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XYmonthdayFFMCDMCDCISItempRHwindrainarea
075marfri86.226.294.35.18.2516.70.00.00
174octtue90.635.4669.16.718.0330.90.00.00
274octsat90.643.7686.96.714.6331.30.00.00
386marfri91.733.377.59.08.3974.00.20.00
486marsun89.351.3102.29.611.4991.80.00.00
586augsun92.385.3488.014.722.2295.40.00.00
686augmon92.388.9495.68.524.1273.10.00.00
786augmon91.5145.4608.210.78.0862.20.00.00
886septue91.0129.5692.67.013.1635.40.00.00
975sepsat92.588.0698.67.122.8404.00.00.00
1075sepsat92.588.0698.67.117.8517.20.00.00
1175sepsat92.873.2713.022.619.3384.00.00.00
1265augfri63.570.8665.30.817.0726.70.00.00
1365sepmon90.9126.5686.57.021.3422.20.00.00
1465sepwed92.9133.3699.69.226.4214.50.00.00
1565sepfri93.3141.2713.913.922.9445.40.00.00
1655marsat91.735.880.87.815.1275.40.00.00
1785octmon84.932.8664.23.016.7474.90.00.00
1864marwed89.227.970.86.315.9354.00.00.00
1964aprsat86.327.497.15.19.3444.50.00.00
2064septue91.0129.5692.67.018.3402.70.00.00
2154sepmon91.878.5724.39.219.1382.70.00.00
2274junsun94.396.3200.056.121.0444.50.00.00
2374augsat90.2110.9537.46.219.5435.80.00.00
2474augsat93.5139.4594.220.323.7325.80.00.00
2574augsun91.4142.4601.410.616.3605.40.00.00
2674sepfri92.4117.9668.012.219.0345.80.00.00
2774sepmon90.9126.5686.57.019.4481.30.00.00
2863sepsat93.4145.4721.48.130.2242.70.00.00
2963sepsun93.5149.3728.68.122.8393.60.00.00
..........................................
48754augtue95.1141.3605.817.726.4343.60.016.40
48844augtue95.1141.3605.817.719.4717.60.046.70
48944augwed95.1141.3605.817.720.6581.30.00.00
49044augwed95.1141.3605.817.728.7334.00.00.00
49144augthu95.8152.0624.113.832.4214.50.00.00
49213augfri95.9158.0633.611.332.4272.20.00.00
49313augfri95.9158.0633.611.327.5294.50.043.32
49466augsat96.0164.0643.014.030.8304.90.08.59
49566augmon96.2175.5661.816.823.9422.20.00.00
49645augmon96.2175.5661.816.832.6263.10.02.77
49734augtue96.1181.1671.214.332.3272.20.014.68
49865augtue96.1181.1671.214.333.3262.70.040.54
49975augtue96.1181.1671.214.327.3634.96.410.82
50086augtue96.1181.1671.214.321.6654.90.80.00
50175augtue96.1181.1671.214.321.6654.90.80.00
50244augtue96.1181.1671.214.320.7694.90.40.00
50324augwed94.5139.4689.120.029.2304.90.01.95
50443augwed94.5139.4689.120.028.9294.90.049.59
50512augthu91.0163.2744.410.126.7351.80.05.80
50612augfri91.0166.9752.67.118.5738.50.00.00
50724augfri91.0166.9752.67.125.9413.60.00.00
50812augfri91.0166.9752.67.125.9413.60.00.00
50954augfri91.0166.9752.67.121.1717.61.42.17
51065augfri91.0166.9752.67.118.2625.40.00.43
51186augsun81.656.7665.61.927.8352.70.00.00
51243augsun81.656.7665.61.927.8322.70.06.44
51324augsun81.656.7665.61.921.9715.80.054.29
51474augsun81.656.7665.61.921.2706.70.011.16
51514augsat94.4146.0614.711.325.6424.00.00.00
51663novtue79.53.0106.71.111.8314.50.00.00
\n", "

517 rows × 13 columns

\n", "
