Add Assignment02

Assignment02_nb.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assignment Sheet 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is part of the 2nd assignment. We will train a Decision Tree classifier using the sklearn library on the Iris dataset.\n",
+    "\n",
+    "Your task is to complete the missing code, where marked with a **TODO**. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import graphviz\n",
+    "\n",
+    "from sklearn import datasets, tree\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report\n",
+    "from os import system\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# For further info https://archive.ics.uci.edu/ml/datasets/iris\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data  \n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploratory data analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Quick look into the data structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(X.shape)\n",
+    "print(y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(X[:5,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using pandas\n",
+    "data = pd.concat([pd.DataFrame(X),pd.DataFrame(y)], axis=1)\n",
+    "data.columns=['a','b','c','d','target']\n",
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exemplary plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8,6))\n",
+    "sns.scatterplot(x=X[:,0], y=X[:,1], hue=y)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Univariate hist_plot 'sepal_length'\n",
+    "class0_index = [i for i, j in enumerate(y) if j==0]\n",
+    "class1_index = [i for i, j in enumerate(y) if j==1]\n",
+    "class2_index = [i for i, j in enumerate(y) if j==2]\n",
+    "\n",
+    "sns.histplot(data=X, x=X[:,0], hue=y, element='step')\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.legend(('class1', 'class2','class3'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Barplot over 'sepal-width'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Boxplot of all features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classification using decision trees "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Split data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train DT classifier using sklearn; Visualization; Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Train a DT classifier "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualize: Export to .png image file\n",
+    "tree.export_graphviz(clf, out_file='tree.dot') \n",
+    "system(\"dot -Tpng tree.dot -o tree1.png\")\n",
+    "Image(\"tree1.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Evaluation the classifier's performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train a second DT classifier using the Entropy instead of the Gini-Index (default)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Train the second classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualize #2\n",
+    "tree.export_graphviz(clf2, out_file='tree2.dot') \n",
+    "system(\"dot -Tpng tree2.dot -o tree2.png\")\n",
+    "Image(\"tree2.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Evaluation"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ "nbformat": 4,
+ "nbformat_minor": 4
+}