diff --git a/docs/tutorials/mab.ipynb b/docs/tutorials/mab.ipynb
index 22c5666..c756b2e 100644
--- a/docs/tutorials/mab.ipynb
+++ b/docs/tutorials/mab.ipynb
@@ -102,14 +102,14 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulli\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mClassicBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulli\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mClassicBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
@@ -169,14 +169,14 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulli\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mClassicBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulli\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mClassicBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
@@ -422,14 +422,14 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulli\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m2\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m3\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mClassicBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulli\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m2\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m3\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m3\u001B[0m\u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mClassicBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
@@ -494,14 +494,14 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulli\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m337\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m369\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m4448\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m4315\u001b[0m\u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m246\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m296\u001b[0m\u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mClassicBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulli\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m337\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m369\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m4448\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m4315\u001B[0m\u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m246\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m296\u001B[0m\u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mClassicBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
diff --git a/docs/tutorials/smab_mo_cc.ipynb b/docs/tutorials/smab_mo_cc.ipynb
index 880654c..3dbb86b 100644
--- a/docs/tutorials/smab_mo_cc.ipynb
+++ b/docs/tutorials/smab_mo_cc.ipynb
@@ -74,9 +74,9 @@
"source": [
"mab = SmabBernoulliMOCC(\n",
" actions={\n",
- " \"a1\": BetaMOCC(counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)], cost=30),\n",
- " \"a2\": BetaMOCC(counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)], cost=10),\n",
- " \"a3\": BetaMOCC(counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)], cost=20),\n",
+ " \"a1\": BetaMOCC(models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)], cost=30),\n",
+ " \"a2\": BetaMOCC(models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)], cost=10),\n",
+ " \"a3\": BetaMOCC(models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)], cost=20),\n",
" }\n",
")"
]
@@ -93,15 +93,15 @@
"SmabBernoulliMOCC(\n",
" actions={\n",
" 'a1': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=30.0\n",
" ),\n",
" 'a2': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=10.0\n",
" ),\n",
" 'a3': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=20.0\n",
" )\n",
" },\n",
@@ -110,23 +110,23 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulliMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m30\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m10\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m20\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mMultiObjectiveCostControlBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulliMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m30\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m10\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m20\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mMultiObjectiveCostControlBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
@@ -180,15 +180,15 @@
"SmabBernoulliMOCC(\n",
" actions={\n",
" 'a1': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=30.0\n",
" ),\n",
" 'a2': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=10.0\n",
" ),\n",
" 'a3': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=20.0\n",
" )\n",
" },\n",
@@ -197,23 +197,23 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulliMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m30\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m10\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m20\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mMultiObjectiveCostControlBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulliMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m30\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m10\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m20\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mMultiObjectiveCostControlBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
@@ -449,15 +449,15 @@
"SmabBernoulliMOCC(\n",
" actions={\n",
" 'a1': BetaMOCC(\n",
- " counters=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
+ " models=[Beta(n_successes=1, n_failures=1), Beta(n_successes=1, n_failures=1)],\n",
" cost=30.0\n",
" ),\n",
" 'a2': BetaMOCC(\n",
- " counters=[Beta(n_successes=7, n_failures=3), Beta(n_successes=7, n_failures=3)],\n",
+ " models=[Beta(n_successes=7, n_failures=3), Beta(n_successes=7, n_failures=3)],\n",
" cost=10.0\n",
" ),\n",
" 'a3': BetaMOCC(\n",
- " counters=[Beta(n_successes=3, n_failures=1), Beta(n_successes=3, n_failures=1)],\n",
+ " models=[Beta(n_successes=3, n_failures=1), Beta(n_successes=3, n_failures=1)],\n",
" cost=20.0\n",
" )\n",
" },\n",
@@ -466,23 +466,23 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulliMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m30\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m7\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m7\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m10\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m3\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m3\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m20\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mMultiObjectiveCostControlBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulliMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m30\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m7\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m3\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m7\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m3\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m10\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m3\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m3\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m20\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mMultiObjectiveCostControlBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
@@ -541,15 +541,15 @@
"SmabBernoulliMOCC(\n",
" actions={\n",
" 'a1': BetaMOCC(\n",
- " counters=[Beta(n_successes=450, n_failures=488), Beta(n_successes=450, n_failures=488)],\n",
+ " models=[Beta(n_successes=450, n_failures=488), Beta(n_successes=450, n_failures=488)],\n",
" cost=30.0\n",
" ),\n",
" 'a2': BetaMOCC(\n",
- " counters=[Beta(n_successes=8541, n_failures=8325), Beta(n_successes=8541, n_failures=8325)],\n",
+ " models=[Beta(n_successes=8541, n_failures=8325), Beta(n_successes=8541, n_failures=8325)],\n",
" cost=10.0\n",
" ),\n",
" 'a3': BetaMOCC(\n",
- " counters=[Beta(n_successes=1110, n_failures=1102), Beta(n_successes=1110, n_failures=1102)],\n",
+ " models=[Beta(n_successes=1110, n_failures=1102), Beta(n_successes=1110, n_failures=1102)],\n",
" cost=20.0\n",
" )\n",
" },\n",
@@ -558,23 +558,23 @@
"\n"
],
"text/plain": [
- "\u001b[1;35mSmabBernoulliMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mactions\u001b[0m=\u001b[1m{\u001b[0m\n",
- " \u001b[32m'a1'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m450\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m488\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m450\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m488\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m30\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a2'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m8541\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m8325\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m8541\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m8325\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m10\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m,\n",
- " \u001b[32m'a3'\u001b[0m: \u001b[1;35mBetaMOCC\u001b[0m\u001b[1m(\u001b[0m\n",
- " \u001b[33mcounters\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1110\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1102\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mBeta\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_successes\u001b[0m=\u001b[1;36m1110\u001b[0m, \u001b[33mn_failures\u001b[0m=\u001b[1;36m1102\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[33mcost\u001b[0m=\u001b[1;36m20\u001b[0m\u001b[1;36m.0\u001b[0m\n",
- " \u001b[1m)\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[33mstrategy\u001b[0m=\u001b[1;35mMultiObjectiveCostControlBandit\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
- "\u001b[1m)\u001b[0m\n"
+ "\u001B[1;35mSmabBernoulliMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mactions\u001B[0m=\u001B[1m{\u001B[0m\n",
+ " \u001B[32m'a1'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m450\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m488\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m450\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m488\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m30\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a2'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m8541\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m8325\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m8541\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m8325\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m10\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m,\n",
+ " \u001B[32m'a3'\u001B[0m: \u001B[1;35mBetaMOCC\u001B[0m\u001B[1m(\u001B[0m\n",
+ " \u001B[33mmodels\u001B[0m=\u001B[1m[\u001B[0m\u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1110\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1102\u001B[0m\u001B[1m)\u001B[0m, \u001B[1;35mBeta\u001B[0m\u001B[1m(\u001B[0m\u001B[33mn_successes\u001B[0m=\u001B[1;36m1110\u001B[0m, \u001B[33mn_failures\u001B[0m=\u001B[1;36m1102\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
+ " \u001B[33mcost\u001B[0m=\u001B[1;36m20\u001B[0m\u001B[1;36m.0\u001B[0m\n",
+ " \u001B[1m)\u001B[0m\n",
+ " \u001B[1m}\u001B[0m,\n",
+ " \u001B[33mstrategy\u001B[0m=\u001B[1;35mMultiObjectiveCostControlBandit\u001B[0m\u001B[1m(\u001B[0m\u001B[1m)\u001B[0m\n",
+ "\u001B[1m)\u001B[0m\n"
]
},
"metadata": {},
diff --git a/pybandits/cmab.py b/pybandits/cmab.py
index a4f2246..e21c41d 100644
--- a/pybandits/cmab.py
+++ b/pybandits/cmab.py
@@ -19,7 +19,7 @@
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
-
+from abc import ABC
from typing import Dict, List, Optional, Set, Union
from numpy import array
@@ -29,21 +29,31 @@
from pybandits.base import ActionId, BinaryReward, CmabPredictions
from pybandits.mab import BaseMab
-from pybandits.model import BayesianLogisticRegression, BayesianLogisticRegressionCC
+from pybandits.model import (
+ BaseBayesianLogisticRegression,
+ BaseBayesianLogisticRegressionMO,
+ BayesianLogisticRegression,
+ BayesianLogisticRegressionCC,
+ BayesianLogisticRegressionMO,
+ BayesianLogisticRegressionMOCC,
+)
from pybandits.strategy import (
BestActionIdentificationBandit,
ClassicBandit,
CostControlBandit,
+ MultiObjectiveBandit,
+ MultiObjectiveCostControlBandit,
+ MultiObjectiveStrategy,
)
-class BaseCmabBernoulli(BaseMab):
+class BaseCmabBernoulli(BaseMab, ABC):
"""
Base model for a Contextual Multi-Armed Bandit for Bernoulli bandits with Thompson Sampling.
Parameters
----------
- actions: Dict[ActionId, BayesianLogisticRegression]
+ actions: Dict[ActionId, Union[BayesianLogisticRegression, BayesianLogisticRegressionMO]]
The list of possible actions, and their associated Model.
strategy: Strategy
The strategy used to select actions.
@@ -54,25 +64,42 @@ class BaseCmabBernoulli(BaseMab):
bandit strategy.
"""
- actions: Dict[ActionId, BayesianLogisticRegression]
+ actions: Dict[ActionId, Union[BaseBayesianLogisticRegression, BaseBayesianLogisticRegressionMO]]
predict_with_proba: bool
predict_actions_randomly: bool
+ @classmethod
+ def _check_single_bayesian_logistic_regression_model(cls, action, first_action_type, first_action):
+ if not isinstance(action, first_action_type):
+ raise AttributeError("All actions should follow the same type.")
+ if not len(action.betas) == len(first_action.betas):
+ raise AttributeError("All actions should have the same number of betas.")
+ if not action.update_method == first_action.update_method:
+ raise AttributeError("All actions should have the same update method.")
+ if not action.update_kwargs == first_action.update_kwargs:
+ raise AttributeError("All actions should have the same update kwargs.")
+
@field_validator("actions", mode="after")
@classmethod
def check_bayesian_logistic_regression_models(cls, v):
action_models = list(v.values())
first_action = action_models[0]
first_action_type = type(first_action)
- for action in action_models[1:]:
- if not isinstance(action, first_action_type):
- raise AttributeError("All actions should follow the same type.")
- if not len(action.betas) == len(first_action.betas):
- raise AttributeError("All actions should have the same number of betas.")
- if not action.update_method == first_action.update_method:
- raise AttributeError("All actions should have the same update method.")
- if not action.update_kwargs == first_action.update_kwargs:
- raise AttributeError("All actions should have the same update kwargs.")
+ if isinstance(first_action, BaseBayesianLogisticRegression):
+ for action in action_models[1:]:
+ if not isinstance(action, first_action_type):
+ raise AttributeError("All actions should follow the same type.")
+ cls._check_single_bayesian_logistic_regression_model(action, first_action_type, first_action)
+ elif isinstance(first_action, BaseBayesianLogisticRegressionMO):
+ first_action = first_action.models[0]
+ first_action_type = type(first_action)
+ for action in action_models:
+ for model in action.models:
+ cls._check_single_bayesian_logistic_regression_model(model, first_action_type, first_action)
+ else:
+ raise NotImplementedError(
+ "Only BaseBayesianLogisticRegression and BaseBayesianLogisticRegressionMO are supported."
+ )
return v
@validate_call(config=dict(arbitrary_types_allowed=True))
@@ -112,7 +139,16 @@ def predict(
if self.predict_actions_randomly:
# check that context has the expected number of columns
- if context.shape[1] != len(list(self.actions.values())[0].betas):
+ first_action_model = list(self.actions.values())[0]
+ if isinstance(first_action_model, BaseBayesianLogisticRegression):
+ expected_length = len(first_action_model.betas)
+ elif isinstance(first_action_model, BaseBayesianLogisticRegressionMO):
+ expected_length = len(first_action_model.models[0].betas)
+ else:
+ raise NotImplementedError(
+ "Only BaseBayesianLogisticRegression and BaseBayesianLogisticRegressionMO are supported."
+ )
+ if context.shape[1] != expected_length:
raise AttributeError("Context must have {n_betas} columns")
selected_actions = choice(list(valid_actions), size=len(context)).tolist() # predict actions randomly
@@ -170,6 +206,7 @@ def update(
rewards = [[1, 1], [1, 0], [1, 1], [1, 0], [1, 1], ...]
"""
self._validate_update_params(actions=actions, rewards=rewards)
+
if len(context) != len(rewards):
raise AttributeError(f"Shape mismatch: actions and rewards should have the same length {len(actions)}.")
@@ -272,3 +309,68 @@ class CmabBernoulliCC(BaseCmabBernoulli):
strategy: CostControlBandit
predict_with_proba: bool = True
predict_actions_randomly: bool = False
+
+
+class BaseCmabBernoulliMO(BaseCmabBernoulli, ABC):
+ """
+ Base model for a Contextual Multi-Armed Bandit with Thompson Sampling implementation, and Multi-Objectives
+ strategy.
+
+ Parameters
+ ----------
+ actions: Dict[ActionId, BetaMO]
+ The list of possible actions, and their associated Model.
+ strategy: Strategy
+ The strategy used to select actions.
+ """
+
+ actions: Dict[ActionId, BaseBayesianLogisticRegressionMO]
+ strategy: MultiObjectiveStrategy
+
+
+class CmabBernoulliMO(BaseCmabBernoulliMO):
+ """
+ Contextual Multi-Armed Bandit with Thompson Sampling, and Multi-Objectives strategy.
+
+ The reward pertaining to an action is a multidimensional vector instead of a scalar value. In this setting,
+ different actions are compared according to Pareto order between their expected reward vectors, and those actions
+ whose expected rewards are not inferior to that of any other actions are called Pareto optimal actions, all of which
+ constitute the Pareto front.
+
+ Reference: Thompson Sampling for Multi-Objective Multi-Armed Bandits Problem (Yahyaa and Manderick, 2015)
+ https://www.researchgate.net/publication/272823659_Thompson_Sampling_for_Multi-Objective_Multi-Armed_Bandits_Problem
+
+ Parameters
+ ----------
+ actions: Dict[ActionId, BayesianLogisticRegressionMO]
+ The list of possible actions, and their associated Model.
+ strategy: MultiObjectiveBandit
+ The strategy used to select actions.
+ """
+
+ actions: Dict[ActionId, BayesianLogisticRegressionMO]
+ strategy: MultiObjectiveBandit
+ predict_with_proba: bool = False
+ predict_actions_randomly: bool = False
+
+
+class CmabBernoulliMOCC(BaseCmabBernoulliMO):
+ """
+ Contextual Multi-Armed Bandit with Thompson Sampling implementation for Multi-Objective (MO) with Cost
+ Control (CC) strategy.
+
+ This Bandit allows the reward to be a multidimensional vector and include a control of the action cost. It merges
+ the Multi-Objective and Cost Control strategies.
+
+ Parameters
+ ----------
+ actions: Dict[ActionId, BayesianLogisticRegressionMOCC]
+ The list of possible actions, and their associated Model.
+ strategy: MultiObjectiveCostControlBandit
+ The strategy used to select actions.
+ """
+
+ actions: Dict[ActionId, BayesianLogisticRegressionMOCC]
+ strategy: MultiObjectiveCostControlBandit
+ predict_with_proba: bool = True
+ predict_actions_randomly: bool = False
diff --git a/pybandits/mab.py b/pybandits/mab.py
index 55de8d9..1a5d32a 100644
--- a/pybandits/mab.py
+++ b/pybandits/mab.py
@@ -37,7 +37,7 @@
Predictions,
PyBanditsBaseModel,
)
-from pybandits.model import Model
+from pybandits.model import Model, ModelMO
from pybandits.strategy import Strategy
from pybandits.utils import extract_argument_names_from_function
@@ -48,7 +48,7 @@ class BaseMab(PyBanditsBaseModel, ABC):
Parameters
----------
- actions : Dict[ActionId, Model]
+ actions : Dict[ActionId, Union[Model,ModelMO]]
The list of possible actions, and their associated Model.
strategy : Strategy
The strategy used to select actions.
@@ -62,14 +62,14 @@ class BaseMab(PyBanditsBaseModel, ABC):
which in turn will be used to instantiate the strategy.
"""
- actions: Dict[ActionId, Model]
+ actions: Dict[ActionId, Union[Model, ModelMO]]
strategy: Strategy
epsilon: Optional[Float01] = None
default_action: Optional[ActionId] = None
def __init__(
self,
- actions: Dict[ActionId, Model],
+ actions: Dict[ActionId, Union[Model, ModelMO]],
epsilon: Optional[Float01] = None,
default_action: Optional[ActionId] = None,
**strategy_kwargs,
@@ -88,27 +88,26 @@ def __init__(
@field_validator("actions", mode="before")
@classmethod
- def at_least_one_action_is_defined(cls, v):
+ def validate_action_configurations(cls, v):
# validate number of actions
if len(v) == 0:
raise AttributeError("At least one action should be defined.")
elif len(v) == 1:
warnings.warn("Only a single action was supplied. This MAB will be deterministic.")
+
# validate that all actions are of the same configuration
action_models = list(v.values())
first_action = action_models[0]
first_action_type = type(first_action)
if any(not isinstance(action, first_action_type) for action in action_models[1:]):
raise AttributeError("All actions should follow the same type.")
- return v
- @model_validator(mode="after")
- def check_default_action(self):
- if not self.epsilon and self.default_action:
- raise AttributeError("A default action should only be defined when epsilon is defined.")
- if self.default_action and self.default_action not in self.actions:
- raise AttributeError("The default action must be valid action defined in the actions set.")
- return self
+ # For multi-objective actions, validate that all actions have the same number of objectives
+ if isinstance(first_action, ModelMO):
+ n_objs_per_action = [len(action_model.models) for action_model in v.values()]
+ if len(set(n_objs_per_action)) != 1:
+ raise ValueError("All actions should have the same number of objectives")
+ return v
@model_validator(mode="after")
def validate_default_action(self):
@@ -203,7 +202,7 @@ def predict(self, forbidden_actions: Optional[Set[ActionId]] = None) -> Predicti
probs: List[Dict[ActionId, Probability]] of shape (n_samples,)
The probabilities of getting a positive reward for each action
ws : List[Dict[ActionId, float]], only relevant for some of the MABs
- The weighted sum of logistic regression logits..
+ The weighted sum of logistic regression logits.
"""
def get_state(self) -> (str, dict):
@@ -224,7 +223,7 @@ def get_state(self) -> (str, dict):
def _select_epsilon_greedy_action(
self,
p: ActionRewardLikelihood,
- actions: Optional[Dict[ActionId, Model]] = None,
+ actions: Optional[Dict[ActionId, Union[Model, ModelMO]]] = None,
) -> ActionId:
"""
Wraps self.strategy.select_action function with epsilon-greedy strategy,
diff --git a/pybandits/model.py b/pybandits/model.py
index 32981f9..936302e 100644
--- a/pybandits/model.py
+++ b/pybandits/model.py
@@ -53,19 +53,72 @@ class Model(PyBanditsBaseModel, ABC):
"""
@abstractmethod
- def sample_proba(self) -> Probability:
+ def sample_proba(self, **kwargs) -> Probability:
"""
Sample the probability of getting a positive reward.
"""
@abstractmethod
- def update(self, rewards: List[Any]):
+ def update(self, rewards: List[Any], **kwargs):
"""
Update the model parameters.
