feat: add decision trees example

colabs/scikit/wandb_decision_tree.ipynb

@@ -0,0 +1,228 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Decision Trees",
+      "provenance": [],
+      "collapsed_sections": [
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+      ],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e6qvOYJV8BQw"
+      },
+      "source": [
+        "## Author: [@SauravMaheshkar](https://twitter.com/MaheshkarSaurav)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BL5XadIr0MbM"
+      },
+      "source": [
+        "# Packages 📦 and Basic Setup\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fZH0PUfw0OBV"
+      },
+      "source": [
+        "## Install Packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_uk-bdosSzwk"
+      },
+      "source": [
+        "%%capture\n",
+        "## Install Sklearn\n",
+        "!pip install -U scikit-learn\n",
+        "## Install the latest version of wandb client 🔥🔥\n",
+        "!pip install -q --upgrade wandb"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UgQLiLtz0STo"
+      },
+      "source": [
+        "## Project Configuration using **`wandb.config`**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OvABqBlB0VhB"
+      },
+      "source": [
+        "import os\n",
+        "import wandb\n",
+        "\n",
+        "## Importing Libraries\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.datasets import load_iris\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
+        "\n",
+        "# Paste your api key here\n",
+        "os.environ[\"WANDB_API_KEY\"] = '...'\n",
+        "\n",
+        "# Feel free to change these and experiment !!\n",
+        "config = wandb.config\n",
+        "config.max_depth = 5\n",
+        "config.min_samples_split = 2\n",
+        "config.clf_criterion = \"gini\"\n",
+        "config.reg_criterion = \"mse\"\n",
+        "config.splitter = \"best\"\n",
+        "config.dataset = \"iris\"\n",
+        "config.test_size = 0.2\n",
+        "config.random_state = 42\n",
+        "config.labels =['setosa', 'versicolor', 'virginica']"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OGV1c4VYJMGa"
+      },
+      "source": [
+        "# 💿 Dataset\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lWnhwnPI2NWD"
+      },
+      "source": [
+        "## Loading the Dataset\n",
+        "iris = load_iris(return_X_y = True, as_frame= True)\n",
+        "dataset = iris[0]\n",
+        "target = iris[1]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kycxKC7ER7lW"
+      },
+      "source": [
+        "# ✍️ Model Architecture\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4wWuB4NZl3h_"
+      },
+      "source": [
+        "## Classification"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vElZpyEQtXzX"
+      },
+      "source": [
+        "run = wandb.init(project='...', entity='...', config = config)\n",
+        "\n",
+        "X, y = load_iris(return_X_y=True)\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",
+        "clf = DecisionTreeClassifier(\n",
+        "    max_depth=config.max_depth,\n",
+        "    min_samples_split=config.min_samples_split,\n",
+        "    criterion=config.clf_criterion,\n",
+        "    splitter=config.splitter\n",
+        ")\n",
+        "clf = clf.fit(x_train,y_train)\n",
+        "\n",
+        "y_pred = clf.predict(x_test)\n",
+        "\n",
+        "# Visualize Confustion Matrix\n",
+        "wandb.sklearn.plot_confusion_matrix(y_test, y_pred, config.labels)\n",
+        "\n",
+        "run.finish()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BX31c0s8MVu5"
+      },
+      "source": [
+        "## Regression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1kBLTtH4MbsY"
+      },
+      "source": [
+        "run = wandb.init(project='...', entity='...', config = config)\n",
+        "\n",
+        "X, y = load_iris(return_X_y=True)\n",
+        "\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",
+        "reg = DecisionTreeRegressor(\n",
+        "    max_depth=config.max_depth,\n",
+        "    min_samples_split=config.min_samples_split,\n",
+        "    criterion=config.reg_criterion,\n",
+        "    splitter=config.splitter\n",
+        ")\n",
+        "\n",
+        "reg = reg.fit(x_train,y_train)\n",
+        "\n",
+        "# All regression plots\n",
+        "wandb.sklearn.plot_regressor(reg, x_train, x_test, y_train, y_test,  model_name='DecisionTreeRegressor')\n",
+        "\n",
+        "run.finish()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}