planning.ipynb

{
 "cells": [
  {
   "metadata": {
    "id": "df7334004bee5154"
   },
   "cell_type": "markdown",
   "source": [
    "<h1>A3I Project - Choreography Planning by Decoupling A* Deterministic Search and Heuristic Computation</h1>\n",
    "<h4 style=\"text-align: center\">Authors: Davide Bombardi, Lorenzo Balzani</h4>\n",
    "<p style=\"text-align: center\">\n",
    "    <a href=\"mailto:davide.bombardi@studio.unibo.it\">davide.bombardi@studio.unibo.it</a> -\n",
    "    <a href=\"mailto:lorenzo.balzani@studio.unibo.it\">lorenzo.balzani@studio.unibo.it</a>\n",
    "</p>\n",
    "\n",
    "\n",
    "<h2>Abstract</h2>\n",
    "<div class=\"abstract\">\n",
    "    <p>\n",
    "  Our project focuses on designing and implementing a customized A* algorithm to generate robotic dance choreographies. This algorithm is inspired by the traditional A* search method but incorporates a unique heuristic powered by a Large Language Model (LLM). The primary goal of the algorithm is to create choreographies that are both dynamic and visually appealing (or “cool”), while adhering to specific constraints. These constraints ensure that the choreographies are functional, safe, and compatible with the capabilities of the robotic platform.\n",
    "    </p>\n",
    "</div>"
   ],
   "id": "df7334004bee5154"
  },
  {
   "metadata": {
    "id": "890a231612e4c9b1"
   },
   "cell_type": "markdown",
   "source": [
    "## Setup\n",
    "Perform the following steps to setup the environment for running the code in this notebook:\n",
    "1. Create a file named `secrets.env` in the root directory of the project and add the following lines:\n",
    "    ```bash\n",
    "    OPENAI_API_KEY=<your_openai_api_key>\n",
    "    OPENAI_API_URL=https://api.openai.com\n",
    "    ```\n",
    "2. Setup the environment by launching the script `./setup_env.sh` in the terminal.\n",
    "3. After activate it with `conda activate a3i`.\n",
    "4. For installing the ipython kernel, please run `python -m ipykernel install --user --name=a3i`."
   ],
   "id": "890a231612e4c9b1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T14:21:27.761455Z",
     "start_time": "2025-01-12T14:21:24.132599Z"
    },
    "id": "346c1ee767482e62"
   },
   "cell_type": "code",
   "source": [
    "import random\n",
    "import re\n",
    "import heapq\n",
    "\n",
    "from time import time\n",
    "from collections import defaultdict\n",
    "from itertools import product\n",
    "\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "from langchain_openai import AzureChatOpenAI\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
    "# Models\n",
    "gpt_4o = AzureChatOpenAI(\n",
    "    deployment_name=\"gpt-4o\",\n",
    "    temperature=0,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "gpt_4o_mini = AzureChatOpenAI(\n",
    "    deployment_name=\"gpt-4o-mini\",\n",
    "    temperature=0,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "load_dotenv(\"secrets.env\")\n",
    "SEED = 42\n",
    "random.seed(SEED)"
   ],
   "id": "346c1ee767482e62",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "id": "e0fc61e0fb53ddf6"
   },
   "cell_type": "markdown",
   "source": [
    "## Position Graph Creation"
   ],
   "id": "e0fc61e0fb53ddf6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T14:21:07.617880Z",
     "start_time": "2025-01-12T14:21:06.957604Z"
    },
    "id": "eeaa4b0ca3e7bb68",
    "outputId": "d2ea8478-36b5-4669-89d0-2b9706d91658"
   },
   "cell_type": "code",
   "source": [
    "initial_state = 'INITIAL_stand_init'\n",
    "final_state = 'FINAL_crouch'\n",
    "\n",
    "# Define the list of nodes\n",
    "nodes = {initial_state, 'rotation_handgun_object', 'right_arm_rotation', 'double_movement_rotation_of_arms',\n",
    "         'arms_opening', 'union_arms', 'move_forward', 'move_backward', 'diagonal_left', 'diagonal_right',\n",
    "         'rotation_foot_left_leg', 'rotation_foot_right_leg', 'play_guitar', 'arms_dance', 'birthday_dance',\n",
    "         'sprinkler_dance', 'workout_legs_and_arms', 'superman', 'mandatory_sit', 'mandatory_wipe_forehead',\n",
    "         'mandatory_hello', 'mandatory_stand', 'mandatory_zero', final_state}\n",
    "\n",
    "# Define the exception nodes for which there are incompatible transitions\n",
    "exception_nodes = {\"mandatory_sit\": {'diagonal_right', 'diagonal_left', 'rotation_foot_right_leg', 'rotation_foot_left_leg',\n",
    "                                     final_state, 'move_backward', 'move_forward', 'play_guitar', 'arms_dance', 'birthday_dance',\n",
    "                                     'sprinkler_dance', 'workout_legs_and_arms', 'superman'}\n",
    "}\n",
    "\n",
    "mandatory_positions = {'mandatory_sit', 'mandatory_wipe_forehead', 'mandatory_hello', 'mandatory_stand', 'mandatory_zero'}\n",
    "intermediate_positions = list(nodes - {initial_state, final_state} - mandatory_positions)\n",
    "\n",
    "# Initialize the directed graph, since there are incompatible transitions between some nodes\n",
    "G = nx.DiGraph()\n",
    "G.add_nodes_from(nodes)\n",
    "\n",
    "# Add edges between all pairs of nodes except the specified exceptions\n",
    "for u, v in product(nodes, repeat=2):\n",
    "    if u == final_state: # Skip adding edges from 'FINAL_crouch' to other nodes, since it is the final node\n",
    "        continue\n",
    "    if v == initial_state: # Skip adding edges to 'INITIAL_stand_init', since it is the initial node\n",
    "        continue\n",
    "    if u in exception_nodes and v in exception_nodes[u]: # Skip adding edges if the pair is in the exception list\n",
    "        continue\n",
    "    G.add_edge(u, v, weight=1)  # Add an edge with a cost of 1\n",
    "\n",
    "# Visualize the graph\n",
    "plt.figure(figsize=(12, 12))\n",
    "pos = nx.spring_layout(G, k=0.15)\n",
    "nx.draw_networkx_nodes(G, pos, node_size=500, node_color='skyblue')\n",
    "nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)\n",
    "nx.draw_networkx_labels(G, pos, font_size=8, font_family='sans-serif')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.title('Positions Graph')\n",
    "plt.show()"
   ],
   "id": "eeaa4b0ca3e7bb68",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "metadata": {
    "id": "9485e0715b382212"
   },
   "cell_type": "markdown",
   "source": [
    "## Graph Statistics"
   ],
   "id": "9485e0715b382212"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T14:21:33.862338Z",
     "start_time": "2025-01-12T14:21:29.498152Z"
    },
    "id": "62f284f78c2b1419",
    "outputId": "4a79a953-6004-403a-ff1e-89ba88d81fcf"
   },
   "cell_type": "code",
   "source": [
    "# 1. Number of nodes\n",
    "num_nodes = G.number_of_nodes()\n",
    "print(f\"1. Number of nodes: {num_nodes}\")\n",
    "\n",
    "# 2. Number of edges\n",
    "num_edges = G.number_of_edges()\n",
    "print(f\"2. Number of edges: {num_edges}\")\n",
    "\n",
    "# 3. Average in-degree and out-degree\n",
    "in_degrees = G.in_degree()\n",
    "out_degrees = G.out_degree()\n",
    "avg_in_degree = sum(dict(in_degrees).values()) / num_nodes\n",
    "avg_out_degree = sum(dict(out_degrees).values()) / num_nodes\n",
    "print(f\"3. Average in-degree: {avg_in_degree:.2f}\\t--\\tAverage out-degree: {avg_out_degree:.2f}\")\n",
    "\n",
    "# 4. Density of the graph\n",
    "density = nx.density(G)\n",
    "print(f\"4. Graph density: {density:.4f}\")\n",
    "\n",
    "# 5. Is the graph strongly connected?\n",
    "is_strongly_connected = nx.is_strongly_connected(G)\n",
    "print(f\"5. Is the graph strongly connected? {is_strongly_connected}\")\n",
    "\n",
    "# 6. Number of strongly connected components\n",
    "num_scc = nx.number_strongly_connected_components(G)\n",
    "print(f\"6. Number of strongly connected components: {num_scc}\")\n",
    "\n",
    "# 7. Average clustering coefficient\n",
    "# For DiGraph, we use the directed version of clustering coefficient\n",
    "avg_clustering = nx.average_clustering(G)\n",
    "print(f\"7. Average clustering coefficient: {avg_clustering:.4f}\")\n",
    "\n",
    "# 8. Top 3 nodes by PageRank\n",
    "pagerank = nx.pagerank(G)\n",
    "top_3_pagerank = sorted(pagerank.items(), key=lambda x: x[1], reverse=True)[:3]\n",
    "print(\"8. Top 3 nodes by PageRank:\")\n",
    "for node, pr in top_3_pagerank:\n",
    "    print(f\"  {node}: {pr:.4f}\")\n",
    "\n",
    "# 9. In-degree and out-degree of 'mandatory_sit'\n",
    "in_degree_mandatory_sit = G.in_degree('mandatory_sit')\n",
    "out_degree_mandatory_sit = G.out_degree('mandatory_sit')\n",
    "print(f\"9. 'mandatory_sit' in-degree: {in_degree_mandatory_sit}\\t--\\tout-degree: {out_degree_mandatory_sit}\")\n",
    "\n",
    "# 10. Average shortest path length (if the graph is strongly connected)\n",
    "if is_strongly_connected:\n",
    "    avg_shortest_path_length = nx.average_shortest_path_length(G)\n",
    "    print(f\"10. Average shortest path length: {avg_shortest_path_length:.4f}\")\n",
    "else:\n",
    "    print(\"10. Graph is not strongly connected; average shortest path length not defined.\")\n",
    "\n",
    "# 11. Number of nodes with zero in-degree (sources)\n",
    "zero_in_degree = [n for n, d in G.in_degree() if d == 0]\n",
    "print(f\"11. Number of nodes with zero in-degree (sources): {len(zero_in_degree)}\")\n",
    "\n",
    "# 12. Number of nodes with zero out-degree (sinks)\n",
    "zero_out_degree = [n for n, d in G.out_degree() if d == 0]\n",
    "print(f\"12. Number of nodes with zero out-degree (sinks): {len(zero_out_degree)}\")\n",
    "\n",
    "# 13. Diameter of the graph (if strongly connected)\n",
    "if is_strongly_connected:\n",
    "    diameter = nx.diameter(G)\n",
    "    print(f\"13. Diameter of the graph: {diameter}\")\n",
    "else:\n",
    "    print(\"13. Graph is not strongly connected; diameter not defined.\")\n",
    "\n",
    "# 14. Eccentricity of the initial state (if strongly connected)\n",
    "if is_strongly_connected:\n",
    "    eccentricity = nx.eccentricity(G, initial_state)\n",
    "    print(f\"14. Eccentricity of '{initial_state}': {eccentricity}\")\n",
    "else:\n",
    "    print(\"14. Graph is not strongly connected; eccentricity not defined.\")"
   ],
   "id": "62f284f78c2b1419",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Number of nodes: 24\n",
      "2. Number of edges: 516\n",
      "3. Average in-degree: 21.50\t--\tAverage out-degree: 21.50\n",
      "4. Graph density: 0.9348\n",
      "5. Is the graph strongly connected? False\n",
      "6. Number of strongly connected components: 3\n",
      "7. Average clustering coefficient: 0.9365\n",
      "8. Top 3 nodes by PageRank:\n",
      "  mandatory_wipe_forehead: 0.0453\n",
      "  right_arm_rotation: 0.0453\n",
      "  double_movement_rotation_of_arms: 0.0453\n",
      "9. 'mandatory_sit' in-degree: 23\t--\tout-degree: 10\n",
      "10. Graph is not strongly connected; average shortest path length not defined.\n",
      "11. Number of nodes with zero in-degree (sources): 1\n",
      "12. Number of nodes with zero out-degree (sinks): 1\n",
      "13. Graph is not strongly connected; diameter not defined.\n",
      "14. Graph is not strongly connected; eccentricity not defined.\n"
     ]
    }
   ],
   "execution_count": null
  },
  {
   "metadata": {
    "id": "8b557b83ee39cad5"
   },
   "cell_type": "markdown",
   "source": [
    "## Graph Waypoints definition"
   ],
   "id": "8b557b83ee39cad5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T14:21:41.047327Z",
     "start_time": "2025-01-12T14:21:40.228032Z"
    },
    "id": "d202d2924ac24915",
    "outputId": "77c08c14-ba99-43ef-fcd1-d2b38215ca30"
   },
   "cell_type": "code",
   "source": [
    "# Define a prompt template for getting waypoints\n",
    "waypoint_prompt = PromptTemplate(\n",
    "    input_variables=['initial_state', 'final_state', 'positions'],\n",
    "    template=\"\"\"\n",
    "    Given the initial state {initial_state} and the final state {final_state}, output a **strictly comma-separated list** of positions from the available ones that create a very cool, elegant and creative movement sequence. Include initial and final state. Do not include any other text in the output, and format the list exactly as: `position1, position2, position3`.\n",
    "\n",
    "    # POSITIONS\n",
    "    {positions}\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "# Create an LLMChain for waypoints\n",
    "waypoint_chain = waypoint_prompt | gpt_4o\n",
    "\n",
    "# Define the available positions as the union of all the nodes except the initial and final states\n",
    "available_positions = list(nodes - {initial_state, final_state})\n",
    "random.shuffle(available_positions)\n",
    "\n",
    "# Run the chain\n",
    "result = waypoint_chain.invoke({\"initial_state\": initial_state, \"final_state\": final_state, \"positions\": available_positions})\n",
    "\n",
    "# Extract the comma-separated list using regex\n",
    "waypoints = re.findall(r'\\b\\w+(?:, \\w+)*\\b', result.content)\n",
    "print(f\"Waypoints sequence: {waypoints}\")"
   ],
   "id": "d202d2924ac24915",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waypoints sequence: ['INITIAL_stand_init', 'mandatory_stand', 'diagonal_left', 'superman', 'rotation_foot_right_leg', 'arms_opening', 'FINAL_crouch']\n"
     ]
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Custom A* Algorithm\n",
    "This function is the implementation of a personalized A\\* algorithm. In our case, the solution space Σ is the set of all the possible choreographies composed of the given moves (i.e. of the moves stored in the set `nodes`). We consider a solution (i.e. a choreography) σ ∈ Σ correct if it satisfies the following constraints:\n",
    "1. The choreography σ doesn't contain any unfeasible transitions, i.e. σ respects all the incompatibilities between moves in the dict `exception_nodes`. In other words, for every move 𝑘 in `exception_nodes.keys()`, if 𝑘 ∈ σ then 𝑘 is not followed by any of the moves in `exception_nodes[𝑘]`;\n",
    "2. The choreography σ contains all the mandatory positions (i.e. moves in the set `mandatory_positions`);\n",
    "3. The choreography σ contains at least `n_intermediate_positions` intermediate positions (i.e. moves in the set `intermediate_positions`). In our case  `n_intermediate_positions = 5`, as stated in the reference paper;\n",
    "4. The execution of σ takes an amount of seconds that is greater than 120 - `duration_tolerance` and lower than 120 + `duration_tolerance`. In our case `duration_tolerance = 5`, as stated in the reference paper.\n",
    "\n",
    "Our A\\* algorithm builds the final choreography by adding a move to the most promising solution found so far at each iteration. In this perspective, the first constraint is achieved by considering only the neighbours of the last node ( i.e. the last move) of the choreography considered at the current iteration of the A\\* algorithm. The second and third constraint are achieved via a simple check on the moves selected in the candidate solution.\n",
    "For the fourth constraint we need to estimate the duration of the execution of each move. This is done once before running A\\* by calling the LLM with prompt `moves_duration_prompt`.\n"
   ],
   "metadata": {
    "id": "NbtfZLSbKZCS"
   },
   "id": "NbtfZLSbKZCS"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### The heuristic\n",
    "A\\* builds the solution by taking at each iteration the most promising solution found so far and by adding a move at the end of the considered solution. Therefore, the definition of the function `f_function` that computes the score of a solution (where the most promising solution is the one with the highest score) is fundamental in order to guarantee that A\\* reaches a good solution in a reasonable amount of time.\n",
    "Once A\\* has considered the most promising solution σ, for every neighbour 𝑘 of the last move of σ, the scoring function `f_function` computes the score of σ + [𝑘] by doing a weighted sum of 4 normalized scores:\n",
    "1. `g_value + h_value`, where:\n",
    "    -  `g_value` is the (normalized) coolness score of σ, computed by calling the LLM with `g_prompt` as prompt, weighted by the (normalized) duration of σ. The prompt takes into account the evaluation targets described in the reference paper. Then `g_value` is the average of the scores given by the LLM in each evaluation target;\n",
    "    - `h_value` is the (normalized) coolness score of 𝑘 if added at the end of σ, computed by calling the LLM with `h_prompt` as prompt, weighted by the (normalized) time left to consider σ a complete choreography. In order to reduce the calls to the LLM, the `h_value` is computed in one call to the LLM for all the neighbours of the last move of σ. Then when dealing with the neighbour 𝑘 we consider the correspondent `h_value`;\n",
    "2. `t_value`\n",
    "3. `duration_bonus`\n",
    "4. `constraint_sat_count`\n"
   ],
   "metadata": {
    "id": "Sklws2sGYrsb"
   },
   "id": "Sklws2sGYrsb"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Setup and Utilities",
   "id": "cae3165ec93d5850"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "max_duration = 120\n",
    "duration_tolerance = 5\n",
    "max_coolness_value = 5\n",
    "debug = False\n",
    "\n",
    "# LLM pricing\n",
    "gpt_4o_mini_costs = {\"prompt\": 0.14311/1e6, \"completion\": 0.5725/1e6}\n",
    "gpt_4o_costs = {\"prompt\": 2.31514/1e6, \"completion\": 9.2606/1e6}"
   ],
   "id": "2a5bdc4ae9a4664a"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def asymmetric_distribution(x, mu=max_duration, sigma=50, alpha=-0.1):\n",
    "    \"\"\"\n",
    "    Computes an asymmetric distribution value for a given input `x`.\n",
    "\n",
    "    The distribution behaves like a normal (Gaussian) distribution for `x <= mu`\n",
    "    and introduces an exponential penalty for `x > mu`. This is useful for scenarios\n",
    "    where values greater than a specified mean (`mu`) should be penalized more heavily\n",
    "    than those below it.\n",
    "\n",
    "    Args:\n",
    "        x (float): The input value for which the distribution is calculated.\n",
    "        mu (float, optional): The mean of the distribution. Default is `max_duration`.\n",
    "        sigma (float, optional): The standard deviation of the normal distribution. Default is 50.\n",
    "        alpha (float, optional): The exponential decay factor for values of `x > mu`. Default is -0.1.\n",
    "\n",
    "    Returns:\n",
    "        float: The calculated value of the asymmetric distribution at `x`.\n",
    "    \"\"\"\n",
    "    if x <= mu:\n",
    "        return np.exp(- (x - mu)**2 / (2 * sigma**2))\n",
    "\n",
    "    return np.exp(- (x - mu)**2 / (2 * sigma**2)) * np.exp(alpha * (x - mu))"
   ],
   "id": "edfb9714922fab65"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Prompts Definition",
   "id": "8d45b54517004c26"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Moves Duration Computation",
   "id": "84d3d30c84ec1f9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:14:19.924338Z",
     "start_time": "2025-01-12T16:14:18.440913Z"
    },
    "id": "c78a51d64a58220b"
   },
   "cell_type": "code",
   "source": [
    "moves_duration_prompt = PromptTemplate(\n",
    "    input_variables=[\"moves\"],\n",
    "    template=\"\"\"\n",
    "    You are a robotics expert analyzing the motion efficiency of a NAO robot. Below is a list of moves the robot will perform. For each move, provide the estimated duration in seconds based on typical execution times for similar robotic actions:\n",
    "\n",
    "    Moves list:\n",
    "    {moves}\n",
    "\n",
    "    ** Criteria **\n",
    "    1.\tProvide an estimate for each move in seconds.\n",
    "\t2.\tConsider the complexity, speed, and transition time for typical NAO robot motions.\n",
    "\n",
    "\tOutput Format:\n",
    "    Provide N mappings (separated by newlines) between move and estimated duration (without unit of measurement) in the format:\n",
    "    `move_name:estimated_duration_in_seconds`\n",
    "\n",
    "    DO NOT OUTPUT ANY OTHER INFORMATION, INCLUDING BACKTICKS.\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "response = (moves_duration_prompt | gpt_4o).invoke(\"\\n- \".join(nodes)).content\n",
    "moves_duration = {value.split(\":\")[0].strip(): int(value.split(\":\")[1].strip()) for value in response.split(\"\\n\")}\n"
   ],
   "id": "c78a51d64a58220b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:15:25.108653Z",
     "start_time": "2025-01-12T16:15:25.105927Z"
    },
    "id": "2209c196bd9d3d1e"
   },
   "cell_type": "code",
   "source": [
    "def compute_total_duration(dance_sequence):\n",
    "    total_duration = 0\n",
    "    for move in dance_sequence:\n",
    "        total_duration += moves_duration[move]\n",
    "    return total_duration"
   ],
   "id": "2209c196bd9d3d1e",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Sequence Evaluation and Heuristic",
   "id": "bc666b31bfb9d7da"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:15:32.596452Z",
     "start_time": "2025-01-12T16:15:32.592515Z"
    },
    "id": "defef91d61e54ff6"
   },
   "cell_type": "code",
   "source": [
    "g_prompt = PromptTemplate(\n",
    "    input_variables=['len_sequence', 'dance_sequence'],\n",
    "    template=\"\"\"\n",
    "Role and Context: You are an expert robotic dance choreographer. Your task is to evaluate a complete choreography for a NAO robot, represented as a sequence of {len_sequence} dance moves or positions. You must provide 7 numerical outputs as the 7 coolness scores of the choreography.\n",
    "\n",
    "Task:\n",
    "Evaluate the given dance sequence based on the following criteria:\n",
    "\n",
    "**Coolness Score:**\n",
    "Rate the choreography on a scale from 0 to 5 in each of the following categories: (1) Storytelling; (2) Rhythm; (3) Movement Technique; (4) Public Involvement; (5) Space Use; (6) Human Characterization; (7) Human Reproducibility.\n",
    "\n",
    "Output Format:\n",
    "Provide ONLY 7 numerical values in the format:\n",
    "`Storytelling_score,Rhythm_score,MovementTechnique_score,PublicInvolvement_score,SpaceUse_score,HumanCharacterization_score,HumanReproducibility_score`\n",
    "\n",
    "Do not include any text, commentary, or explanation.\n",
    "\n",
    "Input:\n",
    "Dance Sequence: {dance_sequence}\n",
    "\"\"\")\n",
    "\n",
    "h_prompt = PromptTemplate(\n",
    "    input_variables=['n_neighbors', 'neighbors_list', 'dance_sequence', 'duration'],\n",
    "    template=\"\"\"\n",
    "    Role and Context: You are an expert robotic dance choreographer. Your task is to improve an incomplete choreography for a NAO robot. You will be provided with:\n",
    "    1. An incomplete list of dance moves or positions (referred to as the current choreography).\n",
    "    2. A list of possible moves to evaluate for inclusion in the choreography (referred to as the possible moves). Your job is to assign a coolness score to each possible move based on how well it improves the choreography. The highest-rated moves will be considered for inclusion.\n",
    "\n",
    "Task: Provide {n_neighbors} numerical scores between 0 and 5, one for each possible move, in the exact order they are listed. The first score corresponds to the first move in the list, the second score to the second move, and so on.\n",
    "\n",
    "Important Guidelines:\n",
    "- Scoring Criteria: Moves should be evaluated based on how much they improve the choreography in storytelling, rhythm, movement technique, public involvement, space use, human characterization and human reproducibility.\n",
    "- Duration: The choreography should have a total duration of {duration} seconds.\n",
    "- Consistency: Rate moves consistently and fairly. Similar moves should receive similar scores.\n",
    "- Diversity: Consider the variety, creativity, and aesthetic appeal of the moves. Give lower scores to moves that are repetitive.\n",
    "- Output Format: Provide ONLY the {n_neighbors} scores in the format: coolness_score_first_move,coolness_score_second_move,...,coolness_score_{n_neighbors}-th_move. Do not include any explanations, text, or comments.\n",
    "\n",
    "Input:\n",
    "Current choreography: {dance_sequence}\n",
    "Possible Moves: {neighbors_list}\n",
    "\"\"\")\n"
   ],
   "id": "defef91d61e54ff6",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Sequence Evaluation via LLM",
   "id": "3673387a05ba5bfc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:21:57.796107Z",
     "start_time": "2025-01-12T16:21:57.779457Z"
    },
    "id": "c4a58597c651819e"
   },
   "cell_type": "code",
   "source": [
    "def g_llm(dance_sequence):\n",
    "    \"\"\"\n",
    "    Evaluate a complete dance_sequence using the LLM.\n",
    "\n",
    "    Parameters:\n",
    "    - dance_sequence (List[str]): The sequence of dance moves.\n",
    "\n",
    "    Output:\n",
    "    - coolness_score (float): The estimated coolness score for the dance_sequence.\n",
    "    \"\"\"\n",
    "    # Remove INITIAL_stand_init from the dance_sequence\n",
    "    if dance_sequence[0] == 'INITIAL_stand_init':\n",
    "        dance_sequence = dance_sequence[1:]\n",
    "\n",
    "    dance_sequence = tuple(dance_sequence)\n",
    "\n",
    "    # Check if the coolness value is already computed and cached\n",
    "    if dance_sequence in heuristic_cache[\"g\"]:\n",
    "        return heuristic_cache[\"g\"][dance_sequence], 0\n",
    "\n",
    "    # Prepare the inputs for the LLM prompt\n",
    "    prompt_inputs = {\n",
    "        'len_sequence': len(dance_sequence),\n",
    "        'dance_sequence': str(list(dance_sequence))\n",
    "    }\n",
    "\n",
    "    # Run the chain, extract and process the response\n",
    "    try:\n",
    "        # Call the LLM to get the coolness score\n",
    "        response = (g_prompt | gpt_4o).invoke(prompt_inputs)\n",
    "        cost = response.response_metadata[\"token_usage\"]\n",
    "        total_cost = cost[\"prompt_tokens\"] * gpt_4o_costs[\"prompt\"] + cost[\"completion_tokens\"] * gpt_4o_costs[\"completion\"]\n",
    "\n",
    "        output_list = response.content.strip().split(\",\")\n",
    "        all_scores = [float(score.strip()) for score in output_list]\n",
    "\n",
    "        assert len(all_scores) == 7\n",
    "        coolness_estimation = sum(all_scores)/len(all_scores)  # average of the scores\n",
    "\n",
    "        # Force the coolness from 0 to max_coolness_score\n",
    "        coolness_estimation = max(0.0, float(coolness_estimation))\n",
    "        coolness_estimation = min(coolness_estimation, 5)\n",
    "\n",
    "        # Store the coolness score in the cache\n",
    "        heuristic_cache[\"g\"][dance_sequence] = coolness_estimation\n",
    "\n",
    "        return heuristic_cache[\"g\"][dance_sequence], total_cost\n",
    "    except ValueError as e:\n",
    "        raise ValueError(f\"Invalid response from the g_LLM: {e}\") from e"
   ],
   "id": "c4a58597c651819e",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Heuristic Computation via LLM",
   "id": "4443bedf83b2a318"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def h_llm(neighbors_list, dance_sequence):\n",
    "    \"\"\"\n",
    "    Give one score for each move in a list of moves neighbors_list that could follow the current dance_sequence, using the LLM.\n",
    "\n",
    "    Parameters:\n",
    "    - neighbors_list (List[str]): The list of possible moves to evaluate.\n",
    "    - dance_sequence (List[str]): The sequence of dance moves.\n",
    "\n",
    "    Output:\n",
    "    - coolness_scores (List[float]): The estimated coolness scores for the possible moves.\n",
    "    \"\"\"\n",
    "    dance_sequence = tuple(dance_sequence)\n",
    "\n",
    "    # Check if the coolness value is already computed and cached\n",
    "    if dance_sequence in heuristic_cache:\n",
    "        return heuristic_cache[\"h\"][dance_sequence], 0\n",
    "\n",
    "    # Prepare the inputs for the LLM prompt\n",
    "    prompt_inputs = {\n",
    "        'n_neighbors': len(neighbors_list),\n",
    "        'neighbors_list': str(neighbors_list),\n",
    "        'dance_sequence': str(list(dance_sequence)),\n",
    "        'duration': max_duration\n",
    "    }\n",
    "\n",
    "    # Run the chain, extract and process the response\n",
    "    try:\n",
    "        # Call the LLM to get the coolness score\n",
    "        response = (h_prompt | gpt_4o).invoke(prompt_inputs)\n",
    "        cost = response.response_metadata[\"token_usage\"]\n",
    "        total_cost = cost[\"prompt_tokens\"] * gpt_4o_costs[\"prompt\"] + cost[\"completion_tokens\"] * gpt_4o_costs[\"completion\"]\n",
    "        coolness_estimations = response.content.strip().split(\",\")\n",
    "\n",
    "        if len(neighbors_list) != len(coolness_estimations):\n",
    "            raise ValueError(\"The number of coolness estimations does not match the number of neighbors\")\n",
    "\n",
    "        for i in range(len(coolness_estimations)):\n",
    "            coolness_estimations[i] = max(0.0, float(coolness_estimations[i]))\n",
    "            coolness_estimations[i] = min(coolness_estimations[i], 5)\n",
    "\n",
    "        # Store the coolness score in the cache\n",
    "        heuristic_cache[\"h\"][dance_sequence] = coolness_estimations\n",
    "\n",
    "        return coolness_estimations, total_cost\n",
    "    except ValueError as e:\n",
    "        raise ValueError(f\"Invalid response from the h_LLM: {e}\") from e"
   ],
   "id": "f092b715fb7998a9"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Combining Past and Future (f-function)",
   "id": "4a268e2cd1321e49"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:26:42.769481Z",
     "start_time": "2025-01-12T16:26:42.764876Z"
    },
    "id": "b29734fb6a1fcd68"
   },
   "cell_type": "code",
   "source": [
    "def f_function(coolness_value, cool_estimation, choreo_duration, new_sequence, current_target, shortest_path, shortest_path_inverted, final_state, mandatory_positions, intermediate_positions, n_intermediate_positions):\n",
    "    \"\"\"\n",
    "    Compute the total score of a choreography as the weighted sum of four elements. This is intended to use within our A* implementation.\n",
    "    1. g + h: the sum of the coolness (g) and heuristic (h) scores\n",
    "    2. t: progress indicator that leads to the next waypoint\n",
    "    3. duration bonus: the more similar to the duration constraints the higher the bonus, i.e. follows an asymmetric distribution where points smaller than the average behave like a normal distribution, whereas points larger than the average are penalized more as they increase in values.\n",
    "    4. constraint satisfaction count: percentage of given satisfied constraints.\n",
    "\n",
    "    Args:\n",
    "        coolness_value (float): A measure of the choreography's coolness. Output of LLM via g-function.\n",
    "        cool_estimation (float): A heuristic estimation of the choreography’s coolness. Output of LLM via h-function.\n",
    "        choreo_duration (float): The duration of the choreography. Output of LLM via g-function.\n",
    "        new_sequence (list[str]): The sequence of moves in the choreography.\n",
    "        current_target (str): The current waypoint target in the choreography.\n",
    "        shortest_path (dict): Mapping (matrix) of nodes to the shortest path lengths for each target (computed via NetworkX), i.e. source in rows and target in columns.\n",
    "        shortest_path_inverted (dict): Inverted mapping of targets to path lengths for each node, i.e. target in rows and source in columns.\n",
    "        final_state (str): The final target state for the choreography.\n",
    "        mandatory_positions (set): Moves that are mandatory in the choreography sequence.\n",
    "        intermediate_positions (set): Optional intermediate moves for added complexity.\n",
    "        n_intermediate_positions (int): The minimum number of distinct intermediate moves required.\n",
    "\n",
    "    Returns:\n",
    "        float: the weighed sum of the 4 components.\n",
    "    \"\"\"\n",
    "\n",
    "    # g and h such that g+h in [0,1], 1 ==> the coolest choreography\n",
    "    g_value = coolness_value * min(choreo_duration, max_duration) / (max_duration * max_coolness_value)\n",
    "    h_value = cool_estimation * max((max_duration-choreo_duration), 0) / (max_duration * max_coolness_value)\n",
    "\n",
    "    # t such that t in [0,1], 1 ==> the shortest path to the target (i.e. target itself)\n",
    "    current_node = new_sequence[-1]\n",
    "    #If the choreography is complete but there are still waypoints t_value = 0\n",
    "    if current_node == final_state and current_target!=final_state:\n",
    "        t_value = 0\n",
    "    #If the choreography is complete and the only waypoint left is final_state t_value = 1\n",
    "    elif current_node == final_state and current_target==final_state:\n",
    "        t_value = 1\n",
    "    #If the choreography is not complete and the only waypoint left is final_state t_value = 1 !!! More freedom if the next waypoint is the final one\n",
    "    elif current_node != final_state and current_target==final_state:\n",
    "        t_value = 1\n",
    "    else:\n",
    "        t_value = 1-(shortest_path[current_node][current_target] / (max(shortest_path_inverted[current_target].values()) + 1))\n",
    "\n",
    "    # duration bonus such that it is in [0,1], 1 ==> choreography has the perfect duration\n",
    "    duration_bonus = asymmetric_distribution(choreo_duration)\n",
    "\n",
    "    # constraint satisfaction counts in [0,1], 1 ==> all constraint are satisfied\n",
    "    n_mandatory_check = len({move for move in new_sequence if move in mandatory_positions})\n",
    "    n_intermediate_check = min(len({move for move in new_sequence if move in intermediate_positions}), n_intermediate_positions)\n",
    "    constraint_sat_count = (n_mandatory_check + n_intermediate_check)/(len(mandatory_positions) + n_intermediate_positions)\n",
    "\n",
    "    if debug:\n",
    "        print(f\"F-FUNCTION -> count={constraint_sat_count}, n_mandatory_check={n_mandatory_check}, n_intermediate_check={n_intermediate_check} \"\"(g+h)={(g_value + h_value)}, choreo_duration={choreo_duration}, duration_bonus={duration_bonus}\")\n",
    "\n",
    "    # final score\n",
    "    final_score = ((g_value + h_value) * gh_weight) + (t_value * t_weight) + (duration_bonus * db_weight) + (constraint_sat_count * csc_weight)\n",
    "\n",
    "    return final_score"
   ],
   "id": "b29734fb6a1fcd68",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Solution Definition",
   "id": "6bc58043b54fed76"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:26:44.501436Z",
     "start_time": "2025-01-12T16:26:44.493089Z"
    },
    "id": "b5ddf80ca2833e6"
   },
   "cell_type": "code",
   "source": [
    "def custom_a_star(graph, initial_state, final_state, targets, mandatory_positions, intermediate_positions, n_intermediate_positions, f_function, timeout=60):\n",
    "    open_set = []\n",
    "    candidate_return = []\n",
    "    total_cost = 0\n",
    "    total_calls = 0\n",
    "    heapq.heappush(open_set, (0, [initial_state], 1))  # (estimated coolness of complete path, uncompleted path, index of current target)\n",
    "    shortest_path = dict(nx.all_pairs_shortest_path_length(graph))\n",
    "\n",
    "    # Used in t-functon\n",
    "    shortest_path_inverted = defaultdict(dict)\n",
    "    for source, target_dict in shortest_path.items():\n",
    "        for target, length in target_dict.items():\n",
    "            shortest_path_inverted[target][source] = length\n",
    "\n",
    "    # Run the A* algorithm\n",
    "    time_now = time()\n",
    "    while open_set:\n",
    "        elapsed_time = time()-time_now\n",
    "\n",
    "        #If timeout is reached and a candidate solution is found, return it.\n",
    "        if candidate_return and elapsed_time > timeout:\n",
    "            candidate_return.sort(key=lambda x: x[0])\n",
    "            if debug:\n",
    "                print(f\"Timeout reached. Returning best candidate so far (score: {candidate_return[0][1]})\")\n",
    "            return candidate_return[0], total_cost, total_calls, elapsed_time\n",
    "        _, current_sequence, current_target_index = heapq.heappop(open_set)\n",
    "        current_target = targets[current_target_index]\n",
    "        current_node = current_sequence[-1]\n",
    "\n",
    "        #If the sequence with the best score is a complete choreography, return it.\n",
    "        if current_node == final_state:\n",
    "            return current_sequence, total_cost, total_calls, elapsed_time\n",
    "\n",
    "        #If the sequence reached a target, update the index to point to the next target\n",
    "        if current_node == current_target:\n",
    "            if current_target_index < len(targets) - 1:\n",
    "                current_target_index += 1\n",
    "                current_target = targets[current_target_index]\n",
    "\n",
    "        n_mandatory_check = len({move for move in current_sequence if move in mandatory_positions})\n",
    "        n_intermediate_check = len({move for move in current_sequence if move in intermediate_positions})\n",
    "\n",
    "        all_constraint_satisfied = n_mandatory_check == len(mandatory_positions) and n_intermediate_check >= n_intermediate_positions\n",
    "\n",
    "        #Estimate the coolness of current_sequence\n",
    "        coolness_value, heuristic_cost = g_llm(current_sequence)\n",
    "        choreo_duration = compute_total_duration(current_sequence)\n",
    "        total_cost += heuristic_cost\n",
    "        total_calls += 1\n",
    "\n",
    "        neighbors_list = list(graph.neighbors(current_node))\n",
    "\n",
    "        if choreo_duration >= max_duration - duration_tolerance and all_constraint_satisfied and final_state in neighbors_list: # Push to heap IFF the max_duration is reached. Hopefully will be returned at next iteration\n",
    "            estimated_coolness = f_function(coolness_value, 0, choreo_duration, current_sequence, current_target, shortest_path, shortest_path_inverted, final_state, mandatory_positions, intermediate_positions, n_intermediate_positions)\n",
    "            candidate_return.append((-estimated_coolness, current_sequence + [final_state]))\n",
    "            if debug:\n",
    "                print(f\"Found a candidate with score {coolness_value} and duration {choreo_duration}\")\n",
    "            heapq.heappush(open_set, (*candidate_return[-1], current_target_index))\n",
    "        # if you receive a longer choreography (+5%), just discard it\n",
    "        elif choreo_duration <= max_duration + duration_tolerance:\n",
    "            # If the duration is not reached, the final state cannot be reach\n",
    "            if final_state in neighbors_list:\n",
    "                neighbors_list.remove(final_state)\n",
    "\n",
    "            # Check the neighbors\n",
    "            coolness_estimations, heuristic_cost = h_llm(neighbors_list, current_sequence)\n",
    "\n",
    "            total_cost += heuristic_cost\n",
    "            total_calls += 1\n",
    "\n",
    "            for move, cool_estimation in zip(neighbors_list, coolness_estimations):\n",
    "                new_sequence = current_sequence + [move]\n",
    "\n",
    "                estimated_coolness = f_function(coolness_value, cool_estimation, choreo_duration, new_sequence, current_target, shortest_path, shortest_path_inverted, final_state, mandatory_positions, intermediate_positions, n_intermediate_positions)\n",
    "                heapq.heappush(open_set, (-estimated_coolness, new_sequence, current_target_index))\n",
    "\n",
    "        if debug:\n",
    "            print(f\"CURRENT sequence: {current_sequence} - Total duration: {choreo_duration}s - Coolness: {coolness_value} - All constraint satisfied: {all_constraint_satisfied}\")\n",
    "            print(f\"CURRENT cost: {total_cost:.6f}€ - Total calls: {total_calls}\\n\")"
   ],
   "id": "b5ddf80ca2833e6",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Solution Execution\n",
    "\n",
    "#### Weights of the 4 Components of the Score\n",
    "\n",
    "- **`gh_weight`**: Higher values indicate greater importance placed on coolness.\n",
    "- **`t_weight`**: Higher values indicate greater importance placed on following the waypoints.\n",
    "- **`db_weight`**: Higher values provide a greater bonus to the choreographer with the right duration (A* checks fewer alternatives).\n",
    "- **`csc_weight`**: Higher values indicate greater importance placed on satisfying the constraint."
   ],
   "id": "51321fae20f94868"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Balanced\n",
    "gh_weight, t_weight, db_weight, csc_weight = 1, 3, 4, 3\n",
    "\n",
    "# Very high Constraint and Waypoints\n",
    "#gh_weight, t_weight, db_weight, csc_weight = 1,10,1,10\n",
    "\n",
    "#High Constraint and Waypoints\n",
    "#gh_weight, t_weight, db_weight, csc_weight = 1,2,1,2\n",
    "\n",
    "#Less importance to waypoints\n",
    "#gh_weight, t_weight, db_weight, csc_weight = 3,1,1,2"
   ],
   "id": "b0b49a7a95e3034b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-12T16:28:09.863731Z",
     "start_time": "2025-01-12T16:27:38.357202Z"
    },
    "id": "8f08f9668aac9921",
    "outputId": "402742e8-0708-4e8a-acd1-a081e6f8a9f0"
   },
   "cell_type": "code",
   "source": [
    "heuristic_cache = {'g': {}, 'h': {}}\n",
    "choreography, cost, n_calls, elapsed_time = custom_a_star(graph=G, initial_state=initial_state, final_state=final_state, targets=waypoints, mandatory_positions=mandatory_positions, intermediate_positions=intermediate_positions, n_intermediate_positions=5, f_function=f_function)\n",
    "print(f\"Choreography: {choreography}\\nCost: {cost:.5f}€\\nNumber of calls to LLM: {n_calls}\\nElapsed time: {elapsed_time:.2f}s\")"
   ],
   "id": "8f08f9668aac9921",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Choreography: ['INITIAL_stand_init', 'mandatory_stand', 'diagonal_left', 'superman', 'rotation_foot_right_leg', 'arms_opening', 'birthday_dance', 'mandatory_wipe_forehead', 'mandatory_hello', 'mandatory_sit', 'mandatory_zero', 'play_guitar', 'double_movement_rotation_of_arms', 'sprinkler_dance', 'workout_legs_and_arms', 'move_backward', 'arms_dance', 'move_forward', 'rotation_handgun_object', 'diagonal_right', 'right_arm_rotation', 'rotation_foot_left_leg', 'arms_dance', 'arms_dance', 'union_arms', 'arms_opening', 'birthday_dance', 'mandatory_hello', 'FINAL_crouch']\n",
      "Cost: 0.06451€\n",
      "Number of calls to LLM: 59\n",
      "Elapsed time: 31.50s\n"
     ]
    }
   ],
   "execution_count": null
  }
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