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preinoso/Port Out Semanal - Cloud.ipynb

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Port Out Semanal - Cloud.ipynb
{
"nbformat_minor": 1,
"cells": [
{
"source": "### Librerias ",
"cell_type": "markdown",
"metadata": {
"collapsed": true
}
},
{
"execution_count": 2,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "Updating HTML index of packages in '.Library'\nMaking 'packages.html' ... done\nUpdating HTML index of packages in '.Library'\nMaking 'packages.html' ... done\nType 'citation(\"pROC\")' for a citation.\n\nAttaching package: \u2018pROC\u2019\n\nThe following objects are masked from \u2018package:stats\u2019:\n\n cov, smooth, var\n\nLoaded gbm 2.1.5\n"
}
],
"source": "install.packages(\"pROC\")\ninstall.packages(\"gbm\")\nlibrary(pROC)\nlibrary(gbm)"
},
{
"execution_count": 3,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "###################### LIBRERIA DE EXTRACCI\u00d3N DE LA TABLA FINAL \n# The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.\nlibrary(projectLib)\nproject <- projectLib::Project$new(projectId=\"fd0c30d9-25f1-4e3f-810c-416cd2079989\", projectToken=\"p-aaf4750349e501d6684581c9b05dadc1813abc2a\")\npc <- project$project_context\n\n###################### FUNCI\u00d3N PARA EXTRACCI\u00d3N \n\nsave_dataframe_to_os = function (dataframe, filename, project) {\n tc <- textConnection(\"conn\", \"w\")\n write.csv(dataframe, tc, row.names=FALSE)\n text.form <- paste(textConnectionValue(tc), collapse = '\\n')\n project$save_data(filename, text.form, overwrite=TRUE)\n close(tc)\n}"
},
{
"source": "### Bases de Datos",
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": 4,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "credentials_1 <- list(endpoint = \"https://s3-api.us-geo.objectstorage.service.networklayer.com\",\napi_key = \"7VbMjlANwy05kvJO-rBWtl_0IxGW__Gx5jWXe1tVcfmt\",secret_key = \"ae83abf130a38301a8509caa1879f766f0478e8104c8b14d\",\niam_service_id = \"iam-ServiceId-445912c6-3e16-434b-ba90-04085dd26ce8\",access_key = \"5087361a91ad44b5b70df89baa658e1a\",\nbucket_name = \"churnwom-donotdelete-pr-kz9daakmsumeio\",file_name = \"PORT_OUT_20190408.csv\",iam_service_endpoint = \"https://iam.bluemix.net/oidc/token\")"
},
{
"execution_count": 5,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": "<table>\n<thead><tr><th scope=col>SEMANA</th><th scope=col>NUMERO_ABONADO</th><th scope=col>RUT</th><th scope=col>ANTIGUEDAD_CONTRATO</th><th scope=col>CARGO_FIJO_LINEA</th><th scope=col>FLAG_MANDATO</th><th scope=col>TOTAL_MB</th><th scope=col>TASA_ROAMING</th><th scope=col>THR_PROMEDIO</th><th scope=col>LATENCIA_PROMEDIO</th><th scope=col>\u22ef</th><th scope=col>FLAG_SS_PLAN_EQUIPO</th><th scope=col>FLAG_SS_RED</th><th scope=col>FLAG_LLAMADO_COMPETENCIA</th><th scope=col>FLAG_NPG</th><th scope=col>FLAG_INGRESO_COMP</th><th scope=col>TASA_LOST_PACK</th><th scope=col>TASA_STR</th><th scope=col>Q_CAMBIO_EQUIPO</th><th scope=col>EDAD</th><th scope=col>FLAG_PORT_OUT</th></tr></thead>\n<tbody>\n\t<tr><td>20181203 </td><td>56968518499</td><td>128009205 </td><td>18 </td><td>14989 </td><td>0 </td><td> 781.40525 </td><td>0.115868 </td><td> 0.751400 </td><td> 261.99260 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.000085 </td><td>0.507176 </td><td>2 </td><td>42 </td><td>1 </td></tr>\n\t<tr><td>20181203 </td><td>56982229487</td><td>14349957K </td><td>10 </td><td>15990 </td><td>0 </td><td>8479.52778 </td><td>0.048890 </td><td>15.864631 </td><td> 38.85989 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.349282 </td><td>0.799341 </td><td>0 </td><td>38 </td><td>1 </td></tr>\n\t<tr><td>20181203 </td><td>56990008364</td><td>108203188 </td><td>12 </td><td>12990 </td><td>0 </td><td> 600.80394 </td><td>0.001841 </td><td> 0.651990 </td><td>1462.74241 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.763636 </td><td>0.347816 </td><td>1 </td><td>47 </td><td>1 </td></tr>\n\t<tr><td>20181203 </td><td>56948975327</td><td>197930462 </td><td> 4 </td><td> 9990 </td><td>0 </td><td>2373.29165 </td><td>0.013619 </td><td> 3.632975 </td><td> 109.94392 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.472282 </td><td>0.857670 </td><td>0 </td><td>23 </td><td>1 </td></tr>\n\t<tr><td>20181203 </td><td>56957494857</td><td>078110732 </td><td>14 </td><td> 9990 </td><td>0 </td><td> 65.28698 </td><td>0.289044 </td><td> 0.002011 </td><td> 54.20869 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>1 </td><td>1 </td><td>0 </td><td>0.787713 </td><td>0.000878 </td><td>0 </td><td>58 </td><td>1 </td></tr>\n\t<tr><td>20181203 </td><td>56978998072</td><td>165005546 </td><td>27 </td><td> 9990 </td><td>0 </td><td> 632.19657 </td><td>0.086881 </td><td> 0.005029 </td><td> 176.58665 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>1 </td><td>1 </td><td>0 </td><td>0.617535 </td><td>0.395607 </td><td>2 </td><td>31 </td><td>1 </td></tr>\n</tbody>\n</table>\n",
"text/latex": "\\begin{tabular}{r|llllllllllllllllllllllll}\n SEMANA & NUMERO\\_ABONADO & RUT & ANTIGUEDAD\\_CONTRATO & CARGO\\_FIJO\\_LINEA & FLAG\\_MANDATO & TOTAL\\_MB & TASA\\_ROAMING & THR\\_PROMEDIO & LATENCIA\\_PROMEDIO & \u22ef & FLAG\\_SS\\_PLAN\\_EQUIPO & FLAG\\_SS\\_RED & FLAG\\_LLAMADO\\_COMPETENCIA & FLAG\\_NPG & FLAG\\_INGRESO\\_COMP & TASA\\_LOST\\_PACK & TASA\\_STR & Q\\_CAMBIO\\_EQUIPO & EDAD & FLAG\\_PORT\\_OUT\\\\\n\\hline\n\t 20181203 & 56968518499 & 128009205 & 18 & 14989 & 0 & 781.40525 & 0.115868 & 0.751400 & 261.99260 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.000085 & 0.507176 & 2 & 42 & 1 \\\\\n\t 20181203 & 56982229487 & 14349957K & 10 & 15990 & 0 & 8479.52778 & 0.048890 & 15.864631 & 38.85989 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.349282 & 0.799341 & 0 & 38 & 1 \\\\\n\t 20181203 & 56990008364 & 108203188 & 12 & 12990 & 0 & 600.80394 & 0.001841 & 0.651990 & 1462.74241 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.763636 & 0.347816 & 1 & 47 & 1 \\\\\n\t 20181203 & 56948975327 & 197930462 & 4 & 9990 & 0 & 2373.29165 & 0.013619 & 3.632975 & 109.94392 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.472282 & 0.857670 & 0 & 23 & 1 \\\\\n\t 20181203 & 56957494857 & 078110732 & 14 & 9990 & 0 & 65.28698 & 0.289044 & 0.002011 & 54.20869 & \u22ef & 0 & 0 & 1 & 1 & 0 & 0.787713 & 0.000878 & 0 & 58 & 1 \\\\\n\t 20181203 & 56978998072 & 165005546 & 27 & 9990 & 0 & 632.19657 & 0.086881 & 0.005029 & 176.58665 & \u22ef & 0 & 0 & 1 & 1 & 0 & 0.617535 & 0.395607 & 2 & 31 & 1 \\\\\n\\end{tabular}\n",
"text/markdown": "\nSEMANA | NUMERO_ABONADO | RUT | ANTIGUEDAD_CONTRATO | CARGO_FIJO_LINEA | FLAG_MANDATO | TOTAL_MB | TASA_ROAMING | THR_PROMEDIO | LATENCIA_PROMEDIO | \u22ef | FLAG_SS_PLAN_EQUIPO | FLAG_SS_RED | FLAG_LLAMADO_COMPETENCIA | FLAG_NPG | FLAG_INGRESO_COMP | TASA_LOST_PACK | TASA_STR | Q_CAMBIO_EQUIPO | EDAD | FLAG_PORT_OUT | \n|---|---|---|---|---|---|\n| 20181203 | 56968518499 | 128009205 | 18 | 14989 | 0 | 781.40525 | 0.115868 | 0.751400 | 261.99260 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.000085 | 0.507176 | 2 | 42 | 1 | \n| 20181203 | 56982229487 | 14349957K | 10 | 15990 | 0 | 8479.52778 | 0.048890 | 15.864631 | 38.85989 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.349282 | 0.799341 | 0 | 38 | 1 | \n| 20181203 | 56990008364 | 108203188 | 12 | 12990 | 0 | 600.80394 | 0.001841 | 0.651990 | 1462.74241 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.763636 | 0.347816 | 1 | 47 | 1 | \n| 20181203 | 56948975327 | 197930462 | 4 | 9990 | 0 | 2373.29165 | 0.013619 | 3.632975 | 109.94392 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.472282 | 0.857670 | 0 | 23 | 1 | \n| 20181203 | 56957494857 | 078110732 | 14 | 9990 | 0 | 65.28698 | 0.289044 | 0.002011 | 54.20869 | \u22ef | 0 | 0 | 1 | 1 | 0 | 0.787713 | 0.000878 | 0 | 58 | 1 | \n| 20181203 | 56978998072 | 165005546 | 27 | 9990 | 0 | 632.19657 | 0.086881 | 0.005029 | 176.58665 | \u22ef | 0 | 0 | 1 | 1 | 0 | 0.617535 | 0.395607 | 2 | 31 | 1 | \n\n\n",
"text/plain": " SEMANA NUMERO_ABONADO RUT ANTIGUEDAD_CONTRATO CARGO_FIJO_LINEA\n1 20181203 56968518499 128009205 18 14989 \n2 20181203 56982229487 14349957K 10 15990 \n3 20181203 56990008364 108203188 12 12990 \n4 20181203 56948975327 197930462 4 9990 \n5 20181203 56957494857 078110732 14 9990 \n6 20181203 56978998072 165005546 27 9990 \n FLAG_MANDATO TOTAL_MB TASA_ROAMING THR_PROMEDIO LATENCIA_PROMEDIO \u22ef\n1 0 781.40525 0.115868 0.751400 261.99260 \u22ef\n2 0 8479.52778 0.048890 15.864631 38.85989 \u22ef\n3 0 600.80394 0.001841 0.651990 1462.74241 \u22ef\n4 0 2373.29165 0.013619 3.632975 109.94392 \u22ef\n5 0 65.28698 0.289044 0.002011 54.20869 \u22ef\n6 0 632.19657 0.086881 0.005029 176.58665 \u22ef\n FLAG_SS_PLAN_EQUIPO FLAG_SS_RED FLAG_LLAMADO_COMPETENCIA FLAG_NPG\n1 0 0 0 0 \n2 0 0 0 0 \n3 0 0 0 0 \n4 0 0 0 0 \n5 0 0 1 1 \n6 0 0 1 1 \n FLAG_INGRESO_COMP TASA_LOST_PACK TASA_STR Q_CAMBIO_EQUIPO EDAD FLAG_PORT_OUT\n1 0 0.000085 0.507176 2 42 1 \n2 0 0.349282 0.799341 0 38 1 \n3 0 0.763636 0.347816 1 47 1 \n4 0 0.472282 0.857670 0 23 1 \n5 0 0.787713 0.000878 0 58 1 \n6 0 0.617535 0.395607 2 31 1 "
},
"metadata": {}
}
],
"source": "library(\"aws.s3\")\nSys.setenv(\"AWS_ACCESS_KEY_ID\" = \"5087361a91ad44b5b70df89baa658e1a\", \"AWS_SECRET_ACCESS_KEY\" = \"ae83abf130a38301a8509caa1879f766f0478e8104c8b14d\")\nurl <- \"s3-api.us-geo.objectstorage.service.networklayer.com\"\nbucket <- \"churnwom-donotdelete-pr-kz9daakmsumeio\"\nheaders <- list(`x-amz-content-sha256`=\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\")\n\nobj <- s3HTTP(verb = \"GET\",bucket = bucket,headers = headers,path = \"ENTRENAMIENTO_SEMANAL_3.csv\",key = Sys.getenv(\"AWS_ACCESS_KEY_ID\"),\nsecret = Sys.getenv(\"AWS_SECRET_ACCESS_KEY\"),check_region = FALSE,base_url = url)\n \nentrenamiento <- read.csv(text = rawToChar(obj$content),sep=';')\nhead(entrenamiento)"
},
{
"source": "### Modelado",
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": 6,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "indexes = sample(1:nrow(entrenamiento), size=0.3*nrow(entrenamiento))\n\nTRAIN = entrenamiento[-indexes,]\nTEST = entrenamiento[indexes,]"
},
{
"execution_count": 7,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "fit2 = gbm(FLAG_PORT_OUT ~\n ANTIGUEDAD_CONTRATO\n+CARGO_FIJO_LINEA\n+as.factor(FLAG_MANDATO) \n+TOTAL_MB \n+TASA_ROAMING \n+THR_PROMEDIO \n+LATENCIA_PROMEDIO \n+Q_CELDAS \n+Q_LLAMADAS\n+MOU \n+as.factor(FLAG_SS_FACT_SUSP) \n+as.factor(FLAG_SS_PLAN_EQUIPO) \n+as.factor(FLAG_SS_RED) \n#+as.factor(FLAG_LLAMADO_COMPETENCIA) \n#+as.factor(FLAG_NPG) \n+as.factor(FLAG_INGRESO_COMP )\n+TASA_LOST_PACK\n+TASA_STR \n+Q_CAMBIO_EQUIPO \n+EDAD \n ,data = TRAIN,distribution = \"bernoulli\",n.trees = 1000,\n shrinkage = 0.01, interaction.depth = 4)"
},
{
"source": "## Base de Testeo",
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": 8,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": "<table>\n<thead><tr><th scope=col>SEMANA</th><th scope=col>NUMERO_ABONADO</th><th scope=col>RUT</th><th scope=col>ANTIGUEDAD_CONTRATO</th><th scope=col>CARGO_FIJO_LINEA</th><th scope=col>FLAG_MANDATO</th><th scope=col>TOTAL_MB</th><th scope=col>TASA_ROAMING</th><th scope=col>THR_PROMEDIO</th><th scope=col>LATENCIA_PROMEDIO</th><th scope=col>\u22ef</th><th scope=col>FLAG_SS_PLAN_EQUIPO</th><th scope=col>FLAG_SS_RED</th><th scope=col>FLAG_LLAMADO_COMPETENCIA</th><th scope=col>FLAG_NPG</th><th scope=col>FLAG_INGRESO_COMP</th><th scope=col>TASA_LOST_PACK</th><th scope=col>TASA_STR</th><th scope=col>Q_CAMBIO_EQUIPO</th><th scope=col>EDAD</th><th scope=col>FLAG_PORT_OUT</th></tr></thead>\n<tbody>\n\t<tr><td>20190429 </td><td>56933112181</td><td>185100359 </td><td>16 </td><td>19990 </td><td>0 </td><td>4266.8182 </td><td>0.000299 </td><td>1.779807 </td><td>1430.20397 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.057822 </td><td>0.442107 </td><td>1 </td><td>24 </td><td>0 </td></tr>\n\t<tr><td>20190429 </td><td>56932048954</td><td>112899545 </td><td>10 </td><td>16990 </td><td>1 </td><td> 189.2470 </td><td>0.081541 </td><td>1.649099 </td><td> 113.41967 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.176404 </td><td>0.308590 </td><td>0 </td><td>51 </td><td>0 </td></tr>\n\t<tr><td>20190429 </td><td>56967505722</td><td>173058837 </td><td>25 </td><td> 9990 </td><td>0 </td><td>3004.3261 </td><td>0.008743 </td><td>2.386512 </td><td> 156.08037 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.848048 </td><td>0.712460 </td><td>8 </td><td>28 </td><td>0 </td></tr>\n\t<tr><td>20190429 </td><td>56971863741</td><td>086403560 </td><td>31 </td><td> 9990 </td><td>0 </td><td> 979.7917 </td><td>0.107319 </td><td>0.151220 </td><td> 457.48336 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.000459 </td><td>0.481094 </td><td>0 </td><td>58 </td><td>0 </td></tr>\n\t<tr><td>20190429 </td><td>56990827049</td><td>094724732 </td><td>16 </td><td> 9990 </td><td>0 </td><td>8718.9881 </td><td>0.000202 </td><td>2.308156 </td><td> 109.08010 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>1 </td><td>1 </td><td>0 </td><td>0.625338 </td><td>0.650853 </td><td>1 </td><td>54 </td><td>1 </td></tr>\n\t<tr><td>20190429 </td><td>56991254859</td><td>060099472 </td><td>22 </td><td>11990 </td><td>0 </td><td> 771.7368 </td><td>0.000446 </td><td>0.000003 </td><td> 65.33712 </td><td>\u22ef </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0.609855 </td><td>0.104636 </td><td>1 </td><td>65 </td><td>0 </td></tr>\n</tbody>\n</table>\n",
"text/latex": "\\begin{tabular}{r|llllllllllllllllllllllll}\n SEMANA & NUMERO\\_ABONADO & RUT & ANTIGUEDAD\\_CONTRATO & CARGO\\_FIJO\\_LINEA & FLAG\\_MANDATO & TOTAL\\_MB & TASA\\_ROAMING & THR\\_PROMEDIO & LATENCIA\\_PROMEDIO & \u22ef & FLAG\\_SS\\_PLAN\\_EQUIPO & FLAG\\_SS\\_RED & FLAG\\_LLAMADO\\_COMPETENCIA & FLAG\\_NPG & FLAG\\_INGRESO\\_COMP & TASA\\_LOST\\_PACK & TASA\\_STR & Q\\_CAMBIO\\_EQUIPO & EDAD & FLAG\\_PORT\\_OUT\\\\\n\\hline\n\t 20190429 & 56933112181 & 185100359 & 16 & 19990 & 0 & 4266.8182 & 0.000299 & 1.779807 & 1430.20397 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.057822 & 0.442107 & 1 & 24 & 0 \\\\\n\t 20190429 & 56932048954 & 112899545 & 10 & 16990 & 1 & 189.2470 & 0.081541 & 1.649099 & 113.41967 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.176404 & 0.308590 & 0 & 51 & 0 \\\\\n\t 20190429 & 56967505722 & 173058837 & 25 & 9990 & 0 & 3004.3261 & 0.008743 & 2.386512 & 156.08037 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.848048 & 0.712460 & 8 & 28 & 0 \\\\\n\t 20190429 & 56971863741 & 086403560 & 31 & 9990 & 0 & 979.7917 & 0.107319 & 0.151220 & 457.48336 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.000459 & 0.481094 & 0 & 58 & 0 \\\\\n\t 20190429 & 56990827049 & 094724732 & 16 & 9990 & 0 & 8718.9881 & 0.000202 & 2.308156 & 109.08010 & \u22ef & 0 & 0 & 1 & 1 & 0 & 0.625338 & 0.650853 & 1 & 54 & 1 \\\\\n\t 20190429 & 56991254859 & 060099472 & 22 & 11990 & 0 & 771.7368 & 0.000446 & 0.000003 & 65.33712 & \u22ef & 0 & 0 & 0 & 0 & 0 & 0.609855 & 0.104636 & 1 & 65 & 0 \\\\\n\\end{tabular}\n",
"text/markdown": "\nSEMANA | NUMERO_ABONADO | RUT | ANTIGUEDAD_CONTRATO | CARGO_FIJO_LINEA | FLAG_MANDATO | TOTAL_MB | TASA_ROAMING | THR_PROMEDIO | LATENCIA_PROMEDIO | \u22ef | FLAG_SS_PLAN_EQUIPO | FLAG_SS_RED | FLAG_LLAMADO_COMPETENCIA | FLAG_NPG | FLAG_INGRESO_COMP | TASA_LOST_PACK | TASA_STR | Q_CAMBIO_EQUIPO | EDAD | FLAG_PORT_OUT | \n|---|---|---|---|---|---|\n| 20190429 | 56933112181 | 185100359 | 16 | 19990 | 0 | 4266.8182 | 0.000299 | 1.779807 | 1430.20397 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.057822 | 0.442107 | 1 | 24 | 0 | \n| 20190429 | 56932048954 | 112899545 | 10 | 16990 | 1 | 189.2470 | 0.081541 | 1.649099 | 113.41967 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.176404 | 0.308590 | 0 | 51 | 0 | \n| 20190429 | 56967505722 | 173058837 | 25 | 9990 | 0 | 3004.3261 | 0.008743 | 2.386512 | 156.08037 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.848048 | 0.712460 | 8 | 28 | 0 | \n| 20190429 | 56971863741 | 086403560 | 31 | 9990 | 0 | 979.7917 | 0.107319 | 0.151220 | 457.48336 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.000459 | 0.481094 | 0 | 58 | 0 | \n| 20190429 | 56990827049 | 094724732 | 16 | 9990 | 0 | 8718.9881 | 0.000202 | 2.308156 | 109.08010 | \u22ef | 0 | 0 | 1 | 1 | 0 | 0.625338 | 0.650853 | 1 | 54 | 1 | \n| 20190429 | 56991254859 | 060099472 | 22 | 11990 | 0 | 771.7368 | 0.000446 | 0.000003 | 65.33712 | \u22ef | 0 | 0 | 0 | 0 | 0 | 0.609855 | 0.104636 | 1 | 65 | 0 | \n\n\n",
"text/plain": " SEMANA NUMERO_ABONADO RUT ANTIGUEDAD_CONTRATO CARGO_FIJO_LINEA\n1 20190429 56933112181 185100359 16 19990 \n2 20190429 56932048954 112899545 10 16990 \n3 20190429 56967505722 173058837 25 9990 \n4 20190429 56971863741 086403560 31 9990 \n5 20190429 56990827049 094724732 16 9990 \n6 20190429 56991254859 060099472 22 11990 \n FLAG_MANDATO TOTAL_MB TASA_ROAMING THR_PROMEDIO LATENCIA_PROMEDIO \u22ef\n1 0 4266.8182 0.000299 1.779807 1430.20397 \u22ef\n2 1 189.2470 0.081541 1.649099 113.41967 \u22ef\n3 0 3004.3261 0.008743 2.386512 156.08037 \u22ef\n4 0 979.7917 0.107319 0.151220 457.48336 \u22ef\n5 0 8718.9881 0.000202 2.308156 109.08010 \u22ef\n6 0 771.7368 0.000446 0.000003 65.33712 \u22ef\n FLAG_SS_PLAN_EQUIPO FLAG_SS_RED FLAG_LLAMADO_COMPETENCIA FLAG_NPG\n1 0 0 0 0 \n2 0 0 0 0 \n3 0 0 0 0 \n4 0 0 0 0 \n5 0 0 1 1 \n6 0 0 0 0 \n FLAG_INGRESO_COMP TASA_LOST_PACK TASA_STR Q_CAMBIO_EQUIPO EDAD FLAG_PORT_OUT\n1 0 0.057822 0.442107 1 24 0 \n2 0 0.176404 0.308590 0 51 0 \n3 0 0.848048 0.712460 8 28 0 \n4 0 0.000459 0.481094 0 58 0 \n5 0 0.625338 0.650853 1 54 1 \n6 0 0.609855 0.104636 1 65 0 "
},
"metadata": {}
}
],
"source": "obj <- s3HTTP(verb = \"GET\",bucket = bucket,headers = headers,path = \"TESTEO_SEMANAL_3.csv\",key = Sys.getenv(\"AWS_ACCESS_KEY_ID\"),\nsecret = Sys.getenv(\"AWS_SECRET_ACCESS_KEY\"),check_region = FALSE,base_url = url)\n\nTest_mes <- read.csv(text = rawToChar(obj$content),sep=';')\nhead(Test_mes)"
},
{
"execution_count": 9,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "A =cbind(Test_mes$NUMERO_ABONADO,Test_mes$FLAG_PORT_OUT,Test_mes$FLAG_LLAMADO_COMPETENCIA,Test_mes$FLAG_NPG,predict(fit2,Test_mes,n.trees = 1000))\ncolnames(A)= c(\"NUMERO_ABONADO\",\"IND_CHURN\",\"LLAMADO_CALL_CENTER\",\"NPG\",\"SCORE\")\nA1 = as.data.frame(A)\n\nQ=quantile(A1$SCORE,seq(0,1,0.1))\naux_decil <- sapply(1:nrow(A1), function (x){\n for(i_q in 1:(length(Q)-1) )\n if (A1$SCORE[x] > Q[i_q] & A1$SCORE[x] <= Q[i_q+1] ) {\n aux <- i_q-1 \n break\n } else {aux <- 0}\n return(aux)},simplify=T)\n\nA2 = cbind(A1,aux_decil)\ncolnames(A2) = c(colnames(A1),\"DECILE\")\nA3=as.data.frame(A2)"
},
{
"execution_count": 21,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": "<ol class=list-inline>\n\t<li>1</li>\n\t<li>275502</li>\n\t<li>551004</li>\n\t<li>826506</li>\n\t<li>1102008</li>\n\t<li>1377510</li>\n</ol>\n",
"text/latex": "\\begin{enumerate*}\n\\item 1\n\\item 275502\n\\item 551004\n\\item 826506\n\\item 1102008\n\\item 1377510\n\\end{enumerate*}\n",
"text/markdown": "1. 1\n2. 275502\n3. 551004\n4. 826506\n5. 1102008\n6. 1377510\n\n\n",
"text/plain": "[1] 1 275502 551004 826506 1102008 1377510"
},
"metadata": {}
}
],
"source": "CORTE = c(1,round(nrow(A3)*(1/5)),round(nrow(A3)*(2/5)),round(nrow(A3)*(3/5)),round(nrow(A3)*(4/5)),round(nrow(A3)*(5/5)))\nCORTE"
},
{
"execution_count": 26,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Time difference of 12.87291 mins"
},
"metadata": {}
}
],
"source": "t1 = Sys.time()\nsave_dataframe_to_os(dataframe=A3[CORTE[1]:CORTE[2],], filename=\"SCORE_SEMANA_1.csv\", project)\nSys.time()-t1"
},
{
"execution_count": 27,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Time difference of 12.28115 mins"
},
"metadata": {}
}
],
"source": "t1 = Sys.time()\nsave_dataframe_to_os(dataframe=A3[(CORTE[2]+1):CORTE[3],],filename=\"SCORE_SEMANA_2.csv\", project)\nSys.time()-t1"
},
{
"execution_count": 28,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Time difference of 12.89193 mins"
},
"metadata": {}
}
],
"source": "t1 = Sys.time()\nsave_dataframe_to_os(dataframe=A3[(CORTE[3]+1):CORTE[4],],filename=\"SCORE_SEMANA_3.csv\", project)\nSys.time()-t1"
},
{
"execution_count": 29,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Time difference of 12.63415 mins"
},
"metadata": {}
}
],
"source": "t1 = Sys.time()\nsave_dataframe_to_os(dataframe=A3[(CORTE[4]+1):CORTE[5],],filename=\"SCORE_SEMANA_4.csv\", project)\nSys.time()-t1"
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "t1 = Sys.time()\nsave_dataframe_to_os(dataframe=A3[(CORTE[5]+1):CORTE[6],],filename=\"SCORE_SEMANA_5.csv\", project)\nSys.time()-t1"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"name": "python3",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"version": "3.6.8",
"name": "python",
"file_extension": ".py",
"pygments_lexer": "ipython3",
"codemirror_mode": {
"version": 3,
"name": "ipython"
}
}
},
"nbformat": 4
}
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