Add ACRE(Abstract Causal REasoning Beyond Covariation) python generators (#199)

* Add acre python generators
* acre: improved prompt & formatting of examples, support arbitrary sizes

---------

Co-authored-by: Andreas Koepf <andreas.koepf@provisio.com>

reasoning_gym/induction/acre/acre.py

@@ -0,0 +1,93 @@
+"""ACRE(Abstract Causal REasoning Beyond Covariation) dataset"""
+
+# Culled and Adapted from https://github.com/WellyZhang/ACRE
+
+from dataclasses import dataclass
+from random import Random
+from typing import Optional
+
+from reasoning_gym.factory import ProceduralDataset, register_dataset
+
+from .blicket import config_control, dist_control, final_parse, serialize
+from .const import ALL_CONFIG_SIZE, ATTR_CONFIG_SIZE
+
+
+# Create blicket questions
+@dataclass
+class ACREDatasetConfig:
+    """Configuration for ACRE dataset generation"""
+
+    train: int = 1  # The default is 1 for training, otherwise 0 for validation and testing
+    size: int = 500  #  Split ratio = 6 : 2 : 2 -> IID : Comp : Sys
+    seed: Optional[int] = None
+
+    def validate(self) -> None:
+        """Validate configuration parameters"""
+        assert self.train in (0, 1), "train must be either 0 or 1"
+        assert self.size > 0, "Dataset size must be positive."
+
+
+class ACREDataset(ProceduralDataset):
+
+    def __init__(self, config: ACREDatasetConfig):
+        super().__init__(config, config.seed, config.size)
+        self.questions = self._generate_questions()
+        self.prompt_template = """You are a researcher studying causal relationships using Blicket experiments. In these experiments, certain objects (called 'blickets') have the hidden property of activating a detector, causing its light to turn on.
+
+Each example shows the results of placing different combinations of objects on the detector. Each object is described by color, material and shape. Your task is to determine whether a new combination of objects will cause the detector to activate.
+
+After observing the previous examples, respond with:
+- "on" if you can determine the detector light will turn on
+- "off" if you can determine the detector light will stay off
+- "undetermined" if there is insufficient evidence to reach a conclusion
+
+Do not use quotation marks in your answer.
+
+Previous experimental results:
+{examples}
+
+New test case:
+{input}
+
+What is the detector light status?"""
+
+    def _generate_questions(self):
+        """
+        Generates questions of particular size
+        """
+
+        iid_size = int(0.6 * self.config.size)
+        comp_size = int(0.2 * self.config.size)
+        sys_size = self.config.size - (iid_size + comp_size)
+        rng = Random(self.seed)
+        iid_questions = config_control(iid_size, self.config.train, ALL_CONFIG_SIZE, "IID", rng)
+        comp_questions = config_control(comp_size, self.config.train, ATTR_CONFIG_SIZE, "Comp", rng)
+        sys_questions = dist_control(sys_size, self.config.train, "Sys", rng)
+
+        questions = []
+        questions.extend(iid_questions)
+        questions.extend(comp_questions)
+        questions.extend(sys_questions)
+        rng.shuffle(questions)
+        final_questions = final_parse(serialized_questions=serialize(questions))
+        return final_questions
+
+    def __getitem__(self, idx: int) -> dict:
+        """Generate a single induction-based list function dataset"""
+        input = self.questions[idx]
+        examples = input["examples"]
+        formatted_examples = ""
+        for object in examples:
+            input_ = ", ".join(" ".join(x) for x in object["input"])
+            output = object["output"]
+            if len(formatted_examples) > 0:
+                formatted_examples += "\n"
+            formatted_examples += f"{input_} → {output}"
+
+        prompt_input = ", ".join(" ".join(x) for x in input["question"]["input"])
+        answer = input["question"]["output"]
+        question = self.prompt_template.format(examples=formatted_examples, input=prompt_input)
+        return {"question": question, "answer": answer, "metadata": {}}
+
+
+register_dataset("acre", ACREDataset, ACREDatasetConfig)