程序代写代做代考 python Untitled

Untitled

In [1]:

from sklearn.neural_network import MLPRegressor

import pandas as pd

from sklearn.preprocessing import StandardScaler
import numpy as np
from sklearn.model_selection import GridSearchCV

In [2]:

data = pd.read_csv(“data169765.csv”, header = None)

In [3]:

X = data.iloc[:, :5]
y = data.iloc[:, 5]

In [15]:

data

Out[15]:

0 1 2 3 4 5
0 3.049700e+00 3.13170 -24.83100 179.990 10.843000 1258.80
1 6.087300e+00 3.43550 -26.99400 59.996 -2.799100 1311.90
2 9.101000e+00 0.57715 79.69900 59.996 -1.261500 1198.00
3 1.207900e+01 0.45136 -36.17300 239.980 -9.370000 1342.10
4 1.500900e+01 10.26000 -21.14300 239.980 0.051995 1350.20
5 1.788000e+01 4.41410 7.05120 -59.996 16.853000 1262.90
6 2.068000e+01 -20.04800 -31.42200 -239.980 -27.761000 1484.50
7 2.339800e+01 10.08200 22.52200 119.990 3.611400 1319.20
8 2.602500e+01 11.48000 6.77220 119.990 -6.084900 1365.00
9 2.854800e+01 -1.25230 43.76000 -179.990 23.560000 1386.30
10 3.095900e+01 12.81300 -30.48800 59.996 7.094000 1440.10
11 3.324800e+01 10.75000 37.00700 0.000 8.922800 1277.10
12 3.540500e+01 9.44840 -64.70000 179.990 15.278000 1163.90
13 3.742300e+01 16.30800 15.77700 -179.990 29.965000 1178.20
14 3.929300e+01 8.54800 36.02500 -0.000 18.834000 1268.10
15 4.100800e+01 20.72000 56.39100 -59.996 24.444000 1294.40
16 4.256200e+01 -11.63500 -4.59750 -119.990 -18.805000 1071.40
17 4.394700e+01 24.50100 -85.32500 0.000 38.278000 1220.90
18 4.515900e+01 -6.55990 -38.91500 -179.990 -27.289000 1299.60
19 4.619200e+01 -3.05640 43.18100 119.990 7.306400 1243.70
20 4.704300e+01 14.93400 89.80800 -119.990 43.318000 1040.00
21 4.770900e+01 -26.55000 -83.43500 0.000 -69.256000 1386.90
22 4.818600e+01 3.97050 37.23700 119.990 -13.642000 1262.70
23 4.847300e+01 -6.88080 -104.52000 59.996 -21.388000 1375.10
24 4.856900e+01 -21.27900 -103.62000 -179.990 -15.199000 1638.50
25 4.847300e+01 -23.99700 -48.90900 239.980 -56.282000 901.22
26 4.818600e+01 -2.48110 44.91500 0.000 1.207200 1255.10
27 4.770900e+01 19.07600 97.29600 0.000 39.323000 675.87
28 4.704300e+01 21.27500 89.56800 -179.990 40.226000 740.13
29 4.619200e+01 23.99000 89.79800 179.990 35.990000 881.35
… … … … … … …
970 -4.704300e+01 14.66100 62.89100 -59.996 41.154000 1272.80
971 -4.770900e+01 13.50800 87.50000 -59.996 -8.155300 1221.80
972 -4.818600e+01 -17.37100 24.38600 -239.980 -49.959000 1143.70
973 -4.847300e+01 14.60000 -59.18500 119.990 -9.279900 1221.50
974 -4.856900e+01 -17.34000 39.83000 179.990 11.128000 1294.80
975 -4.847300e+01 -22.92400 -7.85380 -179.990 -4.062200 1310.10
976 -4.818600e+01 -20.56400 -17.46400 0.000 12.406000 1381.50
977 -4.770900e+01 -3.79530 67.03900 119.990 -4.808700 1322.90
978 -4.704300e+01 12.42800 -3.22990 -59.996 5.101600 1406.00
979 -4.619200e+01 26.99300 116.61000 239.980 35.386000 1270.30
980 -4.515900e+01 17.26600 63.54800 -59.996 39.570000 1070.40
981 -4.394700e+01 -10.41600 -91.06800 59.996 -16.552000 1346.60
982 -4.256200e+01 11.34500 64.33900 59.996 19.630000 1566.50
983 -4.100800e+01 -10.79400 -41.02000 179.990 -45.177000 1171.30
984 -3.929300e+01 14.85300 44.87900 -59.996 10.127000 1266.90
985 -3.742300e+01 2.80770 -35.73600 -239.980 -16.462000 1326.70
986 -3.540500e+01 2.03060 -0.12697 59.996 -22.811000 1154.80
987 -3.324800e+01 13.62500 -29.21500 59.996 6.927800 1418.60
988 -3.095900e+01 -3.14260 -50.90000 -239.980 -44.663000 1175.80
989 -2.854800e+01 14.88800 -8.82180 119.990 23.442000 1394.60
990 -2.602500e+01 7.37600 -0.85256 -179.990 -7.350900 1210.30
991 -2.339800e+01 0.62822 32.86900 59.996 -11.776000 1254.60
992 -2.068000e+01 -12.43100 -21.52800 59.996 4.988700 1177.60
993 -1.788000e+01 23.45600 36.32800 59.996 39.656000 977.15
994 -1.500900e+01 18.45500 -52.39000 -119.990 35.711000 794.32
995 -1.207900e+01 -24.15700 -6.02410 -59.996 -2.408900 1240.60
996 -9.101000e+00 9.64880 63.31000 59.996 18.516000 1480.50
997 -6.087300e+00 7.82180 -3.27640 59.996 22.724000 1346.60
998 -3.049700e+00 14.69500 105.94000 119.990 44.387000 1108.30
999 -4.640700e-13 4.88970 -40.75700 -119.990 -9.860000 1283.70

1000 rows × 6 columns

In [86]:

X.head()

Out[86]:

0 1 2 3 4
0 3.0497 3.13170 -24.831 179.990 10.843000
1 6.0873 3.43550 -26.994 59.996 -2.799100
2 9.1010 0.57715 79.699 59.996 -1.261500
3 12.0790 0.45136 -36.173 239.980 -9.370000
4 15.0090 10.26000 -21.143 239.980 0.051995

In [35]:

y.head()

Out[35]:

0 1258.8
1 1311.9
2 1198.0
3 1342.1
4 1350.2
Name: 5, dtype: float64

In [40]:

reg = MLPRegressor (solver=’lbfgs’, alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)

In [41]:

reg.fit(X, y)

Out[41]:

MLPRegressor(activation=’relu’, alpha=1e-05, batch_size=’auto’, beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver=’lbfgs’, tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)

In [42]:

reg.predict(X)

Out[42]:

array([ 1266.02155648, 1245.01389353, 1238.62640672, 1268.02044991,
1257.08211267, 1243.38281224, 1246.42477883, 1238.01551804,
1236.47509099, 1235.93832116, 1248.09374674, 1232.16485829,
1267.21313042, 1237.61755919, 1240.36845561, 1227.34541373,
1239.40468438, 1262.74820882, 1243.26409727, 1246.41472225,
1230.3041069 , 1262.50686537, 1233.97988114, 1271.18518758,
1270.95204289, 1258.04243063, 1236.51733553, 1229.42331121,
1221.63803328, 1233.39790118, 1225.58228603, 1260.1546772 ,
1271.05885115, 1234.98048422, 1246.22209206, 1242.96719324,
1240.2142514 , 1252.46126931, 1222.83834835, 1246.90462941,
1249.81136844, 1239.86371144, 1267.95366845, 1252.73117099,
1220.06747197, 1249.49416109, 1249.46057681, 1242.43571653,
1254.13547568, 1229.54925775, 1250.39585208, 1273.3536956 ,
1247.49508277, 1267.14290162, 1260.16175918, 1255.62562011,
1272.22028726, 1278.91399759, 1248.80187771, 1248.22278399,
1257.95650874, 1252.21404345, 1289.08666181, 1291.94713173,
1268.32933635, 1259.57620603, 1209.66906519, 1265.32126686,
1265.02062097, 1261.83728159, 1260.31916109, 1210.71187457,
1239.95355562, 1248.38506131, 1251.8668645 , 1285.59095126,
1262.28656983, 1268.06636733, 1223.08440051, 1230.95371875,
1233.90934235, 1246.88506031, 1292.3273911 , 1230.04032242,
1227.5345179 , 1295.98177468, 1271.54428068, 1264.8463089 ,
1255.54207743, 1244.00006846, 1234.56226319, 1272.2061417 ,
1248.10929453, 1228.73233072, 1250.05332011, 1256.92944695,
1249.42709192, 1234.10522671, 1262.6849538 , 1231.43222976,
1276.51426847, 1236.90965986, 1230.83081126, 1258.23994274,
1243.41456547, 1228.88362321, 1252.36921644, 1237.47630205,
1241.74630085, 1271.54144724, 1225.91900756, 1251.57788594,
1243.01370599, 1266.31422782, 1258.04460877, 1233.19117738,
1239.88601692, 1274.29411949, 1245.78544274, 1270.59294333,
1251.66543474, 1237.34896419, 1259.63088202, 1258.11468402,
1253.68103972, 1238.51358835, 1266.99983764, 1245.76059402,
1255.83476361, 1221.7364963 , 1266.1672025 , 1276.52851451,
1245.91566636, 1263.54463671, 1262.18746412, 1240.17103179,
1239.83513391, 1277.13623341, 1240.03265866, 1252.12078448,
1242.32992731, 1256.11039766, 1209.21047238, 1261.62936462,
1265.32602055, 1239.06089924, 1246.17328372, 1260.35423878,
1263.0831453 , 1248.02969522, 1225.15236274, 1225.65045345,
1233.3702758 , 1267.64275726, 1257.47976977, 1243.51545174,
1259.32072266, 1220.70173575, 1238.23420389, 1248.00307957,
1250.86735437, 1256.21902335, 1274.34985728, 1249.52876089,
1270.16931671, 1271.81803244, 1256.47118715, 1241.42116752,
1267.74087589, 1268.9134506 , 1266.23610312, 1250.66639482,
1292.0340843 , 1273.5124165 , 1252.08241409, 1263.56276893,
1279.55144858, 1237.72422149, 1283.96732185, 1301.93881093,
1256.80897812, 1262.85846725, 1241.54953436, 1233.50425823,
1301.13418372, 1254.34107069, 1236.45457754, 1256.94435887,
1289.17589942, 1250.62879902, 1262.02572152, 1252.79067859,
1242.61976561, 1246.67164419, 1245.93563334, 1232.8631613 ,
1243.56376279, 1230.65714241, 1253.70539179, 1256.14459293,
1261.98995822, 1240.01948329, 1238.14942616, 1248.02029334,
1237.83859447, 1250.88610227, 1266.18873182, 1270.67505812,
1226.82605533, 1255.22461548, 1245.98393102, 1234.33203011,
1254.9036339 , 1250.04992133, 1240.57563718, 1254.22168753,
1244.52724829, 1242.0553965 , 1255.77865603, 1228.71045685,
1261.60109573, 1270.22873794, 1239.27842097, 1250.87220925,
1216.58420208, 1229.98968112, 1229.25866349, 1249.81961337,
1244.14454968, 1248.25389783, 1248.37945495, 1221.72388353,
1229.33692446, 1252.6369524 , 1252.76589795, 1230.60060548,
1221.37506261, 1234.3539324 , 1246.64643083, 1247.55363101,
1245.48777584, 1238.57242761, 1266.88645521, 1256.28391341,
1245.78720032, 1259.85830032, 1272.79780844, 1246.40006231,
1233.34540412, 1241.59457973, 1231.93768616, 1241.39205794,
1259.22328868, 1241.76545199, 1216.96296555, 1228.01399006,
1264.74012276, 1250.16144959, 1282.16286712, 1303.13931764,
1248.34783678, 1250.70730516, 1224.9483868 , 1242.44151174,
1243.59631144, 1255.72021696, 1258.6562913 , 1279.50955003,
1265.21819037, 1279.90679157, 1265.58630686, 1251.4898765 ,
1277.18703913, 1262.96076063, 1249.06763041, 1271.79944547,
1274.22410835, 1251.54711351, 1259.64964776, 1252.39019732,
1246.7848189 , 1264.44140248, 1245.58351226, 1238.78188997,
1264.70926428, 1257.59891656, 1245.70807915, 1286.25443341,
1245.73886393, 1261.31507625, 1266.14753055, 1244.42107483,
1233.15446513, 1289.83102399, 1257.02442986, 1268.00746267,
1255.96142956, 1249.22119213, 1256.67611631, 1214.76676948,
1250.42847768, 1275.11228935, 1253.50895749, 1240.67944975,
1229.45745344, 1249.38699047, 1252.28585729, 1256.97695418,
1233.95573448, 1256.71693969, 1232.15155473, 1262.48628692,
1235.01843608, 1261.78154892, 1266.29616101, 1240.08300268,
1241.69283363, 1255.47480153, 1270.93174017, 1226.13695212,
1243.24939063, 1260.81880836, 1274.25117483, 1262.53269508,
1254.31056007, 1232.64484278, 1268.77244202, 1215.69282292,
1238.55300842, 1279.50138485, 1252.39733075, 1246.03764283,
1258.6067219 , 1256.85876375, 1227.19797022, 1258.67051931,
1271.91859052, 1246.13838112, 1246.24165379, 1240.11434223,
1253.38824571, 1238.28428817, 1227.02962911, 1243.76913081,
1268.24941092, 1236.49425424, 1258.57444064, 1258.67233532,
1238.5222067 , 1239.14281649, 1249.42865901, 1247.70822367,
1242.30968043, 1263.16641577, 1259.72844164, 1237.16778064,
1268.47659769, 1264.56252816, 1229.41204247, 1273.09662147,
1206.32737311, 1273.46373011, 1244.33560463, 1257.18286138,
1261.1955591 , 1261.29847912, 1274.0806904 , 1294.46393758,
1249.24071417, 1283.72367688, 1283.38229799, 1271.87732411,
1271.78158396, 1257.23734453, 1260.80813937, 1290.50341634,
1227.77355116, 1286.4112234 , 1294.02233567, 1240.6511789 ,
1261.14780034, 1240.02348136, 1264.31675393, 1266.30402453,
1241.04379359, 1236.68537896, 1274.70054935, 1268.49064656,
1264.8212113 , 1279.88534337, 1240.82828117, 1248.96476716,
1274.64735913, 1232.33662145, 1220.06359528, 1235.28115168,
1236.60522881, 1254.08198922, 1244.6471569 , 1253.3864558 ,
1256.59455938, 1224.54950641, 1239.24355619, 1225.66959995,
1275.22367026, 1238.1011631 , 1260.0270334 , 1250.66754638,
1246.99570868, 1240.29304771, 1260.25869587, 1262.59414528,
1252.06059872, 1240.45233591, 1233.9377692 , 1282.45732958,
1244.61470091, 1209.78169474, 1243.76621048, 1243.55605206,
1260.13688966, 1245.29776378, 1256.23767984, 1252.42200994,
1230.72532629, 1232.0626278 , 1243.15669951, 1248.11203504,
1246.57215774, 1235.52807581, 1245.94470419, 1230.47687524,
1236.01220396, 1258.37124012, 1247.32509951, 1239.2244884 ,
1248.29070923, 1244.98084598, 1226.82456461, 1235.36073718,
1220.05519367, 1273.94145274, 1238.82834944, 1256.29778483,
1240.94180574, 1236.97640124, 1254.7912236 , 1220.44528998,
1235.82560404, 1255.09201392, 1244.38672093, 1224.48785577,
1250.82006402, 1268.76512804, 1270.54329492, 1264.9421637 ,
1254.10238843, 1274.63801236, 1260.99908411, 1251.90772056,
1255.99124279, 1246.19312125, 1248.68345134, 1262.94965592,
1238.85115979, 1269.17758788, 1291.63628134, 1282.37589628,
1276.20519866, 1213.2220152 , 1249.65268517, 1266.02221452,
1301.97602427, 1208.43090766, 1263.51838105, 1255.8612824 ,
1254.80920491, 1275.94951548, 1247.42865662, 1251.29468612,
1251.99899028, 1262.78902729, 1246.11203786, 1240.58839013,
1288.14304284, 1246.94192806, 1280.20131808, 1307.39168141,
1260.36673957, 1268.28644699, 1233.4479279 , 1255.53067932,
1264.75343308, 1244.33989266, 1258.811593 , 1295.33308508,
1268.90663384, 1255.10467445, 1231.75795374, 1242.53638652,
1240.38153852, 1244.78705172, 1254.15129951, 1236.3527657 ,
1231.9556515 , 1252.71199609, 1253.4104574 , 1268.18363861,
1257.35062653, 1280.4115058 , 1236.94166426, 1249.87732216,
1238.84845361, 1254.55669653, 1239.15923989, 1266.44103327,
1258.82131456, 1251.58245946, 1267.98114225, 1240.05526608,
1234.41534848, 1252.26121853, 1256.38978231, 1244.89815914,
1255.29530224, 1250.93269524, 1244.14909373, 1242.22412673,
1241.80473255, 1250.13359118, 1253.22253538, 1261.34757835,
1225.06329776, 1253.59899276, 1248.63320389, 1226.81883451,
1227.95689859, 1219.38808654, 1255.12946257, 1270.22602894,
1240.45201378, 1233.80124764, 1223.75958784, 1247.29160758,
1246.26068832, 1233.34296465, 1240.17391391, 1229.53123854,
1246.66700758, 1269.4429122 , 1283.66408459, 1259.18597155,
1268.42365462, 1261.62124799, 1252.27190819, 1222.31708314,
1257.34280925, 1272.86990266, 1243.65128055, 1236.34654716,
1243.93173007, 1239.58696252, 1262.52801923, 1286.71921233,
1231.83611112, 1275.29593141, 1249.66288954, 1272.8801918 ,
1225.57519142, 1285.58332864, 1231.65509264, 1254.00375478,
1229.11636307, 1269.07583832, 1237.22614702, 1299.21980327,
1243.72277633, 1259.65738265, 1277.53843378, 1279.33053617,
1270.17445651, 1273.17799175, 1276.69467452, 1265.81055702,
1253.99211814, 1224.76002936, 1238.69158464, 1284.18196215,
1244.03574252, 1242.20666062, 1265.63039581, 1209.27218953,
1256.50952965, 1220.97705026, 1261.30904036, 1242.11276033,
1246.32261688, 1256.10199677, 1227.97739475, 1217.07432323,
1247.61085281, 1267.79121345, 1254.18286345, 1278.04689916,
1241.86946857, 1243.22421059, 1226.57830371, 1249.71365303,
1264.74181973, 1234.09020564, 1214.32200599, 1241.79608233,
1214.80961019, 1222.47929324, 1240.19670591, 1226.61799443,
1262.23124228, 1263.89399384, 1239.77671608, 1248.14265906,
1246.44769815, 1250.80754567, 1230.48711356, 1237.37160663,
1241.31533398, 1237.36009826, 1245.10264677, 1254.6779196 ,
1220.79231838, 1253.82819283, 1259.9110868 , 1248.33468299,
1255.1381292 , 1259.18159961, 1261.97319725, 1244.53197469,
1239.06508649, 1251.47608431, 1238.1733699 , 1285.91336323,
1249.69430895, 1284.22190544, 1233.26185356, 1258.22390574,
1201.45807305, 1293.45309916, 1232.45106393, 1237.1651955 ,
1225.23418695, 1258.34469744, 1270.91295817, 1236.58850134,
1223.06607651, 1219.48535894, 1257.16415069, 1227.76568528,
1271.49834843, 1266.02140344, 1250.6599818 , 1247.18054142,
1270.25414018, 1276.55510372, 1273.80282176, 1244.51255602,
1245.01013145, 1264.8930945 , 1258.36929276, 1283.52158355,
1227.89296153, 1271.12488477, 1262.31016446, 1282.21848518,
1245.15366634, 1279.76726795, 1243.44961721, 1249.26616814,
1266.56862593, 1259.54473198, 1217.47540841, 1255.99191979,
1271.37203298, 1286.78303964, 1265.26435141, 1257.29617655,
1263.09921 , 1215.14052066, 1259.61704452, 1220.87806675,
1285.90399939, 1263.65169482, 1236.33199845, 1276.4380464 ,
1262.84824798, 1269.10633995, 1256.04797452, 1276.61425995,
1261.85000795, 1247.72565258, 1249.28313657, 1234.87731641,
1249.59446894, 1244.0264508 , 1240.43976889, 1250.57066625,
1242.42785273, 1249.09920692, 1257.14220589, 1234.50691318,
1261.1704824 , 1230.05022048, 1228.2956625 , 1276.82505614,
1236.59455127, 1278.52521122, 1234.64534525, 1257.07474801,
1236.22797134, 1254.09867811, 1277.11811329, 1256.80539988,
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1256.52932628, 1259.14093326, 1278.58430025, 1216.44234674,
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1259.21791084, 1259.35224967, 1231.21616928, 1249.48069396,
1290.0742499 , 1232.97864096, 1289.10232337, 1293.63205907,
1253.81635684, 1269.25039497, 1237.81402668, 1253.22299481,
1245.65912157, 1285.97801399, 1296.05549432, 1245.49586717,
1249.20272492, 1291.6900966 , 1228.79929174, 1283.43722372,
1243.53910895, 1246.64538624, 1287.51306374, 1279.75458975,
1264.09623648, 1234.51460788, 1249.30244295, 1260.60737788,
1303.05505355, 1252.76475758, 1279.64796492, 1273.19105566,
1237.77568115, 1242.27812073, 1247.19503101, 1272.5969614 ,
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1263.28429241, 1229.48058516, 1285.50329874, 1237.59438484,
1242.15980323, 1248.15109662, 1260.51431438, 1273.5146405 ,
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1267.40359355, 1229.2567464 , 1260.31443785, 1276.53174026,
1259.5920821 , 1250.98335458, 1255.60353787, 1236.90238861,
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1257.74283896, 1267.31670132, 1224.29302777, 1247.62977944,
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1233.5118765 , 1217.46029332, 1233.91879892, 1258.16483718,
1254.10568549, 1265.39339425, 1232.95619368, 1271.3407613 ,
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1236.64099147, 1249.09386874, 1241.13644422, 1241.15497251])

In [90]:

scaler = StandardScaler()
scaler.fit(X)
X = scaler.transform(X)

In [53]:

parameters = {‘solver’:(‘lbfgs’, ‘sgd’, ‘adam’), ‘alpha’: 10.0 ** -np.arange(1, 7)}

In [62]:

mlp = MLPRegressor(hidden_layer_sizes=(5, 2), random_state=1)
gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)

In [63]:

gcv.fit(X, y)

Out[63]:

GridSearchCV(cv=None, error_score=’raise’,
estimator=MLPRegressor(activation=’relu’, alpha=0.0001, batch_size=’auto’, beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver=’adam’, tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False),
fit_params=None, iid=True, n_jobs=1,
param_grid={‘solver’: (‘lbfgs’, ‘sgd’, ‘adam’), ‘alpha’: array([ 1.00000e-01, 1.00000e-02, 1.00000e-03, 1.00000e-04,
1.00000e-05, 1.00000e-06])},
pre_dispatch=’2*n_jobs’, refit=True, return_train_score=’warn’,
scoring=’neg_mean_squared_error’, verbose=0)

In [64]:

gcv.best_score_

Out[64]:

-68648.332680830616

In [57]:

gcv.best_estimator_

Out[57]:

MLPRegressor(activation=’relu’, alpha=1.0000000000000001e-05,
batch_size=’auto’, beta_1=0.9, beta_2=0.999, early_stopping=False,
epsilon=1e-08, hidden_layer_sizes=(5, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver=’lbfgs’, tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)

In [61]:

len(gcv.cv_results_[“params”])

Out[61]:

In [65]:

gcv.best_estimator_

Out[65]:

In [67]:

gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)
gcv.fit(X, y)
gcv.best_score_

Out[67]:

-33483.452903206744

In [68]:

gcv.best_estimator_

Out[68]:

MLPRegressor(activation=’relu’, alpha=0.10000000000000001, batch_size=’auto’,
beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver=’lbfgs’, tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)

In [96]:

mlp = MLPRegressor(solver=’lbfgs’, random_state=1)

parameters = {‘hidden_layer_sizes’:[(5, 2), (5,5, 2), (10, 5)] , ‘alpha’: 10.0 ** -np.arange(1, 7)}
gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)
gcv.fit(X, y)
gcv.best_score_

Out[96]:

-29889.254430003857

In [97]:

gcv.predict(X)

Out[97]:

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In [71]:

gcv.best_estimator_

Out[71]:

MLPRegressor(activation=’relu’, alpha=0.01, batch_size=’auto’, beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 5, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver=’lbfgs’, tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)

In [98]:

mlp = MLPRegressor(solver=’lbfgs’, random_state=1)

parameters = {‘hidden_layer_sizes’:[ (5,5, 2), (5, 5, 2, 2)] , ‘alpha’: 10.0 ** -np.arange(1, 7)}
gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)
gcv.fit(X, y/1000)
gcv.best_score_

Out[98]:

-0.030887437092134363

In [73]:

Out[73]:

In [99]:

gcv.predict(X)

Out[99]:

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In [93]:

mlp = MLPRegressor(solver=’lbfgs’, random_state=100)

parameters = {‘hidden_layer_sizes’:[ (5,5, 2), (5, 5, 2, 2)] , ‘alpha’: 10.0 ** -np.arange(1, 7)}
gcv = GridSearchCV(mlp, parameters)
gcv.fit(X, y)
gcv.best_score_

Out[93]:

-0.0066567132002208988

In [95]:

gcv.predict(X)

Out[95]:

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1252.37896701, 1252.37896701, 1252.37896701, 1252.37896701,
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1252.37896701, 1252.37896701, 1252.37896701, 1252.37896701,
1252.37896701, 1252.37896701, 1252.37896701, 1252.37896701])

In [80]:

data

Out[80]:

1000 rows × 6 columns

In [81]:

Out[81]:

array([[ 8.87997132e-02, 2.32091274e-01, -4.28111179e-01,
1.35828922e+00, 4.14280209e-01],
[ 1.77247104e-01, 2.53241287e-01, -4.63121294e-01,
4.73693554e-01, -9.63090057e-02],
[ 2.64998587e-01, 5.42480630e-02, 1.26380157e+00,
4.73693554e-01, -3.87605362e-02],
…,
[ -1.77247104e-01, 5.58607663e-01, -7.92304507e-02,
4.73693554e-01, 8.58955915e-01],
[ -8.87997132e-02, 1.03710758e+00, 1.68853593e+00,
9.15969270e-01, 1.66974708e+00],
[ -1.34993283e-14, 3.54480095e-01, -6.85887917e-01,
-8.53163081e-01, -3.60580590e-01]])

In [82]:

np.max(X)

Out[82]:

3.6923510838800659

In [83]:

np.max(y)

Out[83]:

2119.0999999999999

In [84]:

np.min(y)

Out[84]:

378.07999999999998

In [89]:

gcv.best_estimator_

Out[89]:

MLPRegressor(activation=’relu’, alpha=9.9999999999999995e-07,
batch_size=’auto’, beta_1=0.9, beta_2=0.999, early_stopping=False,
epsilon=1e-08, hidden_layer_sizes=(5, 5, 2, 2),
learning_rate=’constant’, learning_rate_init=0.001, max_iter=200,
momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=1,
shuffle=True, solver=’lbfgs’, tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)

In [5]:

mlp = MLPRegressor(random_state=100)

parameters = {‘hidden_layer_sizes’:[ (5,5, 2), (5, 5, 2, 2)] , ‘alpha’: 10.0 ** -np.arange(1, 7),
‘solver’: (‘lbfgs’, ‘sgd’, ‘adam’), ‘activation’:(‘relu’, ‘logistic’, ‘tanh’)}
gcv = GridSearchCV(mlp, parameters)
gcv.fit(X, y)
print(gcv.best_score_)
print(gcv.best_estimator_)

-0.00606707666593
MLPRegressor(activation=’logistic’, alpha=0.001, batch_size=’auto’,
beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 5, 2, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=100,
shuffle=True, solver=’lbfgs’, tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)

In [9]:

mlp = MLPRegressor(random_state=100)

parameters = {‘hidden_layer_sizes’:[ (5,5, 2), (5, 5, 2, 2), (16, 8, 4, 4, 2)] , ‘alpha’: 10.0 ** -np.arange(1, 7),
‘solver’: (‘lbfgs’, ‘sgd’, ‘adam’), ‘activation’:(‘relu’, ‘logistic’, ‘tanh’)}
gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)
gcv.fit(X, y)
print(gcv.best_score_)
print(gcv.best_estimator_)
print(gcv.predict(X))

//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)
//anaconda/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn’t converged yet.
% self.max_iter, ConvergenceWarning)

-29870.6573206
MLPRegressor(activation=’logistic’, alpha=0.001, batch_size=’auto’,
beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 5, 2, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=100,
shuffle=True, solver=’lbfgs’, tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
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1247.16699799, 1270.93512254, 1271.13769436, 1270.93512254,
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1267.0810749 , 1271.12795022, 1247.16699799, 1271.12067412,
1267.0900479 , 1268.96369125, 1247.16699799, 1271.12067411,
1247.16699799, 1210.46744514, 1271.14457448, 1213.63486755,
1271.12036975, 1247.16698755, 1210.46591681, 1247.16699799,
1247.16398286, 1210.46744827, 1210.46591681, 1246.04524472,
1267.0810749 , 1267.0810749 , 1210.73284622, 1247.16699799,
1267.0810749 , 1210.73284838, 1270.88562008, 1210.46582591,
1210.46751171, 1271.14457448, 1210.46744827, 1267.28280476,
1210.73284867, 1210.88391919, 1267.02300174, 1247.16699935,
1267.0810749 , 1210.46744814, 1267.0810749 , 1210.46744827,
1267.0810749 , 1210.73000037, 1267.0810749 , 1271.14457448,
1210.46744827, 1267.0810749 , 1210.46184158, 1210.46744827,
1210.45506519, 1211.82805954, 1267.0810749 , 1247.16699799,
1210.46591788, 1267.0810749 , 1210.45505023, 1271.12039145,
1210.73286899, 1253.35809932, 1210.46591696, 1271.12795072,
1267.08107513, 1247.16699799, 1210.46565452, 1210.45505171,
1247.16699799, 1267.08224735, 1271.12701768, 1267.0810749 ,
1267.0810749 , 1210.45505023, 1270.93514086, 1247.16699799,
1247.16699799, 1247.16699799, 1267.0810749 , 1270.93512254,
1271.12795072, 1271.12067412, 1271.12067412, 1247.16699799,
1271.12067387, 1267.10284826, 1267.0810749 , 1247.16699724,
1271.12067412, 1271.12032178, 1270.93512254, 1271.12067412,
1267.0810749 , 1262.2055116 , 1271.12067412, 1271.12067412,
1270.77714806, 1271.11276436, 1247.16699799, 1270.93512254,
1271.12067412, 1247.16699799, 1210.92152717, 1271.12795021,
1271.12795073, 1270.93512074, 1267.0810749 , 1267.08107512,
1271.1279507 , 1247.16699799, 1247.16699799, 1247.16699655,
1210.92152714, 1210.65039395, 1247.16699838, 1247.16699799,
1267.0810749 , 1247.16699799, 1232.82721578, 1210.45803162,
1210.46606023, 1268.77547679, 1267.0810749 , 1271.14457448,
1210.46591681, 1267.0810749 , 1210.67771882, 1210.46591684,
1210.46744827, 1210.48921399, 1210.46744827, 1210.73284824,
1210.46744827, 1210.46744827, 1210.46591681, 1210.46591681,
1210.5794626 , 1212.18216972, 1210.73284804, 1210.46591681,
1210.46591681, 1210.45505023, 1210.46744827, 1211.19965688,
1210.46591681, 1267.0810749 , 1247.16699799, 1210.46591681,
1210.46744827, 1210.46744827, 1247.16698495, 1210.45995245,
1210.46744827, 1210.45505318, 1266.18315859, 1210.73284825,
1210.46747301, 1267.0810749 , 1267.0810749 , 1267.0810749 ,
1247.16699799, 1270.88562015, 1267.0810749 , 1247.16699799,
1247.16699799, 1247.13047323, 1247.16699799, 1267.08180938,
1270.88218619, 1267.57838215, 1247.16699234, 1270.93512254,
1271.12795072, 1271.12067289, 1271.14457444, 1271.12067412,
1267.0810749 , 1270.72467219, 1210.45505023, 1271.12067412,
1270.93512254, 1269.34384991, 1270.93512255, 1271.12067412,
1267.08162657, 1271.12874629, 1271.14457448, 1247.16699799,
1270.97967945, 1271.12795072, 1271.12067193, 1271.12795072,
1270.93512286, 1210.49472255, 1270.93512254, 1270.70540074,
1247.16699799, 1247.1821467 , 1270.7089417 , 1210.4631112 ,
1247.16699799, 1271.12795071, 1270.93512254, 1271.13433939,
1210.45607155, 1271.13181385, 1271.12067412, 1247.16699799,
1210.92152714, 1271.14453324, 1270.93512235, 1271.12588755,
1247.16699799, 1271.12795078, 1271.14457448, 1210.46591683,
1267.0810749 , 1267.0810749 , 1267.08138764, 1247.16699799,
1267.08106033, 1247.1672415 , 1247.16699799, 1210.57065732,
1210.46743295, 1210.46744827, 1210.46591681, 1247.16714478,
1210.46461769, 1210.73491758, 1267.0810749 , 1210.46744827,
1210.46586856, 1266.60167366, 1210.46745102, 1210.46591681,
1210.73284824, 1210.7328536 , 1267.0810749 , 1210.73880098,
1210.73294494, 1210.73284825, 1259.1563028 , 1210.46591681,
1210.46744827, 1210.46890478, 1210.46744827, 1210.73284824,
1267.0810749 , 1267.0810749 , 1210.45505023, 1267.0810749 ,
1210.73284824, 1210.73284311, 1247.16699799, 1210.46744828,
1247.16699799, 1210.73284824, 1210.46744832, 1210.46591681,
1271.1206763 , 1210.45505032, 1267.0810749 , 1271.12067568,
1247.16699799, 1271.12794599, 1247.16699799, 1247.16699799,
1247.16699799, 1247.16699799, 1267.0810749 , 1247.16684483,
1271.14457448, 1271.12067412, 1247.16699799, 1271.12795072,
1247.16696668, 1271.12067412, 1271.10821404, 1248.39444714,
1270.93512252, 1271.12067412, 1271.14457448, 1271.12067412,
1270.7574221 , 1271.12795072, 1270.98379553, 1247.16699799,
1271.12067412, 1212.65001137, 1271.12067412, 1271.14457443,
1247.16699799, 1271.12067412, 1271.12067412, 1270.93511773,
1271.12067354, 1247.16699799, 1267.08107508, 1267.0810749 ,
1247.16699799, 1270.90017751, 1267.09632254, 1271.14428737,
1210.4917952 , 1271.14407544, 1247.16699799, 1247.16699799,
1271.12795072, 1247.16699778, 1247.16699799, 1247.16699799,
1247.16699799, 1247.16699799, 1210.45505023, 1247.16699799,
1247.16699799, 1210.46744827, 1247.11984601, 1210.46744827,
1267.0810749 , 1210.69274821, 1267.0810749 , 1271.14444101,
1247.16699799, 1210.46591683, 1210.73913285, 1210.73284864,
1247.16699799, 1210.46591681, 1267.0810749 , 1267.0810749 ,
1210.73282923, 1210.45505023, 1247.16625583, 1210.73284824,
1247.16699799, 1247.15590892, 1267.41010867, 1210.46591684,
1210.46591681, 1210.73285146, 1210.46744827, 1210.46591681,
1210.73168967, 1210.46744827, 1210.73284824, 1210.73284824,
1210.46744827, 1267.0810749 , 1210.46591682, 1210.46745203,
1210.46744827, 1210.46744827, 1210.46591694, 1267.0810749 ,
1210.46591681, 1267.0810749 , 1247.16699799, 1267.0810749 ,
1270.88562613, 1210.45521523, 1247.16699799, 1210.45505043,
1210.46591606, 1267.0810749 , 1267.0810749 , 1234.93443847,
1271.12794682, 1267.0810749 , 1271.12067411, 1267.0810749 ,
1271.12795072, 1271.14147677, 1247.16699799, 1247.90125881,
1269.94597937, 1210.94808544, 1267.08103536, 1267.0810749 ,
1271.12794662, 1247.18569665, 1271.14457448, 1271.12067412,
1271.12795072, 1270.93512254, 1247.1670645 , 1270.93512254,
1271.14457448, 1247.16699799, 1271.12795072, 1247.1670309 ,
1270.93512267, 1271.12795072, 1213.71009237, 1271.12063564,
1271.12795071, 1271.12795079, 1247.16699799, 1270.93543148,
1271.14457448, 1247.16699799, 1271.12795074, 1271.12067412,
1271.12067412, 1267.08498549, 1247.16699799, 1271.12067412,
1271.12067412, 1261.30462726, 1267.0810749 , 1271.12067412,
1271.14457448, 1270.88587872, 1210.46591681, 1247.16699799,
1271.14451705, 1270.90784348, 1263.79040934, 1210.46744831,
1210.46710245, 1210.53327193, 1211.11617122, 1267.0810749 ,
1247.16699799, 1267.08107491, 1210.46744827, 1210.46744828,
1210.45505649, 1267.39811825, 1210.46591681, 1267.0810749 ,
1247.16699799, 1210.73284824, 1270.88562006, 1210.46744827,
1210.46591682, 1267.0810749 , 1210.73284819, 1210.73284824,
1210.73284862, 1210.73285317, 1210.46591681, 1210.46591702,
1210.46744827, 1210.73284819, 1267.0810749 , 1211.10908675,
1210.46591681, 1210.79655102, 1267.0810749 , 1210.46744814,
1210.46591681, 1210.46744826, 1267.07995579, 1267.0810749 ,
1210.73696211, 1247.16457593, 1210.46591681, 1267.0810749 ,
1267.08107489, 1267.0811505 , 1267.0810749 , 1210.46744827,
1247.16699799, 1267.0810749 , 1271.12067412, 1271.14457448,
1269.59975823, 1271.12067416, 1269.55796331, 1247.16699799,
1271.12795072, 1271.12795072, 1247.16699799, 1247.16699799,
1247.16699799, 1247.16699799, 1247.16699799, 1271.12067412,
1247.16699799, 1271.12795072, 1270.93512254, 1271.12795072,
1210.46336987, 1271.12812571, 1270.93512222, 1247.16699801,
1247.67564606, 1271.12795072, 1247.16699799, 1271.12795072,
1270.92887393, 1247.16699799, 1271.12795072, 1271.12795072,
1271.0492834 , 1267.0810749 , 1271.12067412, 1270.70742754,
1270.93512254, 1247.16706315, 1247.16699799, 1271.12067412,
1267.08107624, 1247.16699799, 1271.12795071, 1247.16699799,
1267.0810749 , 1270.92370337, 1271.1420616 , 1247.16699799,
1271.14341577, 1267.08107546, 1247.16699799, 1210.46953858,
1247.16699799, 1247.16699799, 1210.93570566, 1267.0810749 ,
1210.46744827, 1267.0810749 , 1210.46591698, 1267.0810749 ,
1211.43431035, 1210.45506311, 1210.45505023, 1267.0810749 ,
1210.46591681, 1210.46591681, 1210.46744827, 1210.46744804,
1247.16699799, 1214.89160626, 1267.0810749 , 1267.08107479,
1210.46745043, 1210.46744827, 1210.45505023, 1210.46744827,
1210.46744819, 1210.46595909, 1267.08086019, 1210.46592974,
1210.46591681, 1210.46277838, 1210.46745403, 1210.46744827,
1265.09820051, 1247.16699799, 1267.0810749 , 1210.7328392 ,
1210.46591682, 1216.31141064, 1210.46744827, 1267.0810749 ,
1267.08107382, 1271.12795072, 1210.46748315, 1267.08108267,
1210.45505023, 1271.12067453, 1210.56159786, 1247.16699799,
1210.46591681, 1247.16699799, 1271.12067412, 1247.16700018,
1210.46776682, 1210.92152723, 1271.1279507 , 1210.46745382,
1271.10876924, 1270.88549163, 1267.08113555, 1247.16699799,
1267.08107495, 1271.12067255, 1267.0810749 , 1266.47700302,
1240.66924422, 1267.0810749 , 1271.12067412, 1271.12067412,
1247.16699799, 1240.65737815, 1247.16674211, 1271.12067423,
1210.92152714, 1271.12795072, 1270.93512254, 1271.12067412,
1267.0810749 , 1270.92581802, 1270.93500596, 1271.12795072,
1247.16699799, 1271.12795072, 1270.93478451, 1264.02111839,
1271.12795128, 1247.16689916, 1271.12067412, 1247.16699799,
1271.12070044, 1267.08107476, 1247.16699799, 1271.12067412,
1271.12795072, 1271.12846803, 1271.12795072, 1267.0810749 ,
1270.8842571 , 1270.9351105 , 1267.0810749 , 1247.16699799,
1267.08107488, 1245.59190433, 1267.08107488, 1210.73284824,
1247.16699799, 1247.28587053, 1270.88562008, 1210.73287417,
1267.08107554, 1210.46591681, 1267.0810749 , 1271.14457448,
1267.08107484, 1267.0810749 , 1210.46591673, 1267.41031917,
1210.46341268, 1267.0810749 , 1271.1261842 , 1257.45367481,
1210.46744827, 1210.73284824, 1210.46744827, 1267.0810749 ,
1210.46744827, 1210.74518773, 1267.0810749 , 1210.46591681,
1210.46744827, 1267.0810749 , 1210.46744827, 1210.73284827,
1210.46934249, 1210.46591681, 1210.46744827, 1210.73284824,
1210.47496536, 1267.0810749 , 1271.05538056, 1210.46744827,
1271.12809806, 1247.16699799, 1210.95449297, 1267.0810749 ,
1267.0810749 , 1267.0810749 , 1210.46744827, 1247.16699799,
1250.06550152, 1267.0810749 , 1210.45508073, 1271.14417678,
1267.0810749 , 1210.45505023, 1271.1239332 , 1247.16045615,
1267.0810749 , 1210.46593495, 1247.16699799, 1247.16699799,
1271.12796432, 1247.16699799, 1271.12067412, 1271.12067412,
1247.16699799, 1271.12710404, 1247.16699799, 1271.11938484,
1271.12047227, 1267.0810749 , 1271.14044123, 1247.16699802,
1271.12795072, 1271.12067412, 1270.93511995, 1271.12067412,
1210.76070894, 1270.93512254, 1271.12067412, 1267.0810749 ,
1271.12795072, 1270.93154102, 1271.12795061, 1267.08107823,
1271.12795072, 1271.12795072, 1271.12794994, 1271.12067412,
1247.16699799, 1247.16699799, 1247.16699799, 1271.12041575,
1271.12067412, 1212.83246256, 1247.16699768, 1247.16699799,
1267.0810749 , 1210.57415538, 1271.14276256, 1211.78831186,
1247.16699799, 1247.16677173, 1267.0810749 , 1267.0810749 ,
1267.0810749 , 1210.46609852, 1271.14457446, 1210.46591681,
1267.08107495, 1210.46591681, 1267.0810749 , 1247.23308825,
1267.08107433, 1211.10979202, 1210.46744827, 1210.46591869,
1264.69860405, 1267.0810749 , 1210.46744827, 1210.73283243,
1267.41035285, 1267.08107456, 1210.45553928, 1210.46744827,
1210.45834549, 1210.46744827, 1210.45540338, 1210.45505023,
1210.4550538 , 1267.0810749 , 1270.88562008, 1267.08107493,
1210.46591681, 1210.46591681, 1210.46744827, 1210.46570598,
1210.45505312, 1210.46744827, 1267.0810749 , 1210.46591681,
1267.0810749 , 1210.46744827, 1267.0810749 , 1210.46744827,
1247.16699799, 1247.16699799, 1247.16699798, 1271.14457375,
1267.0810749 , 1271.12067934, 1247.16699799, 1247.16699799,
1247.16699799, 1210.46744827, 1210.46799753, 1271.00986669,
1267.08067803, 1271.12553282, 1247.16699799, 1271.12784758,
1247.16699799, 1247.16699801, 1247.16699799, 1267.08107818,
1270.93512261, 1271.12062883, 1210.46001105, 1270.92962954,
1247.16699799, 1271.12067412, 1270.93512258, 1270.93511874,
1271.12067412, 1241.49291651, 1271.12791054, 1271.12067412,
1271.12067412, 1247.16699799, 1271.12067412, 1267.0810749 ,
1271.12795072, 1247.16699799, 1271.12795071, 1247.16699806,
1271.12794285, 1271.12795072, 1270.93513572, 1267.08206681,
1271.12067411, 1267.0810749 , 1267.0810749 , 1247.16699799,
1271.12795072, 1271.12795072, 1271.09302909, 1271.12795072,
1247.16653013, 1247.16699799, 1267.80099537, 1267.0810749 ,
1247.16699799, 1271.12067412, 1269.49668511, 1247.16699799,
1210.45505023, 1247.16699799, 1247.16699799, 1210.46591681,
1210.73284825, 1210.45649962, 1267.0810749 , 1267.0810749 ,
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1210.46744827, 1247.16699799, 1212.05100667, 1210.73284824,
1210.46591743, 1210.46591681, 1210.46744827, 1210.46724453,
1267.0810749 , 1267.0810749 , 1210.73284824, 1210.73284868,
1210.46744827, 1210.46604608, 1210.46592456, 1267.0810749 ,
1210.46744827, 1210.46744827, 1210.46744827, 1267.0810749 ,
1210.46762581, 1247.16643795, 1247.16699799, 1267.0810749 ,
1267.0810749 , 1267.0810749 , 1267.0810749 , 1264.96337655,
1270.88561997, 1267.08107492, 1271.04261144, 1270.93508562,
1247.16699799, 1210.47923807, 1271.12667483, 1247.16699799,
1271.12067412, 1210.45524885, 1210.86596225, 1271.12794774,
1271.11855902, 1267.0810749 , 1271.14457442, 1271.12712749,
1247.16699799, 1247.16699799, 1268.60692294, 1270.93512713,
1247.16699799, 1219.8509847 , 1240.76632914, 1240.66982589,
1247.16699799, 1271.14457448, 1271.12067411, 1247.16699799,
1271.12795072, 1271.12058777, 1270.93512254, 1267.0810749 ,
1240.71537128, 1271.12795072, 1267.62043456, 1271.12067412,
1270.93512222, 1247.16699799, 1271.12067663, 1271.14456522,
1247.16699799, 1271.12067386, 1247.16699799, 1271.12053598,
1271.12068669, 1210.92385085, 1247.166998 , 1247.167001 ,
1251.854297 , 1267.08108345, 1218.34100688, 1247.16699799])

In [8]:

mlp = MLPRegressor(random_state=100)

parameters = {‘hidden_layer_sizes’:[ (16, 8, 4)] , ‘alpha’: 10.0 ** -np.arange(1, 7), }
gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)
gcv.fit(X, y)
print(gcv.best_score_)
print(gcv.best_estimator_)

-139250.603805
MLPRegressor(activation=’relu’, alpha=1.0000000000000001e-05,
batch_size=’auto’, beta_1=0.9, beta_2=0.999, early_stopping=False,
epsilon=1e-08, hidden_layer_sizes=(16, 8, 4),
learning_rate=’constant’, learning_rate_init=0.001, max_iter=200,
momentum=0.9, nesterovs_momentum=True, power_t=0.5,
random_state=100, shuffle=True, solver=’adam’, tol=0.0001,
validation_fraction=0.1, verbose=False, warm_start=False)

In [ ]:

In [10]:

finalModel = MLPRegressor(activation=’logistic’, alpha=0.001, batch_size=’auto’,
beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 5, 2, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=100,
shuffle=True, solver=’lbfgs’, tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)

finalModel.fit(X, y)

Out[10]:

MLPRegressor(activation=’logistic’, alpha=0.001, batch_size=’auto’,
beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 5, 2, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=100,
shuffle=True, solver=’lbfgs’, tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)

In [11]:

from sklearn.externals import joblib
joblib.dump(finalModel, ‘finalModel.pkl’)

Out[11]:

[‘finalModel.pkl’]

In [12]:

loadModel = joblib.load(‘finalModel.pkl’)

In [14]:

loadModel.predict(X)

Out[14]:

In [29]:

mlp = MLPRegressor(random_state=100, solver=’adam’)

parameters = {‘hidden_layer_sizes’:[ (3, 2)] , ‘alpha’: 10.0 ** -np.arange(1, 7), }
gcv = GridSearchCV(mlp, parameters, scoring = ‘neg_mean_squared_error’)
gcv.fit(X, y)
print(gcv.best_score_)
print(gcv.best_estimator_)

-1594417.88656
MLPRegressor(activation=’relu’, alpha=0.10000000000000001, batch_size=’auto’,
beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(3, 2), learning_rate=’constant’,
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=100,
shuffle=True, solver=’adam’, tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)

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