The R package OpenML is an interface to make interactions with the OpenML server as comfortable as possible. For example, the users can download and upload files, run their implementations on specific tasks and get predictions in the correct form directly via R commands. In this tutorial, we will show the most important functions of this package and give examples on standard workflows.
For general information on what OpenML is, please have a look at the README file or visit the official OpenML website.
After installation and before making practical use of the package, in most cases it is desirable to setup a configuration file to simplify further steps. Afterwards, there are different basic stages when using this package or OpenML, respectively:
DataSets, Tasks, Flows, Runs, RunEvaluations, EvaluationMeasures, and TaskTypes)listOMLdata.frameDataSets, Tasks, Runs, Predictions, and Flows)getOMLrunTaskMlrOMLTask and LearnerOMLMlrRun, OMLRunuploadOMLRunInstallation works as in any other package using
install.packages("OpenML")To install the current development version use the devtools package and run
devtools::install_github("openml/openml-r")Using the OpenML package also requires a reader for the ARFF file format. By default farff is used. Alternatively, the RWeka package can be used. You can install the packages with the following calls.
install.packages(c("farff", "RWeka"))All examples in this tutorial are given with a READ-ONLY API key.
With this key you can read all the information from the server but not write data sets, tasks, flows, and runs to the server. This key allows to emulate uploading to the server but doesn’t allow to really store data. If one wants to write data to a server, one has to get a personal API key. The process of how to obtain a key is shown in the configuration section.
Important: Please do not write meaningless data to the server such as copies of already existing data sets, tasks, or runs (such as the ones from this tutorial)! One instance of the Iris data set should be enough for everyone. :D
In this paragraph you can find an example on how to download a task from the server, print some information about it to the console, and produce a run which is then uploaded to the server. For detailed information on OpenML terminology (task, run, etc.) see the OpenML guide.
library("OpenML")
## temporarily set API key to read only key
setOMLConfig(apikey = "c1994bdb7ecb3c6f3c8f3b35f4b47f1f")## OpenML configuration:
## server : https://www.openml.org/api/v1
## cachedir : L:\GitRepository\openml-r\inst\tests\cache
## verbosity : 0
## arff.reader : farff
## confirm.upload : FALSE
## apikey : ***************************47f1f# download a task (whose ID is 1L)
task = getOMLTask(task.id = 1L)
task##
## OpenML Task 1 :: (Data ID = 1)
## Task Type : Supervised Classification
## Data Set : anneal :: (Version = 2, OpenML ID = 1)
## Target Feature(s) : class
## Tags : basic, study_1, study_7, under100k, under1m
## Estimation Procedure : Stratified crossvalidation (1 x 10 folds)
## Evaluation Measure(s): predictive_accuracyThe task contains information on the following:
In the next line, randomForest is used as a classifier and run with the help of the mlr package. Note that one needs to run the algorithm locally and that mlr will automatically load the package that is needed to run the specified classifier.
# define the classifier (usually called "flow" within OpenML)
library("mlr")
lrn = makeLearner("classif.randomForest")
# upload the new flow (with information about the algorithm and settings);
# if this algorithm already exists on the server, one will receive a message
# with the ID of the existing flow
flow.id = uploadOMLFlow(lrn)
# the last step is to perform a run and upload the results
run.mlr = runTaskMlr(task, lrn)
run.id = uploadOMLRun(run.mlr)Following this very brief example, we will explain the single steps of the OpenML package in more detail in the next sections.
Interacting with the OpenML server requires an API key. For demonstration purposes, we have created a public read-only API key ("c1994bdb7ecb3c6f3c8f3b35f4b47f1f"), which will be used in this tutorial to make the examples executable. However, for a full-fledged usage of the OpenML package, you need your personal API.
In order to receive your own API key
You can set your own OpenML configuration either just temporarily for the current R session via setOMLConfig or permanently via saveOMLConfig. In order to create a permanent configuration file using default values and at the same time setting your personal API key, run
saveOMLConfig(apikey = "c1994bdb7ecb3c6f3c8f3b35f4b47f1f")where "c1994bdb7ecb3c6f3c8f3b35f4b47f1f" should be replaced with your personal API key. It is noteworthy that basically everybody who has access to your computer can read the configuration file and thus see your API key. With your API key other users have full access to your account via the API, so please handle it with care!
It is also possible to manually create a file ~/.openml/config in your home directory – you can use the R command path.expand("~/.openml/config") to get the full path to the configuration file on the operating system. The config file consists of key = value pairs, note that the values are not quoted. An exemplary minimal config file might look as follows:
apikey=c1994bdb7ecb3c6f3c8f3b35f4b47f1fThe config file may contain the following information:
server:
http://www.openml.org/api/v1cachedir:
file.path(tempdir(), "cache").verbosity:
0: normal output1: info output (default)2: debug outputarff.reader:
RWeka: this is the standard Java parser used in Wekafarff: the farff package provides a newer, faster parser without any Java requirementsconfirm.upload:
FALSE) one does not need to confirm the upload decisionapikey:
If you manually modify the config file, you need to reload the modified config file to the current R session using loadOMLConfig(). You can query the current configuration using
getOMLConfig()## OpenML configuration:
## server : https://www.openml.org/api/v1
## cachedir : L:\GitRepository\openml-r\inst\tests\cache
## verbosity : 0
## arff.reader : farff
## confirm.upload : FALSE
## apikey : ***************************47f1fThe configuration file and some related things are also explained in the OpenML Wiki.
Once the config file is set up, you are ready to go!
In this stage, we want to list basic information about the various OpenML objects:
For each of these objects, we have a function to query the information, beginning with listOML. All of these functions return a data.frame, even in case the result consists of a single column or has zero observations (i.e., rows).
Note that the listOML* functions only list information on the corresponding objects – they do not download the respective objects. Information on actually downloading specific objects is covered in the next section.
To browse the OpenML data base for appropriate data sets, you can use listOMLDataSets() in order to get basic data characteristics (number of features, instances, classes, missing values, etc.) for each data set. By default, listOMLDataSets() returns only data sets that have an active status on OpenML:
datasets = listOMLDataSets() # returns active data setsThe resulting data.frame contains the following information for each of the listed data sets:
data.idstatus ("active", "in_preparation" or "deactivated") of the data setname of the data setmajority.class.size)str(datasets)## 'data.frame': 2908 obs. of 16 variables:
## $ data.id : int 2 3 4 5 6 7 8 9 10 11 ...
## $ name : chr "anneal" "kr-vs-kp" "labor" "arrhythmia" ...
## $ version : int 1 1 1 1 1 1 1 1 1 1 ...
## $ status : chr "active" "active" "active" "active" ...
## $ format : chr "ARFF" "ARFF" "ARFF" "ARFF" ...
## $ tags : chr "" "" "" "" ...
## $ majority.class.size : int 684 1669 37 245 813 57 NA 67 81 288 ...
## $ max.nominal.att.distinct.values : int 7 3 3 13 26 24 NA 22 8 3 ...
## $ minority.class.size : int 8 1527 20 2 734 1 NA 3 2 49 ...
## $ number.of.classes : int 5 2 2 13 26 24 0 6 4 3 ...
## $ number.of.features : int 39 37 17 280 17 70 6 26 19 5 ...
## $ number.of.instances : int 898 3196 57 452 20000 226 345 205 148 625 ...
## $ number.of.instances.with.missing.values: int 898 0 56 384 0 222 0 46 0 0 ...
## $ number.of.missing.values : int 22175 0 326 408 0 317 0 59 0 0 ...
## $ number.of.numeric.features : int 6 0 8 206 16 0 6 15 3 4 ...
## $ number.of.symbolic.features : int 33 37 9 74 1 70 0 11 16 1 ...head(datasets[, 1:5])## data.id name version status format
## 1 2 anneal 1 active ARFF
## 2 3 kr-vs-kp 1 active ARFF
## 3 4 labor 1 active ARFF
## 4 5 arrhythmia 1 active ARFF
## 5 6 letter 1 active ARFF
## 6 7 audiology 1 active ARFFTo find a specific data set, you can now query the resulting datasets object. Suppose we want to find the iris data set.
subset(datasets, name == "iris")## data.id name version status format tags majority.class.size
## 53 61 iris 1 active ARFF 50
## 811 969 iris 3 active ARFF 100
## 2586 41510 iris 9 active ARFF NA
## 2587 41511 iris 10 active ARFF 50
## 2620 41567 iris 11 active ARFF NA
## 2621 41568 iris 12 active ARFF 50
## 2622 41582 iris 13 active ARFF NA
## 2623 41583 iris 14 active ARFF 50
## 2873 41996 iris 15 active ARFF NA
## 2874 41997 iris 16 active ARFF 50
## 2876 42002 iris 17 active ARFF NA
## 2877 42003 iris 18 active ARFF 50
## 2880 42010 iris 19 active ARFF NA
## 2881 42011 iris 20 active ARFF 50
## 2882 42015 iris 21 active ARFF NA
## 2883 42016 iris 22 active ARFF 50
## 2884 42020 iris 23 active ARFF NA
## 2885 42021 iris 24 active ARFF 50
## 2886 42025 iris 25 active ARFF NA
## 2887 42026 iris 26 active ARFF 50
## 2888 42030 iris 27 active ARFF NA
## 2889 42031 iris 28 active ARFF 50
## 2890 42035 iris 29 active ARFF NA
## 2891 42036 iris 30 active ARFF 50
## 2892 42040 iris 31 active ARFF NA
## 2893 42041 iris 32 active ARFF 50
## 2894 42045 iris 33 active ARFF NA
## 2895 42046 iris 34 active ARFF 50
## 2896 42050 iris 35 active ARFF NA
## 2897 42051 iris 36 active ARFF 50
## 2898 42055 iris 37 active ARFF NA
## 2899 42056 iris 38 active ARFF 50
## 2904 42065 iris 39 active ARFF NA
## 2905 42066 iris 40 active ARFF 50
## 2906 42070 iris 41 active ARFF NA
## 2907 42071 iris 42 active ARFF 50
## max.nominal.att.distinct.values minority.class.size number.of.classes
## 53 3 50 3
## 811 2 50 2
## 2586 3 NA NA
## 2587 3 50 3
## 2620 3 NA NA
## 2621 3 50 3
## 2622 3 NA NA
## 2623 3 50 3
## 2873 3 NA NA
## 2874 3 50 3
## 2876 3 NA NA
## 2877 3 50 3
## 2880 3 NA NA
## 2881 3 50 3
## 2882 3 NA NA
## 2883 3 50 3
## 2884 3 NA NA
## 2885 3 50 3
## 2886 3 NA NA
## 2887 3 50 3
## 2888 3 NA NA
## 2889 3 50 3
## 2890 3 NA NA
## 2891 3 50 3
## 2892 3 NA NA
## 2893 3 50 3
## 2894 3 NA NA
## 2895 3 50 3
## 2896 3 NA NA
## 2897 3 50 3
## 2898 3 NA NA
## 2899 3 50 3
## 2904 3 NA NA
## 2905 3 50 3
## 2906 3 NA NA
## 2907 3 50 3
## number.of.features number.of.instances
## 53 5 150
## 811 5 150
## 2586 5 150
## 2587 5 150
## 2620 5 150
## 2621 5 150
## 2622 5 150
## 2623 5 150
## 2873 5 150
## 2874 5 150
## 2876 5 150
## 2877 5 150
## 2880 5 150
## 2881 5 150
## 2882 5 150
## 2883 5 150
## 2884 5 150
## 2885 5 150
## 2886 5 150
## 2887 5 150
## 2888 5 150
## 2889 5 150
## 2890 5 150
## 2891 5 150
## 2892 5 150
## 2893 5 150
## 2894 5 150
## 2895 5 150
## 2896 5 150
## 2897 5 150
## 2898 5 150
## 2899 5 150
## 2904 5 150
## 2905 5 150
## 2906 5 150
## 2907 5 150
## number.of.instances.with.missing.values number.of.missing.values
## 53 0 0
## 811 0 0
## 2586 0 0
## 2587 0 0
## 2620 0 0
## 2621 0 0
## 2622 0 0
## 2623 0 0
## 2873 0 0
## 2874 0 0
## 2876 0 0
## 2877 0 0
## 2880 0 0
## 2881 0 0
## 2882 0 0
## 2883 0 0
## 2884 0 0
## 2885 0 0
## 2886 0 0
## 2887 0 0
## 2888 0 0
## 2889 0 0
## 2890 0 0
## 2891 0 0
## 2892 0 0
## 2893 0 0
## 2894 0 0
## 2895 0 0
## 2896 0 0
## 2897 0 0
## 2898 0 0
## 2899 0 0
## 2904 0 0
## 2905 0 0
## 2906 0 0
## 2907 0 0
## number.of.numeric.features number.of.symbolic.features
## 53 4 1
## 811 4 1
## 2586 4 1
## 2587 4 1
## 2620 4 1
## 2621 4 1
## 2622 4 1
## 2623 4 1
## 2873 4 1
## 2874 4 1
## 2876 4 1
## 2877 4 1
## 2880 4 1
## 2881 4 1
## 2882 4 1
## 2883 4 1
## 2884 4 1
## 2885 4 1
## 2886 4 1
## 2887 4 1
## 2888 4 1
## 2889 4 1
## 2890 4 1
## 2891 4 1
## 2892 4 1
## 2893 4 1
## 2894 4 1
## 2895 4 1
## 2896 4 1
## 2897 4 1
## 2898 4 1
## 2899 4 1
## 2904 4 1
## 2905 4 1
## 2906 4 1
## 2907 4 1As you can see, there are two data sets called iris. We want to use the original data set with three classes, which is stored under the data set ID (data.id) 61, 41511, 41568, 41583, 41997, 42003, 42011, 42016, 42021, 42026, 42031, 42036, 42041, 42046, 42051, 42056, 42066, 42071. You can also have a closer look at the data set on the corresponding OpenML web page (http://openml.org/d/61, 41511, 41568, 41583, 41997, 42003, 42011, 42016, 42021, 42026, 42031, 42036, 42041, 42046, 42051, 42056, 42066, 42071).
Each OpenML task is a bundle that encapsulates information on various objects:
"Supervised Classification" or "Supervised Regression""predictive accuracy" for a classification taskListing the tasks can be done via
tasks = listOMLTasks()The resulting data.frame contains for each of the listed tasks information on:
task.idtask.typetarget.featuretags which can be used for labelling the taskestimation.procedure (aka resampling strategy)evaluation.measures used for measuring the performance of the learner / flow on the taskstr(tasks)## 'data.frame': 5000 obs. of 25 variables:
## $ task.id : int 2 3 4 5 6 7 8 9 10 11 ...
## $ task.type : chr "Supervised Classification" "Supervised Classification" "Supervised Classification" "Supervised Classification" ...
## $ data.id : int 2 3 4 5 6 7 8 9 10 11 ...
## $ name : chr "anneal" "kr-vs-kp" "labor" "arrhythmia" ...
## $ status : chr "active" "active" "active" "active" ...
## $ format : chr "ARFF" "ARFF" "ARFF" "ARFF" ...
## $ estimation.procedure : chr "10-fold Crossvalidation" "10-fold Crossvalidation" "10-fold Crossvalidation" "10-fold Crossvalidation" ...
## $ evaluation.measures : chr "predictive_accuracy" NA "predictive_accuracy" "predictive_accuracy" ...
## $ target.feature : chr "class" "class" "class" "class" ...
## $ cost.matrix : chr NA NA NA NA ...
## $ source.data.labeled : chr NA NA NA NA ...
## $ target.feature.event : chr NA NA NA NA ...
## $ target.feature.left : chr NA NA NA NA ...
## $ target.feature.right : chr NA NA NA NA ...
## $ quality.measure : chr NA NA NA NA ...
## $ majority.class.size : int 684 1669 37 245 813 57 NA 67 81 288 ...
## $ max.nominal.att.distinct.values : int 7 3 3 13 26 24 NA 22 8 3 ...
## $ minority.class.size : int 8 1527 20 2 734 1 NA 3 2 49 ...
## $ number.of.classes : int 5 2 2 13 26 24 0 6 4 3 ...
## $ number.of.features : int 39 37 17 280 17 70 6 26 19 5 ...
## $ number.of.instances : int 898 3196 57 452 20000 226 345 205 148 625 ...
## $ number.of.instances.with.missing.values: int 898 0 56 384 0 222 0 46 0 0 ...
## $ number.of.missing.values : int 22175 0 326 408 0 317 0 59 0 0 ...
## $ number.of.numeric.features : int 6 0 8 206 16 0 6 15 3 4 ...
## $ number.of.symbolic.features : int 33 37 9 74 1 70 0 11 16 1 ...For some data sets, there may be more than one task available on the OpenML server. For example, one can look for "Supervised Classification" tasks that are available for data set 61 via
head(subset(tasks, task.type == "Supervised Classification" & data.id == 61L)[, 1:5])## task.id task.type data.id name status
## 51 59 Supervised Classification 61 iris active
## 263 289 Supervised Classification 61 iris active
## 428 1823 Supervised Classification 61 iris active
## 535 1939 Supervised Classification 61 iris active
## 580 1992 Supervised Classification 61 iris active
## 3298 7306 Supervised Classification 61 iris activeA flow is the definition and implementation of a specific algorithm workflow or script, i.e., a flow is essentially the code / implementation of the algorithm.
flows = listOMLFlows()
str(flows)## 'data.frame': 13433 obs. of 6 variables:
## $ flow.id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ full.name : chr "openml.evaluation.EuclideanDistance(1.0)" "openml.evaluation.PolynomialKernel(1.0)" "openml.evaluation.RBFKernel(1.0)" "openml.evaluation.area_under_roc_curve(1.0)" ...
## $ name : chr "openml.evaluation.EuclideanDistance" "openml.evaluation.PolynomialKernel" "openml.evaluation.RBFKernel" "openml.evaluation.area_under_roc_curve" ...
## $ version : int 1 1 1 1 1 1 1 1 1 1 ...
## $ external.version: chr "" "" "" "" ...
## $ uploader : int 1 1 1 1 1 1 1 1 1 1 ...flows[56:63, 1:4]## flow.id full.name name version
## 56 56 weka.ZeroR(1) weka.ZeroR 1
## 57 57 weka.OneR(1) weka.OneR 1
## 58 58 weka.NaiveBayes(1) weka.NaiveBayes 1
## 59 59 weka.JRip(1) weka.JRip 1
## 60 60 weka.J48(1) weka.J48 1
## 61 61 weka.REPTree(1) weka.REPTree 1
## 62 62 weka.DecisionStump(1) weka.DecisionStump 1
## 63 63 weka.HoeffdingTree(1) weka.HoeffdingTree 1A run is an experiment, which is executed on a given combination of task, flow and setup (i.e., the explicit parameter configuration of a flow). The corresponding results are stored as a run result. Both objects, i.e., runs and run results, can be listed via listOMLRuns or listOMLRunEvaluations, respectively. As each of those objects is defined with a task, setup and flow, you can extract runs and run results with specific combinations of task.id, setup.id and/or flow.id. For instance, listing all runs for task 59 (supervised classification on iris) can be done with
runs = listOMLRuns(task.id = 59L) # must be specified with the task, setup and/or implementation ID
head(runs)## run.id task.id setup.id flow.id uploader error.message
## 1 81 59 12 67 1 <NA>
## 2 161 59 13 70 1 <NA>
## 3 234 59 1 56 1 <NA>
## 4 447 59 6 61 1 <NA>
## 5 473 59 18 77 1 <NA>
## 6 491 59 7 62 1 <NA># one of the IDs (here: task.id) must be supplied
run.results = listOMLRunEvaluations(task.id = 59L)
str(run.results)## 'data.frame': 4441 obs. of 33 variables:
## $ run.id : int 81 161 234 447 473 491 550 6088 6157 6158 ...
## $ task.id : int 59 59 59 59 59 59 59 59 59 59 ...
## $ setup.id : int 12 13 1 6 18 7 16 11 12 3 ...
## $ flow.id : int 67 70 56 61 77 62 75 66 67 58 ...
## $ flow.name : chr "weka.BayesNet_K2(1)" "weka.SMO_PolyKernel(1)" "weka.ZeroR(1)" "weka.REPTree(1)" ...
## $ flow.version : chr "1" "1" "1" "1" ...
## $ flow.source : chr "weka" "weka" "weka" "weka" ...
## $ learner.name : chr "BayesNet_K2" "SMO_PolyKernel" "ZeroR" "REPTree" ...
## $ data.name : chr "iris" "iris" "iris" "iris" ...
## $ upload.time : chr "2014-04-07 00:05:11" "2014-04-07 00:55:32" "2014-04-07 01:33:24" "2014-04-07 06:26:27" ...
## $ area.under.roc.curve : num 0.983 0.977 0.5 0.967 0.978 ...
## $ average.cost : num 0 0 0 0 0 0 0 0 0 0 ...
## $ build.cpu.time : num NA NA NA NA NA NA NA NA NA NA ...
## $ build.memory : num NA NA NA NA NA NA NA NA NA NA ...
## $ f.measure : num 0.94 0.96 0.167 0.927 0.947 ...
## $ kappa : num 0.91 0.94 0 0.89 0.92 0.5 0.95 0.93 0.91 0.93 ...
## $ kb.relative.information.score: num 1.39e+02 9.09e+01 -6.80e-05 1.31e+02 1.38e+02 ...
## $ mean.absolute.error : num 0.0384 0.2311 0.4444 0.0671 0.0392 ...
## $ mean.prior.absolute.error : num 0.444 0.444 0.444 0.444 0.444 ...
## $ number.of.instances : num 150 150 150 150 150 150 150 150 150 150 ...
## $ precision : num 0.94 0.96 0.111 0.927 0.947 ...
## $ predictive.accuracy : num 0.94 0.96 0.333 0.927 0.947 ...
## $ prior.entropy : num 1.58 1.58 1.58 1.58 1.58 ...
## $ recall : num 0.94 0.96 0.333 0.927 0.947 ...
## $ relative.absolute.error : num 0.0863 0.52 1 0.151 0.0881 ...
## $ root.mean.prior.squared.error: num 0.471 0.471 0.471 0.471 0.471 ...
## $ root.mean.squared.error : num 0.16 0.288 0.471 0.211 0.178 ...
## $ root.relative.squared.error : num 0.339 0.611 1 0.447 0.377 ...
## $ scimark.benchmark : num 1981 1980 2011 1887 1998 ...
## $ total.cost : num 0 0 0 0 0 0 0 0 0 0 ...
## $ usercpu.time.millis : num NA NA NA NA NA NA NA NA NA NA ...
## $ usercpu.time.millis.testing : num NA NA NA NA NA NA NA NA NA NA ...
## $ usercpu.time.millis.training : num NA NA NA NA NA NA NA NA NA NA ...Analogously to the previous listings, one can list further objects simply by calling the respective functions.
listOMLDataSetQualities()
listOMLEstimationProcedures()
listOMLEvaluationMeasures()
listOMLTaskTypes()Users can download data sets, tasks, flows and runs from the OpenML server. The package provides special representations for each object, which will be discussed here.
To directly download a data set, e.g., when you want to run a few preliminary experiments, one can use the function getOMLDataSet. The function accepts a data set ID as input and returns the corresponding OMLDataSet:
iris.data = getOMLDataSet(data.id = 61L) # the iris data set has the data set ID 61The following call returns an OpenML task object for a supervised classification task on the iris data:
task = getOMLTask(task.id = 59L)
task##
## OpenML Task 59 :: (Data ID = 61)
## Task Type : Supervised Classification
## Data Set : iris :: (Version = 1, OpenML ID = 61)
## Target Feature(s) : class
## Tags : basic, study_1, study_7, under100k, under1m
## Estimation Procedure : Stratified crossvalidation (1 x 10 folds)
## Evaluation Measure(s): predictive_accuracyThe corresponding "OMLDataSet" object can be accessed by
task$input$data.set##
## Data Set 'iris' :: (Version = 1, OpenML ID = 61)
## Collection Date : 1936
## Creator(s) : R.A. Fisher
## Default Target Attribute: classand the class of the task can be shown with the next line
task$task.type## [1] "Supervised Classification"Also, it is possible to extract the data set itself via
iris.data = task$input$data.set$data
head(iris.data)## sepallength sepalwidth petallength petalwidth class
## 0 5.1 3.5 1.4 0.2 Iris-setosa
## 1 4.9 3.0 1.4 0.2 Iris-setosa
## 2 4.7 3.2 1.3 0.2 Iris-setosa
## 3 4.6 3.1 1.5 0.2 Iris-setosa
## 4 5.0 3.6 1.4 0.2 Iris-setosa
## 5 5.4 3.9 1.7 0.4 Iris-setosaAside from tasks and data sets, one can also download flows – by calling getOMLFlow with the specific flow.id
flow = getOMLFlow(flow.id = 2700L)
flow##
## Flow 'classif.randomForest' :: (Version = 47, Flow ID = 2700)
## External Version : R_3.1.2-734b029d
## Dependencies : mlr_2.9, randomForest_4.6.12
## Number of Flow Parameters: 16
## Number of Flow Components: 0To download the results of one run, including all server and user computed metrics, you have to define the corresponding run ID. For all runs that are actually related to the task, the corresponding ID can be extracted from the runs object, which was created in the previous section. Here we use a run of task 59, which has the run.id 525534. Single OpenML runs can be downloaded with the function getOMLRun:
task.list = listOMLRuns(task.id = 59L)
task.list[281:285, ]## run.id task.id setup.id flow.id uploader error.message
## 281 7244063 59 5275959 6952 1 <NA>
## 282 7245683 59 5277579 6952 1 <NA>
## 283 7245684 59 5277580 6952 1 <NA>
## 284 7245686 59 5277582 6952 1 <NA>
## 285 7245687 59 5277583 6952 1 <NA>run = getOMLRun(run.id = 524027L)
run##
## OpenML Run 524027 :: (Task ID = 59, Flow ID = 2393)
## User ID : 970
## Learner : classif.randomForest(43)
## Task type: Supervised ClassificationEach OMLRun object is a list object, which stores additional information on the run. For instance, the flow of the previously downloaded run has some non-default settings for hyperparameters, which can be obtained by:
run$parameter.setting # retrieve the list of parameter settings## $seed
## (parameter of component 2393) seed = 1
##
## $kind
## (parameter of component 2393) kind = Mersenne-Twister
##
## $normal.kind
## (parameter of component 2393) normal.kind = InversionIf the underlying flow has hyperparameters that are different from the default values of the corresponding learner, they are also shown, otherwise the default hyperparameters are used (but not explicitly listed).
All the data that served as input for the run, including data set IDs and the URL to the data, is stored in input.data:
run$input.data##
## ** Data Sets **
## data.id name
## 1 61 iris
## url
## 1 https://www.openml.org/data/download/61/dataset_61_iris.arff
##
## ** Files **
## data frame with 0 columns and 0 rows
##
## ** Evaluations **
## data frame with 0 columns and 0 rowsPredictions made by an uploaded run are stored within the predictions element and can be retrieved via
head(run$predictions, 10)## repeat fold row_id prediction truth
## 1 0 0 43 Iris-setosa Iris-setosa
## 2 0 0 14 Iris-setosa Iris-setosa
## 3 0 0 37 Iris-setosa Iris-setosa
## 4 0 0 23 Iris-setosa Iris-setosa
## 5 0 0 10 Iris-setosa Iris-setosa
## 6 0 0 99 Iris-versicolor Iris-versicolor
## 7 0 0 87 Iris-versicolor Iris-versicolor
## 8 0 0 97 Iris-versicolor Iris-versicolor
## 9 0 0 62 Iris-versicolor Iris-versicolor
## 10 0 0 92 Iris-versicolor Iris-versicolor
## confidence.Iris-setosa confidence.Iris-versicolor
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 6 0 1
## 7 0 1
## 8 0 1
## 9 0 1
## 10 0 1
## confidence.Iris-virginica
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0The output above shows predictions, ground truth information about classes and task-specific information, e.g., about the confidence of a classifier (for every observation) or in which fold a data point has been placed.
The modularized structure of OpenML allows to apply the implementation of an algorithm to a specific task and there exist multiple possibilities to do this.
If one is working with mlr, one can specify an RLearner object and use the function runTaskMlr to create the desired "OMLMlrRun" object. The task is created the same way as in the previous sections:
task = getOMLTask(task.id = 59L)
library("mlr")
lrn = makeLearner("classif.rpart")
run.mlr = runTaskMlr(task, lrn)
run.mlr## $run
##
## OpenML Run NA :: (Task ID = 59, Flow ID = NA)
##
## $bmr
## task.id learner.id acc.test.join timetrain.test.sum
## 1 iris classif.rpart 0.94 0.08
## timepredict.test.sum
## 1 0
##
## $flow
##
## Flow 'mlr.classif.rpart' :: (Version = NA, Flow ID = NA)
## External Version : R_3.6.1-v2.6c782c80
## Dependencies : R_3.6.1, OpenML_1.9, mlr_2.15.0, rpart_4.1.15
## Number of Flow Parameters: 14
## Number of Flow Components: 0
##
## attr(,"class")
## [1] "OMLMlrRun"Note that locally created runs don’t have a run ID or flow ID yet. These are assigned by the OpenML server after uploading the run.
If you are not using mlr, you will have to invest some more time and effort to get things done since this is not supported yet. So, unless you have good reasons to do otherwise, we strongly encourage to use mlr. If the algorithm you want to use is not integrated in mlr yet, you can integrate it yourself (see the tutorial) or open an issue on mlr GitHub repository and hope someone else will do it for you.
The following section gives an overview on how one can contribute building blocks (i.e. data sets, flows and runs) to the OpenML server.
A data set contains information that can be stored on OpenML and used by OpenML tasks and runs. This example shows how a very simple data set can be taken from R, converted to an OpenML data set and afterwards uploaded to the server. The corresponding workflow consists of the following three steps:
makeOMLDataSetDescription: create the description object of an OpenML data setmakeOMLDataSet: convert the data set into an OpenML data setuploadOMLDataSet: upload the data set to the serverdata("airquality")
dsc = "Daily air quality measurements in New York, May to September 1973.
This data is taken from R."
cit = "Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983)
Graphical Methods for Data Analysis. Belmont, CA: Wadsworth."
## (1) Create the description object
desc = makeOMLDataSetDescription(name = "airquality",
description = dsc,
creator = "New York State Department of Conservation (ozone data) and the National
Weather Service (meteorological data)",
collection.date = "May 1, 1973 to September 30, 1973",
language = "English",
licence = "GPL-2",
url = "https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html",
default.target.attribute = "Ozone",
citation = cit,
tags = "R")
## (2) Create the OpenML data set
air.data = makeOMLDataSet(desc = desc,
data = airquality,
colnames.old = colnames(airquality),
colnames.new = colnames(airquality),
target.features = "Ozone")
## (3) Upload the OpenML data set to the server
## Because this is a simple data set which is generally already available in R
## please do not actually upload it to the server!
## The code would be:
#dataset.id = uploadOMLDataSet(air.data)
#dataset.idAlternatively you can enter data directly on the OpenML website.
A flow is an implementation of a single algorithm or a script. Each mlr learner can be considered an implementation of a flow, which can be uploaded to the server with the function uploadOMLFlow. If the flow has already been uploaded to the server (either by you or someone else), one receives a message that the flow already exists and the flow.id is returned from the function. Otherwise, the flow will be uploaded, receive its own flow.id and return that ID.
library("mlr")
lrn = makeLearner("classif.randomForest")
flow.id = uploadOMLFlow(lrn)
flow.idIn addition to uploading data sets or flows, one can also upload runs (which a priori have to be created, e.g., using mlr):
## choose 2 flows (i.e., mlr-learners)
learners = list(
makeLearner("classif.kknn"),
makeLearner("classif.randomForest")
)
## pick 3 random tasks
task.ids = c(57, 59, 2382)
for (lrn in learners) {
for (id in task.ids) {
task = getOMLTask(id)
res = runTaskMlr(task, lrn)$run
run.id = uploadOMLRun(res) # upload results
}
}Before your run will be uploaded to the server, uploadOMLRun checks whether the flow that created this run is already available on the server. If the flow does not exist on the server, it will (automatically) be uploaded as well.
Now, you should have gotten an idea on how to use our package. However, as there is always room for improvement, we are more than happy to receive your feedback. So, in case
please open an issue in the issue tracker of our GitHub repository.