Created
October 18, 2013 19:37
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| { | |
| "metadata": { | |
| "name": "" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import numpy as np\n", | |
| "from pylab import plt\n", | |
| "from sklearn import svm, datasets\n", | |
| "import pandas\n", | |
| "plt.rcParams['figure.figsize'] = 10, 8\n", | |
| "\n", | |
| "# import some data to play with\n", | |
| "iris = datasets.load_iris()\n", | |
| "iris_data = pandas.DataFrame(iris.data, columns=iris.feature_names)\n", | |
| "iris_species = iris.target" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "scatter(iris_data['sepal length (cm)'], iris_data['sepal width (cm)'], c=iris_species, s=75)\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 8, | |
| "text": [ | |
| "<matplotlib.collections.PathCollection at 0x107d7dc10>" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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dpzGJiIh/lSqxcrlctGnThmrVqtGzZ0+aN29eaL/FYuG7776jdevW9OvXjx07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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x107f07b10>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "h = .02 # step size in the mesh\n", | |
| "X = iris_data.iloc[:, :2].as_matrix()\n", | |
| "lin_svc = svm.LinearSVC().fit(X, iris_species)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 9 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# create a mesh to plot in\n", | |
| "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", | |
| "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", | |
| "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", | |
| " np.arange(y_min, y_max, h))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 10 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "Z = lin_svc.predict(np.c_[xx.ravel(), yy.ravel()])\n", | |
| "\n", | |
| "# Put the result into a color plot\n", | |
| "Z = Z.reshape(xx.shape)\n", | |
| "plt.contourf(xx, yy, Z)\n", | |
| "plt.axis('off')\n", | |
| "\n", | |
| "# Plot also the training points\n", | |
| "plt.scatter(X[:, 0], X[:, 1], c=iris_species, s=75)\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 11, | |
| "text": [ | |
| "<matplotlib.collections.PathCollection at 0x107ef8c50>" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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qOD9WA/37WB9krDXPRaRlqKdI2rAK4Ds0DUS1FgJfYgM0ZxJ+TtBqbBDnJbRu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\nIiIigEKRiIiICKBQJCIiIgLA/wPUCydLz5vx8gAAAABJRU5ErkJggg==\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x107e86510>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 11 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "lin_svc.predict(X)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 13, | |
| "text": [ | |
| "array([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, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n", | |
| " 0, 0, 0, 0, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1,\n", | |
| " 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 1, 1, 2,\n", | |
| " 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1,\n", | |
| " 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n", | |
| " 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2])" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 13 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from sklearn.cross_validation import cross_val_score" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 14 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from sklearn." | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Help on function cross_val_score in module sklearn.cross_validation:\n", | |
| "\n", | |
| "cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, score_func=None, pre_dispatch='2*n_jobs')\n", | |
| " Evaluate a score by cross-validation\n", | |
| " \n", | |
| " Parameters\n", | |
| " ----------\n", | |
| " estimator : estimator object implementing 'fit'\n", | |
| " The object to use to fit the data.\n", | |
| " \n", | |
| " X : array-like of shape at least 2D\n", | |
| " The data to fit.\n", | |
| " \n", | |
| " y : array-like, optional, default: None\n", | |
| " The target variable to try to predict in the case of\n", | |
| " supervised learning.\n", | |
| " \n", | |
| " scoring : string, callable or None, optional, default: None\n", | |
| " A string (see model evaluation documentation) or\n", | |
| " a scorer callable object / function with signature\n", | |
| " ``scorer(estimator, X, y)``.\n", | |
| " \n", | |
| " cv : cross-validation generator, optional, default: None\n", | |
| " A cross-validation generator. If None, a 3-fold cross\n", | |
| " validation is used or 3-fold stratified cross-validation\n", | |
| " when y is supplied and estimator is a classifier.\n", | |
| " \n", | |
| " n_jobs : integer, optional\n", | |
| " The number of CPUs to use to do the computation. -1 means\n", | |
| " 'all CPUs'.\n", | |
| " \n", | |
| " verbose : integer, optional\n", | |
| " The verbosity level.\n", | |
| " \n", | |
| " fit_params : dict, optional\n", | |
| " Parameters to pass to the fit method of the estimator.\n", | |
| " \n", | |
| " pre_dispatch : int, or string, optional\n", | |
| " Controls the number of jobs that get dispatched during parallel\n", | |
| " execution. Reducing this number can be useful to avoid an\n", | |
| " explosion of memory consumption when more jobs get dispatched\n", | |
| " than CPUs can process. This parameter can be:\n", | |
| " \n", | |
| " - None, in which case all the jobs are immediately\n", | |
| " created and spawned. Use this for lightweight and\n", | |
| " fast-running jobs, to avoid delays due to on-demand\n", | |
| " spawning of the jobs\n", | |
| " \n", | |
| " - An int, giving the exact number of total jobs that are\n", | |
| " spawned\n", | |
| " \n", | |
| " - A string, giving an expression as a function of n_jobs,\n", | |
| " as in '2*n_jobs'\n", | |
| " \n", | |
| " Returns\n", | |
| " -------\n", | |
| " scores : array of float, shape=(len(list(cv)),)\n", | |
| " Array of scores of the estimator for each run of the cross validation.\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 15 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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