Repository: causalincentives/pycid (language: python)

Issue:
Speed up mixed NE method
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Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index 72b57c3..e41d9ba 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,3 +138,7 @@ dmypy.json
 
 # Cython debug symbols
 cython_debug/
+
+# gambit
+*.efg
+*.nfg
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c558c9a..2e0a35e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/ambv/black
-    rev: 22.1.0
+    rev: 22.3.0
     hooks:
       - id: black
         args: [--safe]
diff --git a/notebooks/MACID_Basics_Tutorial.ipynb b/notebooks/MACID_Basics_Tutorial.ipynb
index b49d8e9..0bb95dd 100644
--- a/notebooks/MACID_Basics_Tutorial.ipynb
+++ b/notebooks/MACID_Basics_Tutorial.ipynb
@@ -30,6 +30,14 @@
      "text": [
       "Autosaving every 60 seconds\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/shreshth/anaconda3/envs/pycid_39_local/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
     }
    ],
    "source": [
@@ -203,7 +211,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -355,7 +363,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -517,7 +525,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -540,7 +548,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -563,7 +571,7 @@
      "data": {
       "image/png": 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3m5xUpPRQ6Uqx1apYi/2P7yeyQuRl+4bHDufXJ36levnqRCdEM3jhYPKKeCJOpCxQ6UqJCfEPYd0j65jZYyazUmZR+Y3KrD24tugTRTyYSldKXN8mfTk56iTNb2hOu2ntuG/ufdjz7GbHEjGFSlcM4e/jz9IHl5LYJ5GvfvmK0P+GsmT3ErNjiRhOpSuGuqvuXaQ+nUqnWp3oMrsLnWd1JiO7GOtBiLgpla4Yzma18fl9n7Pq4VVsOrqJSmMr8cn2T8yOJWIIla6Ypm2Ntvz25G/0bdKXvvP60ubDNvye9bvZsURKlEpXTGW1WpncdTLJQ5I5ePog4WPDmbhpotmxREqMSldKhajwKA6PPMxjrR5j+JLhRE2M4uiZo2bHEnE5la6UKmNvH8uu4bvItGdS450avLrqVbMjibiUSldKndohtdn96G7G3DqGl1a8RJ1367AndY/ZsURcQqUrpdaz7Z7l8MjDBPoEUm98PZ5Y+oTZkUSum0pXSrUqgVVIHpLM+DvHM37jeKq9VY3kY8lmxxIpNpWuuIWhLYdy/Mnj1KxQk+aTmhP3RZwW0BG3pNIVt1HBtwJr4tbwSc9PmLNjDmFvhLHm4BqzY4lcE5WuuJ37G99P6tOpxFaN5eZpN9Pz055k27PNjiVyVVS64pZ8bb4s6beExX0Xs2zfMkLHhpL4c6LZsUSKpNIVt9a5TmdOPXWKLnW70HVOVzrN6KQFdKRUU+mK27NZbcy5dw5r49aSfDyZ0LGhzNo2y+xYIgVS6YrHaB3RmuNPHGdA1AD6L+jP36f8ndSMVLNjiVxEpSsexWq1knBPAilDUjiafpQqb1bhvQ3vmR1LJJ9KVzxS4/DGHBxxkCdaP8HjXz9OkwlNOHzmsNmxRFS64tlev+119jy6h+y8bCLfieT/Vv6f2ZGkjFPpiseLrBjJz8N/5vWOrzNm1RhqjavFrlO7zI4lZZRKV8qMUTeN4siII1TwrUCD9xsw4qsRepRYDKfSlTKlcmBltsRv4YO7P2Bi0kSqvV2NLce2mB1LyhCVrpRJjzR/hBNPnqBuSF1iJsXw0IKHdNUrhlDpSpkV5BvEqodX8Vmvz5i7cy6VxlZixf4VZscSD6fSlTLv3ob3cuqpU7SJaEOH6R3oPqe7FtCREqPSFcG5gE5in0S+efAbvtv/HSH/DeGLn74wO5Z4IJWuyF90rNWRk0+dpFv9bnT/tDsdpnfgbPZZs2OJB1HpilzCZrXxcc+P2fDIBn448QNh/w1jevL0Ao/9du+3LNm9xOCE4s5UuiJX0LJaS449cYy45nHEfRlH7ORYTmaczN9/MuMkPT/ryQP/e4DTWadNTCruRKUrUgir1cr7d73PjqE7OH7uOFXfrMo7698BIH5hPJn2TM7bz/Pct8+ZG1TchsXhcFxxZ0xMjCMpKcnAOCKl2+jlo/n3mn9TNbAqJzNPkmnPBMDP5kfS4CQahjU0OaGUBhaLZbPD4YgpaJ+udEWuwb86/IttQ7Zx9OzR/MIFyLJnMfDLgVzxIiY3FxYtgiFDICoKbrgBqlWDNm3guedg61aDvoGYzWZ2ABF38+a6N7FZbeTm5uZvc+Bg+/HtzP9pPj0a9PjzYIcDZs6EJ5+ErCxIT7/4w44ehY0bYdw4qFULpk6Fli0N+iZiBl3pilyDVQdWMWfHHM7nnr9s37mccwxJHEJmzoUr4PR0uP12GDYMfvvt8sL9Q24uZGTAjh3Qvj08/7yzrMUjqXRFrsHLK14m15FLULkg/Gx+l+3/LeM3hi8ZDmfPQtu2sHo1nDt39X9AZia8846zqFW8Hkk30kSuwbnsc+xN28uhM4c4dPoQ+3/fz67UXexL28fR9KOcOHcCBw5+WRdL7ZUpziGF4ggIgAkToH9/134BMURhN9JUuiIu5HA42PXJeGrGjcTnvP36PiwwEPbsgcqVXRNODKPZCyIGsVgs1H9ren7hRgJ+QHmgAtAGSAD+WETyO+BWIPjCsRex2+H990s6shhMpSviSrt3w86dF21aCKQDB4BngP8AAy/sCwDigLEFfVZWFowfr7FdD6PSFXGlNWvAWvBfq2CgK/ApMB3YAcQCDwK1rvR5mZlw8KDrc4ppVLoirrRpU5GzFWKB6sDqq/k8b29ITr7+XFJqqHRFXOnkyaKPAaoCqVdzYF4enNZiOp5EpSviSn6Xz90tyBEg5GoOtFjA1/d6Ekkpo9IVcaXmzaFcuUIP2YSzdNte7Wc2aHCdoaQ0UemKuFJsrHMctgBngESgN9APaIJz6lgWkAM4Lvz+orezZWerdD2MFrwRcaVWraB8eedjwBfcg/MvmhVoCIwEhlzYtwrnPN0/+AHtgRUAXl7QqxfY9NfUk+i/pogrWa3w9NPORWvOnWN/EYffgvMKtyAOH28so0a5NJ6YT8MLIq72z3/C3/7mvAlWTHm+5ZjczMH4rFXkOfKKPkHchkpXxNVsNpg/H4KCine+jw/WOnW5Zc4GZqXM4vaZt3PwtB6Q8BQqXZGSUK8erFoFoaFFzma4SEAANG4Mq1dTLyKaNXFr6FizIy0mtWDa1mlXfjOFuA2VrkhJiYqCXbugSxfn/N3Cboj5+Tnn4z77LGzYABUqAM7XwT/b7lm+7f8t4zaMo9ucbvx69ldj8kuJUOmKlKSQEPjf/yApCeLioHp155SygADw93f+vn59GD3aucbC888XWM5R4VFsHLSRqPAoohOi+eyHz0z4MuIKWk9XxGhnzzofF7ZanS+ovMK83ivZcHgDAxYMoGmVprx/1/uE+oeWUFApLq2nK1KaBAZCZCTUqHHNhQvQqnortsZvpWr5qkQlRJG4K9H1GaXEqHRF3JCftx9v3fEWs3vM5rEljzHwi4GcOX/G7FhyFVS6Im6sfWR7tg3Zhs1qI2piFMv3LTc7khRBpSvi5sqXK88H93xAwt0J9J/fn8eWPEZGTobZseQKVLoiHqJznc5sH7qdtKw0miY05ftD35sdSQqg0hXxIBX9KjKz+0z+fdu/6flZT55Z9gzn7efNjiV/odIV8UA9GvRg25Bt/HzqZ2Imx7D12FazI8kFKl0RD1U5oDLz7pvH0zc9zR2z7mDMyjHk5OaYHavMU+mKeDCLxUK/qH5sid/C2kNraTO1DTt/21n0iVJiVLoiZUD1oOos6buEQc0H0f6j9ry17i1y83LNjlUmqXRFygiLxcLgFoPZ8MgGFvy0gFun38retL1mxypzVLoiZUytirVY8dAKut/YnVZTWpGQlKAlIw2k0hUpg6wWKyNaj2DVQ6v4cOuHdP64M4fPHDY7Vpmg0hUpwxqENWDdwHW0q9GO5h80Z+a2mbrqLWEqXZEyzma1Mfrm0Sx9cCljvx9Lj896cOLcCbNjeSyVrogA0LRKUzYN2kSDSg2ITohm3o/zzI7kkVS6IpKvnK0cr3V8jXn3zeOZZc/Qb14/0jLTzI7lUVS6InKZ1hGtSR6STKhfKE0mNmHJ7iVmR/IYKl0RKZC/tz/j7hzHjO4zGLpoKIMXDib9fLrZsdyeSldECtWhZgdShqaQ58gjOiGalftXmh3Jral0RaRIQeWCmNJ1Cu/d+R595vVhxFcjyMzJNDuWW1LpishV61KvCylDUjh+7jjNPmjGxiMbzY7kdlS6InJNQv1Dmd1zNmNuHUPXT7oyevlosnOzzY7lNlS6IlIsvRr1YtuQbWw/sZ2Wk1uy7ddtZkdyCypdESm28MBwFty/gJF/H0mnmZ14bfVr2PPsZscq1VS6InJdLBYLA5oOYPPgzXy3/zvaTm3Lzyd/NjtWqaXSFRGXiAiO4Ot+X9M/uj9tp7Vl3Ppx5DnyzI5V6qh0RcRlrBYrw1oOY93AdczdOZeOMzqyL22f2bFKFZWuiLhcnZA6rHxoJV3qdiF2SiyTN0/WkpEXqHRFpER4Wb14ss2TrBiwgoTNCXSZ3YWj6UfNjmU6la6IlKhGlRuxfuB6WlVrRbMPmjF7++wyfdWr0hWREuft5c1Lt7zE4j6LeXX1q/Sa24vfzv1mdixTqHRFxDAtqrZg8+DN1K5Ym+iEaBb8tMDsSIZT6YqIoXxtvvyn03+Y22suo74ZxYAFA/g963ezYxlGpSsipripxk0kxydT3qc8UROjWLpnqdmRDKHSFRHTBPgEMP6u8UztNpVBCwcxbNEwzmafvey4tMw0Xl/9ukfcgFPpiojpbqt1GylDUsi0ZxKdEM3qA6sv2j/wy4E8v/x55v8036SErqPSFZFSIdg3mGndpvH2HW9z/+f38+TSJ8myZ/H5zs/5es/XOHAwfPFwt19GUqUrIqVK1/pdSRmawsHTB4maGMVDCx4iIycDgNPnTzN+43iTE14fS2FjJDExMY6kpCQD44iIODkcDqITotl+YvtF2wN9Ajn4+EEq+lUs8LwccjlMGkc5zWkyAAshBFCVYKpRAS8DrjUtFstmh8MRU9A+W4n/6SIixTAjZQZ70vZctj0nN4cXvnuB8XddfMV7lixWspsUjmDFgp1ccnFeVHphxXahbFtQg7bUwRfvkv8SBdCVroiUOofPHKbB+Aaczbl8JgM45/ruGLqD2iG1AdjOERaxHTt55FH4DAcvrPjgRXeaUYcwl2eHwq90NaYrIqXOzG0zybBnEFQuiPI+5fGyeF20P8ueRdyXcQCs4RcSSSGb3CILFyCXPDLJ4TOSSOZwieQvjK50RaTUcTgcpGWlsTdtL3tS97AnbQ87Tuzgp5M/cfD0QVIzU3HgYO6wZewOyyGH3GL9OTas9CGWSEJdml9juiLiViwWCyF+IYT4hRBT9fLuys7NJvnUDyyvdLzYhQtgJ4//sYVHuRUfg+pQpSsibsfHy4dTlb2x43wd0NuRcZw9/jtWmxdWLythDSOI7t+BFoM7Y7X+OYpqz85hYvSjZKdn8sTh6QCcx04SB2hDbUOyq3RFxO3kkEsyhy8aw+2z8EVq39aUrNPn2L9yB0v+3yQOb9hF92mP5x+zduw8AsKCyU7PzN9mJ4/17KM1tbBgKfHsupEmIm7nCL9jvUJB+gYHcGPXVvT69Cm2Tf+W4zv2A5C271dSZn1Hu2d7XXbOeeykkVGSkfOpdEXE7RzjdP7QwpVUj61PUPVKHFi9E4DFj37Aba/1x+bnc9mxFiwc43SJZL2USldE3M4ZMsktonQBylcNITM1nR/nf09ebh4Nurcp8Lhc8jjLeVfHLJBKV0Tc0NWNvZ45cgrfCgEsfWoad707uIQzXR3dSBMRtxOCPzashQ4xHNm0i/Qjp6gSFcnv+08wtd3TAORm28k6ncHYKv14ZP2bVIwMx4aVCvgZkl2lKyJu5waCsWKFAko360wGB1Y5Zy9E9buF6q0bMPLQR/n7D33/I4uHJxC/ZRwBYUGAc3jhBoINya7SFRG3U5Xgy2YvzL7n/7DavLBYLYQ1jKD1yH/QcsidWL28KF/lzxXJ/EICsVgtF20Lwpcgg6509RiwiLil7/iZdewtchZDUbzxojMNaUYNFyXTgjci4oHaUNslj+4G4UsU1V2Q6OqodEXELZXDRk+a5a+TWxzeeHEvzQ1Z2PwPKl0RcVs1qURXoopVvN540ZsYwgkqgWRXphtpIuLWGlONYPz4nK1kUfQyj954EYQvvWhBZcoblPJPKl0RcXsRhDCcW9jKQb5nLxlk44U1/6k1rwtzeivgx03UJopqF6acGU+lKyIewRsvYqlJSyI5QxbHOE06WQAE40dVggnE1+SUKl0R8TAWLATjR7BB826vlW6kiYgYSKUrImIgla6IiIFUuiIiBlLpiogYSKUrImIgla6IiIEKXdrRYrH8BhwwLo6IiEf4m8PhCCtoR6GlKyIirqXhBRERA6l0RUQMpNIVETGQSldExEAqXRERA/1/Vn5jew09ibAAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -586,7 +594,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -745,7 +753,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -773,6 +781,16 @@
     "outputId": "39ddb73f-e882-410e-e666-b07daa07707f"
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/shreshth/anaconda3/envs/pycid_39_local/lib/python3.9/site-packages/pycid/core/macid_base.py:59: UserWarning: adding DecisionDomain to non-decision node D1\n",
+      "  warn(f\"adding DecisionDomain to non-decision node {variable}\")\n",
+      "/Users/shreshth/anaconda3/envs/pycid_39_local/lib/python3.9/site-packages/pycid/core/macid_base.py:59: UserWarning: adding DecisionDomain to non-decision node D2\n",
+      "  warn(f\"adding DecisionDomain to non-decision node {variable}\")\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -864,7 +882,7 @@
     }
    ],
    "source": [
-    "macid.get_all_pure_ne()"
+    "macid.get_ne(solver=\"enumpure\")"
    ]
   },
   {
@@ -909,7 +927,7 @@
     }
    ],
    "source": [
-    "all_pure_ne = macid.get_all_pure_ne()\n",
+    "all_pure_ne = macid.get_ne(solver=\"enumpure\")\n",
     "macid.policy_profile_assignment(all_pure_ne[0])"
    ]
   },
@@ -1077,7 +1095,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1100,7 +1118,7 @@
      "data": {
       "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1154,7 +1172,7 @@
     }
    ],
    "source": [
-    "macid.get_all_pure_ne()"
+    "macid.get_ne(solver=\"enumpure\")"
    ]
   },
   {
@@ -1220,16 +1238,7 @@
     }
    ],
    "source": [
-    "macid.get_all_pure_ne_in_sg(decisions_in_sg=[\"D2\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2a-B-FPaWnv5"
-   },
-   "source": [
-    "To find all SPE in this MACIM:"
+    "macid.get_ne_in_sg(solver=\"enumpure\", decisions_in_sg=[\"D2\"])"
    ]
   },
   {
@@ -1256,7 +1265,7 @@
     }
    ],
    "source": [
-    "macid.get_all_pure_spe()"
+    "macid.get_spe(solver=\"enumpure\")"
    ]
   },
   {
@@ -1276,7 +1285,8 @@
    },
    "outputs": [],
    "source": [
-    "macid.add_cpds(*macid.get_all_pure_spe()[0])"
+    "# add the decision rules for the SPE\n",
+    "macid.add_cpds(*macid.get_spe()[0])"
    ]
   },
   {
@@ -1365,6 +1375,7 @@
    },
    "outputs": [],
    "source": [
+    "macid.remove_all_decision_rules()   # remove previous decision rules\n",
     "macid.add_cpds(cpd_u2)"
    ]
   },
@@ -1397,7 +1408,7 @@
     }
    ],
    "source": [
-    "macid.get_all_pure_spe()"
+    "macid.get_spe()"
    ]
   },
   {
@@ -1417,7 +1428,7 @@
     }
    ],
    "source": [
-    "print(len(macid.get_all_pure_spe()))"
+    "print(len(macid.get_spe()))"
    ]
   },
   {
@@ -1466,7 +1477,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1509,7 +1520,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
diff --git a/pycid/__init__.py b/pycid/__init__.py
index dc78b1e..5b13579 100644
--- a/pycid/__init__.py
+++ b/pycid/__init__.py
@@ -1,3 +1,4 @@
 from pycid.analyze import *  # noqa
 from pycid.core import *  # noqa
+from pycid.export import *  # noqa
 from pycid.random import *  # noqa
diff --git a/pycid/core/causal_bayesian_network.py b/pycid/core/causal_bayesian_network.py
index 2bf5799..e8855ad 100644
--- a/pycid/core/causal_bayesian_network.py
+++ b/pycid/core/causal_bayesian_network.py
@@ -172,7 +172,6 @@ def query(
 
         with np.errstate(invalid="ignore"):  # Suppress numpy warnings for 0/0
             factor = bp.query(query, context, show_progress=False)
-            factor = bp.query(query, context, show_progress=False)
         return factor
 
     def intervene(self, intervention: Dict[str, Outcome]) -> None:
diff --git a/pycid/core/macid.py b/pycid/core/macid.py
index b8cf4f6..604ab3d 100644
--- a/pycid/core/macid.py
+++ b/pycid/core/macid.py
@@ -2,7 +2,7 @@
 
 import copy
 import itertools
-from typing import Dict, Iterable, List, Optional, Tuple
+from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 import networkx as nx
 from pgmpy.factors.discrete import TabularCPD
@@ -10,32 +10,57 @@
 from pycid.core.cpd import DecisionDomain, StochasticFunctionCPD
 from pycid.core.macid_base import MACIDBase
 from pycid.core.relevance_graph import CondensedRelevanceGraph
+from pycid.export.gambit import behavior_to_cpd, macid_to_efg, pygambit_ne_solver
+
+Outcome = Any
 
 
 class MACID(MACIDBase):
     """A Multi-Agent Causal Influence Diagram"""
 
-    def get_all_pure_ne(self) -> List[List[StochasticFunctionCPD]]:
+    def get_ne(self, solver: Optional[str] = None) -> List[List[StochasticFunctionCPD]]:
         """
-        Return a list of all pure Nash equilbiria in the MACID.
-        - Each NE comes as a list of FunctionCPDs, one for each decision node in the MACID.
+        Return a list of Nash equilbiria in the MACID using pygambit solvers.
+        By default, this does the following:
+        - 2-player games: uses solver='enummixed' to find all mixed NE
+        - N-player games: uses solver='enumpure' to find all pure NE.
+          If no pure NE exist, uses solver='simpdiv' to find 1 mixed NE if it exists.
+        To change this behavior, use the 'solver' argument. The following solvers are available:
+            - "enumpure": enumerate all pure NEs in the MACID.
+                - for arbitrary N-player games
+            - "enummixed": Valid for enumerate all mixed NEs in the MACID by computing the
+              extreme points of the set of equilibria.
+                - for 2-player games only
+            - "lcp": Compute NE using the Linear Complementarity Program (LCP) solver.
+                - for 2-player games only
+            - "lp": Compute one NE using the Linear Programming solver.
+                - for 2-player, constant sum games only
+            - "simpdiv": Compute one mixed NE using the Simplicial Subdivision.
+                - for arbitrary N-player games
+            - "ipa": Compute one mixed NE using the Iterative Partial Assignment solver
+                - for arbitrary N-player games
+            - "gnm": Compute one mixed NE using the global newton method
+                - for arbitrary N-player games
+        See pygambit docs for more details
+        https://gambitproject.readthedocs.io/en/latest/pyapi.html#module-pygambit.nash
+        Each NE comes as a list of FunctionCPDs, one for each decision node in the MACID.
         """
-        return self.get_all_pure_ne_in_sg()
-
-    def joint_pure_policies(self, decisions: Iterable[str]) -> List[Tuple[StochasticFunctionCPD, ...]]:
-        """return a list of tuples of all joint pure policies in the MACID. A joint pure policy assigns a
-        pure decision rule to every decision node in the MACID."""
-        all_dec_decision_rules = list(map(self.pure_decision_rules, decisions))
-        return list(itertools.product(*all_dec_decision_rules))
+        return self.get_ne_in_sg(solver=solver)
 
-    def get_all_pure_ne_in_sg(
-        self, decisions_in_sg: Optional[Iterable[str]] = None
+    def get_ne_in_sg(
+        self,
+        decisions_in_sg: Optional[Iterable[str]] = None,
+        solver: Optional[str] = None,
     ) -> List[List[StochasticFunctionCPD]]:
         """
-        Return a list of all pure Nash equilbiria in a MACID subgame.
-
+        Return a list of NE in a MACID subgame.
+        By default, this does the following:
+        - 2-player games: uses solver='enummixed' to find all mixed NE
+        - N-player games: uses solver='enumpure' to find all pure NE.
+          If no pure NE exist, uses solver='simpdiv' to find 1 mixed NE if it exists.
+        Use the 'solver' argument to change this behavior (see get_ne method for details).
         - Each NE comes as a list of FunctionCPDs, one for each decision node in the MAID subgame.
-        - If decisions_in_sg is not specified, this method finds all pure NE in the full MACID.
+        - If decisions_in_sg is not specified, this method finds NE in the full MACID.
         - If the MACID being operated on already has function CPDs for some decision nodes, it is
         assumed that these have already been optimised and so these are not changed.
         """
@@ -44,55 +69,41 @@ def get_all_pure_ne_in_sg(
             decisions_in_sg = self.decisions
         else:
             decisions_in_sg = set(decisions_in_sg)  # For efficient membership checks
-
         agents_in_sg = list({self.decision_agent[dec] for dec in decisions_in_sg})
-        agent_decs_in_sg = {
-            agent: [dec for dec in self.agent_decisions[agent] if dec in decisions_in_sg] for agent in agents_in_sg
-        }
 
         # impute random decisions to non-instantiated, irrelevant decision nodes
-        macid = self.copy()
-        for d in macid.decisions:
-            if not macid.is_s_reachable(decisions_in_sg, d) and isinstance(macid.get_cpds(d), DecisionDomain):
-                macid.impute_random_decision(d)
-
-        # NE finder
-        all_pure_ne_in_sg: List[List[StochasticFunctionCPD]] = []
-        for pp in self.joint_pure_policies(decisions_in_sg):
-            macid.add_cpds(*pp)  # impute the policy profile
-
-            for a in agents_in_sg:  # check that each agent is happy
-                eu_pp_agent_a = macid.expected_utility({}, agent=a)
-                macid.add_cpds(*macid.optimal_pure_policies(agent_decs_in_sg[a])[0])
-                max_eu_agent_a = macid.expected_utility({}, agent=a)
-                if max_eu_agent_a > eu_pp_agent_a:  # not an NE
-                    break
-            else:
-                # it's an NE
-                all_pure_ne_in_sg.append(list(pp))
-
-        return all_pure_ne_in_sg
+        sg_macid = self.copy()
+        for d in sg_macid.decisions:
+            if not sg_macid.is_s_reachable(decisions_in_sg, d) and isinstance(sg_macid.get_cpds(d), DecisionDomain):
+                sg_macid.impute_random_decision(d)
+
+        # pygambit NE solver
+        efg, parents_to_infoset = macid_to_efg(sg_macid, decisions_in_sg, agents_in_sg)
+        ne_behavior_strategies = pygambit_ne_solver(efg, solver_override=solver)
+        ne_in_sg = [
+            behavior_to_cpd(sg_macid, parents_to_infoset, strat, decisions_in_sg) for strat in ne_behavior_strategies
+        ]
 
-    def policy_profile_assignment(self, partial_policy: Iterable[StochasticFunctionCPD]) -> Dict:
-        """Return a dictionary with the joint or partial policy profile assigned -
-        ie a decision rule for each of the MACIM's decision nodes."""
-        pp: Dict[str, Optional[TabularCPD]] = {d: None for d in self.decisions}
-        pp.update({cpd.variable: cpd for cpd in partial_policy})
-        return pp
+        return ne_in_sg
 
-    def get_all_pure_spe(self) -> List[List[StochasticFunctionCPD]]:
-        """Return a list of all pure subgame perfect Nash equilbiria (SPE) in the MACIM
+    def get_spe(self, solver: Optional[str] = None) -> List[List[StochasticFunctionCPD]]:
+        """Return a list of subgame perfect Nash equilbiria (SPE) in the MACIM.
+        By default, this finds mixed eq using the 'enummixed' pygambit solver for 2-player subgames, and
+        pure eq using the 'enumpure' pygambit solver for N-player games. If pure NE do not exist,
+        it uses the 'simpdiv' solver to find a mixed eq.
+        Use the 'solver' argument to change this behavior (see get_ne method for details).
         - Each SPE comes as a list of FunctionCPDs, one for each decision node in the MACID.
         """
         spes: List[List[StochasticFunctionCPD]] = [[]]
 
+        macid = self.copy()
         # backwards induction over the sccs in the condensed relevance graph (handling tie-breaks)
-        for scc in reversed(CondensedRelevanceGraph(self).get_scc_topological_ordering()):
+        for scc in reversed(CondensedRelevanceGraph(macid).get_scc_topological_ordering()):
             extended_spes = []
             for partial_profile in spes:
-                self.add_cpds(*partial_profile)
-                all_ne_in_sg = self.get_all_pure_ne_in_sg(scc)
-                for ne in all_ne_in_sg:
+                macid.add_cpds(*partial_profile)
+                ne_in_sg = macid.get_ne_in_sg(decisions_in_sg=scc, solver=solver)
+                for ne in ne_in_sg:
                     extended_spes.append(partial_profile + list(ne))
             spes = extended_spes
         return spes
@@ -120,6 +131,19 @@ def decs_in_each_maid_subgame(self) -> List[set]:
 
         return [set(itertools.chain.from_iterable(i)) for i in dec_subgames]
 
+    def joint_pure_policies(self, decisions: Iterable[str]) -> List[Tuple[StochasticFunctionCPD, ...]]:
+        """return a list of tuples of all joint pure policies in the MACID. A joint pure policy assigns a
+        pure decision rule to every decision node in the MACID."""
+        all_dec_decision_rules = list(map(self.pure_decision_rules, decisions))
+        return list(itertools.product(*all_dec_decision_rules))
+
+    def policy_profile_assignment(self, partial_policy: Iterable[StochasticFunctionCPD]) -> Dict:
+        """Return a dictionary with the joint or partial policy profile assigned -
+        ie a decision rule for each of the MACIM's decision nodes."""
+        pp: Dict[str, Optional[TabularCPD]] = {d: None for d in self.decisions}
+        pp.update({cpd.variable: cpd for cpd in partial_policy})
+        return pp
+
     def copy_without_cpds(self) -> MACID:
         """copy the MACID structure"""
         new = MACID()
diff --git a/pycid/examples/story_macids.py b/pycid/examples/story_macids.py
index 088aac8..22f8c17 100644
--- a/pycid/examples/story_macids.py
+++ b/pycid/examples/story_macids.py
@@ -58,7 +58,8 @@ def battle_of_the_sexes() -> MACID:
         | Football | 0, 0     | 2, 3     |
         +----------+----------+----------+
 
-    This game has two pure NE: (Opera, Football) and (Football, Opera)
+    This game has two pure NE: (Opera, Opera) and (Football, Football)
+    and 1 mixed NE: (0.4, 0.6), (0,6, 0.4)
     """
     macid = MACID(
         [("D_F", "U_F"), ("D_F", "U_M"), ("D_M", "U_M"), ("D_M", "U_F")],
@@ -143,7 +144,7 @@ def taxi_competition() -> MACID:
         | cheap    |     5    |   1      |
         +----------+----------+----------+
 
-    There are 3 pure strategy NE and 1 pure SPE.
+    There are 3 pure strategy NE, 1 mixed strategy NE and 1 pure SPE.
     """
     macid = MACID(
         [("D1", "D2"), ("D1", "U1"), ("D1", "U2"), ("D2", "U2"), ("D2", "U1")],
diff --git a/pycid/export/__init__.py b/pycid/export/__init__.py
new file mode 100644
index 0000000..aa3e99d
--- /dev/null
+++ b/pycid/export/__init__.py
@@ -0,0 +1,1 @@
+from pycid.export.gambit import behavior_to_cpd, macid_to_efg, macid_to_gambit_file, pygambit_ne_solver  # noqa
diff --git a/pycid/export/gambit.py b/pycid/export/gambit.py
new file mode 100644
index 0000000..a673dfa
--- /dev/null
+++ b/pycid/export/gambit.py
@@ -0,0 +1,267 @@
+from __future__ import annotations
+
+import itertools
+from collections import defaultdict
+from functools import partial, update_wrapper
+from typing import Any, Callable, Dict, Hashable, Iterable, KeysView, List, Mapping, Optional, Set, Tuple, Union
+from warnings import warn
+
+import pygambit
+
+from pycid.core.cpd import StochasticFunctionCPD
+from pycid.core.macid_base import MACIDBase
+
+Outcome = Any
+
+
+def macid_to_efg(
+    macid: MACIDBase,
+    decisions_in_sg: Optional[Union[KeysView[str], Set[str]]] = None,
+    agents_in_sg: Optional[Iterable[Hashable]] = None,
+) -> Tuple[pygambit.Game, Mapping[Tuple[Hashable, Tuple[Tuple[Any, Any], ...]], pygambit.Infoset]]:
+    """
+    Creates a pygambit EFG from a MACID:
+    1) Finds the MACID nodes needed for the EFG (decision nodes and informational parents S = {D u Pa_D})
+    2) Creats an ordering of these nodes such that X_j precedes X_i in the ordering if and only if X_j
+    is a descendant of X_i in the MACID.
+    3) Labels each node X_i with a partial instantiation of the splits in the path to X_i in the EFG.
+    Args:
+    - macid: The MACID object to convert to a pygambit EFG.
+      Expected input is a MACID object, but also allows for CIDs to be converted to EFGs.
+    - decisions_in_sg: The decisions to include in the EFG. If None, all decisions are included.
+    - agents_in_sg: The agents to include in the EFG. If None, all agents are included.
+    Returns:
+    - game: The pygambit game object. This can be used for further manipulation of the game within pygambit.
+    - parents_to_infoset: A mapping from (agent, (parent, instantiation), ...) to pygambit infoset.
+    """
+
+    # can use on a subgame if copying, else do the whole game
+    if decisions_in_sg is None:
+        decisions_in_sg = macid.decisions
+    if agents_in_sg is None:
+        agents_in_sg = macid.agents
+
+    # choose only relevant nodes
+    game_tree_nodes = set(
+        list(decisions_in_sg) + [parent for dec in decisions_in_sg for parent in macid.get_parents(dec)]
+    )
+    # topologically order them
+    sorted_game_tree_nodes = macid.get_valid_order(game_tree_nodes)
+    # create the pygambit efg
+    game = pygambit.Game.new_tree()
+
+    agent_to_player = _add_players(game, agents_in_sg)
+
+    # key is instantiation of parents, value is pygambit infoset
+    parents_to_infoset: Dict[Tuple[Hashable, Tuple[Tuple[Any, Any], ...]], pygambit.Infoset] = defaultdict(dict)
+    # nodes referenced in the game tree. Root has node_idx (0,), rest are (0, n, m, ...)
+    # state is a dict of node_idx:state of partial instantiations of nodes
+    node_idx_to_state: Dict[Tuple[int, ...], Dict[str, Any]] = defaultdict(dict)
+    # get cardinality of each node
+    num_children = [1] + [len(macid.model.domain[node]) for node in sorted_game_tree_nodes]
+    range_num_children = [list(range(x)) for x in num_children]
+
+    # grow the tree in topological order, breadth first
+    for i, node in enumerate(sorted_game_tree_nodes, start=1):
+        # iterate over all possible parents of the node
+        for node_idx in itertools.product(*range_num_children[:i]):
+            # get current node
+            cur_node = _get_cur_node(game, node_idx)
+            parents = macid.get_parents(node)
+            parents_actions = {parent: node_idx_to_state[node_idx][parent] for parent in parents}
+
+            # if the node is a decision, consider infosets
+            if node in decisions_in_sg:
+                # get agent and domain
+                agent = macid.decision_agent[node]
+                player = agent_to_player[agent]
+                actions = macid.model.domain[node]
+                parents_actions_tuple = (agent, tuple(parents_actions.items()))  # hashable
+                # check if this matches an existing infoset
+                if parents_actions_tuple in parents_to_infoset:
+                    cur_infoset = parents_to_infoset[parents_actions_tuple]
+                    cur_node.append_move(cur_infoset)
+                # else create a new infoset
+                else:
+                    cur_infoset = cur_node.append_move(player, len(actions))
+                    # label with the node for easy reference
+                    cur_infoset.label = node
+                    # add to infosets
+                    parents_to_infoset[parents_actions_tuple] = cur_infoset
+                # add state info
+                for action_idx, action in enumerate(actions):
+                    cur_infoset.actions[action_idx].label = str(action)
+                    state_info = node_idx_to_state[node_idx].copy()
+                    state_info.update({node: action})
+                    node_idx_to_state[node_idx + (action_idx,)] = state_info
+            else:
+                # otherwise is a chance node
+                factor = macid.query([node], context=parents_actions)
+                move = cur_node.append_move(game.players.chance, factor.cardinality)
+                # add state info
+                actions = macid.model.domain[node]
+                for action_idx, prob in enumerate(factor.values):
+                    move.actions[action_idx].label = str(actions[action_idx])
+                    move.actions[action_idx].prob = pygambit.Decimal(prob)
+                    state_info = node_idx_to_state[node_idx].copy()
+                    state_info.update({node: actions[action_idx]})
+                    node_idx_to_state[node_idx + (action_idx,)] = state_info
+
+    game = _add_payoffs(macid, game, range_num_children, node_idx_to_state, agents_in_sg)
+
+    return game, parents_to_infoset
+
+
+def macid_to_gambit_file(macid: MACIDBase, filename: str = "macid.efg") -> bool:
+    """Converts MACID to a ".efg" file for use with GAMBIT GUI.
+    Args:
+    - macid: The MACID object to convert to a pygambit EFG.
+    - filename: The filename to save the EFG to (default: macid.efg)."""
+    game, _ = macid_to_efg(macid)
+    with open(filename, "w") as f:
+        f.write(game.write())
+    print("\nGambit .efg file has been created from the macid")
+
+    return True
+
+
+def pygambit_ne_solver(
+    game: pygambit.Game, solver_override: Optional[str] = None
+) -> List[pygambit.lib.libgambit.MixedStrategyProfile]:
+    """Uses pygambit to find the Nash equilibria of the EFG.
+    Default solver is enummixed for 2 player games. This finds all NEs.
+    For non-2-player games, the default is enumpure which finds all pure NEs.
+    If no pure NEs are found, then simpdiv is used to find a mixed NE if it exists.
+    If a specific solver is desired, it can be passed as a string, but if it is not compatible
+    with the game, a warning will be raised and it will be ignored. We need to do this because
+    enummixed is not compatible for non-2-player games.
+    Returns a list of behaviour strategies corresponding to NEs.
+    """
+    # check if a 2 player game, if so, default to enummixed, else enumpure
+    two_player = True if len(game.players) == 2 else False
+    if solver_override is None:
+        solver = "enummixed" if two_player else "enumpure"
+    elif solver_override in ["enummixed", "lcp", "lp"] and not two_player:
+        warn(f"Solver {solver_override} not allowed for non-2 player games. Using 'enumpure' instead.")
+        solver = "enumpure"
+    else:
+        solver = solver_override
+
+    if solver == "enummixed":
+        mixed_strategies = pygambit.nash.enummixed_solve(game, rational=False)
+    elif solver == "enumpure":
+        mixed_strategies = pygambit.nash.enumpure_solve(game)
+        # if no pure NEs found, try simpdiv if not overridden by user
+        if len(mixed_strategies) == 0 and solver_override is None:
+            warn("No pure NEs found using enumpure. Trying simpdiv.")
+            mixed_strategies = pygambit.nash.simpdiv_solve(game)
+    elif solver == "lcp":
+        mixed_strategies = pygambit.nash.lcp_solve(game, rational=False)
+    elif solver == "lp":
+        mixed_strategies = pygambit.nash.lp_solve(game, rational=False)
+    elif solver == "simpdiv":
+        mixed_strategies = pygambit.nash.simpdiv_solve(game)
+    elif solver == "ipa":
+        mixed_strategies = pygambit.nash.ipa_solve(game)
+    elif solver == "gnm":
+        mixed_strategies = pygambit.nash.gnm_solve(game)
+    else:
+        raise ValueError(f"Solver {solver} not recognised")
+    # convert to behavior strategies
+    behavior_strategies = [x.as_behavior() if solver not in ["lp", "lcp"] else x for x in mixed_strategies]
+
+    return behavior_strategies
+
+
+def behavior_to_cpd(
+    macid: MACIDBase,
+    parents_to_infoset: Mapping[Tuple[Hashable, Tuple[Tuple[str, Any], ...]], pygambit.Infoset],
+    behavior: pygambit.lib.libgambit.MixedStrategyProfile,
+    decisions_in_sg: Union[KeysView[str], Set[str]] = None,
+) -> List[StochasticFunctionCPD]:
+    """Convert a pygambit behavior strategy to list of CPDs for each decision node.
+    Args:
+    - macid: The MACID object relating to the behavior strategy.
+    - parents_to_infoset: A mapping from (agent, (parent, instantiation), ...) to pygambit infoset.
+    - behavior: The pygambit behavior strategy.
+    - decisions_in_sg: The decisions in the subgame. If None, use all decisions in the MACID.
+    Returns:
+    - cpds: A list of CPDs for each decision node.
+    """
+
+    def _action_prob_given_parents(node: Any, **pv: Outcome) -> Mapping[str, float]:
+        """Takes the parent instantiation and outputs the prob from the infoset"""
+        pv_tuple = (macid.decision_agent[node], tuple(pv.items()))
+        # get the infoset for the node
+        infoset = parents_to_infoset[pv_tuple]
+        # if the infoset does not exist, this is not a valid parent instantiation
+        # TODO this was needed previously, but probably not needed anymore
+        if not infoset:
+            return {}
+        # get the action probs for the infoset
+        action_probs = {macid.model.domain[node][i]: float(prob) for i, prob in enumerate(behavior[infoset])}
+        return action_probs
+
+    def _wrapped_partial(func: Callable, *args: str) -> Callable:
+        """Adds __name__ and __doc__ to partial functions"""
+        partial_func = partial(func, *args)
+        update_wrapper(partial_func, func)
+        return partial_func
+
+    if decisions_in_sg is None:
+        decisions_in_sg = macid.decisions
+
+    # require domain to get cpd.values in the same order as in macid
+    cpds = [
+        StochasticFunctionCPD(
+            variable=node,
+            stochastic_function=_wrapped_partial(_action_prob_given_parents, node),
+            cbn=macid,
+            domain=macid.model.domain[node],
+        )
+        for node in decisions_in_sg
+    ]
+    return cpds
+
+
+def _add_players(game: pygambit.Game, agents_in_sg: Iterable[Hashable]) -> Dict[Hashable, pygambit.Player]:
+    """add players to the pygambit game"""
+    agent_to_player = {}
+    for agent in agents_in_sg:
+        player = game.players.add(agent)
+        agent_to_player[agent] = player
+
+    return agent_to_player
+
+
+def _add_payoffs(
+    macid: MACIDBase,
+    game: pygambit.Game,
+    range_num_children: List[List],
+    node_idx_to_state: Dict[Tuple[int, ...], Dict[str, Any]],
+    agents_in_sg: Iterable[Hashable],
+) -> pygambit.Game:
+    """add payoffs to the game as leave nodes"""
+    for node_idx in itertools.product(*range_num_children):
+        cur_node = _get_cur_node(game, node_idx)
+        context = node_idx_to_state[node_idx]
+        # name outcome as a string of the node_idx
+        payoff_tuple = game.outcomes.add(str(node_idx))
+        for i, agent in enumerate(agents_in_sg):
+            payoff = macid.expected_utility(context=context, agent=agent)
+            payoff_tuple[i] = pygambit.Decimal(payoff)
+        cur_node.outcome = payoff_tuple
+
+    return game
+
+
+def _get_cur_node(game: pygambit.Game, idx: Tuple[int, ...]) -> pygambit.Node:
+    """Returns the current node in the game tree given the index of the node."""
+    cur_node = game.root
+    # first entry is the root node
+    if len(idx) == 1:
+        return cur_node
+    # traverse the tree
+    for i in idx[1:]:
+        cur_node = cur_node.children[i]
+    return cur_node
diff --git a/pycid/random/random_macid.py b/pycid/random/random_macid.py
index 10c6233..92f8867 100644
--- a/pycid/random/random_macid.py
+++ b/pycid/random/random_macid.py
@@ -24,8 +24,6 @@ def random_macid(
     -----------
     number_of nodes: The total number of nodes in the MACID.
 
-    number_of_agents: The number of agents in the MACID.
-
     agent_decisions_num: A Tuple giving the number of decision nodes for each agent. For example, (1,2,1)
     means that the first agent has 1 decision, the second agent has two, and the third agent has 1.
 
diff --git a/setup.cfg b/setup.cfg
index 1100dc7..ded4a7d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -30,7 +30,7 @@ install_requires =
 	networkx
 	numpy
 	pgmpy==0.1.17
-	nashpy
+	pygambit==16.0.2
 setup_requires = setuptools_scm
 
 [options.extras_require]
@@ -86,7 +86,7 @@ include_trailing_comma=True
 force_grid_wrap=0
 use_parentheses=True
 skip_glob=*/third_party/*
-known_third_party=matplotlib,nbconvert,nbformat,networkx,numpy,pgmpy,pytest,setuptools
+known_third_party=matplotlib,nbconvert,nbformat,networkx,numpy,pgmpy,pygambit,pytest,setuptools
 
 [tool:pytest]
 # Don't show deprecation warning for dependencies


Test patch:
diff --git a/tests/test_export.py b/tests/test_export.py
new file mode 100644
index 0000000..84e43a0
--- /dev/null
+++ b/tests/test_export.py
@@ -0,0 +1,53 @@
+import sys
+import unittest
+
+import pytest
+
+from pycid.examples.story_macids import matching_pennies, taxi_competition
+from pycid.export.gambit import behavior_to_cpd, macid_to_efg, macid_to_gambit_file, pygambit_ne_solver
+
+
+class TestExport(unittest.TestCase):
+    # @unittest.skip("")
+    def test_macid_to_efg(self) -> None:
+        macid = taxi_competition()
+        game, _ = macid_to_efg(macid)
+        self.assertEqual(len(game.players), 2)
+        self.assertEqual(len(game.players[0].strategies), 2)
+        self.assertEqual(len(game.players[1].strategies), 4)
+        self.assertEqual(len(game.actions), 6)
+        self.assertEqual(len(game.outcomes), 4)
+        self.assertEqual(len(game.infosets), 3)
+
+    # @unittest.skip("")
+    def test_macid_to_gambit_file(self) -> None:
+        macid = taxi_competition()
+        self.assertTrue(macid_to_gambit_file(macid, "taxi_competition.efg"))
+
+    # @unittest.skip("")
+    def test_behavior_to_cpd(self) -> None:
+        macid = taxi_competition()
+        game, parents_to_infoset = macid_to_efg(macid)
+        ne_behavior_strategies = pygambit_ne_solver(game)
+        ne = [behavior_to_cpd(macid, parents_to_infoset, strat) for strat in ne_behavior_strategies]
+        self.assertEqual(len(ne), 4)
+        self.assertEqual(ne[0][0].domain, ["e", "c"])
+        self.assertEqual(ne[0][0].domain, ["e", "c"])
+
+    # @unittest.skip("")
+    def test_pygambit_ne_solver(self) -> None:
+        # pygambit NE solver
+        macid = taxi_competition()
+        game, _ = macid_to_efg(macid)
+        self.assertEqual(len(pygambit_ne_solver(game)), 4)
+        self.assertEqual(len(pygambit_ne_solver(game, solver_override="enumpure")), 3)
+        self.assertEqual(len(pygambit_ne_solver(game, solver_override="gnm")), 1)
+        macid2 = matching_pennies()
+        game2, _ = macid_to_efg(macid2)
+        self.assertEqual(len(pygambit_ne_solver(game2)), 1)
+        self.assertEqual(len(pygambit_ne_solver(game2, solver_override="enumpure")), 0)
+        self.assertEqual(len(pygambit_ne_solver(game2, solver_override="simpdiv")), 1)
+
+
+if __name__ == "__main__":
+    pytest.main(sys.argv)
diff --git a/tests/test_macid.py b/tests/test_macid.py
index 05f20bc..aae681f 100644
--- a/tests/test_macid.py
+++ b/tests/test_macid.py
@@ -49,40 +49,88 @@ def test_policy_profile_assignment(self) -> None:
         self.assertTrue(np.array_equal(d1_cpd.values, np.array([0.5, 0.5])))
 
     # @unittest.skip("")
-    def test_get_all_pure_ne(self) -> None:
+    def test_get_ne(self) -> None:
         macid = prisoners_dilemma()
-        self.assertEqual(len(macid.get_all_pure_ne()), 1)
-        pne = macid.get_all_pure_ne()[0]
+        self.assertEqual(len(macid.get_ne()), 1)
+        pne = macid.get_ne()[0]
         macid.add_cpds(*pne)
         self.assertEqual(macid.expected_utility({}, agent=1), -2)
         self.assertEqual(macid.expected_utility({}, agent=2), -2)
 
         macid2 = battle_of_the_sexes()
-        self.assertEqual(len(macid2.get_all_pure_ne()), 2)
+        self.assertEqual(len(macid2.get_ne()), 3)
+        self.assertEqual(len(macid2.get_ne("enumpure")), 2)
+        self.assertEqual(len(macid2.get_ne(solver="enummixed")), 3)
+        nes = macid2.get_ne(solver="enummixed")
+        joint_policy = macid2.policy_profile_assignment(nes[1])  # mixed ne (order not guaranteed?)
+        cpd_df = joint_policy["D_F"]
+        cpd_dm = joint_policy["D_M"]
+        self.assertTrue(np.allclose(cpd_df.values, np.array([0.6, 0.4])))
+        self.assertTrue(np.allclose(cpd_dm.values, np.array([0.4, 0.6])))
 
