{ "metadata": { "name": "", "signature": "sha256:06afc3740970c2fb16b9294e88b7cb433bf037ae0e70385f8c2415edd736cc44" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com) for PyCon 2015. Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_pycon2015/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Scikit-Learn: Machine Learning with Python\n", "\n", "This session will cover the basics of Scikit-Learn, a popular package containing a collection of tools for machine learning written in Python. See more at http://scikit-learn.org." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outline\n", "\n", "**Main Goal:** To introduce the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package.\n", "\n", "- Definition of machine learning\n", "- Data representation in scikit-learn\n", "- Introduction to the Scikit-learn API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About Scikit-Learn\n", "\n", "[Scikit-Learn](http://github.com/scikit-learn/scikit-learn) is a Python package designed to give access to **well-known** machine learning algorithms within Python code, through a **clean, well-thought-out API**. It has been built by hundreds of contributors from around the world, and is used across industry and academia.\n", "\n", "Scikit-Learn is built upon Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python. As such, scikit-learn is not specifically designed for extremely large datasets, though there is [some work](https://github.com/ogrisel/parallel_ml_tutorial) in this area.\n", "\n", "For this short introduction, I'm going to stick to questions of in-core processing of small to medium datasets with Scikit-learn." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is Machine Learning?\n", "\n", "In this section we will begin to explore the basic principles of machine learning.\n", "Machine Learning is about building programs with **tunable parameters** (typically an\n", "array of floating point values) that are adjusted automatically so as to improve\n", "their behavior by **adapting to previously seen data.**\n", "\n", "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n", "algorithms can be seen as building blocks to make computers learn to behave more\n", "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n", "like a database system would do.\n", "\n", "We'll take a look at two very simple machine learning tasks here.\n", "The first is a **classification** task: the figure shows a\n", "collection of two-dimensional data, colored according to two different class\n", "labels. A classification algorithm may be used to draw a dividing boundary\n", "between the two clusters of points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "# set seaborn plot defaults.\n", "# This can be safely commented out\n", "import seaborn; seaborn.set()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Import the example plot from the figures directory\n", "from fig_code import plot_sgd_separator\n", "plot_sgd_separator()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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eD0pKipt5k1Dj+qZ6jF8sPLzunpTu3Xv4BKXcTUINr00mkyrXO6ltGLhE1Gm4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\npX1wAWDy5CsxefKVbfpM1Lk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"text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may seem like a trivial task, but it is a simple version of a very important concept.\n", "By drawing this separating line, we have learned a model which can **generalize** to new\n", "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n", "could now **predict** whether it's a blue or a red point.\n", "\n", "If you'd like to see the source code used to generate this, you can either open the\n", "code in the `figures` directory, or you can load the code using the `%load` magic command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Uncomment the %load command to load the contents of the file\n", "# %load fig_code/sgd_separator.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next simple task we'll look at is a **regression** task: a simple best-fit line\n", "to a set of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from fig_code import plot_linear_regression\n", "plot_linear_regression()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, this is an example of fitting a model to data, such that the model can make\n", "generalizations about new data. The model has been **learned** from the training\n", "data, and can be used to predict the result of test data:\n", "here, we might be given an x-value, and the model would\n", "allow us to predict the y value. Again, this might seem like a trivial problem,\n", "but it is a basic example of a type of operation that is fundamental to\n", "machine learning tasks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Representation of Data in Scikit-learn\n", "\n", "Machine learning is about creating models from data: for that reason, we'll start by\n", "discussing how data can be represented in order to be understood by the computer. Along\n", "with this, we'll build on our matplotlib examples from the previous section and show some\n", "examples of how to visualize data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most machine learning algorithms implemented in scikit-learn expect data to be stored in a\n", "**two-dimensional array or matrix**. The arrays can be\n", "either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\n", "The size of the array is expected to be `[n_samples, n_features]`\n", "\n", "- **n_samples:** The number of samples: each sample is an item to process (e.g. classify).\n", " A sample can be a document, a picture, a sound, a video, an astronomical object,\n", " a row in database or CSV file,\n", " or whatever you can describe with a fixed set of quantitative traits.\n", "- **n_features:** The number of features or distinct traits that can be used to describe each\n", " item in a quantitative manner. Features are generally real-valued, but may be boolean or\n", " discrete-valued in some cases.\n", "\n", "The number of features must be fixed in advance. However it can be very high dimensional\n", "(e.g. millions of features) with most of them being zeros for a given sample. This is a case\n", "where `scipy.sparse` matrices can be useful, in that they are\n", "much more memory-efficient than numpy arrays." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Simple Example: the Iris Dataset\n", "\n", "As an example of a simple dataset, we're going to take a look at the\n", "iris data stored by scikit-learn.