CausalKnowledgeTransfer-4-SafeRL.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Causal Knowledge Transfer for Safe Reinforcement Learning",
   "id": "9b3534bf33e6bed9"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Train Agents\n",
    "#### Sumo Network File\n",
    "When editing the sumo network (nets/simple_unprotected_right.net.xml) never edit the xml directly. Instead, go to nets/netconfig and make desired changes there. Generate the new net.xml by executing generate config.sh\n",
    "#### Sumo Route File\n",
    "This is part of the generate_config.sh now.\n",
    "#### Reward Function\n",
    "* TODO: Come up with a fitting reward function that penalises collisions\n",
    "#### RL Training\n",
    "* TODO: Come up with Hyperparameters for the training loop\n",
    "\n",
    "**Desired Output: Trained_Model.zip**"
   ],
   "id": "76a3883d1cec1ee8"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Creating the Sumo Environment",
   "id": "696c12a4c9cece3a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-16T03:20:20.076373Z",
     "start_time": "2024-12-16T03:20:18.463756Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pathlib import Path\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "# Environment Variables (Ensure SUMO_HOME is properly set)\n",
    "sumo_home = %env SUMO_HOME\n",
    "\n",
    "# Constants / Parameters\n",
    "SPEED = 22.22\n",
    "FRICTION = 1.0\n",
    "INSERT_PROBABILITY = 0.1\n",
    "DURATION = 3600\n",
    "REPEAT_PERIOD = 10\n",
    "DEFAULT_DECEL = 4.5\n",
    "DEFAULT_EMERGENCY_DECEL = 9.0\n",
    "\n",
    "# File Paths\n",
    "config_directory = Path().joinpath('nets', '2lane_unprotected_right')\n",
    "config_files = {\n",
    "    'netccfg_edges': config_directory.joinpath('netconfig', 'edges.edg.xml'),\n",
    "    'netccfg': config_directory.joinpath('2lane_unprotected_right.netccfg'),\n",
    "    'duarcfg': config_directory.joinpath('2lane_unprotected_right.duarcfg'),\n",
    "    'net.xml': config_directory.joinpath('2lane_unprotected_right.net.xml'),\n",
    "    'rou.xml': config_directory.joinpath('2lane_unprotected_right.rou.xml'),\n",
    "    'routes.rou.xml': config_directory.joinpath('routes.rou.xml'),\n",
    "    'config.rou.xml': config_directory.joinpath('config.rou.xml'),\n",
    "    'experimental.rou.xml': config_directory.joinpath('experimental.rou.xml'),\n",
    "}\n",
    "findAllRoutes = Path(sumo_home).joinpath('tools', 'findAllRoutes.py')\n",
    "vehicle2flow = Path(sumo_home).joinpath('tools', 'route', 'vehicle2flow.py')\n",
    "\n",
    "\n",
    "# Update Friction Coefficients in Edge Configuration\n",
    "def update_friction_coefficients(file_path, friction):\n",
    "    edges_xml_tree = ET.parse(file_path)\n",
    "    edges_xml_root = edges_xml_tree.getroot()\n",
    "    for param in edges_xml_root.findall(\".//lane/param[@key='frictionCoefficient']\"):\n",
    "        param.set('value', str(friction))\n",
    "    edges_xml_tree.write(file_path)\n",
    "\n",
    "\n",
    "update_friction_coefficients(config_files['netccfg_edges'], FRICTION)\n",
    "\n",
    "# Execute SUMO Tools\n",
    "! netconvert --configuration-file {config_files['netccfg']}\n",
    "! python {findAllRoutes} -n {config_files['net.xml']} -o {config_files['routes.rou.xml']} -s southJunction,westJunction -t junctionEast,junctionNorth\n",
    "! duarouter --configuration-file {config_files['duarcfg']}\n",
    "! python {vehicle2flow} {config_files['config.rou.xml']} -o {config_files['rou.xml']} -e {DURATION} -r {REPEAT_PERIOD}\n",
    "\n",
    "# Update Vehicle Configuration for Friction Adjusted Braking Distance\n",
    "def update_vehicle_type_parameters(file_path, speed, default_decel, default_emergency_decel, friction):\n",
    "    tree = ET.parse(file_path)\n",
    "    root = tree.getroot()\n",
    "    for vType in root.findall('vType'):\n",
    "        vClass = vType.attrib.get('vClass')\n",
