Package bigml. May 20, 2015

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1 Type Package Title Bindings for the BigML API Version Date Package bigml May 20, 2015 The 'bigml' package contains bindings for the BigML API. The package includes methods that provide straightforward access to basic API functionality, as well as methods that accommodate idiomatic R data types and concepts. License LGPL-3 URL BugReports Imports RJSONIO, RCurl, plyr Collate 'bigml-internal.r' 'formencodeurl.r' 'bigml-package.r' 'createdataset.r' 'createmodel.r' 'createprediction.r' 'createsource.r' 'getdataset.r' 'getmodel.r' 'getprediction.r' 'getsource.r' 'Datasets.R' 'Models.R' 'Sources.R' 'quickdataset.r' 'quickmodel.r' 'quickprediction.r' 'quicksource.r' 'setcredentials.r' 'deleteresource.r' NeedsCompilation no Author Leon Hwang [cre, aut] Maintainer Repository CRAN Date/Publication :18:53 R topics documented: bigml-package createdataset createmodel createprediction

2 2 bigml-package createsource deleteresource formencodeurl getdataset getmodel getprediction getsource Datasets Models Sources quickdataset quickmodel quickprediction quicksource setcredentials Index 32 bigml-package R bindings for BigML API Package: bigml Type: Package Version: Date: License: GPL (>= 2) LazyLoad: yes Details A set of methods that enable straightforward usage of the BigML API. The methods use R idioms and native datatypes where appropriate, while also providing access to more conventional API usage. Examples ## Not run: # set default credentials

3 createdataset 3 setcredentials( user, key ) model = quickmodel(iris, Species ) quickprediction(model, c(petal.width=0.2, Petal.Length=1.4)) # use specific credentials quickprediction(model, c(petal.width=0.2, Petal.Length=1.4), user= someuser, api_key= somekey ) # most recent sources Sources() # specify limit and offset Models(limit=15,offset=300) # specify filter criteria Datasets(size gt= ) ## End(Not run) createdataset Creating BigML Datasets Creating BigML Datasets createdataset(source_id, field_ids = NULL, = NULL, size = NULL,...) source_id field_ids size The relevant source id used to build the dataset. A of field ids and field properties. See example. The for the dataset. The amount (in bytes) of the source to use for creating the dataset.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. Details This function needs to use id information from existing R resources. See the references for more details.

4 4 createdataset category code content_type created credits description fields file_ md5 data.frame (or if flatten=false) number_of_datasets number_of_models number_of_predictions private resource size source_parser status tags type updated AsIs References See Also Other dataset methods: getdataset; Datasets; quickdataset

5 createmodel 5 Examples ## Not run: # simple create dataset example createdataset("source/1") # configure a number of different parameters createdataset("source/2", field_ids=c( ), = test, size=10) ## End(Not run) createmodel Creating BigML Models Creating BigML Models createmodel(dataset_id, input_field_ids = NULL, = NULL, objective_field_ids = NULL, range = NULL,...) Details dataset_id the relevant dataset_id used to create the model. input_field_ids a vector of field ids to use for training. the to give to the model. objective_field_ids a vector of objective fields used for training. range a vector of two values that define a range of instances from the dataset to train on.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. This function needs to use id information from existing R resources. See the references for more details. model_return

6 6 createprediction References See Also Other model methods: getmodel; Models; quickmodel Examples ## Not run: # simple example m1 = createmodel("dataset/1") # configure a number of different parameters m2 = createmodel("dataset/2", input_field_ids=c( ), objective_field_ids= , = test, range = c(10,1000)) ## End(Not run) createprediction Creating BigML Predictions Creating BigML Predictions createprediction(model_id, input_field_ids, = NULL, prediction_only = TRUE,...) Details model_id string; the model id input_field_ids a of input field ids and values to make a prediction for (see example). string; The given for the prediction. prediction_only : Indicating whether the prediction should be returned as a simple value, or if the full response object should be returned.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. This function needs to use id information from existing R resources. See the references for more details.

7 createprediction 7 atomic or value if prediction_only is TRUE, else return: category code created credits dataset dataset_status description fields input_data locale model model_status objective_fields prediction prediction_path private resource source source_status status tags updated AsIs References See Also Other prediction methods: getprediction; quickprediction

8 8 createsource Examples ## Not run: # simple example m1 = createprediction("model/1", input_field_ids = c( = somevalue, =9999)) # configure a number of different parameters m2 = createprediction("model/2", input_field_ids = c( = somevalue, =9999), = new prediction ) ## End(Not run) createsource Creating BigML Sources Creating BigML Sources createsource(file_, = base(file_), header = TRUE, locale = "en-us", missing_tokens = c("na"), quote = "\"", separator = ",", trim = TRUE, flatten = TRUE,...) file_ header locale A string giving a file location A string specifying the of the source ; TRUE if data contains information, false otherwise. A string giving the locale (defaults to en-us). missing_tokens A vector of strings that will be used to specify missing values in a file. quote separator trim Details flatten A string specifying the quoting used. the separator used when a file is specified. A value indicating whether white space should be trimmed. A value indicating whether or not the returned field objects should be "flattened" into a data frame.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. createsource

9 createsource 9 category code content_type created credits description fields data.frame (or if flatten=false) file_ md5 number_of_datasets number_of_models number_of_predictions private resource size source_parser status tags AsIs type updated References See Also Other source methods: getsource; Sources; quicksource Examples ## Not run: # simple example m1 = createsource("/tmp/iris.csv") ## End(Not run)

10 10 deleteresource deleteresource Deleting BigML Resources Deleting BigML Resources deleteresource(resource_id,...) resource_id the resource to delete.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. Details This function deletes bigml resources referenced by their resource id. TRUE if successful, FALSE otherwise. Examples ## Not run: # replace with your valid credentials: deleteresource("source/1") ## End(Not run)

11 formencodeurl 11 formencodeurl A simple function to turn d arguments into a form-encoded string A simple function to turn d arguments into a form-encoded string formencodeurl(a,...) a something... arbitrary d arguments that will become part of a form-encoded url. Details This function is called in every BigML API function. It helps build the URL that requests are forwarded to. It automatically adds any default user and api key settings specified by setcredentials. However, it also can be used to access advanced options that are otherwise undocumented here. For instance, it s possible to filter and/or sort on a number of different api requests, using a number of different fields (e.g., see the documentation on ing and sorting datasets.) Other usage includes specifying user and api_key for individual API requests; or limit or offset parameters useful for paging through requests. Finally, it s possible to enable a simple debug mode by passing debug=true. This will print the url request string to the screen, along with any posted json objects. form-encoded string result Examples ## Not run: formencodeurl(user="user1", api_key="test", limit=100, debug=true) # "?user=user1&api_key=test&limit=100&debug=true" ## End(Not run)

12 12 getdataset getdataset Retrieving a BigML Dataset Retrieving a BigML Dataset getdataset(source_id, include_overview = TRUE,...) Details source_id A string giving the of the source id. include_overview A value indicating whether to provide a simple data frame overview of fields.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. This function needs to use id information from existing R resources. See the references for more details. category code content_type created credits description fields file_ md5 data.frame (or if flatten=false) number_of_datasets number_of_models number_of_predictions private

13 getmodel 13 resource size source_parser status tags type updated AsIs References See Also Other dataset methods: createdataset; Datasets; quickdataset getmodel Retrieving a BigML Model Retrieving a BigML Model getmodel(model_id,...) model_id A string giving the model id.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. Details This function needs to use id information from existing R resources. See the references for more details.

14 14 getmodel category code columns created credits dataset dataset_status description input_fields locale max_columns max_rows model number_of_predictions objective_fields private range resource rows size source source_status status tags updated AsIs References See Also Other model methods: createmodel; Models; quickmodel

15 getprediction 15 getprediction Retrieving a BigML Prediction Retrieving a BigML Prediction getprediction(prediction_id,...) Details prediction_id the id of the prediction resource.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. This function needs to use id information from existing R resources. See the references for more details. atomic or value if prediction_only is TRUE, else return: category code created credits dataset dataset_status description fields input_data locale model model_status objective_fields prediction

16 16 getsource prediction_path private resource source source_status status tags updated AsIs References See Also Other prediction methods: createprediction; quickprediction getsource Retrieving a BigML Source Retrieving a BigML Source getsource(source_id, flatten = TRUE) Details source_id flatten A value giving the of the source. A value indicating whether to flatten the response into a data frame.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. This function needs to use id information from existing R resources. See the references for more details.

17 getsource 17 category code content_type created credits description fields file_ md5 data.frame (or if flatten=false) number_of_datasets number_of_models number_of_predictions private resource size source_parser status tags type updated AsIs References See Also Other source methods: createsource; Sources; quicksource

18 18 Datasets Datasets Listing BigML Datasets Listing BigML Datasets Datasets(flatten = TRUE, datasets_only = TRUE,...) flatten datasets_only A value indicating whether to flatten the response into a dataframe. A value indicating whether to only return the data frame of field information (only valid if flatten is TRUE).... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. If flatten is TRUE, and datasets_only = TRUE a data frame of: category code columns created credits description locale number_of_models number_of_predictions private resource rows size source source_status status.bytes

19 Models 19 status.code status.elapsed status.message status.serialized_rows updated If flatten is TRUE and datasets_only = FALSE a of: meta datasets fields data.frame data.frame If flatten is FALSE a of: meta objects see references for more details References See Also Other dataset methods: createdataset; getdataset; quickdataset Models Listing BigML Datasets Listing BigML Datasets Models(flatten = TRUE, models_only = TRUE,...)

20 20 Models flatten models_only A value indicating whether to flatten the response into a data frame. A value indicating whether to only return the data frame of model information (only valid if flatten is TRUE).... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. If flatten is TRUE, and models_only = TRUE a data frame of: category code columns created credits dataset dataset_status description locale max_columns max_rows number_of_predictions objective_fields private resource rows size source source_status updated If flatten is TRUE and models_only = FALSE a of: meta models data.frame If flatten is FALSE a of: meta objects see references for more details

21 Sources 21 References See Also Other model methods: createmodel; getmodel; quickmodel Sources Listing BigML Sources Listing BigML Sources Sources(flatten = TRUE, sources_only = TRUE,...) flatten sources_only A value indicating whether to flatten the response into a dataframe. A value indicating whether to only return the data frame of source information (only valid if flatten is TRUE).... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. If flatten is TRUE, and sources_only = TRUE a data frame of: category code content_type created credits description file_ md5 factor factor factor factor factor factor number_of_datasets

22 22 Sources number_of_models number_of_predictions private resource factor size source_parser.header source_parser.locale factor source_parser.missing_tokens factor source_parser.quote factor source_parser.separator factor source_parser.trim status.code status.elapsed status.message factor type updated factor If flatten is TRUE and sources_only = FALSE a of: meta sources fields data.frame data.frame If flatten is FALSE a of: meta objects see references for more details References See Also Other source methods: createsource; getsource; quicksource

23 quickdataset 23 quickdataset Quickly Creating BigML Datasets Quickly Creating BigML Datasets quickdataset(data, fields = s(data), = paste(deparse(substitute(data)), " s dataset", sep = ""), size = NULL,...) data fields size Details A matrix or data frame containing data to upload to bigml. A vector of s in data that should be used for creating the dataset. A string giving the for the dataset. A value giving the amount (in bytes) of the source to use.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. quickdataset will take its "data" dataframe argument and attempt to create an equivalent BigML dataset using quicksource. R "" class fields will become "" fields in the BigML dataset. R "" class fields become "text" fields. R "factor" fields become "categorical" fields. However, if there are too many factors, BigML may convert the field to text. It is possible to specify the fields to include using the fields argument. This can be a a simple of s that were present in the data argument. See references for more details. category code content_type created credits description fields file_ md5 data.frame (or if flatten=false)

24 24 quickdataset number_of_datasets number_of_models number_of_predictions private resource size source_parser status tags type updated AsIs References See Also Other dataset methods: createdataset; getdataset; Datasets Other quick methods: quickmodel; quickprediction; quicksource Examples ## Not run: # simple example iris.d = quickdataset(iris) # configure a number of different parameters iris.d2 = quickdataset(iris, fields = c( Species, Sepal.length ), = test, size=10000) ## End(Not run)

25 quickmodel 25 quickmodel Quickly Creating BigML Models Quickly Creating BigML Models quickmodel(data, input_fields = s(data), objective_fields = tail(s(data), n = 1), = paste(deparse(substitute(data)), " s model", sep = ""), range = NULL,...) data A matrix or data frame containing data to upload to bigml. input_fields A vector of string s to use for training. objective_fields A single string value to use as an objective field (objective_fields is plural for future use). range A string giving the of the model. A two element vector that defines a range over the dataset in which to train on.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. Details quickmodel will take its "data" dataframe argument and attempt to create a dataset using quickdataset. It is possible to specify the input_fields and objective_fields using the simple s from the data argument. category code columns created credits dataset dataset_status description

26 26 quickmodel input_fields locale max_columns max_rows model number_of_predictions objective_fields private range resource rows size source source_status status tags updated AsIs References See Also Other model methods: createmodel; getmodel; Models Other quick methods: quickdataset; quickprediction; quicksource

27 quickprediction 27 quickprediction Quickly Creating BigML Predictions Quickly Creating BigML Predictions quickprediction(model, values, = NULL, prediction_only = TRUE,...) Details model values A string or response object containing a valid model id value. A d vector or of elements to retrieve a prediction for A string giving the of the prediction. prediction_only if TRUE, only the predicted value is returned. Otherwise, the full API response is returned.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. quickprediction can operate on a model id string, or a model response object from an earlier request. The values are a of d elements that are used as input. atomic or value if prediction_only is TRUE, else return: category code created credits dataset dataset_status description fields input_data locale model model_status

28 28 quickprediction objective_fields prediction prediction_path private resource source source_status status tags updated AsIs A or string value giving the prediction. References See Also Other prediction methods: createprediction; getprediction Other quick methods: quickdataset; quickmodel; quicksource Examples ## Not run: quickprediction("model/1", (Sepal.Width=3.5, Petal.Length=1.4)) # setosa ## End(Not run)

29 quicksource 29 quicksource Quickly Creating BigML Sources Quickly Creating BigML Sources quicksource(data, = deparse(substitute(data)), header =!is.null(s(data)), locale = "en-us", missing_tokens = c("na"), quote = "\"", trim = TRUE, flatten = TRUE,...) data header locale A matrix or data frame containing data to upload to bigml. A string giving the of the source. A value indicating whether to use the first row of data as a header row. A string indicating the desired locale. missing_tokens A vector ing strings that should be treated as missing. quote trim Details flatten A string giving the quote to use. A value indicating whether to trim white space. A value indicating whether to flatten the response into a data frame.... Arbitrary d arguments that are passed on to formencodeurl in order to create form-encoded URL options. quicksource will take its "data" dataframe argument and attempt to create an equivalent BigML source. It does this by converting the dataframe to a csv file, compressing it, and uploading it directly to BigML. Generally, it s better to use quickdataset, since this method attempts to preserve any type information in the data frame. category code content_type created credits description fields data.frame (or if flatten=false)

30 30 quicksource file_ md5 number_of_datasets number_of_models number_of_predictions private resource size source_parser status tags type updated AsIs Note It is not currently possible to retrieve the original file from BigML, but it is possible to delete it. References See Also Other quick methods: quickdataset; quickmodel; quickprediction Other source methods: createsource; getsource; Sources

31 setcredentials 31 setcredentials Set BigML API authentication credentials Set BigML API authentication credentials setcredentials(user, api_key) user api_key use the given user for all subsequent API requests use the given api key for all subsequent API requests Details This function sets default user and api_key information for subsequent BigML API access calls. The relevent user and key are stored in the R system environment variables. So, it s also possible to set these variables by setting BIGMLUSER and BIGMLAPIKEY in an.renviron file. Examples ## Not run: # replace with your valid credentials: setcredentials( some_user, some_key ) ## End(Not run)

32 Index Topic package bigml-package, 2 bigml (bigml-package), 2 bigml-package, 2 createdataset, 3, 13, 19, 24 createmodel, 5, 14, 21, 26 createprediction, 6, 16, 28 createsource, 8, 17, 22, 30 deleteresource, 10 formencodeurl, 3, 5, 6, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23, 25, 27, 29 getdataset, 4, 12, 19, 24 getmodel, 6, 13, 21, 26 getprediction, 7, 15, 28 getsource, 9, 16, 22, 30 Datasets, 4, 13, 18, 24 Models, 6, 14, 19, 26 Sources, 9, 17, 21, 30 quickdataset, 4, 13, 19, 23, 25, 26, quickmodel, 6, 14, 21, 24, 25, 28, 30 quickprediction, 7, 16, 24, 26, 27, 30 quicksource, 9, 17, 22 24, 26, 28, 29 setcredentials, 11, 31 32

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