"""
-class BaseBeta(Model):
+class ModelMO(PyBanditsBaseModel):
+ """
+ Multi-objective extension of Model.
+
+ Parameters
+ ----------
+ models : List[Model]
+ List of models.
+ """
+
+ models: List[Model] = Field(..., min_length=1)
+
+ @validate_call
+ def sample_proba(self, **kwargs) -> List[Probability]:
+ """
+ Sample the probability of getting a positive reward.
+
+ Returns
+ -------
+ prob: List[Probability]
+ Probabilities of getting a positive reward for each objective.
+ """
+ return [x.sample_proba(**kwargs) for x in self.models]
+
+ @validate_call
+ def update(self, rewards: List[List[BinaryReward]], **kwargs):
+ """
+ Update the Beta model using the provided rewards.
+
+ Parameters
+ ----------
+ rewards: List[List[BinaryReward]]
+ A list of rewards, where each reward is in turn a list containing the reward of the Beta model
+ associated to each objective.
+ For example, `[[1, 1], [1, 0], [1, 1], [1, 0], [1, 1]]`.
+ kwargs: Dict[str, Any]
+ Additional arguments for the Bayesian Logistic Regression MO child model.
+ """
+ if any(len(x) != len(self.models) for x in rewards):
+ raise AttributeError("The shape of rewards is incorrect")
+
+ for i, model in enumerate(self.models):
+ model.update(rewards=[r[i] for r in rewards], **kwargs)
+
+
+class ModelCC(PyBanditsBaseModel, ABC):
+ """
+ Class to augment prior distributions with cost control.
+ """
+
+ cost: NonNegativeFloat
+
+
+class BaseBeta(Model, ABC):
"""
Beta Distribution model for Bernoulli multi-armed bandits.
@@ -82,7 +135,7 @@ class BaseBeta(Model):
@model_validator(mode="before")
@classmethod
- def both_or_neither_counters_are_defined(cls, values):
+ def both_or_neither_models_are_defined(cls, values):
if hasattr(values, "n_successes") != hasattr(values, "n_failures"):
raise ValueError("Either both or neither n_successes and n_failures should be specified.")
return values
@@ -136,63 +189,42 @@ def sample_proba(self) -> Probability:
class Beta(BaseBeta):
"""
Beta Distribution model for Bernoulli multi-armed bandits.
+
+ Parameters
+ ----------
+ n_successes: PositiveInt = 1
+ Counter of the number of successes.
+ n_failures: PositiveInt = 1
+ Counter of the number of failures.
"""
-class BetaCC(BaseBeta):
+class BetaCC(BaseBeta, ModelCC):
"""
Beta Distribution model for Bernoulli multi-armed bandits with cost control.
Parameters
----------
+ n_successes: PositiveInt = 1
+ Counter of the number of successes.
+ n_failures: PositiveInt = 1
+ Counter of the number of failures.
cost: NonNegativeFloat
Cost associated to the Beta distribution.
"""
- cost: NonNegativeFloat
-
-class BetaMO(Model):
+class BetaMO(ModelMO):
"""
Beta Distribution model for Bernoulli multi-armed bandits with multi-objectives.
Parameters
----------
- counters: List[Beta] of shape (n_objectives,)
+ models : List[Beta] of shape (n_objectives,)
List of Beta distributions.
"""
- counters: List[Beta]
-
- @validate_call
- def sample_proba(self) -> List[Probability]:
- """
- Sample the probability of getting a positive reward.
-
- Returns
- -------
- prob: List[Probability]
- Probabilities of getting a positive reward for each objective.
- """
- return [x.sample_proba() for x in self.counters]
-
- @validate_call
- def update(self, rewards: List[List[BinaryReward]]):
- """
- Update the Beta model using the provided rewards.
-
- Parameters
- ----------
- rewards: List[List[BinaryReward]]
- A list of rewards, where each reward is in turn a list containing the reward of the Beta model
- associated to each objective.
- For example, `[[1, 1], [1, 0], [1, 1], [1, 0], [1, 1]]`.
- """
- if any(len(x) != len(self.counters) for x in rewards):
- raise AttributeError("The shape of rewards is incorrect")
-
- for i, counter in enumerate(self.counters):
- counter.update([r[i] for r in rewards])
+ models: List[Beta]
@classmethod
def cold_start(cls, n_objectives: PositiveInt, **kwargs) -> "BetaMO":
@@ -216,28 +248,26 @@ def cold_start(cls, n_objectives: PositiveInt, **kwargs) -> "BetaMO":
Returns
-------
- blr: BayesianLogisticRegrssion
+ beta_mo: BetaMO
The Bayesian Logistic Regression model.
"""
- counters = n_objectives * [Beta()]
- blr = cls(counters=counters, **kwargs)
- return blr
+ models = n_objectives * [Beta()]
+ beta_mo = cls(models=models, **kwargs)
+ return beta_mo
-class BetaMOCC(BetaMO):
+class BetaMOCC(BetaMO, ModelCC):
"""
Beta Distribution model for Bernoulli multi-armed bandits with multi-objectives and cost control.
Parameters
----------
- counters: List[BetaCC] of shape (n_objectives,)
+ models: List[Beta] of shape (n_objectives,)
List of Beta distributions.
cost: NonNegativeFloat
Cost associated to the Beta distribution.
"""
- cost: NonNegativeFloat
-
class StudentT(PyBanditsBaseModel):
"""
@@ -258,9 +288,9 @@ class StudentT(PyBanditsBaseModel):
nu: confloat(allow_inf_nan=False) = 5.0
-class BayesianLogisticRegression(Model):
+class BaseBayesianLogisticRegression(Model, ABC):
"""
- Base Bayesian Logistic Regression model.
+ Bayesian Logistic Regression model.
It is modeled as:
@@ -465,7 +495,7 @@ def cold_start(
update_method: UpdateMethods = "MCMC",
update_kwargs: Optional[dict] = None,
**kwargs,
- ) -> "BayesianLogisticRegression":
+ ) -> "BaseBayesianLogisticRegression":
"""
Utility function to create a Bayesian Logistic Regression model or child model with cost control,
with default parameters.
@@ -492,7 +522,7 @@ def cold_start(
Returns
-------
- blr: BayesianLogisticRegrssion
+ blr: BayesianLogisticRegression
The Bayesian Logistic Regression model.
"""
return cls(
@@ -504,7 +534,32 @@ def cold_start(
)
-class BayesianLogisticRegressionCC(BayesianLogisticRegression):
+class BayesianLogisticRegression(BaseBayesianLogisticRegression):
+ """
+ Bayesian Logistic Regression model.
+
+ It is modeled as:
+
+ y = sigmoid(alpha + beta1 * x1 + beta2 * x2 + ... + betaN * xN)
+
+ where the alpha and betas coefficients are Student's t-distributions.
+
+ Parameters
+ ----------
+ alpha: StudentT
+ Student's t-distribution of the alpha coefficient.
+ betas: StudentT
+ Student's t-distributions of the betas coefficients.
+ update_method : UpdateMethods, defaults to "MCMC"
+ The strategy for computing posterior quantities of the Bayesian models in the update function. Such as Markov
+ chain Monte Carlo ("MCMC") or Variational Inference ("VI"). Check UpdateMethods in pybandits.model for the
+ full list.
+ update_kwargs : Optional[dict], uses default values if not specified
+ Additional arguments to pass to the update method.
+ """
+
+
+class BayesianLogisticRegressionCC(BaseBayesianLogisticRegression, ModelCC):
"""
Bayesian Logistic Regression model with cost control.
@@ -530,4 +585,107 @@ class BayesianLogisticRegressionCC(BayesianLogisticRegression):
Cost associated to the Bayesian Logistic Regression model.
"""
+
+class BaseBayesianLogisticRegressionMO(ModelMO, ABC):
+ """
+ Bayesian Logistic Regression model with multi-objectives.
+
+ It is modeled as:
+
+ y = sigmoid(alpha + beta1 * x1 + beta2 * x2 + ... + betaN * xN)
+
+ where the alpha and betas coefficients are Student's t-distributions.
+
+ Parameters
+ ----------
+ models: List[BayesianLogisticRegression] of shape (n_objectives,)
+ List of Bayesian Logistic Regression.
+ """
+
+ models: List[BayesianLogisticRegression] = Field(..., min_length=1)
+
+ @classmethod
+ def cold_start(
+ cls,
+ n_objectives: PositiveInt,
+ n_features: PositiveInt,
+ update_method: UpdateMethods = "MCMC",
+ update_kwargs: Optional[dict] = None,
+ **kwargs,
+ ) -> "BaseBayesianLogisticRegressionMO":
+ """
+ Utility function to create a Multi-Objective Bayesian Logistic Regression model or child model with cost control,
+ with default parameters.
+
+ It is modeled as:
+
+ y = sigmoid(alpha + beta1 * x1 + beta2 * x2 + ... + betaN * xN)
+
+ where the alpha and betas coefficients are Student's t-distributions.
+
+ Parameters
+ ----------
+ n_objectives : PositiveInt
+ The number of objectives.
+ n_features : PositiveInt
+ The number of betas of the Bayesian Logistic Regression model. This is also the number of features expected
+ after in the context matrix.
+ update_method : UpdateMethods, defaults to "MCMC"
+ The strategy for computing posterior quantities of the Bayesian models in the update function. Such as Markov
+ chain Monte Carlo ("MCMC") or Variational Inference ("VI"). Check UpdateMethods in pybandits.model for the
+ full list.
+ update_kwargs : Optional[dict], uses default values if not specified
+ Additional arguments to pass to the update method.
+ kwargs: Dict[str, Any]
+ Additional arguments for the Bayesian Logistic Regression child model.
+
+ Returns
+ -------
+ blr_mo: BayesianLogisticRegressionMO
+ The Multi-Objective Bayesian Logistic Regression model.
+ """
+ return cls(
+ models=[
+ BayesianLogisticRegression.cold_start(
+ n_features=n_features, update_method=update_method, update_kwargs=update_kwargs
+ )
+ for _ in range(n_objectives)
+ ],
+ **kwargs,
+ )
+
+
+class BayesianLogisticRegressionMO(BaseBayesianLogisticRegressionMO):
+ """
+ Bayesian Logistic Regression model with multi-objectives.
+
+ It is modeled as:
+
+ y = sigmoid(alpha + beta1 * x1 + beta2 * x2 + ... + betaN * xN)
+
+ where the alpha and betas coefficients are Student's t-distributions.
+
+ Parameters
+ ----------
+ models: List[BayesianLogisticRegression] of shape (n_objectives,)
+ List of Bayesian Logistic Regression.
+ """
+
+
+class BayesianLogisticRegressionMOCC(BaseBayesianLogisticRegressionMO, ModelCC):
+ """
+ Bayesian Logistic Regression model with multi-objectives and cost control.
+
+ It is modeled as:
+
+ y = sigmoid(alpha + beta1 * x1 + beta2 * x2 + ... + betaN * xN)
+
+ where the alpha and betas coefficients are Student's t-distributions.
+
+ Parameters
+ ----------
+ models: List[BayesianLogisticRegression] of shape (n_objectives,)
+ List of Bayesian Logistic Regression.
cost: NonNegativeFloat
+ Cost associated to the Beta distribution.
+ """
diff --git a/pybandits/smab.py b/pybandits/smab.py
index c1b2958..d240a19 100644
--- a/pybandits/smab.py
+++ b/pybandits/smab.py
@@ -19,12 +19,11 @@
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
-
-
+from abc import ABC
from collections import defaultdict
from typing import Dict, List, Optional, Set, Union
-from pydantic import PositiveInt, field_validator, validate_call
+from pydantic import PositiveInt, validate_call
from pybandits.base import (
ActionId,
@@ -40,23 +39,23 @@
CostControlBandit,
MultiObjectiveBandit,
MultiObjectiveCostControlBandit,
- Strategy,
+ MultiObjectiveStrategy,
)
-class BaseSmabBernoulli(BaseMab):
+class BaseSmabBernoulli(BaseMab, ABC):
"""
Base model for a Stochastic Bernoulli Multi-Armed Bandit with Thompson Sampling.
Parameters
----------
- actions: Dict[ActionId, BaseBeta]
+ actions: Dict[ActionId, Union[Beta, BetaMO]]
The list of possible actions, and their associated Model.
strategy: Strategy
The strategy used to select actions.
"""
- actions: Dict[ActionId, BaseBeta]
+ actions: Dict[ActionId, Union[BaseBeta, BetaMO]]
@validate_call
def predict(
@@ -189,7 +188,7 @@ class SmabBernoulliCC(BaseSmabBernoulli):
strategy: CostControlBandit
-class BaseSmabBernoulliMO(BaseSmabBernoulli):
+class BaseSmabBernoulliMO(BaseSmabBernoulli, ABC):
"""
Base model for a Stochastic Bernoulli Multi-Armed Bandit with Thompson Sampling implementation, and Multi-Objectives
strategy.
@@ -203,15 +202,7 @@ class BaseSmabBernoulliMO(BaseSmabBernoulli):
"""
actions: Dict[ActionId, BetaMO]
- strategy: Strategy
-
- @field_validator("actions", mode="after")
- @classmethod
- def all_actions_have_same_number_of_objectives(cls, actions: Dict[ActionId, BetaMO]):
- n_objs_per_action = [len(beta.counters) for beta in actions.values()]
- if len(set(n_objs_per_action)) != 1:
- raise ValueError("All actions should have the same number of objectives")
- return actions
+ strategy: MultiObjectiveStrategy
class SmabBernoulliMO(BaseSmabBernoulliMO):
@@ -234,7 +225,6 @@ class SmabBernoulliMO(BaseSmabBernoulliMO):
The strategy used to select actions.
"""
- actions: Dict[ActionId, BetaMO]
strategy: MultiObjectiveBandit
diff --git a/pybandits/strategy.py b/pybandits/strategy.py
index 014f753..ccf9a9e 100644
--- a/pybandits/strategy.py
+++ b/pybandits/strategy.py
@@ -30,7 +30,7 @@
from typing_extensions import Self
from pybandits.base import ActionId, Float01, Probability, PyBanditsBaseModel
-from pybandits.model import Beta, BetaMOCC, Model
+from pybandits.model import Beta, BetaMOCC, Model, ModelMO
class Strategy(PyBanditsBaseModel, ABC):
@@ -243,7 +243,7 @@ def _average(cls, p_of_action: Union[Probability, List[Probability]]):
def _evaluate_and_select(
cls,
p: Union[Dict[ActionId, Probability], Dict[ActionId, List[Probability]]],
- actions: Dict[ActionId, Model],
+ actions: Dict[ActionId, Union[Model, ModelMO]],
feasible_actions: List[ActionId],
) -> ActionId:
"""
diff --git a/pybandits/utils.py b/pybandits/utils.py
index 62e6af7..1f03c07 100644
--- a/pybandits/utils.py
+++ b/pybandits/utils.py
@@ -3,7 +3,9 @@
from pydantic import validate_call
-JSONSerializable = Union[str, int, float, bool, None, List["JSONSerializable"], Dict[str, "JSONSerializable"]]
+Simple = Union[str, int, float, bool, None]
+
+JSONSerializable = Union[Simple, List["JSONSerializable"], Dict[str, "JSONSerializable"]]
@validate_call
@@ -21,6 +23,40 @@ def to_serializable_dict(d: Dict[str, Any]) -> Dict[str, JSONSerializable]:
return json.loads(json.dumps(d, default=dict))
+@validate_call
+def update_nested_struct(
+ d: Union[Dict[str, Any], List, Simple], other: Union[Dict[str, Any], List, Simple]
+) -> Union[Dict[str, Any], List, Simple]:
+ """
+ Update a nested combination of dictionaries and lists with another dictionary, recursively.
+
+ Parameters
+ ----------
+ d : Union[Dict[str, Any], List, Simple]
+ Nested combination of dictionaries and lists to update.
+ other : Union[Dict[str, Any], List, Simple]
+ Nested combination of dictionaries and lists to update with.
+
+ Returns
+ -------
+ d : Union[Dict[str, Any], List, Simple]
+ Updated nested combination of dictionaries and lists.
+
+ """
+ if isinstance(d, dict) and isinstance(other, dict):
+ for key, value in other.items():
+ if key in d:
+ d[key] = update_nested_struct(d[key], value)
+ else:
+ d[key] = value
+ elif isinstance(d, list) and isinstance(other, list):
+ assert len(d) == len(other)
+ for i, (d_value, other_value) in enumerate(zip(d, other)):
+ d[i] = update_nested_struct(d_value, other_value)
+
+ return d
+
+
@validate_call
def extract_argument_names_from_function(function_handle: Callable, is_class_method: bool = False) -> List[str]:
"""
diff --git a/tests/test_cmab.py b/tests/test_cmab.py
index 73d39a9..47d280c 100644
--- a/tests/test_cmab.py
+++ b/tests/test_cmab.py
@@ -30,10 +30,23 @@
from pydantic import NonNegativeFloat, ValidationError
from pybandits.base import Float01
-from pybandits.cmab import CmabBernoulli, CmabBernoulliBAI, CmabBernoulliCC
-from pybandits.model import BayesianLogisticRegression, BayesianLogisticRegressionCC, StudentT, UpdateMethods
-from pybandits.strategy import BestActionIdentificationBandit, ClassicBandit, CostControlBandit
-from pybandits.utils import to_serializable_dict
+from pybandits.cmab import CmabBernoulli, CmabBernoulliBAI, CmabBernoulliCC, CmabBernoulliMO, CmabBernoulliMOCC
+from pybandits.model import (
+ BayesianLogisticRegression,
+ BayesianLogisticRegressionCC,
+ BayesianLogisticRegressionMO,
+ BayesianLogisticRegressionMOCC,
+ StudentT,
+ UpdateMethods,
+)
+from pybandits.strategy import (
+ BestActionIdentificationBandit,
+ ClassicBandit,
+ CostControlBandit,
+ MultiObjectiveBandit,
+ MultiObjectiveCostControlBandit,
+)
+from pybandits.utils import to_serializable_dict, update_nested_struct
from tests.test_utils import is_serializable
literal_update_methods = get_args(UpdateMethods)
@@ -199,7 +212,7 @@ def run_update(context):
run_update(context=context)
-@settings(deadline=10000)
+@settings(deadline=30000)
@given(st.just(100), st.just(3), st.sampled_from(literal_update_methods))
def test_cmab_update_not_all_actions(n_samples, n_feat, update_method):
actions = np.random.choice(["a3", "a4"], size=n_samples).tolist()
@@ -410,7 +423,7 @@ def test_cmab_from_state(state, update_method):
assert isinstance(cmab, CmabBernoulli)
actual_actions = to_serializable_dict(cmab.actions) # Normalize the dict
- expected_actions = {k: {**v, **state["actions"][k]} for k, v in actual_actions.items()}
+ expected_actions = update_nested_struct(state["actions"], actual_actions)
assert expected_actions == actual_actions
# Ensure get_state and from_state compatibility
@@ -540,7 +553,7 @@ def test_cmab_bai_predict(n_samples, n_features):
assert len(selected_actions) == len(probs) == len(weighted_sums) == n_samples
-@settings(deadline=10000)
+@settings(deadline=30000)
@given(st.just(100), st.just(3), st.sampled_from(literal_update_methods))
def test_cmab_bai_update(n_samples, n_features, update_method):
actions = np.random.choice(["a1", "a2"], size=n_samples).tolist()
@@ -641,7 +654,7 @@ def test_cmab_bai_from_state(state, update_method):
assert isinstance(cmab, CmabBernoulliBAI)
