nbs/docs/tutorials/06_finetuning.ipynb

{
 "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: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": {
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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": "cf9e168a",
   "metadata": {},
   "source": [
    "### 3.1 Control the level of fine-tuning with `finetune_depth`\n",
    "It is also possible to control the depth of fine-tuning with the `finetune_depth` parameter.\n",
    "\n",
    "`finetune_depth` takes values among `[1, 2, 3, 4, 5]`. By default, it is set to 1, which means a small amount of fine-tuning is applied, whereas a value of 5 means the maximum amount of fine-tuning is done. Increasing `finetune_depth` increases the time to generate predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446f47ef",
   "metadata": {},
   "source": [
    "Let's run a small experiment to see how `finetune_depth` impacts the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a55e52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:-24]\n",
    "test = df[-24:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f71b895c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: MS\n",
      "WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: MS\n",
      "WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: MS\n",
      "WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    }
   ],
   "source": [
    "depths = [1, 2, 3]\n",
    "\n",
    "test = test.copy()\n",
    "\n",
    "for depth in depths:\n",
    "    preds_df = nixtla_client.forecast(\n",
    "    df=train, \n",
    "    h=24, \n",
    "    finetune_steps=10,\n",
    "    finetune_depth=depth,\n",
    "    time_col='timestamp', \n",
    "    target_col='value')\n",
    "\n",
    "    preds = preds_df['TimeGPT'].values\n",
    "\n",
    "    test.loc[:,f'TimeGPT_depth{depth}'] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "019b17f6",
   "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>unique_id</th>\n",
       "      <th>metric</th>\n",
       "      <th>TimeGPT_depth1</th>\n",
       "      <th>TimeGPT_depth2</th>\n",
       "      <th>TimeGPT_depth3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>mae</td>\n",
       "      <td>57.164810</td>\n",
       "      <td>23.850386</td>\n",
       "      <td>20.334634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>mse</td>\n",
       "      <td>3913.393147</td>\n",
       "      <td>786.861702</td>\n",
       "      <td>577.173268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id metric  TimeGPT_depth1  TimeGPT_depth2  TimeGPT_depth3\n",
       "0          0    mae       57.164810       23.850386       20.334634\n",
       "1          0    mse     3913.393147      786.861702      577.173268"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['unique_id'] = 0\n",
    "\n",
    "evaluation = evaluate(test, metrics=[mae, mse], time_col=\"timestamp\", target_col=\"value\")\n",
    "evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fc9cba-7c6e-4aef-9c68-e05d4fe8f7ba",
   "metadata": {},
   "source": [
    "As you can see, increasing the depth of fine-tuning can greatly improve the performance of the model. However, 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. It's recommended to monitor the model's performance during fine-tuning and adjust as needed. Be aware that more `finetune_steps` and a larger value of `finetune_depth` 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."
   ]
  }
 ],
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