{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02134a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6d8f223",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c6c0333",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce98fab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da753996-54f8-4244-a34e-7316b0c01827",
   "metadata": {},
   "source": [
    "# Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a62889-d81e-462e-b235-c1eba1096da9",
   "metadata": {},
   "source": [
    "Fine-tuning is a powerful process for utilizing TimeGPT more effectively. Foundation models such as TimeGPT are pre-trained on vast amounts of data, capturing wide-ranging features and patterns. These models can then be specialized for specific contexts or domains. With fine-tuning, the model's parameters are refined to forecast a new task, allowing it to tailor its vast pre-existing knowledge towards the requirements of the new data. Fine-tuning thus serves as a crucial bridge, linking TimeGPT's broad capabilities to your tasks specificities.\n",
    "\n",
    "Concretely, the process of fine-tuning consists of performing a certain number of training iterations on your input data minimizing the forecasting error. The forecasts will then be produced with the updated model. To control the number of iterations, use the `finetune_steps` argument of the `forecast` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "448eaf77-0a40-4b5b-88a2-31de99f404bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/06_finetuning.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()    \n",
    "    colab_badge('docs/tutorials/06_finetuning')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ec4f03",
   "metadata": {},
   "source": [
    "## 1. Import packages\n",
    "First, we import the required packages and initialize the Nixtla client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98942108-d427-42d6-81f8-fa0bb5859395",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nixtla import NixtlaClient\n",
    "from utilsforecast.losses import mae, mse\n",
    "from utilsforecast.evaluation import evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64178d1c-957e-4a04-ab64-fde332b1840c",
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57a38e6",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    "> \n",
    "> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
    "> \n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd61549-0b00-4a42-a98e-239fa4fae5e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2e5387",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b78cc83e-7d34-4c37-906d-8c7ed1a977fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp  value\n",
       "0  1949-01-01    112\n",
       "1  1949-02-01    118\n",
       "2  1949-03-01    132\n",
       "3  1949-04-01    129\n",
       "4  1949-05-01    121"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09be4766",
   "metadata": {},
   "source": [
    "## 3. Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5b9060",
   "metadata": {},
   "source": [
    "Here, `finetune_steps=10` means the model will go through 10 iterations of training on your time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a683abc7-190c-40a6-a4e8-41a4c64bd773",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: MS\n",
      "INFO:nixtla.nixtla_client:Querying model metadata...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    }
   ],
   "source": [
    "timegpt_fcst_finetune_df = nixtla_client.forecast(\n",
    "    df=df, h=12, finetune_steps=10,\n",
    "    time_col='timestamp', target_col='value',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac469746",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.forecast(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "545ffdac-f166-417b-993f-78f51b0db6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, timegpt_fcst_finetune_df, \n",
    "    time_col='timestamp', target_col='value',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fc9cba-7c6e-4aef-9c68-e05d4fe8f7ba",
   "metadata": {},
   "source": [
    "Keep in mind that fine-tuning can be a bit of trial and error. You might need to adjust the number of `finetune_steps` based on your specific needs and the complexity of your data. Usually, a larger value of `finetune_steps` works better for large datasets.\n",
    "\n",
    "It's recommended to monitor the model's performance during fine-tuning and adjust as needed. Be aware that more `finetune_steps` may lead to longer training times and could potentially lead to overfitting if not managed properly. \n",
    "\n",
    "Remember, fine-tuning is a powerful feature, but it should be used thoughtfully and carefully."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c546351",
   "metadata": {},
   "source": [
    "For a detailed guide on using a specific loss function for fine-tuning, check out the [Fine-tuning with a specific loss function](https://docs.nixtla.io/docs/tutorials-fine_tuning_with_a_specific_loss_function) tutorial.\n",
    "\n",
    "Read also our detailed tutorial on [controlling the level of fine-tuning](https://docs.nixtla.io/docs/tutorials-finetune_depth_finetuning) using `finetune_depth`."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}