" ], "text/plain": [ " X Y month day FFMC DMC DC ISI temp RH wind rain area\n", "0 7 5 mar fri 86.2 26.2 94.3 5.1 8.2 51 6.7 0.0 0.00\n", "1 7 4 oct tue 90.6 35.4 669.1 6.7 18.0 33 0.9 0.0 0.00\n", "2 7 4 oct sat 90.6 43.7 686.9 6.7 14.6 33 1.3 0.0 0.00\n", "3 8 6 mar fri 91.7 33.3 77.5 9.0 8.3 97 4.0 0.2 0.00\n", "4 8 6 mar sun 89.3 51.3 102.2 9.6 11.4 99 1.8 0.0 0.00\n", "5 8 6 aug sun 92.3 85.3 488.0 14.7 22.2 29 5.4 0.0 0.00\n", "6 8 6 aug mon 92.3 88.9 495.6 8.5 24.1 27 3.1 0.0 0.00\n", "7 8 6 aug mon 91.5 145.4 608.2 10.7 8.0 86 2.2 0.0 0.00\n", "8 8 6 sep tue 91.0 129.5 692.6 7.0 13.1 63 5.4 0.0 0.00\n", "9 7 5 sep sat 92.5 88.0 698.6 7.1 22.8 40 4.0 0.0 0.00\n", "10 7 5 sep sat 92.5 88.0 698.6 7.1 17.8 51 7.2 0.0 0.00\n", "11 7 5 sep sat 92.8 73.2 713.0 22.6 19.3 38 4.0 0.0 0.00\n", "12 6 5 aug fri 63.5 70.8 665.3 0.8 17.0 72 6.7 0.0 0.00\n", "13 6 5 sep mon 90.9 126.5 686.5 7.0 21.3 42 2.2 0.0 0.00\n", "14 6 5 sep wed 92.9 133.3 699.6 9.2 26.4 21 4.5 0.0 0.00\n", "15 6 5 sep fri 93.3 141.2 713.9 13.9 22.9 44 5.4 0.0 0.00\n", "16 5 5 mar sat 91.7 35.8 80.8 7.8 15.1 27 5.4 0.0 0.00\n", "17 8 5 oct mon 84.9 32.8 664.2 3.0 16.7 47 4.9 0.0 0.00\n", "18 6 4 mar wed 89.2 27.9 70.8 6.3 15.9 35 4.0 0.0 0.00\n", "19 6 4 apr sat 86.3 27.4 97.1 5.1 9.3 44 4.5 0.0 0.00\n", "20 6 4 sep tue 91.0 129.5 692.6 7.0 18.3 40 2.7 0.0 0.00\n", "21 5 4 sep mon 91.8 78.5 724.3 9.2 19.1 38 2.7 0.0 0.00\n", "22 7 4 jun sun 94.3 96.3 200.0 56.1 21.0 44 4.5 0.0 0.00\n", "23 7 4 aug sat 90.2 110.9 537.4 6.2 19.5 43 5.8 0.0 0.00\n", "24 7 4 aug sat 93.5 139.4 594.2 20.3 23.7 32 5.8 0.0 0.00\n", "25 7 4 aug sun 91.4 142.4 601.4 10.6 16.3 60 5.4 0.0 0.00\n", "26 7 4 sep fri 92.4 117.9 668.0 12.2 19.0 34 5.8 0.0 0.00\n", "27 7 4 sep mon 90.9 126.5 686.5 7.0 19.4 48 1.3 0.0 0.00\n", "28 6 3 sep sat 93.4 145.4 721.4 8.1 30.2 24 2.7 0.0 0.00\n", "29 6 3 sep sun 93.5 149.3 728.6 8.1 22.8 39 3.6 0.0 0.00\n", ".. .. .. ... ... ... ... ... ... ... .. ... ... ...\n", "487 5 4 aug tue 95.1 141.3 605.8 17.7 26.4 34 3.6 0.0 16.40\n", "488 4 4 aug tue 95.1 141.3 605.8 17.7 19.4 71 7.6 0.0 46.70\n", "489 4 4 aug wed 95.1 141.3 605.8 17.7 20.6 58 1.3 0.0 0.00\n", "490 4 4 aug wed 95.1 141.3 605.8 17.7 28.7 33 4.0 0.0 0.00\n", "491 4 4 aug thu 95.8 152.0 624.1 13.8 32.4 21 4.5 0.0 0.00\n", "492 1 3 aug fri 95.9 158.0 633.6 11.3 32.4 27 2.2 0.0 0.00\n", "493 1 3 aug fri 95.9 158.0 633.6 11.3 27.5 29 4.5 0.0 43.32\n", "494 6 6 aug sat 96.0 164.0 643.0 14.0 30.8 30 4.9 0.0 8.59\n", "495 6 6 aug mon 96.2 175.5 661.8 16.8 23.9 42 2.2 0.0 0.00\n", "496 4 5 aug mon 96.2 175.5 661.8 16.8 32.6 26 3.1 0.0 2.77\n", "497 3 4 aug tue 96.1 181.1 671.2 14.3 32.3 27 2.2 0.0 14.68\n", "498 6 5 aug tue 96.1 181.1 671.2 14.3 33.3 26 2.7 0.0 40.54\n", "499 7 5 aug tue 96.1 181.1 671.2 14.3 27.3 63 4.9 6.4 10.82\n", "500 8 6 aug tue 96.1 181.1 671.2 14.3 21.6 65 4.9 0.8 0.00\n", "501 7 5 aug tue 96.1 181.1 671.2 14.3 21.6 65 4.9 0.8 0.00\n", "502 4 4 aug tue 96.1 181.1 671.2 14.3 20.7 69 4.9 0.4 0.00\n", "503 2 4 aug wed 94.5 139.4 689.1 20.0 29.2 30 4.9 0.0 1.95\n", "504 4 3 aug wed 94.5 139.4 689.1 20.0 28.9 29 4.9 0.0 49.59\n", "505 1 2 aug thu 91.0 163.2 744.4 10.1 26.7 35 1.8 0.0 5.80\n", "506 1 2 aug fri 91.0 166.9 752.6 7.1 18.5 73 8.5 0.0 0.00\n", "507 2 4 aug fri 91.0 166.9 752.6 7.1 25.9 41 3.6 0.0 0.00\n", "508 1 2 aug fri 91.0 166.9 752.6 7.1 25.9 41 3.6 0.0 0.00\n", "509 5 4 aug fri 91.0 166.9 752.6 7.1 21.1 71 7.6 1.4 2.17\n", "510 6 5 aug fri 91.0 166.9 752.6 7.1 18.2 62 5.4 0.0 0.43\n", "511 8 6 aug sun 81.6 56.7 665.6 1.9 27.8 35 2.7 0.0 0.00\n", "512 4 3 aug sun 81.6 56.7 665.6 1.9 27.8 32 2.7 0.0 6.44\n", "513 2 4 aug sun 81.6 56.7 665.6 1.9 21.9 71 5.8 0.0 54.29\n", "514 7 4 aug sun 81.6 56.7 665.6 1.9 21.2 70 6.7 0.0 11.16\n", "515 1 4 aug sat 94.4 146.0 614.7 11.3 25.6 42 4.0 0.0 0.00\n", "516 6 3 nov tue 79.5 3.0 106.7 1.1 11.8 31 4.5 0.0 0.00\n", "\n", "[517 rows x 13 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "f = open('forestfires.csv','r')\n", "data = pd.read_csv('forestfires.