actual_actions = to_serializable_dict(cmab.actions) # Normalize the dict
- expected_actions = {k: {**v, **state["actions"][k]} for k, v in actual_actions.items()}
+ expected_actions = update_nested_struct(state["actions"], actual_actions)
assert expected_actions == actual_actions
expected_exploit_p = cmab.strategy.get_expected_value_from_state(state, "exploit_p")
@@ -776,7 +789,7 @@ def test_cmab_cc_predict(n_samples, n_features):
assert len(selected_actions) == len(probs) == len(weighted_sums) == n_samples
-@settings(deadline=10000)
+@settings(deadline=20000)
@given(st.just(100), st.just(3), st.sampled_from(literal_update_methods))
def test_cmab_cc_update(n_samples, n_features, update_method):
actions = np.random.choice(["a1", "a2"], size=n_samples).tolist()
@@ -888,7 +901,7 @@ def test_cmab_cc_from_state(state, update_method):
assert isinstance(cmab, CmabBernoulliCC)
actual_actions = to_serializable_dict(cmab.actions) # Normalize the dict
- expected_actions = {k: {**v, **state["actions"][k]} for k, v in actual_actions.items()}
+ expected_actions = update_nested_struct(state["actions"], actual_actions)
assert expected_actions == actual_actions
expected_subsidy_factor = cmab.strategy.get_expected_value_from_state(state, "subsidy_factor")
@@ -903,6 +916,422 @@ def test_cmab_cc_from_state(state, update_method):
########################################################################################################################
+# cmabBernoulli with strategy=MultiObjectiveBandit()
+
+
+@given(
+ st.lists(st.integers(min_value=1), min_size=6, max_size=6),
+ st.integers(min_value=2, max_value=100),
+)
+def test_can_init_cmab_mo(a_list, n_features):
+ a, b, c, d, e, f = a_list
+ model = BayesianLogisticRegressionMO(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ]
+ )
+
+ s = CmabBernoulliMO(
+ actions={
+ "a1": model.model_copy(deep=True),
+ "a2": model.model_copy(deep=True),
+ },
+ )
+ assert s.actions["a1"] == model
+ assert s.actions["a2"] == model
+ assert s.strategy == MultiObjectiveBandit()
+
+
+@given(st.lists(st.integers(min_value=1), min_size=7, max_size=7), st.integers(min_value=2, max_value=100))
+def test_bad_init_cmab_mo(a_list, n_features):
+ a, b, c, d, e, f, g = a_list
+ with pytest.raises(ValueError):
+ BayesianLogisticRegressionMO(
+ models=[
+ BayesianLogisticRegressionCC(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()], cost=g),
+ BayesianLogisticRegressionCC(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()], cost=g),
+ BayesianLogisticRegressionCC(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()], cost=g),
+ ]
+ )
+
+
+@settings(deadline=500)
+@given(st.integers(min_value=1), st.integers(min_value=1), st.integers(min_value=2, max_value=100))
+def test_all_actions_must_have_same_number_of_objectives_cmab_mo(mu, sigma, n_features):
+ blr = BayesianLogisticRegression(alpha=StudentT(mu=mu, sigma=sigma), betas=n_features * [StudentT()])
+ with pytest.raises(ValueError):
+ CmabBernoulliMO(
+ actions={
+ "a1": BayesianLogisticRegressionMO(models=[blr.model_copy(deep=True), blr.model_copy(deep=True)]),
+ "a2": BayesianLogisticRegressionMO(models=[blr.model_copy(deep=True), blr.model_copy(deep=True)]),
+ "a3": BayesianLogisticRegressionMO(
+ models=[blr.model_copy(deep=True), blr.model_copy(deep=True), blr.model_copy(deep=True)]
+ ),
+ },
+ )
+
+
+def test_cmab_mo_predict(n_samples=1000, n_objectives=3, n_features=10):
+ s = CmabBernoulliMO.cold_start(action_ids={"a1", "a2"}, n_objectives=n_objectives, n_features=n_features)
+ context = np.random.uniform(low=-1.0, high=1.0, size=(n_samples, n_features))
+ forbidden = None
+ s.predict(context=context, forbidden_actions=forbidden)
+
+ forbidden = ["a1"]
+ predicted_actions, _, _ = s.predict(context=context, forbidden_actions=forbidden)
+
+ assert "a1" not in predicted_actions
+
+ forbidden = ["a1", "a2"]
+ with pytest.raises(ValueError):
+ s.predict(context=context, forbidden_actions=forbidden)
+
+ forbidden = ["a1", "a2", "a3"]
+ with pytest.raises(ValueError):
+ s.predict(context=context, forbidden_actions=forbidden)
+
+ forbidden = ["a1", "a3"]
+ with pytest.raises(ValueError):
+ s.predict(context=context, forbidden_actions=forbidden)
+
+
+def test_cmab_mo_update(action_ids={"a1", "a2"}, n_samples=10, n_objectives=3, n_features=10):
+ context = np.random.uniform(low=-1.0, high=1.0, size=(n_samples, n_features))
+ rewards = [np.random.choice([0, 1], size=n_objectives).tolist() for _ in range(n_samples)]
+ actions = np.random.choice(list(action_ids), size=n_samples).tolist()
+ mab = CmabBernoulliMO.cold_start(action_ids=action_ids, n_objectives=n_objectives, n_features=n_features)
+ assert all(
+ [
+ mab.actions[a] == BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+ for a in action_ids
+ ]
+ )
+
+ mab.update(actions=actions, rewards=rewards, context=context)
+ assert all(
+ [
+ mab.actions[a] != BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+ for a in set(action_ids)
+ ]
+ )
+
+
+@given(st.lists(st.integers(min_value=1), min_size=6, max_size=6), st.integers(min_value=2, max_value=100))
+def test_cmab_mo_get_state(a_list, n_features):
+ a, b, c, d, e, f = a_list
+ actions = {
+ "a1": BayesianLogisticRegressionMO(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ]
+ ),
+ "a2": BayesianLogisticRegressionMO(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ]
+ ),
+ }
+ cmab = CmabBernoulliMO(actions=actions)
+ expected_state = to_serializable_dict(
+ {
+ "actions": actions,
+ "strategy": {},
+ "epsilon": None,
+ "default_action": None,
+ "predict_actions_randomly": False,
+ "predict_with_proba": False,
+ }
+ )
+
+ class_name, cmab_state = cmab.get_state()
+ assert class_name == "CmabBernoulliMO"
+ assert cmab_state == expected_state
+
+ assert is_serializable(cmab_state), "Internal state is not serializable"
+
+
+@settings(deadline=500)
+@given(
+ state=st.fixed_dictionaries(
+ {
+ "actions": st.dictionaries(
+ keys=st.text(min_size=1, max_size=10),
+ values=st.fixed_dictionaries(
+ {
+ "models": st.lists(
+ st.fixed_dictionaries(
+ {
+ "alpha": st.fixed_dictionaries(
+ {
+ "mu": st.integers(min_value=1, max_value=100),
+ "sigma": st.integers(min_value=1, max_value=100),
+ },
+ ),
+ "betas": st.lists(
+ st.fixed_dictionaries(
+ {
+ "mu": st.integers(min_value=1, max_value=100),
+ "sigma": st.integers(min_value=1, max_value=100),
+ },
+ ),
+ min_size=2,
+ max_size=2,
+ ),
+ },
+ ),
+ min_size=3,
+ max_size=3,
+ )
+ }
+ ),
+ min_size=2,
+ ),
+ "strategy": st.fixed_dictionaries({}),
+ }
+ )
+)
+def test_cmab_mo_from_state(state):
+ cmab = CmabBernoulliMO.from_state(state)
+ assert isinstance(cmab, CmabBernoulliMO)
+
+ actual_actions = to_serializable_dict(cmab.actions)
+ expected_actions = update_nested_struct(state["actions"], actual_actions)
+ assert expected_actions == actual_actions
+
+ # Ensure get_state and from_state compatibility
+ new_cmab = globals()[cmab.get_state()[0]].from_state(state=cmab.get_state()[1])
+ assert new_cmab == cmab
+
+
+########################################################################################################################
+
+
+# cmabBernoulli with strategy=MultiObjectiveCostControlBandit()
+
+
+@given(st.lists(st.integers(min_value=1), min_size=8, max_size=8), st.integers(min_value=2, max_value=100))
+def test_can_init_cmab_mo_cc(a_list, n_features):
+ a, b, c, d, e, f, g, h = a_list
+ model1 = BayesianLogisticRegressionMOCC(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ],
+ cost=g,
+ )
+ model2 = model1.model_copy(deep=True, update={"cost": h})
+ s = CmabBernoulliMOCC(
+ actions={"a1": model1.model_copy(deep=True), "a2": model2.model_copy(deep=True)},
+ )
+ assert s.actions["a1"] == model1
+ assert s.actions["a2"] == model2
+ assert s.strategy == MultiObjectiveCostControlBandit()
+
+
+@given(st.lists(st.integers(min_value=1), min_size=7, max_size=7), st.integers(min_value=2, max_value=100))
+def test_bad_init_cmab_mocc(a_list, n_features):
+ a, b, c, d, e, f, g = a_list
+ with pytest.raises(ValueError):
+ BayesianLogisticRegressionMOCC(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ]
+ )
+ with pytest.raises(ValueError):
+ BayesianLogisticRegressionMOCC(
+ models=[
+ BayesianLogisticRegressionCC(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()], cost=g),
+ BayesianLogisticRegressionCC(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()], cost=g),
+ BayesianLogisticRegressionCC(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()], cost=g),
+ ]
+ )
+
+
+@settings(deadline=500)
+@given(st.integers(min_value=1), st.integers(min_value=1), st.integers(min_value=2, max_value=100), st.just(1))
+def test_all_actions_must_have_same_number_of_objectives_cmab_mo_cc(mu, sigma, n_features, cost):
+ blr = BayesianLogisticRegression(alpha=StudentT(mu=mu, sigma=sigma), betas=n_features * [StudentT()])
+ with pytest.raises(ValueError):
+ CmabBernoulliMO(
+ actions={
+ "a1": BayesianLogisticRegressionMOCC(
+ models=[blr.model_copy(deep=True), blr.model_copy(deep=True)], cost=cost
+ ),
+ "a2": BayesianLogisticRegressionMOCC(
+ models=[blr.model_copy(deep=True), blr.model_copy(deep=True)], cost=cost
+ ),
+ "a3": BayesianLogisticRegressionMOCC(
+ models=[blr.model_copy(deep=True), blr.model_copy(deep=True), blr.model_copy(deep=True)], cost=cost
+ ),
+ },
+ )
+
+
+def test_cmab_mo_cc_predict(n_samples=10, n_objectives=3, n_features=10):
+ context = np.random.uniform(low=-1.0, high=1.0, size=(n_samples, n_features))
+
+ s = CmabBernoulliMOCC.cold_start(
+ action_ids_cost={"a1": 1, "a2": 2}, n_objectives=n_objectives, n_features=n_features
+ )
+
+ forbidden = None
+ s.predict(context=context, forbidden_actions=forbidden)
+
+ forbidden = ["a1"]
+ predicted_actions, _, _ = s.predict(context=context, forbidden_actions=forbidden)
+
+ assert "a1" not in predicted_actions
+
+ forbidden = ["a1", "a2"]
+ with pytest.raises(ValueError):
+ s.predict(context=context, forbidden_actions=forbidden)
+
+ forbidden = ["a1", "a2", "a3"]
+ with pytest.raises(ValueError):
+ s.predict(context=context, forbidden_actions=forbidden)
+
+ forbidden = ["a1", "a3"]
+ with pytest.raises(ValueError):
+ s.predict(context=context, forbidden_actions=forbidden)
+
+
+def test_cmab_mo_cc_update(action_ids_cost={"a1": 1, "a2": 2}, n_samples=10, n_objectives=3, n_features=10):
+ context = np.random.uniform(low=-1.0, high=1.0, size=(n_samples, n_features))
+ rewards = [np.random.choice([0, 1], size=n_objectives).tolist() for _ in range(n_samples)]
+ actions = np.random.choice(list(action_ids_cost), size=n_samples).tolist()
+ action_ids = set(action_ids_cost.keys())
+ mab = CmabBernoulliMOCC.cold_start(
+ action_ids_cost=action_ids_cost, n_objectives=n_objectives, n_features=n_features
+ )
+ assert all(
+ [
+ mab.actions[a]
+ == BayesianLogisticRegressionMOCC.cold_start(
+ n_objectives=n_objectives, n_features=n_features, cost=action_ids_cost[a]
+ )
+ for a in action_ids
+ ]
+ )
+
+ mab.update(actions=actions, rewards=rewards, context=context)
+ assert all(
+ [
+ mab.actions[a]
+ != BayesianLogisticRegressionMOCC.cold_start(
+ n_objectives=n_objectives, n_features=n_features, cost=action_ids_cost[a]
+ )
+ for a in set(action_ids)
+ ]
+ )
+
+
+@given(st.lists(st.integers(min_value=1), min_size=8, max_size=8), st.integers(min_value=2, max_value=100))
+def test_cmab_mo_cc_get_state(a_list, n_features):
+ a, b, c, d, e, f, g, h = a_list
+
+ actions = {
+ "a1": BayesianLogisticRegressionMOCC(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ],
+ cost=g,
+ ),
+ "a2": BayesianLogisticRegressionMOCC(
+ models=[
+ BayesianLogisticRegression(alpha=StudentT(mu=a, sigma=b), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=c, sigma=d), betas=n_features * [StudentT()]),
+ BayesianLogisticRegression(alpha=StudentT(mu=e, sigma=f), betas=n_features * [StudentT()]),
+ ],
+ cost=h,
+ ),
+ }
+ cmab = CmabBernoulliMOCC(actions=actions)
+ expected_state = to_serializable_dict(
+ {
+ "actions": actions,
+ "strategy": {},
+ "epsilon": None,
+ "default_action": None,
+ "predict_actions_randomly": False,
+ "predict_with_proba": True,
+ }
+ )
+
+ class_name, cmab_state = cmab.get_state()
+ assert class_name == "CmabBernoulliMOCC"
+ assert cmab_state == expected_state
+
+ assert is_serializable(cmab_state), "Internal state is not serializable"
+
+
+@settings(deadline=500)
+@given(
+ state=st.fixed_dictionaries(
+ {
+ "actions": st.dictionaries(
+ keys=st.text(min_size=1, max_size=10),
+ values=st.fixed_dictionaries(
+ {
+ "models": st.lists(
+ st.fixed_dictionaries(
+ {
+ "alpha": st.fixed_dictionaries(
+ {
+ "mu": st.integers(min_value=1, max_value=100),
+ "sigma": st.integers(min_value=1, max_value=100),
+ },
+ ),
+ "betas": st.lists(
+ st.fixed_dictionaries(
+ {
+ "mu": st.integers(min_value=1, max_value=100),
+ "sigma": st.integers(min_value=1, max_value=100),
+ },
+ ),
+ min_size=2,
+ max_size=2,
+ ),
+ },
+ ),
+ min_size=3,
+ max_size=3,
+ ),
+ "cost": st.floats(min_value=0),
+ }
+ ),
+ min_size=2,
+ ),
+ "strategy": st.fixed_dictionaries({}),
+ }
+ )
+)
+def test_cmab_mo_cc_from_state(state):
+ cmab = CmabBernoulliMOCC.from_state(state)
+ assert isinstance(cmab, CmabBernoulliMOCC)
+
+ actual_actions = to_serializable_dict(cmab.actions)
+ expected_actions = update_nested_struct(state["actions"], actual_actions)
+ assert expected_actions == actual_actions
+
+ # Ensure get_state and from_state compatibility
+ new_cmab = globals()[cmab.get_state()[0]].from_state(state=cmab.get_state()[1])
+ assert new_cmab == cmab
+
+
+########################################################################################################################
+
+
# Cmab with epsilon-greedy super strategy
@@ -949,3 +1378,31 @@ def test_epsilon_greedy_cmab_cc_predict(n_samples, n_features):
assert len(selected_actions) == n_samples
assert probs == n_samples * [{"a1": 0.5, "a2": 0.5}]
assert weighted_sums == n_samples * [{"a1": 0, "a2": 0}]
+
+
+def test_epsilon_greddy_cmab_mo_predict(
+ action_ids={"a1", "a2"}, n_samples=10, n_objectives=3, n_features=10, epsilon=0.1, default_action="a1"
+):
+ s = CmabBernoulliMO.cold_start(
+ action_ids=action_ids,
+ n_objectives=n_objectives,
+ n_features=n_features,
+ epsilon=epsilon,
+ default_action=default_action,
+ )
+ context = np.random.uniform(low=-1.0, high=1.0, size=(n_samples, n_features))
+ s.predict(context=context)
+
+
+def test_epsilon_greddy_smab_mo_cc_predict(
+ action_ids_cost={"a1": 1, "a2": 2}, n_samples=10, n_objectives=3, n_features=10, epsilon=0.1, default_action="a1"
+):
+ s = CmabBernoulliMOCC.cold_start(
+ action_ids_cost=action_ids_cost,
+ n_objectives=n_objectives,
+ n_features=n_features,
+ epsilon=epsilon,
+ default_action=default_action,
+ )
+ context = np.random.uniform(low=-1.0, high=1.0, size=(n_samples, n_features))
+ s.predict(context=context)
diff --git a/tests/test_model.py b/tests/test_model.py
index 2041cf2..a241cee 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -30,6 +30,8 @@
from pybandits.model import (
BayesianLogisticRegression,
BayesianLogisticRegressionCC,
+ BayesianLogisticRegressionMO,
+ BayesianLogisticRegressionMOCC,
Beta,
BetaCC,
BetaMO,
@@ -55,7 +57,7 @@ def test_can_init_beta(success_counter, failure_counter):
assert (b.n_successes, b.n_failures) == (1, 1)
-def test_both_or_neither_counters_are_defined():
+def test_both_or_neither_models_are_defined():
with pytest.raises(ValidationError):
Beta(n_successes=0)
with pytest.raises(ValidationError):
@@ -112,21 +114,21 @@ def test_can_init_betaCC(a_float):
def test_can_init_base_beta_mo():
# init with default params
- b = BetaMO(counters=[Beta(), Beta()])
- assert b.counters[0].n_successes == 1 and b.counters[0].n_failures == 1
- assert b.counters[1].n_successes == 1 and b.counters[1].n_failures == 1
+ b = BetaMO(models=[Beta(), Beta()])
+ assert b.models[0].n_successes == 1 and b.models[0].n_failures == 1
+ assert b.models[1].n_successes == 1 and b.models[1].n_failures == 1
# init with empty dict
- b = BetaMO(counters=[{}, {}])
- assert b.counters[0] == Beta()
+ b = BetaMO(models=[{}, {}])
+ assert b.models[0] == Beta()
# invalid init with BetaCC instead of Beta
with pytest.raises(ValidationError):
- BetaMO(counters=[BetaCC(cost=1), BetaCC(cost=1)])
+ BetaMO(models=[BetaCC(cost=1), BetaCC(cost=1)])
def test_calculate_proba_beta_mo():
- b = BetaMO(counters=[Beta(), Beta()])
+ b = BetaMO(models=[Beta(), Beta()])
b.sample_proba()
@@ -139,12 +141,12 @@ def test_beta_update_mo(rewards1, rewards2):
rewards1, rewards2 = rewards1[:min_len], rewards2[:min_len]
rewards = [[a, b] for a, b in zip(rewards1, rewards2)]
- b = BetaMO(counters=[Beta(n_successes=11, n_failures=22), Beta(n_successes=33, n_failures=44)])
+ b = BetaMO(models=[Beta(n_successes=11, n_failures=22), Beta(n_successes=33, n_failures=44)])
