diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/.DS_Store differ diff --git a/data prep.ipynb b/data prep.ipynb new file mode 100644 index 0000000..6014031 --- /dev/null +++ b/data prep.ipynb @@ -0,0 +1,6726 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import linear_model\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "import catboost\n", + "from sklearn import preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import Dropout\n", + "from keras.utils import np_utils\n", + "from keras.constraints import maxnorm\n", + "from keras.wrappers.scikit_learn import KerasRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import keras" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "data_24h = pd.read_csv('MODIS_C6_Russia_and_Asia_24h.csv')\n", + "data_7d = pd.read_csv('MODIS_C6_Russia_and_Asia_7d.csv')\n", + "data_virs_24h = pd.read_csv('VNP14IMGTDL_NRT_Russia_and_Asia_24h.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13841, 13)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_7d.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "124" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(data_24h['confidence'] == 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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monthdayFFMCDMCDCISItempRHwindrainarea
138jultue85.848.3313.43.918.0422.70.00.36
139septue91.0129.5692.67.021.7382.20.00.43
140sepmon90.9126.5686.57.021.9391.80.00.47
141augwed95.599.9513.313.223.3314.50.00.55
142augfri90.1108.0529.812.521.2518.90.00.61
143julsat90.051.3296.38.716.6535.40.00.71
144augwed95.599.9513.313.223.8325.40.00.77
145augthu95.2131.7578.810.427.4224.00.00.90
146marmon90.139.786.66.213.2405.40.00.95
147septue84.473.4671.93.224.2283.60.00.96
148augtue94.8108.3647.117.017.4436.70.01.07
149septhu93.780.9685.217.923.7254.50.01.12
150junfri92.556.4433.37.123.2395.40.01.19
151julsun90.168.6355.27.224.8292.20.01.36
152julsat90.151.2424.16.224.6431.80.01.43
153sepfri94.385.1692.315.920.1474.90.01.46
154sepsat93.4145.4721.48.129.6272.70.01.46
155augsun94.8108.3647.117.016.4471.30.01.56
156sepsat93.4145.4721.48.128.6272.20.01.61
157augwed92.1111.2654.19.618.4453.60.01.63
158augwed92.1111.2654.19.620.5354.00.01.64
159sepfri92.4117.9668.012.219.0345.80.01.69
160marmon90.139.786.66.216.1293.10.01.75
161augthu95.2131.7578.810.420.3414.00.01.90
162marsat90.650.1100.47.815.2318.50.01.94
163sepsat92.5121.1674.48.617.8561.80.01.95
164sepsun89.790.0704.44.817.8672.20.02.01
165marthu84.918.255.03.05.3704.50.02.14
166augwed92.1111.2654.19.616.6470.90.02.29
167augwed96.0127.1570.516.523.4334.50.02.51
....................................
471mayfri89.625.473.75.718.0404.00.038.48
472junmon88.296.2229.04.714.3794.00.01.94
473junsat90.561.1252.69.424.5503.10.070.32
474junthu93.0103.8316.710.826.4352.70.010.08
475junthu93.7121.7350.218.022.7409.40.03.19
476julthu93.585.3395.09.927.2281.30.01.76
477julsun93.7101.3423.414.726.1454.00.07.36
478julsun93.7101.3423.414.718.2824.50.02.21
479julmon89.2103.9431.66.422.6574.90.0278.53
480julthu93.2114.4560.09.530.2254.50.02.75
482augsun94.9130.3587.114.123.4405.80.01.29
484augsun94.9130.3587.114.133.1254.00.026.43
485augmon95.0135.5596.321.330.6283.60.02.07
486augtue95.1141.3605.817.724.1436.30.02.00
487augtue95.1141.3605.817.726.4343.60.016.40
488augtue95.1141.3605.817.719.4717.60.046.70
493augfri95.9158.0633.611.327.5294.50.043.32
494augsat96.0164.0643.014.030.8304.90.08.59
496augmon96.2175.5661.816.832.6263.10.02.77
497augtue96.1181.1671.214.332.3272.20.014.68
498augtue96.1181.1671.214.333.3262.70.040.54
499augtue96.1181.1671.214.327.3634.96.410.82
503augwed94.5139.4689.120.029.2304.90.01.95
504augwed94.5139.4689.120.028.9294.90.049.59
505augthu91.0163.2744.410.126.7351.80.05.80
509augfri91.0166.9752.67.121.1717.61.42.17
510augfri91.0166.9752.67.118.2625.40.00.43
512augsun81.656.7665.61.927.8322.70.06.44
513augsun81.656.7665.61.921.9715.80.054.29
514augsun81.656.7665.61.921.2706.70.011.16
\n", + "

270 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " month day FFMC DMC DC ISI temp RH wind rain area\n", + "138 jul tue 85.8 48.3 313.4 3.9 18.0 42 2.7 0.0 0.36\n", + "139 sep tue 91.0 129.5 692.6 7.0 21.7 38 2.2 0.0 0.43\n", + "140 sep mon 90.9 126.5 686.5 7.0 21.9 39 1.8 0.0 0.47\n", + "141 aug wed 95.5 99.9 513.3 13.2 23.3 31 4.5 0.0 0.55\n", + "142 aug fri 90.1 108.0 529.8 12.5 21.2 51 8.9 0.0 0.61\n", + "143 jul sat 90.0 51.3 296.3 8.7 16.6 53 5.4 0.0 0.71\n", + "144 aug wed 95.5 99.9 513.3 13.2 23.8 32 5.4 0.0 0.77\n", + "145 aug thu 95.2 131.7 578.8 10.4 27.4 22 4.0 0.0 0.90\n", + "146 mar mon 90.1 39.7 86.6 6.2 13.2 40 5.4 0.0 0.95\n", + "147 sep tue 84.4 73.4 671.9 3.2 24.2 28 3.6 0.0 0.96\n", + "148 aug tue 94.8 108.3 647.1 17.0 17.4 43 6.7 0.0 1.07\n", + "149 sep thu 93.7 80.9 685.2 17.9 23.7 25 4.5 0.0 1.12\n", + "150 jun fri 92.5 56.4 433.3 7.1 23.2 39 5.4 0.0 1.19\n", + "151 jul sun 90.1 68.6 355.2 7.2 24.8 29 2.2 0.0 1.36\n", + "152 jul sat 90.1 51.2 424.1 6.2 24.6 43 1.8 0.0 1.43\n", + "153 sep fri 94.3 85.1 692.3 15.9 20.1 47 4.9 0.0 1.46\n", + "154 sep sat 93.4 145.4 721.4 8.1 29.6 27 2.7 0.0 1.46\n", + "155 aug sun 94.8 108.3 647.1 17.0 16.4 47 1.3 0.0 1.56\n", + "156 sep sat 93.4 145.4 721.4 8.1 28.6 27 2.2 0.0 1.61\n", + "157 aug wed 92.1 111.2 654.1 9.6 18.4 45 3.6 0.0 1.63\n", + "158 aug wed 92.1 111.2 654.1 9.6 20.5 35 4.0 0.0 1.64\n", + "159 sep fri 92.4 117.9 668.0 12.2 19.0 34 5.8 0.0 1.69\n", + "160 mar mon 90.1 39.7 86.6 6.2 16.1 29 3.1 0.0 1.75\n", + "161 aug thu 95.2 131.7 578.8 10.4 20.3 41 4.0 0.0 1.90\n", + "162 mar sat 90.6 50.1 100.4 7.8 15.2 31 8.5 0.0 1.94\n", + "163 sep sat 92.5 121.1 674.4 8.6 17.8 56 1.8 0.0 1.95\n", + "164 sep sun 89.7 90.0 704.4 4.8 17.8 67 2.2 0.0 2.01\n", + "165 mar thu 84.9 18.2 55.0 3.0 5.3 70 4.5 0.0 2.14\n", + "166 aug wed 92.1 111.2 654.1 9.6 16.6 47 0.9 0.0 2.29\n", + "167 aug wed 96.0 127.1 570.5 16.5 23.4 33 4.5 0.0 2.51\n", + ".. ... ... ... ... ... ... ... .. ... ... ...\n", + "471 may fri 89.6 25.4 73.7 5.7 18.0 40 4.0 0.0 38.48\n", + "472 jun mon 88.2 96.2 229.0 4.7 14.3 79 4.0 0.0 1.94\n", + "473 jun sat 90.5 61.1 252.6 9.4 24.5 50 3.1 0.0 70.32\n", + "474 jun thu 93.0 103.8 316.7 10.8 26.4 35 2.7 0.0 10.08\n", + "475 jun thu 93.7 121.7 350.2 18.0 22.7 40 9.4 0.0 3.19\n", + "476 jul thu 93.5 85.3 395.0 9.9 27.2 28 1.3 0.0 1.76\n", + "477 jul sun 93.7 101.3 423.4 14.7 26.1 45 4.0 0.0 7.36\n", + "478 jul sun 93.7 101.3 423.4 14.7 18.2 82 4.5 0.0 2.21\n", + "479 jul mon 89.2 103.9 431.6 6.4 22.6 57 4.9 0.0 278.53\n", + "480 jul thu 93.2 114.4 560.0 9.5 30.2 25 4.5 0.0 2.75\n", + "482 aug sun 94.9 130.3 587.1 14.1 23.4 40 5.8 0.0 1.29\n", + "484 aug sun 94.9 130.3 587.1 14.1 33.1 25 4.0 0.0 26.43\n", + "485 aug mon 95.0 135.5 596.3 21.3 30.6 28 3.6 0.0 2.07\n", + "486 aug tue 95.1 141.3 605.8 17.7 24.1 43 6.3 0.0 2.00\n", + "487 aug tue 95.1 141.3 605.8 17.7 26.4 34 3.6 0.0 16.40\n", + "488 aug tue 95.1 141.3 605.8 17.7 19.4 71 7.6 0.0 46.70\n", + "493 aug fri 95.9 158.0 633.6 11.3 27.5 29 4.5 0.0 43.32\n", + "494 aug sat 96.0 164.0 643.0 14.0 30.8 30 4.9 0.0 8.59\n", + "496 aug mon 96.2 175.5 661.8 16.8 32.6 26 3.1 0.0 2.77\n", + "497 aug tue 96.1 181.1 671.2 14.3 32.3 27 2.2 0.0 14.68\n", + "498 aug tue 96.1 181.1 671.2 14.3 33.3 26 2.7 0.0 40.54\n", + "499 aug tue 96.1 181.1 671.2 14.3 27.3 63 4.9 6.4 10.82\n", + "503 aug wed 94.5 139.4 689.1 20.0 29.2 30 4.9 0.0 1.95\n", + "504 aug wed 94.5 139.4 689.1 20.0 28.9 29 4.9 0.0 49.59\n", + "505 aug thu 91.0 163.2 744.4 10.1 26.7 35 1.8 0.0 5.80\n", + "509 aug fri 91.0 166.9 752.6 7.1 21.1 71 7.6 1.4 2.17\n", + "510 aug fri 91.0 166.9 752.6 7.1 18.2 62 5.4 0.0 0.43\n", + "512 aug sun 81.6 56.7 665.6 1.9 27.8 32 2.7 0.0 6.44\n", + "513 aug sun 81.6 56.7 665.6 1.9 21.9 71 5.8 0.0 54.29\n", + "514 aug sun 81.6 56.7 665.6 1.9 21.2 70 6.7 0.0 11.16\n", + "\n", + "[270 rows x 11 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_forest_kaggle[data_forest_kaggle['area'] != 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "data_forest_kaggle.drop(columns=['X', 'Y'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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monthdayFFMCDMCDCISItempRHwindrainarea
count517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000
mean7.4758224.25918890.644681110.872340547.9400399.02166318.88916844.2882014.0176020.02166312.847292
std2.2759902.0729295.52011164.046482248.0661924.5594775.80662516.3174691.7916530.29595963.655818
min1.0000001.00000018.7000001.1000007.9000000.0000002.20000015.0000000.4000000.0000000.000000
25%7.0000002.00000090.20000068.600000437.7000006.50000015.50000033.0000002.7000000.0000000.000000
50%8.0000005.00000091.600000108.300000664.2000008.40000019.30000042.0000004.0000000.0000000.520000
75%9.0000006.00000092.900000142.400000713.90000010.80000022.80000053.0000004.9000000.0000006.570000
max12.0000007.00000096.200000291.300000860.60000056.10000033.300000100.0000009.4000006.4000001090.840000
\n", + "
" + ], + "text/plain": [ + " month day FFMC DMC DC ISI \\\n", + "count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \n", + "mean 7.475822 4.259188 90.644681 110.872340 547.940039 9.021663 \n", + "std 2.275990 2.072929 5.520111 64.046482 248.066192 4.559477 \n", + "min 1.000000 1.000000 18.700000 1.100000 7.900000 0.000000 \n", + "25% 7.000000 2.000000 90.200000 68.600000 437.700000 6.500000 \n", + "50% 8.000000 5.000000 91.600000 108.300000 664.200000 8.400000 \n", + "75% 9.000000 6.000000 92.900000 142.400000 713.900000 10.800000 \n", + "max 12.000000 7.000000 96.200000 291.300000 860.600000 56.100000 \n", + "\n", + " temp RH wind rain area \n", + "count 517.000000 517.000000 517.000000 517.000000 517.000000 \n", + "mean 18.889168 44.288201 4.017602 0.021663 12.847292 \n", + "std 5.806625 16.317469 1.791653 0.295959 63.655818 \n", + "min 2.200000 15.000000 0.400000 0.000000 0.000000 \n", + "25% 15.500000 33.000000 2.700000 0.000000 0.000000 \n", + "50% 19.300000 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monthdaytempRHwindrainarea
0358.2516.70.00
110218.0330.90.00
210614.6331.30.00
3358.3974.00.20
43711.4991.80.00
58722.2295.40.00
68124.1273.10.00
7818.0862.20.00
89213.1635.40.00
99622.8404.00.00
109617.8517.20.00
119619.3384.00.00
128517.0726.70.00
139121.3422.20.00
149326.4214.50.00
159522.9445.40.00
163615.1275.40.00
1710116.7474.90.00
183315.9354.00.00
19469.3444.50.00
209218.3402.70.00
219119.1382.70.00
226721.0444.50.00
238619.5435.80.00
248623.7325.80.00
258716.3605.40.00
269519.0345.80.00
279119.4481.30.00
289630.2242.70.00
299722.8393.60.00
........................
4878226.4343.60.016
4888219.4717.60.046
4898320.6581.30.00
4908328.7334.00.00
4918432.4214.50.00
4928532.4272.20.00
4938527.5294.50.043
4948630.8304.90.08
4958123.9422.20.00
4968132.6263.10.02
4978232.3272.20.014
4988233.3262.70.040
4998227.3634.96.410
5008221.6654.90.80
5018221.6654.90.80
5028220.7694.90.40
5038329.2304.90.01
5048328.9294.90.049
5058426.7351.80.05
5068518.5738.50.00
5078525.9413.60.00
5088525.9413.60.00
5098521.1717.61.42
5108518.2625.40.00
5118727.8352.70.00
5128727.8322.70.06
5138721.9715.80.054
5148721.2706.70.011
5158625.6424.00.00
51611211.8314.50.00
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517 rows × 7 columns

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42 4.0 0.0 0\n", + "516 11 2 11.8 31 4.5 0.0 0\n", + "\n", + "[517 rows x 7 columns]" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_class" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "from math import sin, cos, sqrt, atan2, radians" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [], + "source": [ + "user_coordinates = [55.753960, 37.620393]" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [], + "source": [ + "data_distance = pd.read_csv('MODIS_C6_Russia_and_Asia_24h (1).csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [], + "source": [ + "lat_long = list(zip(data_distance['latitude'], data_distance['longitude']))" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [ 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{}, + "output_type": "execute_result" + } + ], + "source": [ + "lat_long" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [], + "source": [ + "from math import sin, cos, sqrt, atan2, radians\n", + "\n", + "def calculate_distance(lat1, lon1, lat2, lon2):\n", + "# approximate radius of earth in km\n", + " R = 6373.0\n", + " \n", + " lat1 = radians(lat1)\n", + " lon1 = radians(lon1)\n", + " lat2 = radians(lat2)\n", + " lon2 = radians(lon2)\n", + " \n", + " dlon = lon2 - lon1\n", + " dlat = lat2 - lat1\n", + "\n", + " a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n", + " c = 2 * atan2(sqrt(a), sqrt(1 - a))\n", + "\n", + " distance = R * c\n", + " \n", + " return distance" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [], + "source": [ + "from math import sin, cos, sqrt, atan2, radians\n", + "\n", + "def min_distance(user_coordinates_lat, user_coordinates_lon):\n", + " \n", + " def calculate_distance(lat1, lon1, lat2, lon2):\n", + " # approximate radius of earth in km\n", + " R = 6373.0\n", + " \n", + " lat1 = radians(lat1)\n", + " lon1 = radians(lon1)\n", + " lat2 = radians(lat2)\n", + " lon2 = radians(lon2)\n", + "\n", + " dlon = lon2 - lon1\n", + " dlat = lat2 - lat1\n", + "\n", + " a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n", + " c = 2 * atan2(sqrt(a), sqrt(1 - a))\n", + "\n", + " distance = R * c\n", + "\n", + " return distance\n", + " \n", + " data_distance = pd.read_csv('MODIS_C6_Russia_and_Asia_24h (1).csv')\n", + " lat_long = list(zip(data_distance['latitude'], data_distance['longitude']))\n", + " distance = []\n", + " coor = []\n", + " for i, j in lat_long:\n", + " distance.append(calculate_distance(user_coordinates[0], user_coordinates[1], i, j))\n", + " coor.append([j, i])\n", + " \n", + " dist_coor = sorted(list(zip(distance, coor)))\n", + " \n", + " \n", + " \n", + " return dist_coor[0][0], dist_coor[0][1]" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(176.2744053941077, [37.709, 54.17])" + ] + }, + "execution_count": 338, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_distance(user_coordinates[0], user_coordinates[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1195.4971863361702" + ] + }, + "execution_count": 285, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calculate_distance(user_coordinates[0], user_coordinates[1], 45.040216, 38.975996)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [], + "source": [ + "import staticmap" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [], + "source": [ + "coordinates = [55.753960, 37.620393]" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": {}, + "outputs": [], + "source": [ + "from staticmap import StaticMap, Line\n", + "def img_fire(user_coordinates_lat, user_coordinates_lon):\n", + " m = StaticMap(800, 800, 80)\n", + "\n", + " coordinates = [[min_distance(user_coordinates_lat, user_coordinates_lon)[1][0],\n", + " min_distance(user_coordinates_lat, user_coordinates_lon)[1][1]]]\n", + " line_outline = Line(coordinates, 'white', 6)\n", + " line = Line(coordinates, '#D2322D', 10)\n", + "\n", + " m.add_line(line_outline)\n", + " m.add_line(line)\n", + "\n", + " image = m.render()\n", + " image.save('ferry.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 356, + "metadata": {}, + "outputs": [], + "source": [ + "img_fire(user_coordinates[0], user_coordinates[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 387, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data_forest_kaggle.to_csv('new_csv.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 395, + "metadata": {}, + "outputs": [], + "source": [ + "data_forest_kaggle = pd.read_csv('new_csv.csv')\n", + "data_forest_kaggle_target = data_forest_kaggle['area']\n", + "data_forest_kaggle.drop(columns=['area', 'Unnamed: 0'], inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array([97.17329458, 25.87481046, 20.50757591, 63.53986266, 63.25718493,\n", + " 27.33511692, 19.80833096, 23.74328342])" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data_prep.ipynb b/data_prep.ipynb deleted file mode 100644 index 248616e..0000000 --- a/data_prep.ipynb +++ /dev/null @@ -1,21040 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "from matplotlib import pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import linear_model\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import cross_val_score" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "import catboost\n", - "from sklearn import preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "from keras.layers import Dropout\n", - "from keras.utils import np_utils\n", - "from keras.constraints import maxnorm\n", - "from keras.wrappers.scikit_learn import KerasRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import KFold" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n", - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import keras" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "data_24h = pd.read_csv('MODIS_C6_Russia_and_Asia_24h.csv')\n", - "data_7d = pd.read_csv('MODIS_C6_Russia_and_Asia_7d.csv')\n", - "data_virs_24h = pd.read_csv('VNP14IMGTDL_NRT_Russia_and_Asia_24h.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(13841, 13)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_7d.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "124" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum(data_24h['confidence'] == 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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37548.22026132.07253367.00.430.382018-10-19412Nhigh1.0NRT293.28.2D
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38448.41955133.41417367.00.480.402018-10-19412Nhigh1.0NRT363.2251.5D
38848.42392133.41980367.00.480.402018-10-19412Nhigh1.0NRT355.088.0D
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41048.43845133.44972367.00.480.402018-10-19412Nhigh1.0NRT356.4184.1D
46748.92891132.56001367.00.460.392018-10-19412Nhigh1.0NRT291.28.6D
49449.34221130.20580367.00.400.372018-10-19412Nhigh1.0NRT332.250.8D
50049.34484130.19925367.00.400.372018-10-19412Nhigh1.0NRT363.8127.4D
51249.34864130.17513367.00.400.372018-10-19412Nhigh1.0NRT290.67.5D
57651.07117108.66190367.00.480.642018-10-19412Nhigh1.0NRT296.819.0D
..........................................
884550.35387116.82705367.00.370.582018-10-20354Nhigh1.0NRT291.313.3D
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465 rows × 13 columns

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138jultue85.848.3313.43.918.0422.70.00.36
139septue91.0129.5692.67.021.7382.20.00.43
140sepmon90.9126.5686.57.021.9391.80.00.47
141augwed95.599.9513.313.223.3314.50.00.55
142augfri90.1108.0529.812.521.2518.90.00.61
143julsat90.051.3296.38.716.6535.40.00.71
144augwed95.599.9513.313.223.8325.40.00.77
145augthu95.2131.7578.810.427.4224.00.00.90
146marmon90.139.786.66.213.2405.40.00.95
147septue84.473.4671.93.224.2283.60.00.96
148augtue94.8108.3647.117.017.4436.70.01.07
149septhu93.780.9685.217.923.7254.50.01.12
150junfri92.556.4433.37.123.2395.40.01.19
151julsun90.168.6355.27.224.8292.20.01.36
152julsat90.151.2424.16.224.6431.80.01.43
153sepfri94.385.1692.315.920.1474.90.01.46
154sepsat93.4145.4721.48.129.6272.70.01.46
155augsun94.8108.3647.117.016.4471.30.01.56
156sepsat93.4145.4721.48.128.6272.20.01.61
157augwed92.1111.2654.19.618.4453.60.01.63
158augwed92.1111.2654.19.620.5354.00.01.64
159sepfri92.4117.9668.012.219.0345.80.01.69
160marmon90.139.786.66.216.1293.10.01.75
161augthu95.2131.7578.810.420.3414.00.01.90
162marsat90.650.1100.47.815.2318.50.01.94
163sepsat92.5121.1674.48.617.8561.80.01.95
164sepsun89.790.0704.44.817.8672.20.02.01
165marthu84.918.255.03.05.3704.50.02.14
166augwed92.1111.2654.19.616.6470.90.02.29
167augwed96.0127.1570.516.523.4334.50.02.51
....................................
471mayfri89.625.473.75.718.0404.00.038.48
472junmon88.296.2229.04.714.3794.00.01.94
473junsat90.561.1252.69.424.5503.10.070.32
474junthu93.0103.8316.710.826.4352.70.010.08
475junthu93.7121.7350.218.022.7409.40.03.19
476julthu93.585.3395.09.927.2281.30.01.76
477julsun93.7101.3423.414.726.1454.00.07.36
478julsun93.7101.3423.414.718.2824.50.02.21
479julmon89.2103.9431.66.422.6574.90.0278.53
480julthu93.2114.4560.09.530.2254.50.02.75
482augsun94.9130.3587.114.123.4405.80.01.29
484augsun94.9130.3587.114.133.1254.00.026.43
485augmon95.0135.5596.321.330.6283.60.02.07
486augtue95.1141.3605.817.724.1436.30.02.00
487augtue95.1141.3605.817.726.4343.60.016.40
488augtue95.1141.3605.817.719.4717.60.046.70
493augfri95.9158.0633.611.327.5294.50.043.32
494augsat96.0164.0643.014.030.8304.90.08.59
496augmon96.2175.5661.816.832.6263.10.02.77
497augtue96.1181.1671.214.332.3272.20.014.68
498augtue96.1181.1671.214.333.3262.70.040.54
499augtue96.1181.1671.214.327.3634.96.410.82
503augwed94.5139.4689.120.029.2304.90.01.95
504augwed94.5139.4689.120.028.9294.90.049.59
505augthu91.0163.2744.410.126.7351.80.05.80
509augfri91.0166.9752.67.121.1717.61.42.17
510augfri91.0166.9752.67.118.2625.40.00.43
512augsun81.656.7665.61.927.8322.70.06.44
513augsun81.656.7665.61.921.9715.80.054.29
514augsun81.656.7665.61.921.2706.70.011.16
\n", - "

270 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " month day FFMC DMC DC ISI temp RH wind rain area\n", - "138 jul tue 85.8 48.3 313.4 3.9 18.0 42 2.7 0.0 0.36\n", - "139 sep tue 91.0 129.5 692.6 7.0 21.7 38 2.2 0.0 0.43\n", - "140 sep mon 90.9 126.5 686.5 7.0 21.9 39 1.8 0.0 0.47\n", - "141 aug wed 95.5 99.9 513.3 13.2 23.3 31 4.5 0.0 0.55\n", - "142 aug fri 90.1 108.0 529.8 12.5 21.2 51 8.9 0.0 0.61\n", - "143 jul sat 90.0 51.3 296.3 8.7 16.6 53 5.4 0.0 0.71\n", - "144 aug wed 95.5 99.9 513.3 13.2 23.8 32 5.4 0.0 0.77\n", - "145 aug thu 95.2 131.7 578.8 10.4 27.4 22 4.0 0.0 0.90\n", - "146 mar mon 90.1 39.7 86.6 6.2 13.2 40 5.4 0.0 0.95\n", - "147 sep tue 84.4 73.4 671.9 3.2 24.2 28 3.6 0.0 0.96\n", - "148 aug tue 94.8 108.3 647.1 17.0 17.4 43 6.7 0.0 1.07\n", - "149 sep thu 93.7 80.9 685.2 17.9 23.7 25 4.5 0.0 1.12\n", - "150 jun fri 92.5 56.4 433.3 7.1 23.2 39 5.4 0.0 1.19\n", - "151 jul sun 90.1 68.6 355.2 7.2 24.8 29 2.2 0.0 1.36\n", - "152 jul sat 90.1 51.2 424.1 6.2 24.6 43 1.8 0.0 1.43\n", - "153 sep fri 94.3 85.1 692.3 15.9 20.1 47 4.9 0.0 1.46\n", - "154 sep sat 93.4 145.4 721.4 8.1 29.6 27 2.7 0.0 1.46\n", - "155 aug sun 94.8 108.3 647.1 17.0 16.4 47 1.3 0.0 1.56\n", - "156 sep sat 93.4 145.4 721.4 8.1 28.6 27 2.2 0.0 1.61\n", - "157 aug wed 92.1 111.2 654.1 9.6 18.4 45 3.6 0.0 1.63\n", - "158 aug wed 92.1 111.2 654.1 9.6 20.5 35 4.0 0.0 1.64\n", - "159 sep fri 92.4 117.9 668.0 12.2 19.0 34 5.8 0.0 1.69\n", - "160 mar mon 90.1 39.7 86.6 6.2 16.1 29 3.1 0.0 1.75\n", - "161 aug thu 95.2 131.7 578.8 10.4 20.3 41 4.0 0.0 1.90\n", - "162 mar sat 90.6 50.1 100.4 7.8 15.2 31 8.5 0.0 1.94\n", - "163 sep sat 92.5 121.1 674.4 8.6 17.8 56 1.8 0.0 1.95\n", - "164 sep sun 89.7 90.0 704.4 4.8 17.8 67 2.2 0.0 2.01\n", - "165 mar thu 84.9 18.2 55.0 3.0 5.3 70 4.5 0.0 2.14\n", - "166 aug wed 92.1 111.2 654.1 9.6 16.6 47 0.9 0.0 2.29\n", - "167 aug wed 96.0 127.1 570.5 16.5 23.4 33 4.5 0.0 2.51\n", - ".. ... ... ... ... ... ... ... .. ... ... ...\n", - "471 may fri 89.6 25.4 73.7 5.7 18.0 40 4.0 0.0 38.48\n", - "472 jun mon 88.2 96.2 229.0 4.7 14.3 79 4.0 0.0 1.94\n", - "473 jun sat 90.5 61.1 252.6 9.4 24.5 50 3.1 0.0 70.32\n", - "474 jun thu 93.0 103.8 316.7 10.8 26.4 35 2.7 0.0 10.08\n", - "475 jun thu 93.7 121.7 350.2 18.0 22.7 40 9.4 0.0 3.19\n", - "476 jul thu 93.5 85.3 395.0 9.9 27.2 28 1.3 0.0 1.76\n", - "477 jul sun 93.7 101.3 423.4 14.7 26.1 45 4.0 0.0 7.36\n", - "478 jul sun 93.7 101.3 423.4 14.7 18.2 82 4.5 0.0 2.21\n", - "479 jul mon 89.2 103.9 431.6 6.4 22.6 57 4.9 0.0 278.53\n", - "480 jul thu 93.2 114.4 560.0 9.5 30.2 25 4.5 0.0 2.75\n", - "482 aug sun 94.9 130.3 587.1 14.1 23.4 40 5.8 0.0 1.29\n", - "484 aug sun 94.9 130.3 587.1 14.1 33.1 25 4.0 0.0 26.43\n", - "485 aug mon 95.0 135.5 596.3 21.3 30.6 28 3.6 0.0 2.07\n", - "486 aug tue 95.1 141.3 605.8 17.7 24.1 43 6.3 0.0 2.00\n", - "487 aug tue 95.1 141.3 605.8 17.7 26.4 34 3.6 0.0 16.40\n", - "488 aug tue 95.1 141.3 605.8 17.7 19.4 71 7.6 0.0 46.70\n", - "493 aug fri 95.9 158.0 633.6 11.3 27.5 29 4.5 0.0 43.32\n", - "494 aug sat 96.0 164.0 643.0 14.0 30.8 30 4.9 0.0 8.59\n", - "496 aug mon 96.2 175.5 661.8 16.8 32.6 26 3.1 0.0 2.77\n", - "497 aug tue 96.1 181.1 671.2 14.3 32.3 27 2.2 0.0 14.68\n", - "498 aug tue 96.1 181.1 671.2 14.3 33.3 26 2.7 0.0 40.54\n", - "499 aug tue 96.1 181.1 671.2 14.3 27.3 63 4.9 6.4 10.82\n", - "503 aug wed 94.5 139.4 689.1 20.0 29.2 30 4.9 0.0 1.95\n", - "504 aug wed 94.5 139.4 689.1 20.0 28.9 29 4.9 0.0 49.59\n", - "505 aug thu 91.0 163.2 744.4 10.1 26.7 35 1.8 0.0 5.80\n", - "509 aug fri 91.0 166.9 752.6 7.1 21.1 71 7.6 1.4 2.17\n", - "510 aug fri 91.0 166.9 752.6 7.1 18.2 62 5.4 0.0 0.43\n", - "512 aug sun 81.6 56.7 665.6 1.9 27.8 32 2.7 0.0 6.44\n", - "513 aug sun 81.6 56.7 665.6 1.9 21.9 71 5.8 0.0 54.29\n", - "514 aug sun 81.6 56.7 665.6 1.9 21.2 70 6.7 0.0 11.16\n", - "\n", - "[270 rows x 11 columns]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_forest_kaggle[data_forest_kaggle['area'] != 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "data_forest_kaggle.drop(columns=['X', 'Y'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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"source": [ - "data_dummy = pd.get_dummies(data_forest_kaggle)\n", - "target_dummy = data_dummy['area']\n", - "data_dummy.drop(columns=['area'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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std2.2759902.0729295.52011164.046482248.0661924.5594775.80662516.3174691.7916530.29595963.655818
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75%9.0000006.00000092.900000142.400000713.90000010.80000022.80000053.0000004.9000000.0000006.570000
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" - ], - "text/plain": [ - " month day FFMC DMC DC ISI \\\n", - "count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \n", - "mean 7.475822 4.259188 90.644681 110.872340 547.940039 9.021663 \n", - "std 2.275990 2.072929 5.520111 64.046482 248.066192 4.559477 \n", - "min 1.000000 1.000000 18.700000 1.100000 7.900000 0.000000 \n", - "25% 7.000000 2.000000 90.200000 68.600000 437.700000 6.500000 \n", - "50% 8.000000 5.000000 91.600000 108.300000 664.200000 8.400000 \n", - "75% 9.000000 6.000000 92.900000 142.400000 713.900000 10.800000 \n", - "max 12.000000 7.000000 96.200000 291.300000 860.600000 56.100000 \n", - "\n", - " temp RH wind rain area \n", - "count 517.000000 517.000000 517.000000 517.000000 517.000000 \n", - "mean 18.889168 44.288201 4.017602 0.021663 12.847292 \n", - "std 5.806625 16.317469 1.791653 0.295959 63.655818 \n", - "min 2.200000 15.000000 0.400000 0.000000 0.000000 \n", - "25% 15.500000 33.000000 2.700000 0.000000 0.000000 \n", - "50% 19.300000 42.000000 4.000000 0.000000 0.520000 \n", - "75% 22.800000 53.000000 4.900000 0.000000 6.570000 \n", - "max 33.300000 100.000000 9.400000 6.400000 1090.840000 " - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_forest_kaggle.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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270 rows × 7 columns

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"164 9 7 17.8 67 2.2 0.0 2.01\n", - "165 3 4 5.3 70 4.5 0.0 2.14\n", - "166 8 3 16.6 47 0.9 0.0 2.29\n", - "167 8 3 23.4 33 4.5 0.0 2.51\n", - ".. ... ... ... .. ... ... ...\n", - "471 5 5 18.0 40 4.0 0.0 38.48\n", - "472 6 1 14.3 79 4.0 0.0 1.94\n", - "473 6 6 24.5 50 3.1 0.0 70.32\n", - "474 6 4 26.4 35 2.7 0.0 10.08\n", - "475 6 4 22.7 40 9.4 0.0 3.19\n", - "476 7 4 27.2 28 1.3 0.0 1.76\n", - "477 7 7 26.1 45 4.0 0.0 7.36\n", - "478 7 7 18.2 82 4.5 0.0 2.21\n", - "479 7 1 22.6 57 4.9 0.0 278.53\n", - "480 7 4 30.2 25 4.5 0.0 2.75\n", - "482 8 7 23.4 40 5.8 0.0 1.29\n", - "484 8 7 33.1 25 4.0 0.0 26.43\n", - "485 8 1 30.6 28 3.6 0.0 2.07\n", - "486 8 2 24.1 43 6.3 0.0 2.00\n", - "487 8 2 26.4 34 3.6 0.0 16.40\n", - "488 8 2 19.4 71 7.6 0.0 46.70\n", - "493 8 5 27.5 29 4.5 0.0 43.32\n", - "494 8 6 30.8 30 4.9 0.0 8.59\n", - "496 8 1 32.6 26 3.1 0.0 2.77\n", - "497 8 2 32.3 27 2.2 0.0 14.68\n", - "498 8 2 33.3 26 2.7 0.0 40.54\n", - "499 8 2 27.3 63 4.9 6.4 10.82\n", - "503 8 3 29.2 30 4.9 0.0 1.95\n", - "504 8 3 28.9 29 4.9 0.0 49.59\n", - "505 8 4 26.7 35 1.8 0.0 5.80\n", - "509 8 5 21.1 71 7.6 1.4 2.17\n", - "510 8 5 18.2 62 5.4 0.0 0.43\n", - "512 8 7 27.8 32 2.7 0.0 6.44\n", - "513 8 7 21.9 71 5.8 0.0 54.29\n", - "514 8 7 21.2 70 6.7 0.0 11.16\n", - "\n", - "[270 rows x 7 columns]" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_forest_kaggle[data_forest_kaggle['area'] > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = data_forest_kaggle.values\n", - "\n", - "\n", - "X = dataset[:,0:6]\n", - "Y = dataset[:,6]" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [], - "source": [ - "Y = np.array(Y).reshape((len(Y), 1))\n", - "#Y.reshape(-1, 1)\n", - "\n", - "# normalize the dataset\n", - "scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))\n", - "Y = scaler.fit_transform(Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results: -0.00 (0.01) MSE\n" - ] - } - ], - "source": [ - "def baseline_model():\n", - " # create model\n", - " model = Sequential()\n", - " model.add(Dense(10, input_dim=6, kernel_initializer='uniform', activation='relu'))\n", - " #model.add(Dropout(0.2))\n", - " #model.add(Dense(5, kernel_initializer='uniform', activation='relu', kernel_constraint=maxnorm(3)))\n", - " #model.add(Dropout(0.2))\n", - " model.add(Dense(3, kernel_initializer='uniform', activation='relu'))\n", - " model.add(Dense(1, kernel_initializer='uniform', activation='relu'))\n", - " \n", - " # compile model\n", - " model.compile(loss='mean_squared_error', optimizer='adam')\n", - " return model\n", - "\n", - "\n", - "# fix random seed for reproducibility\n", - "seed = 7\n", - "np.random.seed(seed)\n", - "\n", - "# evaluate model with standardized dataset\n", - "estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=600, batch_size=5, verbose=0)\n", - "estimator.fit(X, Y)\n", - "\n", - "kfold = KFold(n_splits=10, random_state=seed)\n", - "results = cross_val_score(estimator, X, Y, cv=kfold)\n", - "print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_trian, y_test = train_test_split(X, Y, test_size=0.