From 9a60c190e1c00afdff939a832ec42c31c75f77d2 Mon Sep 17 00:00:00 2001
From: manueh51 <manueh51@mi.fu-berlin.de>
Date: Tue, 12 Oct 2021 11:25:13 +0000
Subject: [PATCH] Delete Assignment02_nb.ipynb
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delete mode 100644 Assignment02_nb.ipynb
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-{
- "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
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
--
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