Adding the colab notebooks for the example notebooks

example/colab/chronos.ipynb

@@ -0,0 +1,213 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOHc9Zna3DRB0e4ryzjGrBk",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Showmick119/Samay/blob/main/chronos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Install Dependencies**"
+      ],
+      "metadata": {
+        "id": "tpaQlDVlxetV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "259--YZOr1Lo"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install samay-0.1.0-cp311-cp311-linux_x86_64.whl"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade -U numpy --force"
+      ],
+      "metadata": {
+        "id": "VGf7Oy_JxpYO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Importing Requirements**"
+      ],
+      "metadata": {
+        "id": "0cbiLmxU2Lw-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import sys\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "\n",
+        "src_path = os.path.abspath(os.path.join(\"..\", \"src\"))\n",
+        "if src_path not in sys.path:\n",
+        "    sys.path.insert(0, src_path)\n",
+        "\n",
+        "from samay.model import ChronosModel\n",
+        "from samay.dataset import ChronosDataset\n",
+        "# from tsfmproject.utils import load_args\n",
+        "\n",
+        "# arg_path = \"../config/timesfm.json\"\n",
+        "# args = load_args(arg_path)\n",
+        "repo = \"amazon/chronos-t5-small\"\n",
+        "chronos_model = ChronosModel(repo=repo)"
+      ],
+      "metadata": {
+        "id": "APfhHKAU1qM3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Loading Dataset**"
+      ],
+      "metadata": {
+        "id": "Gt2KUswn2UtL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://github.com/kage08/Samay/refs/heads/main/data/data/ETTh1.csv"
+      ],
+      "metadata": {
+        "id": "mnIsa2TgJgCN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = ChronosDataset(name=\"ett\", datetime_col='date', path='../src/tsfmproject/models/moment/data/ETTh1.csv',\n",
+        "                             mode='train', batch_size=8)\n",
+        "val_dataset = ChronosDataset(name=\"ett\", datetime_col='date', path='../src/tsfmproject/models/moment/data/ETTh1.csv',\n",
+        "                             mode='test', batch_size=8)"
+      ],
+      "metadata": {
+        "id": "MyUXzOME1sgo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Visualize the zero-shot forecasting**"
+      ],
+      "metadata": {
+        "id": "OgcxscvD3Ee8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "chronos_model.plot(val_dataset, horizon_len=64, quantile_levels=[0.1, 0.5, 0.9])"
+      ],
+      "metadata": {
+        "id": "K9Zmx3li1u5k"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Evaluate the zero-shot Chronos Model**"
+      ],
+      "metadata": {
+        "id": "2I0VE7Uu3Jqt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "metrics = chronos_model.evaluate(val_dataset, horizon_len=64, quantile_levels=[0.1, 0.5, 0.9])\n",
+        "print(metrics)"
+      ],
+      "metadata": {
+        "id": "Z90jyPsp1ykH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Finetune Chronos Model on the ETT dataset**"
+      ],
+      "metadata": {
+        "id": "iuzgw9uA3idf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "chronos_model.finetune(train_dataset)"
+      ],
+      "metadata": {
+        "id": "LeFA_RVg12IS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Evaluate the Finetuned Chronos Model**"
+      ],
+      "metadata": {
+        "id": "DE5-HDC53n4_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "metrics = chronos_model.evaluate(val_dataset, horizon_len=64, quantile_levels=[0.1, 0.5, 0.9])\n",
+        "print(metrics)"
+      ],
+      "metadata": {
+        "id": "XF2bgAXi125y"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}