{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Predictive power metrics",
   "id": "eb9fb4a1a68e633"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "!pip install amlgym",
   "id": "3a2c1c2a35c2e0f2"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Learn a model to be evaluated:",
   "id": "243b51efc337c73b"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from amlgym.benchmarks import get_domain_path, get_trajectories_path\n",
    "from amlgym.algorithms import get_algorithm\n",
    "from amlgym.util.util import empty_domain\n",
    "\n",
    "# Get learning algorithm\n",
    "learner = get_algorithm('nolam')\n",
    "\n",
    "# Get an input domain with no preconditions and effects\n",
    "domain_ref_path = get_domain_path('blocksworld')\n",
    "domain_empty_path = empty_domain(domain_ref_path)\n",
    "\n",
    "# Get a set of trajectories to learn from\n",
    "traj_paths = get_trajectories_path('blocksworld')\n",
    "\n",
    "# Learn a domain model\n",
    "model = learner.learn(domain_empty_path, traj_paths)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Print the learned model",
   "id": "c37e9a2fcb11330c"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "print(model)",
   "id": "6ca1abc82297796c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Save the learned model to a file:",
   "id": "e57ba7ed0e528f13"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "domain_eval_path = 'domain_learned.pddl'\n",
    "with open(domain_eval_path, 'w') as f:\n",
    "    f.write(model)"
   ],
   "id": "180a5e01e627481f"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Get the test set of states necessary to evaluate predicted applicability and predicted effects metrics:",
   "id": "c2d26df0bdcd4500"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from amlgym.benchmarks import get_problems_path, get_test_states\n",
    "\n",
    "test_states = get_test_states('blocksworld', kind='predictive_power')\n",
    "problem_paths = get_problems_path('blocksworld', kind='predictive_power')\n",
    "\n",
    "# get first problem file path\n",
    "problem_path = problem_paths[0]\n",
    "\n",
    "# get test set of state for a single problem\n",
    "test_states = test_states[problem_path.split('/')[-1]]"
   ],
   "id": "7442d5009738bfdc"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Create a simulator defined by the learned domain model and an environment simulator defined by a reference domain model",
   "id": "bbfdd327bae98dd0"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from amlgym.modeling.UPEnv import UPEnv\n",
    "simulator_learned = UPEnv(domain_eval_path, problem_path)\n",
    "simulator_ref = UPEnv(domain_ref_path, problem_path)"
   ],
   "id": "f23f15d513a88d04"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Evaluate the predicted applicability and predicted effects metrics with a learned domain simulator and environment simulator.",
   "id": "4bba85de1fe660c"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from amlgym.metrics import predictive_power\n",
    "predictive_metrics = predictive_power(simulator_learned,\n",
    "                                      simulator_ref,\n",
    "                                      test_states)\n",
    "print(predictive_metrics)"
   ],
   "id": "5cb4c2dae24c8453"
  }
 ],
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   "display_name": "Python 3",
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