{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Naive Bayes Classification\n", "\n", "Over the next few tutorials, we are going to gain an intuition for classification by using multiple algorithms. The datasets and examples being shown are from the [Classifier Comparision](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) We are also going to be appling each algorithm to the Iris dataset we previously worked with, using examples from [here](http://machinelearningmastery.com/get-your-hands-dirty-with-scikit-learn-now/)\n", "\n", "\n", "For now I am just presenting the results of the algorithm for you to gain intuition, later we will learn cross validation, model evaluation, and parameter tuning.\n", "\n", "In this tutorial, we are going to go over naive bayes classification. The corresponding lecture notes (http://datascienceguide.github.io/naive-bayes-classifier/) provides the intuition and explanation for naive bayes. Let us jump into a simple example by first creating 3 datasets for classification and applying naive bayes. Later in the tutorial we will use naive bayes to classify the Iris dataset (and an example with some text data).\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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79uG//13Is89GMXKkNwcObJdEXpjkZZqoPb6Q6fzCwd6fNEl38fHRmaCtoJ/1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\nXfrTPx7aFqRvvvkWP78gvL2NNG0aTmx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oAKZNa4DReA1W63bmzfvIKzYDcaUtW7awav587DYbLe68k5tbtSqzDwPOIh2g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\nZ05w8uQRli//isDAwIue9/7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nZmbSvmVLcotGg4RUq8byX34hLCzMqfWcs2/fPpo0aYXZ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\ntwAAAABJRU5ErkJggg==\n", 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import make_moons, make_circles, make_classification\n", "from sklearn.naive_bayes import GaussianNB\n", "%matplotlib inline\n", "\n", "\n", "h = .02 # step size in the mesh\n", "\n", "names = [\"Naive Bayes\"]\n", "classifiers = [\n", " GaussianNB()]\n", "\n", "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n", " random_state=1, n_clusters_per_class=1)\n", "rng = np.random.RandomState(2)\n", "X += 2 * rng.uniform(size=X.shape)\n", "linearly_separable = (X, y)\n", "\n", "datasets = [make_moons(noise=0.3, random_state=0),\n", " make_circles(noise=0.2, factor=0.5, random_state=1),\n", " linearly_separable\n", " ]\n", "\n", "#figure = plt.figure(figsize=(27, 9))\n", "i = 1\n", "# iterate over datasets\n", "for ds in datasets:\n", " # preprocess dataset, split into training and test part\n", " X, y = ds\n", " X = StandardScaler().fit_transform(X)\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n", "\n", " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", " # just plot the dataset first\n", " cm = plt.cm.RdBu\n", " cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", " plt.figure(i)\n", " # Plot the training points\n", " plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n", "\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lHk0FjpkAl9NZmeaqqFxW2NwujudmY9LpiDGfq+ewK/U0/VxO+nmXy8MdNlac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x4b1796Rlyxbk5eURFhZ21WaJ8/L35nh5Cdszj+OS3IT4BdIvLhmfP6Ve1anUdAqLoSjjKN2p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\n+FatKafYm7tffiF3ShOBXBAEQWiU5h58q09dB9+g6e1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plt.figure(figsize=(7, 9))\n", "i = 1\n", "# iterate over datasets\n", "for ds in datasets:\n", " # preprocess dataset, split into training and test part\n", " X, y = ds\n", " X = StandardScaler().fit_transform(X)\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n", "\n", " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", " # just plot the dataset first\n", " cm = plt.cm.RdBu\n", " cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", " ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n", " # Plot the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " i += 1\n", "\n", " # iterate over classifiers\n", " for name, clf in zip(names, classifiers):\n", " ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test)\n", "\n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, m_max]x[y_min, y_max].