Fix/arguments sample() simulate() mismatch (#129)

* Fix/arguments sample() simulate() mismatch

* bump version

* format

* Remove unused import:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "FSRS-Optimizer"
-version = "5.0.4"
+version = "5.0.5"
 readme = "README.md"
 dependencies = [
     "matplotlib>=3.7.0",

src/fsrs_optimizer/fsrs_optimizer.py

@@ -1272,9 +1272,7 @@ def preview_sequence(self, test_rating_sequence: str, requestRetention: float):
                     (
                         f"{ivl}d"
                         if ivl < 30
-                        else f"{ivl / 30:.1f}m"
-                        if ivl < 365
-                        else f"{ivl / 365:.1f}y"
+                        else f"{ivl / 30:.1f}m" if ivl < 365 else f"{ivl / 365:.1f}y"
                     )
                     for ivl in map(int, t_history.split(","))
                 ]
@@ -1330,7 +1328,7 @@ def find_optimal_retention(
         self,
         learn_span=365,
         max_ivl=36500,
-        loss_aversion=1,
+        loss_aversion=2.5,
         verbose=True,
     ):
         """should not be called before predict_memory_states"""
@@ -1425,9 +1423,7 @@ def moving_average(data, window_size=365 // 20):
         ax.legend()
         ax.grid(True)
 
-        simulate_config["deck_size"] = 20000
-        simulate_config["max_cost_perday"] = 1200
-        simulate_config["learn_limit_perday"] = math.inf
+        simulate_config["loss_aversion"] = 1
         fig6 = workload_graph(simulate_config)
 
         return (fig1, fig2, fig3, fig4, fig5, fig6)

src/fsrs_optimizer/fsrs_simulator.py

@@ -1,6 +1,5 @@
 import math
 import numpy as np
-import pandas as pd
 from matplotlib import pyplot as plt
 from tqdm import trange
 
@@ -230,7 +229,6 @@ def mean_reversion(init, current):
         learn_cnt_per_day[today] = np.sum(true_learn)
         memorized_cnt_per_day[today] = card_table[col["retrievability"]].sum()
         cost_per_day[today] = card_table[col["cost"]][true_review | true_learn].sum()
-    card_table = pd.DataFrame(card_table.T, columns=columns)
     return (
         card_table,
         review_cnt_per_day,
@@ -278,9 +276,9 @@ def sample(
             deck_size,
             learn_span,
             max_cost_perday,
-            max_ivl,
             learn_limit_perday,
             review_limit_perday,
+            max_ivl,
             learn_costs,
             review_costs,
             first_rating_prob,
@@ -296,106 +294,21 @@ def sample(
     return np.mean(memorization)
 
 
-def bracket(xa=0.75, xb=0.95, maxiter=20, **kwargs):
-    u_lim = 0.95
-    l_lim = 0.75
-
-    grow_limit = 100.0
-    gold = 1.6180339
-    verysmall_num = 1e-21
-
-    fa = sample(xa, **kwargs)
-    fb = sample(xb, **kwargs)
-    funccalls = 2
-
-    if fa < fb:  # Switch so fa > fb
-        xa, xb = xb, xa
-        fa, fb = fb, fa
-    xc = max(min(xb + gold * (xb - xa), u_lim), l_lim)
-    fc = sample(xc, **kwargs)
-    funccalls += 1
-
-    iter = 0
-    while fc < fb:
-        tmp1 = (xb - xa) * (fb - fc)
-        tmp2 = (xb - xc) * (fb - fa)
-        val = tmp2 - tmp1
-        if np.abs(val) < verysmall_num:
-            denom = 2.0 * verysmall_num
-        else:
-            denom = 2.0 * val
-        w = max(min((xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom), u_lim), l_lim)
-        wlim = max(min(xb + grow_limit * (xc - xb), u_lim), l_lim)
-
-        if iter > maxiter:
-            print("Failed to converge")
-            break
-
-        iter += 1
-        if (w - xc) * (xb - w) > 0.0:
-            fw = sample(w, **kwargs)
-            funccalls += 1
-            if fw < fc:
-                xa = max(min(xb, u_lim), l_lim)
-                xb = max(min(w, u_lim), l_lim)
-                fa = fb
-                fb = fw
-                break
-            elif fw > fb:
-                xc = max(min(w, u_lim), l_lim)
-                fc = fw
-                break
-            w = max(min(xc + gold * (xc - xb), u_lim), l_lim)
-            fw = sample(w, **kwargs)
-            funccalls += 1
-        elif (w - wlim) * (wlim - xc) >= 0.0:
-            w = wlim
-            fw = sample(w, **kwargs)
-            funccalls += 1
-        elif (w - wlim) * (xc - w) > 0.0:
-            fw = sample(w, **kwargs)
-            funccalls += 1
-            if fw < fc:
-                xb = max(min(xc, u_lim), l_lim)
-                xc = max(min(w, u_lim), l_lim)
-                w = max(min(xc + gold * (xc - xb), u_lim), l_lim)
-                fb = fc
-                fc = fw
-                fw = sample(w, **kwargs)
-                funccalls += 1
-        else:
-            w = max(min(xc + gold * (xc - xb), u_lim), l_lim)
-            fw = sample(w, **kwargs)
-            funccalls += 1
-        xa = max(min(xb, u_lim), l_lim)
-        xb = max(min(xc, u_lim), l_lim)
-        xc = max(min(w, u_lim), l_lim)
-        fa = fb
-        fb = fc
-        fc = fw
-
-    return xa, xb, xc, fa, fb, fc, funccalls
-
-
 def brent(tol=0.01, maxiter=20, **kwargs):
     mintol = 1.0e-11
     cg = 0.3819660
 
-    xa, xb, xc, fa, fb, fc, funccalls = bracket(
-        xa=0.75, xb=0.95, maxiter=maxiter, **kwargs
-    )
+    funccalls = 0
+    xb = 0.75
+    fb = sample(xb, **kwargs)
 
     #################################
     # BEGIN
     #################################
     x = w = v = xb
     fw = fv = fx = fb
-    if xa < xc:
-        a = xa
-        b = xc
-    else:
-        a = xc
-        b = xa
+    a = 0.75
+    b = 0.95
     deltax = 0.0
     iter = 0
 

tests/simulator_test.py

@@ -9,13 +9,8 @@ def test_simulate(self):
             learn_cnt_per_day,
             memorized_cnt_per_day,
             cost_per_day,
-        ) = simulate(w=DEFAULT_PARAMETER)
-        card_table.to_csv("card_table.csv", index=False)
-        print(review_cnt_per_day)
-        print(learn_cnt_per_day)
-        print(memorized_cnt_per_day)
-        print(cost_per_day)
-        # assert memorized_cnt_per_day[-1] == 3145.3779679589484
+        ) = simulate(w=DEFAULT_PARAMETER, request_retention=0.9)
+        assert memorized_cnt_per_day[-1] == 5918.574208243532
 
     def test_optimal_retention(self):
         default_params = {
@@ -29,4 +24,4 @@ def test_optimal_retention(self):
             "loss_aversion": 2.5,
         }
         r = optimal_retention(**default_params)
-        assert r == 0.7791796050312
+        assert r == 0.8346739534878145