add notebook

kiliautoml/utils/mapper/Tuto_Mapper_AutoML.ipynb

@@ -0,0 +1,144 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L9_fNq8zqAH_"
+      },
+      "source": [
+        "# Create Mapper from Kili Dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aKejop6ok_ne"
+      },
+      "source": [
+        "## Import requirements"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "850KFigkX0v9",
+        "outputId": "e18c0bfe-d128-4110-ff8f-d899fe84b491"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install img2vec_pytorch\n",
+        "!pip install kili\n",
+        "!pip install gudhi\n",
+        "!pip install kmapper\n",
+        "!pip install datasets\n",
+        "!pip install transformers\n",
+        "import os\n",
+        "import sys\n",
+        "from kili.client import Kili\n",
+        "\n",
+        "git_token = getpass('Github token: ')\n",
+        "os.environ['GITHUB_TOKEN'] = git_token\n",
+        "!git clone --branch feature/ml-345-mapper https://[email protected]/kili-technology/automl.git\n",
+        "\n",
+        "kili_api_key = getpass('Kili API Key: ')\n",
+        "os.environ[\"KILI_API_KEY\"] =  kili_api_key\n",
+        "api_key = os.environ[\"KILI_API_KEY\"]\n",
+        "\n",
+        "kili = Kili(api_key=api_key)\n",
+        "\n",
+        "%cd /content/automl"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AvCWOIzvXNtb"
+      },
+      "source": [
+        "## Create Mapper"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HJHIvtiAic0I"
+      },
+      "outputs": [],
+      "source": [
+        "# Image Classification - Roman Number -  project id = cl2yugghg0iw10m0i8wh05o00\n",
+        "# Text Classification - Tweet Emotions - project_id = cl1uwuqvt5exk0mtha6lchugl"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IW53iJtMj0Bk"
+      },
+      "outputs": [],
+      "source": [
+        "\"\"\"\n",
+        "Mapper argument: \n",
+        "--api-endpoint, --api-key, --project-id , --target-job, --max-assets : \n",
+        "--assets-repository : Required, where to store downloaded assets\n",
+        "--asset-status-in: if None or TODO or ONGOING included, mapper.py will not use labels in the asset assignment\n",
+        "--cv-folds: Number of cv-folds used to compute predictions. Used only if not (None or TODO or ONGOING included in asset-status-in). \n",
+        "--focus-class: only use assets with labels or predictions inside focus-class\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zt4hKCPohk0t",
+        "outputId": "8788e04b-5819-4b9c-8ebe-536d4fdce967"
+      },
+      "outputs": [],
+      "source": [
+        "!python mapper.py --project-id cl2yugghg0iw10m0i8wh05o00  --assets-repository /content/assets --asset-status-in labeled,reviewed --focus-class ix,x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XB1PvyRQaIIR",
+        "outputId": "27d1b412-afa7-475e-a2d7-85d48abe1b89"
+      },
+      "outputs": [],
+      "source": [
+        "!python mapper.py --project-id cl1uwuqvt5exk0mtha6lchugl  --assets-repository /content/assets --asset-status-in labeled,reviewed"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "machine_shape": "hm",
+      "name": "Tuto_Mapper_AutoML.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}