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DateClosing PriceIBITFBTCBITBARKBBTCOEZBCBRRRHODLBTCWGBTCTotal
011-01-202446368.58594111.7227.0237.965.317.450.129.410.61.0-95.1655.3
112-01-202442853.16797386.0195.317.439.828.40.020.20.00.0-484.1203.0
216-01-202443154.94531212.7102.050.2122.331.90.015.37.30.0-594.4-52.7
317-01-202442742.65234371.4358.168.250.357.61.21.24.81.6-460.6453.8
418-01-202441262.05859145.5177.920.141.858.80.09.32.30.0-582.3-126.6
519-01-202441618.40625201.5222.356.762.663.40.010.414.22.9-590.443.6
622-01-202439507.36719260.6158.741.665.05.64.79.76.80.4-640.5-87.4
723-01-202439845.55078160.1157.726.361.80.01.10.02.20.0-515.3-106.1
824-01-202440077.0742266.2125.719.124.919.91.29.14.50.4-429.3-158.3
925-01-202439933.80859170.7101.020.016.10.00.06.50.00.0-394.1-79.8
1026-01-202441816.8710987.1100.130.946.40.01.21.82.40.0-255.114.8
1129-01-202443288.24609198.4208.220.017.23.00.00.00.00.0-191.7255.1
1230-01-202442952.60938299.2119.221.916.86.32.50.00.02.1-220.7247.3
1331-01-202442582.60547116.2232.017.814.81.50.00.62.40.0-187.7197.6
1401-02-202443075.77344163.935.84.215.90.00.00.00.00.7-182.038.5
1502-02-202443185.85938105.878.911.522.60.02.50.02.40.9-144.680.0
1605-02-202442658.66797137.338.00.00.00.00.00.00.00.7-107.968.1
1706-02-202443084.6718845.237.711.38.60.00.00.02.41.1-72.733.6
1807-02-202444318.2226656.2130.121.43.38.65.11.20.00.9-80.8146.0
1908-02-202445301.56641204.1128.360.586.413.40.01.910.31.7-101.6405.0
2009-02-202447147.19922250.7188.429.1136.5-17.41.41.42.70.5-51.8541.5
2112-02-202449958.22266374.7151.933.040.0-20.80.01.18.50.0-95.0493.4
2213-02-202449742.44141493.1163.610.840.00.00.00.00.01.6-72.8636.3
2314-02-202451826.69531224.3118.947.2101.5-37.59.01.02.93.6-131.2339.7
2415-02-202451938.55469330.997.4120.288.91.33.07.42.90.0-174.6477.4
2516-02-202452160.20313191.4116.720.9140.01.00.07.90.02.8-150.4330.3
2620-02-202452284.87500154.371.711.127.40.00.00.05.92.2-137.0135.6
2721-02-202451839.1796996.552.50.010.71.03.00.00.00.0-199.3-35.6
2822-02-202451304.97266125.1158.97.96.70.00.01.22.94.4-55.7251.4
2923-02-202450731.94922167.552.512.034.50.01.50.08.70.0-44.2232.5
3026-02-202454522.40234111.8243.337.2130.64.47.90.06.20.9-22.4519.9
3127-02-202457085.37109520.2126.018.45.42.616.60.09.73.6-125.6576.9
3228-02-202462504.78906612.1245.29.923.80.00.00.0-3.42.2-216.4673.4
3329-02-202461198.38281603.944.821.79.9-1.55.40.07.00.0-598.992.3
3401-03-202462440.63281202.549.342.355.10.05.40.0-1.80.0-492.4-139.6
3504-03-202468330.41406420.1404.690.938.2-25.77.83.7-5.7-3.2-368.0562.7
3605-03-202463801.19922788.3125.63.763.7-14.23.60.03.56.6-332.5648.3
3706-03-202466106.80469281.7205.728.641.33.05.840.70.01.4-276.2332.0
3807-03-202466925.48438244.2473.441.442.10.00.041.81.92.5-374.8472.5
3908-03-202468300.09375336.3130.38.01.7-7.68.041.47.80.0-302.9223.0
4011-03-202472123.90625562.9215.549.813.0-9.70.043.4118.85.8-494.1505.4
4112-03-202471481.28906849.051.624.693.0-19.70.039.682.93.0-79.01045.0
4213-03-202473083.50000586.5281.55.644.60.019.14.116.52.3-276.5683.7
4314-03-202471396.59375345.413.70.03.50.04.09.413.80.0-257.1132.7
4415-03-202469403.77344139.8155.620.50.00.02.01.215.83.3-139.4198.8
4518-03-202467548.59375451.55.917.62.70.00.04.85.70.0-642.5-154.3
4619-03-202461912.7734475.239.62.50.00.00.00.00.00.0-443.5-326.2
4720-03-202467913.6718849.312.918.623.3-10.219.02.99.30.0-386.6-261.5
4821-03-202465491.39063233.42.912.02.04.23.84.71.80.0-358.8-94.0
4922-03-202463778.7617218.918.116.35.44.529.625.50.00.0-169.9-51.6
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" + ], + "text/plain": [ + " Date Closing Price IBIT FBTC BITB ARKB BTCO EZBC BRRR \\\n", + "0 11-01-2024 46368.58594 111.7 227.0 237.9 65.3 17.4 50.1 29.4 \n", + "1 12-01-2024 42853.16797 386.0 195.3 17.4 39.8 28.4 0.0 20.2 \n", + "2 16-01-2024 43154.94531 212.7 102.0 50.2 122.3 31.9 0.0 15.3 \n", + "3 17-01-2024 42742.65234 371.4 358.1 68.2 50.3 57.6 1.2 1.2 \n", + "4 18-01-2024 41262.05859 145.5 177.9 20.1 41.8 58.8 0.0 9.3 \n", + "5 19-01-2024 41618.40625 201.5 222.3 56.7 62.6 63.4 0.0 10.4 \n", + "6 22-01-2024 39507.36719 260.6 158.7 41.6 65.0 5.6 4.7 9.7 \n", + "7 23-01-2024 39845.55078 160.1 157.7 26.3 61.8 0.0 1.1 0.0 \n", + "8 24-01-2024 40077.07422 66.2 125.7 19.1 24.9 19.9 1.2 9.1 \n", + "9 25-01-2024 39933.80859 170.7 101.0 20.0 16.1 0.0 0.0 6.5 \n", + "10 26-01-2024 41816.87109 87.1 100.1 30.9 46.4 0.0 1.2 1.8 \n", + "11 29-01-2024 43288.24609 198.4 208.2 20.0 17.2 3.0 0.0 0.0 \n", + "12 30-01-2024 42952.60938 299.2 119.2 21.9 16.8 6.3 2.5 0.0 \n", + "13 31-01-2024 42582.60547 116.2 232.0 17.8 14.8 1.5 0.0 0.6 \n", + "14 01-02-2024 43075.77344 163.9 35.8 4.2 15.9 0.0 0.0 0.0 \n", + "15 02-02-2024 43185.85938 105.8 78.9 11.5 22.6 0.0 2.5 0.0 \n", + "16 05-02-2024 42658.66797 137.3 38.0 0.0 0.0 0.0 0.0 0.0 \n", + "17 06-02-2024 43084.67188 45.2 37.7 11.3 8.6 0.0 0.0 0.0 \n", + "18 07-02-2024 44318.22266 56.2 130.1 21.4 3.3 8.6 5.1 1.2 \n", + "19 08-02-2024 45301.56641 204.1 128.3 60.5 86.4 13.4 0.0 1.9 \n", + "20 09-02-2024 47147.19922 250.7 188.4 29.1 136.5 -17.4 1.4 1.4 \n", + "21 12-02-2024 49958.22266 374.7 151.9 33.0 40.0 -20.8 0.0 1.1 \n", + "22 13-02-2024 49742.44141 493.1 163.6 10.8 40.0 0.0 0.0 0.0 \n", + "23 14-02-2024 51826.69531 224.3 118.9 47.2 101.5 -37.5 9.0 1.0 \n", + "24 15-02-2024 51938.55469 330.9 97.4 120.2 88.9 1.3 3.0 7.4 \n", + "25 16-02-2024 52160.20313 191.4 116.7 20.9 140.0 1.0 0.0 7.9 \n", + "26 20-02-2024 52284.87500 154.3 71.7 11.1 27.4 0.0 0.0 0.0 \n", + "27 21-02-2024 51839.17969 96.5 52.5 0.0 10.7 1.0 3.0 0.0 \n", + "28 22-02-2024 51304.97266 125.1 158.9 7.9 6.7 0.0 0.0 1.2 \n", + "29 23-02-2024 50731.94922 167.5 52.5 12.0 34.5 0.0 1.5 0.0 \n", + "30 26-02-2024 54522.40234 111.8 243.3 37.2 130.6 4.4 7.9 0.0 \n", + "31 27-02-2024 57085.37109 520.2 126.0 18.4 5.4 2.6 16.6 0.0 \n", + "32 28-02-2024 62504.78906 612.1 245.2 9.9 23.8 0.0 0.0 0.0 \n", + "33 29-02-2024 61198.38281 603.9 44.8 21.7 9.9 -1.5 5.4 0.0 \n", + "34 01-03-2024 62440.63281 202.5 49.3 42.3 55.1 0.0 5.4 0.0 \n", + "35 04-03-2024 68330.41406 420.1 404.6 90.9 38.2 -25.7 7.8 3.7 \n", + "36 05-03-2024 63801.19922 788.3 125.6 3.7 63.7 -14.2 3.6 0.0 \n", + "37 06-03-2024 66106.80469 281.7 205.7 28.6 41.3 3.0 5.8 40.7 \n", + "38 07-03-2024 66925.48438 244.2 473.4 41.4 42.1 0.0 0.0 41.8 \n", + "39 08-03-2024 68300.09375 336.3 130.3 8.0 1.7 -7.6 8.0 41.4 \n", + "40 11-03-2024 72123.90625 562.9 215.5 49.8 13.0 -9.7 0.0 43.4 \n", + "41 12-03-2024 71481.28906 849.0 51.6 24.6 93.0 -19.7 0.0 39.6 \n", + "42 13-03-2024 73083.50000 586.5 281.5 5.6 44.6 0.0 19.1 4.1 \n", + "43 14-03-2024 71396.59375 345.4 13.7 0.0 3.5 0.0 4.0 9.4 \n", + "44 15-03-2024 69403.77344 139.8 155.6 20.5 0.0 0.0 2.0 1.2 \n", + "45 18-03-2024 67548.59375 451.5 5.9 17.6 2.7 0.0 0.0 4.8 \n", + "46 19-03-2024 61912.77344 75.2 39.6 2.5 0.0 0.0 0.0 0.0 \n", + "47 20-03-2024 67913.67188 49.3 12.9 18.6 23.3 -10.2 19.0 2.9 \n", + "48 21-03-2024 65491.39063 233.4 2.9 12.0 2.0 4.2 3.8 4.7 \n", + "49 22-03-2024 63778.76172 18.9 18.1 16.3 5.4 4.5 29.6 25.5 \n", + "\n", + " HODL BTCW GBTC Total \n", + "0 10.6 1.0 -95.1 655.3 \n", + "1 0.0 0.0 -484.1 203.0 \n", + "2 7.3 0.0 -594.4 -52.7 \n", + "3 4.8 1.6 -460.6 453.8 \n", + "4 2.3 0.0 -582.3 -126.6 \n", + "5 14.2 2.9 -590.4 43.6 \n", + "6 6.8 0.4 -640.5 -87.4 \n", + "7 2.2 0.0 -515.3 -106.1 \n", + "8 4.5 0.4 -429.3 -158.3 \n", + "9 0.0 0.0 -394.1 -79.8 \n", + "10 2.4 0.0 -255.1 14.8 \n", + "11 0.0 0.0 -191.7 255.1 \n", + "12 0.0 2.1 -220.7 247.3 \n", + "13 2.4 0.0 -187.7 197.6 \n", + "14 0.0 0.7 -182.0 38.5 \n", + "15 2.4 0.9 -144.6 80.0 \n", + "16 0.0 0.7 -107.9 68.1 \n", + "17 2.4 1.1 -72.7 33.6 \n", + "18 0.0 0.9 -80.8 146.0 \n", + "19 10.3 1.7 -101.6 405.0 \n", + "20 2.7 0.5 -51.8 541.5 \n", + "21 8.5 0.0 -95.0 493.4 \n", + "22 0.0 1.6 -72.8 636.3 \n", + "23 2.9 3.6 -131.2 339.7 \n", + "24 2.9 0.0 -174.6 477.4 \n", + "25 0.0 2.8 -150.4 330.3 \n", + "26 5.9 2.2 -137.0 135.6 \n", + "27 0.0 0.0 -199.3 -35.6 \n", + "28 2.9 4.4 -55.7 251.4 \n", + "29 8.7 0.0 -44.2 232.5 \n", + "30 6.2 0.9 -22.4 519.9 \n", + "31 9.7 3.6 -125.6 576.9 \n", + "32 -3.4 2.2 -216.4 673.4 \n", + "33 7.0 0.0 -598.9 92.3 \n", + "34 -1.8 0.0 -492.4 -139.6 \n", + "35 -5.7 -3.2 -368.0 562.7 \n", + "36 3.5 6.6 -332.5 648.3 \n", + "37 0.0 1.4 -276.2 332.0 \n", + "38 1.9 2.5 -374.8 472.5 \n", + "39 7.8 0.0 -302.9 223.0 \n", + "40 118.8 5.8 -494.1 505.4 \n", + "41 82.9 3.0 -79.0 1045.0 \n", + "42 16.5 2.3 -276.5 683.7 \n", + "43 13.8 0.0 -257.1 132.7 \n", + "44 15.8 3.3 -139.4 198.8 \n", + "45 5.7 0.0 -642.5 -154.3 \n", + "46 0.0 0.0 -443.5 -326.2 \n", + "47 9.3 0.0 -386.6 -261.5 \n", + "48 1.8 0.0 -358.8 -94.0 \n", + "49 0.0 0.0 -169.9 -51.6 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('BETF Final.csv')\n", + "df.head(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3871e794", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateClosing PriceIBITFBTCBITBARKBBTCOEZBCBRRRHODLBTCWGBTCTotal
4720-03-202467913.6718849.312.918.623.3-10.219.02.99.30.0-386.6-261.5
4821-03-202465491.39063233.42.912.02.04.23.84.71.80.0-358.8-94.0
4922-03-202463778.7617218.918.116.35.44.529.625.50.00.0-169.9-51.6
5025-03-202469958.8125035.5261.814.00.018.520.511.24.00.0-350.115.4
5126-03-202469987.83594162.2279.116.773.626.726.329.915.80.0-212.3418.0
5227-03-202469455.34375323.81.50.0200.74.84.05.11.91.5-299.8243.5
5328-03-202470744.9531395.168.167.027.63.90.06.020.00.0-104.9182.8
5401-04-202469702.14844165.944.01.1-0.34.20.00.02.00.0-302.6-85.7
5502-04-202465446.97266150.544.84.3-87.50.00.03.75.60.0-81.939.5
5603-04-202465980.8125042.0116.723.00.00.03.80.00.03.1-75.1113.5
5704-04-202468508.84375144.0106.611.212.00.00.03.415.50.0-79.3213.4
5805-04-202467837.64063308.883.07.40.02.70.00.00.00.0-198.9203.0
5908-04-202471631.3593821.36.340.39.30.00.00.00.02.3-303.3-223.8
6009-04-202469139.01563128.73.03.80.00.00.00.00.00.0-154.9-19.4
6110-04-202470587.8828133.376.324.37.30.00.00.00.00.0-17.5123.7
6211-04-202470060.60938192.14.611.10.00.00.08.40.00.0-124.991.3
6312-04-202467195.86719111.10.00.00.00.00.00.00.00.0-166.2-55.1
6415-04-202463426.2109473.40.00.00.00.00.00.00.00.0-110.1-36.7
6516-04-202463811.8632825.81.40.0-12.90.01.81.73.60.0-79.4-58.0
6617-04-202461276.6914118.10.0-7.3-42.70.00.00.00.00.0-133.1-165.0
6718-04-202463512.7539118.837.412.89.50.00.00.07.20.0-90.0-4.3
6819-04-202463843.5703129.354.84.912.53.91.90.0-1.80.0-45.859.7
6922-04-202466837.6796919.734.82.222.62.77.70.07.50.0-35.062.2
7023-04-202466407.2734437.94.423.233.3-0.31.90.0-1.90.0-66.931.6
7124-04-202464276.898440.05.60.04.20.00.00.00.00.0-130.4-120.6
7225-04-202464481.707030.0-22.6-6.0-31.30.01.9-20.20.00.0-139.4-217.6
7326-04-202463755.320310.0-2.8-3.85.40.00.00.00.00.0-82.4-83.6
7429-04-202463841.121090.0-6.96.8-31.30.01.82.70.00.0-24.7-51.6
7530-04-202460636.855470.0-35.3-34.33.6-2.40.00.00.00.0-93.2-161.6
7601-05-202458254.01172-36.9-191.1-29.0-98.1-5.4-13.4-9.7-6.5-6.2-167.4-563.7
7702-05-202459123.433590.00.00.013.31.53.42.30.00.0-54.9-34.4
7803-05-202462889.8359412.7102.633.528.133.260.935.68.70.063.0378.3
7906-05-202463161.9492221.599.22.175.611.11.80.01.80.03.9217.0
8007-05-202462334.816410.04.10.02.86.00.00.00.00.0-28.6-15.7
8108-05-202461187.941410.00.011.50.00.00.00.00.00.00.011.5
8209-05-202463049.9609414.22.76.84.42.21.80.00.00.0-43.4-11.3
8310-05-202460792.7773412.45.30.00.00.00.00.00.00.6-103.0-84.7
8413-05-202462901.449220.038.620.30.00.00.00.07.10.00.066.0
8514-05-202461552.789060.08.10.0133.15.51.81.21.70.0-50.9100.5
8615-05-202466267.492190.0131.386.338.64.61.93.77.52.127.0303.0
8716-05-202465231.5820393.767.11.462.06.23.818.50.00.04.6257.3
8817-05-202467051.8750038.199.420.810.05.70.06.49.50.031.6221.5
8920-05-202471448.1953166.464.024.068.30.00.00.00.05.29.3237.2
9021-05-202470136.53125290.025.8-4.20.00.00.00.0-5.90.00.0305.7
9122-05-202469122.3359492.074.60.03.50.00.00.00.00.0-16.1154.0
9223-05-202467929.5625089.019.10.02.02.00.00.09.50.0-13.7107.9
9324-05-202468526.10156182.143.76.44.10.00.00.015.60.00.0251.9
9428-05-202468296.21875102.534.33.34.13.40.01.20.01.4-105.245.0
9529-05-202467578.0937524.617.711.04.01.00.00.00.01.1-31.128.3
9630-05-202468364.992191.6119.125.9-99.92.10.00.00.00.00.048.8
\n", + "
" + ], + "text/plain": [ + " Date Closing Price IBIT FBTC BITB ARKB BTCO EZBC BRRR \\\n", + "47 20-03-2024 67913.67188 49.3 12.9 18.6 23.3 -10.2 19.0 2.9 \n", + "48 21-03-2024 65491.39063 233.4 2.9 12.0 2.0 4.2 3.8 4.7 \n", + "49 22-03-2024 63778.76172 18.9 18.1 16.3 5.4 4.5 29.6 25.5 \n", + "50 25-03-2024 69958.81250 35.5 261.8 14.0 0.0 18.5 20.5 11.2 \n", + "51 26-03-2024 69987.83594 162.2 279.1 16.7 73.6 26.7 26.3 29.9 \n", + "52 27-03-2024 69455.34375 323.8 1.5 0.0 200.7 4.8 4.0 5.1 \n", + "53 28-03-2024 70744.95313 95.1 68.1 67.0 27.6 3.9 0.0 6.0 \n", + "54 01-04-2024 69702.14844 165.9 44.0 1.1 -0.3 4.2 0.0 0.0 \n", + "55 02-04-2024 65446.97266 150.5 44.8 4.3 -87.5 0.0 0.0 3.7 \n", + "56 03-04-2024 65980.81250 42.0 116.7 23.0 0.0 0.0 3.8 0.0 \n", + "57 04-04-2024 68508.84375 144.0 106.6 11.2 12.0 0.0 0.0 3.4 \n", + "58 05-04-2024 67837.64063 308.8 83.0 7.4 0.0 2.7 0.0 0.0 \n", + "59 08-04-2024 71631.35938 21.3 6.3 40.3 9.3 0.0 0.0 0.0 \n", + "60 09-04-2024 69139.01563 128.7 3.0 3.8 0.0 0.0 0.0 0.0 \n", + "61 10-04-2024 70587.88281 33.3 76.3 24.3 7.3 0.0 0.0 0.0 \n", + "62 11-04-2024 70060.60938 192.1 4.6 11.1 0.0 0.0 0.0 8.4 \n", + "63 12-04-2024 67195.86719 111.1 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "64 15-04-2024 63426.21094 73.4 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "65 16-04-2024 63811.86328 25.8 1.4 0.0 -12.9 0.0 1.8 1.7 \n", + "66 17-04-2024 61276.69141 18.1 0.0 -7.3 -42.7 0.0 0.0 0.0 \n", + "67 18-04-2024 63512.75391 18.8 37.4 12.8 9.5 0.0 0.0 0.0 \n", + "68 19-04-2024 63843.57031 29.3 54.8 4.9 12.5 3.9 1.9 0.0 \n", + "69 22-04-2024 66837.67969 19.7 34.8 2.2 22.6 2.7 7.7 0.0 \n", + "70 23-04-2024 66407.27344 37.9 4.4 23.2 33.3 -0.3 1.9 0.0 \n", + "71 24-04-2024 64276.89844 0.0 5.6 0.0 4.2 0.0 0.0 0.0 \n", + "72 25-04-2024 64481.70703 0.0 -22.6 -6.0 -31.3 0.0 1.9 -20.2 \n", + "73 26-04-2024 63755.32031 0.0 -2.8 -3.8 5.4 0.0 0.0 0.0 \n", + "74 29-04-2024 63841.12109 0.0 -6.9 6.8 -31.3 0.0 1.8 2.7 \n", + "75 30-04-2024 60636.85547 0.0 -35.3 -34.3 3.6 -2.4 0.0 0.0 \n", + "76 01-05-2024 58254.01172 -36.9 -191.1 -29.0 -98.1 -5.4 -13.4 -9.7 \n", + "77 02-05-2024 59123.43359 0.0 0.0 0.0 13.3 1.5 3.4 2.3 \n", + "78 03-05-2024 62889.83594 12.7 102.6 33.5 28.1 33.2 60.9 35.6 \n", + "79 06-05-2024 63161.94922 21.5 99.2 2.1 75.6 11.1 1.8 0.0 \n", + "80 07-05-2024 62334.81641 0.0 4.1 0.0 2.8 6.0 0.0 0.0 \n", + "81 08-05-2024 61187.94141 0.0 0.0 11.5 0.0 0.0 0.0 0.0 \n", + "82 09-05-2024 63049.96094 14.2 2.7 6.8 4.4 2.2 1.8 0.0 \n", + "83 10-05-2024 60792.77734 12.4 5.3 0.0 0.0 0.0 0.0 0.0 \n", + "84 13-05-2024 62901.44922 0.0 38.6 20.3 0.0 0.0 0.0 0.0 \n", + "85 14-05-2024 61552.78906 0.0 8.1 0.0 133.1 5.5 1.8 1.2 \n", + "86 15-05-2024 66267.49219 0.0 131.3 86.3 38.6 4.6 1.9 3.7 \n", + "87 16-05-2024 65231.58203 93.7 67.1 1.4 62.0 6.2 3.8 18.5 \n", + "88 17-05-2024 67051.87500 38.1 99.4 20.8 10.0 5.7 0.0 6.4 \n", + "89 20-05-2024 71448.19531 66.4 64.0 24.0 68.3 0.0 0.0 0.0 \n", + "90 21-05-2024 70136.53125 290.0 25.8 -4.2 0.0 0.0 0.0 0.0 \n", + "91 22-05-2024 69122.33594 92.0 74.6 0.0 3.5 0.0 0.0 0.0 \n", + "92 23-05-2024 67929.56250 89.0 19.1 0.0 2.0 2.0 0.0 0.0 \n", + "93 24-05-2024 68526.10156 182.1 43.7 6.4 4.1 0.0 0.0 0.0 \n", + "94 28-05-2024 68296.21875 102.5 34.3 3.3 4.1 3.4 0.0 1.2 \n", + "95 29-05-2024 67578.09375 24.6 17.7 11.0 4.0 1.0 0.0 0.0 \n", + "96 30-05-2024 68364.99219 1.6 119.1 25.9 -99.9 2.1 0.0 0.0 \n", + "\n", + " HODL BTCW GBTC Total \n", + "47 9.3 0.0 -386.6 -261.5 \n", + "48 1.8 0.0 -358.8 -94.0 \n", + "49 0.0 0.0 -169.9 -51.6 \n", + "50 4.0 0.0 -350.1 15.4 \n", + "51 15.8 0.0 -212.3 418.0 \n", + "52 1.9 1.5 -299.8 243.5 \n", + "53 20.0 0.0 -104.9 182.8 \n", + "54 2.0 0.0 -302.6 -85.7 \n", + "55 5.6 0.0 -81.9 39.5 \n", + "56 0.0 3.1 -75.1 113.5 \n", + "57 15.5 0.0 -79.3 213.4 \n", + "58 0.0 0.0 -198.9 203.0 \n", + "59 0.0 2.3 -303.3 -223.8 \n", + "60 0.0 0.0 -154.9 -19.4 \n", + "61 0.0 0.0 -17.5 123.7 \n", + "62 0.0 0.0 -124.9 91.3 \n", + "63 0.0 0.0 -166.2 -55.1 \n", + "64 0.0 0.0 -110.1 -36.7 \n", + "65 3.6 0.0 -79.4 -58.0 \n", + "66 0.0 0.0 -133.1 -165.0 \n", + "67 7.2 0.0 -90.0 -4.3 \n", + "68 -1.8 0.0 -45.8 59.7 \n", + "69 7.5 0.0 -35.0 62.2 \n", + "70 -1.9 0.0 -66.9 31.6 \n", + "71 0.0 0.0 -130.4 -120.6 \n", + "72 0.0 0.0 -139.4 -217.6 \n", + "73 0.0 0.0 -82.4 -83.6 \n", + "74 0.0 0.0 -24.7 -51.6 \n", + "75 0.0 0.0 -93.2 -161.6 \n", + "76 -6.5 -6.2 -167.4 -563.7 \n", + "77 0.0 0.0 -54.9 -34.4 \n", + "78 8.7 0.0 63.0 378.3 \n", + "79 1.8 0.0 3.9 217.0 \n", + "80 0.0 0.0 -28.6 -15.7 \n", + "81 0.0 0.0 0.0 11.5 \n", + "82 0.0 0.0 -43.4 -11.3 \n", + "83 0.0 0.6 -103.0 -84.7 \n", + "84 7.1 0.0 0.0 66.0 \n", + "85 1.7 0.0 -50.9 100.5 \n", + "86 7.5 2.1 27.0 303.0 \n", + "87 0.0 0.0 4.6 257.3 \n", + "88 9.5 0.0 31.6 221.5 \n", + "89 0.0 5.2 9.3 237.2 \n", + "90 -5.9 0.0 0.0 305.7 \n", + "91 0.0 0.0 -16.1 154.0 \n", + "92 9.5 0.0 -13.7 107.9 \n", + "93 15.6 0.0 0.0 251.9 \n", + "94 0.0 1.4 -105.2 45.0 \n", + "95 0.0 1.1 -31.1 28.3 \n", + "96 0.0 0.0 0.0 48.8 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21afe173-1314-4c1e-92ec-675b255c6e40", + "metadata": {}, + "outputs": [], + "source": [ + "y = df['Closing Price']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bfbf16ce-edd0-4f00-b37d-c9478dbe2a85", + "metadata": {}, + "outputs": [], + "source": [ + "X = df[['IBIT', 'FBTC', 'BITB', 'ARKB', 'BTCO',\n", + " 'EZBC', 'BRRR', 'HODL', 'BTCW',\n", + " 'GBTC', ]]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "49eb3045-46b9-4712-a441-df17f9dd4faa", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=410)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "106492f1-1cae-4212-b255-7bda2c3766de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lm = LinearRegression()\n", + "lm.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5209097e-4b05-415c-b802-4cebc0de3b69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([58584.97710189, 57237.27061252, 60140.16733252, 46216.56317528,\n", + " 58507.31490116, 58533.07641301, 66534.01292466, 63148.14582792,\n", + " 62705.60488961, 61359.61664871, 62344.18849227, 60997.52871926,\n", + " 76116.85194294, 64724.60268899, 52862.43899159, 61076.85460689,\n", + " 61534.08337201, 60118.45313438, 60485.88736219, 63904.48276049,\n", + " 59855.77398138, 63033.36241698, 54393.84769647, 61397.36211783,\n", + " 62143.19797152, 41154.67586064, 57029.01185936, 61262.7028767 ,\n", + " 63218.925657 , 63481.27228506])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = lm.predict(X_test)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f9ef6a4f-4ce7-4394-959a-c53de1076992", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(y_test,predictions)\n", + "plt.xlabel('Y Test')\n", + "plt.ylabel('Predicted Y')\n", + "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d254c308-f6eb-4898-aa7c-5cb90bbb3f08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 7131.197876838957\n", + "MSE: 75121198.18853344\n", + "RMSE: 8667.248593904149\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "\n", + "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n", + "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n", + "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1fd8c400-7e3e-4eec-bc69-750a96e8266d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30.0000993818507" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2_score(y_test, predictions)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8baea92f-9789-422e-9437-7d7b0f93c596", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted and Actual Values:\n", + " Actual Predicted\n", + "18 44318.22266 58584.977102\n", + "10 41816.87109 57237.270613\n", + "70 66407.27344 60140.167333\n", + "2 43154.94531 46216.563175\n", + "22 49742.44141 58507.314901\n", + "33 61198.38281 58533.076413\n", + "23 51826.69531 66534.012925\n", + "66 61276.69141 63148.145828\n", + "72 64481.70703 62705.604890\n", + "90 70136.53125 61359.616649\n", + "77 59123.43359 62344.188492\n", + "80 62334.81641 60997.528719\n", + "40 72123.90625 76116.851943\n", + "75 60636.85547 64724.602689\n", + "38 66925.48438 52862.438992\n", + "26 52284.87500 61076.854607\n", + "71 64276.89844 61534.083372\n", + "87 65231.58203 60118.453134\n", + "15 43185.85938 60485.887362\n", + "39 68300.09375 63904.482760\n", + "61 70587.88281 59855.773981\n", + "92 67929.56250 63033.362417\n", + "7 39845.55078 54393.847696\n", + "63 67195.86719 61397.362118\n", + "73 63755.32031 62143.197972\n", + "5 41618.40625 41154.675861\n", + "50 69958.81250 57029.011859\n", + "62 70060.60938 61262.702877\n", + "69 66837.67969 63218.925657\n", + "43 71396.59375 63481.272285\n" + ] + } + ], + "source": [ + "results_df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})\n", + "print(\"Predicted and Actual Values:\")\n", + "print(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "388f56e2-5d56-44d9-9eea-f0e037afeb37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,8))\n", + "sns.histplot(x= 'Closing Price',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4c963ce9-cea3-46d8-8e97-505e69e48cbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['expenses'] = np.log(df['Closing Price'])\n", + "plt.figure(figsize = (12,8))\n", + "sns.histplot(x= 'Closing Price',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a40264c3-7161-44d4-96ca-e6f5cbf9f459", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[61446.02685479]\n" + ] + } + ], + "source": [ + "new_data = pd.DataFrame({\n", + " 'IBIT': [120],\n", + " 'FBTC': [175],\n", + " 'BITB': [5],\n", + " 'ARKB': [50],\n", + " 'BTCO': [1],\n", + " 'EZBC': [2],\n", + " 'BRRR': [3],\n", + " 'HODL': [4],\n", + " 'BTCW': [5],\n", + " 'GBTC': [-20]\n", + "})\n", + "\n", + "# Predicting the expenses for the new data\n", + "predicted_btc_closing_price = lm.predict(new_data)\n", + "\n", + "print(predicted_btc_closing_price )\n", + "\n", + "#print(f\"Predicted btc closing price: {predicted_btc_closing_price:.2f}\")\n", + "\n", + "# Convert the predicted log expenses back to the original scale\n", + "#predicted_value = np.exp(predicted_btc_closing_price[0])\n", + " \n", + "#print(f\"Predicted value: {predicted_value:.2f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ETF-3-A-XGB-V1.ipynb b/ETF-3-A-XGB-V1.ipynb new file mode 100644 index 0000000..3b2ace9 --- /dev/null +++ b/ETF-3-A-XGB-V1.ipynb @@ -0,0 +1,2332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "id": "f4beac85-78c6-464e-8b20-b17611cc6541", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import xgboost as xgb" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "40ff97f0-159e-4391-9416-dc572dd85ad9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateClosing PriceIBITFBTCBITBARKBBTCOEZBCBRRRHODLBTCWGBTCTotal
011-01-202446368.58594111.7227.0237.965.317.450.129.410.61.0-95.1655.3
112-01-202442853.16797386.0195.317.439.828.40.020.20.00.0-484.1203.0
216-01-202443154.94531212.7102.050.2122.331.90.015.37.30.0-594.4-52.7
317-01-202442742.65234371.4358.168.250.357.61.21.24.81.6-460.6453.8
418-01-202441262.05859145.5177.920.141.858.80.09.32.30.0-582.3-126.6
519-01-202441618.40625201.5222.356.762.663.40.010.414.22.9-590.443.6
622-01-202439507.36719260.6158.741.665.05.64.79.76.80.4-640.5-87.4
723-01-202439845.55078160.1157.726.361.80.01.10.02.20.0-515.3-106.1
824-01-202440077.0742266.2125.719.124.919.91.29.14.50.4-429.3-158.3
925-01-202439933.80859170.7101.020.016.10.00.06.50.00.0-394.1-79.8
1026-01-202441816.8710987.1100.130.946.40.01.21.82.40.0-255.114.8
1129-01-202443288.24609198.4208.220.017.23.00.00.00.00.0-191.7255.1
1230-01-202442952.60938299.2119.221.916.86.32.50.00.02.1-220.7247.3
1331-01-202442582.60547116.2232.017.814.81.50.00.62.40.0-187.7197.6
1401-02-202443075.77344163.935.84.215.90.00.00.00.00.7-182.038.5
1502-02-202443185.85938105.878.911.522.60.02.50.02.40.9-144.680.0
1605-02-202442658.66797137.338.00.00.00.00.00.00.00.7-107.968.1
1706-02-202443084.6718845.237.711.38.60.00.00.02.41.1-72.733.6
1807-02-202444318.2226656.2130.121.43.38.65.11.20.00.9-80.8146.0
1908-02-202445301.56641204.1128.360.586.413.40.01.910.31.7-101.6405.0
2009-02-202447147.19922250.7188.429.1136.5-17.41.41.42.70.5-51.8541.5
2112-02-202449958.22266374.7151.933.040.0-20.80.01.18.50.0-95.0493.4
2213-02-202449742.44141493.1163.610.840.00.00.00.00.01.6-72.8636.3
2314-02-202451826.69531224.3118.947.2101.5-37.59.01.02.93.6-131.2339.7
2415-02-202451938.55469330.997.4120.288.91.33.07.42.90.0-174.6477.4
2516-02-202452160.20313191.4116.720.9140.01.00.07.90.02.8-150.4330.3
2620-02-202452284.87500154.371.711.127.40.00.00.05.92.2-137.0135.6
2721-02-202451839.1796996.552.50.010.71.03.00.00.00.0-199.3-35.6
2822-02-202451304.97266125.1158.97.96.70.00.01.22.94.4-55.7251.4
2923-02-202450731.94922167.552.512.034.50.01.50.08.70.0-44.2232.5
3026-02-202454522.40234111.8243.337.2130.64.47.90.06.20.9-22.4519.9
3127-02-202457085.37109520.2126.018.45.42.616.60.09.73.6-125.6576.9
3228-02-202462504.78906612.1245.29.923.80.00.00.0-3.42.2-216.4673.4
3329-02-202461198.38281603.944.821.79.9-1.55.40.07.00.0-598.992.3
3401-03-202462440.63281202.549.342.355.10.05.40.0-1.80.0-492.4-139.6
3504-03-202468330.41406420.1404.690.938.2-25.77.83.7-5.7-3.2-368.0562.7
3605-03-202463801.19922788.3125.63.763.7-14.23.60.03.56.6-332.5648.3
3706-03-202466106.80469281.7205.728.641.33.05.840.70.01.4-276.2332.0
3807-03-202466925.48438244.2473.441.442.10.00.041.81.92.5-374.8472.5
3908-03-202468300.09375336.3130.38.01.7-7.68.041.47.80.0-302.9223.0
4011-03-202472123.90625562.9215.549.813.0-9.70.043.4118.85.8-494.1505.4
4112-03-202471481.28906849.051.624.693.0-19.70.039.682.93.0-79.01045.0
4213-03-202473083.50000586.5281.55.644.60.019.14.116.52.3-276.5683.7
4314-03-202471396.59375345.413.70.03.50.04.09.413.80.0-257.1132.7
4415-03-202469403.77344139.8155.620.50.00.02.01.215.83.3-139.4198.8
4518-03-202467548.59375451.55.917.62.70.00.04.85.70.0-642.5-154.3
4619-03-202461912.7734475.239.62.50.00.00.00.00.00.0-443.5-326.2
4720-03-202467913.6718849.312.918.623.3-10.219.02.99.30.0-386.6-261.5
4821-03-202465491.39063233.42.912.02.04.23.84.71.80.0-358.8-94.0
4922-03-202463778.7617218.918.116.35.44.529.625.50.00.0-169.9-51.6
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" + ], + "text/plain": [ + " Date Closing Price IBIT FBTC BITB ARKB BTCO EZBC BRRR \\\n", + "0 11-01-2024 46368.58594 111.7 227.0 237.9 65.3 17.4 50.1 29.4 \n", + "1 12-01-2024 42853.16797 386.0 195.3 17.4 39.8 28.4 0.0 20.2 \n", + "2 16-01-2024 43154.94531 212.7 102.0 50.2 122.3 31.9 0.0 15.3 \n", + "3 17-01-2024 42742.65234 371.4 358.1 68.2 50.3 57.6 1.2 1.2 \n", + "4 18-01-2024 41262.05859 145.5 177.9 20.1 41.8 58.8 0.0 9.3 \n", + "5 19-01-2024 41618.40625 201.5 222.3 56.7 62.6 63.4 0.0 10.4 \n", + "6 22-01-2024 39507.36719 260.6 158.7 41.6 65.0 5.6 4.7 9.7 \n", + "7 23-01-2024 39845.55078 160.1 157.7 26.3 61.8 0.0 1.1 0.0 \n", + "8 24-01-2024 40077.07422 66.2 125.7 19.1 24.9 19.9 1.2 9.1 \n", + "9 25-01-2024 39933.80859 170.7 101.0 20.0 16.1 0.0 0.0 6.5 \n", + "10 26-01-2024 41816.87109 87.1 100.1 30.9 46.4 0.0 1.2 1.8 \n", + "11 29-01-2024 43288.24609 198.4 208.2 20.0 17.2 3.0 0.0 0.0 \n", + "12 30-01-2024 42952.60938 299.2 119.2 21.9 16.8 6.3 2.5 0.0 \n", + "13 31-01-2024 42582.60547 116.2 232.0 17.8 14.8 1.5 0.0 0.6 \n", + "14 01-02-2024 43075.77344 163.9 35.8 4.2 15.9 0.0 0.0 0.0 \n", + "15 02-02-2024 43185.85938 105.8 78.9 11.5 22.6 0.0 2.5 0.0 \n", + "16 05-02-2024 42658.66797 137.3 38.0 0.0 0.0 0.0 0.0 0.0 \n", + "17 06-02-2024 43084.67188 45.2 37.7 11.3 8.6 0.0 0.0 0.0 \n", + "18 07-02-2024 44318.22266 56.2 130.1 21.4 3.3 8.6 5.1 1.2 \n", + "19 08-02-2024 45301.56641 204.1 128.3 60.5 86.4 13.4 0.0 1.9 \n", + "20 09-02-2024 47147.19922 250.7 188.4 29.1 136.5 -17.4 1.4 1.4 \n", + "21 12-02-2024 49958.22266 374.7 151.9 33.0 40.0 -20.8 0.0 1.1 \n", + "22 13-02-2024 49742.44141 493.1 163.6 10.8 40.0 0.0 0.0 0.0 \n", + "23 14-02-2024 51826.69531 224.3 118.9 47.2 101.5 -37.5 9.0 1.0 \n", + "24 15-02-2024 51938.55469 330.9 97.4 120.2 88.9 1.3 3.0 7.4 \n", + "25 16-02-2024 52160.20313 191.4 116.7 20.9 140.0 1.0 0.0 7.9 \n", + "26 20-02-2024 52284.87500 154.3 71.7 11.1 27.4 0.0 0.0 0.0 \n", + "27 21-02-2024 51839.17969 96.5 52.5 0.0 10.7 1.0 3.0 0.0 \n", + "28 22-02-2024 51304.97266 125.1 158.9 7.9 6.7 0.0 0.0 1.2 \n", + "29 23-02-2024 50731.94922 167.5 52.5 12.0 34.5 0.0 1.5 0.0 \n", + "30 26-02-2024 54522.40234 111.8 243.3 37.2 130.6 4.4 7.9 0.0 \n", + "31 27-02-2024 57085.37109 520.2 126.0 18.4 5.4 2.6 16.6 0.0 \n", + "32 28-02-2024 62504.78906 612.1 245.2 9.9 23.8 0.0 0.0 0.0 \n", + "33 29-02-2024 61198.38281 603.9 44.8 21.7 9.9 -1.5 5.4 0.0 \n", + "34 01-03-2024 62440.63281 202.5 49.3 42.3 55.1 0.0 5.4 0.0 \n", + "35 04-03-2024 68330.41406 420.1 404.6 90.9 38.2 -25.7 7.8 3.7 \n", + "36 05-03-2024 63801.19922 788.3 125.6 3.7 63.7 -14.2 3.6 0.0 \n", + "37 06-03-2024 66106.80469 281.7 205.7 28.6 41.3 3.0 5.8 40.7 \n", + "38 07-03-2024 66925.48438 244.2 473.4 41.4 42.1 0.0 0.0 41.8 \n", + "39 08-03-2024 68300.09375 336.3 130.3 8.0 1.7 -7.6 8.0 41.4 \n", + "40 11-03-2024 72123.90625 562.9 215.5 49.8 13.0 -9.7 0.0 43.4 \n", + "41 12-03-2024 71481.28906 849.0 51.6 24.6 93.0 -19.7 0.0 39.6 \n", + "42 13-03-2024 73083.50000 586.5 281.5 5.6 44.6 0.0 19.1 4.1 \n", + "43 14-03-2024 71396.59375 345.4 13.7 0.0 3.5 0.0 4.0 9.4 \n", + "44 15-03-2024 69403.77344 139.8 155.6 20.5 0.0 0.0 2.0 1.2 \n", + "45 18-03-2024 67548.59375 451.5 5.9 17.6 2.7 0.0 0.0 4.8 \n", + "46 19-03-2024 61912.77344 75.2 39.6 2.5 0.0 0.0 0.0 0.0 \n", + "47 20-03-2024 67913.67188 49.3 12.9 18.6 23.3 -10.2 19.0 2.9 \n", + "48 21-03-2024 65491.39063 233.4 2.9 12.0 2.0 4.2 3.8 4.7 \n", + "49 22-03-2024 63778.76172 18.9 18.1 16.3 5.4 4.5 29.6 25.5 \n", + "\n", + " HODL BTCW GBTC Total \n", + "0 10.6 1.0 -95.1 655.3 \n", + "1 0.0 0.0 -484.1 203.0 \n", + "2 7.3 0.0 -594.4 -52.7 \n", + "3 4.8 1.6 -460.6 453.8 \n", + "4 2.3 0.0 -582.3 -126.6 \n", + "5 14.2 2.9 -590.4 43.6 \n", + "6 6.8 0.4 -640.5 -87.4 \n", + "7 2.2 0.0 -515.3 -106.1 \n", + "8 4.5 0.4 -429.3 -158.3 \n", + "9 0.0 0.0 -394.1 -79.8 \n", + "10 2.4 0.0 -255.1 14.8 \n", + "11 0.0 0.0 -191.7 255.1 \n", + "12 0.0 2.1 -220.7 247.3 \n", + "13 2.4 0.0 -187.7 197.6 \n", + "14 0.0 0.7 -182.0 38.5 \n", + "15 2.4 0.9 -144.6 80.0 \n", + "16 0.0 0.7 -107.9 68.1 \n", + "17 2.4 1.1 -72.7 33.6 \n", + "18 0.0 0.9 -80.8 146.0 \n", + "19 10.3 1.7 -101.6 405.0 \n", + "20 2.7 0.5 -51.8 541.5 \n", + "21 8.5 0.0 -95.0 493.4 \n", + "22 0.0 1.6 -72.8 636.3 \n", + "23 2.9 3.6 -131.2 339.7 \n", + "24 2.9 0.0 -174.6 477.4 \n", + "25 0.0 2.8 -150.4 330.3 \n", + "26 5.9 2.2 -137.0 135.6 \n", + "27 0.0 0.0 -199.3 -35.6 \n", + "28 2.9 4.4 -55.7 251.4 \n", + "29 8.7 0.0 -44.2 232.5 \n", + "30 6.2 0.9 -22.4 519.9 \n", + "31 9.7 3.6 -125.6 576.9 \n", + "32 -3.4 2.2 -216.4 673.4 \n", + "33 7.0 0.0 -598.9 92.3 \n", + "34 -1.8 0.0 -492.4 -139.6 \n", + "35 -5.7 -3.2 -368.0 562.7 \n", + "36 3.5 6.6 -332.5 648.3 \n", + "37 0.0 1.4 -276.2 332.0 \n", + "38 1.9 2.5 -374.8 472.5 \n", + "39 7.8 0.0 -302.9 223.0 \n", + "40 118.8 5.8 -494.1 505.4 \n", + "41 82.9 3.0 -79.0 1045.0 \n", + "42 16.5 2.3 -276.5 683.7 \n", + "43 13.8 0.0 -257.1 132.7 \n", + "44 15.8 3.3 -139.4 198.8 \n", + "45 5.7 0.0 -642.5 -154.3 \n", + "46 0.0 0.0 -443.5 -326.2 \n", + "47 9.3 0.0 -386.6 -261.5 \n", + "48 1.8 0.0 -358.8 -94.0 \n", + "49 0.0 0.0 -169.9 -51.6 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('BETF Final.csv')\n", + "df.head(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3871e794", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateClosing PriceIBITFBTCBITBARKBBTCOEZBCBRRRHODLBTCWGBTCTotal
4720-03-202467913.6718849.312.918.623.3-10.219.02.99.30.0-386.6-261.5
4821-03-202465491.39063233.42.912.02.04.23.84.71.80.0-358.8-94.0
4922-03-202463778.7617218.918.116.35.44.529.625.50.00.0-169.9-51.6
5025-03-202469958.8125035.5261.814.00.018.520.511.24.00.0-350.115.4
5126-03-202469987.83594162.2279.116.773.626.726.329.915.80.0-212.3418.0
5227-03-202469455.34375323.81.50.0200.74.84.05.11.91.5-299.8243.5
5328-03-202470744.9531395.168.167.027.63.90.06.020.00.0-104.9182.8
5401-04-202469702.14844165.944.01.1-0.34.20.00.02.00.0-302.6-85.7
5502-04-202465446.97266150.544.84.3-87.50.00.03.75.60.0-81.939.5
5603-04-202465980.8125042.0116.723.00.00.03.80.00.03.1-75.1113.5
5704-04-202468508.84375144.0106.611.212.00.00.03.415.50.0-79.3213.4
5805-04-202467837.64063308.883.07.40.02.70.00.00.00.0-198.9203.0
5908-04-202471631.3593821.36.340.39.30.00.00.00.02.3-303.3-223.8
6009-04-202469139.01563128.73.03.80.00.00.00.00.00.0-154.9-19.4
6110-04-202470587.8828133.376.324.37.30.00.00.00.00.0-17.5123.7
6211-04-202470060.60938192.14.611.10.00.00.08.40.00.0-124.991.3
6312-04-202467195.86719111.10.00.00.00.00.00.00.00.0-166.2-55.1
6415-04-202463426.2109473.40.00.00.00.00.00.00.00.0-110.1-36.7
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9630-05-202468364.992191.6119.125.9-99.92.10.00.00.00.00.048.8
\n", + "
" + ], + "text/plain": [ + " Date Closing Price IBIT FBTC BITB ARKB BTCO EZBC BRRR \\\n", + "47 20-03-2024 67913.67188 49.3 12.9 18.6 23.3 -10.2 19.0 2.9 \n", + "48 21-03-2024 65491.39063 233.4 2.9 12.0 2.0 4.2 3.8 4.7 \n", + "49 22-03-2024 63778.76172 18.9 18.1 16.3 5.4 4.5 29.6 25.5 \n", + "50 25-03-2024 69958.81250 35.5 261.8 14.0 0.0 18.5 20.5 11.2 \n", + "51 26-03-2024 69987.83594 162.2 279.1 16.7 73.6 26.7 26.3 29.9 \n", + "52 27-03-2024 69455.34375 323.8 1.5 0.0 200.7 4.8 4.0 5.1 \n", + "53 28-03-2024 70744.95313 95.1 68.1 67.0 27.6 3.9 0.0 6.0 \n", + "54 01-04-2024 69702.14844 165.9 44.0 1.1 -0.3 4.2 0.0 0.0 \n", + "55 02-04-2024 65446.97266 150.5 44.8 4.3 -87.5 0.0 0.0 3.7 \n", + "56 03-04-2024 65980.81250 42.0 116.7 23.0 0.0 0.0 3.8 0.0 \n", + "57 04-04-2024 68508.84375 144.0 106.6 11.2 12.0 0.0 0.0 3.4 \n", + "58 05-04-2024 67837.64063 308.8 83.0 7.4 0.0 2.7 0.0 0.0 \n", + "59 08-04-2024 71631.35938 21.3 6.3 40.3 9.3 0.0 0.0 0.0 \n", + "60 09-04-2024 69139.01563 128.7 3.0 3.8 0.0 0.0 0.0 0.0 \n", + "61 10-04-2024 70587.88281 33.3 76.3 24.3 7.3 0.0 0.0 0.0 \n", + "62 11-04-2024 70060.60938 192.1 4.6 11.1 0.0 0.0 0.0 8.4 \n", + "63 12-04-2024 67195.86719 111.1 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "64 15-04-2024 63426.21094 73.4 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "65 16-04-2024 63811.86328 25.8 1.4 0.0 -12.9 0.0 1.8 1.7 \n", + "66 17-04-2024 61276.69141 18.1 0.0 -7.3 -42.7 0.0 0.0 0.0 \n", + "67 18-04-2024 63512.75391 18.8 37.4 12.8 9.5 0.0 0.0 0.0 \n", + "68 19-04-2024 63843.57031 29.3 54.8 4.9 12.5 3.9 1.9 0.0 \n", + "69 22-04-2024 66837.67969 19.7 34.8 2.2 22.6 2.7 7.7 0.0 \n", + "70 23-04-2024 66407.27344 37.9 4.4 23.2 33.3 -0.3 1.9 0.0 \n", + "71 24-04-2024 64276.89844 0.0 5.6 0.0 4.2 0.0 0.0 0.0 \n", + "72 25-04-2024 64481.70703 0.0 -22.6 -6.0 -31.3 0.0 1.9 -20.2 \n", + "73 26-04-2024 63755.32031 0.0 -2.8 -3.8 5.4 0.0 0.0 0.0 \n", + "74 29-04-2024 63841.12109 0.0 -6.9 6.8 -31.3 0.0 1.8 2.7 \n", + "75 30-04-2024 60636.85547 0.0 -35.3 -34.3 3.6 -2.4 0.0 0.0 \n", + "76 01-05-2024 58254.01172 -36.9 -191.1 -29.0 -98.1 -5.4 -13.4 -9.7 \n", + "77 02-05-2024 59123.43359 0.0 0.0 0.0 13.3 1.5 3.4 2.3 \n", + "78 03-05-2024 62889.83594 12.7 102.6 33.5 28.1 33.2 60.9 35.6 \n", + "79 06-05-2024 63161.94922 21.5 99.2 2.1 75.6 11.1 1.8 0.0 \n", + "80 07-05-2024 62334.81641 0.0 4.1 0.0 2.8 6.0 0.0 0.0 \n", + "81 08-05-2024 61187.94141 0.0 0.0 11.5 0.0 0.0 0.0 0.0 \n", + "82 09-05-2024 63049.96094 14.2 2.7 6.8 4.4 2.2 1.8 0.0 \n", + "83 10-05-2024 60792.77734 12.4 5.3 0.0 0.0 0.0 0.0 0.0 \n", + "84 13-05-2024 62901.44922 0.0 38.6 20.3 0.0 0.0 0.0 0.0 \n", + "85 14-05-2024 61552.78906 0.0 8.1 0.0 133.1 5.5 1.8 1.2 \n", + "86 15-05-2024 66267.49219 0.0 131.3 86.3 38.6 4.6 1.9 3.7 \n", + "87 16-05-2024 65231.58203 93.7 67.1 1.4 62.0 6.2 3.8 18.5 \n", + "88 17-05-2024 67051.87500 38.1 99.4 20.8 10.0 5.7 0.0 6.4 \n", + "89 20-05-2024 71448.19531 66.4 64.0 24.0 68.3 0.0 0.0 0.0 \n", + "90 21-05-2024 70136.53125 290.0 25.8 -4.2 0.0 0.0 0.0 0.0 \n", + "91 22-05-2024 69122.33594 92.0 74.6 0.0 3.5 0.0 0.0 0.0 \n", + "92 23-05-2024 67929.56250 89.0 19.1 0.0 2.0 2.0 0.0 0.0 \n", + "93 24-05-2024 68526.10156 182.1 43.7 6.4 4.1 0.0 0.0 0.0 \n", + "94 28-05-2024 68296.21875 102.5 34.3 3.3 4.1 3.4 0.0 1.2 \n", + "95 29-05-2024 67578.09375 24.6 17.7 11.0 4.0 1.0 0.0 0.0 \n", + "96 30-05-2024 68364.99219 1.6 119.1 25.9 -99.9 2.1 0.0 0.0 \n", + "\n", + " HODL BTCW GBTC Total \n", + "47 9.3 0.0 -386.6 -261.5 \n", + "48 1.8 0.0 -358.8 -94.0 \n", + "49 0.0 0.0 -169.9 -51.6 \n", + "50 4.0 0.0 -350.1 15.4 \n", + "51 15.8 0.0 -212.3 418.0 \n", + "52 1.9 1.5 -299.8 243.5 \n", + "53 20.0 0.0 -104.9 182.8 \n", + "54 2.0 0.0 -302.6 -85.7 \n", + "55 5.6 0.0 -81.9 39.5 \n", + "56 0.0 3.1 -75.1 113.5 \n", + "57 15.5 0.0 -79.3 213.4 \n", + "58 0.0 0.0 -198.9 203.0 \n", + "59 0.0 2.3 -303.3 -223.8 \n", + "60 0.0 0.0 -154.9 -19.4 \n", + "61 0.0 0.0 -17.5 123.7 \n", + "62 0.0 0.0 -124.9 91.3 \n", + "63 0.0 0.0 -166.2 -55.1 \n", + "64 0.0 0.0 -110.1 -36.7 \n", + "65 3.6 0.0 -79.4 -58.0 \n", + "66 0.0 0.0 -133.1 -165.0 \n", + "67 7.2 0.0 -90.0 -4.3 \n", + "68 -1.8 0.0 -45.8 59.7 \n", + "69 7.5 0.0 -35.0 62.2 \n", + "70 -1.9 0.0 -66.9 31.6 \n", + "71 0.0 0.0 -130.4 -120.6 \n", + "72 0.0 0.0 -139.4 -217.6 \n", + "73 0.0 0.0 -82.4 -83.6 \n", + "74 0.0 0.0 -24.7 -51.6 \n", + "75 0.0 0.0 -93.2 -161.6 \n", + "76 -6.5 -6.2 -167.4 -563.7 \n", + "77 0.0 0.0 -54.9 -34.4 \n", + "78 8.7 0.0 63.0 378.3 \n", + "79 1.8 0.0 3.9 217.0 \n", + "80 0.0 0.0 -28.6 -15.7 \n", + "81 0.0 0.0 0.0 11.5 \n", + "82 0.0 0.0 -43.4 -11.3 \n", + "83 0.0 0.6 -103.0 -84.7 \n", + "84 7.1 0.0 0.0 66.0 \n", + "85 1.7 0.0 -50.9 100.5 \n", + "86 7.5 2.1 27.0 303.0 \n", + "87 0.0 0.0 4.6 257.3 \n", + "88 9.5 0.0 31.6 221.5 \n", + "89 0.0 5.2 9.3 237.2 \n", + "90 -5.9 0.0 0.0 305.7 \n", + "91 0.0 0.0 -16.1 154.0 \n", + "92 9.5 0.0 -13.7 107.9 \n", + "93 15.6 0.0 0.0 251.9 \n", + "94 0.0 1.4 -105.2 45.0 \n", + "95 0.0 1.1 -31.1 28.3 \n", + "96 0.0 0.0 0.0 48.8 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "21afe173-1314-4c1e-92ec-675b255c6e40", + "metadata": {}, + "outputs": [], + "source": [ + "y = df['Closing Price']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "bfbf16ce-edd0-4f00-b37d-c9478dbe2a85", + "metadata": {}, + "outputs": [], + "source": [ + "X = df[['IBIT', 'FBTC', 'BITB', 'ARKB', 'BTCO',\n", + " 'EZBC', 'BRRR', 'HODL', 'BTCW',\n", + " 'GBTC', ]]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "49eb3045-46b9-4712-a441-df17f9dd4faa", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=410)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "106492f1-1cae-4212-b255-7bda2c3766de", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "#lm = LinearRegression()\n", + "#lm.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e8649d1d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=410, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "XGBRegressor(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=100, n_jobs=None,\n", + " num_parallel_tree=None, random_state=410, ...)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit the XGBoost model\n", + "\n", + "\n", + "xgb_model = xgb.XGBRegressor(n_estimators=100, random_state=410)\n", + "xgb_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5209097e-4b05-415c-b802-4cebc0de3b69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([57026.3 , 57109.89 , 68121.38 , 46540.348, 61123.418, 61416.383,\n", + " 52103.418, 62789.984, 61848.367, 62689.957, 61015.64 , 62924.355,\n", + " 56717.832, 61962.96 , 59422.047, 51762.324, 62287.754, 66328.1 ,\n", + " 45815.62 , 68623.61 , 65385.88 , 65655.62 , 44231.605, 60906.49 ,\n", + " 62002.926, 40723.25 , 59347.02 , 68672.375, 62167.168, 68079.04 ],\n", + " dtype=float32)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = xgb_model.predict(X_test)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f9ef6a4f-4ce7-4394-959a-c53de1076992", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(y_test,predictions)\n", + "plt.xlabel('Y Test')\n", + "plt.ylabel('Predicted Y')\n", + "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d254c308-f6eb-4898-aa7c-5cb90bbb3f08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 4354.513281416666\n", + "MSE: 38604668.01492843\n", + "RMSE: 6213.265487240057\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "\n", + "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n", + "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n", + "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1fd8c400-7e3e-4eec-bc69-750a96e8266d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "64.02715891645452" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2_score(y_test, predictions)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "8baea92f-9789-422e-9437-7d7b0f93c596", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted and Actual Values:\n", + " Actual Predicted\n", + "18 44318.22266 57026.300781\n", + "10 41816.87109 57109.890625\n", + "70 66407.27344 68121.382812\n", + "2 43154.94531 46540.347656\n", + "22 49742.44141 61123.417969\n", + "33 61198.38281 61416.382812\n", + "23 51826.69531 52103.417969\n", + "66 61276.69141 62789.984375\n", + "72 64481.70703 61848.367188\n", + "90 70136.53125 62689.957031\n", + "77 59123.43359 61015.640625\n", + "80 62334.81641 62924.355469\n", + "40 72123.90625 56717.832031\n", + "75 60636.85547 61962.960938\n", + "38 66925.48438 59422.046875\n", + "26 52284.87500 51762.324219\n", + "71 64276.89844 62287.753906\n", + "87 65231.58203 66328.101562\n", + "15 43185.85938 45815.621094\n", + "39 68300.09375 68623.609375\n", + "61 70587.88281 65385.878906\n", + "92 67929.56250 65655.617188\n", + "7 39845.55078 44231.605469\n", + "63 67195.86719 60906.488281\n", + "73 63755.32031 62002.925781\n", + "5 41618.40625 40723.250000\n", + "50 69958.81250 59347.019531\n", + "62 70060.60938 68672.375000\n", + "69 66837.67969 62167.167969\n", + "43 71396.59375 68079.039062\n" + ] + } + ], + "source": [ + "results_df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})\n", + "print(\"Predicted and Actual Values:\")\n", + "print(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "388f56e2-5d56-44d9-9eea-f0e037afeb37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,8))\n", + "sns.histplot(x= 'Closing Price',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4c963ce9-cea3-46d8-8e97-505e69e48cbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['expenses'] = np.log(df['Closing Price'])\n", + "plt.figure(figsize = (12,8))\n", + "sns.histplot(x= 'Closing Price',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a40264c3-7161-44d4-96ca-e6f5cbf9f459", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[56899.145]\n" + ] + } + ], + "source": [ + "new_data = pd.DataFrame({\n", + " 'IBIT': [120],\n", + " 'FBTC': [175],\n", + " 'BITB': [5],\n", + " 'ARKB': [50],\n", + " 'BTCO': [1],\n", + " 'EZBC': [2],\n", + " 'BRRR': [3],\n", + " 'HODL': [4],\n", + " 'BTCW': [5],\n", + " 'GBTC': [-20]\n", + "})\n", + "\n", + "# Predicting the expenses for the new data\n", + "predicted_btc_closing_price = xgb_model.predict(new_data)\n", + "\n", + "print(predicted_btc_closing_price )\n", + "\n", + "#print(f\"Predicted btc closing price: {predicted_btc_closing_price:.2f}\")\n", + "\n", + "# Convert the predicted log expenses back to the original scale\n", + "#predicted_value = np.exp(predicted_btc_closing_price[0])\n", + " \n", + "#print(f\"Predicted value: {predicted_value:.2f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/GizathonHackathon.md b/GizathonHackathon.md index 5b2656a..d2dec87 100644 --- a/GizathonHackathon.md +++ b/GizathonHackathon.md @@ -1614,3 +1614,83 @@ giza agents create --endpoint-id 234 --name gizaTest1 --description diabetesTest + + + + + + +## etfXBG.ipynb + + + + + +### transpile + +### Model ID 690 + +### Version 1 + +``` +$ giza transpile etf_xgb1.json --output-path etf_xgb1 +[giza][2024-06-03 19:00:12.216] No model id provided, checking if model exists ✅ +[giza][2024-06-03 19:00:12.229] Model name is: etf_xgb1 +[giza][2024-06-03 19:00:12.813] Model Created with id -> 690! ✅ +[giza][2024-06-03 19:00:13.400] Version Created with id -> 1! ✅ +[giza][2024-06-03 19:00:13.413] Sending model for transpilation ✅ +[giza][2024-06-03 19:01:06.244] Transpilation is fully compatible. Version compiled and Sierra is saved at Giza ✅ +[giza][2024-06-03 19:01:07.583] Downloading model ✅ +[giza][2024-06-03 19:01:07.594] model saved at: etf_xgb1 +``` + + + +### Deploy an inference endpoint + + + +```bash +$ giza endpoints deploy --model-id 690 --version-id 1 +▰▰▰▰▱▱▱ Creating endpoint! +[giza][2024-06-03 19:03:08.874] Endpoint is successful ✅ +[giza][2024-06-03 19:03:08.880] Endpoint created with id -> 261 ✅ +[giza][2024-06-03 19:03:08.882] Endpoint created with endpoint URL: https://endpoint-giza1-690-1-62d762c5-7i3yxzspbq-ew.a.run.app 🎉 + + +``` + + + + + +### Ape account + +already did it. + + + +### Create and Agent using the CLI + + + +```bash +giza agents create --model-id --version-id --name --description + +giza agents create --model-id 665 --version-id 1 --name gizaTest1 --description diabetesTest + +--model-id 665 --version-id 1 +``` + + + + + + + + + + + + + diff --git a/INS-PRE-LIN-V1.ipynb b/INS-PRE-LIN-V1.ipynb new file mode 100644 index 0000000..6a523c3 --- /dev/null +++ b/INS-PRE-LIN-V1.ipynb @@ -0,0 +1,855 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ceb4bd25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: matplotlib in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.9.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (4.52.4)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy>=1.23 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (1.25.2)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (24.0)\n", + "Requirement already satisfied: pillow>=8 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (2.9.0.post0)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2.1 -> 24.0\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4cc4fbb0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: scipy in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.13.1)\n", + "Requirement already satisfied: joblib in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.4.2)\n", + "Requirement already satisfied: threadpoolctl in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.5.0)\n", + "Requirement already satisfied: numpy<2.3,>=1.22.4 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from scipy) (1.25.2)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2.1 -> 24.0\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "pip install scipy joblib threadpoolctl" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "91c30dd3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9abe745a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age sex bmi children smoker region expenses\n", + "0 18 male 16.0 0 no northeast 1694.80\n", + "1 18 male 17.3 2 yes northeast 12829.46\n", + "2 18 female 20.8 0 no southeast 1607.51\n", + "3 18 male 21.5 0 no northeast 1702.46\n", + "4 18 male 21.6 0 yes northeast 13747.87" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('insurance.csv')\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a0bed65b", + "metadata": {}, + "outputs": [], + "source": [ + "df['children'] = df['children'].astype('object')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b4d18442", + "metadata": {}, + "outputs": [], + "source": [ + "#plt.figure(figsize = (12,8))\n", + "#sns.histplot(x= 'expenses',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9a556fa9", + "metadata": {}, + "outputs": [], + "source": [ + "#df['expenses'] = np.sqrt(df['expenses'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1db969a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age sex bmi children smoker region expenses\n", + "0 18 male 16.0 0 no northeast 1694.80\n", + "1 18 male 17.3 2 yes northeast 12829.46\n", + "2 18 female 20.8 0 no southeast 1607.51\n", + "3 18 male 21.5 0 no northeast 1702.46\n", + "4 18 male 21.6 0 yes northeast 13747.87\n", + ".. ... ... ... ... ... ... ...\n", + "95 19 male 27.7 0 yes southwest 16297.85\n", + "96 19 male 27.8 0 no northwest 1635.73\n", + "97 19 female 27.9 0 yes southwest 16884.92\n", + "98 19 female 27.9 3 no northwest 18838.70\n", + "99 19 female 28.3 0 yes southwest 17081.08\n", + "\n", + "[100 rows x 7 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "061322f4", + "metadata": {}, + "outputs": [], + "source": [ + "#plt.figure(figsize = (12,8))\n", + "#sns.histplot(x= 'expenses',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c4370a40", + "metadata": {}, + "outputs": [], + "source": [ + "#df['expenses'] = np.log(df['expenses'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f590c6f2", + "metadata": {}, + "outputs": [], + "source": [ + "#plt.figure(figsize = (12,8))\n", + "#sns.histplot(x= 'expenses',data = df,kde = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "eaf15bbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['sex', 'children', 'smoker', 'region'], dtype='object')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_columns = df.select_dtypes(include = 'object').columns\n", + "cat_columns" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8455e103", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sathy\\AppData\\Local\\Temp\\ipykernel_8692\\3569672404.py:1: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", + " df = pd.get_dummies(df,cat_columns,drop_first = True)\n" + ] + } + ], + "source": [ + "df = pd.get_dummies(df,cat_columns,drop_first = True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e219c99f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['age', 'bmi', 'expenses', 'sex_male', 'children_1', 'children_2',\n", + " 'children_3', 'children_4', 'children_5', 'smoker_yes',\n", + " 'region_northwest', 'region_southeast', 'region_southwest'],\n", + " dtype='object')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f15bdae8", + "metadata": {}, + "outputs": [], + "source": [ + "y = df['expenses']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "aa67442b", + "metadata": {}, + "outputs": [], + "source": [ + "X = df[['age', 'bmi', 'sex_male', 'children_1', 'children_2',\n", + " 'children_3', 'children_4', 'children_5', 'smoker_yes',\n", + " 'region_northwest', 'region_southeast', 'region_southwest']]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "64a0cebb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X , y, test_size=0.3, random_state=410)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "edc5e3e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lm = LinearRegression()\n", + "lm.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7fa4c308", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.34213062e+04, 9.02428491e+03, 1.44413595e+04, 3.99278919e+03,\n", + " 1.23372952e+04, 3.97106058e+04, 3.57606553e+03, 6.07775282e+03,\n", + " 1.19883929e+04, -4.89858045e+02, 1.53681778e+04, 3.52133354e+03,\n", + " 1.61444651e+04, 1.14533542e+04, 7.15664623e+03, 3.19830288e+03,\n", + " 1.05835618e+04, 1.84293316e+04, 2.56113549e+04, 6.21990893e+03,\n", + " 4.37536457e+03, 1.44178598e+04, 1.06389016e+04, 1.09807691e+04,\n", + " 3.01742216e+04, 2.63967831e+03, 1.03446238e+04, 5.49791893e+03,\n", + " 1.32750086e+04, -3.06669551e+02, 1.50974585e+04, 6.60945264e+03,\n", + " 3.67945273e+03, 1.30963679e+04, 5.33306574e+03, 2.89928740e+03,\n", + " 1.22551937e+04, 2.68291328e+04, 1.47496093e+04, 6.84622080e+03,\n", + " 6.58613636e+03, 5.95127022e+03, 3.28401838e+03, 7.13604911e+03,\n", + " 1.57252560e+04, 1.16417001e+04, -1.49824853e+03, 9.31602495e+03,\n", + " 4.50836713e+03, 4.02407673e+03, 5.97415442e+03, 9.21570037e+03,\n", + " 1.41970684e+04, 9.15066198e+02, 4.48192636e+03, 6.52202952e+03,\n", + " 3.47356954e+04, 9.16501133e+03, 4.20929068e+03, 3.85364703e+03,\n", + " 9.57809798e+03, 9.22417473e+03, 1.11083336e+04, 6.70002994e+03,\n", + " 2.92445479e+03, 1.34335465e+04, 1.30764484e+04, 1.64224669e+04,\n", + " 1.10655478e+04, 1.53688151e+03, 1.32174637e+04, 6.40515719e+03,\n", + " 2.61429319e+03, 1.45858373e+03, 3.14172095e+04, 2.21392927e+03,\n", + " 1.07430004e+04, 1.15950316e+04, 3.68527138e+04, 1.03532828e+04,\n", + " 3.88882980e+04, 5.31940425e+03, 1.21210486e+04, 9.81812474e+03,\n", + " 7.38481134e+03, 1.21198443e+03, 4.44481062e+03, 6.75415139e+03,\n", + " 2.97436601e+04, 4.16132846e+03, 1.42610971e+04, 1.57887134e+04,\n", + " 1.20996435e+04, 1.10988893e+04, 2.80328722e+04, 1.06795517e+04,\n", + " 3.91326959e+03, 6.71920415e+03, 1.08345782e+04, 1.98621192e+03,\n", + " 4.54129730e+03, 2.82835711e+04, 7.03161233e+03, 7.44665201e+03,\n", + " 8.58889108e+03, 7.92519010e+03, 2.86135194e+04, 9.05676886e+03,\n", + " 3.70580328e+04, 1.49966169e+04, 3.95659736e+04, 2.42308622e+03,\n", + " 1.09051032e+04, 1.30337890e+04, 6.78610944e+03, 3.41735393e+04,\n", + " 2.60061014e+03, 8.26941278e+03, 9.73363106e+03, 6.42484400e+03,\n", + " 3.35080089e+04, 1.29047301e+04, 3.16011970e+04, 2.15882317e+03,\n", + " 2.16126781e+03, 1.50634937e+04, 1.33887694e+04, 9.94937601e+03,\n", + " 8.29866157e+03, 2.83975302e+04, 5.06959243e+03, 4.36955131e+03,\n", + " 3.97546453e+04, 2.79998309e+04, 3.52313272e+04, 2.69222855e+04,\n", + " 3.23144153e+03, 1.13670452e+04, 3.42453651e+04, 2.68542648e+04,\n", + " 7.76808676e+03, 1.27059214e+04, 3.01440066e+04, 4.05882792e+04,\n", + " 2.49965370e+03, 8.61510382e+03, 2.55357340e+03, 3.61929655e+04,\n", + " 1.71250360e+04, 3.68034696e+03, 2.34552754e+04, 3.86058578e+04,\n", + " 9.08108143e+03, 3.51056482e+04, 6.61766629e+02, 5.32932855e+03,\n", + " 1.22525082e+04, 1.46020522e+04, 3.82254603e+03, 1.53398207e+04,\n", + " 3.79032993e+03, 2.52273623e+03, 1.15112325e+04, 1.58415859e+04,\n", + " 1.51890810e+04, 6.08788978e+03, 1.11184203e+04, 1.27771014e+04,\n", + " 2.79503270e+04, 1.58149708e+04, 3.99195580e+03, 4.17730766e+02,\n", + " 8.32904824e+03, -2.20210155e+03, 8.47916595e+03, 1.33860529e+04,\n", + " 7.89195051e+03, 2.39148027e+04, 4.99691058e+03, 3.59687472e+04,\n", + " 3.34331521e+04, 2.73300693e+04, 1.10866274e+03, 1.77925125e+04,\n", + " 3.18049630e+04, 1.39571017e+04, 1.69949228e+04, 1.06275492e+04,\n", + " 9.94945527e+03, 7.32853618e+03, 3.32556491e+03, 1.31126481e+04,\n", + " 1.30458138e+04, 8.73551232e+03, 9.76573799e+03, 9.66122991e+03,\n", + " 3.17358653e+04, 7.64095231e+03, 3.17295549e+04, 1.17879472e+04,\n", + " 1.79094445e+04, 7.58969243e+03, 2.87111370e+04, 3.83440156e+04,\n", + " 1.63496210e+04, 3.62708599e+03, 2.58909898e+04, 6.61186185e+03,\n", + " 4.67184482e+03, 4.53765134e+03, 1.14650923e+04, 1.19337839e+03,\n", + " 3.29436678e+04, 8.87449713e+03, 1.22551855e+04, 3.72504443e+04,\n", + " 5.98253111e+03, 1.12148510e+04, 3.13845893e+04, 9.40325074e+03,\n", + " 1.18879505e+04, 7.37965278e+03, 7.19907728e+03, 2.92853476e+04,\n", + " 6.45372579e+03, 1.44564353e+04, 3.46169867e+03, 1.21078620e+04,\n", + " 1.67091846e+04, 1.60340329e+04, 5.98224364e+03, 1.12284691e+03,\n", + " 2.52273623e+03, 3.77256537e+04, 1.06749361e+04, 2.10569845e+03,\n", + " 4.78152530e+03, 9.54739718e+03, 3.07472942e+04, 2.65121285e+03,\n", + " 1.51269410e+04, 2.49325369e+03, 8.15444739e+03, 1.03566041e+04,\n", + " 3.82697734e+04, 9.46323351e+03, 2.92445479e+03, 1.40182418e+04,\n", + " 7.37480050e+03, 2.60894229e+04, 1.23894718e+04, 1.42168249e+03,\n", + " 6.87535501e+03, 3.58835776e+04, 1.40877534e+04, 5.45105217e+03,\n", + " 2.78471381e+04, 5.75157872e+03, 4.17775193e+03, 2.92195329e+03,\n", + " 1.29627345e+04, 1.10540788e+04, 1.36761588e+04, 3.59774360e+04,\n", + " 9.13743351e+03, 2.09804711e+03, 5.95080806e+03, 3.75741470e+03,\n", + " 2.70936848e+04, 1.52143687e+04, 5.50530700e+03, 1.42084884e+04,\n", + " 5.55483602e+03, 3.70323117e+03, 1.61488359e+03, 2.48536288e+04,\n", + " 1.08816948e+04, 3.31789523e+04, 2.97236590e+03, 2.03199211e+03,\n", + " 1.26753946e+04, 9.70573864e+03, 6.43961959e+03, 1.52376877e+04,\n", + " 2.55919442e+04, 1.32149628e+04, 9.66269114e+03, 6.42500690e+03,\n", + " 5.53486028e+03, 6.71341657e+03, 3.24645738e+04, 1.34450719e+04,\n", + " 7.85224442e+03, 8.49731843e+03, 1.01583260e+04, 1.35922944e+04,\n", + " 9.32320424e+03, 9.65122212e+03, 1.12180141e+04, 8.61457092e+03,\n", + " 4.72328446e+03, 9.72001067e+03, 3.17881940e+04, 3.26166589e+04,\n", + " 1.13023105e+04, 3.12864522e+04, 1.21138381e+04, 6.53848881e+03,\n", + " 1.35371529e+04, 7.18234531e+03, 1.31402501e+04, -5.77618718e+01,\n", + " 1.00067765e+04, 8.77880522e+03, 1.22053608e+04, 3.20657100e+04,\n", + " 3.36001583e+04, 9.43596442e+03, 4.21423511e+03, 7.56497881e+03,\n", + " 1.03629787e+04, 3.64079554e+03, 1.50453474e+04, 1.74167923e+03,\n", + " 1.14072481e+04, 5.04410170e+03, 8.92744098e+03, 5.48459780e+03,\n", + " 1.06346333e+04, 1.30680417e+04, 3.61868265e+03, 2.73892327e+04,\n", + " 4.23715474e+03, 3.70069594e+03, 3.11068578e+04, 1.10966742e+04,\n", + " 1.43621817e+04, 6.56156818e+03, 4.06100818e+03, 2.26966919e+03,\n", + " 1.15184143e+04, 1.23928450e+04, 7.99616229e+02, 1.23996217e+04,\n", + " 1.12034711e+04, 1.38878167e+04, 1.59231395e+04, 1.29351661e+04,\n", + " 5.09947162e+03, 9.05544965e+03, 3.77618237e+04, 3.37747729e+04,\n", + " 3.54439077e+04, 1.14954025e+04, 1.00501572e+04, 3.40254962e+04,\n", + " 1.01523924e+04, 1.27793939e+04, 7.82823933e+03, 7.50295944e+03,\n", + " 3.22665999e+03, 1.18592880e+03, 3.29220390e+04, -9.92428572e+02,\n", + " 1.24898207e+03, 2.78758127e+04, 8.84687248e+03, 4.66668626e+03,\n", + " 3.66915719e+03, 1.26862159e+04, 2.36129824e+04, 3.74767598e+04,\n", + " 1.49580022e+04, 2.81988851e+04, 7.82766398e+03, -5.86658698e+02,\n", + " 3.98217123e+04, 1.03792845e+04, 3.60374256e+04, 8.76600547e+03,\n", + " 1.27492993e+04, 3.20503291e+04, -4.20249123e+02, -5.09954972e+02,\n", + " -1.11216367e+01, 3.86876816e+04, 8.55511280e+03, 1.53441727e+04,\n", + " 6.78483034e+03, 3.64918494e+04, 9.64824459e+03, 1.22808016e+02,\n", + " 1.13201586e+04, -5.34457643e+02, 1.56840152e+04, 1.74059918e+04,\n", + " 2.85830488e+04, 5.67165107e+03, 3.36264249e+04, 2.40005997e+03,\n", + " 1.42116514e+04, 3.05461182e+03])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = lm.predict(X_test)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "15882de4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(y_test,predictions)\n", + "plt.xlabel('Y Test')\n", + "plt.ylabel('Predicted Y')\n", + "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2) " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5e1e92ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 4215.726336166608\n", + "MSE: 37267420.1821046\n", + "RMSE: 6104.704757980078\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "\n", + "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n", + "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n", + "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "42f4acb5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "74.79294576853582" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2_score(y_test, predictions)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0cd6d0fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted and Actual Values:\n", + " Actual Predicted\n", + "1005 9877.61 13421.306184\n", + "842 9500.57 9024.284906\n", + "1007 9880.07 14441.359477\n", + "40 2207.70 3992.789189\n", + "969 10118.42 12337.295195\n", + "... ... ...\n", + "480 4562.84 5671.651069\n", + "930 23306.55 33626.424873\n", + "204 2156.75 2400.059974\n", + "863 8733.23 14211.651426\n", + "291 2523.17 3054.611823\n", + "\n", + "[402 rows x 2 columns]\n" + ] + } + ], + "source": [ + "results_df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})\n", + "print(\"Predicted and Actual Values:\")\n", + "print(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f0c4f419", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted expenses: 6424.958578640477\n" + ] + } + ], + "source": [ + "new_data = pd.DataFrame({\n", + " 'age': [29],\n", + " 'bmi': [31.1],\n", + " 'sex_male': [1],\n", + " 'children_1': [1],\n", + " 'children_2': [0],\n", + " 'children_3': [1],\n", + " 'children_4': [0],\n", + " 'children_5': [0],\n", + " 'smoker_yes': [0],\n", + " #'region_northeast':[0],\n", + " 'region_northwest': [0],\n", + " 'region_southeast': [0],\n", + " 'region_southwest': [1]\n", + "})\n", + "\n", + "# Predicting the expenses for the new data\n", + "predicted_expenses = lm.predict(new_data)\n", + "print(f\"Predicted expenses: {predicted_expenses[0]}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/etfXBG.ipynb b/etfXBG.ipynb new file mode 100644 index 0000000..4cfeb5a --- /dev/null +++ b/etfXBG.ipynb @@ -0,0 +1,1489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "afb5d31a-296a-4fb1-90ac-f9a2136e27a4", + "metadata": {}, + "source": [ + "### testing the XGBoost Diabetes example to transpile\n", + "##### used conda env giza from ll laptop" + ] + }, + { + "cell_type": "markdown", + "id": "78186bb5-98ce-471c-93d3-ca19386ca744", + "metadata": {}, + "source": [ + "## Create and Train an XGBoost Model\n", + "### We'll start by creating a simple XGBoost model using Scikit-Learn and train it on diabetes dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ae27a3e5-d5d6-487d-83f8-d1f61f3a5f22", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost as xgb\n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", + "# import xgboost as xgb" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b60fabc3-dc33-4f66-9350-d5611479bb30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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925-01-202439933.80859170.7101.020.016.10.00.06.50.00.0-394.1-79.8
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" + ], + "text/plain": [ + " Date Closing Price IBIT FBTC BITB ARKB BTCO EZBC BRRR \\\n", + "0 11-01-2024 46368.58594 111.7 227.0 237.9 65.3 17.4 50.1 29.4 \n", + "1 12-01-2024 42853.16797 386.0 195.3 17.4 39.8 28.4 0.0 20.2 \n", + "2 16-01-2024 43154.94531 212.7 102.0 50.2 122.3 31.9 0.0 15.3 \n", + "3 17-01-2024 42742.65234 371.4 358.1 68.2 50.3 57.6 1.2 1.2 \n", + "4 18-01-2024 41262.05859 145.5 177.9 20.1 41.8 58.8 0.0 9.3 \n", + "5 19-01-2024 41618.40625 201.5 222.3 56.7 62.6 63.4 0.0 10.4 \n", + "6 22-01-2024 39507.36719 260.6 158.7 41.6 65.0 5.6 4.7 9.7 \n", + "7 23-01-2024 39845.55078 160.1 157.7 26.3 61.8 0.0 1.1 0.0 \n", + "8 24-01-2024 40077.07422 66.2 125.7 19.1 24.9 19.9 1.2 9.1 \n", + "9 25-01-2024 39933.80859 170.7 101.0 20.0 16.1 0.0 0.0 6.5 \n", + "\n", + " HODL BTCW GBTC Total \n", + "0 10.6 1.0 -95.1 655.3 \n", + "1 0.0 0.0 -484.1 203.0 \n", + "2 7.3 0.0 -594.4 -52.7 \n", + "3 4.8 1.6 -460.6 453.8 \n", + "4 2.3 0.0 -582.3 -126.6 \n", + "5 14.2 2.9 -590.4 43.6 \n", + "6 6.8 0.4 -640.5 -87.4 \n", + "7 2.2 0.0 -515.3 -106.1 \n", + "8 4.5 0.4 -429.3 -158.3 \n", + "9 0.0 0.0 -394.1 -79.8 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('BETF Final.csv')\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bdfb88e1-3d85-4e4c-b1a5-5f47d3d0693a", + "metadata": {}, + "outputs": [], + "source": [ + "y = df['Closing Price']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "18f4920c-9306-4bf8-947d-d419a3138232", + "metadata": {}, + "outputs": [], + "source": [ + "X = df[['IBIT', 'FBTC', 'BITB', 'ARKB', 'BTCO',\n", + " 'EZBC', 'BRRR', 'HODL', 'BTCW',\n", + " 'GBTC', ]]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a23d5292-6a9b-41b8-b1d8-297b19cae50b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=410)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9df3f7dc-c1b6-428e-aa11-0baa65a1f40b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7536532e-c34c-4815-8c06-24e53ee22d5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
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" + ], + "text/plain": [ + "XGBRegressor(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=100, n_jobs=None,\n", + " num_parallel_tree=None, random_state=410, ...)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xgb_model = xgb.XGBRegressor(n_estimators=100, random_state=410)\n", + "xgb_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e4c2d8e4-5b51-47f5-b421-898d5142250c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([57026.3 , 57109.89 , 68121.38 , 46540.348, 61123.418, 61416.383,\n", + " 52103.418, 62789.984, 61848.367, 62689.957, 61015.64 , 62924.355,\n", + " 56717.832, 61962.96 , 59422.047, 51762.324, 62287.754, 66328.1 ,\n", + " 45815.62 , 68623.61 , 65385.88 , 65655.62 , 44231.605, 60906.49 ,\n", + " 62002.926, 40723.25 , 59347.02 , 68672.375, 62167.168, 68079.04 ],\n", + " dtype=float32)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = xgb_model.predict(X_test)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5650a200-f157-4c3d-b352-282380f05abd", + "metadata": {}, + "outputs": [], + "source": [ + "# import xgboost as xgb\n", + "# from sklearn.datasets import load_diabetes\n", + "# from sklearn.model_selection import train_test_split\n", + "\n", + "# data = load_diabetes()\n", + "# X, y = data.data, data.target\n", + "\n", + "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# # Increase the number of trees and maximum depth\n", + "# n_estimators = 2 # Increase the number of trees\n", + "# max_depth = 6 # Increase the maximum depth of each tree\n", + "\n", + "# xgb_reg = xgb.XGBRegressor(n_estimators=n_estimators, max_depth=max_depth)\n", + "# xgb_reg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "10569dc0-0494-4370-b84f-365d4a18dfd3", + "metadata": {}, + "source": [ + "## Save the model\n", + "### Save the model in Json format" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "abe18478-37c7-4b40-b549-8e8dd3324c0e", + "metadata": {}, + "outputs": [], + "source": [ + "from giza.zkcook import serialize_model\n", + "serialize_model(xgb_model, \"etf_xgb1.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "becd66e2-de26-4358-adcc-fc058a6698e8", + "metadata": {}, + "source": [ + "## Transpile your model to Orion Cairo\n", + "### We will use Giza-CLI to transpile our saved model to Orion Cairo." + ] + }, + { + "cell_type": "markdown", + "id": "31c49d75-4ff7-4b3b-b4f3-8fe09c06dd34", + "metadata": {}, + "source": [ + "\n", + "$ giza transpile etf_xgb1.json --output-path etf_xgb1\n", + "[giza][2024-06-03 19:00:12.216] No model id provided, checking if model exists ✅\n", + "[giza][2024-06-03 19:00:12.229] Model name is: etf_xgb1\n", + "[giza][2024-06-03 19:00:12.813] Model Created with id -> 690! ✅\n", + "[giza][2024-06-03 19:00:13.400] Version Created with id -> 1! ✅\n", + "[giza][2024-06-03 19:00:13.413] Sending model for transpilation ✅\n", + "[giza][2024-06-03 19:01:06.244] Transpilation is fully compatible. Version compiled and Sierra is saved at Giza ✅\n", + "[giza][2024-06-03 19:01:07.583] Downloading model ✅\n", + "[giza][2024-06-03 19:01:07.594] model saved at: etf_xgb1\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd00d799-97cb-44a2-bde5-03a433b5f507", + "metadata": {}, + "source": [ + "## Deploy an inference endpoint\n", + "### Now that our model is transpiled to Cairo we can deploy an endpoint to run verifiable inferences. We will use Giza CLI again to run and deploy an endpoint. Ensure to replace model-id and version-id with your ids provided during transpilation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f989d383-1aaf-4bf1-b6be-8fbb63e61e1c", + "metadata": {}, + "source": [ + "$ giza endpoints deploy --model-id 690 --version-id 1\n", + "▰▰▰▰▱▱▱ Creating endpoint!\n", + "[giza][2024-06-03 19:03:08.874] Endpoint is successful ✅\n", + "[giza][2024-06-03 19:03:08.880] Endpoint created with id -> 261 ✅\n", + "[giza][2024-06-03 19:03:08.882] Endpoint created with endpoint URL: https://endpoint-giza1-690-1-62d762c5-7i3yxzspbq-ew.a.run.app 🎉" + ] + }, + { + "cell_type": "markdown", + "id": "b8e6bac9-3052-40e2-85c8-6b89a8367ec8", + "metadata": {}, + "source": [ + "### Create and Agent using the CLI" + ] + }, + { + "cell_type": "markdown", + "id": "10fc7376-6f3e-497d-b38b-d1e120cc8ea9", + "metadata": {}, + "source": [ + "$ giza agents create --model-id 690 --version-id 1 --name etfXGB --description etfXGB\n", + "[giza][2024-06-03 19:11:50.498] Creating agent ✅\n", + "[giza][2024-06-03 19:11:50.506] Using model id and version id to create agent\n", + "[giza][2024-06-03 19:11:50.780] Select an existing account to create the agent.\n", + "[giza][2024-06-03 19:11:50.787] Available accounts are:\n", + "┌──────────┐\n", + "│ Accounts │\n", + "├──────────┤\n", + "│ giza1 │\n", + "└──────────┘\n", + "Enter the account name: giza1\n", + "{\n", + " \"id\": 34,\n", + " \"name\": \"etfXGB\",\n", + " \"description\": \"etfXGB\",\n", + " \"parameters\": {\n", + " \"model_id\": 690,\n", + " \"version_id\": 1,\n", + " \"endpoint_id\": 261,\n", + " \"account\": \"giza1\"\n", + " },\n", + " \"created_date\": \"2024-06-04T02:11:57.598948\",\n", + " \"last_update\": \"2024-06-04T02:11:57.598948\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "29ebae06-8a57-40e1-b9d5-990936f896e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.04534098, -0.04464164, -0.00620595, -0.01599898, 0.1250187 ,\n", + 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" [-0.06000263, -0.04464164, 0.00133873, -0.02977038, -0.00707277,\n", + " -0.02166853, 0.01182372, -0.00259226, 0.03181246, -0.05492509],\n", + " [ 0.00538306, -0.04464164, 0.05846277, -0.04354178, -0.07311851,\n", + " -0.07239858, 0.019187 , -0.0763945 , -0.05140387, -0.02593034],\n", + " [-0.09632802, -0.04464164, -0.06979687, -0.06764174, -0.01945635,\n", + " -0.01070833, 0.01550536, -0.03949338, -0.04688253, -0.07977773],\n", + " [ 0.02717829, 0.05068012, 0.01750591, -0.03321323, -0.00707277,\n", + " 0.04597154, -0.06549067, 0.07120998, -0.09643495, -0.05906719],\n", + " [ 0.01991321, -0.04464164, -0.04069594, -0.01599898, -0.00844872,\n", + " -0.0175976 , 0.05232174, -0.03949338, -0.03074792, 0.00306441],\n", + " [-0.05273755, 0.05068012, -0.01806189, 0.08040085, 0.08924393,\n", + " 0.10766179, -0.03971921, 0.1081111 , 0.03606033, -0.04249877],\n", + " [-0.02730979, -0.04464164, 0.06492964, -0.00222757, -0.02496016,\n", + " -0.01728445, 0.02286863, -0.03949338, -0.0611758 , -0.0632093 ],\n", + " [-0.02367725, -0.04464164, -0.046085 , -0.03321323, 0.03282986,\n", + " 0.03626394, 0.03759519, -0.00259226, -0.03324559, 0.01134862],\n", + " [ 0.03807591, 0.05068012, 0.06169621, 0.02187239, -0.0442235 ,\n", + " -0.03482076, -0.04340085, -0.00259226, 0.01990749, -0.01764613],\n", + " [-0.02730979, -0.04464164, -0.01806189, -0.04009893, -0.00294491,\n", + " -0.01133463, 0.03759519, -0.03949338, -0.0089434 , -0.05492509],\n", + " [-0.0382074 , -0.04464164, -0.0547075 , -0.07797029, -0.03321588,\n", + " -0.08649026, 0.14068104, -0.0763945 , -0.01919845, -0.0052198 ],\n", + " [-0.02367725, -0.04464164, 0.03043966, -0.00567042, 0.08236416,\n", + " 0.09200436, -0.01762938, 0.07120998, 0.03304307, 0.00306441],\n", + " [-0.04183994, -0.04464164, 0.04121778, -0.02632753, -0.03183992,\n", + " -0.03043668, -0.03603757, 0.00294291, 0.03365381, -0.01764613],\n", + " [-0.06000263, 0.05068012, 0.05415152, -0.01944183, -0.04972731,\n", + " -0.04891244, 0.02286863, -0.03949338, -0.04398377, -0.0052198 ],\n", + " [ 0.01628068, 0.05068012, -0.04500719, 0.0631866 , 0.01081462,\n", + " -0.00037443, 0.06336665, -0.03949338, -0.03074792, 0.03620126],\n", + " [-0.04183994, -0.04464164, -0.06548562, -0.04009893, -0.00569682,\n", + " 0.01434355, -0.04340085, 0.03430886, 0.00702714, -0.01350402],\n", + " [ 0.07440129, -0.04464164, 0.01858372, 0.0631866 , 0.06172487,\n", + " 0.04284006, 0.00814208, -0.00259226, 0.05803805, -0.05906719],\n", + " [-0.05273755, 0.05068012, -0.01159501, 0.0563009 , 0.05622106,\n", + " 0.07290231, -0.03971921, 0.07120998, 0.03056363, -0.0052198 ],\n", + " [ 0.05260606, 0.05068012, -0.02452876, 0.0563009 , -0.00707277,\n", + " -0.00507166, -0.02131102, -0.00259226, 0.02671684, -0.03835666],\n", + " [-0.10722563, -0.04464164, -0.03422907, -0.06764174, -0.06348684,\n", + " -0.07051969, 0.00814208, -0.03949338, -0.00061174, -0.07977773],\n", + " [-0.06726771, 0.05068012, -0.01267283, -0.04009893, -0.01532849,\n", + " 0.00463594, -0.0581274 , 0.03430886, 0.01919647, -0.03421455],\n", + " [-0.07453279, 0.05068012, -0.01806189, 0.00810098, -0.01945635,\n", + " -0.02480001, -0.06549067, 0.03430886, 0.06731774, -0.01764613],\n", + " [-0.00188202, 0.05068012, 0.03043966, 0.05285804, 0.03970963,\n", + " 0.05661859, -0.03971921, 0.07120998, 0.02539508, 0.02791705],\n", + " [ 0.05987114, -0.04464164, -0.02129532, 0.08728655, 0.04521344,\n", + " 0.03156671, -0.04708248, 0.07120998, 0.07912244, 0.13561183],\n", + " [-0.06000263, 0.05068012, -0.0105172 , -0.01486283, -0.04972731,\n", + " -0.02354742, -0.0581274 , 0.0158583 , -0.00991877, -0.03421455],\n", + " [ 0.06713621, -0.04464164, -0.06117437, -0.04009893, -0.02633611,\n", + " -0.02448686, 0.03391355, -0.03949338, -0.0561531 , -0.05906719],\n", + " [ 0.0090156 , 0.05068012, -0.03961813, 0.02875809, 0.03833367,\n", + " 0.0735286 , -0.07285395, 0.1081111 , 0.01556846, -0.04664087],\n", + " [-0.02730979, 0.05068012, 0.06061839, 0.04941519, 0.08511607,\n", + " 0.08636769, -0.00290283, 0.03430886, 0.03781053, 0.04862759],\n", + " [-0.04547248, -0.04464164, 0.03906215, 0.00121528, 0.01631843,\n", + " 0.01528299, -0.02867429, 0.02655962, 0.04452873, -0.02593034],\n", + " [ 0.04534098, 0.05068012, 0.01966154, 0.03908664, 0.02044629,\n", + " 0.02593004, 0.00814208, -0.00259226, -0.00330084, 0.01963284],\n", + " [ 0.01264814, -0.04464164, -0.02021751, -0.01599898, 0.01219057,\n", + " 0.02123281, -0.07653559, 0.1081111 , 0.0598794 , -0.02178823],\n", + " [-0.0854304 , -0.04464164, -0.00405033, -0.00911327, -0.00294491,\n", + " 0.00776743, 0.02286863, -0.03949338, -0.0611758 , -0.01350402],\n", + " [-0.05637009, -0.04464164, -0.01159501, -0.03321323, -0.0469754 ,\n", + " -0.04765985, 0.00446045, -0.03949338, -0.00797714, -0.08806194],\n", + " [-0.04910502, -0.04464164, -0.06440781, -0.10207025, -0.00294491,\n", + " -0.01540556, 0.06336665, -0.04724262, -0.03324559, -0.05492509],\n", + " [-0.02730979, -0.04464164, -0.06009656, -0.02977038, 0.04658939,\n", + " 0.01998022, 0.12227286, -0.03949338, -0.05140387, -0.00936191],\n", + " [ 0.00175052, -0.04464164, -0.06548562, -0.00567042, -0.00707277,\n", + " -0.01947649, 0.04127682, -0.03949338, -0.00330084, 0.00720652],\n", + " [ 0.01264814, -0.04464164, -0.02560657, -0.04009893, -0.03046397,\n", + " -0.04515466, 0.0780932 , -0.0763945 , -0.07213275, 0.01134862],\n", + " [-0.02730979, -0.04464164, -0.06332999, -0.05042748, -0.08962994,\n", + " -0.10433972, 0.05232174, -0.0763945 , -0.0561531 , -0.06735141],\n", + " [-0.02367725, -0.04464164, -0.06979687, -0.06419889, -0.05935898,\n", + " -0.05047819, 0.019187 , -0.03949338, -0.08913335, -0.05078298],\n", + " [-0.06363517, -0.04464164, 0.03582872, -0.02288468, -0.03046397,\n", + " -0.01885019, -0.00658447, -0.00259226, -0.02595311, -0.05492509]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4c39550c-a76f-4f8c-ab1d-69166b17eebf", + "metadata": {}, + "outputs": [], + "source": [ + "new_data = pd.DataFrame({\n", + " 'IBIT': [120],\n", + " 'FBTC': [175],\n", + " 'BITB': [5],\n", + " 'ARKB': [50],\n", + " 'BTCO': [1],\n", + " 'EZBC': [2],\n", + " 'BRRR': [3],\n", + " 'HODL': [4],\n", + " 'BTCW': [5],\n", + " 'GBTC': [-20]\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c0519c27-e968-4636-9176-6d9827b7c7b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([62310.82 , 62310.82 , 62310.78 , 62310.82 , 62310.78 , 62310.82 ,\n", + " 60597.242, 60597.242, 60597.242, 60597.242, 60597.28 , 62310.82 ,\n", + " 60597.28 , 62310.78 , 60597.242, 60597.242, 62310.78 , 62310.78 ,\n", + " 60597.242, 62310.78 , 60597.28 , 62310.82 , 60597.28 , 62310.78 ,\n", + " 60597.28 , 62310.78 , 62310.78 , 62310.78 , 60597.28 , 62310.78 ,\n", + " 62310.82 , 60597.28 , 60597.242, 62310.78 , 62310.82 , 60597.242,\n", + " 60597.242, 60597.242, 62310.78 , 62310.82 , 60597.28 , 62310.78 ,\n", + " 62310.78 , 62310.78 , 62310.82 , 60597.28 , 60597.28 , 62310.82 ,\n", + " 60597.28 , 60597.28 , 62310.82 , 60597.28 , 62310.78 , 62310.82 ,\n", + " 60597.242, 60597.28 , 60597.28 , 62310.78 , 60597.28 , 60597.28 ,\n", + " 60597.28 , 60597.28 , 60597.242, 62310.78 , 60597.28 , 62310.82 ,\n", + " 60597.242, 62310.78 , 60597.28 , 60597.242, 60597.242, 60597.242,\n", + " 62310.82 , 60597.242, 62310.82 , 62310.78 , 60597.242, 60597.28 ,\n", + " 62310.78 , 62310.82 , 60597.28 , 60597.28 , 60597.28 , 60597.28 ,\n", + " 62310.82 , 62310.82 , 60597.28 , 60597.28 , 60597.28 ],\n", + " dtype=float32)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = xgb_model.predict(X_test)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "de4e4b6a-eb2d-45cc-a4f1-400ea3481a99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([56899.145], dtype=float32)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions1 = xgb_model.predict(new_data)\n", + "predictions1" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "464c2266-4832-46c1-bfcb-417d907e87f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([62310.82 , 62310.82 , 62310.78 , 62310.82 , 62310.78 , 62310.82 ,\n", + " 60597.242, 60597.242, 60597.242, 60597.242, 60597.28 , 62310.82 ,\n", + " 60597.28 , 62310.78 , 60597.242, 60597.242, 62310.78 , 62310.78 ,\n", + " 60597.242, 62310.78 , 60597.28 , 62310.82 , 60597.28 , 62310.78 ,\n", + " 60597.28 , 62310.78 , 62310.78 , 62310.78 , 60597.28 , 62310.78 ,\n", + " 62310.82 , 60597.28 , 60597.242, 62310.78 , 62310.82 , 60597.242,\n", + " 60597.242, 60597.242, 62310.78 , 62310.82 , 60597.28 , 62310.78 ,\n", + " 62310.78 , 62310.78 , 62310.82 , 60597.28 , 60597.28 , 62310.82 ,\n", + " 60597.28 , 60597.28 , 62310.82 , 60597.28 , 62310.78 , 62310.82 ,\n", + " 60597.242, 60597.28 , 60597.28 , 62310.78 , 60597.28 , 60597.28 ,\n", + " 60597.28 , 60597.28 , 60597.242, 62310.78 , 60597.28 , 62310.82 ,\n", + " 60597.242, 62310.78 , 60597.28 , 60597.242, 60597.242, 60597.242,\n", + " 62310.82 , 60597.242, 62310.82 , 62310.78 , 60597.242, 60597.28 ,\n", + " 62310.78 , 62310.82 , 60597.28 , 60597.28 , 60597.28 , 60597.28 ,\n", + " 62310.82 , 62310.82 , 60597.28 , 60597.28 , 60597.28 ],\n", + " dtype=float32)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "id": "377dcf19-aff5-456a-9776-bb30dcc7f954", + "metadata": {}, + "source": [ + "## Run a verifiable inference\n", + "##### To streamline verifiable inference, you might consider using the endpoint URL obtained after transpilation. However, this approach requires manual serialization of the input for the Cairo program and handling the deserialization process. To make this process more user-friendly and keep you within a Python environment, we've introduced a Python SDK designed to facilitate the creation of ML workflows and execution of verifiable predictions. When you initiate a prediction, our system automatically retrieves the endpoint URL you deployed earlier, converts your input into Cairo-compatible format, executes the prediction, and then converts the output back into a numpy object. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e5c142bd-60ac-4dd6-9e5c-0d7f84613b35", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "66247cbc-4dd0-4a93-80a8-13172668783e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.09256398, -0.04464164, 0.03690653, 0.02187239, -0.02496016,\n", + " -0.01665815, 0.00077881, -0.03949338, -0.02251653, -0.02178823])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test[1, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5dbf104f-5868-4084-a496-321ae40e4aee", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An error occurred in predict: 503 Server Error: Service Unavailable for url: https://endpoint-giza1-690-1-62d762c5-7i3yxzspbq-ew.a.run.app/cairo_run\n", + "Deployment predict error: Service Unavailable\n", + "An error occurred in predict: 503 Server Error: Service Unavailable for url: https://endpoint-giza1-690-1-62d762c5-7i3yxzspbq-ew.a.run.app/cairo_run\n" + ] + }, + { + "ename": "HTTPError", + "evalue": "503 Server Error: Service Unavailable for url: https://endpoint-giza1-690-1-62d762c5-7i3yxzspbq-ew.a.run.app/cairo_run", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mHTTPError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[32], line 56\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 50\u001b[0m \u001b[38;5;66;03m# data = load_diabetes()\u001b[39;00m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# X, y = data.data, data.target\u001b[39;00m\n\u001b[0;32m 53\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(\n\u001b[0;32m 54\u001b[0m X, y, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m\n\u001b[0;32m 55\u001b[0m )\n\u001b[1;32m---> 56\u001b[0m _, proof_id \u001b[38;5;241m=\u001b[39m \u001b[43mexecution\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProof ID: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mproof_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[1;32mIn[32], line 39\u001b[0m, in \u001b[0;36mexecution\u001b[1;34m()\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m X_test\n\u001b[0;32m 26\u001b[0m new_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[0;32m 27\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIBIT\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;241m120\u001b[39m],\n\u001b[0;32m 28\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFBTC\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;241m175\u001b[39m],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGBTC\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m20\u001b[39m]\n\u001b[0;32m 37\u001b[0m })\n\u001b[1;32m---> 39\u001b[0m (result, proof_id) \u001b[38;5;241m=\u001b[39m \u001b[43mprediction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mMODEL_ID\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mVERSION_ID\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# (result, proof_id) = prediction(new_data, MODEL_ID, VERSION_ID)\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \n\u001b[0;32m 42\u001b[0m \n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# print(f\"Predicted value for input {new_data.flatten()[0]} is {result}\")\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredicted value for input \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39mflatten()[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[1;32mIn[32], line 14\u001b[0m, in \u001b[0;36mprediction\u001b[1;34m(input, model_id, version_id)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprediction\u001b[39m(\u001b[38;5;28minput\u001b[39m, model_id, version_id):\n\u001b[0;32m 12\u001b[0m model \u001b[38;5;241m=\u001b[39m GizaModel(\u001b[38;5;28mid\u001b[39m\u001b[38;5;241m=\u001b[39mmodel_id, version\u001b[38;5;241m=\u001b[39mversion_id)\n\u001b[1;32m---> 14\u001b[0m (result, proof_id) \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_feed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverifiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_category\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mXGB\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result, proof_id\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\giza\\agents\\model.py:333\u001b[0m, in \u001b[0;36mGizaModel.predict\u001b[1;34m(self, input_file, input_feed, verifiable, fp_impl, custom_output_dtype, model_category, job_size, dry_run)\u001b[0m\n\u001b[0;32m 331\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 332\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred in predict: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 333\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\giza\\agents\\model.py:297\u001b[0m, in \u001b[0;36mGizaModel.predict\u001b[1;34m(self, input_file, input_feed, verifiable, fp_impl, custom_output_dtype, model_category, job_size, dry_run)\u001b[0m\n\u001b[0;32m 295\u001b[0m error_message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDeployment predict error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 296\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(error_message)\n\u001b[1;32m--> 297\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m 299\u001b[0m body \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mjson()\n\u001b[0;32m 300\u001b[0m serialized_output \u001b[38;5;241m=\u001b[39m body[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\giza\\agents\\model.py:292\u001b[0m, in \u001b[0;36mGizaModel.predict\u001b[1;34m(self, input_file, input_feed, verifiable, fp_impl, custom_output_dtype, model_category, job_size, dry_run)\u001b[0m\n\u001b[0;32m 289\u001b[0m response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muri, json\u001b[38;5;241m=\u001b[39mpayload)\n\u001b[0;32m 291\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 292\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 294\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred in predict: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\requests\\models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1019\u001b[0m http_error_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 1020\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Server Error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mreason\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for url: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1021\u001b[0m )\n\u001b[0;32m 1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[1;32m-> 1024\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", + "\u001b[1;31mHTTPError\u001b[0m: 503 Server Error: Service Unavailable for url: https://endpoint-giza1-690-1-62d762c5-7i3yxzspbq-ew.a.run.app/cairo_run" + ] + } + ], + "source": [ + "import xgboost as xgb\n", + "from sklearn.datasets import load_diabetes\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from giza.agents.model import GizaModel\n", + "\n", + "\n", + "MODEL_ID = 690 # Update with your model ID\n", + "VERSION_ID = 1 # Update with your version ID\n", + "\n", + "def prediction(input, model_id, version_id):\n", + " model = GizaModel(id=model_id, version=version_id)\n", + "\n", + " (result, proof_id) = model.predict(\n", + " input_feed={\"input\": input}, verifiable=True, model_category=\"XGB\"\n", + " )\n", + "\n", + " return result, proof_id\n", + "\n", + "\n", + "def execution():\n", + " # The input data type should match the model's expected input\n", + " # input = X_test[1, :]\n", + " input = X_test\n", + "\n", + " new_data = pd.DataFrame({\n", + " 'IBIT': [120],\n", + " 'FBTC': [175],\n", + " 'BITB': [5],\n", + " 'ARKB': [50],\n", + " 'BTCO': [1],\n", + " 'EZBC': [2],\n", + " 'BRRR': [3],\n", + " 'HODL': [4],\n", + " 'BTCW': [5],\n", + " 'GBTC': [-20]\n", + "})\n", + "\n", + " (result, proof_id) = prediction(input, MODEL_ID, VERSION_ID)\n", + " # (result, proof_id) = prediction(new_data, MODEL_ID, VERSION_ID)\n", + "\n", + " \n", + " # print(f\"Predicted value for input {new_data.flatten()[0]} is {result}\")\n", + " print(f\"Predicted value for input {input.flatten()[0]} is {result}\")\n", + "\n", + " return result, proof_id\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " # data = load_diabetes()\n", + " # X, y = data.data, data.target\n", + "\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42\n", + " )\n", + " _, proof_id = execution()\n", + " print(f\"Proof ID: {proof_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d55f279-3629-4615-8ebf-d6547e73e49a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7b44f1d7-6254-4380-aaa4-ecd141fc17e7", + "metadata": {}, + "source": [ + "## Download the proof\n", + "#### Initiating a verifiable inference sets off a proving job on our server, sparing you the complexities of installing and configuring the prover yourself. Upon completion, you can download your proof.\n", + "\n", + "First, let's check the status of the proving job to ensure that it has been completed." + ] + }, + { + "cell_type": "markdown", + "id": "f25f8d23-0d83-412a-9e3b-fca989972f0a", + "metadata": {}, + "source": [ + "$ giza endpoints get-proof --endpoint-id 234 --proof-id \"10c164e6c2364ab6b5491702127979a6\"\n", + "[giza][2024-05-30 00:40:39.691] Getting proof from endpoint 234 ✅\n", + "{\n", + " \"id\": 967,\n", + " \"job_id\": 1121,\n", + " \"metrics\": {\n", + " \"proving_time\": 17.249508\n", + " },\n", + " \"created_date\": \"2024-05-30T07:33:12.532659\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "4f427ceb-61d7-4ffc-ba4f-15d4dad1ec2c", + "metadata": {}, + "source": [ + "Once the proof is ready, you can download it." + ] + }, + { + "cell_type": "markdown", + "id": "43c280b5-8b29-4a7d-8b0e-a5b90e408852", + "metadata": {}, + "source": [ + "$ giza endpoints download-proof --endpoint-id 234 --proof-id \"10c164e6c2364ab6b5491702127979a6\" --output-path zk_xgboost.proof\n", + "[giza][2024-05-30 00:51:52.048] Getting proof from endpoint 234 ✅\n", + "[giza][2024-05-30 00:51:53.800] Proof downloaded to zk_xgboost.proof ✅\n", + "(giza3)" + ] + }, + { + "cell_type": "markdown", + "id": "a36fb812-b3ef-4c4e-a142-c3c9263be989", + "metadata": {}, + "source": [ + "## Verify the proof\n", + "#### Finally, you can verify the proof." + ] + }, + { + "cell_type": "markdown", + "id": "139ac5f2-0bd8-40d7-88c3-75e162febfb8", + "metadata": {}, + "source": [ + "$ giza verify --proof-id 967\n", + "[giza][2024-05-30 00:56:05.847] Verifying proof...\n", + "[giza][2024-05-30 00:56:07.140] Verification result: True\n", + "[giza][2024-05-30 00:56:07.145] Verification time: 0.454667226\n", + "(giza3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8859806a-01d8-40d9-a445-cbe3c8a733fd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "giza3", + "language": "python", + "name": "giza3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/etf_xgb1.json b/etf_xgb1.json new file mode 100644 index 0000000..bd407d1 --- /dev/null +++ b/etf_xgb1.json @@ -0,0 +1 @@ +{"model_type": "xgboost", "opt_type": 0, "learner": {"attributes": {}, "feature_names": ["IBIT", "FBTC", "BITB", "ARKB", "BTCO", "EZBC", "BRRR", "HODL", "BTCW", "GBTC"], "feature_types": ["float", "float", "float", "float", "float", "float", "float", "float", "float", "float"], "gradient_booster": {"model": {"gbtree_model_param": {"num_parallel_tree": "1", "num_trees": "100"}, "iteration_indptr": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 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"version": [2, 0, 3]} \ No newline at end of file diff --git a/etf_xgb1/Scarb.lock b/etf_xgb1/Scarb.lock new file mode 100644 index 0000000..361c239 --- /dev/null +++ b/etf_xgb1/Scarb.lock @@ -0,0 +1,6 @@ +# Code generated by scarb DO NOT EDIT. +version = 1 + +[[package]] +name = "etf_xgb1" +version = "0.1.0" diff --git a/etf_xgb1/Scarb.toml b/etf_xgb1/Scarb.toml new file mode 100644 index 0000000..6b17785 --- /dev/null +++ b/etf_xgb1/Scarb.toml @@ -0,0 +1,6 @@ +[package] +name = "etf_xgb1" +version = "0.1.0" + +[cairo] +enable-gas = false diff --git a/etf_xgb1/src/lib.cairo b/etf_xgb1/src/lib.cairo new file mode 100644 index 0000000..d405ec0 --- /dev/null +++ b/etf_xgb1/src/lib.cairo @@ -0,0 +1,717 @@ +mod xgb_inference; + +fn main(input_vector: Span) -> i32 { + let tree_0 = xgb_inference::Tree { + base_weights: array![-57, 305765060, -573309670, 137595320, 632931500, -1194513700, 44210187, 523003860, -100529570, 393762600, 237895210, -399656180, 47984300, 490603000, -768138900, 137050160, 698849460, -760967720, 403176500, 33704297, 125374540, -62713380, 233314040, -368167110, 160230000, -15777364, 59282495, 86264030, 236407800, 57417346, -1060411800, -400474880, 603954250, -70038990, 67430450, -391869460, -164846790, 63103770, -243317480, 233603760, -18989102].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 0, 0, 0, 21, 23, 25, 27, 29, 31, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 35, 37, 39, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 0, 0, 0, 22, 24, 26, 28, 30, 32, 0, 0, 34, 0, 0, 0, 0, 0, 0, 0, 0, 36, 38, 40, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 4, 5, 1, 0, 0, 4, 0, 9, 0, 0, 0, 0, 6, 1, 0, 1, 9, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![11920000, 150000, 150000, 1810000, 3810000, 42010000, 480000, 1270000, -10520000, 160000, 237895210, -399656180, 47984300, 60000, 23200000, 0, 150000, -42930000, 3800000, 33704297, 125374540, 58650000, 233314040, -368167110, 160230000, -15777364, 59282495, 86264030, 236407800, 57417346, 100000, 1890000, 120000, -70038990, 67430450, -391869460, -164846790, 63103770, -243317480, 233603760, -18989102].span() +}; + let tree_1 = xgb_inference::Tree { + base_weights: array![-450793, -754002000, 127277390, 78540533, -368024020, 27218976, 228524240, 310650950, -268161100, 142584180, 508852400, 148177880, -521483640, 252229830, -416623600, 555128560, 55494336, 52975070, -716501560, -67122390, 105061040, 19339220, -206819820, 58880900, 186092880, 130702880, -142485470, -236130740, 40786642].span(), + left_children: array![1, 3, 5, 0, 0, 7, 0, 9, 11, 13, 15, 0, 17, 19, 21, 23, 0, 25, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 0, 10, 12, 14, 16, 0, 18, 20, 22, 24, 0, 26, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 1, 7, 0, 0, 0, 0, 2, 9, 2, 5, 0, 3, 9, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-38660000, 5160000, 1550000, 78540533, -368024020, 11170000, 228524240, 1200000, -22070000, 1120000, 1910000, 148177880, 670000, -15490000, 160000, 160000, 55494336, 40000, 38600000, -67122390, 105061040, 19339220, -206819820, 58880900, 186092880, 130702880, -142485470, -236130740, 40786642].span() +}; + let tree_2 = xgb_inference::Tree { + base_weights: array![140665, 113101090, -486468900, 3516940, 386841650, -267119900, -13042635, 238260800, -254877150, 177248390, 240408980, 272553660, -145406680, 310631130, -165063480, -392440620, 118046080, -61690137, 361407420, 33270703, 103006140, 186649370, 427649800, 156702600, -169554260, -665662600, -1858057, -47788480, 34668634, 133342660, 74802080, -3342422, 62262775, 34872363, 135613730, -2347383, 72863556, 25724240, -227604050, 69136940, -56201416, -1428281, 35089220].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 0, 17, 19, 0, 21, 23, 25, 0, 27, 29, 0, 0, 31, 33, 35, 0, 37, 39, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 0, 18, 20, 0, 22, 24, 26, 0, 28, 30, 0, 0, 32, 34, 36, 0, 38, 40, 0, 0, 0, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 8, 9, 3, 1, 0, 2, 8, 7, 0, 5, 0, 0, 0, 0, 5, 0, 0, 8, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![560000, 140000, -21640000, 670000, 6810000, -267119900, 3720000, 40000, 930000, 177248390, 120000, 2460000, -145406680, 9200000, 2580000, 180000, 118046080, 19840000, 360000, 33270703, 103006140, 0, 300000, 1270000, -169554260, 1890000, 0, -47788480, 34668634, 133342660, 58650000, -3342422, 62262775, 34872363, 135613730, -2347383, 72863556, 25724240, -227604050, 69136940, -56201416, -1428281, 35089220].span() +}; + let tree_3 = xgb_inference::Tree { + base_weights: array![1780101, -427130660, 74446670, 136142870, -206394780, 15705566, 134087400, 17085782, 56057056, 182404900, -133694030, 97127454, 351478520, 283302080, -260297600, 152871850, -136195900, 128959810, 49687357, 90892596, 24789375, 85942737, -329298730, 20741469, 60303674, 39959140, -141656250, -135447720, 9320567].span(), + left_children: array![1, 3, 5, 7, 0, 9, 0, 0, 0, 11, 13, 15, 17, 19, 21, 23, 25, 0, 0, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 0, 10, 0, 0, 0, 12, 14, 16, 18, 20, 22, 24, 26, 0, 0, 0, 0, 0, 28, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 1, 7, 0, 0, 0, 0, 0, 0, 2, 9, 8, 4, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-38660000, 5160000, 1550000, 20410000, -206394780, 9510000, 134087400, 17085782, 56057056, 2050000, -22070000, 40000, 220000, 600000, 3580000, 1880000, 2580000, 128959810, 49687357, 90892596, 24789375, 85942737, 30880000, 20741469, 60303674, 39959140, -141656250, -135447720, 9320567].span() +}; + let tree_4 = xgb_inference::Tree { + base_weights: array![4692090, 69243340, -273735770, 2267330, 236541650, -148399540, -13701340, 137836450, -146972070, 104494130, 154071280, 159289770, -89235820, 186215170, -126421580, -339203170, 41144568, 85084420, 92902423, 15617462, 64027423, 96859660, 274736900, 106557810, -123803970, -460442240, 29150626, 201670510, -44261475, 112013620, -19552383, 19866927, 60483813, 16007637, 88721796, -7989141, 55940160, 14084590, -161890090, 18208536, 68529180, -49548166, 35378320, 47198392, 8210186].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 0, 17, 19, 0, 21, 23, 25, 27, 29, 0, 0, 0, 31, 33, 35, 0, 37, 0, 39, 41, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 0, 18, 20, 0, 22, 24, 26, 28, 30, 0, 0, 0, 32, 34, 36, 0, 38, 0, 40, 42, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 8, 9, 3, 1, 0, 2, 8, 3, 0, 7, 0, 0, 0, 0, 7, 0, 3, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 5, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![560000, 140000, -21640000, 670000, 6810000, -148399540, 3720000, 40000, 2330000, 104494130, 1030000, 2460000, -89235820, 9200000, 2580000, 270000, 9650000, 6500000, 92902423, 15617462, 64027423, 4370000, 300000, 1270000, -123803970, 3810000, 29150626, 160000, 360000, 330000, -19552383, 19866927, 60483813, 16007637, 88721796, -7989141, 55940160, 14084590, -161890090, 18208536, 68529180, -49548166, 35378320, 47198392, 8210186].span() +}; + let tree_5 = xgb_inference::Tree { + base_weights: array![5319830, 53763100, -203768090, 3127144, 180157540, -4756529, -112100980, 54479960, -215513230, 230728560, 82176450, -67045470, 127431800, 122330570, -80107030, -337532890, 100363800, 97310840, 274006030, -45473828, 42699400, 12493946, 51221954, 88668480, 67734120, -115053320, 63639970, -21274884, -135933640, 47549124, -2385410, 37851642, 1609043, 14960449, 303050980, -16619531, -3843692, 10578498, 52555700, 30453050, -35898224, 20990450, 100702170].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 17, 19, 0, 21, 23, 25, 27, 29, 31, 33, 35, 0, 0, 0, 37, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 18, 20, 0, 22, 24, 26, 28, 30, 32, 34, 36, 0, 0, 0, 38, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 42, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 8, 8, 8, 4, 9, 0, 3, 4, 3, 4, 0, 0, 1, 3, 8, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![560000, 140000, 40000, 40000, 100000, -46060000, -112100980, 1480000, 100000, 860000, 270000, -67045470, 2460000, 6400000, 2330000, 70000, 2580000, 15870000, 380000, 19840000, 42699400, 12493946, 51221954, 10250000, 67734120, -115053320, 12020000, -21274884, -135933640, 47549124, -2385410, 37851642, 1609043, 14960449, -180000, -16619531, -3843692, 10578498, 52555700, 30453050, -35898224, 20990450, 100702170].span() +}; + let tree_6 = xgb_inference::Tree { + base_weights: array![3448030, -187138510, 35692392, 54196970, -298310420, 11274458, 249505080, 4040899, 26456836, -104695460, -34324884, 172118850, -16343193, 22465782, 80835516, 55378930, 12716367, -72467490, 108219810, 73895050, -122259310, 165075050, 37837110, -1604649, 34322540, -48324448, 8257344, 15180176, 67091880, -1980029, 19745836].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 0, 0, 0, 0, 15, 17, 0, 0, 0, 0, 19, 21, 23, 25, 27, 29, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 0, 0, 0, 0, 16, 18, 0, 0, 0, 0, 20, 22, 24, 26, 28, 30, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 1, 7, 0, 0, 9, 3, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 1, 7, 0, 8, 1, 7, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-38660000, 5160000, 1580000, 20410000, 28170000, -27650000, 200000, 4040899, 26456836, -104695460, -34324884, 45150000, -4340000, 22465782, 80835516, 55378930, 12716367, 3580000, 170000, 1270000, 220000, 290000, 720000, -1604649, 34322540, -48324448, 8257344, 15180176, 67091880, -1980029, 19745836].span() +}; + let tree_7 = xgb_inference::Tree { + base_weights: array![4701339, -141849290, 29460745, 45562207, -228677400, 150491920, 4665297, 3232695, 22488283, -79568580, -27459885, 180725670, 59959863, 63829755, -48098120, 198836720, 10808907, -2583750, 25706448, 8581725, 128342720, -91678440, 119712760, 15835782, 65246910, 51758954, -166987760, 47499670, 10883408, 21519482, -145938780, 4376133, 41346143, -10278340, 18808084, -6658946, -68485547, -77594653, 29548010, -52805380, 9915118].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 0, 0, 0, 0, 15, 17, 19, 21, 23, 0, 0, 0, 25, 27, 29, 31, 0, 0, 33, 35, 0, 0, 37, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 0, 0, 0, 0, 16, 18, 20, 22, 24, 0, 0, 0, 26, 28, 30, 32, 0, 0, 34, 36, 0, 0, 38, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 1, 9, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 1, 7, 9, 0, 0, 0, 1, 4, 3, 1, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-38660000, 5160000, -22070000, 20410000, 28170000, 470000, 9650000, 3232695, 22488283, -79568580, -27459885, 61210000, 25070000, 3960000, 1550000, -36800000, 10808907, -2583750, 25706448, 3770000, 480000, 860000, 4400000, 15835782, 65246910, 0, 160000, 47499670, 10883408, 110000, 24330000, 4376133, 41346143, -10278340, 18808084, -6658946, -68485547, -77594653, 29548010, -52805380, 9915118].span() +}; + let tree_8 = xgb_inference::Tree { + base_weights: array![3000643, -13956160, 131921190, 10718164, -115602830, 11075860, 47230548, 64021610, -18681238, 8434600, -166632810, 8074052, 91322560, -197106800, 12725970, -58695514, -24433888, -28150780, 19727228, 37104453, 113793550, 11288145, -79559125, 99767840, -25183687, -10296973, -519141, -7148, 18559375, 38411996, 14210860, -3006602, 39770425, -33419284, 7764074].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 0, 15, 17, 19, 21, 23, 0, 0, 25, 0, 27, 29, 0, 0, 31, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 0, 16, 18, 20, 22, 24, 0, 0, 26, 0, 28, 30, 0, 0, 32, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 4, 3, 1, 0, 0, 0, 0, 1, 0, 1, 6, 1, 0, 3, 0, 0, 0, 0, 7, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1550000, 560000, 440000, 3580000, 4200000, 11075860, 47230548, 1880000, 3860000, 8434600, 18840000, 60000, 300000, 1890000, 400000, -58695514, -24433888, 1270000, 19727228, 190000, 1810000, 11288145, -79559125, 4370000, 2330000, -10296973, -519141, -7148, 18559375, 38411996, 14210860, -3006602, 39770425, -33419284, 7764074].span() +}; + let tree_9 = xgb_inference::Tree { + base_weights: array![2196582, -85904724, 17091777, 27531445, -138453300, 5989425, 40739790, 2110391, 13353282, -52018800, -20669180, 17524043, -112861560, 41979883, -16621378, -11088828, -45341956, 25171777, 93610547, -54021625, -263151, 13220639, -5439258, 34310577, 8343086, 26989874, -8035719].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 0, 0, 0, 0, 13, 15, 17, 19, 0, 0, 21, 23, 0, 25, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 0, 0, 0, 0, 14, 16, 18, 20, 0, 0, 22, 24, 0, 26, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 7, 4, 0, 3, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-38660000, 5160000, 24520000, 20410000, 15890000, 20820000, 40739790, 2110391, 13353282, -52018800, -20669180, 13730000, 11620000, 2400000, 13980000, -11088828, -45341956, 190000, 420000, -54021625, 200000, 13220639, -5439258, 34310577, 8343086, 26989874, -8035719].span() +}; + let tree_10 = xgb_inference::Tree { + base_weights: array![691291, -10350576, 84799120, 40034375, -30727362, 6965313, 30436993, -4407254, 62622460, -49368588, 47251694, -32445392, 43792056, 203438, 68192114, 55364060, -64190295, 25187227, 14905925, -10954835, -2424375, 16319884, 3386543, 28131270, 46878710, 1247813, 20137617, 3639106, -86951730, -8785677, 8684444, 3341680, 17080644, -45918400, 12707798, 4239522, -30619550, -4393184, 439629].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 15, 17, 19, 21, 0, 23, 25, 27, 0, 29, 0, 0, 0, 0, 0, 31, 0, 0, 33, 35, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 16, 18, 20, 22, 0, 24, 26, 28, 0, 30, 0, 0, 0, 0, 0, 32, 0, 0, 34, 36, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 1, 3, 0, 9, 0, 0, 6, 1, 7, 7, 0, 1, 0, 6, 1, 3, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1550000, 3580000, 440000, 1880000, -1610000, 6965313, 30436993, 60000, 140000, 0, 170000, 1270000, 0, 203438, 60000, 5160000, 860000, 25187227, 9940000, -10954835, -2424375, 16319884, 3386543, 28131270, 270000, 1247813, 20137617, 3860000, -1020000, 160000, 8684444, 3341680, 17080644, -45918400, 12707798, 4239522, -30619550, -4393184, 439629].span() +}; + let tree_11 = xgb_inference::Tree { + base_weights: array![1722145, -5299659, 75164060, 5916361, -53460770, 27223712, 8800156, -5022684, 47145773, 13306152, -77194880, 18787590, -41142944, 73159910, -16668945, 4287109, 6054493, -31984033, -32337344, -1414557, 42474643, 3522960, -81237415, 26236618, 11100176, -10224181, 5334903, 1555488, 373711, -10842891, -2567227, 5355190, -25327657, 5322037, 19275743, -39030646, 10946661, -33521484, -4553027].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 15, 17, 19, 21, 23, 25, 27, 0, 0, 29, 31, 33, 35, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 16, 18, 20, 22, 24, 26, 28, 0, 0, 30, 32, 34, 36, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 4, 8, 0, 0, 0, 0, 0, 9, 0, 1, 9, 3, 0, 1, 0, 0, 0, 1, 9, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1580000, 560000, 280000, 26060000, 4200000, 27223712, 8800156, 11620000, -19170000, 2460000, 15890000, -4580000, 1250000, 45150000, 12830000, 1420000, 6054493, -31984033, 23200000, -7270000, 1880000, 110000, 2010000, 26236618, 11100176, -10224181, 5334903, 1555488, 373711, -10842891, -2567227, 5355190, -25327657, 5322037, 19275743, -39030646, 10946661, -33521484, -4553027].span() +}; + let tree_12 = xgb_inference::Tree { + base_weights: array![757399, -4765253, 58619010, 23046835, -17298810, 21098439, 7040157, 12127539, 53379220, -46831290, -6972257, 22416434, -11978946, 17620197, 4794024, 28410043, -23812944, -2047656, 47173047, 46131772, 13438889, -56636720, 2688350, -6838220, 7319180, 15989864, 1562110, -1424824, 17177368, -2608477, 7786485, -24412105, -4264395, 14277037, -7838971].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 0, 15, 17, 0, 0, 0, 19, 21, 23, 25, 27, 29, 31, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 0, 16, 18, 0, 0, 0, 20, 22, 24, 26, 28, 30, 32, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 1, 8, 8, 1, 0, 0, 8, 1, 0, 2, 1, 0, 0, 0, 3, 2, 6, 5, 0, 3, 9, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1580000, 3770000, 280000, 90000, 3860000, 21098439, 7040157, 40000, 1810000, -46831290, 1130000, 300000, -11978946, 17620197, 4794024, 400000, 2300000, 60000, 1910000, 9200000, 1200000, -18200000, 170000, -6838220, 7319180, 15989864, 1562110, -1424824, 17177368, -2608477, 7786485, -24412105, -4264395, 14277037, -7838971].span() +}; + let tree_13 = xgb_inference::Tree { + base_weights: array![654722, -6602728, 29119363, 19343144, -20833691, 64112660, 8710781, -4736133, 27685312, -30046173, -15705860, 21162540, 5759414, 29895053, -18452500, -20236035, 5252734, 36804080, 12006139, -1082609, -40369623, 1986797, 9967500, -6416544, -1006289, -7527539, -850313, 48591470, 9921973, -5425352, 27659296, -16795480, 21023672, -53247516, 20305860, 16775743, 1792969, -1833281, 5190977, 9123223, 2498027, -1402942, -26151390, 10343053, -2332588, -22557443, -6728692, -397910, 9535547].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 0, 27, 29, 31, 33, 0, 0, 0, 0, 0, 0, 35, 37, 0, 39, 41, 43, 45, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 0, 28, 30, 32, 34, 0, 0, 0, 0, 0, 0, 36, 38, 0, 40, 42, 44, 46, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![0, 0, 2, 2, 0, 0, 4, 6, 9, 0, 0, 0, 0, 2, 1, 0, 0, 4, 1, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9, 1, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![30880000, 4520000, 790000, 250000, 4930000, 61210000, 100000, 60000, -2240000, -30046173, 16750000, 21162540, 5759414, 1100000, 20570000, 1270000, 5252734, 130000, 3960000, -10520000, 540000, 1986797, 9967500, -6416544, -1006289, -7527539, -850313, 2580000, 1880000, -5425352, 630000, -10790000, 11670000, -18770000, 20410000, 16775743, 1792969, -1833281, 5190977, 9123223, 2498027, -1402942, -26151390, 10343053, -2332588, -22557443, -6728692, -397910, 9535547].span() +}; + let tree_14 = xgb_inference::Tree { + base_weights: array![262931, -3457037, 39278775, -10323461, 21312834, 5636719, 16051642, 11002298, -20682709, 33021262, 8403857, -9466719, 18028665, -31845276, -12023731, 12316348, 16732715, -5489356, 5351297, -17652246, 3491309, 27393054, -2432969, 17175640, -26076883, -3431426, 8980704, -8448307, 508125, -6022031, -1558301, 11253961, 3538289, -10182423, 4178848, 1281782, 11606339, -11504904, 3912656, -3078985, -722754].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 15, 17, 19, 21, 0, 23, 0, 25, 27, 0, 29, 0, 31, 33, 35, 37, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 16, 18, 20, 22, 0, 24, 0, 26, 28, 0, 30, 0, 32, 34, 36, 38, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 9, 1, 1, 7, 0, 0, 0, 1, 2, 7, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 6, 0, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1580000, -3110000, 15870000, 3770000, 170000, 5636719, 16051642, 1880000, 3860000, 1120000, 720000, 590000, 13730000, -31845276, 1130000, 12316348, 160000, 2460000, 5351297, 1270000, 3491309, 60000, 16590000, 130000, 540000, -3431426, 8980704, 160000, 508125, -6022031, -1558301, 11253961, 3538289, -10182423, 4178848, 1281782, 11606339, -11504904, 3912656, -3078985, -722754].span() +}; + let tree_15 = xgb_inference::Tree { + base_weights: array![474713, -5064055, 22200252, 11689512, -14230925, 13091884, 9260547, -5277012, 14156332, 5632650, -23196204, 22334961, -8280859, 18880437, 5300635, -21207910, 16935938, -53385070, -5849380, 9105157, 3221866, -4926445, 786016, 3321973, 23697482, -10247070, 16678671, 10504688, -17451855, -1777969, 24314732, -14758907, -24400266, -16523438, 669800, -1287383, 3924258, 8803165, 2791055, -3689258, -614356, 5594180, 1320645, -1082227, 5809336, 12030704, 2993766, -8011289, 631836, -1348937, 7366719].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 0, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 0, 0, 0, 0, 35, 37, 39, 41, 43, 0, 0, 45, 47, 0, 0, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 0, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 0, 0, 0, 0, 36, 38, 40, 42, 44, 0, 0, 46, 48, 0, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![0, 0, 2, 0, 3, 0, 4, 0, 9, 2, 3, 0, 1, 9, 1, 0, 9, 2, 9, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 9, 1, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![30880000, 4520000, 790000, 0, 440000, 13091884, 100000, -5277012, -2240000, 110000, 1720000, 45150000, 12830000, -10160000, 9940000, 9510000, -33250000, 1130000, -58230000, 9105157, 3221866, -4926445, 786016, 2130000, 130000, 160000, 630000, 7520000, -17451855, -1777969, -15040000, 5480000, -24400266, -16523438, 2990000, -1287383, 3924258, 8803165, 2791055, -3689258, -614356, 5594180, 1320645, -1082227, 5809336, 12030704, 2993766, -8011289, 631836, -1348937, 7366719].span() +}; + let tree_16 = xgb_inference::Tree { + base_weights: array![201126, -2213019, 31061954, -6599977, 13926115, 2119492, 13411485, -2605451, -27744873, 22242578, 4908447, -8897384, 14914485, -14824922, -12284961, 11008887, 8374688, -3252832, 10455782, -20636644, 10750451, 18281392, -3318223, -5325322, -901823, 5979141, -2363379, -5746484, 634219, 3558047, 725742, -8895557, 2053008, 4262217, -4609922, 2160469, 7894297, -596543, 190723, -2063731, -522188].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 15, 17, 19, 21, 0, 23, 25, 0, 27, 29, 31, 33, 35, 0, 0, 37, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 16, 18, 20, 22, 0, 24, 26, 0, 28, 30, 32, 34, 36, 0, 0, 38, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 9, 0, 4, 7, 0, 0, 4, 4, 0, 7, 7, 1, 0, 4, 3, 0, 0, 0, 8, 9, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, -3110000, 37470000, 560000, 170000, 2119492, 13411485, 270000, 1110000, 1240000, 720000, 350000, 20820000, -14824922, 2670000, -1290000, 8374688, 2460000, 4200000, 140000, -7510000, 3580000, -3318223, -5325322, 15050000, 5979141, -2363379, 160000, 634219, 3558047, 725742, -8895557, 2053008, 4262217, -4609922, 2160469, 7894297, -596543, 190723, -2063731, -522188].span() +}; + let tree_17 = xgb_inference::Tree { + base_weights: array![270835, -1653601, 24849454, 9284071, -5807314, 5651953, 10729141, 1548698, 15317500, -39665314, -1735284, 2108789, 434590, -5369987, 11539551, 10472696, 9261893, 5637774, -17693379, -11821172, 99777, -3440000, 545020, 5636758, -1531406, -421992, 10771289, 3048057, -18097917, -408867, 490586, 3725173, -112559, 1927434, -2998696, -12283888, -1601695].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 17, 19, 0, 0, 21, 23, 0, 25, 0, 0, 0, 27, 0, 29, 0, 0, 0, 31, 33, 35, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 18, 20, 0, 0, 22, 24, 0, 26, 0, 0, 0, 28, 0, 30, 0, 0, 0, 32, 34, 36, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 1, 0, 1, 1, 0, 0, 6, 1, 0, 9, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 3580000, 37470000, 300000, 3860000, 16390000, 10729141, 190000, 530000, 1890000, -58230000, 2108789, 434590, 160000, 270000, 10472696, 590000, 5637774, -17693379, -11821172, 20820000, -3440000, 1880000, 5636758, -1531406, -421992, 1910000, 630000, 1590000, -408867, 490586, 3725173, -112559, 1927434, -2998696, -12283888, -1601695].span() +}; + let tree_18 = xgb_inference::Tree { + base_weights: array![25661, -1730463, 18460677, -4960055, 9927399, 2587688, 10145391, 6057215, -10322806, 15610603, 3713672, -338889, 11780295, -15973164, -5976367, 7579102, 5921836, -2849219, 2466375, 6375911, -10326367, 5834668, 8901798, 6174219, -11780007, 4026914, -1492910, -2043281, 741667, -1301719, 2816016, -4066758, -95684, 181133, 4305469, 201108, 4691514, -7238894, -899858, 5977, 327773].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 15, 17, 19, 21, 0, 23, 25, 0, 27, 0, 29, 31, 33, 0, 35, 37, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 16, 18, 20, 22, 0, 24, 26, 0, 28, 0, 30, 32, 34, 0, 36, 38, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 9, 1, 1, 7, 0, 0, 9, 1, 0, 1, 9, 0, 0, 2, 3, 0, 0, 0, 3, 2, 3, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1580000, -3110000, 28150000, 3770000, 170000, 2587688, 10145391, -15490000, 3860000, 1240000, 9940000, -27650000, 11170000, -15973164, 1130000, -1290000, 5921836, 160000, 2466375, 270000, 430000, 540000, 8901798, 130000, 3820000, 4026914, -1492910, -2043281, 2460000, -1301719, 2816016, -4066758, -95684, 181133, 4305469, 201108, 4691514, -7238894, -899858, 5977, 327773].span() +}; + let tree_19 = xgb_inference::Tree { + base_weights: array![216033, -2120244, 11295345, 2279795, -11780317, 539297, 16605957, -3802714, 7429244, -21275356, 2747900, -2250820, 2520469, 22248593, 1620352, 2624721, -18166277, 19348567, 2785409, -29816210, 1125195, 12702500, -10382324, 26165430, 987188, 7672119, -3518694, -6001367, -8499219, 313652, 22800078, 6116051, -2846373, -7201953, -36153970, 4565886, -1379531, 633750, 4446563, -4489180, 504375, 2046446, 9101875, -22002, 3700219, -2961856, 1114541, -2346328, -81328, 7747412, 1605235, 2812793, 551035, -3063721, 1569375, -385898, -2854981, -14540420, -2305156, 1645313, 409336].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 0, 0, 23, 0, 25, 27, 29, 31, 33, 35, 37, 39, 41, 0, 43, 45, 47, 0, 0, 49, 51, 53, 55, 57, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 0, 0, 24, 0, 26, 28, 30, 32, 34, 36, 38, 40, 42, 0, 44, 46, 48, 0, 0, 50, 52, 54, 56, 58, 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![8, 8, 3, 9, 8, 2, 0, 9, 9, 9, 4, 0, 0, 3, 0, 6, 7, 1, 2, 1, 0, 3, 0, 0, 0, 0, 1, 4, 0, 0, 2, 7, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![220000, 40000, 860000, -15490000, 140000, 1860000, 61210000, -19170000, -8190000, -5570000, 550000, -2250820, 2520469, 7360000, 1620352, 410000, 170000, 140000, 1130000, 3740000, 11620000, 440000, 30880000, 2150000, 987188, 10250000, 10260000, 340000, -8499219, 313652, 3350000, 180000, 5480000, 1270000, 570000, 2580000, -1379531, 633750, 4446563, -4489180, 504375, 2046446, 9101875, -22002, 3700219, -2961856, 1114541, -2346328, -81328, 7747412, 1605235, 2812793, 551035, -3063721, 1569375, -385898, -2854981, -14540420, -2305156, 1645313, 409336].span() +}; + let tree_20 = xgb_inference::Tree { + base_weights: array![123995, -1594889, 10253008, -7731973, -733654, 4842920, 6378672, -2847587, 6113532, 7058984, -1203516, 3689551, -6239141, 10009989, 1939941, 2550498, 834727, -842988, 301406, -1525558, 6971172, -9870509, -3159612, 3286192, 651914, -1931445, 4649062, -1646524, 347180, 6404414, 900521, 2180977, -1900345, -1277227, 78906, 1803750, 520781].span(), + left_children: array![1, 3, 5, 0, 7, 9, 0, 11, 13, 15, 17, 19, 21, 23, 25, 0, 0, 0, 0, 27, 29, 0, 31, 0, 0, 33, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 8, 10, 0, 12, 14, 16, 18, 20, 22, 24, 26, 0, 0, 0, 0, 28, 30, 0, 32, 0, 0, 34, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![0, 9, 1, 0, 9, 7, 0, 1, 7, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![37470000, -58230000, 27910000, -7731973, -3110000, 870000, 6378672, 3770000, 170000, 15560000, 58650000, 300000, 3860000, 8300000, 9940000, 2550498, 834727, -842988, 301406, 1880000, 530000, -9870509, 200000, 3286192, 651914, 160000, 200000, -1646524, 347180, 6404414, 900521, 2180977, -1900345, -1277227, 78906, 1803750, 520781].span() +}; + let tree_21 = xgb_inference::Tree { + base_weights: array![130463, -2141953, 6103639, 1214180, -14151349, 14801619, 950601, -3225206, 4993004, -19698047, 479883, 1818820, 7329766, 4042791, -3211289, 918880, -15012422, 13771223, 1601103, -23823958, -1464063, 783633, -887520, -1785078, 7628014, -2220391, 5997852, -5773047, -7795489, 214512, 4871836, 3287974, -4703223, -463066, -27971330, 633789, -1526328, 2838328, 609063, -2017902, 1866035, 504180, 2320898, -2173086, -204199, 174492, 2412914, -2004024, 184102, -2561778, -9208360].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 0, 0, 23, 0, 25, 27, 29, 31, 33, 0, 0, 0, 35, 37, 39, 41, 43, 0, 0, 0, 45, 47, 0, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 0, 0, 24, 0, 26, 28, 30, 32, 34, 0, 0, 0, 36, 38, 40, 42, 44, 0, 0, 0, 46, 48, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![8, 8, 9, 9, 9, 5, 4, 9, 9, 4, 0, 0, 0, 2, 0, 7, 1, 1, 0, 0, 0, 0, 0, 7, 7, 0, 9, 4, 0, 0, 0, 6, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![140000, 40000, -22070000, -15490000, -5570000, 470000, 480000, -18770000, -8190000, 570000, 11620000, 1818820, 7329766, 1860000, -3211289, 190000, 9920000, 140000, 14550000, 1270000, -1464063, 783633, -887520, 170000, 850000, 25070000, -49240000, 150000, -7795489, 214512, 4871836, 190000, 17070000, -463066, 3740000, 633789, -1526328, 2838328, 609063, -2017902, 1866035, 504180, 2320898, -2173086, -204199, 174492, 2412914, -2004024, 184102, -2561778, -9208360].span() +}; + let tree_22 = xgb_inference::Tree { + base_weights: array![-11001, -833551, 10519844, 1777279, -4045838, 1343906, 5202071, -84141, 9500893, -8200938, -1308145, 2658315, -5834883, 678828, 3583078, 921680, -9266146, 5715402, -3023877, -10635416, 1092734, 1840317, -3672266, -4198125, -5190938, 682457, 4124414, -2989014, 939750, -3664125, -411563, -173555, 496553, 1062593, -474844, -1578750, 164766, -1650439, 152285].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 0, 15, 17, 19, 0, 0, 21, 23, 25, 27, 29, 31, 33, 35, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 0, 16, 18, 20, 0, 0, 22, 24, 26, 28, 30, 32, 34, 36, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 7, 0, 2, 7, 0, 0, 2, 8, 0, 1, 8, 6, 0, 0, 3, 0, 0, 8, 5, 0, 9, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 240000, 45150000, 2300000, 270000, 1343906, 5202071, 1120000, 40000, -8200938, 15870000, 40000, 740000, 678828, 3583078, 6500000, 26060000, 10250000, 90000, 300000, 2130000, -7900000, 37140000, 23200000, -5190938, 682457, 4124414, -2989014, 939750, -3664125, -411563, -173555, 496553, 1062593, -474844, -1578750, 164766, -1650439, 152285].span() +}; + let tree_23 = xgb_inference::Tree { + base_weights: array![-94468, -777960, 8673125, 1334874, -3373963, 3471875, 4421719, -92057, 7259599, -6560781, -1193967, 1229141, 239414, 2653746, -3419671, 543047, 9077344, 607724, -7601172, 1113648, 4094297, -10815000, 619961, 1029219, 3509453, -2136377, 2030729, -3238360, -4412285, -816445, 785645, -4211133, 311016, 749609, -2443301, -1204141, 699180, 1362279, -446032, -1279102, 129434].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 17, 0, 0, 19, 21, 0, 23, 25, 27, 29, 0, 31, 33, 0, 0, 35, 37, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 18, 0, 0, 20, 22, 0, 24, 26, 28, 30, 0, 32, 34, 0, 0, 36, 38, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 7, 0, 2, 7, 4, 0, 0, 8, 0, 1, 0, 0, 0, 4, 0, 0, 7, 0, 9, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 240000, 45150000, 2300000, 270000, 2840000, 4421719, 13730000, 40000, -6560781, 15870000, 1229141, 239414, 11170000, 100000, 543047, 4520000, 720000, 26060000, -15490000, 4094297, 18210000, 2190000, 1029219, 3509453, 37140000, 14550000, 23200000, -4412285, -816445, 785645, -4211133, 311016, 749609, -2443301, -1204141, 699180, 1362279, -446032, -1279102, 129434].span() +}; + let tree_24 = xgb_inference::Tree { + base_weights: array![-99494, 2408189, -1809632, 818325, 8101302, -5013390, 668587, 2557188, -2170703, 533724, 11751660, 723549, -7942155, 3493658, -5418262, -57386, 7797813, -5812305, 618815, -307090, 547266, 4137618, 844570, -1105469, 1754649, -15304558, -496931, 5269597, -640495, 380371, -8973515, -1718506, 3279199, 2776875, 294609, -63105, -2282852, 705908, -1899740, -906016, 606836, -859922, -5165672, -1455404, 788086, 2960742, 7909843, -936641, 1380859, 523398, -556875, -2006380, -3884844, -786699, 198594, -335801, 1535547, 49688, -904570, -25078, 259453, -742148, -98320, 202266, 957321, 306914, 2812734, 110039, 478984, -139863, -763008].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 0, 0, 0, 0, 39, 0, 41, 43, 45, 47, 49, 51, 53, 55, 0, 0, 0, 0, 0, 57, 0, 59, 0, 0, 61, 0, 63, 65, 0, 67, 0, 0, 69, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 0, 0, 0, 0, 40, 0, 42, 44, 46, 48, 50, 52, 54, 56, 0, 0, 0, 0, 0, 58, 0, 60, 0, 0, 62, 0, 64, 66, 0, 68, 0, 0, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 6, 2, 8, 2, 1, 4, 0, 8, 0, 2, 1, 4, 3, 5, 1, 3, 0, 6, 0, 0, 0, 0, 1, 0, 0, 1, 3, 8, 3, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1130000, 190000, 2050000, 40000, 490000, 3770000, 480000, 10250000, 90000, 16220000, 740000, 3430000, 210000, 7360000, 140000, 5480000, 200000, 1270000, 60000, -307090, 547266, 4137618, 844570, 300000, 1754649, 160000, 22700000, 3980000, 60000, 5510000, 11180000, 60000, 2460000, 2776875, 294609, -63105, -2282852, 705908, 11170000, -906016, 2130000, -859922, -5165672, 12830000, 788086, 2080000, 5160000, -936641, 11620000, 523398, -556875, 1420000, -3884844, -786699, 198594, -335801, 1535547, 49688, -904570, -25078, 259453, -742148, -98320, 202266, 957321, 306914, 2812734, 110039, 478984, -139863, -763008].span() +}; + let tree_25 = xgb_inference::Tree { + base_weights: array![-61868, -531989, 5968047, 840072, -2216891, 2230371, 3137813, -53633, 4550223, -4215469, -819821, 808828, 124980, 1397489, -3095625, 308047, 5754219, 587667, -4925168, -443389, 3901259, -5569075, 491641, 632031, 2245078, 1772596, -1189193, -675977, -7943066, 707344, -455859, 2694053, -40469, -1963781, -102715, -35479, 439688, -108262, 945337, -862441, 490957, -360469, 135117, -3167754, -1065391].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 17, 0, 0, 19, 21, 0, 23, 25, 27, 29, 31, 33, 35, 0, 0, 37, 39, 41, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 18, 0, 0, 20, 22, 0, 24, 26, 28, 30, 32, 34, 36, 0, 0, 38, 40, 42, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 7, 0, 2, 7, 4, 0, 2, 8, 0, 6, 0, 0, 2, 6, 0, 0, 9, 2, 9, 3, 1, 2, 0, 0, 3, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 240000, 45150000, 2300000, 270000, 2840000, 3137813, 1120000, 40000, -4215469, 650000, 808828, 124980, 370000, 740000, 308047, 4520000, -7900000, 3720000, -27650000, 200000, 12560000, 2030000, 632031, 2245078, 670000, 100000, 6640000, 4230000, 707344, -455859, 2694053, -40469, -1963781, -102715, -35479, 439688, -108262, 945337, -862441, 490957, -360469, 135117, -3167754, -1065391].span() +}; + let tree_26 = xgb_inference::Tree { + base_weights: array![-38965, -561930, 3953516, 767937, -2507656, 1978125, 2667188, 3958, 3536865, -3372344, -1259477, 709594, 202031, 1743497, -2108622, 395889, 4603281, 21658, -4892969, 744312, 2659571, -6045234, 70547, 505625, 1796016, 893870, -1871745, -1535547, -2321563, -463008, 490645, -2349258, 164590, 290078, -1199531, -153738, 1255869, 278125, -1050879, -64805, -571016].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 17, 0, 0, 19, 21, 0, 23, 25, 27, 29, 0, 31, 33, 0, 0, 35, 37, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 18, 0, 0, 20, 22, 0, 24, 26, 28, 30, 0, 32, 34, 0, 0, 36, 38, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 7, 1, 2, 7, 8, 0, 0, 8, 0, 6, 0, 0, 0, 4, 0, 0, 7, 0, 9, 0, 0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1550000, 240000, 28150000, 2300000, 270000, 40000, 2667188, 13730000, 40000, -3372344, 650000, 709594, 202031, 11170000, 100000, 395889, 4520000, 870000, 11180000, -15490000, 2659571, 18210000, 2190000, 505625, 1796016, 720000, 6620000, 1420000, -2321563, -463008, 490645, -2349258, 164590, 290078, -1199531, -153738, 1255869, 278125, -1050879, -64805, -571016].span() +}; + let tree_27 = xgb_inference::Tree { + base_weights: array![-29188, -364478, 4268359, 134773, -2782209, 1556934, 2267110, 445450, -3860059, -93294, -5007422, 536563, 129316, -67871, 2119401, -2498906, 121914, 387773, -628984, -571348, -2242656, 943750, -1356727, 3740104, 427455, -60508, -253984, -335357, 521140, 451574, -873945, 365938, 1408594, 364922, -308984].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 17, 19, 0, 0, 21, 23, 0, 0, 0, 25, 0, 0, 27, 29, 31, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 18, 20, 0, 0, 22, 24, 0, 0, 0, 26, 0, 0, 28, 30, 32, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 4, 0, 1, 8, 4, 0, 3, 1, 3, 0, 0, 0, 3, 3, 0, 0, 0, 3, 0, 0, 2, 0, 9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 560000, 45150000, 22700000, 40000, 2840000, 2267110, 3980000, 24330000, 1070000, 23340000, 536563, 129316, 440000, 7360000, -2498906, 121914, 387773, 4000000, -571348, -2242656, 110000, 3810000, -30330000, 2590000, -60508, -253984, -335357, 521140, 451574, -873945, 365938, 1408594, 364922, -308984].span() +}; + let tree_28 = xgb_inference::Tree { + base_weights: array![-29096, 1261830, -909994, 446162, 4178255, -2237956, 123893, -1912305, 1207530, -86458, 1899668, 502679, -3650163, -2008008, 711311, -1058164, -223047, 2925817, -1306055, -299883, 260977, -628581, 1093535, -7214584, -73493, 1393511, -1453841, 263802, -252539, 575000, 4186998, -1513301, -51395, -1714713, 343164, -2545518, -934727, -487565, 361641, 969416, 1290469, -686191, 142057, 127207, -8496, 515859, -42305, 177422, 1652086, 189000, -350977, -618106, -153516, -14297, 146797, -240117, -39316, 358396, -395156, -45996, 109922].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 0, 21, 23, 0, 25, 0, 27, 29, 31, 0, 0, 33, 0, 35, 37, 39, 41, 43, 0, 45, 47, 0, 49, 51, 53, 0, 0, 55, 0, 57, 0, 0, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 0, 22, 24, 0, 26, 0, 28, 30, 32, 0, 0, 34, 0, 36, 38, 40, 42, 44, 0, 46, 48, 0, 50, 52, 54, 0, 0, 56, 0, 58, 0, 0, 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 6, 2, 0, 2, 1, 9, 1, 8, 0, 0, 1, 4, 0, 4, 0, 0, 9, 2, 0, 0, 1, 0, 7, 1, 6, 4, 0, 0, 3, 1, 0, 6, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1130000, 190000, 2050000, 2460000, 490000, 3770000, -58230000, 290000, 40000, 16220000, 1899668, 3430000, 210000, -2008008, 630000, -1058164, 1270000, -19170000, 110000, -299883, 260977, 300000, 1093535, 270000, 22700000, 4070000, 1990000, 160000, -252539, 0, 150000, -1513301, 60000, 160000, 2130000, -2545518, -934727, 12830000, 361641, 12020000, 1290469, -686191, 1420000, 127207, -8496, 515859, -42305, 177422, 1652086, 189000, -350977, -618106, -153516, -14297, 146797, -240117, -39316, 358396, -395156, -45996, 109922].span() +}; + let tree_29 = xgb_inference::Tree { + base_weights: array![-64838, -305481, 3028359, 931914, -848646, 1048731, 1642031, -674051, 1668332, -1418877, -458826, 357188, 93457, -2403203, 2432552, 138037, 3178906, -1706836, -192483, -897598, -7207, 986543, 108105, 437612, -293848, 374023, 5189063, -380107, 1096875, -130488, 196660, 141953, 11484, 1909453, 249258, 301460, -302867].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 17, 0, 0, 19, 21, 23, 25, 0, 27, 0, 0, 0, 0, 29, 0, 31, 33, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 18, 0, 0, 20, 22, 24, 26, 0, 28, 0, 0, 0, 0, 30, 0, 32, 34, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 1, 0, 0, 1, 4, 0, 6, 9, 0, 9, 0, 0, 1, 1, 1, 2, 0, 6, 0, 0, 0, 0, 3, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 3770000, 45150000, 1880000, 3960000, 2840000, 1642031, 60000, -16740000, -1418877, -58230000, 357188, 93457, 290000, 0, 3430000, 370000, -1706836, 4070000, -897598, -7207, 986543, 108105, 270000, -293848, 7520000, 100000, 9650000, 1096875, -130488, 196660, 141953, 11484, 1909453, 249258, 301460, -302867].span() +}; + let tree_30 = xgb_inference::Tree { + base_weights: array![-11070, 916936, -644560, 606279, 1395703, -816065, 932344, 1208789, -538867, -1440385, 320495, -460039, 1647412, -2215723, 496317, -1971609, 1185078, 1247042, -435764, 3023134, -106201, -568750, -1073496, 1026250, -552344, -2546984, 494063, 103945, 392432, 604547, -448828, 584570, -1043359, 279668, 1173965, 263789, -393457, -6152, -249785, 134688, 378438, -387246, 138691, -925820, 33398, 24727, 172910, -223418, 21445, 25547, 216797, -559805, -151688].span(), + left_children: array![1, 3, 5, 7, 0, 9, 0, 11, 13, 15, 17, 0, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 0, 39, 41, 43, 45, 0, 0, 0, 47, 49, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 0, 10, 0, 12, 14, 16, 18, 0, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 0, 40, 42, 44, 46, 0, 0, 0, 48, 50, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 1, 6, 8, 0, 7, 0, 3, 8, 8, 5, 0, 3, 1, 1, 0, 3, 3, 9, 9, 9, 0, 0, 2, 0, 5, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1130000, 27910000, 4070000, 40000, 1395703, 720000, 932344, -3130000, 90000, 220000, 120000, -460039, 400000, 3740000, 12570000, 37140000, 200000, 4130000, -12560000, -19170000, -4420000, 1270000, -1073496, 370000, 12870000, 540000, 1760000, 103945, 392432, 604547, 23340000, 6620000, 5480000, 279668, 1173965, 263789, -393457, -6152, -249785, 134688, 378438, -387246, 138691, -925820, 33398, 24727, 172910, -223418, 21445, 25547, 216797, -559805, -151688].span() +}; + let tree_31 = xgb_inference::Tree { + base_weights: array![19640, -148035, 2161953, -2769531, -18800, 1186289, 725293, -1311914, 65625, -105725, 792539, 277930, 18281, 579420, -406005, 85104, 1946484, -1063867, -166330, -51563, 552422, 1472109, -6123, 497871, -106246].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 0, 0, 13, 0, 0, 0, 15, 17, 19, 21, 0, 23, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 0, 0, 14, 0, 0, 0, 16, 18, 20, 22, 0, 24, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![0, 9, 0, 0, 6, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![58650000, -58230000, 61210000, 28170000, 3960000, 1186289, 84900000, -1311914, 65625, 3770000, 792539, 277930, 18281, 11170000, 3860000, 3580000, 13730000, -1063867, 210000, -51563, 552422, 1472109, -6123, 497871, -106246].span() +}; + let tree_32 = xgb_inference::Tree { + base_weights: array![47949, -147855, 1534961, 348036, -875859, 793862, 1008398, -14938, 1019291, -1377149, -353261, 1017656, 46875, 455593, -2599121, 179297, 1521875, 24121, -2151856, 347695, 67852, -800726, 976758, -211406, -898711, 461719, -184922, 210879, 561797, 397881, -283686, -117344, -1115098, -582891, 410977, 640059, 77099, 154658, 36973, -389203, 38401].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 17, 19, 0, 21, 23, 25, 27, 29, 31, 0, 0, 33, 35, 0, 0, 37, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 18, 20, 0, 22, 24, 26, 28, 30, 32, 0, 0, 34, 36, 0, 0, 38, 0, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 7, 1, 3, 7, 8, 0, 3, 2, 0, 6, 0, 0, 2, 0, 9, 9, 4, 0, 0, 0, 9, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1550000, 240000, 28150000, 1720000, 270000, 40000, 1008398, 1480000, 1760000, -1377149, 930000, 16390000, 46875, 110000, 16590000, -4580000, -35880000, -1020000, 11180000, 347695, 67852, -10520000, 560000, -211406, -898711, 1100000, -184922, 210879, 561797, 397881, 1280000, -117344, -1115098, -582891, 410977, 640059, 77099, 154658, 36973, -389203, 38401].span() +}; + let tree_33 = xgb_inference::Tree { + base_weights: array![20663, -107129, 1652266, 25410, -2101563, 191045, 857109, -1209158, 234574, -1335410, 49648, -570459, -947813, 1134375, 72987, 141574, -833203, 403750, 529219, 464399, -561675, -84258, 110016, 27344, 174531, 280566, -84160, -312188, 50127].span(), + left_children: array![1, 3, 5, 7, 9, 0, 0, 11, 13, 0, 0, 15, 0, 17, 19, 21, 0, 23, 0, 25, 27, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 0, 12, 14, 0, 0, 16, 0, 18, 20, 22, 0, 24, 0, 26, 28, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 1, 0, 9, 1, 0, 0, 3, 9, 0, 0, 9, 0, 1, 8, 0, 0, 8, 0, 5, 8, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 23200000, 45150000, -38660000, 24330000, 191045, 857109, 6370000, -22070000, -1335410, 49648, -39410000, -947813, 1770000, 60000, 9200000, -833203, 40000, 529219, 150000, 220000, -84258, 110016, 27344, 174531, 280566, -84160, -312188, 50127].span() +}; + let tree_34 = xgb_inference::Tree { + base_weights: array![-5038, -112079, 1366094, -51, -1792481, 493359, 728555, -1041493, 176798, -1135078, 39727, 173984, 35039, -500293, -805664, 114799, 510117, 102734, -708223, 347583, -245387, -67383, 83578, -128932, 187383, -381914, -1016].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 0, 0, 0, 17, 0, 19, 0, 21, 0, 23, 25, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 0, 0, 0, 18, 0, 20, 0, 22, 0, 24, 26, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 1, 0, 9, 1, 4, 0, 3, 6, 0, 0, 0, 0, 9, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![27910000, 23200000, 45150000, -38660000, 24330000, 2840000, 728555, 6370000, 4070000, -1135078, 39727, 173984, 35039, -39410000, -805664, 60000, 510117, 9200000, -708223, 1880000, 90000, -67383, 83578, -128932, 187383, -381914, -1016].span() +}; + let tree_35 = xgb_inference::Tree { + base_weights: array![-25207, 380148, -301420, 24355, 1680664, -880444, 66496, -1170859, 337603, -27734, 2541797, -1586589, 24023, -684844, 251781, -514609, -70820, 1085156, -45551, -136348, 123867, 873203, 215273, -2820469, -35391, -417070, 424665, 141829, 433594, -474089, 695592, -365703, -1044531, -179557, 120573, 349297, -104062, 431087, -136088, 109898, -282026, 423428, -58242, -65918, -14883, 51914, 2344, 8281, -60313, -177305, 173320, 42826, -183234].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 0, 27, 0, 0, 0, 29, 0, 0, 0, 0, 31, 33, 0, 35, 37, 0, 39, 41, 0, 0, 43, 45, 0, 47, 49, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 0, 28, 0, 0, 0, 30, 0, 0, 0, 0, 32, 34, 0, 36, 38, 0, 40, 42, 0, 0, 44, 46, 0, 48, 50, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 6, 2, 1, 2, 7, 9, 0, 1, 0, 2, 4, 2, 0, 6, 0, 0, 0, 9, 0, 0, 0, 0, 0, 1, 0, 2, 4, 0, 9, 7, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1130000, 190000, 2010000, 290000, 490000, 270000, -58230000, 1880000, 530000, 16220000, 740000, 210000, 1280000, -684844, 4070000, -514609, -70820, 1085156, -5180000, -136348, 123867, 873203, 215273, 4930000, 3430000, -417070, 1740000, 130000, 433594, -21230000, 170000, -365703, -1044531, 2130000, 20250000, 349297, 1770000, 160000, 15050000, 109898, -282026, 423428, -58242, -65918, -14883, 51914, 2344, 8281, -60313, -177305, 173320, 42826, -183234].span() +}; + let tree_36 = xgb_inference::Tree { + base_weights: array![-20404, 329241, -258689, 47164, 1355664, -713647, 31235, -936797, 303618, -23698, 2051270, -1270269, 1758, -582129, 187719, -411719, -56680, 922383, -21137, -115898, 105234, 698555, 182930, -2256406, -30078, -354492, 339621, 93164, 368555, 494629, -394318, -292578, -835625, -152604, 102474, 931510, -83438, 301335, -106160, 216445, -164004, -337477, 55162, -56016, -12656, 44121, 1992, 341250, 77930, 19688, -41133, -150703, 126019, 41587, -124453].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 0, 27, 0, 0, 0, 29, 0, 0, 0, 0, 31, 33, 0, 35, 37, 0, 39, 41, 0, 0, 43, 45, 47, 49, 51, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 0, 28, 0, 0, 0, 30, 0, 0, 0, 0, 32, 34, 0, 36, 38, 0, 40, 42, 0, 0, 44, 46, 48, 50, 52, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 6, 2, 1, 2, 7, 9, 0, 1, 0, 2, 4, 2, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 4, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1130000, 190000, 2010000, 290000, 490000, 270000, -58230000, 1880000, 530000, 16220000, 740000, 210000, 1280000, -582129, 4070000, -411719, -56680, 922383, 9510000, -115898, 105234, 698555, 182930, 4930000, 3430000, -354492, 1740000, 130000, 368555, 8300000, 13980000, -292578, -835625, 2130000, 20250000, 1890000, 630000, 160000, 200000, 216445, -164004, -337477, 55162, -56016, -12656, 44121, 1992, 341250, 77930, 19688, -41133, -150703, 126019, 41587, -124453].span() +}; + let tree_37 = xgb_inference::Tree { + base_weights: array![-19164, 358047, -172736, -154297, 712467, -326391, 546441, -824219, 889160, 783984, 302095, -483819, 287109, 1216992, 8333, -336885, -151172, 454863, 174740, 5915, 656328, -667940, 295480, 960156, -209440, 464453, 33516, 171094, -63438, -13418, -54609, -10781, 89414, -101406, 54844, 66602, 261563, -518555, -682793, 469688, -93359, 26250, 366563, -178320, 131641, 24297, 66211, 8750, -59453, -426523, -115215, 190938, 43906, 1699, -43711, 60703, -12070].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 21, 23, 25, 27, 0, 29, 0, 31, 33, 35, 37, 39, 41, 43, 0, 0, 45, 0, 0, 0, 0, 0, 47, 0, 0, 0, 49, 0, 51, 53, 0, 0, 0, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 22, 24, 26, 28, 0, 30, 0, 32, 34, 36, 38, 40, 42, 44, 0, 0, 46, 0, 0, 0, 0, 0, 48, 0, 0, 0, 50, 0, 52, 54, 0, 0, 0, 56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 1, 5, 6, 1, 9, 9, 0, 0, 0, 9, 0, 7, 1, 8, 0, 0, 0, 0, 8, 0, 1, 6, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![3770000, 300000, 580000, 190000, 530000, -4340000, -22070000, 1880000, 160000, 783984, -10490000, 37140000, 170000, 35810000, 100000, -336885, 2930000, 454863, 25070000, 90000, 1270000, 22700000, 140000, 1240000, 720000, 464453, 33516, 11620000, -63438, -13418, -54609, -10781, 89414, 1770000, 54844, 66602, 261563, 3860000, -682793, 15560000, 42010000, 26250, 366563, -178320, 4200000, 24297, 66211, 8750, -59453, -426523, -115215, 190938, 43906, 1699, -43711, 60703, -12070].span() +}; + let tree_38 = xgb_inference::Tree { + base_weights: array![1275, -1026823, 47988, -477539, 15469, -70628, 324336, 145431, -541250, 1092500, 64014, 337939, -331133, -1400625, -101420, 97793, 360791, 277783, -133116, 22917, 776680, -404414, -68359, -372005, -588711, -642057, 90061, -172526, 136992, 35648, -107531, -59082, 203027, 457102, 7422, 74625, -111539, -32637, -134766, -4922, -284004, 42469, 6164, 35039, -112676].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 0, 0, 27, 29, 31, 33, 0, 35, 37, 0, 39, 41, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 0, 0, 28, 30, 32, 34, 0, 36, 38, 0, 40, 42, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 3, 0, 0, 3, 3, 8, 3, 0, 2, 0, 2, 6, 1, 0, 0, 0, 6, 6, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 4000000, -477539, 15469, 1480000, 5030000, 70000, 1720000, 15050000, 2460000, 9200000, 110000, 60000, 5480000, 97793, 360791, 2460000, 170000, 190000, 14550000, -404414, 170000, 16590000, -588711, 6620000, 410000, 160000, 136992, 35648, -107531, -59082, 203027, 457102, 7422, 74625, -111539, -32637, -134766, -4922, -284004, 42469, 6164, 35039, -112676].span() +}; + let tree_39 = xgb_inference::Tree { + base_weights: array![-11546, -872917, 27782, -405938, 13125, 280880, -72982, 516587, -196987, -290290, 220238, -201641, 858203, -234492, 30729, 123665, -565418, 561084, 9738, -140859, -17285, 22559, 1272266, 53555, -176563, -126504, 352300, -1422813, -192157, 51660, 256922, 203181, -160742, 21563, -21289, 53672, -26758, 506156, -120352, -69199, -10254, 239492, 33248, -96035, -485537, 36343, -173359, 527, 20313, 108516, -1846, -111656, 43125].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 21, 0, 23, 25, 27, 29, 31, 0, 33, 35, 37, 0, 39, 0, 41, 43, 45, 47, 0, 49, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 22, 0, 24, 26, 28, 30, 32, 0, 34, 36, 38, 0, 40, 0, 42, 44, 46, 48, 0, 50, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 1, 0, 0, 4, 3, 9, 4, 0, 3, 0, 2, 0, 2, 1, 0, 3, 4, 0, 0, 0, 9, 0, 0, 0, 2, 0, 3, 2, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 3770000, -405938, 13125, 130000, 3820000, -15490000, 260000, 9650000, 5030000, 0, 110000, -234492, 370000, 4370000, 13980000, 4000000, 130000, -140859, 2150000, 1270000, -2240000, 53555, 2130000, -126504, 560000, 10250000, 1250000, 1760000, 256922, 2460000, 23200000, 21563, -21289, 53672, -26758, 506156, -120352, -69199, -10254, 239492, 33248, -96035, -485537, 36343, -173359, 527, 20313, 108516, -1846, -111656, 43125].span() +}; + let tree_40 = xgb_inference::Tree { + base_weights: array![-17515, -742057, 15684, -345059, 11133, 189640, -75604, -44974, 559592, -252876, 97249, -230039, 270964, 521836, 194678, -403802, 61719, 169358, -135286, -19336, -715234, 250938, -220898, -240000, 689355, -74922, -884635, -124512, 198359, 281179, -5566, -99883, 46777, -102148, 43340, -50918, -270938, -107109, 28125, -199336, 9668, -87773, 334258, -98687, 188242, -80771, -423086, -58828, -10586, 74150, 469, 109541, 12891, 15402, -60586, 26172, -11191].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 21, 0, 23, 25, 27, 29, 31, 33, 35, 0, 37, 39, 41, 43, 45, 47, 49, 51, 53, 0, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 22, 0, 24, 26, 28, 30, 32, 34, 36, 0, 38, 40, 42, 44, 46, 48, 50, 52, 54, 0, 56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 2, 0, 0, 2, 2, 1, 2, 4, 4, 1, 3, 0, 6, 3, 1, 2, 4, 1, 0, 0, 0, 0, 2, 3, 9, 0, 0, 7, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 740000, -345059, 11133, 380000, 2050000, 4370000, 420000, 300000, 630000, 3580000, 400000, 521836, 60000, 1250000, 12600000, 3350000, 1740000, 140000, 9200000, 250938, 10250000, 1880000, 490000, 930000, -21230000, 2130000, 20250000, 870000, 12020000, -99883, 8630000, -102148, 43340, -50918, -270938, -107109, 28125, -199336, 9668, -87773, 334258, -98687, 188242, -80771, -423086, -58828, -10586, 74150, 469, 109541, 12891, 15402, -60586, 26172, -11191].span() +}; + let tree_41 = xgb_inference::Tree { + base_weights: array![-17837, -630729, 10292, -293320, 9492, 129032, -75220, 30146, 486589, -288281, 55625, -396016, 140707, -74609, 785840, -551514, 10986, 20573, 134531, -182539, -15469, 443555, -15755, -74590, 41016, 267383, 70430, 29531, -341982, -75846, 54297, -27170, 140402, -59010, 44602, -96211, 110977, -49980, -7313, -39059, 27096, 56680, -22617].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 0, 0, 0, 0, 33, 0, 0, 0, 0, 35, 0, 37, 0, 39, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 0, 0, 0, 0, 34, 0, 0, 0, 0, 36, 0, 38, 0, 40, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 2, 0, 0, 6, 2, 1, 2, 4, 6, 0, 1, 0, 2, 0, 1, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 1130000, -293320, 9492, 190000, 2010000, 290000, 490000, 210000, 4070000, 1880000, 530000, 16220000, 740000, 6620000, 20570000, 220000, 134531, -182539, -15469, 443555, 4400000, -74590, 41016, 267383, 70430, 1200000, -341982, 2130000, 54297, 13130000, 19840000, -59010, 44602, -96211, 110977, -49980, -7313, -39059, 27096, 56680, -22617].span() +}; + let tree_42 = xgb_inference::Tree { + base_weights: array![-11340, -536068, 12683, -249316, 8086, 159231, -45589, 28958, 518203, -527734, -1719, -35073, 138809, 376992, 19434, -63477, -221426, 359277, -36058, -210889, 170871, -15430, 34805, 191875, -72246, -212964, 70110, -123188, 27451, 189961, -3516, -2145, -248584, 67383, -20539].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 0, 0, 21, 0, 0, 23, 25, 27, 29, 0, 0, 0, 0, 31, 33, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 0, 0, 22, 0, 0, 24, 26, 28, 30, 0, 0, 0, 0, 32, 34, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 1, 0, 0, 0, 1, 1, 0, 0, 2, 6, 0, 0, 0, 0, 0, 3, 3, 1, 1, 0, 0, 0, 0, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 3770000, -249316, 8086, 11170000, 3960000, 3580000, 13730000, 4930000, 210000, 60000, 138809, 376992, 25070000, -63477, -221426, 400000, 1720000, 290000, 0, -15430, 34805, 191875, -72246, 1480000, -18770000, -123188, 27451, 189961, -3516, -2145, -248584, 67383, -20539].span() +}; + let tree_43 = xgb_inference::Tree { + base_weights: array![-7721, -455599, 12754, -211934, 6914, 144346, -39600, 285807, -85889, -258919, 32601, -100234, 491357, 35391, -231797, -328906, 76641, 346250, -15909, -162891, -4167, 256417, 320391, -110391, -5508, -433789, -36914, 128203, 3281, -189974, 79759, -58535, -14766, 3281, -5156, 107949, -54609, -55137, -164109, -16797, 3047, 9648, -192686, 57566, -22195].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 21, 0, 23, 25, 0, 27, 29, 31, 33, 35, 0, 0, 0, 37, 39, 0, 0, 41, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 22, 0, 24, 26, 0, 28, 30, 32, 34, 36, 0, 0, 0, 38, 40, 0, 0, 42, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 1, 0, 0, 4, 1, 9, 2, 4, 1, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 9, 0, 0, 0, 0, 2, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 3770000, -211934, 6914, 100000, 5480000, -15490000, 370000, 130000, 8300000, 430000, 7520000, 35391, 2130000, 17070000, 76641, 9510000, 1720000, 0, 2150000, -2240000, 320391, -110391, -5508, 9200000, 2590000, 128203, 3281, 1250000, 5510000, -58535, -14766, 3281, -5156, 107949, -54609, -55137, -164109, -16797, 3047, 9648, -192686, 57566, -22195].span() +}; + let tree_44 = xgb_inference::Tree { + base_weights: array![-7698, -387240, 9671, -180117, 5859, 112623, -31283, 17682, 374922, -371973, -304, -29548, 101836, 272344, 14746, -39727, -163594, 271094, -26144, -158301, 121819, -34245, 24258, 132969, -36797, -84476, 126725, -86695, 13389, 145313, -5801, -12539, -2871, -66730, 8255, 167, 75840].span(), + left_children: array![1, 3, 5, 0, 0, 7, 9, 11, 13, 15, 17, 19, 0, 0, 21, 0, 0, 23, 25, 27, 29, 31, 0, 0, 0, 33, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 0, 0, 8, 10, 12, 14, 16, 18, 20, 0, 0, 22, 0, 0, 24, 26, 28, 30, 32, 0, 0, 0, 34, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![9, 0, 1, 0, 0, 0, 1, 1, 0, 0, 2, 6, 0, 0, 0, 0, 0, 3, 5, 1, 1, 0, 0, 0, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![-58230000, 28170000, 3770000, -180117, 5859, 11170000, 3960000, 3580000, 13730000, 4930000, 210000, 60000, 101836, 272344, 25070000, -39727, -163594, 400000, 360000, 290000, 0, 16590000, 24258, 132969, -36797, 2000000, 110000, -86695, 13389, 145313, -5801, -12539, -2871, -66730, 8255, 167, 75840].span() +}; + let tree_45 = xgb_inference::Tree { + base_weights: array![-2683, 62634, -82661, 243799, 13861, -597266, -26576, 58203, 231445, -102665, 145000, -228340, -40430, -153105, 8929, -69336, 123516, 68917, -202521, -111816, 218522, -119531, 57440, -117500, 25723, 86543, -15755, -271045, -14844, -60059, -15625, 328516, -1172, 37734, -286230, 117458, -35634, -43799, -527, 3828, -9961, -37922, -115225, 4063, -15000, -6094, -938, 118984, 24844, 22266, -45410, 22266, -3398, -98164, -24492, -26914, 48984, -23297, 4055].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 25, 27, 29, 31, 33, 35, 37, 0, 0, 39, 41, 43, 0, 45, 47, 49, 51, 53, 55, 57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 26, 28, 30, 32, 34, 36, 38, 0, 0, 40, 42, 44, 0, 46, 48, 50, 52, 54, 56, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 1, 0, 9, 6, 0, 1, 9, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 2, 2, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 590000, 210000, 7520000, 5480000, 12510000, -58230000, 190000, 231445, 3770000, -30330000, -228340, -40430, -153105, 2460000, 150000, 123516, 1890000, 17070000, 18210000, 300000, 1420000, 630000, 2930000, 25723, 86543, 810000, 10250000, 20410000, -60059, 45150000, 2190000, 15870000, 2860000, 740000, 300000, 2670000, -43799, -527, 3828, -9961, -37922, -115225, 4063, -15000, -6094, -938, 118984, 24844, 22266, -45410, 22266, -3398, -98164, -24492, -26914, 48984, -23297, 4055].span() +}; + let tree_46 = xgb_inference::Tree { + base_weights: array![-5078, 51881, -74735, 212695, 8707, -507552, -27384, 55692, 196758, -76769, 105000, -194063, -34336, -130137, 2623, -51693, 105000, -327474, -21510, 297852, 31966, -16262, 76875, -91172, 21855, -25605, -121758, 59766, -82335, 119687, -820, -30773, 165137, -135781, 10428, -33955, -469, 73535, -3594, -34434, -3926, -44531, 15840, 7617, 60977, -79336, -11250, 26066, -6500].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 25, 27, 29, 31, 33, 0, 35, 0, 0, 0, 37, 39, 0, 0, 41, 43, 45, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 26, 28, 30, 32, 34, 0, 36, 0, 0, 0, 38, 40, 0, 0, 42, 44, 46, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 1, 0, 9, 6, 0, 2, 2, 0, 0, 0, 6, 1, 0, 0, 1, 0, 7, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 590000, 210000, 7520000, 5480000, 12510000, -58230000, 190000, 196758, 110000, 640000, -194063, -34336, -130137, 3960000, 150000, 105000, 10250000, 3770000, 61210000, 350000, 300000, 76875, 2930000, 21855, -25605, -121758, 1890000, 17070000, 119687, -820, 1680000, 14400000, 290000, 440000, -33955, -469, 73535, -3594, -34434, -3926, -44531, 15840, 7617, 60977, -79336, -11250, 26066, -6500].span() +}; + let tree_47 = xgb_inference::Tree { + base_weights: array![-3826, 45086, -63659, 185010, 7523, -431380, -23424, 52176, 167227, -60478, 84089, -164941, -29180, -110625, 2079, -38281, 89238, -278255, -12891, -87598, 134310, -13990, 65391, -31211, 20605, -21738, -103477, 46484, -70654, -44355, -18229, 95781, 65391, 32988, -34066, 18574, -4141, 62520, -1925, -5578, -35420, -7500, -703, 39660, -20361, -50479, -2065].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 25, 27, 29, 31, 33, 0, 0, 35, 0, 0, 37, 39, 0, 41, 0, 43, 0, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 26, 28, 30, 32, 34, 0, 0, 36, 0, 0, 38, 40, 0, 42, 0, 44, 0, 46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 1, 0, 9, 6, 0, 2, 9, 0, 0, 0, 6, 0, 0, 0, 9, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 590000, 210000, 7520000, 5480000, 12510000, -58230000, 190000, 167227, 110000, -30330000, -164941, -29180, -110625, 3960000, 160000, 89238, 10250000, -8190000, 18210000, 640000, 210000, 65391, -31211, 1270000, -21738, -103477, 1890000, 1150000, -44355, 45150000, 95781, 15870000, 32988, 3430000, 18574, -4141, 62520, -1925, -5578, -35420, -7500, -703, 39660, -20361, -50479, -2065].span() +}; + let tree_48 = xgb_inference::Tree { + base_weights: array![-3619, 36678, -52898, 159521, 3793, -366667, -18615, 46931, 142148, -103795, 33766, -140215, -24785, -94043, 3111, -29557, 75879, -87949, -23372, 104395, 499, -10489, 55547, -24961, 18066, -11396, 1172, 193652, 12109, -88225, 52214, -101875, 9834, 15762, -3281, -1172, 78242, -4365, 17813, -42832, 35859, 7884, 50625, 11836, -44121, 19779, -7344].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 0, 25, 27, 29, 31, 0, 0, 33, 0, 0, 35, 37, 39, 41, 43, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 0, 26, 28, 30, 32, 0, 0, 34, 0, 0, 36, 38, 40, 42, 44, 46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 3, 0, 9, 6, 0, 1, 0, 0, 0, 0, 6, 0, 0, 0, 1, 8, 0, 4, 0, 0, 0, 0, 0, 1, 0, 7, 5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 590000, 210000, 7520000, 200000, 12510000, -58230000, 190000, 142148, 3860000, 9510000, -140215, -24785, -94043, 3960000, 160000, 75879, -87949, 4930000, 40000, 18210000, 300000, 55547, -24961, 1270000, -11396, 1172, 1770000, 4930000, 930000, 790000, 1240000, 2330000, 15762, -3281, -1172, 78242, -4365, 17813, -42832, 35859, 7884, 50625, 11836, -44121, 19779, -7344].span() +}; + let tree_49 = xgb_inference::Tree { + base_weights: array![-2120, 13864, -96211, -9033, 58965, -79922, -47700, 10568, -188086, -32275, 110577, -13337, -33594, 46354, -51535, -119180, 14063, 10156, -59961, 146680, -7324, -10547, 11816, 107983, -68359, 5966, -59033, 117, 6211, -30312, -31230, 61068, 220078, -15234, 7227, -1172, 18359, -20537, 46190, -74766, -4380, -7526, 29141, -938, -10898, -7734, 33281, 10313, 77373, 1699, 6563].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 17, 19, 21, 0, 23, 25, 0, 27, 0, 29, 31, 33, 0, 35, 37, 39, 41, 0, 0, 0, 43, 0, 45, 47, 0, 0, 0, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 18, 20, 22, 0, 24, 26, 0, 28, 0, 30, 32, 34, 0, 36, 38, 40, 42, 0, 0, 0, 44, 0, 46, 48, 0, 0, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 6, 2, 1, 9, 0, 3, 5, 1, 9, 5, 3, 0, 8, 9, 0, 0, 0, 2, 5, 1, 0, 0, 0, 2, 9, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4160000, 190000, 4230000, 22700000, -33250000, -79922, 6830000, 120000, 24330000, -49240000, 2630000, 3980000, -33594, 70000, -4420000, -119180, 11620000, 10156, 2000000, 120000, 3430000, -10547, 11180000, 160000, 110000, -5090000, -59033, 117, 6211, 6620000, -31230, 5250000, 110000, -15234, 7227, -1172, 20410000, -20537, 46190, -74766, -4380, -7526, 29141, -938, -10898, -7734, 33281, 10313, 77373, 1699, 6563].span() +}; + let tree_50 = xgb_inference::Tree { + base_weights: array![-5090, 24897, -41683, 126953, 1794, -211484, -8695, 21549, 113906, -32248, 42526, -97148, -28646, -67910, 8383, -44688, 52910, 35400, -78516, -55078, 71517, 3164, -16055, -781, 35625, -14971, -3574, 7589, 34512, -165495, -40799, -26543, -14453, 51328, 34492, 18809, -12287, 5713, -1729, -10898, -63574, -14136, 1465, -5859, -645, 25625, -10055, 1141, -25693].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 21, 0, 23, 25, 0, 27, 29, 31, 33, 0, 0, 35, 0, 0, 0, 37, 0, 39, 41, 0, 43, 0, 45, 0, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 22, 0, 24, 26, 0, 28, 30, 32, 34, 0, 0, 36, 0, 0, 0, 38, 0, 40, 42, 0, 44, 0, 46, 0, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 1, 2, 9, 6, 0, 1, 9, 0, 0, 0, 6, 1, 0, 1, 2, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 530000, 260000, 7520000, 5480000, 1840000, -58230000, 190000, 113906, 3770000, -30330000, -97148, 1240000, -67910, 3960000, 140000, 52910, 3740000, 110000, 18210000, 640000, 3164, -16055, 210000, 35625, -14971, -3574, 810000, 34512, 10250000, 2590000, -26543, 45150000, 51328, 15560000, 18809, 7560000, 5713, -1729, -10898, -63574, -14136, 1465, -5859, -645, 25625, -10055, 1141, -25693].span() +}; + let tree_51 = xgb_inference::Tree { + base_weights: array![-3763, 20909, -33884, 109989, 769, -223047, -13147, 20768, 96797, -50732, 17219, -86719, -13652, -57715, 126, -35078, 45000, -98047, -2734, 62891, -4036, -20488, 45920, -12852, -4688, -54023, -10677, 5625, -12793, -195, 100781, 21635, -58984, 28460, -43499, 29609, 16741, -7852, 3047, -5918, -1172, 1641, -1172, 44805, 5586, -3293, 29326, -4297, -23320, 17754, -23379, -20977, 5414, 9463, -674].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 25, 27, 29, 31, 33, 35, 0, 0, 0, 37, 0, 39, 41, 43, 45, 47, 49, 51, 0, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 26, 28, 30, 32, 34, 36, 0, 0, 0, 38, 0, 40, 42, 44, 46, 48, 50, 52, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 3, 0, 9, 6, 0, 9, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 9, 2, 4, 0, 0, 0, 0, 0, 0, 0, 8, 7, 1, 1, 9, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 530000, 210000, 7520000, 200000, 12510000, -58230000, 190000, 96797, -10520000, 9510000, -86719, -13652, -57715, 15190000, 160000, 45000, 3860000, 590000, 1810000, -7900000, 560000, 340000, -12852, -4688, -54023, 9200000, 5625, 160000, 2150000, 40000, 620000, 5250000, 6400000, -3110000, 29609, 970000, -7852, 3047, -5918, -1172, 1641, -1172, 44805, 5586, -3293, 29326, -4297, -23320, 17754, -23379, -20977, 5414, 9463, -674].span() +}; + let tree_52 = xgb_inference::Tree { + base_weights: array![-3039, 17588, -28226, 94754, 159, -189583, -10560, 19141, 82266, -43115, 14000, -73711, -11602, -49043, 739, -28047, 38262, -83398, -2266, 50439, -2973, -42676, 17243, -9346, -2344, -45938, -9115, 4805, -10840, 0, 80703, -43136, 20703, 7031, -28242, 30420, -20768, -6680, 2578, -4297, -59, 1406, -938, 35859, 4492, -20801, 17109, 10534, -5068, 4922, -4570, -14297, 11641, -586, -17227].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 25, 27, 29, 31, 33, 35, 0, 0, 0, 37, 0, 39, 41, 43, 45, 47, 49, 0, 51, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 26, 28, 30, 32, 34, 36, 0, 0, 0, 38, 0, 40, 42, 44, 46, 48, 50, 0, 52, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 3, 0, 9, 6, 0, 9, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 8, 7, 9, 1, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 530000, 210000, 7520000, 200000, 12510000, -58230000, 190000, 82266, -10520000, 9510000, -73711, -11602, -49043, 2460000, 140000, 38262, 3860000, 590000, 1810000, 18210000, 1420000, 970000, -9346, -2344, -45938, 9200000, 4805, 4520000, 2150000, 40000, 930000, -9000000, 11920000, -28242, 300000, 7560000, -6680, 2578, -4297, -59, 1406, -938, 35859, 4492, -20801, 17109, 10534, -5068, 4922, -4570, -14297, 11641, -586, -17227].span() +}; + let tree_53 = xgb_inference::Tree { + base_weights: array![-3320, 14998, -25668, 81920, -110, -161068, -10776, 17839, 69961, -36279, 11469, -62637, -9844, -41660, -1242, -21953, 32520, -70801, -1406, -25000, 24301, -7219, 24023, -8398, -2578, -39023, -7682, 4102, -8594, -8073, -18984, -3555, 49727, 20742, -12816, -5684, 2227, -4395, -508, 176, -5078, 17227, -5013, 23066, 2156, -9902, 1258].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 0, 0, 0, 21, 23, 0, 25, 27, 29, 31, 33, 0, 0, 0, 0, 35, 0, 37, 39, 0, 41, 43, 0, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 0, 0, 0, 22, 24, 0, 26, 28, 30, 32, 34, 0, 0, 0, 0, 36, 0, 38, 40, 0, 42, 44, 0, 46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![4, 1, 4, 0, 3, 0, 9, 6, 0, 9, 9, 0, 0, 0, 6, 0, 0, 1, 1, 6, 1, 7, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![130000, 530000, 210000, 7520000, 200000, 12510000, -58230000, 190000, 69961, -10520000, -30330000, -62637, -9844, -41660, 3960000, 160000, 32520, 3860000, 590000, 510000, 5480000, 0, 24023, -8398, -2578, -39023, 9200000, 4102, 160000, 5160000, -18984, 1890000, 15560000, 20742, 190000, -5684, 2227, -4395, -508, 176, -5078, 17227, -5013, 23066, 2156, -9902, 1258].span() +}; + let tree_54 = xgb_inference::Tree { + base_weights: array![-2625, 4959, -47109, 22249, -5923, -35391, -26128, -12500, 77691, -27362, 20910, 4818, -35658, 5220, -48984, 59414, 37891, -49888, 15479, 2170, 37326, 117, 2051, -14941, -14238, -17041, 48438, -33164, -5957, 625, 75000, -3460, -84277, 38750, -17480, 9821, -4922, 10352, 49089, -5234, -1113, -488, -12656, 22656, -4922, 1289, -3242, -20977, 10723, 0, 30000, -7359, 9844, -41689, -7102, 17109, 2266, -10371, -78, -1230, 6387, 3711, 645, 2871, 16523].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 17, 19, 21, 23, 25, 27, 0, 29, 31, 33, 35, 37, 0, 0, 39, 0, 41, 43, 0, 45, 47, 49, 51, 53, 55, 57, 59, 0, 61, 63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 18, 20, 22, 24, 26, 28, 0, 30, 32, 34, 36, 38, 0, 0, 40, 0, 42, 44, 0, 46, 48, 50, 52, 54, 56, 58, 60, 0, 62, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 2, 2, 2, 0, 6, 8, 2, 7, 4, 0, 0, 0, 3, 0, 6, 6, 0, 3, 6, 0, 0, 0, 0, 1, 3, 0, 1, 0, 2, 1, 4, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4160000, 740000, 4230000, 380000, 2010000, -35391, 140000, 70000, 420000, 480000, 100000, 20410000, 11180000, 9200000, 200000, 59414, 60000, 60000, 16390000, 7360000, 60000, 117, 2051, 9650000, -14238, 1290000, 400000, -33164, 270000, 1880000, 490000, 9740000, 210000, 1630000, 17070000, 4520000, -4922, 12560000, 1420000, -5234, -1113, -488, -12656, 22656, -4922, 1289, -3242, -20977, 10723, 0, 30000, -7359, 9844, -41689, -7102, 17109, 2266, -10371, -78, -1230, 6387, 3711, 645, 2871, 16523].span() +}; + let tree_55 = xgb_inference::Tree { + base_weights: array![-2102, 14807, -16670, 5729, 53190, -35742, 5447, -28472, 21628, 28008, 9766, -1519, -58431, 16462, -26484, -8545, -28184, 50508, 4123, -2227, 5391, -9877, 18490, -90848, -10872, 29167, -5339, -11914, -12715, -6230, 5625, -33398, 21124, -4805, 1094, 0, 8320, -127500, 521, -7773, 3125, 16699, 6641, 4766, -19922, -4141, -938, -18281, -1318, -6375, 12695, -12383, -51367, -3516, 3750, 1523, -410, -938, 3691, -8789, -2109].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 21, 23, 25, 27, 29, 0, 0, 31, 0, 0, 33, 35, 37, 39, 41, 43, 45, 0, 0, 0, 47, 49, 0, 0, 0, 0, 51, 53, 0, 55, 0, 57, 0, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 22, 24, 26, 28, 30, 0, 0, 32, 0, 0, 34, 36, 38, 40, 42, 44, 46, 0, 0, 0, 48, 50, 0, 0, 0, 0, 52, 54, 0, 56, 0, 58, 0, 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 9, 7, 2, 2, 1, 2, 0, 2, 0, 0, 1, 8, 9, 0, 9, 0, 0, 3, 0, 0, 0, 0, 4, 7, 2, 0, 0, 0, 0, 0, 2, 9, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, -2470000, 720000, 370000, 740000, 4930000, 3720000, 11170000, 420000, 28008, 160000, 3960000, 50000, -7900000, 11180000, -10490000, -28184, 50508, 670000, -2227, 5391, 25070000, 16220000, 1340000, 350000, 1630000, 4200000, 9650000, -12715, -6230, 5625, 790000, -35880000, -4805, 1094, 0, 8320, 9940000, 6640000, -7773, 37470000, 16699, 12830000, 4766, 17070000, -4141, -938, -18281, -1318, -6375, 12695, -12383, -51367, -3516, 3750, 1523, -410, -938, 3691, -8789, -2109].span() +}; + let tree_56 = xgb_inference::Tree { + base_weights: array![-1930, 11560, -13545, 23711, -8023, -28281, 3581, 9910, 42891, -53281, 18012, -564, -46712, 12528, -22188, -4715, 42383, -176, -19893, 35078, -2656, -7422, 15625, -101823, -25508, 22569, -4622, -9766, -10781, -15625, 12148, 22383, 7617, 2813, 14727, 5208, -2891, -12266, 938, 0, 7031, -39258, -6563, -11806, -22324, 12979, 5013, 3828, -16504, -3359, -820, -8438, 469, -1875, 4297, 410, 1934, -820, -4189, -9883, -318, -410, 3555, -7441, -1641].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 21, 23, 25, 27, 29, 31, 0, 0, 33, 35, 37, 39, 41, 43, 45, 47, 49, 0, 51, 0, 0, 53, 0, 0, 55, 0, 57, 0, 0, 0, 0, 0, 59, 0, 0, 61, 0, 63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 22, 24, 26, 28, 30, 32, 0, 0, 34, 36, 38, 40, 42, 44, 46, 48, 50, 0, 52, 0, 0, 54, 0, 0, 56, 0, 58, 0, 0, 0, 0, 0, 60, 0, 0, 62, 0, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 0, 7, 0, 3, 1, 2, 9, 0, 1, 0, 1, 3, 9, 0, 3, 2, 0, 0, 1, 3, 0, 0, 0, 6, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, 13730000, 720000, 11170000, 1680000, 4930000, 3720000, -2470000, 42891, 3740000, 29920000, 3960000, 1590000, -7900000, 11180000, 1200000, 740000, -176, -19893, 11910000, 2490000, 25070000, 16220000, 12510000, 970000, 1630000, 4200000, 9650000, -10781, 40000, 12148, 22383, 160000, 2813, 14727, 30880000, -2891, 150000, 938, 0, 7031, -39258, -6563, 9940000, -22324, 12979, 2380000, 3828, 17070000, -3359, -820, -8438, 469, -1875, 4297, 410, 1934, -820, -4189, -9883, -318, -410, 3555, -7441, -1641].span() +}; + let tree_57 = xgb_inference::Tree { + base_weights: array![-1528, 10193, -11624, 20137, -5889, -23516, 2235, 8409, 36445, -41406, 14497, -1953, -40980, 9515, -18594, -3013, 33659, -20977, -16797, 4785, 13828, 8125, -10026, -67773, -7357, 17578, -4167, -8008, -9141, -11621, 32422, 17891, 5762, -176, -6602, -3281, 4102, 5977, 195, -3750, 352, -102148, 651, -5508, 2734, 10020, 4102, 3047, -13867, -2734, -703, -599, -9111, 2285, 12305, -664, 4453, 664, -2305, -469, 820, -8965, -34883, -2930, 3223, 1289, -293, -264, 2813, -6328, -1328].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 21, 23, 25, 27, 29, 31, 0, 33, 35, 0, 37, 39, 41, 43, 45, 47, 49, 0, 51, 53, 0, 55, 0, 0, 57, 0, 0, 59, 0, 0, 61, 63, 0, 65, 0, 67, 0, 69, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 22, 24, 26, 28, 30, 32, 0, 34, 36, 0, 38, 40, 42, 44, 46, 48, 50, 0, 52, 54, 0, 56, 0, 0, 58, 0, 0, 60, 0, 0, 62, 64, 0, 66, 0, 68, 0, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 0, 7, 0, 3, 1, 2, 9, 0, 0, 6, 2, 8, 9, 0, 3, 2, 0, 0, 9, 0, 6, 0, 4, 7, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, 13730000, 720000, 11170000, 1680000, 9920000, 3720000, -2470000, 36445, 13980000, 2550000, 490000, 50000, -7900000, 11180000, 1200000, 740000, -20977, 16590000, -21640000, 13828, 60000, 37140000, 1340000, 350000, 1630000, 4200000, 9650000, -9141, 130000, 160000, 17891, 1240000, -176, -6602, 30880000, 4102, 5977, 16220000, -3750, 352, 2460000, 6640000, -5508, 37470000, 10020, 2380000, 3047, 17070000, -2734, -703, -599, -9111, 2285, 12305, -664, 4453, 664, -2305, -469, 820, -8965, -34883, -2930, 3223, 1289, -293, -264, 2813, -6328, -1328].span() +}; + let tree_58 = xgb_inference::Tree { + base_weights: array![-1028, 8752, -9459, 17285, -5048, -18672, 1302, 7340, 30938, -17813, 4427, -1016, -33026, 7171, -15391, -1674, 27148, -2273, 11719, 8496, -6306, -73047, -16016, 13585, -3646, -1875, -7793, -26367, 7457, 14297, 4980, -13574, 3662, 130, 5039, -7747, 352, -28125, -4746, -32109, 3281, 7734, 3190, 5469, -14583, -11172, 938, 10430, 375, -1465, 2969, -176, -5313, -797, 3193, -645, 703, 59, -2813, -1797, -14258, 2773, -1133, -625, 1904, 2422, -352, -5391, -1172].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 0, 17, 19, 21, 23, 25, 27, 29, 31, 0, 33, 35, 37, 39, 41, 43, 0, 0, 45, 47, 0, 49, 51, 53, 55, 0, 57, 0, 0, 0, 59, 61, 0, 63, 65, 67, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 0, 18, 20, 22, 24, 26, 28, 30, 32, 0, 34, 36, 38, 40, 42, 44, 0, 0, 46, 48, 0, 50, 52, 54, 56, 0, 58, 0, 0, 0, 60, 62, 0, 64, 66, 68, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 0, 7, 0, 0, 1, 2, 9, 0, 0, 6, 2, 3, 9, 0, 1, 2, 3, 0, 1, 0, 0, 1, 2, 3, 0, 0, 0, 7, 0, 0, 0, 9, 0, 0, 2, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, 13730000, 720000, 11170000, 13980000, 9920000, 3720000, -2470000, 30938, -17813, 2550000, 210000, 1590000, -7900000, 11180000, 290000, 740000, 1680000, 11719, 270000, 37140000, 12510000, 15890000, 1630000, 3450000, -1875, -7793, 1880000, 0, 14297, 160000, 16590000, -21640000, 2930000, 5039, 490000, 352, -28125, -4746, 6500000, 5510000, 7734, 12830000, 4200000, 17070000, -11172, 938, 10430, 375, -1465, 2969, -176, -5313, -797, 3193, -645, 703, 59, -2813, -1797, -14258, 2773, -1133, -625, 1904, 2422, -352, -5391, -1172].span() +}; + let tree_59 = xgb_inference::Tree { + base_weights: array![-850, 6451, -9265, -5258, 12281, -54948, -4388, 3711, -26367, 26250, 5501, -820, -23906, 696, -18457, 9717, -13542, -1563, -15117, 10723, 2632, 5748, -6445, -12148, -4362, -2637, 13616, -5449, -645, -1289, 586, -22266, 7480, -2474, 10590, -12333, 5469, -2314, 1563, 5391, -1875, -10723, -1758, -18, 5640, -1797, 234, 5977, 781, -1318, -5156, 2578, 469, -293, 996].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 27, 29, 0, 0, 31, 33, 35, 0, 37, 0, 39, 0, 0, 0, 0, 41, 43, 45, 47, 49, 51, 0, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 28, 30, 0, 0, 32, 34, 36, 0, 38, 0, 40, 0, 0, 0, 0, 42, 44, 46, 48, 50, 52, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 2, 2, 1, 0, 1, 0, 0, 9, 2, 0, 0, 0, 0, 0, 1, 7, 4, 0, 1, 0, 5, 0, 0, 0, 0, 2, 4, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 420000, 4930000, 4030000, 210000, 3580000, 26250, 0, -820, -23906, -7900000, 6700000, 0, 2460000, 11170000, -15117, 10723, 300000, 620000, 100000, -12148, 15190000, -2637, 190000, -5449, -645, -1289, 586, 740000, 300000, 6640000, 1630000, 5250000, 10100000, -2314, 11180000, 5391, -1875, -10723, -1758, -18, 5640, -1797, 234, 5977, 781, -1318, -5156, 2578, 469, -293, 996].span() +}; + let tree_60 = xgb_inference::Tree { + base_weights: array![-994, 4772, -7629, -5288, 9813, -46745, -3464, 2070, -22363, 22266, 4036, -703, -20332, 577, -14648, 6885, -11458, -1302, -12832, -596, 18034, -1339, 4141, -9727, -3320, -4948, 11654, -4629, -527, -1055, 469, 9141, -4015, 9453, 9141, 5859, -3955, -1787, 1302, -2227, 0, 5508, -352, 5234, -2336, 3223, 645, -859, 3281, -164, -2478, -234, 820].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 27, 29, 0, 31, 33, 35, 0, 0, 37, 39, 41, 0, 0, 0, 0, 0, 43, 45, 0, 47, 49, 0, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 28, 30, 0, 32, 34, 36, 0, 0, 38, 40, 42, 0, 0, 0, 0, 0, 44, 46, 0, 48, 50, 0, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 2, 2, 1, 0, 3, 0, 0, 1, 2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 420000, 4930000, 4030000, 210000, 3580000, 22266, 4000000, -703, -20332, 24520000, 6700000, 140000, 2460000, 11170000, -12832, 0, 20410000, 4200000, 4141, -9727, 15190000, 0, 400000, -4629, -527, -1055, 469, 9141, 560000, 19840000, 9141, 200000, -7900000, -1787, 11180000, -2227, 0, 5508, -352, 5234, -2336, 3223, 645, -859, 3281, -164, -2478, -234, 820].span() +}; + let tree_61 = xgb_inference::Tree { + base_weights: array![-666, 4980, -5532, -5820, 9460, -10957, 803, 1606, -10898, 18867, 4173, -234, -19709, -804, 4219, -5339, 11621, 7793, 1897, 4883, -3125, -33724, -2409, 5301, -4616, -6445, -2083, 1445, 4805, -20964, 5407, 521, 2695, -4036, 352, -17070, -7910, -7813, 2246, 3555, 313, -7861, 3027, -2266, -469, 0, -938, -645, -8789, -2344, 2836, -117, 352, 0, -1453, -3867, 1055, -2930, -586, 898, 0, 352, -469, -4531, -879, -176, 1328].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 0, 17, 19, 21, 23, 0, 25, 27, 0, 29, 31, 33, 35, 37, 39, 41, 43, 45, 0, 0, 47, 49, 51, 0, 53, 0, 0, 55, 57, 59, 0, 61, 63, 65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 0, 18, 20, 22, 24, 0, 26, 28, 0, 30, 32, 34, 36, 38, 40, 42, 44, 46, 0, 0, 48, 50, 52, 0, 54, 0, 0, 56, 58, 60, 0, 62, 64, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 0, 2, 1, 1, 9, 0, 0, 1, 2, 8, 3, 0, 9, 0, 0, 1, 1, 0, 4, 7, 0, 1, 2, 0, 0, 0, 0, 9, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, 370000, 720000, 11170000, 420000, 9920000, 28150000, -10490000, -10898, 18867, 0, 210000, 50000, 1250000, 4219, -15490000, 1270000, 7793, 290000, 270000, 37140000, 570000, 350000, 4200000, 15190000, 110000, 7520000, 1445, 4805, 160000, -35880000, 2930000, 2695, 490000, 352, -17070, 6640000, 12870000, 37470000, 3555, 19140000, 270000, 11180000, -2266, -469, 0, -938, -645, -8789, -2344, 2836, -117, 352, 0, -1453, -3867, 1055, -2930, -586, 898, 0, 352, -469, -4531, -879, -176, 1328].span() +}; + let tree_62 = xgb_inference::Tree { + base_weights: array![-569, -1300, 7266, 2796, -6445, 19531, -952, -30859, -1502, 4948, 16055, 5241, -4446, -14805, -2083, 6855, -3391, -2891, 6621, -703, 8301, -13281, 3288, 117, -1055, -5742, -1937, -1367, -78, 820, -674, -156, 3398, -5879, -914, 415, 3633, 1787, -1004].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 13, 15, 17, 0, 19, 21, 0, 23, 0, 25, 27, 0, 29, 31, 33, 35, 0, 0, 0, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 14, 16, 18, 0, 20, 22, 0, 24, 0, 26, 28, 0, 30, 32, 34, 36, 0, 0, 0, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 4, 0, 1, 4, 0, 0, 2, 7, 6, 0, 3, 0, 0, 0, 0, 9, 0, 0, 1, 1, 7, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 130000, 7266, 530000, 260000, 7520000, 9510000, 1840000, 0, 190000, 16055, 200000, 18210000, -14805, 1240000, 6855, -58230000, 160000, 6621, 590000, 1770000, 350000, 790000, 117, -1055, -5742, 210000, -1367, -78, 820, -674, -156, 3398, -5879, -914, 415, 3633, 1787, -1004].span() +}; + let tree_63 = xgb_inference::Tree { + base_weights: array![-172, -793, 6211, 4467, -3464, -3177, 16710, 617, -6207, 1136, -12031, 13594, 7471, -3190, 2204, -12480, -3245, -2979, 9082, -8379, -1074, 130, 5898, -977, -1523, -1116, 4834, -5060, 3646, -117, -3164, 3828, -293, -703, 39, 5625, -2203, 117, -469, -750, 469, 2842, 47, -787, -2607, 1594, -703].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 13, 15, 17, 19, 0, 21, 23, 25, 0, 27, 29, 31, 0, 33, 35, 0, 37, 0, 39, 41, 43, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 14, 16, 18, 20, 0, 22, 24, 26, 0, 28, 30, 32, 0, 34, 36, 0, 38, 0, 40, 42, 44, 46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 2, 0, 8, 2, 9, 0, 0, 3, 0, 1, 4, 3, 0, 1, 1, 3, 0, 0, 0, 0, 0, 0, 4, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 740000, 6211, 380000, 11620000, 70000, 420000, -10300000, 12510000, 9200000, 200000, 13594, 4930000, 420000, 930000, -12480, 20570000, 4370000, 400000, -8379, 11170000, 160000, 5898, 2150000, -1523, 100000, 10100000, 270000, 40460000, -117, -3164, 3828, -293, -703, 39, 5625, -2203, 117, -469, -750, 469, 2842, 47, -787, -2607, 1594, -703].span() +}; + let tree_64 = xgb_inference::Tree { + base_weights: array![-23, 2861, -3357, -3215, 5906, -25260, -1055, 898, -12695, 11602, 2930, -762, -10605, 849, -6396, -2846, 7227, -1042, -7148, 226, 11003, 2902, -2109, -4336, -1302, -326, -2695, 3047, -234, -586, 117, 4805, -1541, 1852, 5273, -1758, 4332, -6406, 1823, -2832, 1172, -1055, 645, 3203, -1121, -117, -879, 2109, 536, -2656, -547, -234, 996, -234, -977, 0, 527].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 27, 29, 0, 31, 33, 35, 37, 0, 39, 41, 0, 0, 0, 0, 0, 0, 43, 0, 0, 45, 47, 49, 51, 53, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 28, 30, 0, 32, 34, 36, 38, 0, 40, 42, 0, 0, 0, 0, 0, 0, 44, 0, 0, 46, 48, 50, 52, 54, 56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 2, 0, 1, 0, 3, 0, 0, 9, 2, 1, 3, 0, 0, 1, 0, 7, 9, 0, 1, 9, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 420000, 4930000, 4030000, 9200000, 3580000, 11602, 4000000, -762, -10605, -7900000, 6700000, 4370000, 400000, 11170000, -7148, 0, 20410000, 480000, -4340000, -4336, 15190000, -15490000, -2695, 3047, -234, -586, 117, 4805, 560000, 1852, 5273, 12570000, 1630000, 17070000, 1420000, 7460000, 11180000, -1055, 645, 3203, -1121, -117, -879, 2109, 536, -2656, -547, -234, 996, -234, -977, 0, 527].span() +}; + let tree_65 = xgb_inference::Tree { + base_weights: array![-40, 2439, -2905, -2855, 5094, -21484, -951, 586, -10742, 9844, 2572, -645, -9023, 543, -5127, -2455, 5762, -781, -6094, -781, 8247, -2669, 1584, -3477, -1042, -326, -2285, 2422, -176, -469, 117, 2204, -4336, 5449, 260, -117, -1113, 3602, -391, -703, 1042, -674, 703, 4102, 81, 791, -898, 2383, 368, -742, 656, 0, 469].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 27, 29, 0, 31, 33, 35, 37, 0, 39, 41, 0, 0, 0, 0, 0, 43, 0, 0, 45, 0, 0, 47, 49, 0, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 28, 30, 0, 32, 34, 36, 38, 0, 40, 42, 0, 0, 0, 0, 0, 44, 0, 0, 46, 0, 0, 48, 50, 0, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 2, 0, 1, 0, 4, 0, 0, 7, 2, 1, 3, 0, 0, 4, 4, 1, 9, 0, 1, 9, 0, 0, 0, 0, 0, 1, 0, 0, 6, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 420000, 4930000, 4030000, 9200000, 3580000, 9844, 300000, -645, -9023, 480000, 6700000, 4370000, 400000, 11170000, -6094, 220000, 420000, 12570000, -8190000, -3477, 15190000, -10790000, -2285, 2422, -176, -469, 117, 0, -4336, 5449, 970000, -117, -1113, 1630000, 5480000, -703, 11180000, -674, 703, 4102, 81, 791, -898, 2383, 368, -742, 656, 0, 469].span() +}; + let tree_66 = xgb_inference::Tree { + base_weights: array![-34, 1974, -2356, -2434, 4188, -18229, -690, 430, -8984, 8320, 2051, -527, -7676, -6771, -14, 1910, -1934, -521, -5156, 123, 7813, -3281, 234, -732, 3438, 293, 2227, -352, 117, 3457, -1150, 1359, 3633, 879, -2163, 586, 1406, -996, 385, -1162, 1105, 768, -938, -1406, -281].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 0, 27, 0, 29, 31, 0, 0, 33, 35, 37, 0, 0, 0, 0, 39, 0, 0, 41, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 0, 28, 0, 30, 32, 0, 0, 34, 36, 38, 0, 0, 0, 0, 40, 0, 0, 42, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 9, 1, 1, 0, 3, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 4, 0, 0, 8, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 420000, 4930000, -58230000, 8300000, 3580000, 8320, 4000000, -527, -7676, 28170000, 23200000, 5480000, -1934, 11170000, -5156, 0, 20410000, -3281, 234, 120000, 37470000, 0, 2227, -352, 117, 3457, 300000, 1359, 3633, 40000, 180000, 586, 1406, -996, 385, -1162, 1105, 768, -938, -1406, -281].span() +}; + let tree_67 = xgb_inference::Tree { + base_weights: array![-80, 1605, -2026, -2133, 3484, -15495, -612, 313, -7715, 7031, 1676, -469, -6504, 459, -3613, -1786, 3906, -521, -4395, 123, 6315, 1855, -977, -2383, -846, -260, -1641, 1719, -234, -352, 117, 2930, -955, 1055, 3047, -625, 3174, -2511, 977, -557, 781, -820, 234, 1846, -836, -469, 156, 117, 1430, -1195, 234, 439, 0, 59, 293].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 27, 29, 0, 31, 33, 35, 37, 0, 39, 41, 0, 0, 0, 0, 0, 0, 43, 0, 0, 45, 47, 49, 51, 0, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 28, 30, 0, 32, 34, 36, 38, 0, 40, 42, 0, 0, 0, 0, 0, 0, 44, 0, 0, 46, 48, 50, 52, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 2, 0, 1, 0, 3, 0, 0, 9, 2, 1, 3, 0, 0, 1, 0, 9, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 3, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 420000, 4930000, 4030000, 9200000, 3580000, 7031, 4000000, -469, -6504, -7940000, 6700000, 4370000, 400000, 11170000, -4395, 0, 20410000, -30330000, 2050000, -2383, 15190000, 0, -1641, 1719, -234, -352, 117, 2930, 0, 1055, 3047, 6640000, 670000, 930000, 11620000, -557, 11180000, -820, 234, 1846, -836, -469, 156, 117, 1430, -1195, 234, 439, 0, 59, 293].span() +}; + let tree_68 = xgb_inference::Tree { + base_weights: array![-57, 2455, -1156, 5329, -2881, -1546, 2578, 1986, 5078, -10156, 1237, 4219, -2180, 675, 2461, -3750, -820, 0, 445, 3164, 0, -4053, 176, -1563, 1709, 391, -586, -7199, 320, -2031, 854, -703, -156, 723, -78, -4043, -664, 653, -703, -674, -176, -70, 686].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 0, 15, 17, 19, 21, 23, 0, 0, 0, 0, 0, 0, 25, 27, 29, 31, 33, 0, 0, 35, 37, 39, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 0, 16, 18, 20, 22, 24, 0, 0, 0, 0, 0, 0, 26, 28, 30, 32, 34, 0, 0, 36, 38, 40, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![3, 0, 6, 0, 7, 7, 0, 6, 0, 0, 1, 0, 8, 9, 0, 0, 0, 0, 0, 0, 1, 7, 3, 0, 7, 0, 0, 3, 5, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![410000, 13730000, 4070000, 9200000, 170000, 0, 2578, 190000, 5078, 13980000, 300000, 3810000, 50000, -10790000, 2461, -3750, -820, 0, 445, 3164, 27910000, 710000, 930000, 0, 170000, 391, -586, 1680000, 120000, 12870000, 220000, -703, -156, 723, -78, -4043, -664, 653, -703, -674, -176, -70, 686].span() +}; + let tree_69 = xgb_inference::Tree { + base_weights: array![-29, 1341, -1611, -1653, 2844, -11589, -560, 39, -5469, 12598, 941, -293, -4922, -4948, -70, 998, -1289, -260, -3164, 5234, -293, -368, 4492, -2344, 117, -472, 1875, -1563, 1953, -234, 117, -2133, 4297, 1660, 2031, 1060, -1042, 293, 820, -586, -117, 723, -117, -3223, -156, 1875, 273, 625, 59, 586, -234, -867, -92].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 0, 0, 23, 25, 27, 0, 29, 0, 0, 0, 31, 33, 0, 0, 35, 37, 39, 41, 0, 0, 43, 45, 47, 0, 49, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 0, 0, 24, 26, 28, 0, 30, 0, 0, 0, 32, 34, 0, 0, 36, 38, 40, 42, 0, 0, 44, 46, 48, 0, 50, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 8, 2, 0, 9, 1, 1, 0, 3, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 9, 1, 0, 0, 1, 0, 0, 2, 0, 0, 0, 2, 1, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![240000, 370000, 270000, 70000, 560000, 4930000, -58230000, 8300000, 3580000, 13730000, 4000000, -293, -4922, 28170000, 23200000, 140000, -1289, 11170000, -3164, 5234, -293, -3110000, 11910000, -2344, 117, 4930000, 37470000, 0, 210000, -234, 117, 1880000, 1120000, 6810000, 2031, 1630000, 1280000, 293, 820, -586, -117, 723, -117, -3223, -156, 1875, 273, 625, 59, 586, -234, -867, -92].span() +}; + let tree_70 = xgb_inference::Tree { + base_weights: array![-155, 1099, -1235, 2520, -1172, -3223, -270, 1131, 4336, -6641, 1997, -994, -4160, 678, -2490, -391, 3795, -234, -7910, 4375, -781, 1250, -2455, -36, 1476, -1992, -949, -2109, 4004, 684, 1934, -2695, -703, 117, 2070, 651, -586, 2474, -391, -3438, 0, 195, -352, 2500, 156, -492, 156, -91, -2754, 1816, 391, 59, 234, 879, 234, -176, 0, -78, -1641, -176, 176, 146, -195, 176, 850, 156, -78].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 17, 19, 21, 0, 23, 25, 27, 29, 0, 31, 33, 35, 37, 39, 41, 43, 0, 45, 47, 49, 0, 0, 0, 0, 0, 0, 51, 0, 53, 55, 57, 59, 61, 0, 63, 65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 18, 20, 22, 0, 24, 26, 28, 30, 0, 32, 34, 36, 38, 40, 42, 44, 0, 46, 48, 50, 0, 0, 0, 0, 0, 0, 52, 0, 54, 56, 58, 60, 62, 0, 64, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 0, 6, 0, 3, 6, 2, 1, 0, 1, 0, 9, 0, 3, 2, 9, 4, 0, 0, 1, 3, 0, 2, 1, 3, 0, 1, 9, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, 13730000, 110000, 11170000, 1680000, 60000, 3720000, 4370000, 4336, 3740000, 29920000, -8190000, -4160, 2760000, 4230000, -3110000, 220000, -234, 18210000, 11910000, 2490000, 16750000, 1280000, 15870000, 5030000, -1992, 15190000, -4580000, 0, 684, 1934, -2695, -703, 117, 2070, 30880000, -586, 1890000, 58650000, 4400000, 160000, 1250000, -352, 1420000, 1740000, -492, 156, -91, -2754, 1816, 391, 59, 234, 879, 234, -176, 0, -78, -1641, -176, 176, 146, -195, 176, 850, 156, -78].span() +}; + let tree_71 = xgb_inference::Tree { + base_weights: array![-121, 891, -992, -1445, 1868, -3223, -365, 87, -2285, 3633, 852, -335, -3516, -3516, -14, -911, 1563, -391, 3571, -2474, 1016, -1699, 117, -309, 1406, -1328, 176, 117, 547, 949, -6510, 2051, -586, -879, -234, 0, 1270, 781, -716, 234, 586, -439, -117, 776, -318, -2344, -586, -273, 59, 430, 117, -273, 492, -117, -664].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 0, 0, 17, 19, 0, 21, 23, 25, 27, 29, 31, 33, 35, 0, 0, 37, 39, 41, 0, 0, 0, 43, 45, 0, 47, 0, 0, 0, 49, 51, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 0, 0, 18, 20, 0, 22, 24, 26, 28, 30, 32, 34, 36, 0, 0, 38, 40, 42, 0, 0, 0, 44, 46, 0, 48, 0, 0, 0, 50, 52, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![7, 2, 7, 0, 2, 1, 9, 9, 0, 0, 4, 0, 0, 0, 1, 1, 1, 4, 4, 0, 7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![180000, 370000, 270000, 11170000, 420000, 18840000, -58230000, -10490000, -2285, 3633, 300000, 4930000, -3516, 28170000, 23200000, 4370000, 590000, 220000, 420000, 2460000, 190000, -1699, 117, 4200000, 37470000, 3580000, 176, 117, 547, 7340000, 1880000, 2051, 1760000, -879, -234, 0, 16750000, 160000, -5090000, 234, 586, -439, -117, 776, -318, -2344, -586, -273, 59, 430, 117, -273, 492, -117, -664].span() +}; + let tree_72 = xgb_inference::Tree { + base_weights: array![-172, 1094, -686, 2455, -1786, -4688, -416, -391, 4080, -1992, 130, -176, -1934, 998, -1250, -1302, 391, 2051, 3047, 195, -293, -1563, 2103, -4375, -601, -469, -117, 234, -117, 98, 3203, -117, 0, 130, -732, 651, 4297, -8333, 313, -1465, -234, 117, -39, 1230, -59, -117, 176, -234, 449, 1582, 59, -762, -2988, -242, 211].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 25, 27, 29, 0, 0, 31, 33, 35, 37, 39, 0, 0, 0, 0, 41, 43, 0, 0, 45, 0, 47, 49, 51, 0, 0, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 26, 28, 30, 0, 0, 32, 34, 36, 38, 40, 0, 0, 0, 0, 42, 44, 0, 0, 46, 0, 48, 50, 52, 0, 0, 54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 4, 1, 9, 4, 0, 7, 2, 0, 0, 2, 0, 0, 9, 7, 0, 1, 2, 0, 0, 0, 1, 4, 0, 9, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![3770000, 130000, 3860000, -15490000, 260000, 4930000, 170000, 430000, 7520000, -1992, 110000, -176, -1934, -35880000, 270000, 0, 810000, 110000, 3047, 195, 11170000, 5160000, 300000, 12510000, -58230000, -469, -117, 234, -117, 1270000, -2240000, -117, 0, 9200000, -732, 200000, 29920000, 2460000, 313, -1465, 15190000, 117, -39, 1230, -59, -117, 176, -234, 449, 1582, 59, -762, -2988, -242, 211].span() +}; + let tree_73 = xgb_inference::Tree { + base_weights: array![-201, 840, -622, 1981, -1563, -1298, 514, -326, 3299, -1699, 65, -2490, -24, -1230, 998, -352, 391, 1563, 2578, 156, -293, -1536, -2520, -639, 1107, 1563, -781, 234, -117, 1230, 614, -117, 0, -4036, -841, -1283, 391, 498, 0, 2148, 78, -293, 0, -59, 381, -176, -1641, -11, -1055, -586, -88, -39, 234, -59, 59, 938, 164, 117, -78].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 21, 23, 0, 25, 0, 27, 29, 0, 0, 31, 33, 0, 35, 37, 39, 41, 0, 0, 0, 43, 0, 0, 45, 47, 49, 51, 0, 53, 55, 57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 22, 24, 0, 26, 0, 28, 30, 0, 0, 32, 34, 0, 36, 38, 40, 42, 0, 0, 0, 44, 0, 0, 46, 48, 50, 52, 0, 54, 56, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 4, 5, 9, 4, 2, 9, 2, 0, 0, 2, 2, 1, 0, 2, 0, 1, 1, 0, 0, 0, 1, 0, 9, 3, 8, 2, 0, 0, 0, 1, 0, 0, 0, 5, 6, 0, 0, 0, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![3770000, 130000, 190000, -15490000, 260000, 1840000, -58230000, 430000, 7520000, -1699, 110000, 1760000, 13130000, -1230, 6050000, -352, 810000, 0, 2578, 156, 11170000, 3860000, -2520, -7510000, 4460000, 280000, 23790000, 234, -117, 1230, 150000, -117, 0, 4930000, 120000, 740000, 1240000, 498, 26060000, 420000, 380000, -293, 0, -59, 381, -176, -1641, -11, -1055, -586, -88, -39, 234, -59, 59, 938, 164, 117, -78].span() +}; + let tree_74 = xgb_inference::Tree { + base_weights: array![-155, 742, -518, 1730, -1339, -3516, -316, -195, 2821, -1465, 65, -176, -1406, 994, -697, -313, 488, 1318, 2227, 156, -293, 49, 2637, -2648, -67, 234, -59, 1055, 502, -117, 0, -1302, 716, 234, 898, -2168, -1172, -1055, 181, -23, 391, -469, -117, 293, 39, -820, -59, -4, 820].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 0, 21, 23, 0, 25, 27, 0, 0, 29, 31, 33, 35, 37, 0, 0, 0, 39, 0, 0, 41, 43, 0, 0, 0, 45, 0, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 0, 22, 24, 0, 26, 28, 0, 0, 30, 32, 34, 36, 38, 0, 0, 0, 40, 0, 0, 42, 44, 0, 0, 0, 46, 0, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 4, 1, 9, 4, 0, 2, 4, 0, 0, 2, 0, 0, 2, 2, 0, 1, 1, 0, 0, 0, 0, 1, 0, 9, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![3770000, 130000, 3860000, -15490000, 260000, 4930000, 680000, 0, 7520000, -1465, 110000, -176, -1406, 490000, 1840000, -313, 810000, 0, 2227, 156, 11170000, 9200000, 4400000, 12510000, -58230000, 234, -59, 1055, 590000, -117, 0, 2460000, 400000, 234, 898, -2168, 9740000, -1055, 4070000, -23, 391, -469, -117, 293, 39, -820, -59, -4, 820].span() +}; + let tree_75 = xgb_inference::Tree { + base_weights: array![-132, 837, -376, -477, 2669, -470, 703, -2188, 1328, 1875, 703, 238, -964, -684, -1230, 879, 195, 59, 234, 879, -18, -5566, -296, -293, -78, 0, 117, 358, -426, -1992, -352, -879, -98, -29, 305, -381, 15, -179, 186].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 15, 0, 17, 19, 21, 23, 0, 0, 25, 0, 0, 0, 27, 29, 31, 0, 0, 0, 0, 33, 35, 0, 0, 0, 37, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 16, 0, 18, 20, 22, 24, 0, 0, 26, 0, 0, 0, 28, 30, 32, 0, 0, 0, 0, 34, 36, 0, 0, 0, 38, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![1, 1, 6, 6, 1, 0, 0, 1, 0, 0, 1, 7, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 9, 0, 0, 0, 0, 3, 5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![1290000, 300000, 4070000, 60000, 530000, 11620000, 703, 140000, 160000, 1875, 590000, 0, 13980000, 0, -1230, 879, 25070000, 59, 234, 879, 120000, 110000, -58230000, -293, -78, 0, 117, 930000, 200000, -1992, -352, -879, 13130000, -29, 305, -381, 15, -179, 186].span() +}; + let tree_76 = xgb_inference::Tree { + base_weights: array![-92, 595, -391, -443, 2734, 762, -507, -781, 391, 1602, 625, -850, -284, -273, -879, 0, 146, 0, 313, 308, -706, 167, -293, 59, -59, -670, 811, -3776, -293, -146, 234, -398, 195, -39, 425, -176, -1523, 67, -277].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 0, 17, 0, 19, 21, 0, 0, 0, 23, 0, 25, 27, 29, 0, 0, 0, 31, 33, 35, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 0, 18, 0, 20, 22, 0, 0, 0, 24, 0, 26, 28, 30, 0, 0, 0, 32, 34, 36, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 1, 7, 0, 0, 2, 0, 0, 0, 0, 0, 7, 2, 0, 0, 0, 0, 0, 9, 7, 9, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![680000, 380000, 0, 190000, 13730000, 762, 990000, 11170000, 2930000, 1602, 16590000, -850, 240000, 110000, -879, 0, 146, 16220000, 313, -33250000, 270000, -10790000, -293, 59, -59, 4000000, 670000, 4930000, 16390000, -146, 234, -398, 195, -39, 425, -176, -1523, 67, -277].span() +}; + let tree_77 = xgb_inference::Tree { + base_weights: array![-75, 484, -317, -391, 2288, 645, -416, -710, 391, 1289, 625, -2604, -260, -273, -762, 0, 146, 0, 313, -938, -234, -723, 105, -614, 391, 59, -59, -1256, 446, -703, 297, -215, 0, 352, -78, -29, -720, 23, 273, 172, -78].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 0, 17, 19, 21, 23, 0, 0, 0, 25, 0, 0, 0, 27, 29, 31, 33, 0, 0, 35, 37, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 0, 18, 20, 22, 24, 0, 0, 0, 26, 0, 0, 0, 28, 30, 32, 34, 0, 0, 36, 38, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 1, 7, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 3, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![680000, 380000, 0, 190000, 13730000, 645, 790000, 11170000, 2930000, 1289, 16590000, 1880000, 2010000, 9200000, -762, 0, 146, 16220000, 313, -938, -234, 18210000, -58230000, 8640000, 9510000, 59, -59, 6640000, 20570000, -703, 4160000, -215, 0, 352, -78, -29, -720, 23, 273, 172, -78].span() +}; + let tree_78 = xgb_inference::Tree { + base_weights: array![-6, 484, -220, -208, 1897, -355, 1016, -564, 279, 1016, 625, 81, -837, 439, -117, -98, -645, -521, 703, 0, 313, 977, -206, -3320, -239, -176, 56, -234, 0, 313, 130, 59, -59, 1660, -78, -664, 273, 0, -1328, -586, -23, 59, -117, 0, 59, 586, 117, -318, 59, -33, 264, -107, 151].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 21, 23, 0, 0, 25, 0, 27, 29, 31, 0, 33, 35, 37, 39, 0, 41, 0, 0, 0, 43, 0, 0, 45, 0, 47, 49, 0, 0, 0, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 22, 24, 0, 0, 26, 0, 28, 30, 32, 0, 34, 36, 38, 40, 0, 42, 0, 0, 0, 44, 0, 0, 46, 0, 48, 50, 0, 0, 0, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 6, 1, 0, 4, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 9, 1, 0, 9, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![680000, 380000, 2990000, 3860000, 13730000, 150000, 29920000, 3580000, 9200000, 1016, 16590000, 2150000, 260000, 439, -117, 0, -645, 2460000, 400000, 16220000, 313, -2240000, 10260000, 1240000, -58230000, -176, 1290000, -234, 0, 313, 10250000, 59, -59, 1890000, -78, 18210000, 350000, 0, -1328, -586, 15190000, 59, -117, 0, 59, 586, 117, -318, 59, -33, 264, -107, 151].span() +}; + let tree_79 = xgb_inference::Tree { + base_weights: array![-6, 446, -203, -130, 1618, -543, 105, -477, 335, 1055, 716, 410, -692, 483, -547, -98, -527, -391, 703, 0, 322, -1050, 223, 178, 893, -469, -260, -176, 56, -176, 0, 313, 130, 59, -59, -326, -2578, -156, 234, -49, 176, 352, 130, 0, -234, 59, -117, 0, 59, 73, -352, -1094, -195, 59, -88, 39, -117, 59, 0, 39, -117].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 0, 21, 23, 25, 27, 0, 29, 31, 33, 0, 35, 37, 39, 41, 0, 43, 0, 45, 0, 0, 0, 47, 0, 0, 49, 51, 53, 0, 55, 0, 0, 57, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 0, 22, 24, 26, 28, 0, 30, 32, 34, 0, 36, 38, 40, 42, 0, 44, 0, 46, 0, 0, 0, 48, 0, 0, 50, 52, 54, 0, 56, 0, 0, 58, 60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 2, 1, 1, 1, 2, 1, 0, 0, 2, 0, 4, 1, 2, 0, 0, 0, 3, 0, 0, 4, 1, 9, 3, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![680000, 380000, 2010000, 3860000, 530000, 0, 4160000, 3580000, 9200000, 1055, 490000, 410, 300000, 12560000, 4230000, 0, -527, 2460000, 400000, 16220000, 322, 130000, 20570000, 390000, 4460000, -469, 170000, -176, 1290000, -176, 0, 313, 10250000, 59, -59, 1250000, 12510000, 300000, 234, 170000, 176, 352, 11620000, 37470000, -234, 59, -117, 0, 59, 73, -352, -1094, -195, 59, -88, 39, -117, 59, 0, 39, -117].span() +}; + let tree_80 = xgb_inference::Tree { + base_weights: array![23, 409, -146, -104, 1451, 352, -199, -434, 335, 938, 651, -469, -104, 0, -1302, -391, 703, 0, 293, -160, 352, -176, 195, -117, -469, -176, 0, 313, 130, 59, -59, 207, -377, 0, 117, 0, 59, -18, 258, -781, -32].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 13, 15, 0, 17, 0, 19, 21, 23, 25, 27, 29, 0, 31, 0, 0, 33, 0, 0, 0, 0, 0, 35, 0, 0, 37, 39, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 14, 16, 0, 18, 0, 20, 22, 24, 26, 28, 30, 0, 32, 0, 0, 34, 0, 0, 0, 0, 0, 36, 0, 0, 38, 40, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 1, 1, 1, 0, 2, 1, 0, 0, 2, 0, 6, 0, 0, 0, 3, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![680000, 380000, 0, 3860000, 530000, 352, 790000, 1290000, 9200000, 938, 490000, -469, 4070000, 0, 11170000, 2460000, 400000, 16220000, 293, 1250000, 352, -176, 40000, -117, -469, -176, 0, 313, 10250000, 59, -59, 930000, 1680000, 0, 117, 0, 59, -18, 258, -781, -32].span() +}; + let tree_81 = xgb_inference::Tree { + base_weights: array![40, 374, -127, 629, -456, -343, 391, 39, 1215, -352, 195, -456, 176, -130, 694, -174, 293, 781, 446, 0, 117, 0, -684, -313, 117, 78, 352, -146, 65, 195, -117, -859, 614, -703, -473, 0, -117, 117, -260, 0, 59, -410, -117, 39, 234, -211, 94, 0, -117].span(), + left_children: array![1, 3, 5, 7, 9, 11, 13, 15, 17, 0, 19, 21, 0, 23, 25, 27, 0, 0, 29, 0, 0, 31, 33, 35, 0, 37, 0, 0, 39, 0, 0, 41, 43, 0, 45, 0, 0, 0, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 14, 16, 18, 0, 20, 22, 0, 24, 26, 28, 0, 0, 30, 0, 0, 32, 34, 36, 0, 38, 0, 0, 40, 0, 0, 42, 44, 0, 46, 0, 0, 0, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![3, 8, 0, 0, 2, 4, 3, 6, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 1, 0, 0, 3, 3, 0, 0, 2, 0, 0, 8, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![440000, 70000, 28170000, 9200000, 370000, 2670000, 4000000, 190000, 13730000, -352, 4520000, 1480000, 176, 58650000, 12570000, 160000, 293, 781, 4930000, 0, 117, 930000, 1590000, 30880000, 117, 380000, 352, -146, 40000, 195, -117, 1880000, 560000, -703, 15890000, 0, -117, 117, 37140000, 0, 59, -410, -117, 39, 234, -211, 94, 0, -117].span() +}; + let tree_82 = xgb_inference::Tree { + base_weights: array![69, -138, 268, -12, -1432, 703, 138, -195, 52, -586, -59, -58, 781, 335, -184, 81, -879, 332, -130, 178, 205, -352, -49, -352, 187, -313, -59, 59, -117, 151, -94, 33, -234, 18, 234].span(), + left_children: array![1, 3, 5, 7, 9, 0, 11, 0, 13, 0, 0, 15, 17, 19, 21, 23, 25, 0, 27, 29, 0, 0, 31, 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 0, 12, 0, 14, 0, 0, 16, 18, 20, 22, 24, 26, 0, 28, 30, 0, 0, 32, 0, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![0, 0, 0, 4, 0, 0, 1, 0, 4, 0, 0, 6, 1, 3, 1, 0, 1, 0, 0, 2, 0, 0, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![12870000, 11620000, 13730000, 0, 12510000, 703, 20570000, -195, 100000, -586, -59, 970000, 35810000, 930000, 290000, 13980000, 15890000, 332, 37470000, 740000, 205, -352, 140000, -352, 1060000, -313, -59, 59, -117, 151, -94, 33, -234, 18, 234].span() +}; + let tree_83 = xgb_inference::Tree { + base_weights: array![63, 353, -65, -26, 1116, -116, 352, -195, 146, 586, 651, -232, 284, -558, 260, 195, 313, 31, -721, -130, 391, -234, 0, 586, -260, 78, 0, 547, -93, -527, -488, 0, -59, 188, 78, 78, 234, -117, 0, 234, -59, -98, 59, -211, 117, 0, 59].span(), + left_children: array![1, 3, 5, 7, 9, 11, 0, 13, 0, 0, 15, 17, 19, 21, 23, 25, 0, 27, 29, 31, 33, 0, 0, 35, 37, 0, 0, 39, 41, 0, 43, 0, 0, 0, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 6, 8, 10, 12, 0, 14, 0, 0, 16, 18, 20, 22, 24, 26, 0, 28, 30, 32, 34, 0, 0, 36, 38, 0, 0, 40, 42, 0, 44, 0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![2, 2, 6, 0, 1, 4, 0, 9, 0, 0, 0, 4, 7, 1, 7, 0, 0, 1, 4, 0, 8, 0, 0, 0, 0, 0, 0, 9, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![680000, 380000, 4070000, 13980000, 530000, 570000, 352, -10490000, 146, 586, 16590000, 150000, 170000, 3860000, 180000, 16220000, 313, 810000, 210000, 30880000, 40000, -234, 0, 1270000, 2460000, 78, 0, -2240000, 10260000, -527, 15890000, 0, -59, 188, 23340000, 78, 234, -117, 0, 234, -59, -98, 59, -211, 117, 0, 59].span() +}; + let tree_84 = xgb_inference::Tree { + base_weights: array![98, 64, 352, 372, -75, 52, 1004, -179, 284, -98, 146, 59, 1107, 16, -541, -130, 391, 117, -234, 59, 1250, 240, -210, -469, -326, 0, -59, 188, 78, -39, 117, 469, 156, 195, -29, -215, 59, -23, -313, 0, 59].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 13, 15, 17, 0, 0, 19, 21, 23, 25, 27, 29, 0, 0, 31, 33, 35, 0, 37, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 14, 16, 18, 0, 0, 20, 22, 24, 26, 28, 30, 0, 0, 32, 34, 36, 0, 38, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 2, 4, 0, 0, 4, 7, 0, 0, 0, 3, 3, 4, 0, 8, 0, 0, 0, 3, 9, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 680000, 352, 380000, 570000, 13980000, 3810000, 150000, 170000, 10250000, 146, 59, -3130000, 1250000, 210000, 30880000, 40000, 2930000, -234, 59, 440000, -7900000, 3860000, -469, 460000, 0, -59, 188, 23340000, -39, 117, 469, 156, 195, -29, -215, 59, -23, -313, 0, 59].span() +}; + let tree_85 = xgb_inference::Tree { + base_weights: array![103, 70, 352, 316, -42, 78, 781, -137, 284, -65, 146, 293, 117, 47, -481, -130, 391, 117, -195, 251, -195, -1074, -195, 0, -59, 188, 78, -39, 117, 127, -156, 78, -167, 0, -430, 29, -273, 0, 59].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 13, 15, 17, 0, 0, 0, 19, 21, 23, 25, 27, 0, 29, 31, 33, 35, 0, 0, 0, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 14, 16, 18, 0, 0, 0, 20, 22, 24, 26, 28, 0, 30, 32, 34, 36, 0, 0, 0, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 2, 4, 0, 9, 4, 7, 0, 0, 0, 0, 9, 4, 0, 8, 0, 0, 5, 7, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 680000, 352, 380000, 570000, 13980000, -15040000, 150000, 170000, 10250000, 146, 293, 117, -7900000, 260000, 30880000, 40000, 2930000, -195, 580000, 170000, 1240000, 460000, 0, -59, 188, 23340000, -39, 117, 127, -156, 78, -167, 0, -430, 29, -273, 0, 59].span() +}; + let tree_86 = xgb_inference::Tree { + base_weights: array![115, 82, 352, 124, -469, 67, 651, -234, 0, 110, -352, 234, 78, 363, 27, -17, 205, -117, 44].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 0, 0, 0, 15, 17, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 0, 0, 0, 16, 18, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 1, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, 22700000, 1740000, -234, 0, 1290000, -352, 234, 78, 1880000, 3960000, -17, 205, -117, 44].span() +}; + let tree_87 = xgb_inference::Tree { + base_weights: array![98, 64, 352, 7, 488, 279, -75, 234, -260, -87, 781, -189, 182, 0, -117, -456, 488, 78, 293, -67, -859, 446, -43, -164, 0, 195, 0, -69, 117, -293, -59, 156, 0, 59, -117].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 13, 15, 17, 19, 21, 0, 0, 23, 25, 0, 0, 27, 29, 31, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 14, 16, 18, 20, 22, 0, 0, 24, 26, 0, 0, 28, 30, 32, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 7, 0, 1, 2, 1, 3, 0, 0, 6, 0, 3, 5, 0, 0, 0, 1, 0, 0, 9, 3, 7, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 1550000, 352, 1290000, 2080000, 300000, 4000000, 234, 9650000, 190000, 1270000, 3450000, 140000, 0, -117, 2930000, 270000, 78, 293, -5180000, 3980000, 870000, 450000, -164, 0, 195, 0, -69, 117, -293, -59, 156, 0, 59, -117].span() +}; + let tree_88 = xgb_inference::Tree { + base_weights: array![103, 70, 352, 13, 488, 279, -66, 234, -260, -87, 781, -391, 21, 0, -117, -456, 488, 78, 293, 0, -195, 586, -45, -164, 0, 195, 0, -59, 29, 234, 0, -98, 29].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 13, 15, 17, 19, 21, 0, 0, 23, 25, 0, 0, 27, 0, 29, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 14, 16, 18, 20, 22, 0, 0, 24, 26, 0, 0, 28, 0, 30, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 7, 0, 1, 2, 1, 1, 0, 0, 6, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 1550000, 352, 1290000, 2080000, 300000, 3960000, 234, 9650000, 190000, 1270000, 2580000, 210000, 0, -117, 2930000, 270000, 78, 293, 160000, -195, 400000, 1840000, -164, 0, 195, 0, -59, 29, 234, 0, -98, 29].span() +}; + let tree_89 = xgb_inference::Tree { + base_weights: array![109, 76, 352, 118, -469, 67, 586, -234, 0, 103, -293, 88, 234, 33, 391, 41, -96, 17, 234].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 0, 0, 0, 15, 17, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 0, 0, 0, 16, 18, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 1, 5, 0, 0, 9, 0, 0, 0, 9, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, 22700000, 1660000, -234, 0, -2470000, -293, 88, 234, -7900000, 2460000, 41, -96, 17, 234].span() +}; + let tree_90 = xgb_inference::Tree { + base_weights: array![115, 82, 352, 124, -469, 74, 586, -234, 0, 103, -234, 88, 234, 33, 391, 41, -96, 17, 234].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 0, 0, 0, 15, 17, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 0, 0, 0, 16, 18, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 1, 5, 0, 0, 9, 0, 0, 0, 9, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, 22700000, 1660000, -234, 0, -2470000, -234, 88, 234, -7900000, 2460000, 41, -96, 17, 234].span() +}; + let tree_91 = xgb_inference::Tree { + base_weights: array![121, 87, 352, 130, -469, 81, 586, -234, 0, 8, 391, 88, 234, 61, -586, 56, 234, 117, -13, 0, -234, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 9, 0, 0, 0, 1, 3, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, -4420000, 2460000, 88, 234, 1290000, 410000, 60000, 234, 117, -13, 0, -234, -94, 195].span() +}; + let tree_92 = xgb_inference::Tree { + base_weights: array![132, 99, 352, 143, -469, 94, 586, -234, 0, 24, 391, 88, 234, -160, 174, 56, 234, -19, -176, 234, 10, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 630000, 60000, 234, -19, -176, 234, 10, -94, 195].span() +}; + let tree_93 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let tree_94 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let tree_95 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let tree_96 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let tree_97 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let tree_98 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let tree_99 = xgb_inference::Tree { + base_weights: array![126, 93, 352, 136, -469, 88, 586, -234, 0, 16, 391, 88, 234, -160, 159, 56, 234, -19, -176, 352, 23, -94, 195].span(), + left_children: array![1, 3, 0, 5, 7, 9, 11, 0, 0, 13, 15, 0, 0, 17, 19, 21, 0, 0, 0, 0, 0, 0, 0].span(), + right_children: array![2, 4, 0, 6, 8, 10, 12, 0, 0, 14, 16, 0, 0, 18, 20, 22, 0, 0, 0, 0, 0, 0, 0].span(), + split_indices: array![6, 2, 0, 1, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0].span(), + split_conditions: array![4070000, 8630000, 352, 24330000, 12020000, -2470000, 1660000, -234, 0, 12870000, 2460000, 88, 234, 10250000, 13730000, 60000, 234, -19, -176, 352, 23, -94, 195].span() +}; + let num_trees: u32 = 100; + let base_score: i32 = 2147483647; + let opt_type: u8 = 0; + let trees: Span = array![tree_0, tree_1, tree_2, tree_3, tree_4, tree_5, tree_6, tree_7, tree_8, tree_9, tree_10, tree_11, tree_12, tree_13, tree_14, tree_15, tree_16, tree_17, tree_18, tree_19, tree_20, tree_21, tree_22, tree_23, tree_24, tree_25, tree_26, tree_27, tree_28, tree_29, tree_30, tree_31, tree_32, tree_33, tree_34, tree_35, tree_36, tree_37, tree_38, tree_39, tree_40, tree_41, tree_42, tree_43, tree_44, tree_45, tree_46, tree_47, tree_48, tree_49, tree_50, tree_51, tree_52, tree_53, tree_54, tree_55, tree_56, tree_57, tree_58, tree_59, tree_60, tree_61, tree_62, tree_63, tree_64, tree_65, tree_66, tree_67, tree_68, tree_69, tree_70, tree_71, tree_72, tree_73, tree_74, tree_75, tree_76, tree_77, tree_78, tree_79, tree_80, tree_81, tree_82, tree_83, tree_84, tree_85, tree_86, tree_87, tree_88, tree_89, tree_90, tree_91, tree_92, tree_93, tree_94, tree_95, tree_96, tree_97, tree_98, tree_99].span(); + let mut result: i32 = xgb_inference::accumulate_scores_from_trees(num_trees, trees, input_vector, 0, 0); + + if opt_type == 1 { + // Implement logic here + } else { + result = result + base_score; + } + + return result; +} \ No newline at end of file diff --git a/etf_xgb1/src/xgb_inference.cairo b/etf_xgb1/src/xgb_inference.cairo new file mode 100644 index 0000000..ed557e7 --- /dev/null +++ b/etf_xgb1/src/xgb_inference.cairo @@ -0,0 +1,38 @@ +use core::array::ArrayTrait; + + +#[derive(Copy, Drop)] +pub struct Tree { + pub base_weights: Span, + pub left_children: Span, + pub right_children: Span, + pub split_indices: Span, + pub split_conditions: Span +} + +pub fn navigate_tree_and_accumulate_score(tree: Tree, features: Span, node: u32) -> i32 { + if *tree.left_children[node] == 0 { + if *tree.right_children[node] == 0{ + return *tree.base_weights[node]; + } + } + let mut next_node: u32 = 0; + let feature_index = *tree.split_indices[node]; + let threshold = *tree.split_conditions[node]; + if *features.at(feature_index) < threshold{ + next_node = *tree.left_children[node]; + } + else{ + next_node = *tree.right_children[node]; + } + navigate_tree_and_accumulate_score(tree, features, next_node) +} + +pub fn accumulate_scores_from_trees(num_trees: u32, trees: Span, features: Span, index:u32, accumulated_score:i32) -> i32{ + if index >= num_trees{ + return accumulated_score; + } + let tree: Tree = *trees[index]; + let score_from_tree: i32 = navigate_tree_and_accumulate_score(tree, features, 0); + accumulate_scores_from_trees(num_trees, trees, features, index + 1, accumulated_score + score_from_tree) +} diff --git a/insurance.csv b/insurance.csv new file mode 100644 index 0000000..253a66b --- /dev/null +++ b/insurance.csv @@ -0,0 +1,1339 @@ +age,sex,bmi,children,smoker,region,expenses +18,male,16,0,no,northeast,1694.8 +18,male,17.3,2,yes,northeast,12829.46 +18,female,20.8,0,no,southeast,1607.51 +18,male,21.5,0,no,northeast,1702.46 +18,male,21.6,0,yes,northeast,13747.87 +18,female,21.7,0,yes,northeast,14283.46 +18,male,21.8,2,no,southeast,11884.05 +18,male,23,0,no,northeast,1704.57 +18,male,23.1,0,no,northeast,1704.7 +18,male,23.2,0,no,southeast,1121.87 +18,male,23.3,1,no,southeast,1711.03 +18,male,23.8,0,no,northeast,1705.62 +18,female,24.1,1,no,southeast,2201.1 +18,female,25.1,0,no,northeast,2196.47 +18,male,25.2,0,yes,northeast,15518.18 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