add files

.gitattributes

@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

data/math23k.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03df80a02b5404e7231abccb6e5511ef6cc05f101b260408dbbac201799997ec
+size 11831918

math23k.py

@@ -1,8 +1,64 @@
+import re
 import string
+from pathlib import Path
+
+import pandas as pd
 
 import datasets
 from datasets import DatasetInfo, load_dataset
 
+ALPHABET = string.ascii_lowercase
+
+
+def temp_list(num_list):
+    return map(lambda x: "temp_" + x, ALPHABET[: len(num_list)])
+
+
+def extract_placeholders(text):
+    pattern = r"<<(.*?)>>"
+    matches = re.findall(pattern, text)
+    return matches
+
+
+def multiple_replace(text, replacement_dict):
+    for k, v in replacement_dict.items():
+        text = text.replace(k, v)
+    return text
+    # if replacement_dict:
+    #     pattern = "|".join(map(re.escape, replacement_dict.keys()))
+    #     return re.sub(pattern, lambda m: replacement_dict[m.group()], text)
+    # else:
+    #     return text
+
+
+def solution_human(solution, num_list):
+    eqs = extract_placeholders(solution)
+    num_list = {key: str(value) for key, value in zip(temp_list(num_list), num_list)}
+
+    modified = []
+    cached = {}
+    for eq in eqs:
+        eq = multiple_replace(eq, num_list)
+        eq = multiple_replace(eq, cached)
+        try:
+            res = eval(eq)
+            be_eval = True
+        except Exception:
+            res = eq
+            be_eval = False
+        cached[eq] = str(res)
+        num_ops = sum([1 for char in eq if char in "+-*/"])
+        if num_ops and be_eval:
+            modified.append(f"{eq}={cached[eq]}")
+        else:
+            modified.append(f"{eq}")
+
+    text = solution
+    for t, rt in zip(eqs, modified):
+        text = text.replace(t, rt, 1)
+
+    return text
+
 
 def get_expre(example):
     seq = example["target_template"]
@@ -27,12 +83,14 @@ def get_expre(example):
 def regular(example):
     if example["id"] in ["17520"]:
         return False
-    alphabet = string.ascii_lowercase
-    num_list = list(map(lambda x: "temp_" + x, alphabet[: len(example["num_list"])]))
+    num_list = list(temp_list(example["num_list"]))
     eqs = example["expression"].format(**dict(zip(num_list, example["num_list"])))
     return eval(eqs) == example["answer"]
 
 
+_DATA_FILES = ["data/math23k.csv"]
+
+
 class DatasetBuilder(datasets.DatasetBuilder):
     def _info(self):
         return DatasetInfo()
@@ -40,10 +98,32 @@ class DatasetBuilder(datasets.DatasetBuilder):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
-    def download_and_prepare(self, *args, **kwargs):
-        return self
+    # def download_and_prepare(self, *args, **kwargs):
+
+    #     return self
+
+    def _download_and_prepare(
+        self, dl_manager, verification_mode, **prepare_split_kwargs
+    ):
+        from datasets import SplitDict
+
+        downloaded_files = dl_manager.download(_DATA_FILES)
+        print(downloaded_files)
+        self.info.solution_files = downloaded_files
+        df=pd.read_csv(downloaded_files[0])
+        print(df)
+        split_dict = SplitDict(dataset_name=self.name)
+        self.info.splits = split_dict
+        self.info.download_size = dl_manager.downloaded_size
 
     def as_dataset(self, split, **kwargs):
+        df = pd.read_csv(self.info.solution_files[0])
         ds = load_dataset("Gxg/Math23K", self.config.name, split=split)
         ds = ds.map(get_expre).filter(regular)
+        ds = ds.add_column("solution", df["answers"])
+        ds = ds.map(
+            lambda exa: {
+                "solution_human": solution_human(exa["solution"], exa["num_list"])
+            }
+        )
         return ds