cogsci-2021-simulations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from scipy.special import softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import simulations\n",
    "import visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Worlds and Actions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining All Possible Actions\n",
    "Given a list of shapes and colors (which define features $\\phi$), produce all possible actions $\\cal{A}$. This implements Eq. 10 from the paper: \n",
    "$$\n",
    "\\phi: A \\rightarrow \\{0,1\\}^K\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All possible actions: [('blue', 'circle'), ('blue', 'square'), ('green', 'circle'), ('green', 'square')]\n"
     ]
    }
   ],
   "source": [
    "test_colors = ['blue', 'green']\n",
    "test_shapes = ['circle', 'square']\n",
    "test_actions = simulations.generate_actions_from_features(test_colors, test_shapes)\n",
    "\n",
    "print(\"All possible actions: {}\".format(test_actions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>green</th>\n",
       "      <th>circle</th>\n",
       "      <th>blue</th>\n",
       "      <th>square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>green circle</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue circle</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green square</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              green  circle  blue  square\n",
       "green circle    1.0     1.0   0.0     0.0\n",
       "blue circle     0.0     1.0   1.0     0.0\n",
       "green square    1.0     0.0   0.0     1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_action_context = simulations.generate_context_from_actions([(\"green\", \"circle\"), (\"blue\", \"circle\"), (\"green\", \"square\")])\n",
    "test_action_context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Possible Worlds\n",
    "\n",
    "Generate a dataframe of all possible world states $w$. This is all combinatoric feature-value sets.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>circle</th>\n",
       "      <th>square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   blue  green  circle  square\n",
       "0    -1     -1      -1      -1\n",
       "1    -1     -1      -1       1\n",
       "2    -1     -1       1      -1\n",
       "3    -1     -1       1       1\n",
       "4    -1      1      -1      -1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features = test_colors + test_shapes\n",
    "test_values = [-1, 1]\n",
    "\n",
    "test_worlds = simulations.generate_worlds_from_feature_values(test_features, test_values)\n",
    "test_worlds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Literal Listener: Beliefs and Actions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Belief Updates\n",
    "Given a message $u$ which consists of a feature-value tuple $(\\phi, \\mathbb{R})$, condition worlds $w$ and return only those with are literally consistent with that utterance. This implements Eq. 3 / 11 from the paper:\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "    P_L(w \\mid u) \\propto \\delta_{[ u ] (w)} P(w)\n",
    "\\end{equation} \n",
    "\n",
    "$$\n",
    "p_L(w_L \\mid u) = p_L(w_L \\mid u_{\\mathbb{1}_K} =  u_{\\mathbb{R}})\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before conditioning, 16 possible worlds.\n",
      "After conditioning, 8 possible worlds.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>circle</th>\n",
       "      <th>square</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    blue  green  circle  square  probability\n",
       "4     -1      1      -1      -1        0.125\n",
       "5     -1      1      -1       1        0.125\n",
       "6     -1      1       1      -1        0.125\n",
       "7     -1      1       1       1        0.125\n",
       "12     1      1      -1      -1        0.125"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from listeners import StatelessLiteralListener\n",
    "\n",
    "# Condition on a message\n",
    "test_message = ('green', 1)\n",
    "test_beliefs = StatelessLiteralListener.condition_worlds_on_message(test_worlds, test_message)\n",
    "\n",
    "print(\"Before conditioning, {} possible worlds.\".format(len(test_worlds)))\n",
    "print(\"After conditioning, {} possible worlds.\".format(len(test_beliefs)))\n",
    "test_beliefs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listener Rewards\n",
    "\n",
    "Given an action dataframe $A$ and beliefs about rewards $w$, assign rewards to all actions. \n",
    "\n",
    "This implements Eq. 11 from the paper:\n",
    "\n",
    "$$\n",
    "R(a, w) = w^\\top\\phi(a)\n",
    "$$\n",
    "\n",
    "It can be called with either a Pandas `Series` or `DataFrame`. If `DataFrame`, will marginalize over beliefs and return expected rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['green circle', 'blue circle', 'green square'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2., 0., 0.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rewards = pd.Series({\"blue\": -1, \"green\": 1, \"circle\": 1, \"square\": -1})\n",
    "print(test_action_context.index)\n",
    "StatelessLiteralListener.estimate_rewards(rewards, test_action_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['green circle', 'blue circle', 'green square'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With a belief dataframe from before:\n",
    "print(test_action_context.index)\n",
    "StatelessLiteralListener.estimate_rewards(test_beliefs, test_action_context)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listener Policy\n",
    "Given the listener's belief distribution and available actions, calculate their distribution over possible actions.\n",
    "\n",
    "This implements Eq. 4 and 5 from the paper, \n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "    R_L(a, u) = \\sum_{W}  R(a, w) P_L(w \\mid u)\n",
    "\\end{equation}\n",
    "\n",
    "$$\n",
    "    \\pi_L(a \\mid u, A) = \\frac{\n",
    "    \\exp\\{\\beta_L \\cdot R_L(a, u)\\}\n",
    "}{\n",
    "\\sum_{a' \\in A} \\exp\\{\\beta_L \\cdot R_L(a', u) \\}\n",
    "}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>green</th>\n",
       "      <th>circle</th>\n",
       "      <th>blue</th>\n",
       "      <th>square</th>\n",
       "      <th>listener_reward</th>\n",
       "      <th>listener_choice_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>green circle</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.487856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue circle</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green square</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.487856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              green  circle  blue  square  listener_reward  \\\n",
       "green circle    1.0     1.0   0.0     0.0              1.0   \n",
       "blue circle     0.0     1.0   1.0     0.0              0.0   \n",
       "green square    1.0     0.0   0.0     1.0              1.0   \n",
       "\n",
       "              listener_choice_prob  \n",
       "green circle              0.487856  \n",
       "blue circle               0.024289  \n",
       "green square              0.487856  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "literal_listener = StatelessLiteralListener(3, test_features, test_worlds)\n",
    "\n",
    "test_message = ('green', 1)\n",
    "literal_listener.action_policy(test_message, test_action_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>green</th>\n",
       "      <th>circle</th>\n",
       "      <th>blue</th>\n",
       "      <th>square</th>\n",
       "      <th>listener_reward</th>\n",
       "      <th>listener_choice_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>green circle</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue circle</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.909443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green square</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              green  circle  blue  square  listener_reward  \\\n",
       "green circle    1.0     1.0   0.0     0.0              0.0   \n",
       "blue circle     0.0     1.0   1.0     0.0              1.0   \n",
       "green square    1.0     0.0   0.0     1.0              0.0   \n",
       "\n",
       "              listener_choice_prob  \n",
       "green circle              0.045279  \n",
       "blue circle               0.909443  \n",
       "green square              0.045279  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_message = ('blue', 1)\n",
    "literal_listener.action_policy(test_message, test_action_context)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speaker Models\n",
    "\n",
    "All of the speaker models implement `BaseSpeaker`, and take an utterance and action context to calculate a utility for that utterance.\n",
    "\n",
    "These utilities are used to choose utterances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Belief-Oriented Speaker\n",
    "The belief-oriented speaker optimizes the probability of a literal listener inferring the true world state.\n",
    "\n",
    "From the paper, this is Eqs. 2 and 3:  \n",
    "\n",
    "\\begin{equation}\n",
    "    U_\\text{Belief}(u \\mid w) = \\log P_L(w \\mid u)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from speakers import BeliefSpeaker, ActionSpeaker, CombinedSpeaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Belief speaker utility:\n",
      "\t('green', 1): -2.0794415416798357\n",
      "\t('blue', 1): -23.025850929940457\n"
     ]
    }
   ],
   "source": [
    "test_rewards = pd.Series({\"green\": 1, \"blue\": -1, \"circle\": 1, \"square\": -1})\n",
    "bad_message = (\"blue\", 1)\n",
    "good_message = (\"green\", 1)\n",
    "\n",
    "test_belief_speaker = BeliefSpeaker(literal_listener, beta=3, w=test_rewards, name=\"Belief\")\n",
    "\n",
    "good_utility = test_belief_speaker.utility(good_message, test_action_context)\n",
    "bad_utility = test_belief_speaker.utility(bad_message, test_action_context)\n",
    "\n",
    "print(\"Belief speaker utility:\")\n",
    "print(\"\\t{}: {}\".format(good_message, good_utility))\n",
    "print(\"\\t{}: {}\".format(bad_message, bad_utility))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Action-Oriented Speaker\n",
    "Will choose based on probability of the single best action. \n",
    "\n",
    "From the paper, this is Eq. 6 and 7:\n",
    "$$\n",
    "a^* \\triangleq \\text{argmax}_{a\\in A} R(a, w)\n",
    "$$\n",
    "\n",
    "$$\n",
    "U_\\text{Action}(u \\mid A, a^*) = \\log[\\pi_L(a^* \\mid u, A)]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action speaker utility:\n",
      "\t('green', 1): -0.7177359186667025\n",
      "\t('blue', 1): -3.0949229564209606\n"
     ]
    }
   ],
   "source": [
    "test_action_speaker = ActionSpeaker(literal_listener, beta=3, w=test_rewards, name=\"Action\")\n",
    "\n",
    "good_utility = test_action_speaker.utility(good_message, test_action_context)\n",
    "bad_utility = test_action_speaker.utility(bad_message, test_action_context)\n",
    "\n",
    "print(\"Action speaker utility:\")\n",
    "print(\"\\t{}: {}\".format(good_message, good_utility))\n",
    "print(\"\\t{}: {}\".format(bad_message, bad_utility))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combined Speaker\n",
    "Will choose based on expected utility of actions. This implements Eq. 8 and 9 from the paper:\n",
    "\n",
    "\\begin{equation}\n",
    "    R_S(u, w, A) = \\sum_{a \\in A} \\pi_L(a \\mid u, A) R(a, w)\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "U_\\text{Combined}(u \\mid A, w) = R_S(u,w, A)\n",
    "\\end{equation}  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action speaker utility:\n",
      "\t('green', 1): 0.9757111023207369\n",
      "\t('blue', 1): 0.09055700148725816\n"
     ]
    }
   ],
   "source": [
    "test_combined_speaker = CombinedSpeaker(literal_listener, beta=3, w=test_rewards, name=\"Combined\")\n",
    "\n",
    "good_utility = test_combined_speaker.utility(good_message, test_action_context)\n",
    "bad_utility = test_combined_speaker.utility(bad_message, test_action_context)\n",
    "\n",
    "print(\"Action speaker utility:\")\n",
    "print(\"\\t{}: {}\".format(good_message, good_utility))\n",
    "print(\"\\t{}: {}\".format(bad_message, bad_utility))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 1: Reference Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our set of shapes / colors\n",
    "exp_1_colors = ['blue', 'green']\n",
    "exp_1_shapes = ['circle', 'square']\n",
    "\n",
    "# Define and plot our reward vector\n",
    "exp_1_rewards = pd.Series({\"green\": 100, \"circle\": 100, \"blue\": -100, \"square\": -100})\n",
    "\n",
    "# Restrict allowable utterances\n",
    "exp_1_utterances = [('blue', 100), ('circle', 100), ('green', 100), ('square', 100)]\n",
    "\n",
    "# Define the decision set\n",
    "exp_1_action_tuples = [('blue', 'circle'), ('green', 'square'), ('green', 'circle')]\n",
    "exp_1_action_context = simulations.generate_context_from_actions(exp_1_action_tuples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker</th>\n",
       "      <th>action_context</th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belief</td>\n",
       "      <td>All</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>All</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Combined</td>\n",
       "      <td>All</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    speaker action_context  prob_truthful  prob_optimal_action  \\\n",
       "0    Belief            All            1.0                  0.5   \n",
       "1    Action            All            1.0                  0.5   \n",
       "2  Combined            All            1.0                  0.5   \n",
       "\n",
       "   expected_rewards  \n",
       "0             100.0  \n",
       "1             100.0  \n",
       "2             100.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_1_features = exp_1_colors + exp_1_shapes\n",
    "exp_1_worlds = simulations.generate_worlds_from_feature_values(exp_1_features, exp_1_rewards.unique())\n",
    "\n",
    "exp_1_listener = StatelessLiteralListener(beta=3, features=exp_1_features, possible_worlds=exp_1_worlds)\n",
    "\n",
    "exp_1_belief_speaker = BeliefSpeaker(exp_1_listener, beta=3, w=exp_1_rewards, name=\"Belief\")\n",
    "exp_1_action_speaker = ActionSpeaker(exp_1_listener, beta=3, w=exp_1_rewards, name=\"Action\")\n",
    "exp_1_combined_speaker = CombinedSpeaker(exp_1_listener, beta=2, w=exp_1_rewards, name=\"Combined\")\n",
    "\n",
    "exp_1_speakers = [exp_1_belief_speaker, exp_1_action_speaker, exp_1_combined_speaker]\n",
    "\n",
    "df = simulations.speakers_utterance_probabilities_single_action_context(exp_1_action_context, exp_1_speakers, exp_1_utterances, exp_1_rewards)\n",
    "simulations.summarize_speakers_performance(df, exp_1_speakers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 2: Single-Shot Bandits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our set of shapes / colors\n",
    "exp_2_colors = ['blue', 'green', 'red']\n",
    "exp_2_shapes = ['circle', 'square', 'triangle']\n",
    "exp_2_features = exp_2_colors + exp_2_shapes\n",
    "\n",
    "# Generate action contexts\n",
    "exp_2_actions = simulations.generate_actions_from_features(exp_2_colors, exp_2_shapes)\n",
    "exp_2_action_contexts = list(itertools.combinations(exp_2_actions, 3))\n",
    "\n",
    "# Define and our reward vector\n",
    "exp_2_rewards = pd.Series({\"green\": 2, \"red\": 0,  \"blue\": -2, \"circle\": 1, \"triangle\": 0, \"square\": -1})\n",
    "\n",
    "# Define allowable utterances as all valid feature / value tuples\n",
    "exp_2_utterances = list(itertools.product(exp_2_features, exp_2_rewards.unique()))\n",
    "\n",
    "# Define a listener\n",
    "exp_2_worlds = simulations.generate_worlds_from_feature_values(exp_2_features, exp_2_rewards.unique())\n",
    "exp_2_listener = StatelessLiteralListener(beta=3, features=exp_2_features, possible_worlds=exp_2_worlds)\n",
    "\n",
    "exp_2_belief_speaker = BeliefSpeaker(exp_2_listener, beta=3, w=exp_2_rewards, name=\"Belief\")\n",
    "exp_2_action_speaker = ActionSpeaker(exp_2_listener, beta=3, w=exp_2_rewards, name=\"Action\")\n",
    "exp_2_combined_speaker = CombinedSpeaker(exp_2_listener, beta=2, w=exp_2_rewards, name=\"Combined\")\n",
    "\n",
    "exp_2_speakers = [exp_2_belief_speaker, exp_2_action_speaker, exp_2_combined_speaker]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker</th>\n",
       "      <th>action_context</th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belief</td>\n",
       "      <td>All</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.482780</td>\n",
       "      <td>-0.052623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>All</td>\n",
       "      <td>0.359497</td>\n",
       "      <td>0.737039</td>\n",
       "      <td>0.455710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Combined</td>\n",
       "      <td>All</td>\n",
       "      <td>0.330118</td>\n",
       "      <td>0.626946</td>\n",
       "      <td>0.481718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    speaker action_context  prob_truthful  prob_optimal_action  \\\n",
       "0    Belief            All       1.000000             0.482780   \n",
       "1    Action            All       0.359497             0.737039   \n",
       "2  Combined            All       0.330118             0.626946   \n",
       "\n",
       "   expected_rewards  \n",
       "0         -0.052623  \n",
       "1          0.455710  \n",
       "2          0.481718  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo_bandit = (('blue', 'square'), ('red', 'triangle'), ('red', 'circle'))\n",
    "demo_action_context = simulations.generate_context_from_actions(demo_bandit)\n",
    "\n",
    "df = simulations.speakers_utterance_probabilities_single_action_context(demo_action_context, exp_2_speakers, exp_2_utterances, exp_2_rewards)\n",
    "visualizations.heatmap_speaker_action_dataframe(df, exp_2_combined_speaker, exp_2_rewards, speaker_two=exp_2_action_speaker, vmax=.08, vmin=-.08)\n",
    "\n",
    "simulations.summarize_speakers_performance(df, exp_2_speakers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average over all possible bandit configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluated over 84 bandit configurations.\n"
     ]
    }
   ],
   "source": [
    "exp_two_full_df = simulations.speaker_utterance_probabilities_multiple_action_contexts(exp_2_action_contexts, exp_2_speakers, exp_2_utterances, exp_2_rewards)\n",
    "\n",
    "print(\"Evaluated over {} bandit configurations.\".format(exp_two_full_df.action_context.nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speaker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>0.329731</td>\n",
       "      <td>0.771643</td>\n",
       "      <td>1.184601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belief</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499266</td>\n",
       "      <td>0.538652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combined</th>\n",
       "      <td>0.359215</td>\n",
       "      <td>0.742426</td>\n",
       "      <td>1.275542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          prob_truthful  prob_optimal_action  expected_rewards\n",
       "speaker                                                       \n",
       "Action         0.329731             0.771643          1.184601\n",
       "Belief         1.000000             0.499266          0.538652\n",
       "Combined       0.359215             0.742426          1.275542"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_two_local = simulations.summarize_speakers_performance(exp_two_full_df, speaker_list=exp_2_speakers)\n",
    "exp_two_local.groupby(\"speaker\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker</th>\n",
       "      <th>action_context</th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belief</td>\n",
       "      <td>All</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499266</td>\n",
       "      <td>0.538652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>All</td>\n",
       "      <td>0.329731</td>\n",
       "      <td>0.489996</td>\n",
       "      <td>0.485632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Combined</td>\n",
       "      <td>All</td>\n",
       "      <td>0.359215</td>\n",
       "      <td>0.499885</td>\n",
       "      <td>0.521622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    speaker action_context  prob_truthful  prob_optimal_action  \\\n",
       "0    Belief            All       1.000000             0.499266   \n",
       "1    Action            All       0.329731             0.489996   \n",
       "2  Combined            All       0.359215             0.499885   \n",
       "\n",
       "   expected_rewards  \n",
       "0          0.538652  \n",
       "1          0.485632  \n",
       "2          0.521622  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generalization: collapse utterance probabilities over action contexts, then evaluate expected rewards\n",
    "exp_two_utterance_probability_df = exp_two_full_df.groupby(\"utterance\").mean().reset_index()\n",
    "simulations.summarize_speakers_performance(exp_two_utterance_probability_df, exp_2_speakers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in exp_2_speakers:\n",
    "    visualizations.heatmap_speaker_action_dataframe(exp_two_full_df, s, exp_2_rewards, vmax=.18, cmap='Oranges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 3: Full Context\n",
    "\n",
    "The speakers now choose an utterance while looking at **all 9** possible bandits, $\\cal{A}$. We generate utterance probabilities based on this single action context, then use the experiment 2 results and re-weight outcomes by the new distribution over utterances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('blue', 'circle'), ('blue', 'square'), ('blue', 'triangle'), ('green', 'circle'), ('green', 'square'), ('green', 'triangle'), ('red', 'circle'), ('red', 'square'), ('red', 'triangle')]\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>utterance</th>\n",
       "      <th>feature</th>\n",
       "      <th>value</th>\n",
       "      <th>truthful</th>\n",
       "      <th>Belief</th>\n",
       "      <th>Action</th>\n",
       "      <th>Combined</th>\n",
       "      <th>Belief_prob</th>\n",
       "      <th>Action_prob</th>\n",
       "      <th>Combined_prob</th>\n",
       "      <th>action_context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(blue, 2)</td>\n",
       "      <td>blue</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-7.103558</td>\n",
       "      <td>-1.985201</td>\n",
       "      <td>5.086263e-21</td>\n",
       "      <td>3.220013e-09</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(blue, 0)</td>\n",
       "      <td>blue</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-2.197225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.086263e-21</td>\n",
       "      <td>7.947630e-03</td>\n",
       "      <td>0.007575</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(blue, -2)</td>\n",
       "      <td>blue</td>\n",
       "      <td>-2</td>\n",
       "      <td>True</td>\n",
       "      <td>-8.047190</td>\n",
       "      <td>-1.792998</td>\n",
       "      <td>0.996286</td>\n",
       "      <td>1.666667e-01</td>\n",
       "      <td>2.672377e-02</td>\n",
       "      <td>0.055560</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(blue, 1)</td>\n",
       "      <td>blue</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-4.193535</td>\n",
       "      <td>-1.728329</td>\n",
       "      <td>5.086263e-21</td>\n",
       "      <td>1.991946e-05</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(blue, -1)</td>\n",
       "      <td>blue</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-1.816348</td>\n",
       "      <td>0.927133</td>\n",
       "      <td>5.086263e-21</td>\n",
       "      <td>2.491582e-02</td>\n",
       "      <td>0.048383</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    utterance feature  value  truthful     Belief    Action  Combined  \\\n",
       "0   (blue, 2)    blue      2     False -23.025851 -7.103558 -1.985201   \n",
       "1   (blue, 0)    blue      0     False -23.025851 -2.197225  0.000000   \n",
       "2  (blue, -2)    blue     -2      True  -8.047190 -1.792998  0.996286   \n",
       "3   (blue, 1)    blue      1     False -23.025851 -4.193535 -1.728329   \n",
       "4  (blue, -1)    blue     -1     False -23.025851 -1.816348  0.927133   \n",
       "\n",
       "    Belief_prob   Action_prob  Combined_prob  \\\n",
       "0  5.086263e-21  3.220013e-09       0.000143   \n",
       "1  5.086263e-21  7.947630e-03       0.007575   \n",
       "2  1.666667e-01  2.672377e-02       0.055560   \n",
       "3  5.086263e-21  1.991946e-05       0.000239   \n",
       "4  5.086263e-21  2.491582e-02       0.048383   \n",
       "\n",
       "                                      action_context  \n",
       "0  ['blue circle' 'blue square' 'blue triangle' '...  \n",
       "1  ['blue circle' 'blue square' 'blue triangle' '...  \n",
       "2  ['blue circle' 'blue square' 'blue triangle' '...  \n",
       "3  ['blue circle' 'blue square' 'blue triangle' '...  \n",
       "4  ['blue circle' 'blue square' 'blue triangle' '...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(exp_2_actions)\n",
    "\n",
    "exp_3_action_context = exp_2_actions\n",
    "exp_three_full_df = simulations.speaker_utterance_probabilities_multiple_action_contexts([exp_3_action_context], exp_2_speakers, exp_2_utterances, exp_2_rewards)\n",
    "exp_three_full_df.drop([\"expected_rewards\", \"prob_optimal_action\"], axis=1, inplace=True)\n",
    "\n",
    "exp_three_full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>utterance</th>\n",
       "      <th>feature</th>\n",
       "      <th>value</th>\n",
       "      <th>truthful</th>\n",
       "      <th>Belief</th>\n",
       "      <th>Action</th>\n",
       "      <th>Combined</th>\n",
       "      <th>Belief_prob</th>\n",
       "      <th>Action_prob</th>\n",
       "      <th>Combined_prob</th>\n",
       "      <th>action_context</th>\n",
       "      <th>expected_rewards</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>(blue, 2)</td>\n",
       "      <td>blue</td>\n",
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       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-7.103558</td>\n",
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       "      <td>5.086263e-21</td>\n",
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       "      <td>0.000143</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>-1.276990e+00</td>\n",
       "      <td>0.104835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(blue, 0)</td>\n",
       "      <td>blue</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-2.197225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.086263e-21</td>\n",
       "      <td>7.947630e-03</td>\n",
       "      <td>0.007575</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>6.410216e-17</td>\n",
       "      <td>0.361111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(blue, -2)</td>\n",
       "      <td>blue</td>\n",
       "      <td>-2</td>\n",
       "      <td>True</td>\n",
       "      <td>-8.047190</td>\n",
       "      <td>-1.792998</td>\n",
       "      <td>0.996286</td>\n",
       "      <td>1.666667e-01</td>\n",
       "      <td>2.672377e-02</td>\n",
       "      <td>0.055560</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>9.591251e-01</td>\n",
       "      <td>0.589918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(blue, 1)</td>\n",
       "      <td>blue</td>\n",
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       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-4.193535</td>\n",
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       "      <td>5.086263e-21</td>\n",
       "      <td>1.991946e-05</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>-1.124562e+00</td>\n",
       "      <td>0.133964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(blue, -1)</td>\n",
       "      <td>blue</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>-23.025851</td>\n",
       "      <td>-1.816348</td>\n",
       "      <td>0.927133</td>\n",
       "      <td>5.086263e-21</td>\n",
       "      <td>2.491582e-02</td>\n",
       "      <td>0.048383</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>8.670348e-01</td>\n",
       "      <td>0.566003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    utterance feature  value  truthful     Belief    Action  Combined  \\\n",
       "0   (blue, 2)    blue      2     False -23.025851 -7.103558 -1.985201   \n",
       "1   (blue, 0)    blue      0     False -23.025851 -2.197225  0.000000   \n",
       "2  (blue, -2)    blue     -2      True  -8.047190 -1.792998  0.996286   \n",
       "3   (blue, 1)    blue      1     False -23.025851 -4.193535 -1.728329   \n",
       "4  (blue, -1)    blue     -1     False -23.025851 -1.816348  0.927133   \n",
       "\n",
       "    Belief_prob   Action_prob  Combined_prob  \\\n",
       "0  5.086263e-21  3.220013e-09       0.000143   \n",
       "1  5.086263e-21  7.947630e-03       0.007575   \n",
       "2  1.666667e-01  2.672377e-02       0.055560   \n",
       "3  5.086263e-21  1.991946e-05       0.000239   \n",
       "4  5.086263e-21  2.491582e-02       0.048383   \n",
       "\n",
       "                                      action_context  expected_rewards  \\\n",
       "0  ['blue circle' 'blue square' 'blue triangle' '...     -1.276990e+00   \n",
       "1  ['blue circle' 'blue square' 'blue triangle' '...      6.410216e-17   \n",
       "2  ['blue circle' 'blue square' 'blue triangle' '...      9.591251e-01   \n",
       "3  ['blue circle' 'blue square' 'blue triangle' '...     -1.124562e+00   \n",
       "4  ['blue circle' 'blue square' 'blue triangle' '...      8.670348e-01   \n",
       "\n",
       "   prob_optimal_action  \n",
       "0             0.104835  \n",
       "1             0.361111  \n",
       "2             0.589918  \n",
       "3             0.133964  \n",
       "4             0.566003  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join Exp 3 utterance probabilities back to Exp 2 utterance outcomes\n",
    "exp_two_utterance_outcomes = exp_two_utterance_probability_df[[\"utterance\", \"expected_rewards\", \"prob_optimal_action\"]].set_index(\"utterance\")\n",
    "exp_three_results = exp_three_full_df.set_index(\"utterance\").join(exp_two_utterance_outcomes).reset_index()\n",
    "exp_three_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker</th>\n",
       "      <th>action_context</th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belief</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499266</td>\n",
       "      <td>0.538652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>0.440370</td>\n",
       "      <td>0.566147</td>\n",
       "      <td>0.748069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Combined</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>0.534047</td>\n",
       "      <td>0.626699</td>\n",
       "      <td>0.948940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    speaker                                     action_context  prob_truthful  \\\n",
       "0    Belief  ['blue circle' 'blue square' 'blue triangle' '...       1.000000   \n",
       "1    Action  ['blue circle' 'blue square' 'blue triangle' '...       0.440370   \n",
       "2  Combined  ['blue circle' 'blue square' 'blue triangle' '...       0.534047   \n",
       "\n",
       "   prob_optimal_action  expected_rewards  \n",
       "0             0.499266          0.538652  \n",
       "1             0.566147          0.748069  \n",
       "2             0.626699          0.948940  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulations.summarize_speakers_performance(exp_three_results, exp_2_speakers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in exp_2_speakers:\n",
    "    visualizations.heatmap_speaker_action_dataframe(exp_three_results, s, exp_2_rewards, cmap=\"Oranges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimal Speakers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_action_speaker = ActionSpeaker(exp_2_listener, beta=1e5, w=exp_2_rewards, name=\"OptimalAction\")\n",
    "optimal_combined_speaker = CombinedSpeaker(exp_2_listener, beta=1e5, w=exp_2_rewards, name=\"OptimalCombined\")\n",
    "optimal_belief_speaker = BeliefSpeaker(exp_2_listener, beta=1e5, w=exp_2_rewards, name=\"OptimalBelief\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_speakers = [optimal_belief_speaker, optimal_action_speaker, optimal_combined_speaker]\n",
    "\n",
    "optimal_full_df = simulations.speaker_utterance_probabilities_multiple_action_contexts(exp_2_action_contexts, optimal_speakers, exp_2_utterances, exp_2_rewards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimal - Experiment 2\n",
    "\n",
    "#### Local Results\n",
    "Optimal speakers do much better locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speaker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OptimalAction</th>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.942236</td>\n",
       "      <td>1.630993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OptimalBelief</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499266</td>\n",
       "      <td>0.538652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OptimalCombined</th>\n",
       "      <td>0.488095</td>\n",
       "      <td>0.942236</td>\n",
       "      <td>1.666575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                 prob_truthful  prob_optimal_action  expected_rewards\n",
       "speaker                                                              \n",
       "OptimalAction         0.458333             0.942236          1.630993\n",
       "OptimalBelief         1.000000             0.499266          0.538652\n",
       "OptimalCombined       0.488095             0.942236          1.666575"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_local_speakers = simulations.summarize_speakers_performance(optimal_full_df, speaker_list=optimal_speakers)\n",
    "optimal_local_speakers.groupby(\"speaker\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generalization\n",
    "But their generalization performance suffers still suffers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker</th>\n",
       "      <th>action_context</th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OptimalBelief</td>\n",
       "      <td>All</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499266</td>\n",
       "      <td>0.538652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OptimalAction</td>\n",
       "      <td>All</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.544040</td>\n",
       "      <td>0.690351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OptimalCombined</td>\n",
       "      <td>All</td>\n",
       "      <td>0.488095</td>\n",
       "      <td>0.550010</td>\n",
       "      <td>0.710317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           speaker action_context  prob_truthful  prob_optimal_action  \\\n",
       "0    OptimalBelief            All       1.000000             0.499266   \n",
       "1    OptimalAction            All       0.458333             0.544040   \n",
       "2  OptimalCombined            All       0.488095             0.550010   \n",
       "\n",
       "   expected_rewards  \n",
       "0          0.538652  \n",
       "1          0.690351  \n",
       "2          0.710317  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generalization: collapse utterance probabilities over action contexts, then evaluate expected rewards\n",
    "optimal_local_utterance_probability_df = optimal_full_df.groupby(\"utterance\").mean().reset_index()\n",
    "simulations.summarize_speakers_performance(optimal_local_utterance_probability_df, optimal_speakers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimal - Experiment 3\n",
    "\n",
    "As before, use utterance-based outcomes from Experiment 2 and join in new distribution over utterances from global context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker</th>\n",
       "      <th>action_context</th>\n",
       "      <th>prob_truthful</th>\n",
       "      <th>prob_optimal_action</th>\n",
       "      <th>expected_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OptimalBelief</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.499266</td>\n",
       "      <td>0.538652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OptimalAction</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.624145</td>\n",
       "      <td>0.957743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OptimalCombined</td>\n",
       "      <td>['blue circle' 'blue square' 'blue triangle' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.731498</td>\n",
       "      <td>1.276990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           speaker                                     action_context  \\\n",
       "0    OptimalBelief  ['blue circle' 'blue square' 'blue triangle' '...   \n",
       "1    OptimalAction  ['blue circle' 'blue square' 'blue triangle' '...   \n",
       "2  OptimalCombined  ['blue circle' 'blue square' 'blue triangle' '...   \n",
       "\n",
       "   prob_truthful  prob_optimal_action  expected_rewards  \n",
       "0            1.0             0.499266          0.538652  \n",
       "1            0.5             0.624145          0.957743  \n",
       "2            1.0             0.731498          1.276990  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_global_utterance_probability_df = simulations.speaker_utterance_probabilities_multiple_action_contexts([exp_3_action_context], optimal_speakers, exp_2_utterances, exp_2_rewards)\n",
    "optimal_global_utterance_probability_df.drop([\"expected_rewards\", \"prob_optimal_action\"], axis=1, inplace=True)\n",
    "\n",
    "exp_two_utterance_outcomes = exp_two_utterance_probability_df[[\"utterance\", \"expected_rewards\", \"prob_optimal_action\"]].set_index(\"utterance\")\n",
    "\n",
    "optimal_global_results = optimal_global_utterance_probability_df.set_index(\"utterance\").join(exp_two_utterance_outcomes).reset_index()\n",
    "\n",
    "simulations.summarize_speakers_performance(optimal_global_results, optimal_speakers)"
   ]
  }
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