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "estimator.fit(X_train, y_trian)" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00034780558739277783" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean_squared_error(y_test, estimator.predict(X_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [], - "source": [ - "data_pred = pd.read_csv('predict.csv', delimiter=';')" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " month day temp RH wind rain\n", - "0 7 1 22.6 57 4.90 0\n", - "1 8 2 32.3 58 4.10 0\n", - "2 3 3 32.3 59 4.11 0\n", - "3 8 2 22.1 60 4.12 0\n", - "4 8 2 22.1 61 4.13 0\n", - "5 4 2 33.2 62 4.14 0\n", - "6 2 2 32.3 63 4.15 0\n", - "7 8 2 32.3 64 4.16 0" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" - ] - }, - "execution_count": 186, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "estimator.predict(data_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - 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] - }, - { - "cell_type": "code", - "execution_count": 238, - "metadata": {}, - "outputs": [], - "source": [ - "model_test = catboost.CatBoostRegressor().load_model('model_fire_pred')" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([97.17329458, 25.87481046, 20.50757591, 63.53986266, 63.25718493,\n", - " 27.33511692, 19.80833096, 23.74328342])" - ] - }, - "execution_count": 239, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_test.predict(data_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0:\tlearn: 79.3845453\ttotal: 7.62ms\tremaining: 7.61s\n", - "1:\tlearn: 79.0284442\ttotal: 12.6ms\tremaining: 6.28s\n", - "2:\tlearn: 78.8299819\ttotal: 17.7ms\tremaining: 5.89s\n", - "3:\tlearn: 78.6780579\ttotal: 21.2ms\tremaining: 5.29s\n", - "4:\tlearn: 78.5639081\ttotal: 24ms\tremaining: 4.78s\n", - 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" - ], - "text/plain": [ - " month day temp RH wind rain \\\n", - "count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \n", - "mean 7.475822 4.259188 18.889168 44.288201 4.017602 0.021663 \n", - "std 2.275990 2.072929 5.806625 16.317469 1.791653 0.295959 \n", - "min 1.000000 1.000000 2.200000 15.000000 0.400000 0.000000 \n", - "25% 7.000000 2.000000 15.500000 33.000000 2.700000 0.000000 \n", - "50% 8.000000 5.000000 19.300000 42.000000 4.000000 0.000000 \n", - "75% 9.000000 6.000000 22.800000 53.000000 4.900000 0.000000 \n", - "max 12.000000 7.000000 33.300000 100.000000 9.400000 6.400000 \n", - "\n", - " area \n", - "count 517.000000 \n", - "mean 12.847292 \n", - "std 63.655818 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 0.520000 \n", - "75% 6.570000 \n", - "max 1090.840000 " - ] - }, - "execution_count": 189, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_forest_kaggle.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 247, - "metadata": {}, - "outputs": [], - "source": [ - "data_class = data_forest_kaggle.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 248, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - } - ], - "source": [ - "data_class[data_class['area'] == 0]['area'] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 249, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \n", - "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n", - "/Users/aleksandrbojko/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "data_class[(data_class['area'] >= 0.01) & (data_class['area'] <= 0.52)]['area'] = 1\n", - "data_class[(data_class['area'] >= 0.53) & (data_class['area'] <= 4)]['area'] = 2\n", - "data_class[(data_class['area'] >= 4.01) & (data_class['area'] <= 7)]['area'] = 3\n", - "data_class[(data_class['area'] >= 7.1) & (data_class['area'] <= max(data_class['area'].values))]['area'] = 4" - ] - }, - { - "cell_type": "code", - "execution_count": 250, - "metadata": {}, - 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monthdaytempRHwindrainarea
0358.2516.70.00
110218.0330.90.00
210614.6331.30.00
3358.3974.00.20
43711.4991.80.00
58722.2295.40.00
68124.1273.10.00
7818.0862.20.00
89213.1635.40.00
99622.8404.00.00
109617.8517.20.00
119619.3384.00.00
128517.0726.70.00
139121.3422.20.00
149326.4214.50.00
159522.9445.40.00
163615.1275.40.00
1710116.7474.90.00
183315.9354.00.00
19469.3444.50.00
209218.3402.70.00
219119.1382.70.00
226721.0444.50.00
238619.5435.80.00
248623.7325.80.00
258716.3605.40.00
269519.0345.80.00
279119.4481.30.00
289630.2242.70.00
299722.8393.60.00
........................