b.update(rewards=rewards)
assert b == BetaMO(
- counters=[
+ models=[
Beta(n_successes=11 + sum(rewards1), n_failures=22 + len(rewards1) - sum(rewards1)),
Beta(n_successes=33 + sum(rewards2), n_failures=44 + len(rewards2) - sum(rewards2)),
]
@@ -157,26 +159,6 @@ def test_beta_update_mo(rewards1, rewards2):
########################################################################################################################
-# BetaMO
-
-
-def test_can_init_beta_mo():
- # init with default params
- b = BetaMO(counters=[Beta(), Beta()])
- assert b.counters == [Beta(), Beta()]
-
- # init with empty dict
- b = BetaMO(counters=[{}, {}])
- assert b.counters == [Beta(), Beta()]
-
- # invalid init with BetaCC instead of Beta
- with pytest.raises(ValidationError):
- BetaMO(counters=[BetaCC(cost=1), BetaCC(cost=1)])
-
-
-########################################################################################################################
-
-
# BetaMOCC
@@ -184,21 +166,21 @@ def test_can_init_beta_mo():
def test_can_init_beta_mo_cc(a_float):
if a_float < 0 or np.isnan(a_float):
with pytest.raises(ValidationError):
- BetaMOCC(counters=[Beta(), Beta()], cost=a_float)
+ BetaMOCC(models=[Beta(), Beta()], cost=a_float)
else:
# init with default params
- b = BetaMOCC(counters=[Beta(), Beta()], cost=a_float)
- assert b.counters == [Beta(), Beta()]
+ b = BetaMOCC(models=[Beta(), Beta()], cost=a_float)
+ assert b.models == [Beta(), Beta()]
assert b.cost == a_float
# init with empty dict
- b = BetaMOCC(counters=[{}, {}], cost=a_float)
- assert b.counters == [Beta(), Beta()]
+ b = BetaMOCC(models=[{}, {}], cost=a_float)
+ assert b.models == [Beta(), Beta()]
assert b.cost == a_float
# invalid init with BetaCC instead of Beta
with pytest.raises(ValidationError):
- BetaMOCC(counters=[BetaCC(cost=1), BetaCC(cost=1)], cost=a_float)
+ BetaMOCC(models=[BetaCC(cost=1), BetaCC(cost=1)], cost=a_float)
########################################################################################################################
@@ -382,3 +364,158 @@ def test_create_default_instance_bayesian_logistic_regression_cc(n_betas, cost):
assert blr == BayesianLogisticRegressionCC(
alpha=StudentT(), betas=[StudentT() for _ in range(n_betas)], cost=cost
)
+
+
+########################################################################################################################
+
+
+# BayesianLogisticRegressionMO
+
+
+@given(st.integers(max_value=10), st.integers(min_value=2, max_value=100))
+def test_can_init_bayesian_logistic_regression_mo(n_objectives, n_features):
+ # at least one blr must be specified
+ model = BayesianLogisticRegression(alpha=StudentT(), betas=[StudentT() for _ in range(n_features)])
+ if n_objectives <= 0:
+ with pytest.raises(ValidationError):
+ BayesianLogisticRegressionMO(models=[model.model_copy(deep=True) for _ in range(n_objectives)])
+ else:
+ blr_mo = BayesianLogisticRegressionMO(models=[model.model_copy(deep=True) for _ in range(n_objectives)])
+ assert all(blr == model for blr in blr_mo.models)
+
+
+@given(st.integers(max_value=10), st.integers(max_value=100))
+def test_create_default_instance_bayesian_logistic_regression_mo(n_objectives, n_features):
+ # at least one beta must be specified
+ if n_objectives <= 0 or n_features <= 0:
+ with pytest.raises(ValidationError):
+ BayesianLogisticRegressionMO.cold_start(n_features=n_features, n_objectives=n_objectives)
+ else:
+ blr_mo = BayesianLogisticRegressionMO.cold_start(n_features=n_features, n_objectives=n_objectives)
+ assert all(
+ blr == BayesianLogisticRegression(alpha=StudentT(), betas=[StudentT() for _ in range(n_features)])
+ for blr in blr_mo.models
+ )
+
+
+@given(
+ st.integers(min_value=1, max_value=100),
+ st.integers(min_value=1, max_value=10),
+ st.integers(min_value=1, max_value=100),
+)
+def test_blr_mo_sample_proba(n_samples, n_objectives, n_features):
+ def sample_proba(context):
+ results = blr_mo.sample_proba(context=context)
+ prob, weighted_sum = zip(*results) # unpack the results
+ assert type(prob) is type(weighted_sum) is tuple # type of the returns must be np.ndarray
+ for p, ws in results:
+ assert type(p) is type(ws) is np.ndarray
+ assert len(p) == len(ws) == n_samples # return 1 sampled probability and ws per each sample
+ assert (np.clip(p, 0, 1) == p).all() # probs must be in the interval [0, 1]
+
+ blr_mo = BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+
+ # context is numpy array
+ context = np.random.uniform(low=-100.0, high=100.0, size=(n_samples, n_features))
+ assert type(context) is np.ndarray
+ sample_proba(context=context)
+
+ # context is python list
+ context = context.tolist()
+ assert type(context) is list
+ sample_proba(context=context)
+
+ # context is pandas DataFrame
+ context = pd.DataFrame(context)
+ assert type(context) is pd.DataFrame
+ sample_proba(context=context)
+
+
+def test_blr_mo_update(n_samples=10, n_objectives=3, n_features=3):
+ def update(context, rewards):
+ blr_mo = BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+ assert all(
+ [
+ blr.alpha == StudentT(mu=0.0, sigma=10.0, nu=5.0)
+ and blr.betas == [StudentT(mu=0.0, sigma=10.0, nu=5.0)] * n_objectives
+ for blr in blr_mo.models
+ ]
+ )
+
+ blr_mo.update(context=context, rewards=rewards)
+
+ assert all(
+ blr.alpha != StudentT(mu=0.0, sigma=10.0, nu=5.0)
+ and blr.betas != [StudentT(mu=0.0, sigma=10.0, nu=5.0)] * n_objectives
+ for blr in blr_mo.models
+ )
+
+ rewards = [np.random.choice([0, 1], size=n_objectives).tolist() for _ in range(n_samples)]
+
+ # context is numpy array
+ context = np.random.uniform(low=-100.0, high=100.0, size=(n_samples, n_features))
+ assert type(context) is np.ndarray
+ update(context=context, rewards=rewards)
+
+ # context is python list
+ context = context.tolist()
+ assert type(context) is list
+ update(context=context, rewards=rewards)
+
+ # context is pandas DataFrame
+ context = pd.DataFrame(context)
+ assert type(context) is pd.DataFrame
+ update(context=context, rewards=rewards)
+
+ # raise an error if len(context) != len(rewards)
+ with pytest.raises(ValueError):
+ blr_mo = BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+ blr_mo.update(context=context, rewards=rewards[1:])
+
+ # raise an error if n_objectives != len(rewards[0])
+ with pytest.raises(AttributeError):
+ blr_mo = BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+ blr_mo.update(context=context, rewards=[rewards[0][:1]] + rewards[1:])
+
+ # raise an error if n_objectives != len(rewards[*])
+ with pytest.raises(AttributeError):
+ blr_mo = BayesianLogisticRegressionMO.cold_start(n_objectives=n_objectives, n_features=n_features)
+ blr_mo.update(context=context, rewards=[reward[:1] for reward in rewards])
+
+
+########################################################################################################################
+
+
+# BayesianLogisticRegressionMOCC
+
+
+@given(
+ st.integers(max_value=10), st.integers(min_value=2, max_value=100), st.floats(allow_nan=False, allow_infinity=False)
+)
+def test_can_init_bayesian_logistic_regression_mocc(n_objectives, n_features, cost):
+ # at least one blr must be specified
+ model = BayesianLogisticRegression(alpha=StudentT(), betas=[StudentT() for _ in range(n_features)])
+ if n_objectives <= 0 or cost < 0:
+ with pytest.raises(ValidationError):
+ BayesianLogisticRegressionMOCC(models=[model.model_copy(deep=True) for _ in range(n_objectives)], cost=cost)
+ else:
+ blr_mo = BayesianLogisticRegressionMOCC(
+ models=[model.model_copy(deep=True) for _ in range(n_objectives)], cost=cost
+ )
+ assert all(blr == model for blr in blr_mo.models)
+
+
+@given(st.integers(max_value=10), st.integers(max_value=100), st.floats(allow_nan=False, allow_infinity=False))
+def test_create_default_instance_bayesian_logistic_regression_mocc(n_objectives, n_features, cost):
+ # at least one beta must be specified
+ if n_objectives <= 0 or n_features <= 0 or cost < 0:
+ with pytest.raises(ValidationError):
+ BayesianLogisticRegressionMOCC.cold_start(n_objectives=n_objectives, n_features=n_features, cost=cost)
+ else:
+ blr_mocc = BayesianLogisticRegressionMOCC.cold_start(
+ n_objectives=n_objectives, n_features=n_features, cost=cost
+ )
+ assert all(
+ blr == BayesianLogisticRegression(alpha=StudentT(), betas=[StudentT() for _ in range(n_features)])
+ for blr in blr_mocc.models
+ )
