Cross validaiton.ipynb

{
    "cells": [
        {
            "cell_type": "code", 
            "execution_count": 1, 
            "metadata": {
                "collapsed": true
            }, 
            "source": "from sklearn.datasets import load_iris\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn import metrics", 
            "outputs": []
        }, 
        {
            "cell_type": "code", 
            "execution_count": 3, 
            "metadata": {
                "collapsed": true
            }, 
            "source": "# read in the iris data\niris = load_iris()\n\n# create X (features) and y (response)\nX = iris.data\ny = iris.target", 
            "outputs": []
        }, 
        {
            "cell_type": "code", 
            "execution_count": 4, 
            "metadata": {}, 
            "source": "# use train/test split with different random_state values\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)\n\n# check classification accuracy of KNN with K=5\nknn = KNeighborsClassifier(n_neighbors=5)\nknn.fit(X_train, y_train)\ny_pred = knn.predict(X_test)\nprint(metrics.accuracy_score(y_test, y_pred))", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "0.973684210526\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "markdown", 
            "source": "### K fold cross validaiton...\nk folds..\nand each fold/segment in turn becomes the test set...\niteratively", 
            "metadata": {}
        }, 
        {
            "cell_type": "code", 
            "execution_count": 37, 
            "metadata": {}, 
            "source": "\n\n# simulate splitting a dataset of 25 observations into 5 folds\nfrom sklearn.cross_validation import KFold\nkf = KFold(25, n_folds=5, shuffle=False)\n\n# print the contents of each training and testing set\nprint('{} {:^61} {}'.format('Iteration', 'Training set observations', 'Testing set observations'))\nfor iteration, data in enumerate(kf, start=1):\n    print( '{:^9}'.format(iteration), data[0],'{:}'.format(data[1]))\n\n", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "Iteration                   Training set observations                   Testing set observations\n    1     [ 5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [0 1 2 3 4]\n    2     [ 0  1  2  3  4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [5 6 7 8 9]\n    3     [ 0  1  2  3  4  5  6  7  8  9 15 16 17 18 19 20 21 22 23 24] [10 11 12 13 14]\n    4     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 20 21 22 23 24] [15 16 17 18 19]\n    5     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24]\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "code", 
            "execution_count": 39, 
            "metadata": {}, 
            "source": "from sklearn.cross_validation import cross_val_score\n\n\n# 10-fold cross-validation with K=5 for KNN (the n_neighbors parameter)\n# 10 is generally recommended\nknn = KNeighborsClassifier(n_neighbors=5)\nscores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\nprint(scores)\n\n", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "[ 1.          0.93333333  1.          1.          0.86666667  0.93333333\n  0.93333333  1.          1.          1.        ]\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "code", 
            "execution_count": 40, 
            "metadata": {}, 
            "source": "# use average accuracy as an estimate of out-of-sample accuracy\nprint(scores.mean())", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "0.966666666667\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "code", 
            "execution_count": 41, 
            "metadata": {}, 
            "source": "# search for an optimal value of K for KNN\n#manually.....\nk_range = list(range(1, 31))\nk_scores = []\nfor k in k_range:\n    knn = KNeighborsClassifier(n_neighbors=k)\n    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n    k_scores.append(scores.mean())\nprint(k_scores)", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "[0.95999999999999996, 0.95333333333333337, 0.96666666666666656, 0.96666666666666656, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.98000000000000009, 0.96666666666666656, 0.96666666666666656, 0.97333333333333338, 0.95999999999999996, 0.96666666666666656, 0.95999999999999996, 0.96666666666666656, 0.95333333333333337, 0.95333333333333337, 0.95333333333333337]\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "code", 
            "execution_count": 42, 
            "metadata": {}, 
            "source": "# ok visual aids to the rescue....\n\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\nplt.plot(k_range, k_scores)\nplt.xlabel('Value of K for KNN')\nplt.ylabel('Cross-Validated Accuracy')\n\n", 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "execution_count": 42, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "<matplotlib.text.Text at 0x7f9e6cbcc0b8>"
                    }
                }, 
                {
                    "output_type": "display_data", 
                    "metadata": {}, 
                    "data": {
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VzXts6NBQkVfVeFvD4sWLWbx48VH37Up5t82qAxgz2wYU53kbMI6Qg3J1tddz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\nTkSuQmCC\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f9e6cbb5908>"
                    }
                }
            ]
        }, 
        {
            "cell_type": "code", 
            "execution_count": 43, 
            "metadata": {}, 
            "source": "# mdoel selection\n#compare KNN with Log reg\n# 10-fold cross-validation with the best KNN model N_neighbours = 20\nknn = KNeighborsClassifier(n_neighbors=20)\nprint(cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean())", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "0.98\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "code", 
            "execution_count": 44, 
            "metadata": {}, 
            "source": "# 10-fold cross-validation with logistic regression\nfrom sklearn.linear_model import LogisticRegression\nlogreg = LogisticRegression()\nprint(cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean())", 
            "outputs": [
                {
                    "output_type": "stream", 
                    "text": "0.953333333333\n", 
                    "name": "stdout"
                }
            ]
        }, 
        {
            "cell_type": "markdown", 
            "source": "## so K NNwins.. :)", 
            "metadata": {}
        }
    ], 
    "nbformat": 4, 
    "nbformat_minor": 1, 
    "metadata": {
        "kernelspec": {
            "language": "python", 
            "display_name": "Python 3.5 (Experimental) with Spark 2.1", 
            "name": "python3-spark21"
        }, 
        "language_info": {
            "pygments_lexer": "ipython3", 
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "name": "python", 
            "file_extension": ".py", 
            "version": "3.5.2", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }
}