Santander Customer Satisfaction¶

Data Science Competition on Kaggle : https://www.kaggle.com/c/santander-customer-satisfaction Final Result : Top 67%.

I used H2O Platform for this competition, H2O is an open source machine learning platform.

The goal is to predict if a customer is satisfied or dissatisfied with their banking experience.

In [1]:

import h2o
h2o.init()

/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:92: DeprecationWarning: DisplayFormatter._ipython_display_formatter_default is deprecated: use @default decorator instead.
  def _ipython_display_formatter_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:98: DeprecationWarning: DisplayFormatter._formatters_default is deprecated: use @default decorator instead.
  def _formatters_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.
  def _deferred_printers_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.
  def _singleton_printers_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.
  def _type_printers_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.
  def _singleton_printers_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.
  def _type_printers_default(self):
/Users/comalada/anaconda/lib/python2.7/site-packages/IPython/core/formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.
  def _deferred_printers_default(self):

H2O cluster uptime:	21 days 55 minutes 38 seconds 440 milliseconds
H2O cluster version:	3.8.1.4
H2O cluster name:	H2O_started_from_python_comalada_kmk341
H2O cluster total nodes:	1
H2O cluster total free memory:	1.28 GB
H2O cluster total cores:	4
H2O cluster allowed cores:	4
H2O cluster healthy:	True
H2O Connection ip:	127.0.0.1
H2O Connection port:	54321
H2O Connection proxy:	None
Python Version:	2.7.11

In [2]:

df = h2o.import_file("/Users/comalada/Documents/h2o_ML/Kaggle-Santander_Customer_Satisfaction/train.csv")

Parse Progress: [##################################################] 100%

In [3]:

df.summary()

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type	int	int	int	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	real	real	real	real	real	real	real	int	real	real	real	int	real	real	real	real	int	int	real	real	real	real	real	int	real	real	int	real	real	int	int	int	int	real	real	real	real	int	int	int	int	int	int	int	real	real	real	real	real	int	int	int	int	int	int	int	real	int	real	int	real	real	real	real	real	int	int	real	real	real	real	int	real	real	real	int	int	real	real	real	real	int	real	real	real	int	int	real	real	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	int	int	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	int
mins	1.0	-999999.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.9	-2895.72	0.0	-4942.26	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-4942.26	0.0	0.0	0.0	0.0	0.0	-0.9	0.0	-4942.26	0.0	0.0	0.0	0.0	0.0	-1.0	-1.0	-1.0	-1.0	0.0	-1.0	0.0	-1.0	0.0	-1.0	0.0	-1.0	-1.0	-1.0	-1.0	-1.0	0.0	-1.0	0.0	-1.0	0.0	-1.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-128.37	-8.04	-922.38	-476.07	-287.67	0.0	-3401.34	-1844.52	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.03	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5163.75	0.0
mean	75964.0507235	-1523.19927651	33.2128650355	86.2082651934	72.3630670876	119.529632202	3.55912983425	6.47269771113	0.412946329913	0.567352407261	3.16071546961	68.8039372534	113.056934491	68.2051404893	113.225057616	137.24276322	68.6180868193	113.792410024	140.40347869	5.47767561168	0.0114575111813	0.00376216785057	0.0	0.0	0.958024204157	0.663759536964	0.000105235464351	2.63088660879e-05	0.0328334648777	0.0285977374375	0.0675217048145	0.0454617205998	0.0522494080505	0.0429360694554	0.0414759273875	0.010168376743	0.00999736911339	2.63088660879e-05	2.63088660879e-05	0.0508550381479	0.023651670613	0.00530123651671	0.00180215732702	0.00144698763483	2.63088660879e-05	2.63088660879e-05	0.00419626414102	0.00363062352013	0.00269665877401	0.0423704288345	0.0378847671665	0.0264272559853	0.0246382530913	0.027558537227	0.0246382530913	0.02363851618	0.02363851618	0.0	0.0	0.0	0.0	0.000105235464351	2.63088660879e-05	0.995488029466	0.732833464878	0.00427519073928	0.00367008681926	0.00121020784004	0.0010786635096	0.0010786635096	0.000749802683504	0.000631412786109	2.63088660879e-05	2.63088660879e-05	0.0722967640095	0.065259142331	0.065259142331	0.880755064457	0.0114180478821	0.00372270455143	0.879281767956	0.0	0.00372270455143	0.00188108392528	0.00169692186267	0.0	0.0	0.0344514601421	0.0113259668508	1.07943962115	2.89404104183	1.99917127072	0.000315706393054	7.89265982636e-05	0.0985398579321	0.0857932123125	0.211247040253	0.138003157064	0.16771902131	0.130307813733	0.124506708761	0.0373322809787	0.0352407261247	7.89265982636e-05	7.89265982636e-05	0.159826361484	0.0726913970008	0.0161404893449	0.0118784530387	0.00887924230466	7.89265982636e-05	7.89265982636e-05	0.0108918705604	0.00808997632202	0.127663772691	0.113772691397	0.0893843725335	0.0893843725335	0.0851617995264	0.0851617995264	0.0202446724546	0.00102604577743	0.0570244672455	0.0782951854775	1.60114443567	0.0939226519337	2.85883977901	4.55390686661	1.62138910813	0.0949486977111	2.91586424625	4.63220205209	0.0	0.0	0.0	0.0	0.000315706393054	7.89265982636e-05	3.37186266772	2.38287292818	0.0201262825572	0.0160615627466	0.0042225730071	0.0042225730071	0.00256511444357	0.00209155485399	7.89265982636e-05	7.89265982636e-05	3.29936858721	0.263535911602	0.418784530387	0.418784530387	2.72494080505	0.0342936069455	0.0111681136543	2.69925019732	0.0	0.0111681136543	3.20414364641	2.2179952644	0.00568271507498	0.005090765588	0.0	0.0	48.4491069455	1028.4682352	0.414475138122	141.226784136	6021.61590095	4993.75297001	1493.68226993	0.513022888713	6487.94826283	69.0962004736	183.405896606	43.409629045	27.3994558011	5925.12024467	76.081633386	72.7356925809	0.0	0.0	0.414475138122	13679.6736582	292.290967245	3.34594080505	12.5323326756	0.670876085241	36.9071937648	4.36860181531	0.0	7191.72539542	96.3527379637	0.0	40.4490791897	263088.660852	263088.660852	48671402.2359	5130228.88643	131544.330141	9208103.12972	4998684.5562	2630886.60851	131544.330426	526177.321679	526177.321705	657721.652052	131544.330426	5524861.87795	48671402.2357	5130228.88643	131544.330196	9208103.12977	4998684.5562	2630886.60851	131544.330426	526177.321679	526177.321705	657721.652052	131544.330426	5524861.87799	0.0	0.231189423836	0.0	0.0180513022889	2823.94908919	619.585009866	98.7887691397	31.1053228098	2.98579321231	0.0481452249408	2.72145382794	127.698209945	13.9645805051	116.782528019	0.0	46.1802430939	0.158210339384	12.5694005525	0.0	0.0157853196527	854.12074704	1932.95443173	1.87480820837	2.51267837411	0.0	1.9136160221	2.78977229676	0.314701262826	0.0	0.0394632991318	3.78713693765	81.43382794	3.94632991318e-05	0.00269665877401	0.0808734543541	0.0921599579058	0.0968692449355	0.0859116022099	0.0665877400684	0.129308076822	32.5493291239	0.0	0.0	0.0758879242305	0.017955801105	0.00153906866614	0.00339384372534	0.00106550907656	0.000315706393054	0.000118389897395	0.0102999210734	0.00185477505919	0.00753749013418	0.187963693765	1.29869771113	1.18488555643	0.560655090766	3.04423835833	0.635872138911	4.02466456196	1.97997895291	0.053604314654	0.102052091555	0.0989213364904	0.0174033149171	5.26177321757e-05	0.00295974743489	0.000105235464351	0.00151275980005	1.59279137069	0.00357800578795	2.19479084451	3.60706393054	0.0749802683504	0.144830307814	0.0024861878453	0.00367008681926	2.11981057616	3.46223362273	0.719415943173	1.21215469613	0.721902131018	1.21582478295	0.0	0.00149960536701	3.94632991318e-05	0.00118389897395	0.0	3.94632991318e-05	0.00493291239148	0.392817679558	0.81499605367	0.120678768745	0.000118389897395	0.000157853196527	0.0	0.000157853196527	0.000236779794791	0.000236779794791	0.0	3.94632991318e-05	0.000157853196527	0.00441988950276	5.39321231255	3.89439621152	4.3634964483	13.6511049724	0.0	1579.13531137	891.365863457	1077.25675572	1048.85644712	68.2754518548	9.50528689818	124.620961721	110.026575375	3997.02332794	613.534443173	5703.00817245	4401.00243923	3639.41993923	556.184177979	4852.26181373	3857.84854223	771.227449092	162.170438832	956.950207182	750.956273086	0.175324388319	0.0	0.513022888713	0.344174033149	91.1718105762	36.4631835043	131.031565904	109.216943567	0.213071033938	0.00191002367798	0.253906866614	0.186629834254	7.93582399369	1.36514601421	12.2155797159	8.784074191	31.5053243883	1.85857498027	76.0261653512	56.6143512234	117235.80943	0.0395685345962
maxs	151838.0	238.0	105.0	210000.0	12888.03	21024.81	8237.82	11073.57	6600.0	6600.0	8237.82	12888.03	16566.81	45990.0	131100.0	47598.09	45990.0	131100.0	47598.09	105000.0	1.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	1.0	1.0	0.0	0.0	6.0	6.0	7.0	15.0	15.0	3.0	3.0	6.0	3.0	111.0	15.0	18.0	6.0	6.0	18.0	18.0	3.0	3.0	18.0	111.0	12.0	36.0	27.0	3.0	3.0	3.0	3.0	9.0	6.0	33.0	33.0	33.0	33.0	117.0	48.0	234.0	351.0	249.0	81.0	468.0	468.0	249.0	81.0	468.0	468.0	0.0	0.0	0.0	0.0	3.0	3.0	114.0	33.0	36.0	27.0	12.0	12.0	12.0	6.0	3.0	3.0	36.0	105.0	114.0	114.0	33.0	6.0	3.0	33.0	0.0	3.0	114.0	18.0	6.0	3.0	0.0	0.0	3000000.0	619329.15	19531.8	240045.0	3008077.32	450000.0	1500000.0	30000.0	1500000.0	450000.0	6119500.14	3000000.0	455858.16	3008077.32	69756.72	69756.72	0.0	0.0	19531.8	3458077.32	6119500.14	12210.78	142078.8	36000.0	60000.0	8192.61	0.0	3008077.32	740006.61	0.0	99.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	0.0	15691.8	0.0	1096.02	840000.0	450000.0	6083691.87	432457.32	36000.0	1260.0	145384.92	1039260.0	210001.35	3410058.66	0.0	450000.0	12027.15	182132.97	0.0	1200.0	1155003.0	2310003.0	96781.44	133730.58	0.0	69622.29	49581.27	13207.32	0.0	3000.0	209834.4	2754476.46	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	30000.0	0.0	0.0	24.0	30.0	12.0	21.0	12.0	6.0	3.0	24.0	9.0	39.0	60.0	123.0	108.0	96.0	234.0	78.0	267.0	3.0	3.0	3.0	3.0	3.0	2.0	3.0	2.0	3.0	3.0	3.0	438.0	600.0	210.0	582.0	24.0	24.0	438.0	438.0	90.0	156.0	90.0	156.0	0.0	3.0	3.0	21.0	0.0	3.0	15.0	180.0	264.0	93.0	6.0	3.0	0.0	3.0	3.0	6.0	0.0	3.0	6.0	39.0	342.0	339.0	510.0	801.0	0.0	812137.26	1542339.36	601428.6	544365.57	231351.99	77586.21	228031.8	177582.0	3000538.14	668335.32	3004185.6	2272859.43	450000.0	304838.7	450000.0	450000.0	840000.0	534000.0	1500000.0	1034482.74	7741.95	0.0	30000.0	18870.99	4210084.23	2368558.95	3998687.46	3525776.88	10430.01	145.2	13793.67	7331.34	50003.88	20385.72	138831.63	91778.73	438329.22	24650.01	681462.9	397884.3	22034738.76	1.0
sigma	43781.9473795	39033.4623639	12.9564858164	1614.75731272	339.315830907	546.266294057	93.155748706	153.737065555	30.6048637661	36.5135132146	95.2682043651	319.605516194	512.154822602	531.89791718	950.086397947	697.712596074	535.47374963	953.578623993	712.767239685	465.391148614	0.106425493238	0.0612214280075	0.0	0.0	0.200535177472	0.472425390866	0.010257961495	0.00512918317133	0.178201700867	0.166674150019	0.250924993655	0.208315922107	0.222531028886	0.202714340765	0.199389562929	0.100325038043	0.0994864408896	0.00512918317133	0.00512918317133	0.219703068253	0.151962406074	0.0726168215642	0.0424138328843	0.0380120095316	0.00512918317133	0.00512918317133	0.0646429460718	0.0601455707347	0.0518596392514	0.20143413153	0.190918807513	0.160403224965	0.155021049206	0.163705274149	0.155021049206	0.151921164877	0.151921164877	0.0	0.0	0.0	0.0	0.010257961495	0.00512918317133	0.0670199354026	0.442482941089	0.0652454556382	0.0604703678123	0.0347672135433	0.0328255109438	0.0328255109438	0.027372437513	0.0251201593296	0.00512918317133	0.00512918317133	0.258980354857	0.246984187974	0.246984187974	0.32407863623	0.106244174191	0.0609006963069	0.325801683231	0.0	0.0609006963069	0.0433309375264	0.0411590160635	0.0	0.0	0.320378506009	0.184626247363	0.909565723744	0.656478967714	1.43190152968	0.0307738844851	0.015387549514	0.534929915158	0.500022450058	0.890235967382	0.637258654068	0.745821815577	0.618826295918	0.598745756698	0.404821878509	0.381372635808	0.015387549514	0.015387549514	0.713022201022	0.610815013511	0.224260188983	0.362480077721	0.288768304407	0.015387549514	0.015387549514	0.180436712204	0.155578917754	0.608481590983	0.573662372167	0.628596239515	0.628596239515	0.61210963526	0.61210963526	0.897617715601	0.180762243658	1.82737355508	2.55821597517	7.32200421698	1.21521783802	10.8863135253	16.8918007064	7.38953858771	1.22850992758	11.1128987607	17.1759000186	0.0	0.0	0.0	0.0	0.0307738844851	0.015387549514	1.3366536576	1.64278656215	0.406623860708	0.331608906994	0.139702396828	0.139702396828	0.101456772402	0.0863382128094	0.015387549514	0.015387549514	2.86821662689	1.65027340997	2.24114087533	2.24114087533	1.13915860148	0.319284967004	0.182702088921	1.10529672918	0.0	0.182702088921	0.944122808984	1.49770334393	0.131350137878	0.123477048191	0.0	0.0	10937.4730191	9852.14007253	83.0967969865	2515.65630213	48144.7139698	32619.1342451	20016.4891485	113.597558837	38416.7539674	2839.61896446	22698.1299551	10934.9661588	2477.81074364	48008.5293417	739.776626037	726.882668778	0.0	0.0	83.0967969865	63014.0781424	23316.398822	126.68015248	797.839556706	141.448597643	472.098862948	113.968756324	0.0	49145.3138785	5218.42836485	0.0	47.3627191511	51291831.7082	51291831.7082	695953653.99	226443467.841	36269040.5736	303310817.974	223522965.125	162179809.193	36269040.5736	72536649.8183	72536649.8183	81097906.4951	36269040.5736	234986852.924	695953653.99	226443467.841	36269040.5736	303310817.974	223522965.125	162179809.193	36269040.5736	72536649.8183	72536649.8183	81097906.4951	36269040.5736	234986852.924	0.0	57.3209584955	0.0	4.09946129459	25334.4681064	11252.9950893	22120.7191637	2457.09128231	226.862983666	6.02269240022	554.249436523	6368.99673096	1151.51086171	13620.4421717	0.0	2859.73659099	43.6213191379	1093.13388273	0.0	4.35228486927	14255.8880999	25355.7174292	388.245021796	508.968830644	0.0	357.405329676	323.814261031	53.4080845993	0.0	10.8807121732	811.976086466	11282.413386	0.00628189946461	0.0518596392514	0.272642470198	0.289253523167	0.295781583537	0.28023531515	0.249308304214	0.33554281267	393.83493949	0.0	0.0	0.552499139252	0.288756547088	0.104919190475	0.174059455629	0.0696628618487	0.0344066109322	0.0188456983938	0.228782264812	0.0991113036437	0.315452688933	0.995016369119	3.45026751156	3.26333316843	2.10441008449	6.20611585538	1.83526701335	10.9303336867	1.29892400358	0.334908300685	0.487687358511	0.486063980031	0.213094760124	0.0102583663427	0.0776479489301	0.0135703135758	0.0624870349354	0.719654609397	0.088174398261	9.13140565962	14.9197256574	2.08940833212	4.28814467733	0.147171515624	0.20206826119	8.7994942845	14.1406503288	3.21099927522	5.16039625794	3.22631441994	5.18109154866	0.0	0.0670568895442	0.0108807121732	0.0985225743459	0.0	0.0108807121732	0.154948802426	2.21585313366	3.55765968054	1.17214820936	0.0243298840418	0.0217609949477	0.0	0.0217609949477	0.0266513163502	0.0307745929743	0.0	0.0108807121732	0.0266519006948	0.264256357791	14.4960954356	10.4168212393	14.4064847941	33.3040121792	0.0	12148.4523984	9888.59699975	9614.90698529	8189.94885234	1733.83822581	519.389156902	2205.2498037	1935.30571326	37773.138997	9292.75272596	46202.5367	35507.1831645	26359.1742227	7182.64253249	31886.6151889	25572.2450549	13082.1558672	4698.86807537	16006.9805653	12422.5183735	34.6255183528	0.0	113.597558837	73.3765127852	15392.4757116	8612.39521233	14956.5262256	13082.1574583	41.8204444521	0.526626469182	52.0787753804	31.8794180363	455.887217988	113.959637255	783.207398897	538.439210545	2013.12539264	147.786584179	4040.3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2	4.0	2.0	23.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	99.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.18	3.0	2.07	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	67333.77	0.0
3	8.0	2.0	37.0	0.0	195.0	195.0	0.0	0.0	0.0	0.0	0.0	195.0	195.0	0.0	0.0	195.0	0.0	0.0	195.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	9.0	0.0	0.0	9.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	6.0	6.0	6.0	3.0	0.0	0.0	3.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	70.62	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	70.62	0.0	0.0	0.0	0.0	34.95	0.0	0.0	70.62	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	15.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	9.0	9.0	0.0	0.0	0.0	0.0	9.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	27.0	3.0	18.0	48.0	0.0	186.09	0.0	91.56	138.84	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	64007.97	0.0
4	10.0	2.0	39.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	3.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	6.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	135003.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	135003.0	0.0	0.0	0.0	0.0	0.0	135003.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	135003.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	135003.0	270003.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	6.0	9.0	3.0	0.0	3.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	6.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.3	40501.08	13501.47	0.0	0.0	0.0	0.0	0.0	0.0	85501.89	85501.89	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	117310.979016	0.0
5	13.0	2.0	23.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	99.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	87975.75	0.0
6	14.0	2.0	27.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.42	3.0	2.13	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	94956.66	0.0
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8	20.0	2.0	45.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	99.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	101962.02	0.0
9	23.0	2.0	25.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.79	0.0	3.0	2.91	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	356463.06	0.0

In [4]:

from h2o.estimators.gbm import H2OGradientBoostingEstimator

In [5]:

df["TARGET"]= df["TARGET"].asfactor()

Split 0.7 Train - 0.1 Validation - 0.2 Test¶

In [66]:

# Split final H2O data table into train test and validation sets
ratios = [0.9,0.05]
frs = df.split_frame(ratios,seed=12345)
train = frs[0]
train.frame_id = "Train"
valid = frs[2]
valid.frame_id = "Validation"
test = frs[1]
test.frame_id = "Test"

In [8]:

# Define Preditors
predictors = df.names[:]
response = "TARGET"
predictors.remove(response)

Gradient Boosting¶

In [57]:

from h2o.grid.grid_search import H2OGridSearch 

ntrees_opt = [15, 16, 17, 18] 
max_depth_opt = [3, 4] 
learn_rate_opt = [ 0.2]

hyper_parameters = {"ntrees": ntrees_opt, "max_depth":max_depth_opt,"learn_rate":learn_rate_opt}

model_gbm = H2OGridSearch(H2OGradientBoostingEstimator(distribution="multinomial"), hyper_params=hyper_parameters)

model_gbm.train(x               =predictors,
               y               ="TARGET",
               training_frame  =train,
               validation_frame=valid
               )

gbm Grid Build Progress: [##################################################] 100%

In [58]:

print model_gbm.sort_by("auc", increasing=False)

Grid Search Results for H2OGradientBoostingEstimator:

Model Id	Hyperparameters: [learn_rate, ntrees, max_depth]	auc
Grid_GBM_Train_model_python_1460226203968_5639_model_7	[0.2, 18, 4]	0.8501685
Grid_GBM_Train_model_python_1460226203968_5639_model_6	[0.2, 17, 4]	0.8493567
Grid_GBM_Train_model_python_1460226203968_5639_model_5	[0.2, 16, 4]	0.8488648
Grid_GBM_Train_model_python_1460226203968_5639_model_4	[0.2, 15, 4]	0.8478010
Grid_GBM_Train_model_python_1460226203968_5639_model_3	[0.2, 18, 3]	0.8381908
Grid_GBM_Train_model_python_1460226203968_5639_model_2	[0.2, 17, 3]	0.8368843
Grid_GBM_Train_model_python_1460226203968_5639_model_1	[0.2, 16, 3]	0.8357914
Grid_GBM_Train_model_python_1460226203968_5639_model_0	[0.2, 15, 3]	0.8351749

In [67]:

#Simple GBM model - Predict Arrest
model_gbm = H2OGradientBoostingEstimator(ntrees         =18,
                                        max_depth      =4,
                                        learn_rate     =0.2, 
                                        #nfolds         =2,
                                        distribution   ="multinomial")

model_gbm.train(x               =predictors,
               y               ="TARGET",
               training_frame  =train,
               validation_frame=valid
               )

gbm Model Build Progress: [##################################################] 100%

AUC : Train & Validation¶

In [70]:

#AUC Train: 0.846304  0.850429711071
#AUC Validation: 0.843999    0.845719714677
print("Train: " + str(model_gbm.auc(train=True)))
print("Validation: " + str(model_gbm.auc(valid=True)))
#model_gbm.plot(metric="AUC")

Train: 0.848617115678
Validation: 0.845816831856

In [69]:

#AUC : 0.851175 0.852239641273
test_performance = model_gbm.model_performance(test)
test_performance

ModelMetricsBinomial: gbm
** Reported on test data. **

MSE: 0.0350234706464
R^2: 0.0901716380185
LogLoss: 0.134912012086
AUC: 0.852239641273
Gini: 0.704479282546

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.137471941587:

	0	1	Error	Rate
0	3469.0	265.0	0.071	(265.0/3734.0)
1	86.0	70.0	0.5513	(86.0/156.0)
Total	3555.0	335.0	0.0902	(351.0/3890.0)

Maximum Metrics: Maximum metrics at their respective thresholds

metric	threshold	value	idx
max f1	0.1374719	0.2851324	99.0
max f2	0.0692502	0.4187192	176.0
max f0point5	0.2257219	0.2670940	32.0
max accuracy	0.3245295	0.9598972	3.0
max precision	0.3245295	0.5	3.0
max recall	0.0068725	1.0	394.0
max specificity	0.5264727	0.9997322	0.0
max absolute_MCC	0.0692502	0.2847757	176.0
max min_per_class_accuracy	0.0394624	0.7897697	251.0

Gains/Lift Table: Avg response rate:  4,01 %

group	cumulative_data_fraction	lower_threshold	lift	cumulative_lift	response_rate	cumulative_response_rate	capture_rate	cumulative_capture_rate	gain	cumulative_gain
1	0.0100257	0.2378949	5.7544379	5.7544379	0.2307692	0.2307692	0.0576923	0.0576923	475.4437870	475.4437870
2	0.0200514	0.2228933	10.2301118	7.9922748	0.4102564	0.3205128	0.1025641	0.1602564	923.0111769	699.2274819
3	0.0318766	0.2117084	3.7945931	6.4350703	0.1521739	0.2580645	0.0448718	0.2051282	279.4593088	543.5070306
4	0.0401028	0.1946925	3.8962340	5.9142834	0.15625	0.2371795	0.0320513	0.2371795	289.6233974	491.4283366
5	0.0501285	0.1720460	2.5575279	5.2429323	0.1025641	0.2102564	0.0256410	0.2628205	155.7527942	424.2932281
6	0.1	0.1026853	4.4987444	4.8717949	0.1804124	0.1953728	0.2243590	0.4871795	349.8744383	387.1794872
7	0.1501285	0.0700410	3.0690335	4.2698455	0.1230769	0.1712329	0.1538462	0.6410256	206.9033531	326.9845451
8	0.2	0.0484875	1.7994978	3.6538462	0.0721649	0.1465296	0.0897436	0.7307692	79.9497753	265.3846154
9	0.3002571	0.0315654	1.0230112	2.7753995	0.0410256	0.1113014	0.1025641	0.8333333	2.3011177	177.5399543
10	0.4002571	0.0228962	0.7692308	2.2741795	0.0308483	0.0912010	0.0769231	0.9102564	-23.0769231	127.4179471
11	0.5015424	0.0185304	0.3797345	1.8916006	0.0152284	0.0758585	0.0384615	0.9487179	-62.0265521	89.1600626
12	0.6033419	0.0155407	0.1259389	1.5936875	0.0050505	0.0639114	0.0128205	0.9615385	-87.4061124	59.3687523
13	0.7015424	0.0105173	0.1305544	1.3888811	0.0052356	0.0556981	0.0128205	0.9743590	-86.9445563	38.8881059
14	0.8015424	0.0090400	0.1923077	1.2395972	0.0077121	0.0497114	0.0192308	0.9935897	-80.7692308	23.9597211
15	0.9231362	0.0068751	0.0527186	1.0832637	0.0021142	0.0434419	0.0064103	1.0	-94.7281401	8.3263715
16	1.0	0.0058277	0.0	1.0	0.0	0.0401028	0.0	1.0	-100.0	0.0

Out[69]:

Random Forest¶

In [111]:

from h2o.estimators.random_forest import H2ORandomForestEstimator

ntrees=[50,100,200]
stopping_rounds=[1,2]
sample_rate = [0.1, 0.2]
seed=1000000

hyper_parameters = {"ntrees": ntrees, "stopping_rounds":stopping_rounds,"sample_rate":sample_rate,
                    "seed":seed}

model_random_forest = H2OGridSearch(H2ORandomForestEstimator(score_each_iteration=True), hyper_params=hyper_parameters)

model_random_forest.train(x               =predictors,
               y               ="TARGET",
               training_frame  =train,
               validation_frame=valid
               )

drf Grid Build Progress: [##################################################] 100%

In [112]:

print model_random_forest.sort_by("auc", increasing=False)

Grid Search Results for H2ORandomForestEstimator:

Model Id	Hyperparameters: [ntrees, sample_rate, seed, stopping_rounds]	auc
Grid_DRF_Train_model_python_1460226203968_1408_model_9	[200, 0.1, 1000000, 2]	0.7823061
Grid_DRF_Train_model_python_1460226203968_1408_model_1	[50, 0.1, 1000000, 2]	0.7823061
Grid_DRF_Train_model_python_1460226203968_1408_model_5	[100, 0.1, 1000000, 2]	0.7823061
Grid_DRF_Train_model_python_1460226203968_1408_model_3	[50, 0.2, 1000000, 2]	0.7820207
Grid_DRF_Train_model_python_1460226203968_1408_model_7	[100, 0.2, 1000000, 2]	0.7820207
Grid_DRF_Train_model_python_1460226203968_1408_model_11	[200, 0.2, 1000000, 2]	0.7820207
Grid_DRF_Train_model_python_1460226203968_1408_model_2	[50, 0.2, 1000000, 1]	0.7788367
Grid_DRF_Train_model_python_1460226203968_1408_model_6	[100, 0.2, 1000000, 1]	0.7788367
Grid_DRF_Train_model_python_1460226203968_1408_model_10	[200, 0.2, 1000000, 1]	0.7788367
Grid_DRF_Train_model_python_1460226203968_1408_model_8	[200, 0.1, 1000000, 1]	0.7765093
Grid_DRF_Train_model_python_1460226203968_1408_model_0	[50, 0.1, 1000000, 1]	0.7765093
Grid_DRF_Train_model_python_1460226203968_1408_model_4	[100, 0.1, 1000000, 1]	0.7765093

In [113]:

print(model_random_forest.auc(train=True))
print("----Validation: " + str(model_random_forest.auc(valid=True)))
#model_gbm.plot(metric="AUC")

{u'Grid_DRF_Train_model_python_1460226203968_1408_model_10': 0.7788366862330695, u'Grid_DRF_Train_model_python_1460226203968_1408_model_11': 0.7820207477348616, u'Grid_DRF_Train_model_python_1460226203968_1408_model_6': 0.7788366862330695, u'Grid_DRF_Train_model_python_1460226203968_1408_model_7': 0.7820207477348616, u'Grid_DRF_Train_model_python_1460226203968_1408_model_4': 0.7765093227261314, u'Grid_DRF_Train_model_python_1460226203968_1408_model_5': 0.7823060793369291, u'Grid_DRF_Train_model_python_1460226203968_1408_model_2': 0.7788366862330695, u'Grid_DRF_Train_model_python_1460226203968_1408_model_3': 0.7820207477348616, u'Grid_DRF_Train_model_python_1460226203968_1408_model_0': 0.7765093227261314, u'Grid_DRF_Train_model_python_1460226203968_1408_model_1': 0.7823060793369291, u'Grid_DRF_Train_model_python_1460226203968_1408_model_8': 0.7765093227261314, u'Grid_DRF_Train_model_python_1460226203968_1408_model_9': 0.7823060793369291}
----Validation: {u'Grid_DRF_Train_model_python_1460226203968_1408_model_10': 0.7811812877646773, u'Grid_DRF_Train_model_python_1460226203968_1408_model_11': 0.7823395488149033, u'Grid_DRF_Train_model_python_1460226203968_1408_model_6': 0.7811812877646773, u'Grid_DRF_Train_model_python_1460226203968_1408_model_7': 0.7823395488149033, u'Grid_DRF_Train_model_python_1460226203968_1408_model_4': 0.7753144531588151, u'Grid_DRF_Train_model_python_1460226203968_1408_model_5': 0.7763287972437195, u'Grid_DRF_Train_model_python_1460226203968_1408_model_2': 0.7811812877646773, u'Grid_DRF_Train_model_python_1460226203968_1408_model_3': 0.7823395488149033, u'Grid_DRF_Train_model_python_1460226203968_1408_model_0': 0.7753144531588151, u'Grid_DRF_Train_model_python_1460226203968_1408_model_1': 0.7763287972437195, u'Grid_DRF_Train_model_python_1460226203968_1408_model_8': 0.7753144531588151, u'Grid_DRF_Train_model_python_1460226203968_1408_model_9': 0.7763287972437195}

In [122]:

model_random_forest = H2ORandomForestEstimator(ntrees=200,
                                        stopping_rounds      =2,
                                        sample_rate     =0.1, 
                                        seed=1000000
                                            )

model_random_forest.train(x               =predictors,
               y               ="TARGET",
               training_frame  =train,
               validation_frame=valid
               )

drf Model Build Progress: [##################################################] 100%

In [123]:

test_performance = model_random_forest.model_performance(test)
test_performance

ModelMetricsBinomial: drf
** Reported on test data. **

MSE: 0.0350591177046
R^2: 0.0709754860633
LogLoss: 0.140125855625
AUC: 0.80658346729
Gini: 0.613166934581

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.131365816061:

	0	1	Error	Rate
0	13524.0	1004.0	0.0691	(1004.0/14528.0)
1	359.0	235.0	0.6044	(359.0/594.0)
Total	13883.0	1239.0	0.0901	(1363.0/15122.0)

Maximum Metrics: Maximum metrics at their respective thresholds

metric	threshold	value	idx
max f1	0.1313658	0.2564103	108.0
max f2	0.0695485	0.3730273	192.0
max f0point5	0.1546340	0.2381746	71.0
max accuracy	0.3056535	0.9608517	1.0
max precision	0.4472917	1.0	0.0
max recall	0.0042575	1.0	388.0
max specificity	0.4472917	1.0	0.0
max absolute_MCC	0.0950517	0.2473182	155.0
max min_per_class_accuracy	0.0449187	0.7239057	248.0

Gains/Lift Table: Avg response rate:  3,93 %

group	cumulative_data_fraction	lower_threshold	lift	cumulative_lift	response_rate	cumulative_response_rate	capture_rate	cumulative_capture_rate	gain	cumulative_gain
1	0.0100516	0.1787496	8.0393408	8.0393408	0.3157895	0.3157895	0.0808081	0.0808081	703.9340776	703.9340776
2	0.0200370	0.1672940	5.5636497	6.8055806	0.2185430	0.2673267	0.0555556	0.1363636	456.3649742	580.5580558
3	0.0300225	0.1600323	4.2148862	5.9439179	0.1655629	0.2334802	0.0420875	0.1784512	321.4886169	494.3917887
4	0.0400079	0.1539491	6.2380315	6.0173248	0.2450331	0.2363636	0.0622896	0.2407407	523.8031529	501.7324763
5	0.0500595	0.1493541	4.5221292	5.7171006	0.1776316	0.2245707	0.0454545	0.2861953	352.2129187	471.7100552
6	0.1000529	0.1201862	3.3337743	4.5262250	0.1309524	0.1777925	0.1666667	0.4528620	233.3774250	352.6225017
7	0.1500463	0.0770777	2.4582376	3.8371997	0.0965608	0.1507272	0.1228956	0.5757576	145.8237579	283.7199674
8	0.2000397	0.0585241	1.3806540	3.2232663	0.0542328	0.1266116	0.0690236	0.6447811	38.0653983	222.3266272
9	0.3000265	0.0438331	0.8418622	2.4296399	0.0330688	0.0954375	0.0841751	0.7289562	-15.8137816	142.9639871
10	0.4000132	0.0328529	0.6061408	1.9738405	0.0238095	0.0775335	0.0606061	0.7895623	-39.3859227	97.3840460
11	0.5	0.0237782	0.8923739	1.7575758	0.0350529	0.0690385	0.0892256	0.8787879	-10.7626084	75.7575758
12	0.5999868	0.0180759	0.6734897	1.5769147	0.0264550	0.0619420	0.0673401	0.9461279	-32.6510252	57.6914670
13	0.6999735	0.0113525	0.2020469	1.3805235	0.0079365	0.0542277	0.0202020	0.9663300	-79.7953076	38.0523548
14	0.7999603	0.0080068	0.1852097	1.2311217	0.0072751	0.0483591	0.0185185	0.9848485	-81.4790319	23.1121666
15	0.8999471	0.0063237	0.0505117	1.0999524	0.0019841	0.0432067	0.0050505	0.9898990	-94.9488269	9.9952423
16	1.0	0.0001298	0.1009567	1.0	0.0039656	0.0392805	0.0101010	1.0	-89.9043308	0.0

Out[123]:

In [124]:

predictions_rf = model_random_forest.predict(df_test)

drf prediction Progress: [##################################################] 100%

In [71]:

#import Test Dataset
df_test = h2o.import_file("/Users/comalada/Documents/h2o_ML/Kaggle-Santander_Customer_Satisfaction/test.csv")

Parse Progress: [##################################################] 100%

Predict with the model build on the test Dataset¶

In [72]:

predictions = model_gbm.predict(df_test)

gbm prediction Progress: [##################################################] 100%

Construct the Dataset to send to Kaggle¶

In [73]:

import pandas as pd

In [74]:

#Parse in panda dataframe 
df_prediction = predictions["p1"].as_data_frame(use_pandas=True)
df_id = df_test["ID"].as_data_frame(use_pandas=True)

In [75]:

merge = pd.concat([df_id,df_prediction],axis=1)

In [76]:

print(df_prediction.count())
print(df_id.count())

p1    75818
dtype: int64
ID    75818
dtype: int64

In [77]:

merge = merge.rename(columns={"p1": "TARGET"})

In [78]:

merge.to_csv("result.csv",sep=",",index=False)

In [41]:

df_test.summary()

	ID	var3	var15	imp_ent_var16_ult1	imp_op_var39_comer_ult1	imp_op_var39_comer_ult3	imp_op_var40_comer_ult1	imp_op_var40_comer_ult3	imp_op_var40_efect_ult1	imp_op_var40_efect_ult3	imp_op_var40_ult1	imp_op_var41_comer_ult1	imp_op_var41_comer_ult3	imp_op_var41_efect_ult1	imp_op_var41_efect_ult3	imp_op_var41_ult1	imp_op_var39_efect_ult1	imp_op_var39_efect_ult3	imp_op_var39_ult1	imp_sal_var16_ult1	ind_var1_0	ind_var1	ind_var2_0	ind_var2	ind_var5_0	ind_var5	ind_var6_0	ind_var6	ind_var8_0	ind_var8	ind_var12_0	ind_var12	ind_var13_0	ind_var13_corto_0	ind_var13_corto	ind_var13_largo_0	ind_var13_largo	ind_var13_medio_0	ind_var13_medio	ind_var13	ind_var14_0	ind_var14	ind_var17_0	ind_var17	ind_var18_0	ind_var18	ind_var19	ind_var20_0	ind_var20	ind_var24_0	ind_var24	ind_var25_cte	ind_var26_0	ind_var26_cte	ind_var26	ind_var25_0	ind_var25	ind_var27_0	ind_var28_0	ind_var28	ind_var27	ind_var29_0	ind_var29	ind_var30_0	ind_var30	ind_var31_0	ind_var31	ind_var32_cte	ind_var32_0	ind_var32	ind_var33_0	ind_var33	ind_var34_0	ind_var34	ind_var37_cte	ind_var37_0	ind_var37	ind_var39_0	ind_var40_0	ind_var40	ind_var41_0	ind_var41	ind_var39	ind_var44_0	ind_var44	ind_var46_0	ind_var46	num_var1_0	num_var1	num_var4	num_var5_0	num_var5	num_var6_0	num_var6	num_var8_0	num_var8	num_var12_0	num_var12	num_var13_0	num_var13_corto_0	num_var13_corto	num_var13_largo_0	num_var13_largo	num_var13_medio_0	num_var13_medio	num_var13	num_var14_0	num_var14	num_var17_0	num_var17	num_var18_0	num_var18	num_var20_0	num_var20	num_var24_0	num_var24	num_var26_0	num_var26	num_var25_0	num_var25	num_op_var40_hace2	num_op_var40_hace3	num_op_var40_ult1	num_op_var40_ult3	num_op_var41_hace2	num_op_var41_hace3	num_op_var41_ult1	num_op_var41_ult3	num_op_var39_hace2	num_op_var39_hace3	num_op_var39_ult1	num_op_var39_ult3	num_var27_0	num_var28_0	num_var28	num_var27	num_var29_0	num_var29	num_var30_0	num_var30	num_var31_0	num_var31	num_var32_0	num_var32	num_var33_0	num_var33	num_var34_0	num_var34	num_var35	num_var37_med_ult2	num_var37_0	num_var37	num_var39_0	num_var40_0	num_var40	num_var41_0	num_var41	num_var39	num_var42_0	num_var42	num_var44_0	num_var44	num_var46_0	num_var46	saldo_var1	saldo_var5	saldo_var6	saldo_var8	saldo_var12	saldo_var13_corto	saldo_var13_largo	saldo_var13_medio	saldo_var13	saldo_var14	saldo_var17	saldo_var18	saldo_var20	saldo_var24	saldo_var26	saldo_var25	saldo_var28	saldo_var27	saldo_var29	saldo_var30	saldo_var31	saldo_var32	saldo_var33	saldo_var34	saldo_var37	saldo_var40	saldo_var41	saldo_var42	saldo_var44	saldo_var46	var36	delta_imp_amort_var18_1y3	delta_imp_amort_var34_1y3	delta_imp_aport_var13_1y3	delta_imp_aport_var17_1y3	delta_imp_aport_var33_1y3	delta_imp_compra_var44_1y3	delta_imp_reemb_var13_1y3	delta_imp_reemb_var17_1y3	delta_imp_reemb_var33_1y3	delta_imp_trasp_var17_in_1y3	delta_imp_trasp_var17_out_1y3	delta_imp_trasp_var33_in_1y3	delta_imp_trasp_var33_out_1y3	delta_imp_venta_var44_1y3	delta_num_aport_var13_1y3	delta_num_aport_var17_1y3	delta_num_aport_var33_1y3	delta_num_compra_var44_1y3	delta_num_reemb_var13_1y3	delta_num_reemb_var17_1y3	delta_num_reemb_var33_1y3	delta_num_trasp_var17_in_1y3	delta_num_trasp_var17_out_1y3	delta_num_trasp_var33_in_1y3	delta_num_trasp_var33_out_1y3	delta_num_venta_var44_1y3	imp_amort_var18_hace3	imp_amort_var18_ult1	imp_amort_var34_hace3	imp_amort_var34_ult1	imp_aport_var13_hace3	imp_aport_var13_ult1	imp_aport_var17_hace3	imp_aport_var17_ult1	imp_aport_var33_hace3	imp_aport_var33_ult1	imp_var7_emit_ult1	imp_var7_recib_ult1	imp_compra_var44_hace3	imp_compra_var44_ult1	imp_reemb_var13_hace3	imp_reemb_var13_ult1	imp_reemb_var17_hace3	imp_reemb_var17_ult1	imp_reemb_var33_hace3	imp_reemb_var33_ult1	imp_var43_emit_ult1	imp_trans_var37_ult1	imp_trasp_var17_in_hace3	imp_trasp_var17_in_ult1	imp_trasp_var17_out_hace3	imp_trasp_var17_out_ult1	imp_trasp_var33_in_hace3	imp_trasp_var33_in_ult1	imp_trasp_var33_out_hace3	imp_trasp_var33_out_ult1	imp_venta_var44_hace3	imp_venta_var44_ult1	ind_var7_emit_ult1	ind_var7_recib_ult1	ind_var10_ult1	ind_var10cte_ult1	ind_var9_cte_ult1	ind_var9_ult1	ind_var43_emit_ult1	ind_var43_recib_ult1	var21	num_var2_0_ult1	num_var2_ult1	num_aport_var13_hace3	num_aport_var13_ult1	num_aport_var17_hace3	num_aport_var17_ult1	num_aport_var33_hace3	num_aport_var33_ult1	num_var7_emit_ult1	num_var7_recib_ult1	num_compra_var44_hace3	num_compra_var44_ult1	num_ent_var16_ult1	num_var22_hace2	num_var22_hace3	num_var22_ult1	num_var22_ult3	num_med_var22_ult3	num_med_var45_ult3	num_meses_var5_ult3	num_meses_var8_ult3	num_meses_var12_ult3	num_meses_var13_corto_ult3	num_meses_var13_largo_ult3	num_meses_var13_medio_ult3	num_meses_var17_ult3	num_meses_var29_ult3	num_meses_var33_ult3	num_meses_var39_vig_ult3	num_meses_var44_ult3	num_op_var39_comer_ult1	num_op_var39_comer_ult3	num_op_var40_comer_ult1	num_op_var40_comer_ult3	num_op_var40_efect_ult1	num_op_var40_efect_ult3	num_op_var41_comer_ult1	num_op_var41_comer_ult3	num_op_var41_efect_ult1	num_op_var41_efect_ult3	num_op_var39_efect_ult1	num_op_var39_efect_ult3	num_reemb_var13_hace3	num_reemb_var13_ult1	num_reemb_var17_hace3	num_reemb_var17_ult1	num_reemb_var33_hace3	num_reemb_var33_ult1	num_sal_var16_ult1	num_var43_emit_ult1	num_var43_recib_ult1	num_trasp_var11_ult1	num_trasp_var17_in_hace3	num_trasp_var17_in_ult1	num_trasp_var17_out_hace3	num_trasp_var17_out_ult1	num_trasp_var33_in_hace3	num_trasp_var33_in_ult1	num_trasp_var33_out_hace3	num_trasp_var33_out_ult1	num_venta_var44_hace3	num_venta_var44_ult1	num_var45_hace2	num_var45_hace3	num_var45_ult1	num_var45_ult3	saldo_var2_ult1	saldo_medio_var5_hace2	saldo_medio_var5_hace3	saldo_medio_var5_ult1	saldo_medio_var5_ult3	saldo_medio_var8_hace2	saldo_medio_var8_hace3	saldo_medio_var8_ult1	saldo_medio_var8_ult3	saldo_medio_var12_hace2	saldo_medio_var12_hace3	saldo_medio_var12_ult1	saldo_medio_var12_ult3	saldo_medio_var13_corto_hace2	saldo_medio_var13_corto_hace3	saldo_medio_var13_corto_ult1	saldo_medio_var13_corto_ult3	saldo_medio_var13_largo_hace2	saldo_medio_var13_largo_hace3	saldo_medio_var13_largo_ult1	saldo_medio_var13_largo_ult3	saldo_medio_var13_medio_hace2	saldo_medio_var13_medio_hace3	saldo_medio_var13_medio_ult1	saldo_medio_var13_medio_ult3	saldo_medio_var17_hace2	saldo_medio_var17_hace3	saldo_medio_var17_ult1	saldo_medio_var17_ult3	saldo_medio_var29_hace2	saldo_medio_var29_hace3	saldo_medio_var29_ult1	saldo_medio_var29_ult3	saldo_medio_var33_hace2	saldo_medio_var33_hace3	saldo_medio_var33_ult1	saldo_medio_var33_ult3	saldo_medio_var44_hace2	saldo_medio_var44_hace3	saldo_medio_var44_ult1	saldo_medio_var44_ult3	var38
type	int	int	int	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	real	real	real	real	real	real	real	int	real	real	real	int	real	real	real	real	int	int	real	real	real	real	real	int	real	real	int	real	real	int	int	int	int	real	real	real	real	int	int	int	int	int	int	int	real	real	real	real	real	int	int	int	int	int	int	int	real	int	real	int	real	real	real	real	real	real	real	int	real	real	real	int	int	int	real	int	int	real	real	real	real	int	int	real	real	int	real	real	real	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	int	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	real	int	real	real	real	real	real	real	real	int	real	real	real	real	real	real	real	real	real	real	real
mins	2.0	-999999.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1831.74	0.0	-3972.24	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-3972.24	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-3972.24	0.0	0.0	0.0	0.0	0.0	-1.0	-1.0	-1.0	-1.0	0.0	0.0	0.0	-1.0	0.0	-1.0	0.0	-1.0	-1.0	-1.0	-1.0	-1.0	0.0	0.0	0.0	-1.0	0.0	-1.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1573.23	-32.85	-1605.15	-1036.08	-118.02	0.0	-3925.92	-869.31	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.06	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.6	-0.6	0.0	0.0	0.0	0.0	1202.73
mean	75874.8305811	-1579.95501068	33.1388324672	83.1643287874	74.31289364	123.136448205	4.57851723865	7.66685536416	0.470645229365	0.672444274447	3.6358929278	69.7343764014	115.469592841	67.4399821942	110.967136036	137.214619879	67.9106274236	111.639580311	140.850512807	4.91715146799	0.0121079427049	0.003917275581	0.0	0.0	0.95700229497	0.663747395078	9.23263604949e-05	2.63789601414e-05	0.0336859321006	0.0298873618402	0.0686116753278	0.0453586219631	0.0532063626052	0.0434857157931	0.0421535783059	0.0107626157377	0.0105779630167	6.59474003535e-05	6.59474003535e-05	0.051874225118	0.0244137276109	0.00514389722757	0.00160911656862	0.00139808488749	1.31894800707e-05	1.31894800707e-05	0.00374581234008	0.00341607533831	0.00244005381308	0.04291856815	0.038051650004	0.0279748872299	0.0258909493788	0.0292806457569	0.0258909493788	0.0247698435728	0.0247698435728	0.0	0.0	0.0	0.0	9.23263604949e-05	2.63789601414e-05	0.995304545095	0.733968186974	0.00433933894326	0.00385132818064	0.00143765332771	0.00123981112665	0.00123981112665	0.000870505684666	0.0007649898441	2.63789601414e-05	2.63789601414e-05	0.0707219921391	0.0642723363845	0.0642723363845	0.88254767997	0.0120815637448	0.00389089662086	0.880925373922	0.0	0.00389089662086	0.00201799045082	0.00183333772983	0.0	0.0	0.0364425334353	0.0117913951832	1.08165607112	2.88984146245	1.99876018887	0.000276979081485	7.91368804242e-05	0.101097364742	0.0896620855206	0.213590440265	0.137065076895	0.171450051439	0.131525495265	0.126579440238	0.0396871455327	0.0377087235221	0.000237410641273	0.00019784220106	0.164486005962	0.0741116885172	0.0155503970034	0.00997124693345	0.00728059299902	3.95684402121e-05	3.95684402121e-05	0.0102482260149	0.00732016143924	0.129230525733	0.114194518452	0.0936189295418	0.0936189295418	0.0889498535968	0.0889498535968	0.0184388931388	0.000633095043393	0.0494605502651	0.0685325384473	1.62661900868	0.0923923078952	2.8837874911	4.60279880767	1.64505790182	0.0930254029386	2.93324804136	4.67133134612	0.0	0.0	0.0	0.0	0.000276979081485	7.91368804242e-05	3.37625629798	2.39005249413	0.0189532828616	0.0153129863621	0.00466907594503	0.00466907594503	0.00284892769527	0.00245324329315	7.91368804242e-05	7.91368804242e-05	3.30459785275	0.25937112559	0.408702418951	0.408702418951	2.73180511224	0.0363238281147	0.0116726898626	2.70410720409	0.0	0.0116726898626	3.20480624654	2.22026431718	0.00613310823287	0.0055791500699	0.0	0.0	8.77917328339	989.100886333	0.297780210504	136.03353755	6390.10689454	5060.71732029	1483.26098961	1.08813210583	6545.06644201	58.1429446833	74.9935214593	3.16547521697	9.48108298821	6322.48286687	78.6395063178	74.8591137988	0.0	0.0	0.297780210504	14060.6055406	202.572765966	3.78039251893	19.6073737107	0.833311350867	37.6856001213	4.78038671556	0.0	7515.53909863	107.971870796	0.0	40.59665251	131894.800694	263789.601388	49724339.8397	4880107.62548	1318948.00673	10287794.4539	3561159.61873	1450842.80763	0.0	1055158.40547	0.0	1582737.60823	131894.800694	4088738.82154	49724339.8399	4880107.62542	1318948.00679	10287794.454	3561159.61873	1450842.80763	0.0	1055158.40547	0.0	1582737.60823	131894.800694	4088738.82155	0.0	0.0352479622253	0.0	0.0216581814345	2799.47703593	559.224566066	22.1657701337	23.7170919834	2.3450608035	0.583176290591	10.9011052784	155.859704556	49.1217094885	50.3812105305	0.0	37.0297290881	0.0	7.28074612889	0.0	0.0	992.796975916	1964.82267483	9.37580482207	7.44915138885	0.0	0.0	3.80586351526	1.7475876441	0.0	0.0741951779261	7.84888680788	31.8700246643	2.63789601414e-05	0.00247962225329	0.0812471972355	0.0918119707721	0.0969558679997	0.0865493682239	0.0680049592445	0.127647788124	32.5885673587	0.0	0.0	0.0747447835606	0.0178849349759	0.0016223060487	0.00344245429845	0.000989211005302	0.000870505684666	0.000158273760848	0.00846764620539	0.00300720145612	0.00767627740114	0.185575984595	1.29970455565	1.18665752196	0.557677596349	3.04403967396	0.640256931072	3.97555989343	1.98290643383	0.0553034899364	0.101163312142	0.0989870479306	0.0189664723417	0.000131894800707	0.00278298029492	7.91368804242e-05	0.00167506396898	1.597945079	0.0036007280593	2.21389379831	3.64330370097	0.0712231923818	0.133187369754	0.00383813870057	0.00498562346672	2.14267060592	3.51011633121	0.726793109816	1.22120077027	0.730631248516	1.22618639373	0.0	0.00106834788573	0.0	0.00102877944551	0.0	0.0	0.00447123374397	0.391490147458	0.817286132581	0.123651375663	0.000316547521697	0.000395684402121	0.0	0.0	0.000276979081485	0.000514389722757	0.0	3.95684402121e-05	0.000593526603181	0.00300720145612	5.41414967422	3.7761745232	4.29962541877	13.4899496162	0.0	1629.27607296	902.370806405	1056.86767773	1060.01450434	60.7643368329	9.36178137118	118.186621515	104.8057977	4359.74154185	704.8761583	6059.6468307	4745.3358287	3581.18466617	501.124805983	4909.47387494	3869.98682411	816.713431243	176.592697908	986.916914189	771.302025508	0.199158247382	0.0	1.21814212984	0.70865018861	36.3235768551	6.20474649819	57.5280677412	47.6885465193	0.138711124007	0.0	0.195139676594	0.160594845551	12.4385587855	1.32740760769	17.4699910312	12.6743494948	63.5978391675	11.4045048669	95.9730254029	70.5043192909	117386.348875
maxs	151837.0	238.0	105.0	240000.0	21093.96	47943.96	21093.96	47943.96	6600.0	6600.0	23799.96	14784.9	28927.89	67500.0	67950.0	72511.77	67500.0	67950.0	72511.77	66000.0	1.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	1.0	1.0	0.0	0.0	6.0	6.0	9.0	12.0	12.0	3.0	3.0	6.0	3.0	42.0	6.0	21.0	6.0	6.0	21.0	21.0	6.0	3.0	21.0	42.0	6.0	66.0	18.0	3.0	3.0	3.0	3.0	6.0	6.0	18.0	18.0	18.0	18.0	78.0	15.0	99.0	144.0	231.0	144.0	333.0	489.0	231.0	144.0	333.0	522.0	0.0	0.0	0.0	0.0	3.0	3.0	45.0	24.0	66.0	18.0	12.0	12.0	6.0	6.0	3.0	3.0	42.0	117.0	84.0	84.0	39.0	6.0	3.0	39.0	0.0	3.0	45.0	15.0	6.0	6.0	0.0	0.0	241299.27	600000.0	12627.0	375060.0	4202599.17	450000.0	1008000.0	36000.0	1008000.0	373312.11	557236.17	240000.0	156316.44	4202599.17	58264.68	58264.68	0.0	0.0	12627.0	4212656.07	557236.17	13522.89	162964.56	54000.0	90000.0	9966.0	0.0	4212656.07	498089.79	0.0	99.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	0.0	9999999999.0	0.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	9999999999.0	0.0	9999999999.0	0.0	9999999999.0	9999999999.0	9999999999.0	0.0	2672.43	0.0	994.47	1008000.0	450000.0	555824.4	339075.0	36497.67	24000.0	526500.0	850460.19	596253.0	458175.42	0.0	366060.0	0.0	211775.58	0.0	0.0	2880000.0	3000000.0	555824.4	199665.84	0.0	0.0	149252.1	35310.6	0.0	5625.33	438202.5	463917.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	18000.0	0.0	0.0	18.0	12.0	39.0	27.0	9.0	12.0	9.0	12.0	45.0	51.0	84.0	78.0	75.0	51.0	129.0	42.0	273.0	3.0	3.0	3.0	3.0	3.0	2.0	3.0	2.0	3.0	3.0	3.0	321.0	450.0	120.0	249.0	33.0	33.0	321.0	429.0	75.0	156.0	75.0	156.0	0.0	3.0	0.0	12.0	0.0	0.0	9.0	60.0	282.0	57.0	6.0	6.0	0.0	0.0	3.0	6.0	0.0	3.0	24.0	45.0	426.0	201.0	402.0	825.0	0.0	678060.0	405001.5	656187.09	482422.35	264702.87	95260.56	375060.0	216012.03	4500000.0	1202339.22	4356643.41	3002214.48	450000.0	283333.32	450000.0	450000.0	1008000.0	420000.03	1008000.0	740000.01	8129.04	0.0	36000.0	18600.0	556062.21	248772.75	556872.24	453902.4	6899.97	0.0	9160.32	8030.16	146605.32	40080.6	162355.8	138054.96	453893.4	217762.23	496198.08	354260.73	28894395.51
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