    "        if vClass and vClass != 'passenger':\n",
    "            raise NotImplementedError(\"Check for non-passenger vehicle classes not implemented\")\n",
    "        vType.attrib.update({\n",
    "            'maxSpeed': str(speed),\n",
    "            'decel': str(default_decel * friction),\n",
    "            'emergencyDecel': str(default_emergency_decel * friction),\n",
    "        })\n",
    "    tree.write(file_path, xml_declaration=True, encoding='UTF-8')\n",
    "\n",
    "\n",
    "update_vehicle_type_parameters(config_files['rou.xml'], SPEED, DEFAULT_DECEL, DEFAULT_EMERGENCY_DECEL, FRICTION)\n",
    "\n",
    "\n",
    "# Update Vehicle Flows for Forcing Unprotected Right Action\n",
    "def update_flows(file_path, insert_probability):\n",
    "    tree = ET.parse(file_path)\n",
    "    root = tree.getroot()\n",
    "    for flow in root.findall('flow'):\n",
    "        match flow.attrib.get('id'):\n",
    "            case 'southEast':\n",
    "                flow.set('period', f\"exp({insert_probability})\")\n",
    "            case 'southNorth':\n",
    "                flow.set('period', f\"exp({insert_probability})\")\n",
    "            case 'westEastTop':\n",
    "                flow.set('period', f\"exp({2 * insert_probability})\")\n",
    "            case id if 'westEastBottom' in id:\n",
    "                flow.set('end', str(float(flow.get('begin')) + 600))\n",
    "                if float(flow.get('begin')) % 1200 == 0:\n",
    "                    flow.set('period', f\"exp({1 * insert_probability})\")\n",
    "                else:\n",
    "                    flow.set('period', f\"exp({0.0001 * insert_probability})\")\n",
    "    tree.write(file_path, xml_declaration=True, encoding='UTF-8')\n",
    "\n",
    "\n",
    "update_flows(config_files['rou.xml'], INSERT_PROBABILITY)"
   ],
   "id": "e95329e734a9e91e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Speed of turning connection 'southJunction_0->junctionEast_0' reduced by 49.05 due to turning radius of 7.70 (length=9.03, angle=90.00).\r\n",
      "Success.\r\n",
      "Success.up to time step: 3200.00\r\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-14T23:29:48.323390Z",
     "start_time": "2024-12-14T23:29:48.112094Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from env.SumoEnvironmentGenerator import SumoEnvironmentGenerator\n",
    "from pathlib import Path\n",
    "\n",
    "net_name = '2lane_unprotected_right'\n",
    "\n",
    "environments = SumoEnvironmentGenerator(\n",
    "    net_file=str(Path().joinpath('nets', net_name, f'{net_name}.net.xml')),\n",
    "    route_file=str(Path().joinpath('nets', net_name, f'{net_name}.rou.xml')),\n",
    "    sumocfg_file=str(Path().joinpath('nets', net_name, f'{net_name}.sumocfg')),\n",
    "    duration=3600,\n",
    "    learning_data_csv_name=str(Path().joinpath('env', 'training_data', 'output.csv')),\n",
    ")"
   ],
   "id": "83958ce226f27b73",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Training and saving the Model",
   "id": "e6f9f4917eb5beff"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from stable_baselines3.a2c import A2C\n",
    "\n",
    "%load_ext tensorboard\n",
    "env = environments.get_training_env()\n",
    "model = A2C(\n",
    "    env=env,\n",
    "    policy='MlpPolicy',\n",
    "    n_steps=100,\n",
    "    # learning_rate=0.001,\n",
    "    # learning_starts=0,\n",
    "    # train_freq=1,\n",
    "    # target_update_interval=500,\n",
    "    # exploration_fraction=0.05,\n",
    "    # exploration_final_eps=0.01,\n",
    "    verbose=1,\n",
    "    tensorboard_log='dqn_sumo_tensorboard'\n",
    ")\n",
    "model.learn(100_000, tb_log_name='a2c_100step_2lane_1delta_minute_1traffic_50speed_new')\n",
    "model.save(Path().joinpath('env', 'training_data_2lane', 'a2c_100step_2lane_1delta_minute_1traffic_50speed_new'))"
   ],
   "id": "8c3f5ee8509cdf52",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Giving the model a test run in an evaluation environment",
