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   "source": [
    "# Time Limit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94210ca1",
   "metadata": {},
   "source": [
    "Some explanations are computationally expensive to compute. For this reason, most explanation methods accept a ```time_limit``` parameter (in seconds). If the time limit is reached, the method stops and returns either an approximation of the desired explanation or ```[]``` if no approximation was found. By default, ```time_limit``` is set to ```None```, giving an unlimited amount of time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4dfe8a0",
   "metadata": {},
   "source": [
    "All explainers expose an ```elapsed_time``` attribute set to the time in seconds taken by the last explanation method call. It is equal to ```Explaining.TIMEOUT``` if the time limit was reached.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3843608",
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   "source": [
    "The following methods accept a ```time_limit``` parameter:\n",
    "\n",
    "| Method | DT | RF | BT | BT (regression) |\n",
    "|:-------|:--:|:--:|:--:|:---------:|\n",
    "| ```sufficient_reason``` | ✓ | ✓ | | ✓ |\n",
    "| ```minimal_sufficient_reason``` | ✓ | ✓ | | |\n",
    "| ```preferred_sufficient_reason``` | ✓ | | | |\n",
    "| ```n_sufficient_reasons``` | ✓ | | | |\n",
    "| ```n_sufficient_reasons_per_attribute``` | ✓ | | | |\n",
    "| ```anchored_reason``` | ✓ | | | |\n",
    "| ```minimal_contrastive_reason``` | | ✓ | ✓ | |\n",
    "| ```majoritary_reason``` | | ✓ | | |\n",
    "| ```preferred_majoritary_reason``` | | ✓ | | |\n",
    "| ```minimal_majoritary_reason``` | | ✓ | | |\n",
    "| ```most_anchored_reason``` | | ✓ | | |\n",
    "| ```tree_specific_reason``` | | | ✓ | ✓ |\n",
    "| ```minimal_tree_specific_reasons``` | | | ✓ | |\n",
    "| ```range_for_partial_instance``` | | | | ✓ |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af439d32",
   "metadata": {},
   "source": [
    "The following example illustrates the usage of ```time_limit``` and ```elapsed_time```:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d7d29af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyxai import Learning, Explaining\n",
    "\n",
    "learner = Learning.Scikitlearn(\"../../dataset/iris.csv\", problem_type=Learning.CLASSIFICATION)\n",
    "model = learner.evaluate(splitting_method=Learning.HOLD_OUT, model_type=Learning.DT)\n",
    "instance, prediction = learner.get_instances(model, n=1, is_correct=True)\n",
    "explainer = Explaining.initialize(model, instance)\n",
    "\n",
    "sufficient_reason = explainer.sufficient_reason(time_limit=10)\n",
    "if explainer.elapsed_time == Explaining.TIMEOUT:\n",
    "    print(\"Timeout reached.\")\n",
    "    print(f\"sufficient_reason: {sufficient_reason} (approximation)\")\n",
    "else:\n",
    "    print(f\"Time to compute: {explainer.elapsed_time:.3f}s\")\n",
    "    print(f\"sufficient_reason: {sufficient_reason}\")\n"
   ]
  }
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