification"
+    input_schema: ClassVar[Features] = Features({"text": Value("string")})
+    # TODO(lewtun): Find a more elegant approach without descriptors.
+    label_schema: ClassVar[Features] = FeaturesWithLazyClassLabel(Features({"labels": ClassLabel}))
     labels: List[str]
     text_column: str = "text"
     label_column: str = "labels"
 
     def __post_init__(self):
-        assert sorted(set(self.labels)) == sorted(self.labels), "Labels must be unique"
+        assert len(self.labels) == len(set(self.labels)), "Labels must be unique"
         # Cast labels to tuple to allow hashing
-        object.__setattr__(self, "labels", tuple(sorted(self.labels)))
-        object.__setattr__(self, "text_column", self.text_column)
-        object.__setattr__(self, "label_column", self.label_column)
-        self.label_schema["labels"] = ClassLabel(names=self.labels)
-        object.__setattr__(self, "label2id", {label: idx for idx, label in enumerate(self.labels)})
-        object.__setattr__(self, "id2label", {idx: label for label, idx in self.label2id.items()})
+        self.__dict__["labels"] = tuple(sorted(self.labels))
 
     @property
     def column_mapping(self) -> Dict[str, str]:
@@ -33,10 +43,10 @@ def column_mapping(self) -> Dict[str, str]:
             self.label_column: "labels",
         }
 
-    @classmethod
-    def from_dict(cls, template_dict: dict) -> "TextClassification":
-        return cls(
-            text_column=template_dict["text_column"],
-            label_column=template_dict["label_column"],
-            labels=template_dict["labels"],
-        )
+    @property
+    def label2id(self):
+        return {label: idx for idx, label in enumerate(self.labels)}
+
+    @property
+    def id2label(self):
+        return {idx: label for idx, label in enumerate(self.labels)}