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    "# Save and Load a Model\n",
    "\n",
    "In this tutorial, we illustrate the process of saving and loading a model using CapyMOA. \n",
    "\n",
    "* We use the SEA synthetic generator as the data source, and the AdaptiveRandomForestClassifier as the learner.\n",
    "* The trained model is saved to a file, specifically 'capymoa_model.pkl'.\n",
    "* Subsequently, we reload the model from the file and resume training and evaluating its performance on the SEA data.\n",
    "* As a final step, we delete the model file."
   ]
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    "## 1. Training and saving the model\n",
    "\n",
    "* We train the model on 5k instances from SEA using the `evaluate_prequential` function\n",
    "* We proceed to save the model with `save_model(learner, \"capymoa_ARF_model.pkl\")`"
   ]
  },
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     "start_time": "2024-05-29T08:18:27.317959Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.96\n"
     ]
    }
   ],
   "source": [
    "from capymoa.classifier import AdaptiveRandomForestClassifier\n",
    "from capymoa.evaluation import prequential_evaluation\n",
    "from capymoa.stream.generator import SEA\n",
    "from capymoa.misc import save_model, load_model\n",
    "\n",
    "stream = SEA()\n",
    "learner = AdaptiveRandomForestClassifier(schema=stream.get_schema(), ensemble_size=10)\n",
    "\n",
    "results = prequential_evaluation(stream=stream, learner=learner, max_instances=5000)\n",
    "\n",
    "print(f\"Accuracy: {results['cumulative'].accuracy():.2f}\")\n",
    "save_model(learner, \"capymoa_ARF_model.pkl\") # Save model to capymoa_model.pkl"
   ]
  },
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   "source": [
    "## 2. Loading and resuming training\n",
    "\n",
    "* We use `os.path.getsize()` to inspect the size (KB) of the saved file.\n",
    "* We don't restart the synthetic stream, we just continue processing it through another call to `prequential_evaluation`\n",
    "* Finally, we observe the accuracy "
   ]
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The saved model size: 618.28 KB\n",
      "Updated accuracy: 89.32\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "model_file = 'capymoa_ARF_model.pkl'\n",
    "\n",
    "model_size = os.path.getsize(model_file)\n",
    "print(f\"The saved model size: {model_size / 1024:.2f} KB\")\n",
    "\n",
    "restored_learner = load_model(\"capymoa_ARF_model.pkl\")  # Load model from capymoa_model.pkl\n",
    "\n",
    "# Train for more 50k instances on the restored model\n",
    "results = prequential_evaluation(stream=stream, learner=restored_learner, max_instances=5000)\n",
    "    \n",
    "print(f\"Updated accuracy: {results['cumulative'].accuracy():.2f}\")"
   ]
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   "cell_type": "markdown",
   "id": "d86a6b63-4a4d-4753-bda5-19a632aad41d",
   "metadata": {},
   "source": [
    "## 3. Cleanup \n",
    "\n",
    "* As a last step, we delete the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e00a292713d154ee",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2024-05-29T08:18:37.739209Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File capymoa_ARF_model.pkl has been deleted.\n"
     ]
    }
   ],
   "source": [
    "if os.path.exists(model_file):\n",
    "    os.remove(model_file)\n",
    "    print(f\"File {model_file} has been deleted.\")\n",
    "else:\n",
    "    print(f\"File {model_file} not found.\")"
   ]
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