4878226.4343.60.016
4888219.4717.60.046
4898320.6581.30.00
4908328.7334.00.00
4918432.4214.50.00
4928532.4272.20.00
4938527.5294.50.043
4948630.8304.90.08
4958123.9422.20.00
4968132.6263.10.02
4978232.3272.20.014
4988233.3262.70.040
4998227.3634.96.410
5008221.6654.90.80
5018221.6654.90.80
5028220.7694.90.40
5038329.2304.90.01
5048328.9294.90.049
5058426.7351.80.05
5068518.5738.50.00
5078525.9413.60.00
5088525.9413.60.00
5098521.1717.61.42
5108518.2625.40.00
5118727.8352.70.00
5128727.8322.70.06
5138721.9715.80.054
5148721.2706.70.011
5158625.6424.00.00
51611211.8314.50.00
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517 rows × 7 columns

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"execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": {}, - "outputs": [], - "source": [ - "from math import sin, cos, sqrt, atan2, radians" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "metadata": {}, - "outputs": [], - "source": [ - "user_coordinates = [55.753960, 37.620393]" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": {}, - "outputs": [], - "source": [ - "data_distance = pd.read_csv('MODIS_C6_Russia_and_Asia_24h (1).csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "metadata": {}, - "outputs": [], - "source": [ - "lat_long = list(zip(data_distance['latitude'], data_distance['longitude']))" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - 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" (50.86600000000001, 59.228),\n", - " (24.895, 53.019),\n", - " (35.075, 36.102),\n", - " (36.954, 35.644),\n", - " (37.061, 34.942),\n", - " ...]" - ] - }, - "execution_count": 273, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lat_long" - ] - }, - { - "cell_type": "code", - "execution_count": 283, - "metadata": {}, - "outputs": [], - "source": [ - "from math import sin, cos, sqrt, atan2, radians\n", - "\n", - "def calculate_distance(lat1, lon1, lat2, lon2):\n", - "# approximate radius of earth in km\n", - " R = 6373.0\n", - " \n", - " lat1 = radians(lat1)\n", - " lon1 = radians(lon1)\n", - " lat2 = radians(lat2)\n", - " lon2 = radians(lon2)\n", - " \n", - " dlon = lon2 - lon1\n", - " dlat = lat2 - lat1\n", - "\n", - " a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n", - " c = 2 * atan2(sqrt(a), sqrt(1 - a))\n", - "\n", - " distance = R * c\n", - " \n", - " return distance" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [], - "source": [ - "from math import sin, cos, sqrt, atan2, radians\n", - "\n", - "def min_distance(user_coordinates_lat, user_coordinates_lon):\n", - " \n", - " def calculate_distance(lat1, lon1, lat2, lon2):\n", - " # approximate radius of earth in km\n", - " R = 6373.0\n", - " \n", - " lat1 = radians(lat1)\n", - " lon1 = radians(lon1)\n", - " lat2 = radians(lat2)\n", - " lon2 = radians(lon2)\n", - "\n", - " dlon = lon2 - lon1\n", - " dlat = lat2 - lat1\n", - "\n", - " a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n", - " c = 2 * atan2(sqrt(a), sqrt(1 - a))\n", - "\n", - " distance = R * c\n", - "\n", - " return distance\n", - " \n", - " data_distance = pd.read_csv('MODIS_C6_Russia_and_Asia_24h (1).csv')\n", - " lat_long = list(zip(data_distance['latitude'], data_distance['longitude']))\n", - " distance = []\n", - " coor = []\n", - " for i, j in lat_long:\n", - " distance.append(calculate_distance(user_coordinates[0], user_coordinates[1], i, j))\n", - " coor.append([j, i])\n", - " \n", - " dist_coor = sorted(list(zip(distance, coor)))\n", - " \n", - " \n", - " \n", - " return dist_coor[0][0], dist_coor[0][1]" - ] - }, - { - "cell_type": "code", - "execution_count": 338, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(176.2744053941077, [37.709, 54.17])" - ] - }, - "execution_count": 338, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "min_distance(user_coordinates[0], user_coordinates[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 285, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1195.4971863361702" - ] - }, - "execution_count": 285, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "calculate_distance(user_coordinates[0], user_coordinates[1], 45.040216, 38.975996)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 291, - "metadata": {}, - "outputs": [], - "source": [ - "import staticmap" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "metadata": {}, - "outputs": [], - "source": [ - "coordinates = [55.753960, 37.620393]" - ] - }, - { - "cell_type": "code", - "execution_count": 355, - "metadata": {}, - "outputs": [], - "source": [ - "from staticmap import StaticMap, Line\n", - "def img_fire(user_coordinates_lat, user_coordinates_lon):\n", - " m = StaticMap(800, 800, 80)\n", - "\n", - " coordinates = [[min_distance(user_coordinates_lat, user_coordinates_lon)[1][0],\n", - " min_distance(user_coordinates_lat, user_coordinates_lon)[1][1]]]\n", - " line_outline = Line(coordinates, 'white', 6)\n", - " line = Line(coordinates, '#D2322D', 10)\n", - "\n", - " m.add_line(line_outline)\n", - " m.add_line(line)\n", - "\n", - " image = m.render()\n", - " image.save('ferry.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 356, - "metadata": {}, - "outputs": [], - "source": [ - "img_fire(user_coordinates[0], user_coordinates[1])" - ] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}