W&B 🔥: K-Fold Cross Validation

w-b-k-fold-cross-validation.ipynb

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  "metadata": {
    "colab": {
      "name": "W&B 🔥: K-Fold Cross Validation",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNtQN06rmH289Xfm/95XefY",
      "include_colab_link": true
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    "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/gist/SauravMaheshkar/3acdbfdb5ae7ca520783606a51b9bc2f/w-b-k-fold-cross-validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euYUBxAHLNHR"
      },
      "source": [
        "# Author: [@SauravMaheshkar](https://twitter.com/MaheshkarSaurav)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0J_WzJU7LSAb"
      },
      "source": [
        "# Packages 📦 and Basic Setup\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0Zyy3i7LTOy"
      },
      "source": [
        "## Install Packages\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmZTdUxJao_1"
      },
      "source": [
        "%%capture\n",
        "\n",
        "## Install the latest version of wandb client 🔥🔥\n",
        "!pip install -q --upgrade wandb\n",
        "\n",
        "import numpy as np\n",
        "from sklearn import svm\n",
        "from sklearn import datasets"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swvAOJ8dLYIt"
      },
      "source": [
        "## Project Configuration using **`wandb.config`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ueyfw3G2Lggm"
      },
      "source": [
        "import os\n",
        "import wandb\n",
        "\n",
        "# Paste your api key here\n",
        "os.environ[\"WANDB_API_KEY\"] = '...'\n",
        "\n",
        "# Initialize the run\n",
        "run = wandb.init(project='...', entity='...')\n",
        "\n",
        "# Feel free to change these and experiment !!\n",
        "config = wandb.config\n",
        "config.dataset = \"Iris\"\n",
        "config.n_splits = 5\n",
        "config.random_state = 21\n",
        "config.shuffle = True\n",
        "config.test_size = 0.4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrcKTKl1MMsg"
      },
      "source": [
        "# The Much Simpler (not always better) : Train Test Split\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xvj1Cx0odzWD"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load and Download the Dataset\n",
        "X, y = datasets.load_iris(return_X_y=True)\n",
        "\n",
        "# Split using train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=config.test_size, random_state=config.random_state)\n",
        "\n",
        "# Create and train a SVC Model\n",
        "clf = svm.SVC(kernel = 'linear', C = 1)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Print the score\n",
        "score = clf.score(X_test, y_test)\n",
        "print(score)\n",
        "\n",
        "wandb.log({\"train_test_split_score\": score})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBOrrb_WMUTl"
      },
      "source": [
        "# K-Fold Cross Validation\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQz1ChUZHTk3"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "# Create KFold instance\n",
        "kfold = KFold(n_splits = config.n_splits, shuffle = config.shuffle, random_state = config.random_state)\n",
        "\n",
        "# Create and train a SVC Model\n",
        "clf = svm.SVC(kernel = 'linear', C = 1)\n",
        "\n",
        "# Iterate over the folds\n",
        "for train_index, test_index in kfold.split(X):\n",
        "\n",
        "  # Split the dataset\n",
        "  X_train, X_test = X[train_index], X[test_index]\n",
        "  y_train, y_test = y[train_index], y[test_index]\n",
        "\n",
        "  clf.fit(X_train, y_train)\n",
        "\n",
        "  # Print the Scores\n",
        "  print(clf.score(X_test, y_test))\n",
        "\n",
        "# Plot the learning curve\n",
        "wandb.sklearn.plot_learning_curve(clf, X_train, y_train)\n",
        "\n",
        "wandb.finish()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}