\n", "The data consists of measurements of three different species of irises.\n", "There are three species of iris in the dataset, which we can picture here:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image, display\n", "display(Image(filename='images/iris_setosa.jpg'))\n", "print(\"Iris Setosa\\n\")\n", "\n", "display(Image(filename='images/iris_versicolor.jpg'))\n", "print(\"Iris Versicolor\\n\")\n", "\n", "display(Image(filename='images/iris_virginica.jpg'))\n", "print(\"Iris Virginica\")" ], "language": "python", "metadata": {}, "outputs": [ { "jpeg": 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T3Mjj93NOUgsM9KrBuaeCSa+sR0s3zpAuFQ25wNucnvVSfRrqHJKhh7Vt2lt5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\nR+NS3ibZDjvXJWjaVwZScYNRMasSDjNQMKhEn//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"metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Setosa\n", "\n" ] }, { "jpeg": 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HB+4waiiwuCwBIJUsfQdQfp/Sjsx2tGlAe5ooJxtLq3KKPPjz35FAfFd1zMFI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\nWlJnW5EIgFHOKhvrgKhCnnFfA2xMk80Iv7nJPNC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"metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Versicolor\n", "\n" ] }, { "jpeg": 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mg6Z4s0pPD2uskFoH/wBC1Zk3S6c2OAD1MecZXI+vFfM/iPwTrHhOSbTNQ0Qw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naQdh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\nwrKSN6b5lY+N9eiOlWcd21uRECFKZ5GO544Fdf8AC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"metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Virginica\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick Question:\n", "\n", "**If we want to design an algorithm to recognize iris species, what might the data be?**\n", "\n", "Remember: we need a 2D array of size `[n_samples x n_features]`.\n", "\n", "- What would the `n_samples` refer to?\n", "\n", "- What might the `n_features` refer to?\n", "\n", "Remember that there must be a **fixed** number of features for each sample, and feature\n", "number ``i`` must be a similar kind of quantity for each sample." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading the Iris Data with Scikit-Learn\n", "\n", "Scikit-learn has a very straightforward set of data on these iris species. The data consist of\n", "the following:\n", "\n", "- Features in the Iris dataset:\n", "\n", " 1. sepal length in cm\n", " 2. sepal width in cm\n", " 3. petal length in cm\n", " 4. petal width in cm\n", "\n", "- Target classes to predict:\n", "\n", " 1. Iris Setosa\n", " 2. Iris Versicolour\n", " 3. Iris Virginica\n", " \n", "``scikit-learn`` embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "iris.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "dict_keys(['target_names', 'DESCR', 'data', 'target', 'feature_names'])" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "n_samples, n_features = iris.data.shape\n", "print((n_samples, n_features))\n", "print(iris.data[0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(150, 4)\n", "[ 5.1 3.5 1.4 0.2]\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "print(iris.data.shape)\n", "print(iris.target.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(150, 4)\n", "(150,)\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "print(iris.target)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2]\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "print(iris.target_names)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['setosa' 'versicolor' 'virginica']\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This data is four dimensional, but we can visualize two of the dimensions\n", "at a time using a simple scatter-plot:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x_index = 0\n", "y_index = 1\n", "\n", "# this formatter will label the colorbar with the correct target names\n", "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", "\n", "plt.scatter(iris.data[:, x_index], iris.data[:, y_index],\n", " c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))\n", "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n", "plt.clim(-0.5, 2.5)\n", "plt.xlabel(iris.feature_names[x_index])\n", "plt.ylabel(iris.feature_names[y_index]);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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EfQYLj4vBRunMD3kWiK3OtIsR4TgpvfmzICKUmLjSs3Uz3IpZprwoKpyoqMjaBS2EEGe4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\nunUzL7+8gFQKhgwZxYABR0Qd6aA89dQMtmxZSzq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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick Exercise:\n", "\n", "**Change** `x_index` **and** `y_index` **in the above script\n", "and find a combination of two parameters\n", "which maximally separate the three classes.**\n", "\n", "This exercise is a preview of **dimensionality reduction**, which we'll see later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other Available Data\n", "They come in three flavors:\n", "\n", "- **Packaged Data:** these small datasets are packaged with the scikit-learn installation,\n", " and can be downloaded using the tools in ``sklearn.datasets.load_*``\n", "- **Downloadable Data:** these larger datasets are available for download, and scikit-learn\n", " includes tools which streamline this process. These tools can be found in\n", " ``sklearn.datasets.fetch_*``\n", "- **Generated Data:** there are several datasets which are generated from models based on a\n", " random seed. These are available in the ``sklearn.datasets.make_*``\n", "\n", "You can explore the available dataset loaders, fetchers, and generators using IPython's\n", "tab-completion functionality. After importing the ``datasets`` submodule from ``sklearn``,\n", "type\n", "\n", " datasets.load_ + TAB\n", "\n", "or\n", "\n", " datasets.fetch_ + TAB\n", "\n", "or\n", "\n", " datasets.make_ + TAB\n", "\n", "to see a list of available functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import datasets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# Type datasets.fetch_ or datasets.load_ in IPython to see all possibilities\n", "\n", "# datasets.fetch_" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# datasets.load_" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next section, we'll use some of these datasets and take a look at the basic principles of machine learning." ] } ], "metadata": {} } ] }