   "id": "4604104a64fb90ae"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-14T23:31:33.957517Z",
     "start_time": "2024-12-14T23:30:16.485335Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#63 (ns green), 86 (incoming collision), 134 (we green), 0739 unprotected good\n",
    "\n",
    "from stable_baselines3.a2c import A2C\n",
    "from pathlib import Path\n",
    "\n",
    "env = environments.get_demonstration_env()\n",
    "model = A2C(env=env, policy='MlpPolicy').load(\n",
    "    Path().joinpath('env', 'agents_paper', 'scratch_s80_f0.5.zip'))\n",
    "\n",
    "rewards = []\n",
    "actions = []\n",
    "obs, info = env.reset()\n",
    "\n",
    "gui = env.sumo.gui\n",
    "gui.setBoundary(gui.DEFAULT_VIEW, 480.0, 480.0, 520.0, 520.0)\n",
    "# gui.addView('3D', in3D=True)\n",
    "screenshots_path_jpg = Path().joinpath('screenshots', 'tmp', 'jpg')\n",
    "screenshots_path_jpg.mkdir(parents=True, exist_ok=True)\n",
    "screenshots_path_svg = Path().joinpath('screenshots', 'tmp', 'svg')\n",
    "screenshots_path_svg.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "#vehicletype = env.sumo.vehicletype\n",
    "#vehicletype.setMaxSpeed('carCustom', SPEED)\n",
    "#vehicletype.setDecel('carCustom', vehicletype.getDecel('carCustom') * FRICTION)\n",
    "#vehicletype.setEmergencyDecel('carCustom',\n",
    "#                              vehicletype.getEmergencyDecel('carCustom') * FRICTION)\n",
    "#for traffic_signal in env.traffic_signals.values():\n",
    "#    for lane in traffic_signal.lanes:\n",
    "#        traffic_signal.sumo.lane.setParameter(lane, 'frictionCoefficient', FRICTION)\n",
    "\n",
    "done = False\n",
    "while not done:\n",
    "    action, _state = model.predict(obs, deterministic=True)\n",
    "    obs, reward, terminated, truncated, info = env.step(action)\n",
    "    rewards.append(reward)\n",
    "    actions.append(action)\n",
    "    done = terminated or truncated\n",
    "    current_step = int(env.sumo.simulation.getTime())\n",
    "    screenshot_path_jpg = screenshots_path_jpg.joinpath(str(current_step).zfill(4) + '.jpg')\n",
    "    screenshot_path_svg = screenshots_path_svg.joinpath(str(current_step).zfill(4) + '.svg')\n",
    "    gui.screenshot(gui.DEFAULT_VIEW, str(screenshot_path_jpg))\n",
    "    gui.screenshot(gui.DEFAULT_VIEW, str(screenshot_path_svg))\n",
    "env.close()\n",
    "\n",
    "print(rewards)\n",
    "print(\" \")\n",
    "print(actions)"
   ],
   "id": "4e7f9cc33d31421c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Retrying in 1 seconds\n",
      "Step #0.00 (0ms ?*RT. ?UPS, TraCI: 3ms, vehicles TOT 0 ACT 0 BUF 0)                      \r\n",
      " Retrying in 1 seconds\n",
      "Could not connect to TraCI server at localhost:61721 [Errno 61] Connection refused\n",
      " Retrying in 1 seconds\n",
      "Could not connect to TraCI server at localhost:61721 [Errno 61] Connection refused\n",
      " Retrying in 1 seconds\n",
      "Could not connect to TraCI server at localhost:61721 [Errno 61] Connection refused\n",
      " Retrying in 1 seconds\n",
      "Could not connect to TraCI server at localhost:61721 [Errno 61] Connection refused\n",
      " Retrying in 1 seconds\n",
      "Could not connect to TraCI server at localhost:61721 [Errno 61] Connection refused\n",
      " Retrying in 1 seconds\n",
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array(2)]\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Produce Traces\n",
    "Run the simulation repeatably to produce traces (data) for causal discovery.\n",
    "\n",
    "#### Data Selection\n",
    "TODO: Select which columns we want to do Causal Discovery on\n",
    "#### Data Summary\n",
    "TODO: Incorporate old data summary script\n",
    "\n",
    "**Desired Output: One CSV File containing all interesting data**\n"
   ],
   "id": "4f83a5026c16fe31"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import itertools\n",
    "from env.SumoTraceGenerator import SumoTraceGenerator\n",
    "from pathlib import Path\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "speeds = [30, 50, 100]\n",
    "frictions = [1.0, 0.8, 0.5]\n",
    "\n",
    "experiments = list(itertools.product(speeds, frictions))\n",
    "\n",
    "for speed, friction in tqdm(experiments):\n",
    "    simulation_output_path = Path().joinpath('data', f'a2c_{int(speed)}_f{friction}')\n",
    "    Path.mkdir(simulation_output_path, parents=True, exist_ok=True)\n",
    "\n",
    "    trace_generator = SumoTraceGenerator()\n",
    "    trace_generator.generate_traces(\n",
    "        env_generator=environments,\n",
    "        path=simulation_output_path,\n",
    "        size=100,\n",
    "        speed_loc=float(speed) / 3.6,\n",
    "        friction_log=friction,\n",
    "        friction_scale=0.1\n",
    "    )"
   ],
   "id": "3ada91211cbe194a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Data Summary",
   "id": "237d6d2f0b9c2225"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:06:39.643001Z",
     "start_time": "2024-12-15T18:05:43.401451Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from statistics import mean\n",
    "import glob\n",
    "from pathlib import Path\n",
    "import xml.etree.ElementTree as ElementTree\n",
    "import pandas as pd\n",
    "\n",
    "data_folder = Path().joinpath('traces_paper')\n",
    "experiments = glob.glob(str(data_folder.joinpath('*')))\n",
    "\n",
    "for experiment in experiments:\n",
    "    experiment_path = Path(experiment)\n",
    "    statistics_files = glob.glob(str(experiment_path.joinpath('*_statistics.xml')))\n",
    "    ids = [path.split('/')[-1].split('_')[0] for path in statistics_files]\n",
    "\n",
    "    data = []\n",
    "    for id in ids:\n",
    "        statistics_file = experiment_path.joinpath(id + '_statistics.xml')\n",
    "        collisions_file = experiment_path.joinpath(id + '_collisions.xml')\n",
    "        ssm_file = experiment_path.joinpath(id + '_ssm.xml')\n",
    "        metadata_file = experiment_path.joinpath(id + '_metadata.xml')\n",
    "        tripinfo_file = experiment_path.joinpath(id + '_tripinfo.xml')\n",
    "        statistics_xml = ElementTree.parse(statistics_file).getroot()\n",
    "        collisions_xml = ElementTree.parse(collisions_file).getroot()\n",
    "        ssm_xml = ElementTree.parse(ssm_file).getroot()\n",
    "        metadata_xml = ElementTree.parse(metadata_file).getroot()\n",
    "        tripinfo_xml = ElementTree.parse(tripinfo_file).getroot()\n",
    "\n",
    "        row = {\n",
    "            'experiment': experiment,\n",
    "            'index': int(id),\n",
    "            'desiredSpeed': float(metadata_xml.find('.//desiredSpeed').text),\n",
    "            'friction': float(metadata_xml.find('.//friction').text)\n",
    "        }\n",
    "\n",
    "        for key, value in {**statistics_xml.find('vehicleTripStatistics').attrib,\n",
    "                           **statistics_xml.find('safety').attrib}.items():\n",
    "            match key:\n",
    "                case 'count' | 'emergencyStops' | 'emergencyBraking':\n",
    "                    row[key] = int(value)\n",
    "                case 'collisions':\n",
    "                    row[key] = int(value)\n",
    "                    row['rearEndCollisions'] = sum(\n",
    "                        'southEast' in child.attrib.get('victim') for child in collisions_xml)\n",
    "                    row['lateralCollisions'] = sum(\n",
    "                        'southEast' in child.attrib.get('collider') for child in collisions_xml)\n",
    "                    row[key] = row['rearEndCollisions'] + row['lateralCollisions']\n",
    "                case _:\n",
    "                    row[key] = float(value)\n",
    "\n",
