mirror of
https://github.com/gsi-upm/sitc
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445 lines
12 KiB
Plaintext
445 lines
12 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, © 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, make predictions with that model and evaluate these 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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"## Loading 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": null,
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"metadata": {},
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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"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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.model_selection 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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"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": null,
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"metadata": {},
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"outputs": [],
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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": null,
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"metadata": {},
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"outputs": [],
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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": null,
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"metadata": {},
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"outputs": [],
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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": null,
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"metadata": {},
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"outputs": [],
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%run util_knn.py\n",
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"\n",
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"plot_classification_iris()"
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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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"## Evaluating the 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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"### Precision, recall and f-score"
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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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"For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
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"\n",
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"* **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",
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"* **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",
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"* **F1-score**: This is the harmonic mean of precision and recall, and tries to combine both in a single number."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(metrics.classification_report(y_test, y_test_pred, target_names=iris.target_names))"
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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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"### Confusion matrix"
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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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"Another useful metric is the confusion matrix"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(metrics.confusion_matrix(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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"We see we classify well all the 'setosa' and 'versicolor' samples. "
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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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"### K-Fold validation"
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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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"In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import cross_val_score, KFold\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
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"model = Pipeline([\n",
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" ('scaler', StandardScaler()),\n",
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" ('kNN', KNeighborsClassifier())\n",
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"])\n",
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"\n",
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"# create a k-fold cross validation iterator of k=10 folds\n",
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"cv = KFold(10, shuffle=True, random_state=33)\n",
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"\n",
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"# by default the score used is the one returned by score method of the estimator (accuracy)\n",
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"scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
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"print(scores)"
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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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"We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.stats import sem\n",
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"def mean_score(scores):\n",
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" return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n",
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"print(mean_score(scores))"
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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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"So, we get an average accuracy of 0.940."
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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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"## Tuning the 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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"We are going to tune the algorithm, and calculate which is the best value for the k hyperparameter."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"k_range = range(1, 21)\n",
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"accuracy = []\n",
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"for k in k_range:\n",
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" m = KNeighborsClassifier(k)\n",
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" m.fit(x_train, y_train)\n",
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" y_test_pred = m.predict(x_test)\n",
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" accuracy.append(metrics.accuracy_score(y_test, y_test_pred))\n",
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"plt.plot(k_range, accuracy)\n",
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"plt.xlabel('k value')\n",
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"plt.ylabel('Accuracy')\n"
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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 result is very dependent of the input data. Execute again the train_test_split and test again how the result changes with k."
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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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"## 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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"* [KNeighborsClassifier API scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n",
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"* [Learning scikit-learn: Machine Learning in Python](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n"
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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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"## Licence\n",
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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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