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 517 entries, 0 to 516\n", "Data columns (total 13 columns):\n", "X 517 non-null int64\n", "Y 517 non-null int64\n", "month 517 non-null object\n", "day 517 non-null object\n", "FFMC 517 non-null float64\n", "DMC 517 non-null float64\n", "DC 517 non-null float64\n", "ISI 517 non-null float64\n", "temp 517 non-null float64\n", "RH 517 non-null int64\n", "wind 517 non-null float64\n", "rain 517 non-null float64\n", "area 517 non-null float64\n", "dtypes: float64(8), int64(3), object(2)\n", "memory usage: 52.6+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def get_data(df):\n", " # month_and_days_in_number\n", " names_month = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']\n", " for j in range(12):\n", " df['month'][df.month == names_month[j]] = j + 1 \n", " names_days = ['mon', 'tue', 'wed', 'thu', 'fri', 'sat', 'sun']\n", " for j in range(7):\n", " df['day'][df.day == names_days[j]] = j + 1 \n", " return df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\admin\\miniconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \"\"\"\n", "c:\\users\\admin\\miniconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \n" ] } ], "source": [ "X_data = data.iloc[:,0:-1]\n", "X_data = get_data(X_data)\n", "X_data = X_data.astype('float64')\n", "from sklearn.preprocessing import scale\n", "X_data_s = scale(X_data)\n", "Y_data = np.array(data['area'], dtype='float64')\n", "Y_data = np.log(1 + Y_data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model\n", "reg_s = linear_model.Ridge(alpha = .5)\n", "reg_s.fit(X_data_s,Y_data) \n", "w1 = reg_s.coef_\n", "reg = linear_model.Ridge(alpha = .5)\n", "reg.fit(X_data,Y_data) \n", "w2 = reg.coef_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## With standardization" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.dot(X_data_s,w1)\n", "y = Y_data\n", "p1 = np.poly1d(np.polyfit(x,y,1))\n", "plt.scatter(x,y,color='r')\n", "plt.plot(x,p1(x),'g')\n", "plt.xlim(-1,1.5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(np.arange(1, 13), np.absolute(w1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Error" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "40.113628301254415" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q = np.linalg.norm(Y_data - np.dot(X_data_s, w1), ord=2)\n", "Q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Without standardization" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.dot(X_data,w2)\n", "y = Y_data\n", "p1 = np.poly1d(np.polyfit(x,y,1))\n", "plt.scatter(x,y,color='r')\n", "plt.plot(x,p1(x),'g')\n", "plt.xlim(-0.5, 3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(np.arange(1, 13), np.absolute(w2))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Error" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3548.8883256085996" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q = np.linalg.norm(Y_data - np.dot(X_data, w1), ord=2)\n", "Q" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }