Cache computation of linear regression for numerical stability

notebooks_jason/max_of_K_all_models.py

@@ -3193,11 +3193,22 @@ def double_singleton_groups(data: pd.DataFrame, column: str) -> pd.DataFrame:
     )
     # Show the plot
     fig.show("png")
+
+
 # %%
 # plt.set_prop_cycle(color=['red', 'green', 'blue'])
 # default_colors
 # cycler(color=plt.cm.Paired.colors)
 # cycler(color=plt.cm.tab20c.colors)
+# %%
+def do_linear_regression(X, Y):
+    model = LinearRegression().fit(X, Y)
+    slope = model.coef_[0]
+    intercept = model.intercept_
+    r_squared = r2_score(Y, model.predict(X))
+    return slope, intercept, r_squared
+
+
 # %%
 plt.rcParams["axes.prop_cycle"] = cycler(color=plt.cm.Paired.colors[::-1])
 
@@ -3295,10 +3306,32 @@ def double_singleton_groups(data: pd.DataFrame, column: str) -> pd.DataFrame:
             X = subgroup["EQKERatioFirstTwoSingularFloat"].values.reshape(-1, 1)
             y = subgroup["normalized-accuracy-bound"].values
 
-            model = LinearRegression().fit(X, y)
-            slope = model.coef_[0]
-            intercept = model.intercept_
-            r_squared = r2_score(y, model.predict(X))
+            # cache for numerical stability
+            # model = LinearRegression().fit(X, y)
+            # slope = model.coef_[0]
+            # intercept = model.intercept_
+            # r_squared = r2_score(y, model.predict(X))
+            with memoshelve_hf_staged(
+                short_name=f"linear_regression_normalized-accuracy-bound-vs-EQKERatioFirstTwoSingularFloat{EXTRA_D_VOCAB_FILE_SUFFIX}"
+            ) as memoshelve_hf:
+                with memoshelve_hf(
+                    (
+                        lambda _sing_upper_bound, _attn_err_handling_key, _best_bound_only, _sorted_seeds: do_linear_regression(
+                            X, y
+                        )
+                    ),
+                    "linear_regression_normalized-accuracy-bound-vs-EQKERatioFirstTwoSingularFloat",
+                    extra_hf_file_suffix=EXTRA_D_VOCAB_FILE_SUFFIX,
+                    get_hash_mem=(lambda x: x[0]),
+                    get_hash=str,
+                ) as memo_do_linear_regression:
+                    slope, intercept, r_squared = memo_do_linear_regression(
+                        sing_upper_bound,
+                        attn_err_handling_key,
+                        best_bound_only,
+                        tuple(sorted(subgroup["seed"].values)),
+                    )
+
             attn_err_handling_key_latex = (
                 LargestWrongLogitQuadraticConfig.transform_description(
                     attn_err_handling_key, latex=True