\n", " if hasattr(clf, \"decision_function\"):\n", " Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n", " else:\n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n", "\n", " # Plot also the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n", " alpha=0.6)\n", "\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " ax.set_title(name)\n", " ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n", " size=15, horizontalalignment='right')\n", " i += 1\n", "\n", "figure.subplots_adjust(left=.02, right=.98)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see the intuition on how naive bayes seperates the different classes. As we can see, if the assumption of independant features, the model might generalize well. The main advantage of the naive bayes method is the simplicity and speed. \n", "\n", "# Naive Bayes on Iris Dataset \n", "\n", "Now we are going to apply a Naive Bayes classifier to the Iris dataset we previously worked with. Note, for this example I am not using cross validation since I just wanted to show standalone,\n", "simple and consistent code for each classifier first. Normally you should always use cross validation!\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GaussianNB()\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 50\n", " 1 0.94 0.94 0.94 50\n", " 2 0.94 0.94 0.94 50\n", "\n", "avg / total 0.96 0.96 0.96 150\n", "\n", "[[50 0 0]\n", " [ 0 47 3]\n", " [ 0 3 47]]\n" ] } ], "source": [ "from sklearn import datasets\n", "from sklearn import metrics\n", "from sklearn.naive_bayes import GaussianNB\n", "# load the iris datasets\n", "dataset = datasets.load_iris()\n", "# fit a Naive Bayes model to the data\n", "model = GaussianNB()\n", "model.fit(dataset.data, dataset.target)\n", "print(model)\n", "# make predictions\n", "expected = dataset.target\n", "predicted = model.predict(dataset.data)\n", "# summarize the fit of the model\n", "print(metrics.classification_report(expected, predicted))\n", "print(metrics.confusion_matrix(expected, predicted))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optional: Text Classification ( Natural Language Processsing)\n", "\n", "For example if you wanted to classify new groups (or emails), naive bayes offers a very fast approach using a \"bag of words\". \n", "\n", "[![](http://www.python-course.eu/images/document_representation.png)](http://www.python-course.eu/images/document_representation.png)\n", "\n", "This example is a bit advanced and outlines the use of a different evaluation metric (ROC), it shows the speed of naive bayes with text." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, 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QvHlzhIeH49dff4WI9Y+rm5tbua+Lioqstrdo0QIAMHz4cLz//vuW7RcuXLAE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\nXTtnF4eIiMhuGJASKYyIYPny5di5cyeSkpLQtGl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from ggplot import *\n", "from sklearn.datasets import fetch_20newsgroups\n", "from sklearn.metrics import roc_curve\n", "\n", "# vectorizer\n", "from sklearn.feature_extraction.text import HashingVectorizer\n", "\n", "# our classifiers\n", "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n", "from sklearn.svm import SVC\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "categories = [\n", " 'alt.atheism',\n", " 'talk.religion.misc',\n", " 'comp.graphics',\n", " 'sci.space'\n", "]\n", "\n", "data_train = fetch_20newsgroups(subset='train', categories=categories,\n", " shuffle=True, random_state=42)\n", "\n", "data_test = fetch_20newsgroups(subset='test', categories=categories,\n", " shuffle=True, random_state=42)\n", "\n", "categories = data_train.target_names\n", "\n", "vectorizer = HashingVectorizer(stop_words='english', non_negative=True, n_features=1000)\n", "X_train = vectorizer.fit_transform(data_train.data)\n", "X_test = vectorizer.transform(data_test.data)\n", "\n", "y_train = data_train.target==0\n", "y_test = data_test.target==0\n", "\n", "\n", "\n", "clfs = [\n", " (\"MultinomialNB\", MultinomialNB()),\n", " (\"BernoulliNB\", BernoulliNB()),\n", "]\n", "\n", "all_results = None\n", "for name, clf in clfs:\n", " clf.fit(X_train.todense(), y_train)\n", " probs = clf.predict_proba(X_test.todense())[:,1]\n", " fpr, tpr, thresh = roc_curve(y_test, probs)\n", " results = pd.DataFrame({\n", " \"name\": name,\n", " \"fpr\": fpr,\n", " \"tpr\": tpr\n", " })\n", " if all_results is None:\n", " all_results = results\n", " else:\n", " all_results = all_results.append(results)\n", "\n", "ggplot(aes(x='fpr', y='tpr', color='name'), data=all_results) + \\\n", " geom_step() + \\\n", " geom_abline(color=\"black\") + \\\n", " ggtitle(\"Text Classification Benchmark on 20 News Groups\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }