vignettes/googleCloudAutoMLTablesR.Rmd
googleCloudAutoMLTablesR.RmdCreate a Google Cloud Storage Bucket and save the name for use in an upcoming step.
Run the following chunk to install the required R packages to complete this tutorial (checking to see if they are installed first and only install if not already):
list.of.packages <- c("remotes", "googleAuthR")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
remotes::install_github("justinjm/googleCloudAutoMLTablesR")Create a file called .Renviron in your project’s working directory and use the following environemtn arguments:
GAR_SERVICE_JSON - path to service account (JSON) keyfile downloaded before and copiedGCAT_DEFAULT_PROJECT_ID - string of your GCP project you configured beforeGCAT_DEFAULT_REGION - region of GCP resorces that can be one of: "us-central1" or "eu"
e.g. your .Renviron should look like:
# .Renviron
GAR_SERVICE_JSON="/Users/me/auth/auth.json"
GCAT_DEFAULT_PROJECT_ID="my-project"
GCAT_DEFAULT_REGION="us-central1"Save the .Renviron file and restart your R session to load the environment variables
Load the 2 R packages we need and then authenticate using the Service account we just created:
library(googleAuthR)
library(googleCloudAutoMLTablesR)
options(googleAuthR.scopes.selected = "https://www.googleapis.com/auth/cloud-platform")
gar_auth_service(json_file = Sys.getenv("GAR_SERVICE_JSON"))To make things easier later on, we set some global arguements for our GCP project and the region of the GCP resources within our project:
projectId <- Sys.getenv("GCAT_DEFAULT_PROJECT_ID")
location <- "us-central1"
gcat_region_set("us-central1")## 2021-04-21 00:46:43> Region set to 'us-central1'
gcat_project_set(projectId)## 2021-04-21 00:46:43> ProjectId set to 'gc-automl-tables-r'
datasets_list <- gcat_list_datasets()
datasets_list## displayName createTime
## 1 test_01_bq 2019-08-24 18:30:35
## 2 test_03_gcs 2020-03-26 19:17:53
## 3 test_02_gcs 2020-03-26 19:06:14
## 4 test_01 2019-06-20 12:26:51
## etag
## 1 AB3BwFoC1imPoD-vw-qs0w4nRobEt9IUonBpsNUv6C4XF5upYKzsO_mav8xtjkR5gmQ=
## 2 AB3BwFpjjrOK1aS80tnMnuXuy-QErsB0uO1El4ahgo0UjXYwhlsnaSqnAoY69AUi9fZb
## 3 AB3BwFr3Mjr-8hl1oh-cORwk5jGce1BxcZLVyiP3pxrtxUcAnduwmG67lZnfTrk2gJfH
## 4 AB3BwFr4ZFc2zgiJmoZ1RHZRC5qP-czmyUcvUuSH9h1f61-zPcXU3nfxuhE9cqomygNt
## name
## 1 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512
## 2 projects/736862006196/locations/us-central1/datasets/TBL4077180430816641024
## 3 projects/736862006196/locations/us-central1/datasets/TBL4897961462904913920
## 4 projects/736862006196/locations/us-central1/datasets/TBL7889488510502043648To simplify code for other API calls, you can set a dataset name to a global environment variable:
gcat_global_dataset("test_01_bq")## Set default dataset name to 'test_01_bq'Then you can retrieve it like so:
## [1] "test_01_bq"The example workflow in the rest of this vignette uses this function to keep the code as concise as possible.
There are 2 options for loading data into AutoML Tables
At a high-level, the workflow is:
To load data into AutoML Tables from Google Cloud storage, first do the following:
us-central1 regiongoogleCloudStorageR
Now we are ready to create an AutoML tables dataset.
Create a AutoML Tables dataset (with a unique displayName):
gcat_dataset <- gcat_create_dataset(displayName = "test_01_bq")
gcat_datasetExecute import from Google Cloud storage:
# set url as seperate parameter to keep line length under 80
gs_url <- "gs://gcatr-dev/bank_marketing.csv"
gcat_import_job <- gcat_import_data(displayName = "test_03_gcs",
input_source = "gcs",
input_url = gs_url)
gcat_import_jobTo load data into AutoML Tables from BigQuery, first do the following:
bigQueryR or bigrquery
Create a AutoML Tables dataset (with a unique displayName):
gcat_dataset <- gcat_create_dataset(displayName = "test_01_bq")
gcat_datasetExecute import from BigQuery:
# set url as seperate parameter to keep line length under 80
bq_url <- "bq://gc-automl-tables-r.gcatr_dev.bank_marketing"
gcat_dataset_import <- gcat_import_data(dataset_display_name = "test_01_bq",
input_source = "bq",
input_url = bq_url)
gcat_dataset_importOnce the import - from GCS or BQ - is completed, you’ll recieve an email notification. Locate the datasetId in the GCP console or via gcat_list_datasets function. (e.g. - will be in the form TBL123456789) Once the data import is completed, we can then sanity check the results.
Now we’re ready to view the results and get the tableSpecId we need for later functions:
dataset <- gcat_get_dataset()
dataset## ==Google Cloud AutoML Tables Dataset==
## displayName: test_01_bq
## exampleCount: 45211
## createTime: 2019-08-24 18:30:35
## primaryTableSpecId: 7338035050660757504
## targetColumnSpecId: 7181143537470144512
## statsUpdateTime: 2019-10-28 19:32:47AutoML Tables automatically detects your data column type. Depending on the type of your label column, AutoML Tables chooses to run a classification or regression model. If your label column contains only numerical values, but they represent categories, change your label column type to categorical by updating your schema.
Lets first view the table schema and the columns
table_spec <- gcat_get_table_specs()
table_spec## ==Google Cloud AutoML Tables Table Spec==
## columnCount: 17
## rowCount: 45211
## validRowCount: 45211
## eTag: AB3BwFrdvTumnUtQljG_URdo5mzep05YDAHZ-dqzpcuc5QmnHdQ55pmtnHphJPBqV18f
column_specs_list <- gcat_list_column_specs()
column_specs_list## name
## 1 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/1416536014435909632
## 2 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/6172337220939153408
## 3 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/3866494211725459456
## 4 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/8478180230152847360
## 5 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/984190450208342016
## 6 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/5595876468635729920
## 7 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/3290033459422035968
## 8 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/7901719477849423872
## 9 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/2137111954815188992
## 10 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/4442954964028882944
## 11 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/6748797973242576896
## 12 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/4875300528256450560
## 13 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/9054640982456270848
## 14 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/263614509829062656
## 15 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/2569457519042756608
## 16 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/7181143537470144512
## 17 projects/736862006196/locations/us-central1/datasets/TBL4800700863335104512/tableSpecs/7338035050660757504/columnSpecs/6028222032863297536
## typeCode displayName dataStats.distinctValueCount
## 1 FLOAT64 V1 77
## 2 CATEGORY V2 12
## 3 CATEGORY V3 3
## 4 CATEGORY V4 4
## 5 CATEGORY V5 2
## 6 FLOAT64 V6 7168
## 7 CATEGORY V7 2
## 8 CATEGORY V8 2
## 9 CATEGORY V9 3
## 10 FLOAT64 V10 31
## 11 CATEGORY V11 12
## 12 FLOAT64 V12 1573
## 13 FLOAT64 V13 48
## 14 FLOAT64 V14 559
## 15 FLOAT64 V15 41
## 16 CATEGORY V16 4
## 17 CATEGORY Class 2
## dataStats.float64Stats.mean dataStats.float64Stats.standardDeviation
## 1 40.9362102 10.618762
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 1362.2720577 3044.765829
## 7 NA NA
## 8 NA NA
## 9 NA NA
## 10 15.8064188 8.322476
## 11 NA NA
## 12 258.1630798 257.527812
## 13 2.7638407 3.098021
## 14 40.1978280 100.128746
## 15 0.5803234 2.303441
## 16 NA NA
## 17 NA NA
## dataStats.float64Stats.quantiles
## 1 18, 32, 38, 47, 95
## 2 NULL
## 3 NULL
## 4 NULL
## 5 NULL
## 6 -8019, 38, 386, 1187, 102127
## 7 NULL
## 8 NULL
## 9 NULL
## 10 1, 8, 15, 20, 31
## 11 NULL
## 12 0, 96, 169, 293, 4918
## 13 1, 1, 2, 3, 63
## 14 -1, -1, -1, -1, 871
## 15 0, 0, 0, 0, 275
## 16 NULL
## 17 NULL
## dataStats.float64Stats.histogramBuckets
## 1 -Infinity, 25.7, 33.4, 41.1, 48.8, 56.5, 64.2, 71.9, 79.6, 87.3, 25.7, 33.4, 41.1, 48.8, 56.5, 64.2, 71.9, 79.6, 87.3, Infinity, 1336, 11747, 12925, 8015, 7066, 3312, 377, 303, 116, 14
## 2 NULL
## 3 NULL
## 4 NULL
## 5 NULL
## 6 -Infinity, 2995.6, 14010.2, 25024.8, 36039.4, 47054, 58068.6, 69083.2, 80097.8, 91112.4, 2995.6, 14010.2, 25024.8, 36039.4, 47054, 58068.6, 69083.2, 80097.8, 91112.4, Infinity, 39587, 5233, 294, 58, 21, 7, 6, 1, 2, 2
## 7 NULL
## 8 NULL
## 9 NULL
## 10 -Infinity, 4, 7, 10, 13, 16, 19, 22, 25, 28, 4, 7, 10, 13, 16, 19, 22, 25, 28, Infinity, 2694, 5287, 5220, 3606, 5136, 5662, 6535, 2291, 2996, 5784
## 11 NULL
## 12 -Infinity, 491.8, 983.6, 1475.4, 1967.2, 2459, 2950.8, 3442.6, 3934.4, 4426.2, 491.8, 983.6, 1475.4, 1967.2, 2459, 2950.8, 3442.6, 3934.4, 4426.2, Infinity, 39677, 4425, 865, 176, 43, 11, 11, 2, NA, 1
## 13 -Infinity, 7.2, 13.4, 19.6, 25.8, 32, 38.2, 44.4, 50.6, 56.8, 7.2, 13.4, 19.6, 25.8, 32, 38.2, 44.4, 50.6, 56.8, Infinity, 42882, 1622, 420, 165, 75, 33, 7, 3, 2, 2
## 14 -Infinity, 86.2, 173.4, 260.6, 347.8, 435, 522.2, 609.4, 696.6, 783.8, 86.2, 173.4, 260.6, 347.8, 435, 522.2, 609.4, 696.6, 783.8, Infinity, 37516, 2299, 2154, 1825, 1229, 98, 39, 18, 18, 15
## 15 -Infinity, 27.5, 55, 82.5, 110, 137.5, 165, 192.5, 220, 247.5, 27.5, 55, 82.5, 110, 137.5, 165, 192.5, 220, 247.5, Infinity, 45190, 18, 2, NA, NA, NA, NA, NA, NA, 1
## 16 NULL
## 17 NULL
## dataStats.validValueCount
## 1 45211
## 2 45211
## 3 45211
## 4 45211
## 5 45211
## 6 45211
## 7 45211
## 8 45211
## 9 45211
## 10 45211
## 11 45211
## 12 45211
## 13 45211
## 14 45211
## 15 45211
## 16 45211
## 17 45211
## dataStats.topCategoryStats
## 1 NULL
## 2 blue-collar, management, technician, admin., services, retired, self-employed, entrepreneur, unemployed, housemaid, student, unknown, 9732, 9458, 7597, 5171, 4154, 2264, 1579, 1487, 1303, 1240, 938, 288
## 3 married, single, divorced, 27214, 12790, 5207
## 4 secondary, tertiary, primary, unknown, 23202, 13301, 6851, 1857
## 5 false, true, 44396, 815
## 6 NULL
## 7 true, false, 25130, 20081
## 8 false, true, 37967, 7244
## 9 cellular, unknown, telephone, 29285, 13020, 2906
## 10 NULL
## 11 may, jul, aug, jun, nov, apr, feb, jan, oct, sep, mar, dec, 13766, 6895, 6247, 5341, 3970, 2932, 2649, 1403, 738, 579, 477, 214
## 12 NULL
## 13 NULL
## 14 NULL
## 15 NULL
## 16 unknown, failure, other, success, 36959, 4901, 1840, 1511
## 17 1, 2, 39922, 5289
## etag
## 1 AB3BwFrs0we5pJkUFHWEsvM7VUFUguSxO8LRDda8CFEmHpHAFtRpGzs-iwwLxmE8ThNM
## 2 AB3BwFpZIHPjPo-M1hLmbUkqCPpddN_k6aGUkaDTzAqYWrLqnxJ_O47J_23rcGq2qbtZ
## 3 AB3BwFq6KmJRKcS9OPZHDj5XgSRxP7mxPMx8z56itGvKQk6-4NaxCGr6hmFelVcjRky_
## 4 AB3BwFpDERe8xTeS5XZonzp2VBWedQXP6-JeDDrwT6LtWYwiase6Z5BdY9rxcorbEj05
## 5 AB3BwFqVzk2nDcMAbjUphs17m5b7SNSOKCkuR20mJWZXrSBrzt_8MJjGsMnrU59XtWcD
## 6 AB3BwFrc7DK9f_L3JpvY7fY0lRL2_tnC384WDHZEEwFYBj176B_Fs6tZ5QBgbR9Fst1Y
## 7 AB3BwFr6IvZAUORe3LT38ISAYDl9SaUzg8kioeKDGyd26WKX2HmR3ZDvThOHe4AYu16A
## 8 AB3BwFrrQKLSY8ImlGwc14joppMNDlaUwTblqHdI-WWNsc9ArI177acAfXWFlivejx8Z
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## 10 AB3BwFqCgVfVBxuJ9DepxZZTHjPLcSwuM4dmcu9s1Scdo5TfYGCfK6fSApUABGQboIXf
## 11 AB3BwFpBpwvNvWAwniCgs6KJNiGfsCfxtfTSLw3LmnxzztwLn3fbGueyyxB4Ae_R_yxN
## 12 AB3BwFrknkaqUu9gl2xnO0InPgYg8I9hVPbllMMS5JehHcr4D0P8G-HLIRMgIDUMUltN
## 13 AB3BwFqeV1kVJ5J4W2x-j1P7Lq7PsgJQwJOVRVrgOj2WxjM1X2eyAp0E2QvwSpqilzRr
## 14 AB3BwFq2sev0SPKSn41k0Ok6_gMXz73kaJADGffca2eAdfXg2M01jZ5ZD4C_kMtj2t6n
## 15 AB3BwFp5bElZv5OdHg969ccumydhcoLBo1oTMvMW5zsuF6XibWL9wtjw0YIVyximSfj4
## 16 AB3BwFp9E9m1P-hh4hI-yN7STYRh7b7NZMSEfg04V7bW-AiFP0vmkSBLYhDNRl4sSJAj
## 17 AB3BwFo4TgCKNN4fqfuW3uTwpH29-W-2tNjBpA-8soJurH-95vIDsUnphZgDPIEbM5BJ
column_spec <- gcat_get_column_spec(columnDisplayName = "V16")
column_spec ## ==Google Cloud AutoML Tables Column Spec==
## displayName: V16
## dataType: CATEGORY
## distinctValueCount: 4
## eTag: AB3BwFr-4W_21h3Yf_VnJ4sfVk7sP8ys3B8pSsO1CP8qiTPMozWsMPIMIBc8FjX8P-AvSet V16 or outcome source as label
dataset <- gcat_set_target_column(columnDisplayName = "V16")
dataset## ==Google Cloud AutoML Tables Dataset==
## displayName: test_01_bq
## exampleCount: 45211
## createTime: 2019-08-24 18:30:35
## primaryTableSpecId: 7338035050660757504
## targetColumnSpecId: 7181143537470144512
## statsUpdateTime: 2019-10-28 19:32:47Training or creating a model is done with gcat_create_model.
This will be a long-lasting operation. You will recieve an email when the model training completes
gcat_model <- gcat_create_model(
datasetDisplayName = "test_01_bq",
columnDisplayName = "V16",
modelDisplayName = "test_01_bq_02",
optimizationObjective = "MINIMIZE_LOG_LOSS",
trainBudgetMilliNodeHours = 1000)
gcat_modelMore details here: Training models|AutoML Tables Documentation
While you wait - or after your latest model training completes - you can view the list trained models:
models_list <- gcat_list_models()
models_list## displayName datasetId createTime
## 1 test_01_bq_01 TBL4800700863335104512 2020-03-13T15:00:23.561254Z
## 2 test_01_bq_01 TBL4800700863335104512 2020-01-27T22:28:05.031904Z
## deploymentState updateTime
## 1 UNDEPLOYED 2020-03-13T16:15:51.507143Z
## 2 UNDEPLOYED 2020-01-27T23:28:31.875753ZAfter the model has trained, you can get the trainied model with the following:
gcat_model <- gcat_get_model(modelDisplayName = "test_01_bq_01")
gcat_model## ==Google Cloud AutoML Tables Model==
## name: projects/736862006196/locations/us-central1/models/TBL5977433391753068544
## displayName: test_01_bq_01
## createTime: 2020-03-13 15:00:23
## deploymentState: UNDEPLOYED
## updateTime: 2020-03-13 16:15:51After training has been completed, you can review various performance statistics on the model, such as the accuracy, precision, recall, and so on. The metrics are returned in a nested data structure:
model_evaluation <- gcat_list_model_evaluations(
projectId = projectId,
locationId = location,
modelDisplayName = "test_01_bq_01"
)
model_evaluation## name
## 1 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/5769820913197085287
## 2 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/1394149522462322254
## 3 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/2754760648355542481
## 4 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/2755184473427583976
## 5 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/3947317502930421411
## 6 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/4236599268158459959
## 7 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/4387717826163459231
## 8 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/6883889233127841644
## 9 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/7542751370421007032
## 10 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/8655258658608986026
## 11 projects/736862006196/locations/us-central1/models/TBL5977433391753068544/modelEvaluations/8707696807938175658
## createTime evaluatedExampleCount annotationSpecId
## 1 2020-03-13 16:15:51 4538 <NA>
## 2 2020-03-13 16:15:51 147 not available
## 3 2020-03-13 16:15:51 147 not available
## 4 2020-03-13 16:15:51 180 not available
## 5 2020-03-13 16:15:51 3713 not available
## 6 2020-03-13 16:15:51 498 not available
## 7 2020-03-13 16:15:51 3713 not available
## 8 2020-03-13 16:15:51 498 not available
## 9 2020-03-13 16:15:51 4538 <NA>
## 10 2020-03-13 16:15:51 4538 <NA>
## 11 2020-03-13 16:15:51 180 not available
## classificationEvaluationMetrics.auPrc
## 1 NA
## 2 0.41128716
## 3 0.58342760
## 4 0.38422626
## 5 1.00000000
## 6 0.72111624
## 7 1.00000000
## 8 0.78081820
## 9 0.93552220
## 10 0.98969203
## 11 0.02053571
## classificationEvaluationMetrics.confidenceMetricsEntry
## 1 NULL
## 2 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.585034, 0.5782313, 0.56462586, 0.5578231, 0.5442177, 0.53741497, 0.52380955, 0.5170068, 0.50340134, 0.49659863, 0.48979592, 0.48979592, 0.47619048, 0.46938777, 0.45578232, 0.4489796, 0.43537414, 0.42857143, 0.414966, 0.40816328, 0.39455783, 0.3877551, 0.37414965, 0.36734694, 0.3537415, 0.3469388, 0.33333334, 0.3265306, 0.3197279, 0.30612245, 0.2993197, 0.2857143, 0.27891156, 0.26530612, 0.2585034, 0.24489796, 0.23809524, 0.2244898, 0.21768707, 0.20408164, 0.19727892, 0.18367347, 0.17687075, 0.1632653, 0.15646258, 0.14965986, 0.13605443, 0.1292517, 0.11564626, 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104, 102, 101, 99, 98, 97, 95, 94, 92, 91, 89, 88, 86, 85, 83, 82, 80, 79, 77, 76, 74, 73, 72, 72, 70, 69, 67, 66, 64, 63, 61, 60, 58, 57, 55, 54, 52, 51, 49, 48, 47, 45, 44, 42, 41, 39, 38, 36, 35, 33, 32, 30, 29, 27, 26, 24, 23, 22, 20, 19, 17, 16, 14, 13, 11, 10, 8, 7, 5, 4, 2, 1, NA, NA, 4391, 643, 608, 590, 487, 470, 358, 355, 338, 281, 234, 225, 217, 206, 195, 191, 180, 180, 178, 172, 170, 169, 161, 156, 150, 139, 138, 128, 114, 111, 109, 102, 98, 95, 91, 90, 83, 78, 76, 74, 65, 63, 62, 58, 56, 55, 55, 51, 51, 51, 46, 46, 46, 43, 38, 35, 34, 34, 34, 28, 27, 27, 26, 25, 25, 21, 21, 20, 18, 18, 18, 17, 15, 13, 13, 13, 13, 11, 11, 10, 9, 9, 9, 8, 8, 8, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 1, 1, NA, NA, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, NA, 0.010839686, 0.014885206, 0.016235838, 0.026671115, 0.028994557, 0.048856784, 0.04948494, 0.054404765, 0.067971945, 0.08744593, 0.09170113, 0.097178034, 0.10232572, 0.110563815, 0.11415918, 0.13264868, 0.13271455, 0.13599184, 0.15219513, 0.15707669, 0.17054676, 0.18047723, 0.18914008, 0.20269331, 0.21863243, 0.21887165, 0.23160394, 0.2528941, 0.26128983, 0.27204555, 0.28802556, 0.29421914, 0.3026201, 0.31597567, 0.31770194, 0.33008754, 0.3427328, 0.35700095, 0.3704303, 0.40097016, 0.40923628, 0.4149381, 0.4273527, 0.4349776, 0.44521782, 0.44884473, 0.4597331, 0.4665845, 0.48056456, 0.49290156, 0.49459127, 0.49685466, 0.5, 0.52081114, 0.53409904, 0.54321784, 0.54358894, 0.5586739, 0.576673, 0.5869421, 0.592259, 0.6046691, 0.60724115, 0.6096879, 0.6239275, 0.625368, 0.62940013, 0.6394073, 0.6409813, 0.64218765, 0.6521648, 0.6566966, 0.66402054, 0.6644736, 0.66792786, 0.66924167, 0.67609066, 0.6769297, 0.67871344, 0.68116325, 0.68571264, 0.68738514, 0.69229865, 0.69288856, 0.69578177, 0.7030954, 0.7041785, 0.7065917, 0.71766347, 0.72284937, 0.7245846, 0.72760206, 0.7358066, 0.73794955, 0.73963493, 0.75132525, 0.75531834, 0.76690173, 0.76974946, 0.77454513, 0.78354204, 0.8139368, 1, NA, 1, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 28, 30, 31, 33, 34, 36, 37, 39, 40, 42, 43, 45, 46, 48, 49, 50, 52, 53, 55, 56, 58, 59, 61, 62, 64, 65, 67, 68, 70, 71, 73, 74, 75, 75, 77, 78, 80, 81, 83, 84, 86, 87, 89, 90, 92, 93, 95, 96, 98, 99, 100, 102, 103, 105, 106, 108, 109, 111, 112, 114, 115, 117, 118, 120, 121, 123, 124, 125, 127, 128, 130, 131, 133, 134, 136, 137, 139, 140, 142, 143, 145, 146, 147, 147, NA, 3748, 3783, 3801, 3904, 3921, 4033, 4036, 4053, 4110, 4157, 4166, 4174, 4185, 4196, 4200, 4211, 4211, 4213, 4219, 4221, 4222, 4230, 4235, 4241, 4252, 4253, 4263, 4277, 4280, 4282, 4289, 4293, 4296, 4300, 4301, 4308, 4313, 4315, 4317, 4326, 4328, 4329, 4333, 4335, 4336, 4336, 4340, 4340, 4340, 4345, 4345, 4345, 4348, 4353, 4356, 4357, 4357, 4357, 4363, 4364, 4364, 4365, 4366, 4366, 4370, 4370, 4371, 4373, 4373, 4373, 4374, 4376, 4378, 4378, 4378, 4378, 4380, 4380, 4381, 4382, 4382, 4382, 4383, 4383, 4383, 4385, 4385, 4385, 4387, 4387, 4387, 4387, 4387, 4387, 4387, 4387, 4387, 4389, 4389, 4390, 4390, 4391, 4391
## 4 1, 0.99444443, 0.98888886, 0.9777778, 0.96666664, 0.95555556, 0.95, 0.9388889, 0.92777777, 0.9166667, 0.90555555, 0.9, 0.8888889, 0.87777776, 0.8666667, 0.85555553, 0.85, 0.8388889, 0.8277778, 0.81666666, 0.8055556, 0.8, 0.7888889, 0.7777778, 0.76666665, 0.75555557, 0.75, 0.73888886, 0.7277778, 0.71666664, 0.70555556, 0.7, 0.6888889, 0.67777777, 0.6666667, 0.65555555, 0.65, 0.6388889, 0.62777776, 0.6166667, 0.60555553, 0.6, 0.5888889, 0.5777778, 0.56666666, 0.5555556, 0.55, 0.5388889, 0.5277778, 0.51666665, 0.50555557, 0.5, 0.4888889, 0.47777778, 0.46666667, 0.45555556, 0.45, 0.43888888, 0.42777777, 0.41666666, 0.40555555, 0.4, 0.3888889, 0.37777779, 0.36666667, 0.35555556, 0.35, 0.33888888, 0.32777777, 0.31666666, 0.30555555, 0.3, 0.2888889, 0.2777778, 0.26666668, 0.25555557, 0.25, 0.23888889, 0.22777778, 0.21666667, 0.20555556, 0.2, 0.18888889, 0.17777778, 0.16666667, 0.15555556, 0.15, 0.1388889, 0.12777779, 0.11666667, 0.10555556, 0.1, 0.08888889, 0.07777778, 0.06666667, 0.055555556, 0.05, 0.03888889, 0.027777778, 0.027777778, 0.016666668, 0.0055555557, NA, NA, 0.03966505, 0.22919334, 0.23146944, 0.23913044, 0.23770492, 0.23593964, 0.24255319, 0.24177396, 0.2511278, 0.25581396, 0.2599681, 0.26086956, 0.26272577, 0.26421404, 0.26440677, 0.26551723, 0.26608697, 0.2677305, 0.26992753, 0.26873857, 0.26703498, 0.26568267, 0.26296297, 0.26070765, 0.26640928, 0.27145708, 0.27162978, 0.2703252, 0.27754238, 0.2822757, 0.28097346, 0.28125, 0.28117913, 0.28438228, 0.28368795, 0.29426435, 0.2969543, 0.30343008, 0.30053192, 0.3, 0.3105413, 0.32047477, 0.3192771, 0.32298136, 0.33663365, 0.33670035, 0.33445945, 0.33448276, 0.34545454, 0.34444445, 0.34210527, 0.34351146, 0.34108528, 0.3467742, 0.34146342, 0.33744857, 0.34177214, 0.3511111, 0.37745097, 0.37878788, 0.37435898, 0.38297874, 0.3888889, 0.38857144, 0.3859649, 0.38554215, 0.40127388, 0.3935484, 0.39333335, 0.4318182, 0.45833334, 0.46153846, 0.45217392, 0.44247788, 0.4848485, 0.49462366, 0.48913044, 0.47777778, 0.47126436, 0.47560975, 0.46835443, 0.4864865, 0.49275362, 0.516129, 0.54545456, 0.54901963, 0.54, 0.5555556, 0.54761904, 0.53846157, 0.54285717, 0.5294118, 0.6956522, 0.7368421, 0.75, 0.71428573, 0.75, 0.7, 0.71428573, 0.71428573, 0.75, 0.5, NA, NaN, 0.07630352, 0.3725286, 0.37513173, 0.3842795, 0.38157895, 0.37843785, 0.38644066, 0.38452786, 0.39526626, 0.4, 0.4039653, 0.40449437, 0.40557668, 0.40616965, 0.40519482, 0.40526316, 0.40529802, 0.40591398, 0.40710384, 0.40440166, 0.4011065, 0.39889196, 0.39444444, 0.39051604, 0.39541548, 0.39941263, 0.3988183, 0.39583334, 0.40184048, 0.40502355, 0.40189874, 0.40127388, 0.3993559, 0.40065682, 0.39800996, 0.4061962, 0.4076655, 0.41144902, 0.40647483, 0.40363637, 0.41054615, 0.41779497, 0.4140625, 0.41434264, 0.42236024, 0.4192872, 0.4159664, 0.41276595, 0.41758242, 0.41333333, 0.40807176, 0.40723982, 0.40182647, 0.40186915, 0.3943662, 0.38770685, 0.38848922, 0.39012346, 0.40104166, 0.3968254, 0.38933334, 0.39130434, 0.3888889, 0.3830986, 0.37606838, 0.3699422, 0.37388724, 0.3641791, 0.35757574, 0.3653846, 0.36666667, 0.36363637, 0.35254237, 0.34129694, 0.34408602, 0.33699635, 0.33088234, 0.31851852, 0.3071161, 0.2977099, 0.2857143, 0.28346458, 0.27309236, 0.2644628, 0.25531915, 0.24242425, 0.2347826, 0.22222222, 0.2072072, 0.19178082, 0.1767442, 0.1682243, 0.15763547, 0.14070351, 0.12244898, 0.10309278, 0.09375, 0.07368421, 0.053475935, 0.053475935, 0.032608695, 0.010989011, NA, NaN, 1, 0.13813676, 0.13561267, 0.12849931, 0.12804039, 0.12781093, 0.12253327, 0.12161542, 0.1142726, 0.11014227, 0.10647086, 0.105323546, 0.103028916, 0.10096374, 0.09958696, 0.09775126, 0.09683341, 0.09476824, 0.09247361, 0.09178522, 0.0913263, 0.0913263, 0.0913263, 0.09109683, 0.08719596, 0.08375402, 0.08306563, 0.08237724, 0.0782469, 0.07526388, 0.07457549, 0.0738871, 0.07273979, 0.07044516, 0.069527306, 0.064938046, 0.06356127, 0.060578246, 0.060348783, 0.05943093, 0.05553006, 0.05254704, 0.051858652, 0.050022945, 0.046122074, 0.045204222, 0.045204222, 0.04428637, 0.04130335, 0.040614963, 0.040156037, 0.039467644, 0.039008718, 0.037173014, 0.037173014, 0.03694355, 0.035796236, 0.033501606, 0.029141808, 0.028223956, 0.027994493, 0.026617715, 0.025240935, 0.024552546, 0.02409362, 0.023405232, 0.021569528, 0.021569528, 0.020881139, 0.01720973, 0.014915098, 0.0144561725, 0.0144561725, 0.0144561725, 0.011702616, 0.010784764, 0.010784764, 0.010784764, 0.010555301, 0.009866911, 0.009637448, 0.008719596, 0.008031207, 0.0068838918, 0.0057365764, 0.0052776504, 0.0052776504, 0.004589261, 0.004359798, 0.004130335, 0.0036714089, 0.0036714089, 0.0016062415, 0.0011473153, 0.0009178522, 0.0009178522, 0.0006883892, 0.0006883892, 0.0004589261, 0.0004589261, 0.00022946305, 0.00022946305, 0.00022946305, NA, 180, 179, 178, 176, 174, 172, 171, 169, 167, 165, 163, 162, 160, 158, 156, 154, 153, 151, 149, 147, 145, 144, 142, 140, 138, 136, 135, 133, 131, 129, 127, 126, 124, 122, 120, 118, 117, 115, 113, 111, 109, 108, 106, 104, 102, 100, 99, 97, 95, 93, 91, 90, 88, 86, 84, 82, 81, 79, 77, 75, 73, 72, 70, 68, 66, 64, 63, 61, 59, 57, 55, 54, 52, 50, 48, 46, 45, 43, 41, 39, 37, 36, 34, 32, 30, 28, 27, 25, 23, 21, 19, 18, 16, 14, 12, 10, 9, 7, 5, 5, 3, 1, NA, NA, 4358, 602, 591, 560, 558, 557, 534, 530, 498, 480, 464, 459, 449, 440, 434, 426, 422, 413, 403, 400, 398, 398, 398, 397, 380, 365, 362, 359, 341, 328, 325, 322, 317, 307, 303, 283, 277, 264, 263, 259, 242, 229, 226, 218, 201, 197, 197, 193, 180, 177, 175, 172, 170, 162, 162, 161, 156, 146, 127, 123, 122, 116, 110, 107, 105, 102, 94, 94, 91, 75, 65, 63, 63, 63, 51, 47, 47, 47, 46, 43, 42, 38, 35, 30, 25, 23, 23, 20, 19, 18, 16, 16, 7, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, NA, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, 2147483647, NA, 0.098490335, 0.101760715, 0.12248515, 0.12431478, 0.12510908, 0.13763396, 0.14055222, 0.15138803, 0.15797327, 0.16453397, 0.16738504, 0.17084953, 0.1722481, 0.17392446, 0.1769764, 0.17806293, 0.18080613, 0.1843759, 0.18572798, 0.1864633, 0.18710487, 0.18730941, 0.18749237, 0.19168098, 0.19409263, 0.19479407, 0.19535717, 0.19897525, 0.20112488, 0.2019484, 0.20281644, 0.20476626, 0.20890462, 0.2096736, 0.21487087, 0.21634713, 0.22320755, 0.22429365, 0.22546695, 0.22829884, 0.23268259, 0.23344873, 0.23690602, 0.2427009, 0.24523659, 0.2458568, 0.2488974, 0.25161597, 0.25299752, 0.2538861, 0.25420013, 0.256123, 0.25896162, 0.25943157, 0.25998875, 0.26291198, 0.2674291, 0.2769538, 0.27968037, 0.2801766, 0.2825233, 0.2845502, 0.28617936, 0.28826725, 0.28971386, 0.29518977, 0.29593045, 0.29974148, 0.30772242, 0.31552455, 0.3163769, 0.316907, 0.31811547, 0.3299896, 0.3357048, 0.33571738, 0.33858973, 0.3418977, 0.3470701, 0.349363, 0.354546, 0.35566008, 0.3603783, 0.36897314, 0.37262967, 0.37390494, 0.38576475, 0.39189568, 0.3963825, 0.40016028, 0.40127927, 0.41861442, 0.4355992, 0.4429252, 0.4461862, 0.45688292, 0.4638157, 0.48475438, 0.5, 0.51109326, 0.518941, 0.54259574, 1, NA, 1, 2, 4, 6, 8, 9, 11, 13, 15, 17, 18, 20, 22, 24, 26, 27, 29, 31, 33, 35, 36, 38, 40, 42, 44, 45, 47, 49, 51, 53, 54, 56, 58, 60, 62, 63, 65, 67, 69, 71, 72, 74, 76, 78, 80, 81, 83, 85, 87, 89, 90, 92, 94, 96, 98, 99, 101, 103, 105, 107, 108, 110, 112, 114, 116, 117, 119, 121, 123, 125, 126, 128, 130, 132, 134, 135, 137, 139, 141, 143, 144, 146, 148, 150, 152, 153, 155, 157, 159, 161, 162, 164, 166, 168, 170, 171, 173, 175, 175, 177, 179, 180, 180, NA, 3756, 3767, 3798, 3800, 3801, 3824, 3828, 3860, 3878, 3894, 3899, 3909, 3918, 3924, 3932, 3936, 3945, 3955, 3958, 3960, 3960, 3960, 3961, 3978, 3993, 3996, 3999, 4017, 4030, 4033, 4036, 4041, 4051, 4055, 4075, 4081, 4094, 4095, 4099, 4116, 4129, 4132, 4140, 4157, 4161, 4161, 4165, 4178, 4181, 4183, 4186, 4188, 4196, 4196, 4197, 4202, 4212, 4231, 4235, 4236, 4242, 4248, 4251, 4253, 4256, 4264, 4264, 4267, 4283, 4293, 4295, 4295, 4295, 4307, 4311, 4311, 4311, 4312, 4315, 4316, 4320, 4323, 4328, 4333, 4335, 4335, 4338, 4339, 4340, 4342, 4342, 4351, 4353, 4354, 4354, 4355, 4355, 4356, 4356, 4357, 4357, 4357, 4358
## 5 1, 1, 0.99973065, 0.990035, 0.9798007, 0.9698357, 0.9598707, 0.95017505, 0.9394021, 0.9302451, 0.91974145, 0.90977645, 0.9000808, 0.88957715, 0.8801508, 0.87018585, 0.86022085, 0.85025585, 0.8397522, 0.82978725, 0.81982225, 0.80985725, 0.79962295, 0.7899273, 0.7802316, 0.7699973, 0.75949365, 0.7495287, 0.739833, 0.7295987, 0.7196337, 0.70966876, 0.69970375, 0.6900081, 0.67977375, 0.66953945, 0.6598438, 0.6496095, 0.63910586, 0.6296795, 0.61998385, 0.60974956, 0.5995152, 0.5892809, 0.5801239, 0.5701589, 0.55965525, 0.54942095, 0.5399946, 0.5297603, 0.5197953, 0.50983036, 0.49932668, 0.4893617, 0.47993535, 0.46997038, 0.45973605, 0.44869378, 0.43872878, 0.42930245, 0.41906813, 0.40829518, 0.39994612, 0.38890386, 0.37920818, 0.36978185, 0.35873955, 0.34931323, 0.33934823, 0.32938325, 0.31699434, 0.30649072, 0.29490978, 0.28871533, 0.27444115, 0.2625909, 0.25289524, 0.24158363, 0.23350391, 0.22569351, 0.21465123, 0.20333962, 0.1885268, 0.17371398, 0.15674657, 0.13870186, 0.11230811, 0.08887692, 0.030433612, 0.016698088, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NaN, 1, 1, 0.9998653, 0.99499255, 0.9897973, 0.9846869, 0.97952455, 0.97445107, 0.96875435, 0.9638621, 0.95819306, 0.952757, 0.9474132, 0.9415621, 0.9362556, 0.93058753, 0.92485887, 0.9190684, 0.9128971, 0.90697676, 0.90099156, 0.8949405, 0.8886561, 0.8826362, 0.8765507, 0.8700548, 0.8633093, 0.85683495, 0.8504644, 0.8436624, 0.8369616, 0.83018273, 0.8233244, 0.8165737, 0.8093635, 0.80206484, 0.7950673, 0.7875918, 0.7798225, 0.77276486, 0.7654198, 0.7575707, 0.74962115, 0.7415692, 0.7342765, 0.72624356, 0.7176653, 0.7091952, 0.7012942, 0.6926056, 0.68403333, 0.67534786, 0.6660679, 0.6571429, 0.6485896, 0.6394284, 0.6298893, 0.619446, 0.60988396, 0.60071605, 0.5906244, 0.57984316, 0.5713736, 0.5600155, 0.5498926, 0.5399135, 0.52804756, 0.51776445, 0.5067364, 0.49554294, 0.4813906, 0.4691816, 0.45549086, 0.4480669, 0.4306847, 0.41595563, 0.40369734, 0.38915402, 0.37860262, 0.3682707, 0.3534368, 0.3379588, 0.3172445, 0.29600734, 0.2710128, 0.243614, 0.20193705, 0.16324511, 0.059069525, 0.032847684, NaN, 3713, 3713, 3712, 3676, 3638, 3601, 3564, 3528, 3488, 3454, 3415, 3378, 3342, 3303, 3268, 3231, 3194, 3157, 3118, 3081, 3044, 3007, 2969, 2933, 2897, 2859, 2820, 2783, 2747, 2709, 2672, 2635, 2598, 2562, 2524, 2486, 2450, 2412, 2373, 2338, 2302, 2264, 2226, 2188, 2154, 2117, 2078, 2040, 2005, 1967, 1930, 1893, 1854, 1817, 1782, 1745, 1707, 1666, 1629, 1594, 1556, 1516, 1485, 1444, 1408, 1373, 1332, 1297, 1260, 1223, 1177, 1138, 1095, 1072, 1019, 975, 939, 897, 867, 838, 797, 755, 700, 645, 582, 515, 417, 330, 113, 62, NA, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 825, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 0.5, 0.99856037, 0.9996118, 0.9997061, 0.99975044, 0.99977165, 0.99979097, 0.99980265, 0.9998153, 0.99982613, 0.9998348, 0.9998417, 0.9998486, 0.99985456, 0.9998615, 0.9998679, 0.9998753, 0.9998807, 0.9998852, 0.9998902, 0.9998946, 0.9998988, 0.99990284, 0.999907, 0.9999105, 0.9999143, 0.99991786, 0.99992144, 0.99992526, 0.99992824, 0.9999317, 0.99993384, 0.9999368, 0.9999404, 0.9999442, 0.99994636, 0.9999496, 0.9999523, 0.9999546, 0.9999571, 0.99995923, 0.99996173, 0.9999645, 0.9999666, 0.9999689, 0.9999714, 0.9999727, 0.99997485, 0.99997663, 0.9999784, 0.99997973, 0.99998116, 0.9999826, 0.9999838, 0.999985, 0.99998665, 0.99998784, 0.9999888, 0.99998975, 0.9999908, 0.999992, 0.9999927, 0.99999356, 0.9999943, 0.9999949, 0.99999535, 0.9999958, 0.9999963, 0.99999666, 0.999997, 0.9999974, 0.9999976, 0.99999774, 0.999998, 0.9999982, 0.99999833, 0.99999845, 0.99999857, 0.9999987, 0.9999988, 0.9999989, 0.99999905, 0.99999917, 0.9999993, 0.9999994, 0.9999995, 0.99999964, 0.99999976, 0.9999999, 1, NA, NA, 1, 37, 75, 112, 149, 185, 225, 259, 298, 335, 371, 410, 445, 482, 519, 556, 595, 632, 669, 706, 744, 780, 816, 854, 893, 930, 966, 1004, 1041, 1078, 1115, 1151, 1189, 1227, 1263, 1301, 1340, 1375, 1411, 1449, 1487, 1525, 1559, 1596, 1635, 1673, 1708, 1746, 1783, 1820, 1859, 1896, 1931, 1968, 2006, 2047, 2084, 2119, 2157, 2197, 2228, 2269, 2305, 2340, 2381, 2416, 2453, 2490, 2536, 2575, 2618, 2641, 2694, 2738, 2774, 2816, 2846, 2875, 2916, 2958, 3013, 3068, 3131, 3198, 3296, 3383, 3600, 3651, 3713
## 6 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.9076305, 0.90361446, 0.89759034, 0.8875502, 0.87751, 0.8674699, 0.85943776, 0.8514056, 0.8493976, 0.83935744, 0.8293173, 0.8192771, 0.80923694, 0.7991968, 0.7891566, 0.77911645, 0.7690763, 0.7590361, 0.74899596, 0.7389558, 0.7289157, 0.7188755, 0.70883536, 0.6987952, 0.68875504, 0.6787149, 0.6686747, 0.65863454, 0.6485944, 0.6385542, 0.62851405, 0.6184739, 0.6084337, 0.59839356, 0.5883534, 0.57831323, 0.56827307, 0.5582329, 0.5481928, 0.53815264, 0.5281125, 0.5180723, 0.50803214, 0.49799198, 0.48995984, 0.47991967, 0.4698795, 0.45983934, 0.4497992, 0.43975905, 0.42971888, 0.41967872, 0.40963855, 0.3995984, 0.38955823, 0.37951806, 0.3694779, 0.35943776, 0.3493976, 0.33935744, 0.32931727, 0.3192771, 0.30923694, 0.29919678, 0.28915662, 0.27911645, 0.26907632, 0.25903615, 0.24899599, 0.23895583, 0.22891566, 0.2188755, 0.20883535, 0.19879518, 0.18875502, 0.17871486, 0.16867469, 0.15863454, 0.14859438, 0.13855422, 0.12851405, 0.118473895, 0.10843374, 0.098393574, 0.08835341, 0.078313254, 0.06827309, 0.058232933, 0.04819277, 0.03815261, 0.02811245, 0.018072288, 0.008032128, NA, NA, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.68277943, 0.6880734, 0.6962617, 0.6971609, 0.69808304, 0.702439, 0.70627064, 0.7090301, 0.7085427, 0.7120954, 0.71453285, 0.7132867, 0.71708184, 0.72232306, 0.7277778, 0.73345935, 0.7337165, 0.739726, 0.750503, 0.7556468, 0.7546778, 0.7584746, 0.76406926, 0.7631579, 0.76222223, 0.7716895, 0.7744186, 0.7772512, 0.7764423, 0.7813268, 0.7825, 0.7917738, 0.79112273, 0.7925532, 0.79403794, 0.7933884, 0.7927171, 0.78977275, 0.7890173, 0.7882353, 0.7897898, 0.78899086, 0.78571427, 0.7923323, 0.7922078, 0.7887789, 0.7878788, 0.78965515, 0.79151946, 0.7906137, 0.795539, 0.7916667, 0.796875, 0.80241936, 0.8016529, 0.79746836, 0.7965368, 0.79555553, 0.79452056, 0.7934272, 0.78846157, 0.7910448, 0.78571427, 0.7842105, 0.77837837, 0.78531075, 0.7882353, 0.7865854, 0.7848101, 0.78289473, 0.8201439, 0.83206105, 0.8455285, 0.8534483, 0.8468468, 0.84761906, 0.8484849, 0.84042555, 0.84090906, 0.8414634, 0.85333335, 0.85507244, 0.85714287, 0.8596491, 0.88, 0.8863636, 0.8717949, 0.85294116, 0.82758623, 0.95, 0.93333334, 0.9, 1, NaN, NaN, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.77931035, 0.78125, 0.7842105, 0.7809187, 0.7775801, 0.77628034, 0.7753623, 0.77372265, 0.77260274, 0.7705069, 0.767658, 0.7626168, 0.76037735, 0.7588179, 0.75722545, 0.75559884, 0.7509804, 0.7492567, 0.74974877, 0.7472081, 0.74157304, 0.73814434, 0.73541665, 0.7295597, 0.7236287, 0.7222222, 0.7176724, 0.71304345, 0.70678335, 0.7027624, 0.6971047, 0.69447577, 0.6878547, 0.6819222, 0.6758939, 0.66898954, 0.6619883, 0.65411764, 0.6469194, 0.63961816, 0.6329723, 0.62545455, 0.6170732, 0.6115906, 0.60545903, 0.5967541, 0.58867925, 0.5812183, 0.57362354, 0.5651613, 0.55801827, 0.54855645, 0.54111403, 0.53351206, 0.5243243, 0.51428574, 0.5048011, 0.49515906, 0.48535565, 0.47538677, 0.46458924, 0.4549356, 0.44380403, 0.43313953, 0.4216691, 0.41185185, 0.4011976, 0.3897281, 0.37804878, 0.36615384, 0.3579278, 0.34658188, 0.33494365, 0.32247558, 0.3087028, 0.29519072, 0.28140703, 0.2668919, 0.25255972, 0.23793103, 0.22338569, 0.20811288, 0.19251336, 0.17657657, 0.16058394, 0.14391144, 0.12662943, 0.10902256, 0.0910816, 0.07335907, 0.054580897, 0.035433073, 0.015936255, NaN, NaN, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.051980197, 0.05049505, 0.048267327, 0.047524754, 0.046782177, 0.04529703, 0.044059407, 0.043069307, 0.043069307, 0.041831683, 0.040841583, 0.04059406, 0.039356437, 0.037871286, 0.03638614, 0.03490099, 0.03440594, 0.032920793, 0.03069307, 0.029455446, 0.02920792, 0.028217822, 0.026980199, 0.026732674, 0.026485149, 0.024752475, 0.0240099, 0.023267327, 0.023019802, 0.022029703, 0.021534653, 0.020049505, 0.01980198, 0.01930693, 0.018811882, 0.018564356, 0.018316831, 0.018316831, 0.018069306, 0.017821781, 0.017326733, 0.017079208, 0.017079208, 0.01608911, 0.015841585, 0.015841585, 0.01559406, 0.0150990095, 0.01460396, 0.014356435, 0.013613861, 0.013613861, 0.012871287, 0.012128713, 0.0118811885, 0.0118811885, 0.011633663, 0.011386138, 0.011138614, 0.010891089, 0.010891089, 0.01039604, 0.01039604, 0.010148515, 0.010148515, 0.009405941, 0.008910891, 0.008663367, 0.0084158415, 0.008168316, 0.006188119, 0.0054455446, 0.0047029704, 0.0042079207, 0.0042079207, 0.003960396, 0.0037128713, 0.0037128713, 0.0034653465, 0.0032178217, 0.0027227723, 0.0024752475, 0.0022277227, 0.001980198, 0.0014851486, 0.0012376237, 0.0012376237, 0.0012376237, 0.0012376237, 0.00024752476, 0.00024752476, 0.00024752476, NA, NA, NA, 452, 452, 452, 452, 452, 452, 452, 452, 452, 452, 450, 447, 442, 437, 432, 428, 424, 423, 418, 413, 408, 403, 398, 393, 388, 383, 378, 373, 368, 363, 358, 353, 348, 343, 338, 333, 328, 323, 318, 313, 308, 303, 298, 293, 288, 283, 278, 273, 268, 263, 258, 253, 248, 244, 239, 234, 229, 224, 219, 214, 209, 204, 199, 194, 189, 184, 179, 174, 169, 164, 159, 154, 149, 144, 139, 134, 129, 124, 119, 114, 109, 104, 99, 94, 89, 84, 79, 74, 69, 64, 59, 54, 49, 44, 39, 34, 29, 24, 19, 14, 9, 4, NA, NA, 210, 210, 210, 210, 210, 210, 210, 210, 210, 210, 204, 195, 192, 189, 183, 178, 174, 174, 169, 165, 164, 159, 153, 147, 141, 139, 133, 124, 119, 118, 114, 109, 108, 107, 100, 97, 94, 93, 89, 87, 81, 80, 78, 76, 75, 74, 74, 73, 72, 70, 69, 69, 65, 64, 64, 63, 61, 59, 58, 55, 55, 52, 49, 48, 48, 47, 46, 45, 44, 44, 42, 42, 41, 41, 38, 36, 35, 34, 33, 25, 22, 19, 17, 17, 16, 15, 15, 14, 13, 11, 10, 9, 8, 6, 5, 5, 5, 5, 1, 1, 1, NA, NA, NA, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 48, 51, 56, 61, 66, 70, 74, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 254, 259, 264, 269, 274, 279, 284, 289, 294, 299, 304, 309, 314, 319, 324, 329, 334, 339, 344, 349, 354, 359, 364, 369, 374, 379, 384, 389, 394, 399, 404, 409, 414, 419, 424, 429, 434, 439, 444, 449, 454, 459, 464, 469, 474, 479, 484, 489, 494, 498, 498, 3830, 3830, 3830, 3830, 3830, 3830, 3830, 3830, 3830, 3830, 3836, 3845, 3848, 3851, 3857, 3862, 3866, 3866, 3871, 3875, 3876, 3881, 3887, 3893, 3899, 3901, 3907, 3916, 3921, 3922, 3926, 3931, 3932, 3933, 3940, 3943, 3946, 3947, 3951, 3953, 3959, 3960, 3962, 3964, 3965, 3966, 3966, 3967, 3968, 3970, 3971, 3971, 3975, 3976, 3976, 3977, 3979, 3981, 3982, 3985, 3985, 3988, 3991, 3992, 3992, 3993, 3994, 3995, 3996, 3996, 3998, 3998, 3999, 3999, 4002, 4004, 4005, 4006, 4007, 4015, 4018, 4021, 4023, 4023, 4024, 4025, 4025, 4026, 4027, 4029, 4030, 4031, 4032, 4034, 4035, 4035, 4035, 4035, 4039, 4039, 4039, 4040, 4040, 4040, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 0.12874669, 0.17249818, 0.20607886, 0.23799963, 0.2757447, 0.3124694, 0.33401486, 0.36625978, 0.39066777, 0.4147814, 0.43940994, 0.45562497, 0.46719584, 0.47828367, 0.48969308, 0.5, 0.5017466, 0.51364875, 0.5206453, 0.52830434, 0.5355014, 0.54587007, 0.55513006, 0.5631731, 0.57071435, 0.5781385, 0.58986884, 0.59474605, 0.5980961, 0.60409987, 0.6087189, 0.6125137, 0.61582667, 0.62625873, 0.62995905, 0.6343181, 0.6370867, 0.64419776, 0.64573413, 0.65551287, 0.66144913, 0.66433054, 0.6679255, 0.67124873, 0.67334795, 0.6748795, 0.6811666, 0.6837567, 0.6851977, 0.68813825, 0.6899942, 0.6953959, 0.6977342, 0.6989829, 0.7006808, 0.70327973, 0.7064875, 0.7088893, 0.7121915, 0.71443975, 0.71855336, 0.7207341, 0.72465324, 0.7256298, 0.7271607, 0.7276305, 0.73076093, 0.7331546, 0.734725, 0.7364718, 0.73909533, 0.7409409, 0.74303246, 0.74739796, 0.7500887, 0.7518257, 0.7542474, 0.7563156, 0.7625581, 0.7671832, 0.7710219, 0.77406055, 0.7746591, 0.7775496, 0.7790793, 0.77982813, 0.78151906, 0.7856331, 0.7882218, 0.79374194, 0.7966358, 0.8003344, 0.803133, 0.8046244, 0.8089103, 0.8112714, 0.81446624, 0.82292235, 0.8271451, 0.83131397, 0.83868814, 0.86160016, 1
## 7 1, 1, 0.99973065, 0.990035, 0.9798007, 0.9698357, 0.9598707, 0.95017505, 0.9394021, 0.9302451, 0.91974145, 0.90977645, 0.9000808, 0.88957715, 0.8801508, 0.87018585, 0.86022085, 0.85025585, 0.8397522, 0.82978725, 0.81982225, 0.80985725, 0.79962295, 0.7899273, 0.7802316, 0.7699973, 0.75949365, 0.7495287, 0.739833, 0.7295987, 0.7196337, 0.70966876, 0.69970375, 0.6900081, 0.67977375, 0.66953945, 0.6598438, 0.6496095, 0.63910586, 0.6296795, 0.61998385, 0.60974956, 0.5995152, 0.5892809, 0.5801239, 0.5701589, 0.55965525, 0.54942095, 0.5399946, 0.5297603, 0.5197953, 0.50983036, 0.49932668, 0.4893617, 0.47993535, 0.46997038, 0.45973605, 0.44869378, 0.43872878, 0.42930245, 0.41906813, 0.40829518, 0.39994612, 0.38890386, 0.37920818, 0.36978185, 0.35873955, 0.34931323, 0.33934823, 0.32938325, 0.31699434, 0.30649072, 0.29490978, 0.28871533, 0.27444115, 0.2625909, 0.25289524, 0.24158363, 0.23350391, 0.22569351, 0.21465123, 0.20333962, 0.1885268, 0.17371398, 0.15674657, 0.13870186, 0.11230811, 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4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, NA, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 175, 175, 175, 175, 175, 175, 177, 179, 180, 180, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4354, 4355, 4355, 4355, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4356, 4357, 4357, 4357, 4358, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 0.098490335, 0.101760715, 0.12248515, 0.12431478, 0.12510908, 0.13763396, 0.14055222, 0.15138803, 0.15797327, 0.16453397, 0.16738504, 0.17084953, 0.1722481, 0.17392446, 0.1769764, 0.17806293, 0.18080613, 0.1843759, 0.18572798, 0.1864633, 0.18710487, 0.18730941, 0.18749237, 0.19168098, 0.19409263, 0.19479407, 0.19535717, 0.19897525, 0.20112488, 0.2019484, 0.20281644, 0.20476626, 0.20890462, 0.2096736, 0.21487087, 0.21634713, 0.22320755, 0.22429365, 0.22546695, 0.22829884, 0.23268259, 0.23344873, 0.23690602, 0.2427009, 0.24523659, 0.2458568, 0.2488974, 0.25161597, 0.25299752, 0.2538861, 0.25420013, 0.256123, 0.25896162, 0.25943157, 0.25998875, 0.26291198, 0.2674291, 0.2769538, 0.27968037, 0.2801766, 0.2825233, 0.2845502, 0.28617936, 0.28826725, 0.28971386, 0.29518977, 0.29593045, 0.29974148, 0.30772242, 0.31552455, 0.3163769, 0.316907, 0.31811547, 0.3299896, 0.3357048, 0.33571738, 0.33858973, 0.3418977, 0.3470701, 0.349363, 0.354546, 0.35566008, 0.3603783, 0.36897314, 0.37262967, 0.37390494, 0.38576475, 0.39189568, 0.3963825, 0.40016028, 0.40127927, 0.41861442, 0.4355992, 0.4429252, 0.4461862, 0.45688292, 0.4638157, 0.48475438, 0.5, 0.51109326, 0.518941, 0.54259574, 1
## classificationEvaluationMetrics.auRoc
## 1 NA
## 2 0.7866810
## 3 0.9751013
## 4 0.9490082
## 5 1.0000000
## 6 0.9357104
## 7 1.0000000
## 8 0.9775270
## 9 0.9676056
## 10 0.9961697
## 11 0.5162154
## classificationEvaluationMetrics.logLoss
## 1 NA
## 2 1.081565e+00
## 3 1.081565e+00
## 4 1.366061e+00
## 5 5.589816e-05
## 6 4.707106e-01
## 7 5.589816e-05
## 8 4.707106e-01
## 9 1.409216e-01
## 10 1.409216e-01
## 11 1.366061e+00
## classificationEvaluationMetrics.confusionMatrix.row
## 1 NULL
## 2 NULL
## 3 NULL
## 4 NULL
## 5 NULL
## 6 NULL
## 7 NULL
## 8 NULL
## 9 452, 2, 44, 0, 151, 6, 23, 0, 59, 2, 86, 0, 0, 0, 0, 3713
## 10 452, 2, 44, 0, 151, 6, 23, 0, 59, 2, 86, 0, 0, 0, 0, 3713
## 11 NULL
## classificationEvaluationMetrics.confusionMatrix.displayName displayName
## 1 NULL <NA>
## 2 NULL success
## 3 NULL success
## 4 NULL other
## 5 NULL unknown
## 6 NULL failure
## 7 NULL unknown
## 8 NULL failure
## 9 failure, other, success, unknown <NA>
## 10 failure, other, success, unknown <NA>
## 11 NULL otherThere are two different prediction modes: online and batch. The following cell shows you how to make a batch prediction.
batch_predictions <- gcat_batch_predict(
modelDisplayName = "test_01_bq_01",
inputSource = "gs://gcatr-dev/bank_marketing_batch_01.csv",
outputTarget = "gs://gcatr-dev/predictions/"
)
batch_predictions## $name
## [1] "projects/736862006196/locations/us-central1/operations/TBL8706366551189618688"
##
## $metadata
## $metadata$`@type`
## [1] "type.googleapis.com/google.cloud.automl.v1beta1.OperationMetadata"
##
## $metadata$createTime
## [1] "2021-04-21T00:46:55.224733Z"
##
## $metadata$updateTime
## [1] "2021-04-21T00:46:55.224733Z"
##
## $metadata$batchPredictDetails
## $metadata$batchPredictDetails$inputConfig
## $metadata$batchPredictDetails$inputConfig$gcsSource
## $metadata$batchPredictDetails$inputConfig$gcsSource$inputUris
## [1] "gs://gcatr-dev/bank_marketing_batch_01.csv"