working example with half-implemented evaluation

syntaxgym/prediction.py

@@ -4,7 +4,14 @@ from typing import Union, Optional as TOptional, List as TList
 from pyparsing import *
 import numpy as np
 
-from syntaxgym.utils import METRICS
+METRICS = {
+    'sum': sum,
+    'mean': np.mean,
+    'median': np.median,
+    'range': np.ptp,
+    'max': max,
+    'min': min
+}
 
 
 # Enable parser packrat (caching)

syntaxgym/syntaxgym.py

@@ -5,12 +5,16 @@ SyntaxGym dataset as used in Hu et al. (2020).
 """
 
 
+from copy import deepcopy
 import json
 from pathlib import Path
+import re
 from typing import List
 
 import datasets
 
+from syntaxgym.prediction import Prediction
+
 
 _CITATION = """
 @inproceedings{Hu:et-al:2020,
@@ -28,11 +32,11 @@ _PROJECT_URL = "https://syntaxgym.org"
 _DOWNLOAD_URL = "https://github.com/cpllab/syntactic-generalization"
 
 
-print("_____HERE")
 SUITE_JSONS = []
 for suite_f in Path("test_suites").glob("*.json"):
     with suite_f.open() as f:
         SUITE_JSONS.append(json.load(f))
+SUITE_JSONS = {suite["meta"]["name"]: suite for suite in SUITE_JSONS}
 
 
 class SyntaxGymSuiteConfig(datasets.BuilderConfig):
@@ -51,11 +55,12 @@ class SyntaxGymSuiteConfig(datasets.BuilderConfig):
 class SyntaxGym(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [SyntaxGymSuiteConfig(suite_json)
-                       for suite_json in SUITE_JSONS]
+                       for suite_json in SUITE_JSONS.values()]
 
     def _info(self):
         condition_spec = {
             "condition_name": datasets.Value("string"),
+            "content": datasets.Value("string"),
             "regions": datasets.Sequence({
                 "region_number": datasets.Value("int32"),
                 "content": datasets.Value("string")
@@ -85,10 +90,50 @@ class SyntaxGym(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, name):
         # DEV: NB suite jsons already loaded because BUILDER_CONFIGS is static
-        suite_jsons = SUITE_JSONS
-
-        suite_json = next(suite for suite in SUITE_JSONS
-                          if suite["meta"]["name"] == name)
+        suite_json = SUITE_JSONS[name]
 
         for item in suite_json["items"]:
+            # Convert to sentence input.
+            for cond in item["conditions"]:
+                cond["content"] = " ".join([region["content"].lstrip()
+                                            for region in cond["regions"]
+                                            if region["content"].strip() != ""])
+                cond["content"] = re.sub(r"\s+,", ",", cond["content"])
+
             yield item["item_number"], item
+
+
+class SyntaxGymMetric(datasets.Metric):
+    """
+    SyntaxGym prediction evaluation metric.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.suite = SUITE_JSONS[self.config_name]
+        self.predictions = [
+            Prediction(idx, p["formula"], "sum")
+            for idx, p in enumerate(self.suite["predictions"])
+        ]
+
+    def _info(self):
+        features = datasets.Features({
+            "conditions": datasets.Sequence({
+                "condition_name": datasets.Value("string"),
+                "regions": datasets.Sequence({
+                    "region_number": datasets.Value("int32"),
+                    "metric_value": {
+                        "sum": datasets.Value("float32")
+                    },
+                }),
+            })
+        })
+        return datasets.MetricInfo(
+            description="TODO",
+            citation=_CITATION,
+            inputs_description="TODO",
+            features=features,
+        )
+
+    def _compute(self, region_surprisals):
+        suite_with_results = deepcopy(self.suite)

test.py

@@ -1,4 +1,16 @@
 import datasets
+import transformers
 
 
 dataset = datasets.load_dataset("syntaxgym", "mvrr_mod")
+metric = datasets.load_metric("syntaxgym", "mvrr_mod")
+
+model = transformers.AutoModelForCausalLM.from_pretrained("gpt2")
+
+
+for item in dataset["test"]:
+    # TODO evaluate surprisals
+
+    print(item)
+
+    break