stability calibration for four last ratings

src/fsrs_optimizer/fsrs_optimizer.py

@@ -1568,23 +1568,38 @@ def calibration_graph(self, dataset=None, verbose=True):
                 title=f"Last rating: {last_rating}",
             )
 
-        fig3 = self.calibration_helper(
+        fig3 = plt.figure()
+        self.calibration_helper(
             dataset[["stability", "p", "y"]].copy(),
             "stability",
             lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))),
             True,
+            fig3.add_subplot(111),
         )
-        fig4 = self.calibration_helper(
+
+        fig4 = plt.figure(figsize=(16, 12))
+        for last_rating in (1, 2, 3, 4):
+            calibration_data = dataset[dataset["last_rating"] == last_rating]
+            if calibration_data.empty:
+                continue
+            self.calibration_helper(
+                calibration_data[["stability", "p", "y"]].copy(),
+                "stability",
+                lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))),
+                True,
+                fig4.add_subplot(2, 2, int(last_rating)),
+            )
+        fig5 = plt.figure()
+        self.calibration_helper(
             dataset[["difficulty", "p", "y"]].copy(),
             "difficulty",
             lambda x: round(x),
             False,
+            fig5.add_subplot(111),
         )
-        return metrics, (fig1, fig2, fig3, fig4)
+        return metrics, (fig1, fig2, fig3, fig4, fig5)
 
-    def calibration_helper(self, calibration_data, key, bin_func, semilogx):
-        fig = plt.figure()
-        ax1 = fig.add_subplot(111)
+    def calibration_helper(self, calibration_data, key, bin_func, semilogx, ax1):
         ax2 = ax1.twinx()
         lns = []
 
@@ -1621,7 +1636,7 @@ def to_percent(temp, position):
         ax2.legend(lns, labs, loc="lower right")
         ax2.grid(linestyle="--")
         ax2.yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))
-        return fig
+        return ax1
 
     def formula_analysis(self):
         analysis_df = self.dataset[self.dataset["i"] > 2].copy()