    "        waiting_times = [float(tripinfo.attrib.get('waitingTime')) for tripinfo in tripinfo_xml.findall('tripinfo')]\n",
    "        average_waiting_time = mean(waiting_times)\n",
    "        row['waitingTime'] = average_waiting_time\n",
    "\n",
    "        data.append(row)\n",
    "\n",
    "        df = pd.DataFrame(data)\n",
    "        df.to_csv(experiment_path.joinpath('.summary.csv'), index=False)"
   ],
   "id": "2e2dd00337e5afd0",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Some Visualization",
   "id": "12e038aa7f8f3099"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "data = Path().joinpath('data_agent', 'data.csv')\n",
    "paper_path = Path().joinpath('paper')\n",
    "df = pd.read_csv(data)\n",
    "\n",
    "sns.displot(data=df, x='friction', y='waitingTime', kind='kde')\n",
    "plt.title('friction - waitingTime, all desiredSpeeds')\n",
    "plt.savefig(paper_path.joinpath('friction-waitingTime.png'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='friction', y='collisions', kind='kde')\n",
    "plt.title('friction - collisions, all desiredSpeeds')\n",
    "plt.savefig(paper_path.joinpath('friction-collisions'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='desiredSpeed', y='waitingTime', kind='kde')\n",
    "plt.title('desiredSpeed - waitingTime, all frictions')\n",
    "plt.savefig(paper_path.joinpath('desiredSpeed-waitingTime'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='desiredSpeed', y='collisions', kind='kde')\n",
    "plt.title('desiredSpeed - collisions, all frictions')\n",
    "plt.savefig(paper_path.joinpath('desiredSpeed-collisions'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='friction', y='emergencyBraking', kind='kde')\n",
    "plt.title('friction - emergencyBraking, all desiredSpeeds')\n",
    "plt.savefig(paper_path.joinpath('friction-emergencyBraking'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='friction', y='speed', kind='kde')\n",
    "plt.title('friction - speed, all desiredSpeeds')\n",
    "plt.savefig(paper_path.joinpath('friction-speed'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='desiredSpeed', y='emergencyBraking', kind='kde')\n",
    "plt.title('desiredSpeed - emergencyBraking, all frictions')\n",
    "plt.savefig(paper_path.joinpath('desiredSpeed-emergencyBraking'), bbox_inches='tight')\n",
    "\n",
    "sns.displot(data=df, x='desiredSpeed', y='speed', kind='kde')\n",
    "plt.title('desiredSpeed - speed, all frictions')\n",
    "plt.savefig(paper_path.joinpath('desiredSpeed-speed'), bbox_inches='tight')\n"
   ],
   "id": "e399ea7eacff5e7d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Concatenate Summary CSVs",
   "id": "8886990b2c39dd7c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:10:16.778762Z",
     "start_time": "2024-12-15T18:10:16.756521Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pathlib import Path\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "data_folder = Path().joinpath('traces_paper')\n",
    "summaries = glob.glob(str(data_folder.joinpath('*', '.summary.csv')))\n",
    "\n",
    "dfs = []\n",
    "for summary in summaries:\n",
    "    agent = Path(summary).parent.name[0:-7]\n",
    "    df = pd.read_csv(summary)\n",
    "    df['agent'] = agent\n",
    "    dfs.append(df)\n",
    "\n",
    "final_df = pd.concat(dfs)[\n",
    "    ['agent', 'desiredSpeed', 'friction', 'speed', 'waitingTime', 'emergencyBraking', 'collisions']]\n",
    "\n",
    "final_df.to_csv(data_folder.joinpath('data.csv'), index=False)"
   ],
   "id": "4df2a21ca3b9e06f",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Causal Discovery\n",
    "Discover causal graph\n",
    "\n",
    "TODO: Decide which discovery algorithm to use\n",
    "\n",
    "TODO: Figure out how to incorporate R code in Jupyter Notebook\n",
    "\n",
    "**Desired Output: Causal Graph XML File**"
   ],
   "id": "1de43ea98a1365db"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# TODO: Find library to compute the adjustments.\n",
    "\n",
    "from castle.common.priori_knowledge import PrioriKnowledge\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from castle.algorithms import PC, DirectLiNGAM\n",
    "from pathlib import Path\n",
    "\n",
    "data_csv = Path().joinpath('data_agent', 'data.csv')\n",
    "df = pd.read_csv(data_csv)\n",
    "\n",
    "columns = dict(enumerate(df.columns))\n",
    "column_indexes = {value: int(key) for key, value in columns.items()}\n",
    "\n",
    "priori_knowledge = PrioriKnowledge(len(columns))\n",
    "independent_variables = ['desiredSpeed', 'friction']\n",
    "outcome_variables = ['waitingTime', 'collisions']\n",
    "forbidden_edges = [\n",
    "    *({'source': column, 'target': 'desiredSpeed'} for column in column_indexes.keys() if column != 'desiredSpeed'),\n",
    "    *({'source': column, 'target': 'friction'} for column in column_indexes.keys() if column != 'friction'),\n",
    "    *({'source': source_node, 'target': target_node} for source_node, target_node in\n",
    "      itertools.permutations(outcome_variables, 2))\n",
    "]\n",
    "priori_knowledge.add_forbidden_edges(\n",
    "    [(column_indexes[edge['source']], column_indexes[edge['target']]) for edge in forbidden_edges])\n",
    "\n",
    "pc = PC(variant='stable', priori_knowledge=priori_knowledge)\n",
    "pc.learn(df.values.tolist())\n",
    "\n",
    "G_PC = nx.DiGraph(pc.causal_matrix)\n",
    "H_PC = nx.relabel_nodes(G_PC, dict(enumerate(df.columns)))\n",
    "\n",
    "color_map = [\n",
    "    'green' if node in independent_variables else\n",
    "    'red' if H_PC.out_degree(node) == 0 else\n",
    "    'yellow' for node in H_PC.nodes\n",
    "]\n",
    "\n",
    "nx.draw(G=H_PC, node_color=color_map, node_size=1200, arrowsize=30, with_labels=True,\n",
    "        pos=nx.circular_layout(H_PC))\n"
   ],
   "id": "ca7bf3cc83299253",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Compute directionality of arrows",
   "id": "aad7e14038dd022a"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from scipy.stats import spearmanr\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns\n",
    "\n",
    "direction_adjusted_edge_confidence = {}\n",
    "\n",
    "for edge in H_PC.edges():\n",
    "\n",
    "    X = edge[0]\n",
    "    Y = edge[1]\n",
    "    assert H_PC.has_edge(X, Y)\n",
    "\n",
    "    X_data = df[X].values.reshape(-1, 1)\n",
    "    Y_data = df[Y].values.reshape(-1, 1)\n",
    "\n",
    "    # X = f(Y)\n",
    "    model_X_Y = LinearRegression()\n",
    "    model_X_Y.fit(Y_data, X_data)\n",
    "    X_pred = model_X_Y.predict(Y_data)\n",
    "    residuals_X = X_data - X_pred\n",
    "\n",
    "    # Y = g(X)\n",
    "    model_Y_X = LinearRegression()\n",
    "    model_Y_X.fit(X_data, Y_data)\n",
    "    Y_pred = model_Y_X.predict(X_data)\n",
    "    residuals_Y = Y_data - Y_pred\n",
    "\n",
    "    #Spearman's rank correlation\n",
    "    corr_X_resid, p_X_resid = spearmanr(X_data.ravel(), residuals_Y.ravel())\n",
    "    corr_Y_resid, p_Y_resid = spearmanr(Y_data.ravel(), residuals_X.ravel())\n",
    "    print(f'corr_{X}_resid:', corr_X_resid)\n",
    "    print(f'corr_{Y}_resid:', corr_Y_resid)\n",
    "\n",
    "    confidence_abs = abs(abs(corr_Y_resid) - abs(corr_X_resid))\n",
    "    if abs(corr_X_resid) < abs(corr_Y_resid):\n",
    "        print(f\"{X} -> {Y}\")\n",
    "        direction_adjusted_edge_confidence[(X, Y)] = confidence_abs\n",
    "    else:\n",
    "        print(f\"{Y} -> {X}\")\n",
    "        direction_adjusted_edge_confidence[(Y, X)] = confidence_abs\n",
    "\n",
    "    # fig, axs = plt.subplots(1, 2, figsize=(12, 6), constrained_layout=True)\n",
    "\n",
    "    # Residuals of waitingTime on speed\n",
    "    #sns.scatterplot(x=X_data.ravel(), y=residuals_Y.ravel(), ax=axs[0])\n",
    "    #axs[0].set_title(f\"Residuals of {Y} ~ {X}\")\n",
    "    #axs[0].set_xlabel(f\"{X}\")\n",
    "    #axs[0].set_ylabel(f\"Residuals ({Y})\")\n",
