{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Семинар 7. Sklearn и линейная регрессия" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 0. Что сегодня?\n", "- Проверочная №2\n", " - 10 баллов максимум\n", " - 15 минут \n", " - составляет аудиторную оценку, каждая проверочная с равными весами\n", " ___\n", " 1. Только одну из ненаписанных проверочных можно написать, если нет уважительной причины; любую можно с уважительной причиной (к примеру, поход к врачу, приносите справку)\n", " 2. самая худшая из написанных проверочных не будет учитываться. Важнее всего, что из написанных проверочных выбирается худшая. Ненаписанные проверочные будут учитываться с нулевыми баллами, но обязательно будут учитываться каждая;\n", " 3. в конце модуля по оценкам мы подведем итоги. Если появиться необходимость, устроим контрольную работу, чтобы вы могли поднять оценку.\n", "- Начнем смотреть на код настоящих Data Scientists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![За работу](doit.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Sklearn Intro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [Sklearn, машинное обучение (часть, обучение с учителем)](http://scikit-learn.org/stable/supervised_learning.html)\n", "- [Sklearn, Линейные модели](http://scikit-learn.org/stable/modules/linear_model.html)\n", "\n", "Что еше?\n", "- metrics" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.datasets import load_boston\n", "from sklearn.linear_model import LinearRegression\n", "from matplotlib import pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "boston = load_boston()\n", "X = boston.data\n", "y = boston.target" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Boston House Prices dataset\n", "\n", "Notes\n", "------\n", "Data Set Characteristics: \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive\n", " \n", " :Median Value (attribute 14) is usually the target\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in $1000's\n", "\n", " :Missing Attribute Values: None\n", "\n", " :Creator: Harrison, D. and Rubinfeld, D.L.\n", "\n", "This is a copy of UCI ML housing dataset.\n", "http://archive.ics.uci.edu/ml/datasets/Housing\n", "\n", "\n", "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", "\n", "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", "prices and the demand for clean air', J. Environ. Economics & Management,\n", "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", "pages 244-261 of the latter.\n", "\n", "The Boston house-price data has been used in many machine learning papers that address regression\n", "problems. \n", " \n", "**References**\n", "\n", " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", "\n" ] } ], "source": [ "print(boston.DESCR)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "boston_df = pd.DataFrame(np.column_stack([boston.data, boston.target]), columns = \n", " np.append(boston.feature_names, \"MEDV_target\"))" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", "0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 \n", "1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 \n", "2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 \n", "3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 \n", "4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 \n", "5 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 \n", "6 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 \n", "7 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 \n", "8 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 \n", "9 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 \n", "10 0.22489 12.5 7.87 0 0.524 6.377 94.3 6.3467 5 311 \n", "11 0.11747 12.5 7.87 0 0.524 6.009 82.9 6.2267 5 311 \n", "12 0.09378 12.5 7.87 0 0.524 5.889 39.0 5.4509 5 311 \n", "13 0.62976 0.0 8.14 0 0.538 5.949 61.8 4.7075 4 307 \n", "14 0.63796 0.0 8.14 0 0.538 6.096 84.5 4.4619 4 307 \n", "15 0.62739 0.0 8.14 0 0.538 5.834 56.5 4.4986 4 307 \n", "16 1.05393 0.0 8.14 0 0.538 5.935 29.3 4.4986 4 307 \n", "17 0.78420 0.0 8.14 0 0.538 5.990 81.7 4.2579 4 307 \n", "18 0.80271 0.0 8.14 0 0.538 5.456 36.6 3.7965 4 307 \n", "19 0.72580 0.0 8.14 0 0.538 5.727 69.5 3.7965 4 307 \n", "20 1.25179 0.0 8.14 0 0.538 5.570 98.1 3.7979 4 307 \n", "21 0.85204 0.0 8.14 0 0.538 5.965 89.2 4.0123 4 307 \n", "22 1.23247 0.0 8.14 0 0.538 6.142 91.7 3.9769 4 307 \n", "23 0.98843 0.0 8.14 0 0.538 5.813 100.0 4.0952 4 307 \n", "24 0.75026 0.0 8.14 0 0.538 5.924 94.1 4.3996 4 307 \n", "25 0.84054 0.0 8.14 0 0.538 5.599 85.7 4.4546 4 307 \n", "26 0.67191 0.0 8.14 0 0.538 5.813 90.3 4.6820 4 307 \n", "27 0.95577 0.0 8.14 0 0.538 6.047 88.8 4.4534 4 307 \n", "28 0.77299 0.0 8.14 0 0.538 6.495 94.4 4.4547 4 307 \n", "29 1.00245 0.0 8.14 0 0.538 6.674 87.3 4.2390 4 307 \n", ".. ... ... ... ... ... ... ... ... ... ... \n", "476 4.87141 0.0 18.10 0 0.614 6.484 93.6 2.3053 24 666 \n", "477 15.02340 0.0 18.10 0 0.614 5.304 97.3 2.1007 24 666 \n", "478 10.23300 0.0 18.10 0 0.614 6.185 96.7 2.1705 24 666 \n", "479 14.33370 0.0 18.10 0 0.614 6.229 88.0 1.9512 24 666 \n", "480 5.82401 0.0 18.10 0 0.532 6.242 64.7 3.4242 24 666 \n", "481 5.70818 0.0 18.10 0 0.532 6.750 74.9 3.3317 24 666 \n", "482 5.73116 0.0 18.10 0 0.532 7.061 77.0 3.4106 24 666 \n", "483 2.81838 0.0 18.10 0 0.532 5.762 40.3 4.0983 24 666 \n", "484 2.37857 0.0 18.10 0 0.583 5.871 41.9 3.7240 24 666 \n", "485 3.67367 0.0 18.10 0 0.583 6.312 51.9 3.9917 24 666 \n", "486 5.69175 0.0 18.10 0 0.583 6.114 79.8 3.5459 24 666 \n", "487 4.83567 0.0 18.10 0 0.583 5.905 53.2 3.1523 24 666 \n", "488 0.15086 0.0 27.74 0 0.609 5.454 92.7 1.8209 4 711 \n", "489 0.18337 0.0 27.74 0 0.609 5.414 98.3 1.7554 4 711 \n", "490 0.20746 0.0 27.74 0 0.609 5.093 98.0 1.8226 4 711 \n", "491 0.10574 0.0 27.74 0 0.609 5.983 98.8 1.8681 4 711 \n", "492 0.11132 0.0 27.74 0 0.609 5.983 83.5 2.1099 4 711 \n", "493 0.17331 0.0 9.69 0 0.585 5.707 54.0 2.3817 6 391 \n", "494 0.27957 0.0 9.69 0 0.585 5.926 42.6 2.3817 6 391 \n", "495 0.17899 0.0 9.69 0 0.585 5.670 28.8 2.7986 6 391 \n", "496 0.28960 0.0 9.69 0 0.585 5.390 72.9 2.7986 6 391 \n", "497 0.26838 0.0 9.69 0 0.585 5.794 70.6 2.8927 6 391 \n", "498 0.23912 0.0 9.69 0 0.585 6.019 65.3 2.4091 6 391 \n", "499 0.17783 0.0 9.69 0 0.585 5.569 73.5 2.3999 6 391 \n", "500 0.22438 0.0 9.69 0 0.585 6.027 79.7 2.4982 6 391 \n", "501 0.06263 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 \n", "502 0.04527 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 \n", "503 0.06076 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 \n", "504 0.10959 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 \n", "505 0.04741 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 \n", "\n", " PTRATIO B LSTAT MEDV_target \n", "0 15.3 396.90 4.98 24.0 \n", "1 17.8 396.90 9.14 21.6 \n", "2 17.8 392.83 4.03 34.7 \n", "3 18.7 394.63 2.94 33.4 \n", "4 18.7 396.90 5.33 36.2 \n", "5 18.7 394.12 5.21 28.7 \n", "6 15.2 395.60 12.43 22.9 \n", "7 15.2 396.90 19.15 27.1 \n", "8 15.2 386.63 29.93 16.5 \n", "9 15.2 386.71 17.10 18.9 \n", "10 15.2 392.52 20.45 15.0 \n", "11 15.2 396.90 13.27 18.9 \n", "12 15.2 390.50 15.71 21.7 \n", "13 21.0 396.90 8.26 20.4 \n", "14 21.0 380.02 10.26 18.2 \n", "15 21.0 395.62 8.47 19.9 \n", "16 21.0 386.85 6.58 23.1 \n", "17 21.0 386.75 14.67 17.5 \n", "18 21.0 288.99 11.69 20.2 \n", "19 21.0 390.95 11.28 18.2 \n", "20 21.0 376.57 21.02 13.6 \n", "21 21.0 392.53 13.83 19.6 \n", "22 21.0 396.90 18.72 15.2 \n", "23 21.0 394.54 19.88 14.5 \n", "24 21.0 394.33 16.30 15.6 \n", "25 21.0 303.42 16.51 13.9 \n", "26 21.0 376.88 14.81 16.6 \n", "27 21.0 306.38 17.28 14.8 \n", "28 