In [81]:

    

import xgboost as xgb
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, matthews_corrcoef

poslabels = ["-%02d" % (i) for i in range(10, 0, -1)] + ["+%02d" % (i) for i in range(1,11)]
# 18 H  17 S  15 E
proplables = ["H%02d" % (i) for i in range(18)] + ["S%02d" % (i) for i in range(17)] + ["E%02d" % (i) for i in range(15)]

cols = ["target"]
for poslabel in poslabels:
    for proplable in proplables:
        cols.append("%s%s" % (poslabel, proplable))
       
# read in data
data_dir="/workspace/chipper_data/chipper-0.3.0-data/"
dtrain = xgb.DMatrix(data_dir + "training_data.ll", feature_names=cols)
dtest = xgb.DMatrix(data_dir + "testing_data.ll", feature_names=cols)

watchlist  = [(dtest,'eval'), (dtrain,'train')]
# specify parameters via map
# 'binary:logistic'
param = {'n_estimators': 10000, 'max_depth':10, 'objective':'reg:logistic'} #, "eval_metric": "auc"}
num_round = 2000
bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=10)
# make prediction
preds = bst.predict(dtest)


    

    




[0]	eval-rmse:0.444096	train-rmse:0.411716
Multiple eval metrics have been passed: 'train-rmse' will be used for early stopping.

Will train until train-rmse hasn't improved in 10 rounds.
[1]	eval-rmse:0.415364	train-rmse:0.355559
[2]	eval-rmse:0.401988	train-rmse:0.315775
[3]	eval-rmse:0.389962	train-rmse:0.289015
[4]	eval-rmse:0.384522	train-rmse:0.267998
[5]	eval-rmse:0.382291	train-rmse:0.250928
[6]	eval-rmse:0.381688	train-rmse:0.238674
[7]	eval-rmse:0.379367	train-rmse:0.228976
[8]	eval-rmse:0.378704	train-rmse:0.217621
[9]	eval-rmse:0.380197	train-rmse:0.20861
[10]	eval-rmse:0.382082	train-rmse:0.199585
[11]	eval-rmse:0.384251	train-rmse:0.191445
[12]	eval-rmse:0.382889	train-rmse:0.184003
[13]	eval-rmse:0.383883	train-rmse:0.179041
[14]	eval-rmse:0.384582	train-rmse:0.176569
[15]	eval-rmse:0.383514	train-rmse:0.168435
[16]	eval-rmse:0.384136	train-rmse:0.159926
[17]	eval-rmse:0.38502	train-rmse:0.153724
[18]	eval-rmse:0.383275	train-rmse:0.147944
[19]	eval-rmse:0.383252	train-rmse:0.145118
[20]	eval-rmse:0.38337	train-rmse:0.140306
[21]	eval-rmse:0.384714	train-rmse:0.136156
[22]	eval-rmse:0.388849	train-rmse:0.132296
[23]	eval-rmse:0.389499	train-rmse:0.126763
[24]	eval-rmse:0.388361	train-rmse:0.125405
[25]	eval-rmse:0.38937	train-rmse:0.121749
[26]	eval-rmse:0.388672	train-rmse:0.114247
[27]	eval-rmse:0.388139	train-rmse:0.110197
[28]	eval-rmse:0.388499	train-rmse:0.105446
[29]	eval-rmse:0.389235	train-rmse:0.103687
[30]	eval-rmse:0.387929	train-rmse:0.099606
[31]	eval-rmse:0.388117	train-rmse:0.096634
[32]	eval-rmse:0.387727	train-rmse:0.094194
[33]	eval-rmse:0.38876	train-rmse:0.091093
[34]	eval-rmse:0.389185	train-rmse:0.087172
[35]	eval-rmse:0.389681	train-rmse:0.08417
[36]	eval-rmse:0.389381	train-rmse:0.080678
[37]	eval-rmse:0.38941	train-rmse:0.07809
[38]	eval-rmse:0.389737	train-rmse:0.074272
[39]	eval-rmse:0.390105	train-rmse:0.07278
[40]	eval-rmse:0.390248	train-rmse:0.070973
[41]	eval-rmse:0.390705	train-rmse:0.067204
[42]	eval-rmse:0.391594	train-rmse:0.065755
[43]	eval-rmse:0.392592	train-rmse:0.06516
[44]	eval-rmse:0.391883	train-rmse:0.064228
[45]	eval-rmse:0.390827	train-rmse:0.061507
[46]	eval-rmse:0.390435	train-rmse:0.059863
[47]	eval-rmse:0.388099	train-rmse:0.057171
[48]	eval-rmse:0.387633	train-rmse:0.055369
[49]	eval-rmse:0.387626	train-rmse:0.053246
[50]	eval-rmse:0.387804	train-rmse:0.050991
[51]	eval-rmse:0.388053	train-rmse:0.049446
[52]	eval-rmse:0.388722	train-rmse:0.047814
[53]	eval-rmse:0.391221	train-rmse:0.044873
[54]	eval-rmse:0.391127	train-rmse:0.043324
[55]	eval-rmse:0.390996	train-rmse:0.041767
[56]	eval-rmse:0.390767	train-rmse:0.041321
[57]	eval-rmse:0.389833	train-rmse:0.039533
[58]	eval-rmse:0.390695	train-rmse:0.037592
[59]	eval-rmse:0.390065	train-rmse:0.036579
[60]	eval-rmse:0.390557	train-rmse:0.036046
[61]	eval-rmse:0.390995	train-rmse:0.034344
[62]	eval-rmse:0.390129	train-rmse:0.033509
[63]	eval-rmse:0.391058	train-rmse:0.032581
[64]	eval-rmse:0.390252	train-rmse:0.031135
[65]	eval-rmse:0.390427	train-rmse:0.030655
[66]	eval-rmse:0.390534	train-rmse:0.030292
[67]	eval-rmse:0.390307	train-rmse:0.028946
[68]	eval-rmse:0.391109	train-rmse:0.028455
[69]	eval-rmse:0.390355	train-rmse:0.027799
[70]	eval-rmse:0.390594	train-rmse:0.026545
[71]	eval-rmse:0.391236	train-rmse:0.025902
[72]	eval-rmse:0.392249	train-rmse:0.025009
[73]	eval-rmse:0.392584	train-rmse:0.024215
[74]	eval-rmse:0.393413	train-rmse:0.023935
[75]	eval-rmse:0.39329	train-rmse:0.023621
[76]	eval-rmse:0.393314	train-rmse:0.023003
[77]	eval-rmse:0.393153	train-rmse:0.022189
[78]	eval-rmse:0.393641	train-rmse:0.021636
[79]	eval-rmse:0.394231	train-rmse:0.0214
[80]	eval-rmse:0.394942	train-rmse:0.020739
[81]	eval-rmse:0.394614	train-rmse:0.020212
[82]	eval-rmse:0.394603	train-rmse:0.019925
[83]	eval-rmse:0.395046	train-rmse:0.019397
[84]	eval-rmse:0.396061	train-rmse:0.018882
[85]	eval-rmse:0.396026	train-rmse:0.018627
[86]	eval-rmse:0.396455	train-rmse:0.018093
[87]	eval-rmse:0.396252	train-rmse:0.017633
[88]	eval-rmse:0.397333	train-rmse:0.017165
[89]	eval-rmse:0.397291	train-rmse:0.016872
[90]	eval-rmse:0.397131	train-rmse:0.016555
[91]	eval-rmse:0.396081	train-rmse:0.016182
[92]	eval-rmse:0.396285	train-rmse:0.015933
[93]	eval-rmse:0.396684	train-rmse:0.015679
[94]	eval-rmse:0.396402	train-rmse:0.015347
[95]	eval-rmse:0.39598	train-rmse:0.015021
[96]	eval-rmse:0.396222	train-rmse:0.014594
[97]	eval-rmse:0.396614	train-rmse:0.01422
[98]	eval-rmse:0.396777	train-rmse:0.013971
[99]	eval-rmse:0.396804	train-rmse:0.013771
[100]	eval-rmse:0.396749	train-rmse:0.013585
[101]	eval-rmse:0.397237	train-rmse:0.013342
[102]	eval-rmse:0.396468	train-rmse:0.013071
[103]	eval-rmse:0.395901	train-rmse:0.012791
[104]	eval-rmse:0.395842	train-rmse:0.012516
[105]	eval-rmse:0.396483	train-rmse:0.012193
[106]	eval-rmse:0.396616	train-rmse:0.011975
[107]	eval-rmse:0.396449	train-rmse:0.011773
[108]	eval-rmse:0.396839	train-rmse:0.011635
[109]	eval-rmse:0.397306	train-rmse:0.011334
[110]	eval-rmse:0.397079	train-rmse:0.011091
[111]	eval-rmse:0.397076	train-rmse:0.010974
[112]	eval-rmse:0.397432	train-rmse:0.010806
[113]	eval-rmse:0.397954	train-rmse:0.010702
[114]	eval-rmse:0.397776	train-rmse:0.010576
[115]	eval-rmse:0.397639	train-rmse:0.010368
[116]	eval-rmse:0.397942	train-rmse:0.01018
[117]	eval-rmse:0.397997	train-rmse:0.010051
[118]	eval-rmse:0.399113	train-rmse:0.009838
[119]	eval-rmse:0.398625	train-rmse:0.009743
[120]	eval-rmse:0.398845	train-rmse:0.009587
[121]	eval-rmse:0.399502	train-rmse:0.009426
[122]	eval-rmse:0.399441	train-rmse:0.009243
[123]	eval-rmse:0.399538	train-rmse:0.009159
[124]	eval-rmse:0.39885	train-rmse:0.009088
[125]	eval-rmse:0.398747	train-rmse:0.009004
[126]	eval-rmse:0.399664	train-rmse:0.008859
[127]	eval-rmse:0.400044	train-rmse:0.008778
[128]	eval-rmse:0.399694	train-rmse:0.008603
[129]	eval-rmse:0.399174	train-rmse:0.008456
[130]	eval-rmse:0.398882	train-rmse:0.008322
[131]	eval-rmse:0.39928	train-rmse:0.008224
[132]	eval-rmse:0.399692	train-rmse:0.008096
[133]	eval-rmse:0.399929	train-rmse:0.00802
[134]	eval-rmse:0.399828	train-rmse:0.007918
[135]	eval-rmse:0.40008	train-rmse:0.007826
[136]	eval-rmse:0.400077	train-rmse:0.007814
[137]	eval-rmse:0.399963	train-rmse:0.007716
[138]	eval-rmse:0.399315	train-rmse:0.007623
[139]	eval-rmse:0.398783	train-rmse:0.007487
[140]	eval-rmse:0.398649	train-rmse:0.007372
[141]	eval-rmse:0.39861	train-rmse:0.007334
[142]	eval-rmse:0.399715	train-rmse:0.007217
[143]	eval-rmse:0.399771	train-rmse:0.007119
[144]	eval-rmse:0.399773	train-rmse:0.007026
[145]	eval-rmse:0.399643	train-rmse:0.006935
[146]	eval-rmse:0.399916	train-rmse:0.006831
[147]	eval-rmse:0.400324	train-rmse:0.006731
[148]	eval-rmse:0.400078	train-rmse:0.006675
[149]	eval-rmse:0.400089	train-rmse:0.006576
[150]	eval-rmse:0.399919	train-rmse:0.006518
[151]	eval-rmse:0.400196	train-rmse:0.006429
[152]	eval-rmse:0.400581	train-rmse:0.006382
[153]	eval-rmse:0.400111	train-rmse:0.006321
[154]	eval-rmse:0.400359	train-rmse:0.006245
[155]	eval-rmse:0.399921	train-rmse:0.006165
[156]	eval-rmse:0.400153	train-rmse:0.006098
[157]	eval-rmse:0.399741	train-rmse:0.006039
[158]	eval-rmse:0.399541	train-rmse:0.006009
[159]	eval-rmse:0.399918	train-rmse:0.005926
[160]	eval-rmse:0.400095	train-rmse:0.005863
[161]	eval-rmse:0.400128	train-rmse:0.005802
[162]	eval-rmse:0.400693	train-rmse:0.005744
[163]	eval-rmse:0.400399	train-rmse:0.005686
[164]	eval-rmse:0.400505	train-rmse:0.005635
[165]	eval-rmse:0.40072	train-rmse:0.005567
[166]	eval-rmse:0.401092	train-rmse:0.005523
[167]	eval-rmse:0.401118	train-rmse:0.005496
[168]	eval-rmse:0.401615	train-rmse:0.005446
[169]	eval-rmse:0.401494	train-rmse:0.005391
[170]	eval-rmse:0.401868	train-rmse:0.005333
[171]	eval-rmse:0.402365	train-rmse:0.005275
[172]	eval-rmse:0.402117	train-rmse:0.00523
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[175]	eval-rmse:0.402164	train-rmse:0.005092
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[180]	eval-rmse:0.401862	train-rmse:0.004893
[181]	eval-rmse:0.402158	train-rmse:0.004865
[182]	eval-rmse:0.401873	train-rmse:0.004833
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[202]	eval-rmse:0.401117	train-rmse:0.00412
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[1856]	eval-rmse:0.409909	train-rmse:0.000459
[1857]	eval-rmse:0.409937	train-rmse:0.000459
[1858]	eval-rmse:0.409941	train-rmse:0.000459
[1859]	eval-rmse:0.409946	train-rmse:0.000459
[1860]	eval-rmse:0.409922	train-rmse:0.000459
[1861]	eval-rmse:0.409926	train-rmse:0.000458
[1862]	eval-rmse:0.409892	train-rmse:0.000458
[1863]	eval-rmse:0.409881	train-rmse:0.000458
[1864]	eval-rmse:0.409803	train-rmse:0.000458
[1865]	eval-rmse:0.409839	train-rmse:0.000458
[1866]	eval-rmse:0.409876	train-rmse:0.000457
[1867]	eval-rmse:0.409854	train-rmse:0.000457
[1868]	eval-rmse:0.409827	train-rmse:0.000457
[1869]	eval-rmse:0.409844	train-rmse:0.000457
[1870]	eval-rmse:0.409809	train-rmse:0.000457
[1871]	eval-rmse:0.409817	train-rmse:0.000456
[1872]	eval-rmse:0.409825	train-rmse:0.000456
[1873]	eval-rmse:0.409843	train-rmse:0.000456
[1874]	eval-rmse:0.409866	train-rmse:0.000456
[1875]	eval-rmse:0.409772	train-rmse:0.000456
[1876]	eval-rmse:0.409791	train-rmse:0.000455
[1877]	eval-rmse:0.409823	train-rmse:0.000455
[1878]	eval-rmse:0.409876	train-rmse:0.000455
[1879]	eval-rmse:0.409821	train-rmse:0.000455
[1880]	eval-rmse:0.409823	train-rmse:0.000455
[1881]	eval-rmse:0.409805	train-rmse:0.000454
[1882]	eval-rmse:0.409809	train-rmse:0.000454
[1883]	eval-rmse:0.409783	train-rmse:0.000454
[1884]	eval-rmse:0.409816	train-rmse:0.000454
[1885]	eval-rmse:0.409808	train-rmse:0.000454
[1886]	eval-rmse:0.409755	train-rmse:0.000454
[1887]	eval-rmse:0.409747	train-rmse:0.000453
[1888]	eval-rmse:0.409769	train-rmse:0.000453
[1889]	eval-rmse:0.409792	train-rmse:0.000453
[1890]	eval-rmse:0.409706	train-rmse:0.000453
[1891]	eval-rmse:0.409752	train-rmse:0.000453
[1892]	eval-rmse:0.409737	train-rmse:0.000453
[1893]	eval-rmse:0.409744	train-rmse:0.000453
[1894]	eval-rmse:0.409801	train-rmse:0.000452
[1895]	eval-rmse:0.40978	train-rmse:0.000452
[1896]	eval-rmse:0.409808	train-rmse:0.000452
[1897]	eval-rmse:0.409778	train-rmse:0.000452
[1898]	eval-rmse:0.409734	train-rmse:0.000452
[1899]	eval-rmse:0.409728	train-rmse:0.000451
[1900]	eval-rmse:0.409766	train-rmse:0.000451
[1901]	eval-rmse:0.409782	train-rmse:0.000451
[1902]	eval-rmse:0.409757	train-rmse:0.000451
[1903]	eval-rmse:0.409755	train-rmse:0.000451
[1904]	eval-rmse:0.409732	train-rmse:0.000451
[1905]	eval-rmse:0.409706	train-rmse:0.00045
[1906]	eval-rmse:0.409752	train-rmse:0.00045
[1907]	eval-rmse:0.409717	train-rmse:0.00045
[1908]	eval-rmse:0.409764	train-rmse:0.00045
[1909]	eval-rmse:0.409852	train-rmse:0.00045
[1910]	eval-rmse:0.409826	train-rmse:0.00045
[1911]	eval-rmse:0.409834	train-rmse:0.000449
[1912]	eval-rmse:0.409803	train-rmse:0.000449
[1913]	eval-rmse:0.409787	train-rmse:0.000449
[1914]	eval-rmse:0.409748	train-rmse:0.000449
[1915]	eval-rmse:0.409661	train-rmse:0.000449
[1916]	eval-rmse:0.409646	train-rmse:0.000448
[1917]	eval-rmse:0.409691	train-rmse:0.000448
[1918]	eval-rmse:0.409685	train-rmse:0.000448
[1919]	eval-rmse:0.409641	train-rmse:0.000448
[1920]	eval-rmse:0.409628	train-rmse:0.000448
[1921]	eval-rmse:0.409655	train-rmse:0.000448
[1922]	eval-rmse:0.409704	train-rmse:0.000448
[1923]	eval-rmse:0.409748	train-rmse:0.000447
[1924]	eval-rmse:0.409736	train-rmse:0.000447
[1925]	eval-rmse:0.409753	train-rmse:0.000447
[1926]	eval-rmse:0.409717	train-rmse:0.000447
[1927]	eval-rmse:0.409709	train-rmse:0.000447
[1928]	eval-rmse:0.409711	train-rmse:0.000446
[1929]	eval-rmse:0.409727	train-rmse:0.000446
[1930]	eval-rmse:0.40972	train-rmse:0.000446
[1931]	eval-rmse:0.409709	train-rmse:0.000446
[1932]	eval-rmse:0.409752	train-rmse:0.000446
[1933]	eval-rmse:0.409791	train-rmse:0.000446
[1934]	eval-rmse:0.409736	train-rmse:0.000446
[1935]	eval-rmse:0.409725	train-rmse:0.000445
[1936]	eval-rmse:0.409686	train-rmse:0.000445
[1937]	eval-rmse:0.409659	train-rmse:0.000445
[1938]	eval-rmse:0.40971	train-rmse:0.000445
[1939]	eval-rmse:0.409738	train-rmse:0.000445
[1940]	eval-rmse:0.409696	train-rmse:0.000445
[1941]	eval-rmse:0.409702	train-rmse:0.000445
[1942]	eval-rmse:0.409798	train-rmse:0.000444
[1943]	eval-rmse:0.409804	train-rmse:0.000444
[1944]	eval-rmse:0.409762	train-rmse:0.000444
[1945]	eval-rmse:0.409752	train-rmse:0.000444
[1946]	eval-rmse:0.409797	train-rmse:0.000444
[1947]	eval-rmse:0.409776	train-rmse:0.000444
[1948]	eval-rmse:0.409804	train-rmse:0.000444
[1949]	eval-rmse:0.409771	train-rmse:0.000444
[1950]	eval-rmse:0.409818	train-rmse:0.000444
[1951]	eval-rmse:0.409842	train-rmse:0.000443
[1952]	eval-rmse:0.409846	train-rmse:0.000443
[1953]	eval-rmse:0.409853	train-rmse:0.000443
[1954]	eval-rmse:0.409866	train-rmse:0.000443
[1955]	eval-rmse:0.409848	train-rmse:0.000443
[1956]	eval-rmse:0.409855	train-rmse:0.000443
[1957]	eval-rmse:0.409865	train-rmse:0.000442
[1958]	eval-rmse:0.409839	train-rmse:0.000442
[1959]	eval-rmse:0.409782	train-rmse:0.000442
[1960]	eval-rmse:0.40977	train-rmse:0.000442
[1961]	eval-rmse:0.409796	train-rmse:0.000442
[1962]	eval-rmse:0.409842	train-rmse:0.000442
[1963]	eval-rmse:0.409881	train-rmse:0.000442
[1964]	eval-rmse:0.409869	train-rmse:0.000441
[1965]	eval-rmse:0.409894	train-rmse:0.000441
[1966]	eval-rmse:0.409936	train-rmse:0.000441
[1967]	eval-rmse:0.409935	train-rmse:0.000441
[1968]	eval-rmse:0.409926	train-rmse:0.000441
[1969]	eval-rmse:0.40982	train-rmse:0.000441
[1970]	eval-rmse:0.409821	train-rmse:0.000441
[1971]	eval-rmse:0.40983	train-rmse:0.000441
[1972]	eval-rmse:0.409758	train-rmse:0.000441
[1973]	eval-rmse:0.409762	train-rmse:0.00044
[1974]	eval-rmse:0.40976	train-rmse:0.00044
[1975]	eval-rmse:0.409731	train-rmse:0.00044
[1976]	eval-rmse:0.40981	train-rmse:0.00044
[1977]	eval-rmse:0.409796	train-rmse:0.000439
[1978]	eval-rmse:0.409802	train-rmse:0.000439
[1979]	eval-rmse:0.409784	train-rmse:0.000439
[1980]	eval-rmse:0.409729	train-rmse:0.000439
[1981]	eval-rmse:0.409734	train-rmse:0.000439
[1982]	eval-rmse:0.409718	train-rmse:0.000439
[1983]	eval-rmse:0.409713	train-rmse:0.000438
[1984]	eval-rmse:0.409658	train-rmse:0.000438
[1985]	eval-rmse:0.40966	train-rmse:0.000438
[1986]	eval-rmse:0.409687	train-rmse:0.000438
[1987]	eval-rmse:0.409665	train-rmse:0.000438
[1988]	eval-rmse:0.40967	train-rmse:0.000438
[1989]	eval-rmse:0.409677	train-rmse:0.000438
[1990]	eval-rmse:0.409682	train-rmse:0.000438
[1991]	eval-rmse:0.409686	train-rmse:0.000437
[1992]	eval-rmse:0.409651	train-rmse:0.000437
[1993]	eval-rmse:0.409652	train-rmse:0.000437
[1994]	eval-rmse:0.40972	train-rmse:0.000437
[1995]	eval-rmse:0.409735	train-rmse:0.000437
[1996]	eval-rmse:0.409734	train-rmse:0.000437
[1997]	eval-rmse:0.409769	train-rmse:0.000437
[1998]	eval-rmse:0.409725	train-rmse:0.000437
[1999]	eval-rmse:0.409665	train-rmse:0.000436

In [30]:

    

#bst.save_model("/tmp/model.txt")
#bst.dump_model("/tmp/dump.txt")


    

In [82]:

    

LR_classification_vector = lambda cutoff: [1 if pred >= cutoff else 0 for pred in preds]

def find_best_mcc():
    best_mcc = 0.0
    best_cutoff = 0.0
    for i in range(1, 100):
        pred_cutoff = i/100.0
        mcc = matthews_corrcoef(dtest.get_label(), LR_classification_vector(pred_cutoff))
        if (mcc > best_mcc):
            best_mcc = mcc
            best_cutoff = pred_cutoff
    return (best_cutoff, best_mcc)

(best_cutoff, best_mcc) = find_best_mcc()
print "** Logistic Regression Report, cutoff= %.2f (MCC=%.3f) **" % (best_cutoff, best_mcc)
print classification_report(dtest.get_label(), LR_classification_vector(best_cutoff))


def print_metrics(name, actual, predicted):
    ((tn, fp), (fn, tp)) = confusion_matrix(actual, predicted)
    sensitivity = 100.0 * tp / (tp + fn)
    specificity = 100.0 * tn / (tn + fp)
    precision = 100.0 * tp / (tp + fp)
    print "%s: sensitivity(recall)=%.1f, specificity=%.1f, precision=%.1f" % (name, sensitivity, specificity, precision)

print_metrics("LR", dtest.get_label(), LR_classification_vector(best_cutoff))

%matplotlib inline
import matplotlib.pyplot as plt
from sklearn import svm, metrics

fpr, tpr, thresholds = metrics.roc_curve(dtest.get_label(), preds, pos_label=1)
roc_auc = metrics.roc_auc_score(dtest.get_label(), preds, average='macro', sample_weight=None)
plt.title('Logistic Regression ROC Curve')
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f'% (roc_auc))
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')

#LR: sensitivity(recall)=77.6, specificity=84.8, precision=86.5


    

    




** Logistic Regression Report, cutoff= 0.21 (MCC=0.631) **
             precision    recall  f1-score   support

        0.0       0.83      0.79      0.81       208
        1.0       0.80      0.84      0.82       208

avg / total       0.82      0.81      0.81       416

LR: sensitivity(recall)=84.1, specificity=78.8, precision=79.9






    
Out[82]:





<matplotlib.text.Text at 0x110c11590>






    

In [83]:

    

importance = bst.get_score(importance_type="weight")
tuples = [(k, importance[k]) for k in importance]
tuples = sorted(tuples, reverse=True, key=lambda x: x[1])
for (key, value) in tuples:
    print "%5s (%d) %s" % (key, value, "*" * (value/2))


    

    




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