         macid3 = matching_pennies()
-        self.assertEqual(len(macid3.get_all_pure_ne()), 0)
+        self.assertEqual(len(macid3.get_ne()), 1)  # gets the mixed NE by default
+        self.assertEqual(len(macid3.get_ne("enumpure")), 0)  # no NE if overriden solver to pure NE only
+        self.assertEqual(len(macid3.get_ne(solver="enummixed")), 1)
+        joint_policy = macid3.policy_profile_assignment(macid3.get_ne(solver="enummixed")[0])
+        cpd_d1 = joint_policy["D1"]
+        cpd_d2 = joint_policy["D2"]
+        self.assertTrue(np.allclose(cpd_d1.values, np.array([0.5, 0.5])))
+        self.assertTrue(np.allclose(cpd_d2.values, np.array([0.5, 0.5])))
+        self.assertEqual(len(macid3.get_ne(solver="lp")), 1)
+        joint_policy = macid3.policy_profile_assignment(macid3.get_ne(solver="lp")[0])
+        cpd_d1 = joint_policy["D1"]
+        cpd_d2 = joint_policy["D2"]
+        self.assertTrue(np.allclose(cpd_d1.values, np.array([0.5, 0.5])))
+        self.assertTrue(np.allclose(cpd_d2.values, np.array([0.5, 0.5])))
+        self.assertEqual(len(macid3.get_ne(solver="lcp")), 1)
+        joint_policy = macid3.policy_profile_assignment(macid3.get_ne(solver="lcp")[0])
+        cpd_d1 = joint_policy["D1"]
+        cpd_d2 = joint_policy["D2"]
+        self.assertTrue(np.allclose(cpd_d1.values, np.array([0.5, 0.5])))
+        self.assertTrue(np.allclose(cpd_d2.values, np.array([0.5, 0.5])))
+        self.assertEqual(len(macid3.get_ne(solver="simpdiv")), 1)
+        joint_policy = macid3.policy_profile_assignment(macid3.get_ne(solver="simpdiv")[0])
+        cpd_d1 = joint_policy["D1"]
+        cpd_d2 = joint_policy["D2"]
+        self.assertTrue(np.allclose(cpd_d1.values, np.array([0.5, 0.5])))
+        self.assertTrue(np.allclose(cpd_d2.values, np.array([0.5, 0.5])))
+        self.assertEqual(len(macid3.get_ne(solver="gnm")), 1)
+        joint_policy = macid3.policy_profile_assignment(macid3.get_ne(solver="gnm")[0])
+        cpd_d1 = joint_policy["D1"]
+        cpd_d2 = joint_policy["D2"]
+        self.assertTrue(np.allclose(cpd_d1.values, np.array([0.5, 0.5])))
+        self.assertTrue(np.allclose(cpd_d2.values, np.array([0.5, 0.5])))
+        self.assertEqual(len(macid3.get_ne(solver="ipa")), 1)
+        joint_policy = macid3.policy_profile_assignment(macid3.get_ne(solver="ipa")[0])
+        cpd_d1 = joint_policy["D1"]
+        cpd_d2 = joint_policy["D2"]
+        self.assertTrue(np.allclose(cpd_d1.values, np.array([0.5, 0.5])))
+        self.assertTrue(np.allclose(cpd_d2.values, np.array([0.5, 0.5])))
 
         macid4 = two_agents_three_actions()
-        self.assertEqual(len(macid4.get_all_pure_ne()), 1)
+        self.assertEqual(len(macid4.get_ne()), 1)
 
     # @unittest.skip("")
-    def test_get_all_pure_ne_in_sg(self) -> None:
+    def test_get_ne_in_sg(self) -> None:
         macid = taxi_competition()
-        ne_in_subgame = macid.get_all_pure_ne_in_sg(decisions_in_sg=["D2"])
+        ne_in_subgame = macid.get_ne_in_sg(decisions_in_sg=["D2"])
         policy_assignment = macid.policy_profile_assignment(ne_in_subgame[0])
         cpd_d2 = policy_assignment["D2"]
         self.assertTrue(np.array_equal(cpd_d2.values, np.array([[0, 1], [1, 0]])))
         self.assertFalse(policy_assignment["D1"])
-        ne_in_full_macid = macid.get_all_pure_ne_in_sg()
-        self.assertEqual(len(ne_in_full_macid), 3)
+        ne_in_full_macid = macid.get_ne_in_sg()
+        self.assertEqual(len(ne_in_full_macid), 4)
         with self.assertRaises(KeyError):
-            macid.get_all_pure_ne_in_sg(decisions_in_sg=["D3"])
+            macid.get_ne_in_sg(decisions_in_sg=["D3"])
+
+        mixed_ne_in_subgame = macid.get_ne_in_sg(decisions_in_sg=["D2"], solver="enummixed")
+        self.assertEqual(len(mixed_ne_in_subgame), 1)
 
     # @unittest.skip("")
-    def test_get_all_pure_spe(self) -> None:
+    def test_get_spe(self) -> None:
         macid = taxi_competition()
-        all_spe = macid.get_all_pure_spe()
+        all_spe = macid.get_spe()
         self.assertTrue(len(all_spe) == 1)
         spe = all_spe[0]
         joint_policy = macid.policy_profile_assignment(spe)
@@ -92,19 +140,19 @@ def test_get_all_pure_spe(self) -> None:
         self.assertTrue(np.array_equal(cpd_d2.values, np.array([[0, 1], [1, 0]])))
 
         macid = modified_taxi_competition()
-        all_spe = macid.get_all_pure_spe()
+        all_spe = macid.get_spe("enumpure")
         self.assertTrue(len(all_spe) == 2)
 
         macid = prisoners_dilemma()
-        all_spe = macid.get_all_pure_spe()
+        all_spe = macid.get_spe("enumpure")
         self.assertTrue(len(all_spe) == 1)
 
         macid = battle_of_the_sexes()
-        all_spe = macid.get_all_pure_spe()
-        self.assertTrue(len(all_spe) == 2)
+        all_spe = macid.get_spe("enumpure")
+        self.assertTrue(len(all_spe) == 2)  # only 2 pure subgame perfect NE
 
         macid3 = basic_different_dec_cardinality()
-        all_spe = macid3.get_all_pure_spe()
+        all_spe = macid3.get_spe("enumpure")
         spe = all_spe[0]
         joint_policy = macid3.policy_profile_assignment(spe)
         cpd_d1 = joint_policy["D1"]
@@ -112,6 +160,22 @@ def test_get_all_pure_spe(self) -> None:
         self.assertTrue(np.array_equal(cpd_d1.values, np.array([0, 1])))
         self.assertTrue(np.array_equal(cpd_d2.values, np.array([[0, 0], [1, 0], [0, 1]])))
 
+        macid = battle_of_the_sexes()
+        all_spe = macid.get_spe(solver="enummixed")
+        self.assertTrue(len(all_spe) == 3)
+        macid = prisoners_dilemma()
+        all_spe = macid.get_spe(solver="enummixed")
+        self.assertTrue(len(all_spe) == 1)
+        macid = taxi_competition()
+        all_spe = macid.get_spe(solver="enummixed")
+        self.assertTrue(len(all_spe) == 1)
+        macid = modified_taxi_competition()
+        all_spe = macid.get_spe(solver="enummixed")
+        self.assertTrue(len(all_spe) == 2)
+        macid = basic_different_dec_cardinality()
+        all_spe = macid.get_spe(solver="enummixed")
+        self.assertTrue(len(all_spe) == 1)
+
 
 if __name__ == "__main__":
     pytest.main(sys.argv)