mirror of
https://github.com/gsi-upm/sitc
synced 2024-11-22 06:22:29 +00:00
568 lines
79 KiB
Plaintext
568 lines
79 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![](files/images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Table of Contents\n",
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"* [kNN Model](#kNN-Model)\n",
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"* [Load data and preprocessing](#Load-data-and-preprocessing)\n",
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"* [Train classifier](#Train-classifier)\n",
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"* [Evaluating the algorithm](#Evaluating-the-algorithm)\n",
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" * [Precision, recall and f-score](#Precision,-recall-and-f-score)\n",
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"\t* [Confusion matrix](#Confusion-matrix)\n",
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"\t* [K-Fold validation](#K-Fold-validation)\n",
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"* [Tuning the algorithm](#Tuning-the-algorithm)\n",
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"* [References](#References)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# kNN Model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The goal of this notebook is to learn how to train a model and make and evaluate predictions.\n",
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"\n",
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"The notebook uses the [kNN (k nearest neighbors) algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load data and preprocessing\n",
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"\n",
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"The first step is loading and preprocessing the data as explained in the previous notebooks."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# library for displaying plots\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# display plots in the notebook \n",
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"%matplotlib inline\n",
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"\n",
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"## First, we repeat the load and preprocessing steps\n",
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"\n",
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"# Load data\n",
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"from sklearn import datasets\n",
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"iris = datasets.load_iris()\n",
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"\n",
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"# Training and test spliting\n",
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"from sklearn.cross_validation import train_test_split\n",
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"\n",
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"x_iris, y_iris = iris.data, iris.target\n",
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"\n",
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"# Test set will be the 25% taken randomly\n",
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"x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
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"\n",
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"# Preprocess: normalize\n",
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"from sklearn import preprocessing\n",
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"scaler = preprocessing.StandardScaler().fit(x_train)\n",
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"x_train = scaler.transform(x_train)\n",
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"x_test = scaler.transform(x_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"## Train classifier"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The usual steps for creating a classifier are:\n",
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"1. Create classifier object\n",
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"2. Call *fit* to train the classifier\n",
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"3. Call *predict* to obtain predictions\n",
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"\n",
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"Once the model is created, the most relevant methods are:\n",
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"* model.fit(x_train, y_train): train the model\n",
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"* model.predict(x): predict\n",
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"* model.score(x, y): evaluate the prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
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" metric_params=None, n_jobs=1, n_neighbors=15, p=2,\n",
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" weights='uniform')"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"import numpy as np\n",
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"\n",
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"# Create kNN model\n",
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"model = KNeighborsClassifier(n_neighbors=15)\n",
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"\n",
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"# Train the model using the training sets\n",
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"model.fit(x_train, y_train) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 1 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
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" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
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" 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 2 0 0 0 2 1 2 1 2 1 2\n",
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" 0]\n",
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"Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
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" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
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" 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
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" 0]\n"
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]
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}
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],
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"source": [
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"print(\"Prediction \", model.predict(x_train))\n",
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"print(\"Expected \", y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy in training 0.964285714286\n"
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]
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}
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],
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"source": [
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"# Evaluate Accuracy in training\n",
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"\n",
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"from sklearn import metrics\n",
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"y_train_pred = model.predict(x_train)\n",
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"print(\"Accuracy in training\", metrics.accuracy_score(y_train, y_train_pred))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy in testing 0.921052631579\n"
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]
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}
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],
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"source": [
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"# Now we evaluate error in testing\n",
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"y_test_pred = model.predict(x_test)\n",
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"print(\"Accuracy in testing \", metrics.accuracy_score(y_test, y_test_pred))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we are going to visualize the Nearest Neighbors classification. It will plot the decision boundaries for each class.\n",
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"\n",
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"We are going to import a function defined in the file [util_knn.py](files/util_knn.py) using the *magic command* **%run**."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"image/png": 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mUhIPUyD14NQynlEFxCORJvD5UFTl0iReRWzbvZuft2zB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\nrkJggg==\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f21a42a1ef0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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SmCVxY4xJYhEncRFJEZElIvJuiNf6iMh29/UlInJnbMM0pu6woRQTS9GMiY8EVgGNwry+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\nJ4sltD0AAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f21a42a1c88>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%run util_knn.py\n",
|
|
"plot_classification_iris()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Evaluating the algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Precision, recall and f-score"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
|
|
"\n",
|
|
"* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n",
|
|
"* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n",
|
|
"* **F1-score**: This is the harmonic mean of precision and recall, and tries to combine both in a single number."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" setosa 1.00 1.00 1.00 8\n",
|
|
" versicolor 0.79 1.00 0.88 11\n",
|
|
" virginica 1.00 0.84 0.91 19\n",
|
|
"\n",
|
|
"avg / total 0.94 0.92 0.92 38\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(metrics.classification_report(y_test, y_test_pred,target_names=iris.target_names))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Confusion matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Another useful metric is the confusion matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[ 8 0 0]\n",
|
|
" [ 0 11 0]\n",
|
|
" [ 0 3 16]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(metrics.confusion_matrix(y_test, y_test_pred))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"source": [
|
|
"We see we classify well all the 'setosa' and 'versicolor' samples. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### K-Fold validation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ 0.93333333 0.8 1. 0.93333333 0.93333333 0.93333333\n",
|
|
" 1. 1. 0.86666667 1. ]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.cross_validation import cross_val_score, KFold\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"\n",
|
|
"# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
|
|
"model = Pipeline([\n",
|
|
" ('scaler', StandardScaler()),\n",
|
|
" ('kNN', KNeighborsClassifier())\n",
|
|
"])\n",
|
|
"\n",
|
|
"# create a k-fold cross validation iterator of k=10 folds\n",
|
|
"\n",
|
|
"cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n",
|
|
"\n",
|
|
"# by default the score used is the one returned by score method of the estimator (accuracy)\n",
|
|
"scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
|
|
"print(scores)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"source": [
|
|
"We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Mean score: 0.940 (+/- 0.021)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from scipy.stats import sem\n",
|
|
"def mean_score(scores):\n",
|
|
" return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n",
|
|
"print(mean_score(scores))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"So, we get an average accuracy of 0.940."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Tuning the algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We are going to tune the algorithm, and calculate which is the best value for the k parameter."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.text.Text at 0x7f21711a9cc0>"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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f64zKjbiIjTPSe0qR17HpJqv5eQ2jOg89FCYMTHKeoLhXcc1WSC5YEMYcxJkn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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f21711cc6d8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"k_range = range(1, 21)\n",
|
|
"accuracy = []\n",
|
|
"for k in k_range:\n",
|
|
" m = KNeighborsClassifier(k)\n",
|
|
" m.fit(x_train, y_train)\n",
|
|
" y_test_pred = m.predict(x_test)\n",
|
|
" accuracy.append(metrics.accuracy_score(y_test, y_test_pred))\n",
|
|
"plt.plot(k_range, accuracy)\n",
|
|
"plt.xlabel('k value')\n",
|
|
"plt.ylabel('Accuracy')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The result is very dependent of the input data. Execute again the train_test_split and test again how the result changes with k."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## References"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* [KNeighborsClassifier API scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n",
|
|
"* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Licence\n",
|
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
|
"\n",
|
|
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.5.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|