Small uipdates

Author: tail-call

cgt-nn-rev9.ipynb

@@ -62,10 +62,10 @@ def calculate_sha1(file_path):
             )
         else:
             print(f"File {file_path} exists and SHA1 matches, skipping download.")
-            if columns is None:
+            if features is None:
                 return pd.read_csv(file_path)
             else:
-                return pd.read_csv(file_path, header=None, names=columns)
+                return pd.read_csv(file_path, header=None, names=features)
     else:
         print(f"Downloading {url} to {file_path}")
         urllib.request.urlretrieve(url, file_path)
@@ -76,10 +76,10 @@ def calculate_sha1(file_path):
                 f"SHA1 mismatch for downloaded file: {file_path}. Expected {sha1}, got {downloaded_sha1}"
             )
 
-    if columns is None:
+    if features is None:
         return pd.read_csv(file_path)
     else:
-        return pd.read_csv(file_path, header=None, names=columns)
+        return pd.read_csv(file_path, header=None, names=features)
 
 
 def download_pmlb(dataset_name: str) -> pd.DataFrame:
@@ -120,7 +120,7 @@ def download_pmlb(dataset_name: str) -> pd.DataFrame:
             url='https://raw.githubusercontent.com/mragpavank/car-evaluation-dataset/refs/heads/master/car_evaluation.csv',
             saved_name='car_evaluation.csv',
             sha1='985852bc1bb34d7cb3c192d6b8e7127cc743e176',
-            columns=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'],
+            features=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'],
         )),
     ),
     Dataset(

cgtnnlib/datasets.py

@@ -0,0 +1,12 @@
+## Regarding Theorem 1.115 from the Seven Sketches
+
+Strategic preorders<em>‼︎</em>
+
+- $P$: preorder of strategies (e.g., ordered by risk)
+- $Q$: be a preorder of game states (e.g., ordered by payoff)
+- A function $f : P \to Q$ that finds the *best possible outcome* for a given strategy is **Left Adjoint**
+- Due to T1.115 this is equivalent to $f$ preserving **joins** 
+  - **Example:** being logical about "OR" choices, like "what's my best outcome if I can use strategy A *or* strategy B?"
+- A function $g: Q \to P$ that finds the **least risky strategy** required to **guarantee** a certain outcome is a **Right Adjoint**.
+- Due to T1.115, this is equivalent to $g$ preserving **meets**.
+  - **Example**: being logical about "AND" requirements, like "what is the single safest strategy I must commit to if I want to guarantee I achieve an outcome of *at least* A **AND** an outcome of *at least* B?"