    "\n",
    "    # Residuals of speed on waitingTime\n",
    "    #sns.scatterplot(x=Y_data.ravel(), y=residuals_X.ravel(), ax=axs[1])\n",
    "    #axs[1].set_title(f\"Residuals of {X} ~ {Y}\")\n",
    "    #axs[1].set_xlabel(f\"{Y}\")\n",
    "    #axs[1].set_ylabel(f\"Residuals ({X})\")\n",
    "\n",
    "    print(\"==============================\")\n",
    "\n",
    "direction_adjusted_edges = []\n",
    "for edge, confidence in direction_adjusted_edge_confidence.items():\n",
    "    if edge not in H_PC.edges():\n",
    "        if confidence < 0.1:\n",
    "            edge = edge[::-1]\n",
    "    direction_adjusted_edges.append(edge)\n",
    "\n",
    "I_PC = H_PC.copy()\n",
    "I_PC.clear_edges()\n",
    "\n",
    "I_PC.add_edges_from(direction_adjusted_edges)\n",
    "print(direction_adjusted_edge_confidence)\n",
    "\n",
    "color_map = [\n",
    "    'green' if node in independent_variables else\n",
    "    'red' if I_PC.out_degree(node) == 0 else\n",
    "    'yellow' for node in H_PC.nodes\n",
    "]\n",
    "\n",
    "nx.draw(G=I_PC, node_color=color_map, node_size=1200, arrowsize=30, with_labels=True,\n",
    "        pos=nx.circular_layout(H_PC))"
   ],
   "id": "c7ad297c8cb3cf4f",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Compute Adjustments",
   "id": "9d66fd2902b8351f"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "c36280120c83ec9f",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Fit MLMs\n",
    "Fit MLMs based on Causal Discovery Graph\n",
    "\n",
    "TODO: Parse Graph XML into MLM parameters / formulae\n",
    "\n",
    "**Desired Output: MLM**"
   ],
   "id": "a3d03cf3105ec494"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_models = {}\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, df, test_size=0.2)\n",
    "\n",
    "for node in H_PC.nodes:\n",
    "    if H_PC.in_degree(node) > 0:\n",
    "        lm_independent_variables = list(H_PC.predecessors(node))\n",
    "        lm_target_variable = node\n",
    "\n",
    "        X_node = X_train[list(set(lm_independent_variables + independent_variables))]\n",
    "        y_node = y_train[lm_target_variable]\n",
    "\n",
    "        model = LinearRegression()\n",
    "        model.fit(X_node[lm_independent_variables], y_node)\n",
    "\n",
    "        model_simple = LinearRegression()\n",
    "        model_simple.fit(X_node[independent_variables], y_node)\n",
    "\n",
    "        y_pred = model.predict(X_test[lm_independent_variables])\n",
    "        mse = mean_squared_error(y_test[lm_target_variable], y_pred)\n",
    "        r2 = r2_score(y_test[lm_target_variable], y_pred)\n",
    "\n",
    "        y_pred_simple = model_simple.predict(X_test[independent_variables])\n",
    "        mse_simple = mean_squared_error(y_test[lm_target_variable], y_pred_simple)\n",
    "        r2_simple = r2_score(y_test[lm_target_variable], y_pred_simple)\n",
    "\n",
    "        linear_models[node] = {'causal_lm': model, 'simple_lm': model_simple}"
   ],
   "id": "829786814c4f2bda",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "causal_emergency_braking_lm = linear_models['emergencyBraking']['causal_lm']\n",
    "# causal_time_loss_lm = linear_models['timeLoss']['causal_lm']\n",
    "causal_rear_end_lm = linear_models['rearEndCollisions']['causal_lm']\n",
    "causal_x_test = X_test.copy()\n",
    "\n",
    "causal_emergency_braking = causal_emergency_braking_lm.predict(X_test[causal_emergency_braking_lm.feature_names_in_])\n",
    "causal_x_test['emergencyBraking'] = causal_emergency_braking\n",
    "\n",
    "# causal_time_loss = causal_time_loss_lm.predict(X_test[causal_time_loss_lm.feature_names_in_])\n",
    "# causal_x_test['timeLoss'] = causal_time_loss\n",
    "\n",
    "# causal_rear_end = causal_rear_end_lm.predict(X_test[causal_rear_end_lm.feature_names_in_])\n",
    "# causal_x_test['rearEndCollisions'] = causal_rear_end\n",
    "\n",
    "for node, model in linear_models.items():\n",
    "    # Causal LM \n",
    "    model_causal = model['causal_lm']\n",
    "    lm_independent_variables = model_causal.feature_names_in_\n",
    "    lm_target_variable = node\n",
    "\n",
    "    causal_y_pred = model_causal.predict(causal_x_test[lm_independent_variables])\n",
    "    causal_mse = mean_squared_error(y_test[lm_target_variable], causal_y_pred)\n",
    "    causal_r2 = r2_score(y_test[lm_target_variable], causal_y_pred)\n",
    "\n",
    "    # Simple LM\n",
    "    model_simple = model['simple_lm']\n",
    "    y_pred_simple = model_simple.predict(X_test[independent_variables])\n",
    "    mse_simple = mean_squared_error(y_test[lm_target_variable], y_pred_simple)\n",
    "    r2_simple = r2_score(y_test[lm_target_variable], y_pred_simple)\n",
    "\n",
    "    print(\"causal\", node, model_causal.feature_names_in_, model_causal.coef_, \"With: mse \", causal_mse, \", r2 \",\n",
    "          causal_r2)\n",
    "    print(\"simple\", node, model_simple.feature_names_in_, model_simple.coef_, \"With: mse \", mse_simple, \", r2 \",\n",
    "          r2_simple)\n",
    "    print(\" \")\n",
    "\n",
    "print(\"Done\")"
   ],
   "id": "c30331585987ddac",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Produce Interventions\n",
    "\n",
    "#### Covariate Shift Distribution\n",
    "* Create a distribution for the covariate (friction) shift\n",
    "* Sample from distribution\n",
    "    * Fulfill Assumption: sparse sample data is representative for covariate shift ground truth\n",
    "* Produce Traces for sparse input data\n",
    "\n",
    "#### Crank MLM the other way\n",
    "* Calculate Intervention Distribution by inputting sparse data into MLM\n",
    "\n",
    "**Desired Output: Intervention Distribution**"
   ],
   "id": "a5c99e021c7da84f"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# TODO",
   "id": "32459df33ac2d588",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Generate Posterior Distributions\n",
    "TODO: Generate Posterior Distributions without intervention\n",
    "\n",
    "TODO: Generate Posterior Distributions with intervention\n",
    "\n",
    "**Desired Output: Two XML Files**"
   ],
   "id": "c86f426933c2843a"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# TODO",
   "id": "730155056916357a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Query\n",
    "Compare Distributions and decide, which part of the model to retrain.\n",
    "\n",
    "TODO: Classify the data / model in parts"
   ],
   "id": "71229ba166fe756f"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# TODO",
   "id": "73190c3130a276a3",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Evaluation\n",
    "\n",
    "#### Agent\n",
    "compare new resulting agent (partially continued training depending on Query) to:\n",
    "* Old agent (Lower performance bound)\n",
    "* Completely newly trained agent (upper performance bound)\n",
    "* (New Agent that is trained completely on new data (without Query))\n",
    "\n",
    "#### Intervention\n",
    "Function: Number of Covariate Shift Samples --> Wasserstein distance: Intervention vs. ground truth (distribution)\n",
    "\n",
    "#### MLM\n",
    "* Wasserstein Distance: Effect of Intervention vs. ground truth effect\n",
    "* Maybe also as a function of the number of retrain samples\n",
    "\n"
   ],
   "id": "6cdb163f340d239c"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# TODO",
   "id": "b28fb56662cf1a7a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Ideas\n",
    "* Maybe no change in friction but rather only in the requirements\n",
    "* Sophisticated Query: Causal Graph of Transfer Learning --> Generate Posterior for different transfer learning options --> Rank and choose best.\n",
    "* Collision / Penalty Factor for managing Safety/Performance tradeoff"
   ],
   "id": "8485d156269d6f45"
  }
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