diff --git a/tests/test_smab.py b/tests/test_smab.py
index f11ec0c..00b216a 100644
--- a/tests/test_smab.py
+++ b/tests/test_smab.py
@@ -20,7 +20,6 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
-import json
from copy import deepcopy
from typing import List
@@ -496,7 +495,7 @@ def test_smab_cc_from_state(state):
assert isinstance(smab, SmabBernoulliCC)
expected_actions = state["actions"]
- actual_actions = json.loads(json.dumps(smab.actions, default=dict)) # Normalize the dict
+ actual_actions = to_serializable_dict(smab.actions) # Normalize the dict
assert expected_actions == actual_actions
expected_subsidy_factor = smab.strategy.get_expected_value_from_state(state, "subsidy_factor")
actual_subsidy_factor = smab.strategy.subsidy_factor
@@ -520,14 +519,14 @@ def test_can_init_smab_mo(a_list):
s = SmabBernoulliMO(
actions={
"a1": BetaMO(
- counters=[
+ models=[
Beta(n_successes=a, n_failures=b),
Beta(n_successes=c, n_failures=d),
Beta(n_successes=e, n_failures=f),
]
),
"a2": BetaMO(
- counters=[
+ models=[
Beta(n_successes=d, n_failures=a),
Beta(n_successes=e, n_failures=b),
Beta(n_successes=f, n_failures=c),
@@ -536,14 +535,14 @@ def test_can_init_smab_mo(a_list):
},
)
assert s.actions["a1"] == BetaMO(
- counters=[
+ models=[
Beta(n_successes=a, n_failures=b),
Beta(n_successes=c, n_failures=d),
Beta(n_successes=e, n_failures=f),
]
)
assert s.actions["a2"] == BetaMO(
- counters=[
+ models=[
Beta(n_successes=d, n_failures=a),
Beta(n_successes=e, n_failures=b),
Beta(n_successes=f, n_failures=c),
@@ -556,9 +555,9 @@ def test_all_actions_must_have_same_number_of_objectives_smab_mo():
with pytest.raises(ValueError):
SmabBernoulliMO(
actions={
- "a1": BetaMO(counters=[Beta(), Beta()]),
- "a2": BetaMO(counters=[Beta(), Beta()]),
- "a3": BetaMO(counters=[Beta(), Beta(), Beta()]),
+ "a1": BetaMO(models=[Beta(), Beta()]),
+ "a2": BetaMO(models=[Beta(), Beta()]),
+ "a3": BetaMO(models=[Beta(), Beta(), Beta()]),
},
)
@@ -602,14 +601,14 @@ def test_smab_mo_get_state(a_list):
actions = {
"a1": BetaMO(
- counters=[
+ models=[
Beta(n_successes=a, n_failures=b),
Beta(n_successes=c, n_failures=d),
Beta(n_successes=e, n_failures=f),
]
),
"a2": BetaMO(
- counters=[
+ models=[
Beta(n_successes=d, n_failures=a),
Beta(n_successes=e, n_failures=b),
Beta(n_successes=f, n_failures=c),
@@ -640,7 +639,7 @@ def test_smab_mo_get_state(a_list):
keys=st.text(min_size=1, max_size=10),
values=st.fixed_dictionaries(
{
- "counters": st.lists(
+ "models": st.lists(
st.fixed_dictionaries(
{
"n_successes": st.integers(min_value=1, max_value=100),
@@ -663,7 +662,7 @@ def test_smab_mo_from_state(state):
assert isinstance(smab, SmabBernoulliMO)
expected_actions = state["actions"]
- actual_actions = json.loads(json.dumps(smab.actions, default=dict)) # Normalize the dict
+ actual_actions = to_serializable_dict(smab.actions)
assert expected_actions == actual_actions
# Ensure get_state and from_state compatibility
@@ -684,7 +683,7 @@ def test_can_init_smab_mo_cc(a_list):
s = SmabBernoulliMOCC(
actions={
"a1": BetaMOCC(
- counters=[
+ models=[
Beta(n_successes=a, n_failures=b),
Beta(n_successes=c, n_failures=d),
Beta(n_successes=e, n_failures=f),
@@ -692,7 +691,7 @@ def test_can_init_smab_mo_cc(a_list):
cost=g,
),
"a2": BetaMOCC(
- counters=[
+ models=[
Beta(n_successes=d, n_failures=a),
Beta(n_successes=e, n_failures=b),
Beta(n_successes=f, n_failures=c),
@@ -702,7 +701,7 @@ def test_can_init_smab_mo_cc(a_list):
},
)
assert s.actions["a1"] == BetaMOCC(
- counters=[
+ models=[
Beta(n_successes=a, n_failures=b),
Beta(n_successes=c, n_failures=d),
Beta(n_successes=e, n_failures=f),
@@ -710,7 +709,7 @@ def test_can_init_smab_mo_cc(a_list):
cost=g,
)
assert s.actions["a2"] == BetaMOCC(
- counters=[
+ models=[
Beta(n_successes=d, n_failures=a),
Beta(n_successes=e, n_failures=b),
Beta(n_successes=f, n_failures=c),
@@ -724,16 +723,14 @@ def test_all_actions_must_have_same_number_of_objectives_smab_mo_cc():
with pytest.raises(ValueError):
SmabBernoulliMOCC(
actions={
- "action 1": BetaMOCC(counters=[Beta(), Beta()], cost=1),
- "action 2": BetaMOCC(counters=[Beta(), Beta()], cost=1),
- "action 3": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=1),
+ "action 1": BetaMOCC(models=[Beta(), Beta()], cost=1),
+ "action 2": BetaMOCC(models=[Beta(), Beta()], cost=1),
+ "action 3": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=1),
},
)
-def test_smab_mo_cc_predict(n_samples: int):
- n_samples = 1000
-
+def test_smab_mo_cc_predict(n_samples=1000):
s = SmabBernoulliMOCC.cold_start(action_ids_cost={"a1": 1, "a2": 2}, n_objectives=2)
forbidden = None
@@ -782,7 +779,7 @@ def test_smab_mo_cc_get_state(a_list):
actions = {
"a1": BetaMOCC(
- counters=[
+ models=[
Beta(n_successes=a, n_failures=b),
Beta(n_successes=c, n_failures=d),
Beta(n_successes=e, n_failures=f),
@@ -790,7 +787,7 @@ def test_smab_mo_cc_get_state(a_list):
cost=g,
),
"a2": BetaMOCC(
- counters=[
+ models=[
Beta(n_successes=d, n_failures=a),
Beta(n_successes=e, n_failures=b),
Beta(n_successes=f, n_failures=c),
@@ -822,7 +819,7 @@ def test_smab_mo_cc_get_state(a_list):
keys=st.text(min_size=1, max_size=10),
values=st.fixed_dictionaries(
{
- "counters": st.lists(
+ "models": st.lists(
st.fixed_dictionaries(
{
"n_successes": st.integers(min_value=1, max_value=100),
@@ -877,9 +874,7 @@ def test_can_instantiate_epsilon_greddy_smab_with_params(a, b):
assert s.actions["action1"] == s.actions["action2"]
-def test_epsilon_greedy_smab_predict(n_samples: int):
- n_samples = 1000
-
+def test_epsilon_greedy_smab_predict(n_samples=1000):
s = SmabBernoulli(
actions={
"a0": Beta(),
@@ -897,14 +892,12 @@ def test_epsilon_greedy_smab_predict(n_samples: int):
_, _ = s.predict(n_samples=n_samples, forbidden_actions=forbidden_actions)
-def test_epsilon_greddy_smabbai_predict(n_samples: int):
- n_samples = 1000
+def test_epsilon_greddy_smabbai_predict(n_samples=1000):
s = SmabBernoulliBAI(actions={"a1": Beta(), "a2": Beta()}, epsilon=0.1, default_action="a1")
_, _ = s.predict(n_samples=n_samples)
-def test_epsilon_greddy_smabcc_predict(n_samples: int):
- n_samples = 1000
+def test_epsilon_greddy_smabcc_predict(n_samples=1000):
s = SmabBernoulliCC(
actions={
"a1": BetaCC(n_successes=1, n_failures=2, cost=10),
@@ -917,18 +910,14 @@ def test_epsilon_greddy_smabcc_predict(n_samples: int):
_, _ = s.predict(n_samples=n_samples)
-def test_epsilon_greddy_smab_mo_predict(n_samples: int):
- n_samples = 1000
-
+def test_epsilon_greddy_smab_mo_predict(n_samples=1000):
s = SmabBernoulliMO.cold_start(action_ids={"a1", "a2"}, n_objectives=3, epsilon=0.1, default_action="a1")
forbidden = None
s.predict(n_samples=n_samples, forbidden_actions=forbidden)
-def test_epsilon_greddy_smab_mo_cc_predict(n_samples: int):
- n_samples = 1000
-
+def test_epsilon_greddy_smab_mo_cc_predict(n_samples=1000):
s = SmabBernoulliMOCC.cold_start(
action_ids_cost={"a1": 1, "a2": 2}, n_objectives=2, epsilon=0.1, default_action="a1"
)
diff --git a/tests/test_strategy.py b/tests/test_strategy.py
index 8c5165f..737886b 100644
--- a/tests/test_strategy.py
+++ b/tests/test_strategy.py
@@ -351,11 +351,11 @@ def test_select_action_mo_cc():
m = MultiObjectiveCostControlBandit()
actions = {
- "a1": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=8),
- "a2": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=2),
- "a3": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=5),
- "a4": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=1),
- "a5": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=7),
+ "a1": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=8),
+ "a2": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=2),
+ "a3": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=5),
+ "a4": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=1),
+ "a5": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=7),
}
p = {
"a1": [0.1, 0.3, 0.5],
@@ -369,9 +369,9 @@ def test_select_action_mo_cc():
assert m.select_action(p=p, actions=actions) == "a4"
actions = {
- "a1": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=2),
- "a2": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=2),
- "a3": BetaMOCC(counters=[Beta(), Beta(), Beta()], cost=5),
+ "a1": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=2),
+ "a2": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=2),
+ "a3": BetaMOCC(models=[Beta(), Beta(), Beta()], cost=5),
}
p = {
"a1": [0.6, 0.1, 0.1],