21.0 387.94 12.80 18.4 \n", "29 21.0 380.23 11.98 21.0 \n", ".. ... ... ... ... \n", "476 20.2 396.21 18.68 16.7 \n", "477 20.2 349.48 24.91 12.0 \n", "478 20.2 379.70 18.03 14.6 \n", "479 20.2 383.32 13.11 21.4 \n", "480 20.2 396.90 10.74 23.0 \n", "481 20.2 393.07 7.74 23.7 \n", "482 20.2 395.28 7.01 25.0 \n", "483 20.2 392.92 10.42 21.8 \n", "484 20.2 370.73 13.34 20.6 \n", "485 20.2 388.62 10.58 21.2 \n", "486 20.2 392.68 14.98 19.1 \n", "487 20.2 388.22 11.45 20.6 \n", "488 20.1 395.09 18.06 15.2 \n", "489 20.1 344.05 23.97 7.0 \n", "490 20.1 318.43 29.68 8.1 \n", "491 20.1 390.11 18.07 13.6 \n", "492 20.1 396.90 13.35 20.1 \n", "493 19.2 396.90 12.01 21.8 \n", "494 19.2 396.90 13.59 24.5 \n", "495 19.2 393.29 17.60 23.1 \n", "496 19.2 396.90 21.14 19.7 \n", "497 19.2 396.90 14.10 18.3 \n", "498 19.2 396.90 12.92 21.2 \n", "499 19.2 395.77 15.10 17.5 \n", "500 19.2 396.90 14.33 16.8 \n", "501 21.0 391.99 9.67 22.4 \n", "502 21.0 396.90 9.08 20.6 \n", "503 21.0 396.90 5.64 23.9 \n", "504 21.0 393.45 6.48 22.0 \n", "505 21.0 396.90 7.88 11.9 \n", "\n", "[506 rows x 14 columns]" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boston_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Модель **Ordinary Least Squares (OLS)** " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Одномерная регрессия" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Fitting a model is trivial: call the ``fit`` method in LinearRegression:\n", "x = X[:, 5].reshape(-1, 1) # AVERAGE NUMBER OF ROOMS PER DWELLING\n", "lr = LinearRegression()\n", "lr.fit(x.reshape(-1, 1), y)\n", "y_predicted = lr.predict(x )" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/kvandy/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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gbEZYGzY8yODgIDAbeDTpZ5lSjG47K3fLFydI7WBoyGLJkrb4c1L7LZ4QHVQA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\ncrnGbK4l9XiBwEyCQed9yvp9FakRSieKJ1LXYbW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(7, 10))\n", "ax = plt.gca()\n", "ax.scatter(x, y, label='Original data', s=10)\n", "ax.plot(x, y_predicted, 'r', label='Fitted line')\n", "ax.set_aspect('auto')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Настолько легко, что можно просто использовать **numpy** " ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Residual: 7.64268509308749\n" ] } ], "source": [ "# np.linal.lstsq implements least-squares linear regression\n", "s, total_error, _, _ = np.linalg.lstsq(x, y)\n", "\n", "rmse = np.sqrt(total_error[0] / len(x))\n", "print('Residual: {}'.format(rmse))" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE: 43.60055177116956\n", "\n", "Same MSE: 43.60055177116956\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error\n", "mse = mean_squared_error(y, y_predicted)\n", "print(\"MSE: {}\".format(mse))\n", "print()\n", "# The instance member `residues_` contains the sum of the squared residues\n", "print(\"Same MSE: {}\".format(lr.residues_/len(x)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$MSE = \\frac1n \\sum_{i=1}^n (y_i - a(x_i))^2 $" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 6.603071389222561\n" ] } ], "source": [ "rmse = np.sqrt(mse)\n", "print('RMSE: {}'.format(rmse))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$RMSE = \\sqrt{\\frac1n \\sum_{i=1}^n (y_i - a(x_i))^2} \\approx \\sqrt{D \\xi}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** *Неравенство Чебышева* **\n", "\n", "Если $E\\xi^2 < \\infty \\rightarrow P(|\\xi - E\\xi| \\geq x) \\leq \\frac{D\\xi}{x^2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Можем оценить погрешность вычислений с помощью подстановки $x = c \\sqrt{D\\xi}$, где $с = \\{2, 3, \\dots\\}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Качество алгоритма?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Сравнение:\n", "- $с=3 \\Rightarrow$ 89% (8/9 часть) всех разниц истинных значений с прогнозами не будут выходить за пределы $6.6\\dot 3 = 19.8$\n", "- сравнение с наилучшим константным прогнозом: Метод МП над $MSE$ => $a(x) = \\bar{y} = \\frac1n \\sum_{i=1}^n y_i$ - *нулевая модель*\n", " По коэффициенту детерминации\n", "$1 - \\frac{\\sum_i (y_i - a(x_i))^2}{(y_i - \\bar{y})} \\approx 1 - \\frac{MSE}{VAR(y)}$\n", "\n", "Если ...=1 - хорошая регрессионная модель, ...=0 - не лучше нулевой модкли, ...< 0 - слишком плохая регрессионная модель " ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " R2 (на обучающих данных): 0.48 ) \n" ] } ], "source": [ "from sklearn.metrics import r2_score\n", "r2 = r2_score(y, lr.predict(x))\n", "print (\" R2 (на обучающих данных): {:.2} ) \" .format(r2))" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " R2 (на обучающих данных): 0.48 ) \n" ] } ], "source": [ "r2 =lr.score(x , y)\n", "print (\" R2 (на обучающих данных): {:.2} ) \" .format(r2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Многмерная регрессия" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = boston.data\n", "y = boston.target\n", "lr.fit(x, y)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R2: 0.740607742865\n", "Ближе к 1, значит лучше!\n" ] } ], "source": [ "p = lr.predict(x)\n", "print(\"R2: \", lr.score(x, y))\n", "print(\"Ближе к 1, значит лучше!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Сравнение моделей" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Fit_intercept - для чего?](http://stackoverflow.com/questions/24393518/python-sklearn-linear-model-linearregression-working-weird)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---Одномерная регрессия---\n", "Mean squared error (of training data): 58.4\n", "Root mean squared error (of training data): 7.64\n", "COD (on training data): 0.31\n", "\n", "---Многомерная регрессия---\n", "Mean squared error (of training data): 43.6\n", "Root mean squared error (of training data): 6.6\n", "COD (on training data): 0.48\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/kvandy/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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JcaHzuKJ9P/HeTyUnRznssIPp1+9AiosHsnXrFurrJ5Js5zqje5Hyaq7hqGoz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\no0ad6slxOwA5OVdywgnHMG+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rUfvXsw9VbW1tDaxXbX9XRlgowCimvYHHVfVPInI4cJaIXKmqqwsnW0Z/J1u+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\nyy+H88+ne8ROMh1Mnvw/XHr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Index number five in the number of rooms\n", "fig,ax = plt.subplots()\n", "ax.scatter(boston.data[:, 5], boston.target)\n", "ax.set_xlabel(\"Average number of rooms (RM)\")\n", "ax.set_ylabel(\"House Price\")\n", "\n", "x = boston.data[:, 5]\n", "# fit (used below) takes a two-dimensional array as input. We use np.atleast_2d\n", "# to convert from one to two dimensional, then transpose to make sure that the\n", "# format matches:\n", "x = np.transpose(np.atleast_2d(x))\n", "\n", "y = boston.target\n", "\n", "lr = LinearRegression(fit_intercept=False)\n", "lr.fit(x, y)\n", "\n", "ax.plot([0, boston.data[:, 5].max() + 1],\n", " [0, lr.predict(boston.data[:, 5].max() + 1)], '-', lw=4)\n", "fig.savefig('Figure1.png')\n", "\n", "print(\"---Одномерная регрессия---\")\n", "\n", "mse = mean_squared_error(y, lr.predict(x))\n", "print(\"Mean squared error (of training data): {:.3}\".format(mse))\n", "\n", "rmse = np.sqrt(mse)\n", "print(\"Root mean squared error (of training data): {:.3}\".format(rmse))\n", "\n", "cod = r2_score(y, lr.predict(x))\n", "print('COD (on training data): {:.2}'.format(cod))\n", "print()\n", "\n", "# Repeat, but fitting an intercept this time:\n", "lr = LinearRegression(fit_intercept=True)\n", "\n", "lr.fit(x, y)\n", "\n", "fig,ax = plt.subplots()\n", "ax.set_xlabel(\"Average number of rooms (RM)\")\n", "ax.set_ylabel(\"House Price\")\n", "ax.scatter(boston.data[:, 5], boston.target)\n", "xmin = x.min()\n", "xmax = x.max()\n", "ax.plot([xmin, xmax], lr.predict([[xmin], [xmax]]) , '-', lw=4)\n", "fig.savefig('Figure2.png')\n", "\n", "print(\"---Многомерная регрессия---\")\n", "\n", "mse = mean_squared_error(y, lr.predict(x))\n", "print(\"Mean squared error (of training data): {:.3}\".format(mse))\n", "\n", "rmse = np.sqrt(mse)\n", "print(\"Root mean squared error (of training data): {:.3}\".format(rmse))\n", "\n", "cod = r2_score(y, lr.predict(x))\n", "print('COD (on training data): {:.2}'.format(cod))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Переобучение" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Штрафы и регуляризация" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Без регуляризации:\n", " $$w^* = \\arg min_{w} ||y - Xw||^2$$\n", "***L1 (Lasso)***:\n", " $$w^* = \\arg min_{w} \\frac1{2n} ||y - Xw||^2 + \\alpha \\sum_i |w_i|$$ \n", "***L2 (Ridge)***:\n", " $$w^* = \\arg min_{w} ||y - Xw||^2+ \\alpha \\sum_i w_i^2$$\n", "***ElasticNet (Ridge)***:\n", " $$w^* = \\arg min_{w} \\frac1{2n}||y - Xw||^2+ \\alpha \\rho \\sum_i |w_i|+ \\frac{\\alpha (1 - \\rho)}{2} \\sum_i w_i^2$$" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method: linear regression\n", "R2 on training: 0.7406077428649428\n", "R2 on 5-fold CV: 0.559652434645513\n", "\n", "\n", "Method: lasso()\n", "R2 on training: 0.6825494617506651\n", "R2 on 5-fold CV: 0.5795682066389939\n", "\n", "\n", "Method: elastic-net(.5)\n", "R2 on training: 0.7055782468808041\n", "R2 on 5-fold CV: 0.6350108245667516\n", "\n", "\n", "Method: lasso(.5)\n", "R2 on training: 0.7139506197036718\n", "R2 on 5-fold CV: 0.589940808802092\n", "\n", "\n", "Method: ridge(.5)\n", "R2 on training: 0.739919756983817\n", "R2 on 5-fold CV: 0.5757684052376045\n", "\n", "\n" ] } ], "source": [ "import numpy as np\n", "from sklearn.cross_validation import KFold\n", "from sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge\n", "from sklearn.metrics import r2_score\n", "from sklearn.datasets import load_boston\n", "boston = load_boston()\n", "x = boston.data\n", "y = boston.target\n", "\n", "for name, met in [\n", " ('linear regression', LinearRegression()),\n", " ('lasso()', Lasso()),\n", " ('elastic-net(.5)', ElasticNet(alpha=0.5)),\n", " ('lasso(.5)', Lasso(alpha=0.5)),\n", " ('ridge(.5)', Ridge(alpha=0.5)),\n", "]:\n", " # Fit on the whole data:\n", " met.fit(x, y)\n", "\n", " # Predict on the whole data:\n", " p = met.predict(x)\n", " r2_train = r2_score(y, p)\n", "\n", " # Now, we use 10 fold cross-validation to estimate generalization error\n", " kf = KFold(len(x), n_folds=5)\n", " p = np.zeros_like(y)\n", " for train, test in kf:\n", " met.fit(x[train], y[train])\n", " p[test] = met.predict(x[test])\n", "\n", " r2_cv = r2_score(y, p)\n", " print('Method: {}'.format(name))\n", " print('R2 on training: {}'.format(r2_train))\n", " print('R2 on 5-fold CV: {}'.format(r2_cv))\n", " print()\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Влияние гиперпараметров $\\alpha, \\rho$ на вектор коэффициентов линейной регрессии " ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(13, 1000)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coefs.shape" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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L/ZlY7M/ESuT+dC6iSKRXzoXkXP9hj5FI/xDTR5r38mWliJyLSArLuYicCwfTwofmvXxa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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "boston = load_boston()\n", "x = boston.data\n", "y = boston.target\n", "\n", "las = Lasso(normalize=1)\n", "alphas = np.logspace(-5, 5, 1000)\n", "alphas, coefs, _= las.path(x, y, alphas=alphas)\n", "\n", "plt.figure(figsize=(15, 10))\n", "ax = plt.gca()\n", "ax.plot(alphas, coefs.T)\n", "ax.set_xscale('log')\n", "ax.set_xlim(alphas.max(), alphas.min())\n", "ax.set_xlabel('Lasso coefficient path as a function of alpha')\n", "ax.set_xlabel('Alpha')\n", "ax.set_ylabel('Coefficient weight')\n", "ax.set_title('Lasso coefficients as a function of the regularization')\n", "fig.savefig('Figure_LassoPath.png')\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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gPStX8oaWFiJyNVAIIYQQBcg9hEKIk6a//x727n0nS5ZcxHnnPU0w2FTukBaM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\nWenbt2/dcQBoEWYIAaAXGDt2bEaOHJlrr7227igAtCEFIQDUzOIyANR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn import linear_model\n", "x = boston.data\n", "y = boston.target\n", "\n", "n_alphas = 200\n", "alphas = np.logspace(-5, 5, 1000)\n", "clf = linear_model.Ridge(fit_intercept=True)\n", "\n", "coefs = []\n", "for a in alphas:\n", " clf.set_params(alpha=a)\n", " clf.fit(x, y)\n", " coefs.append(clf.coef_)\n", "\n", "###############################################################################\n", "# Display results\n", "plt.figure(figsize=(15, 10))\n", "ax = plt.gca()\n", "ax.set_color_cycle(['b', 'r', 'g', 'c', 'k', 'y', 'm'])\n", "\n", "ax.plot(alphas, coefs)\n", "ax.set_xscale('log')\n", "ax.set_xlim(ax.get_xlim()[::-1]) # reverse axis\n", "plt.xlabel('alpha')\n", "plt.ylabel('weights')\n", "plt.title('Ridge coefficients as a function of the regularization')\n", "plt.axis('tight')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "eps=5e-4\n", "from sklearn.linear_model import lasso_path, enet_path\n", "\n", "def paths_figure(x, y):\n", " x /= x.std(axis=0)\n", " alphas_lasso, coefs_lasso, _ = lasso_path(x, y, eps, fit_intercept=False)\n", " alphas_enet, coefs_enet, _ = enet_path(\n", " x, y, eps=eps, l1_ratio=0.8, fit_intercept=False)\n", "\n", "\n", " plt.figure(figsize=(15, 10))\n", " ax = plt.gca()\n", " ax.set_color_cycle(2*['b', 'r', 'g', 'c', 'k'])\n", " l1 = plt.plot(-np.log10(alphas_lasso), coefs_lasso.T)\n", " ax.set_color_cycle(2*['b', 'r', 'g', 'c', 'k'])\n", " l2 = plt.plot(-np.log10(alphas_enet), coefs_enet.T, linestyle='--')\n", "\n", " plt.xlabel('-Log(alpha)')\n", " plt.ylabel('coefficients')\n", " plt.title('Lasso and Elastic-Net Paths')\n", " plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left')\n", " plt.axis('tight')\n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ohLhdu6R582wfujlzrFjJV19JlSsXPzc/n/AGAEFhmiUAAHFuyRLp7rulKVNs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\nZzRyrqSEM8XFnLnoOcqEW4ZeTzMXF0LPlzmWlTt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U1QqFQlFnMBjguuukwcesWbLU61oo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\ny02bNmHcuHFo3rw5AGDEiBFYvHgxrl279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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = boston.data\n", "y = boston.target\n", "paths_figure(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Тут покрасивее" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IjBrWWl6MxbipuZm/tLayOBzm+vp6zq2sxKMBVGSE2F/I8xzpYnoZYwLAU4C/UMdfrLU3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WcRFvZr9hPnJ/8vj/COAKDeaOSL6mo+rqqixmjkjuBgTBZLn3rN6zUa5hcWMt3Xl7zUVLz/uBD9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\n+qjS0tK0Zs0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn import datasets\n", "\n", "diabetes = datasets.load_diabetes()\n", "X = diabetes.data\n", "y = diabetes.target\n", "paths_figure(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Вопросы?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![caption](cat_fat.jpg)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }