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code: If the request is successful you will get a bigml.api.HTTP_CREATED (201) status code. In asynchronous file uploading api.create_source calls, it will contain bigml.api.HTTP_ACCEPTED (202) status code. Otherwise, it will be one of the standard HTTP error codes detailed in the documentation.
resource: The identifier of the new resource.
location: The location of the new resource.
object: The resource itself, as computed by BigML.
error: If an error occurs and the resource cannot be created, it will contain an additional code and a description of the error. In this case, location, and resource will be None.
Resource creation is an asynchronous process, so the API will return a response probably before the resource is totally processed and you’ll need to repeatedly call the API to see the evolution of the resource, reflected in its status (see the Statuses section). The bindings provide methods to help you do that. Please check the waiting for resources section to learn more about them.
Resource creation is almost always asynchronous (with few exceptions, like projects, predictions, and similar prediction-like results for Unsupervised Models like anomaly scores, centroids, etc.) Therefore, when you create a new source, a new dataset or a new model, even if you receive an immediate response from the BigML servers, the full creation of the resource can take from a few seconds to a few days, depending on the size of the resource and BigML’s load. A resource is not fully created until its status is bigml.api.FINISHED, or bigml.api.FAULTY if some error occurs (like the one you would get when trying to upload an empty file, a .ppt or a .doc). See the documentation on status codes for the complete listing of potential states and their semantics.
Depending on your application you might need to import the following constants:
from bigml.api import WAITING from bigml.api import QUEUED from bigml.api import STARTED from bigml.api import IN_PROGRESS from bigml.api import SUMMARIZED from bigml.api import FINISHED from bigml.api import UPLOADING from bigml.api import FAULTY from bigml.api import UNKNOWN from bigml.api import RUNNABLE
Usually, you will simply need to wait until the resource is in the bigml.api.FINISHED state for further processing. If that’s the case, the easiest way is calling the api.ok method and passing as first argument the object that contains your resource:
from bigml.api import BigML api = BigML() # creates a connection to BigML's API source = api.create_source('my_file.csv') # creates a source object api.ok(source) # checks that the source is finished and updates ``source``
In this code, api.create_source will probably return a non-finished source object. Then, api.ok will query its status and update the contents of the source variable with the retrieved information until it reaches a bigml.api.FINISHED or bigml.api.FAULTY status.
As explained in the Create Resources section, the time needed to create a completely finished resource can vary depending on many factors: the size of the data to be used, the type of fields and the platform load, for instance. In BigML, the API will answer to any creation request shortly after receiving the creation call that starts the process. Resources in BigML are any-time, meaning that the result contains partial but correct information at any point of its evolution, so getting the information of a resource which is still in progress can be useful. However, usually you’ll want to wait till the process ends to retrieve and use the resource. The api.ok method is the mechanism provided for that, as:
It waits efficiently between API calls. The sleep time is modified to be adapted to the resoruce process as given in its status.
It adapts the parameters of the API call to minimize the amount of information downloaded in each iteration while waiting for completion.
It modifies the contents of the variable passed as argument to store there the value of the resource returned by the API when it reaches the finished or faulty state.
It allows error handling and retries.
Most of the time, no errors happen and a correctly finished resource is generated. In this case, and following the example in the previous section, the api.ok(source) method would return True and the variable source contents would be like:
Where the object attribute of the dictionary would contain the response of the last get call to the API.
Nonetheless, two kinds of problem can arise when using api.ok, and both will cause the method to return False. Firstly, the HTTP connection that it needs to reach the API might fail. Than will prevent the resource information retrieval and will be reflected in the code and error first-level attributes of the source new contents.
and as the call could not reach the API, the object attribute will not be modified.
In this case, the cause was a transient error, and we can decide that transient error calls should be retried a certain amount of times. Just set an error_retries argument: e.g. api.ok(source, error_retries=10).
The second kind of error appears when the API can be correctly reached and it returns a faulty resource. There’s also a variety of reasons for a resource to end in a bigml.api.FAULTY state, but an example would be trying to create a source by uploading an empty file, or some kind of non-supported file, like an .ipnb file. The API will accept the create task, and add the new resource ID. Afterwards, it will realize that the uploaded contents are not correct, so the api.ok call with get a resource in a faulty status. Let’s see what happens when trying to upload a zip file that does not contain images or a CSV-like file.
In this case, according to the outer code and error attributes (associated to HTTP failures) everything went smoothly, which is correct because the api.ok method was able to connect to the API. However, the object attribute (that contains the API response) will show in the inner code attribute that describes the error and the status information will also contain a message describing the cause of that error. As this particular error is not transient, no retrying will be done even if the error_retries argument is set.
Based on what we’ve seen, a safe way to check if we have been able to create completely a resource in BigML would be checking the return value of the api.ok method.
from bigml.api import BigML api = BigML() source = api.create_source('my_file.csv') # creates a source object if api.ok(source): # code that uses the finished source contents show_fields(source) else: # code that handles the error handle_error(source)
An alternative that can also be used to check for errors is using the raise_on_error argument of the api.ok method, that will cause an error to be raised in both the HTTP problem or faulty resource scenarios.
from bigml.api import BigML from bigml.exceptions import FaultyResourceError api = BigML() source = api.create_source('my_file.csv') # creates a source object try: api.ok(source) except FaultyResourceError: # code that handles the faulty resource error handle_faulty_error(source) except Exception: # code that handles the HTTP connection errors handle_http_error(source)
The api.ok method is repeatedly calling the API but it sleeps for some time between calls. The sleeping time is set by using an exponential function that generates a random number in a range. The upper limit of that range is increasing with the number of retries. When the progress of the resource reaches 80%, the waiting times descend by applying a progress dumping. The parameters like the initial waiting time, the number of retries or the estimate of the maximum elapsed time can be provided to fit every particular case.
Sometimes, it can be useful to report the progress of the resource. To that end, api.ok accepts a progress_cb callback function that will be called every time that the status is checked internally. The progress will be a decimal number in the [0, 1] range
As explained previously, the api.ok method updates the contents of the variable that is given as first argument. If you prefer to wait for the resource without side effects on that variable, you can also use the check_resource function:
that will constantly query the API until the resource gets to a FINISHED or FAULTY state, or can also be used with wait_time (in seconds) and retries arguments to control the polling:
The wait_time value is used as seed to a wait interval that grows exponentially with the number of retries up to the given retries limit.
However, in other scenarios you might need to control the complete evolution of the resource, not only its final states. There, you can query the status of any resource with the status method, which simply returns its value and does not update the contents of the associated variable:
api.status(source) api.status(dataset) api.status(model) api.status(prediction) api.status(evaluation) api.status(ensemble) api.status(batch_prediction) api.status(cluster) api.status(centroid) api.status(batch_centroid) api.status(anomaly) api.status(anomaly_score) api.status(batch_anomaly_score) api.status(sample) api.status(correlation) api.status(statistical_test) api.status(logistic_regression) api.status(association) api.status(association_set) api.status(topic_model) api.status(topic_distribution) api.status(batch_topic_distribution) api.status(time_series) api.status(forecast) api.status(optiml) api.status(fusion) api.status(pca) api.status(projection) api.status(batch_projection) api.status(linear_regression) api.status(script) api.status(execution) api.status(library)
Remember that, consequently, you will need to retrieve the resources explicitly in your code to get the updated information.
A special kind of resource is project. Projects are repositories for resources, intended to fulfill organizational purposes. Each project can contain any other kind of resource, but the project that a certain resource belongs to is determined by the one used in the source they are generated from. Thus, when a source is created and assigned a certain project_id, the rest of resources generated from this source will remain in this project.
The REST calls to manage the project resemble the ones used to manage the rest of resources. When you create a project:
from bigml.api import BigML api = BigML() project = api.create_project({'name': 'my first project'})
the resulting resource is similar to the rest of resources, although shorter:
{'code': 201, 'resource': u'project/54a1bd0958a27e3c4c0002f0', 'location': 'http://bigml.io/andromeda/project/54a1bd0958a27e3c4c0002f0', 'object': {u'category': 0, u'updated': u'2014-12-29T20:43:53.060045', u'resource': u'project/54a1bd0958a27e3c4c0002f0', u'name': u'my first project', u'created': u'2014-12-29T20:43:53.060013', u'tags': [], u'private': True, u'dev': None, u'description': u''}, 'error': None}
and you can use its project id to get, update or delete it:
project = api.get_project('project/54a1bd0958a27e3c4c0002f0') api.update_project(project['resource'], {'description': 'This is my first project'}) api.delete_project(project['resource'])
Important: Deleting a non-empty project will also delete all resources assigned to it, so please be extra-careful when using the api.delete_project call.
To create an external connector to an existing database you need to use the create_external_connector method. The only required parameter is the dictionary that contains the information needed to connect to the particular database/table. The attributes of the connection dictionary needed for the method to work will depend on the type of database used.
For instance, you can create a connection to an Elasticsearch database hosted locally at port 9200 by calling:
from bigml.api import BigML api = BigML() external_connector = api.create_external_connector( \ {"hosts": ["localhost:9200"]}, {"source": "elasticsearch"})
where the first argument contains the infromation about the host and source contains the type of database to connec to (allowed types are: elasticsearch, postgresql, mysql, sqlserver). If no source type is set, postgresql will be used as default value.
You can add other properties to that second argument, like the name to be used for this external connector. All other arguments should be placed in the second parameter:
from bigml.api import BigML api = BigML() external_connector = api.create_external_connector( \ {"hosts": ["localhost:9200"]}, {"source": "elasticsearch", "name": "My elasticsearch"})
To create a source from a local data file, you can use the create_source method. The only required parameter is the path to the data file (or file-like object). You can use a second optional parameter to specify any of the options for source creation described in the BigML API documentation.
Here’s a sample invocation:
from bigml.api import BigML api = BigML() source = api.create_source('./data/iris.csv', {'name': 'my source', 'source_parser': {'missing_tokens': ['?']}})
or you may want to create a source from a file in a remote location:
source = api.create_source('s3://bigml-public/csv/iris.csv', {'name': 'my remote source', 'source_parser': {'missing_tokens': ['?']}})
or maybe reading the content from stdin:
content = StringIO.StringIO(sys.stdin.read()) source = api.create_source(content, {'name': 'my stdin source', 'source_parser': {'missing_tokens': ['?']}})
or from an existing external connector:
content = {"source": "postgresql", "externalconnector_id": "5ea1d2f7c7736e160900001c", "query": "select * from table_name"} source = api.create_source(content, {'name': 'my stdin source', 'source_parser': {'missing_tokens': ['?']}})
or using data stored in a local python variable. The following example shows the two accepted formats:
local = [['a', 'b', 'c'], [1, 2, 3], [4, 5, 6]] local2 = [{'a': 1, 'b': 2, 'c': 3}, {'a': 4, 'b': 5, 'c': 6}] source = api.create_source(local, {'name': 'inline source'})
As already mentioned, source creation is asynchronous. In both these examples, the api.create_source call returns once the file is uploaded. Then source will contain a resource whose status code will be either WAITING or QUEUED.
For local data files you can go one step further and use asynchronous uploading:
source = api.create_source('./data/iris.csv', {'name': 'my source', 'source_parser': {'missing_tokens': ['?']}}, async_load=True)
In this case, the call fills source immediately with a primary resource like:
{'code': 202, 'resource': None, 'location': None, 'object': {'status': {'progress': 0.99, 'message': 'The upload is in progress', 'code': 6}}, 'error': None}
where the source['object'] status is set to UPLOADING and its progress is periodically updated with the current uploading progress ranging from 0 to 1. When upload completes, this structure will be replaced by the real resource info as computed by BigML. Therefore source’s status will eventually be (as it is in the synchronous upload case) WAITING or QUEUED.
You can retrieve the updated status at any time using the corresponding get method. For example, to get the status of our source we would use:
Creating Composite Sources (Images)¶
BigML offers support to use images or collections of CSVs in your Machine Learning models. Uploading images to BigML is as easy as uploading any other file. Each file will be ingested and a new source will be created from it. To build Machine Learning models one typically needs lots of images and they are usually uploaded in batches stored in .zip or .tar files. BigML is able to ingest such a file and creates a composite source from it and for each file contained in the compressed file a component source will be created. Thus, a zip file containg two images can be uploaded to BigML by using the create_source method:
and that operation will create three sources: one per image plus the composite source that contains them.
If you put together a bunch of image sources inside a composite, that composite will also have format “image”. If you create a dataset from it, every row will correspond to one of the images in the composite, and have a column representing the image data, and another its filename. Also, BigML will extract around two hundred features per image by default, representing its gradients histogram, and you can choose several others or add labels to each image. Please, check the complete API documentation about composite sources to learn how to create them, update their contents while they are open (editable) and close them so that you can create datasets and models from them. Closing a source makes it immutable, but any source can be cloned into a new source open to modification.
Images are usually associated to other information, like labels or numeric fields, which can be regarded as additional attributes related to that image. The associated information can be described as annotations for each of the images. These annotations can be provided as a JSON file that contains the properties associated to each image and the name of the image file, that is used as foreign key. The meta information needed to create the structure of the composite source, such as the fields to be associated and their types, should also be included in the annotations file. This is an example of the expected structure of the annotations file:
{"description": "Fruit images to test colour distributions", "images_file": "./fruits_hist.zip", "new_fields": [{"name": "new_label", "optype": "categorical"}], "source_id": null, "annotations": [ {"file": "f1/fruits1f.png", "new_label": "True"}, {"file": "f1/fruits1.png", "new_label": "False"}, {"file": "f2/fruits2e.png", "new_label": "False"}]}
The images_file attribute should contain the path to zip-compressed images file and the “annotations” attribute the corresponding annotations. The new_fields attribute should be a list of the fields used as annotations for the images.
Also, if you prefer to keep your annotations in a separate file, you can point to that file in the annotations attribute:
{"description": "Fruit images to test colour distributions", "images_file": "./fruits_hist.zip", "new_fields": [{"name": "new_label", "optype": "categorical"}], "source_id": null, "annotations": "./annotations_detail.json"}
The created source will contain the fields associated to the uploaded images, plus an additional field named new_label with the values defined in this file.
If a source has already been created from this collection of images, you can provide the ID of this source in the source_id attribute. If the annotations file contains the source ID information, the existing source will be updated to add the new annotations (if still open for editing) or will be cloned (if the source is closed for editing) and the new source will be updated. In both cases, images won’t be uploaded again.
Once you have created a source, you can create a dataset. The only required argument to create a dataset is a source id. You can add all the additional arguments accepted by BigML and documented in the Datasets section of the Developer’s documentation.
For example, to create a dataset named “my dataset” with the first 1024 bytes of a source, you can submit the following request:
dataset = api.create_dataset(source, {"name": "my dataset", "size": 1024})
Upon success, the dataset creation job will be queued for execution, and you can follow its evolution using api.status(dataset).
As for the rest of resources, the create method will return an incomplete object, that can be updated by issuing the corresponding api.get_dataset call until it reaches a FINISHED status. Then you can export the dataset data to a CSV file using:
You can also extract samples from an existing dataset and generate a new one with them using the api.create_dataset method. The first argument should be the origin dataset and the rest of arguments that set the range or the sampling rate should be passed as a dictionary. For instance, to create a new dataset extracting the 80% of instances from an existing one, you could use:
dataset = api.create_dataset(origin_dataset, {"sample_rate": 0.8})
Similarly, if you want to split your source into training and test datasets, you can set the sample_rate as before to create the training dataset and use the out_of_bag option to assign the complementary subset of data to the test dataset. If you set the seed argument to a value of your choice, you will ensure a deterministic sampling, so that each time you execute this call you will get the same datasets as a result and they will be complementary:
origin_dataset = api.create_dataset(source) train_dataset = api.create_dataset( origin_dataset, {"name": "Dataset Name | Training", "sample_rate": 0.8, "seed": "my seed"}) test_dataset = api.create_dataset( origin_dataset, {"name": "Dataset Name | Test", "sample_rate": 0.8, "seed": "my seed", "out_of_bag": True})
Sometimes, like for time series evaluations, it’s important that the data in your train and test datasets is ordered. In this case, the split cannot be done at random. You will need to start from an ordered dataset and decide the ranges devoted to training and testing using the range attribute:
origin_dataset = api.create_dataset(source) train_dataset = api.create_dataset( origin_dataset, {"name": "Dataset Name | Training", "range": [1, 80]}) test_dataset = api.create_dataset( origin_dataset, {"name": "Dataset Name | Test", "range": [81, 100]})
It is also possible to generate a dataset from a list of datasets (multidataset):
dataset1 = api.create_dataset(source1) dataset2 = api.create_dataset(source2) multidataset = api.create_dataset([dataset1, dataset2])
Clusters can also be used to generate datasets containing the instances grouped around each centroid. You will need the cluster id and the centroid id to reference the dataset to be created. For instance,
cluster = api.create_cluster(dataset) cluster_dataset_1 = api.create_dataset(cluster, args={'centroid': '000000'})
would generate a new dataset containing the subset of instances in the cluster associated to the centroid id 000000.
Existing datasets can also be cloned:
Once you have created a dataset you can create a model from it. If you don’t select one, the model will use the last field of the dataset as objective field. The only required argument to create a model is a dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Models section of the Developer’s documentation.
For example, to create a model only including the first two fields and the first 10 instances in the dataset, you can use the following invocation:
model = api.create_model(dataset, { "name": "my model", "input_fields": ["000000", "000001"], "range": [1, 10]})
Again, the model is scheduled for creation, and you can retrieve its status at any time by means of api.status(model).
Models can also be created from lists of datasets. Just use the list of ids as the first argument in the api call
model = api.create_model([dataset1, dataset2], { "name": "my model", "input_fields": ["000000", "000001"], "range": [1, 10]})
And they can also be generated as the result of a clustering procedure. When a cluster is created, a model that predicts if a certain instance belongs to a concrete centroid can be built by providing the cluster and centroid ids:
model = api.create_model(cluster, { "name": "model for centroid 000001", "centroid": "000001"})
if no centroid id is provided, the first one appearing in the cluster is used.
Existing models can also be cloned:
If your dataset has no fields showing the objective information to predict for the training data, you can still build a cluster that will group similar data around some automatically chosen points (centroids). Again, the only required argument to create a cluster is the dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Clusters section of the Developer’s documentation.
Let’s create a cluster from a given dataset:
cluster = api.create_cluster(dataset, {"name": "my cluster", "k": 5})
that will create a cluster with 5 centroids.
Existing clusters can also be cloned:
If your problem is finding the anomalous data in your dataset, you can build an anomaly detector, that will use iforest to single out the anomalous records. Again, the only required argument to create an anomaly detector is the dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Anomaly detectors section of the Developer’s documentation.
Let’s create an anomaly detector from a given dataset:
anomaly = api.create_anomaly(dataset, {"name": "my anomaly"})
that will create an anomaly resource with a top_anomalies block of the most anomalous points.
Existing anomaly detectors can also be cloned:
To find relations between the field values you can create an association discovery resource. The only required argument to create an association is a dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Association section of the Developer’s documentation.
For example, to create an association only including the first two fields and the first 10 instances in the dataset, you can use the following invocation:
association = api.create_association(dataset, { \ "name": "my association", "input_fields": ["000000", "000001"], \ "range": [1, 10]})
Again, the association is scheduled for creation, and you can retrieve its status at any time by means of api.status(association).
Associations can also be created from lists of datasets. Just use the list of ids as the first argument in the api call
association = api.create_association([dataset1, dataset2], { \ "name": "my association", "input_fields": ["000000", "000001"], \ "range": [1, 10]})
Existing associations can also be cloned:
To find which topics do your documents refer to you can create a topic model. The only required argument to create a topic model is a dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Topic Model section of the Developer’s documentation.
For example, to create a topic model including exactly 32 topics you can use the following invocation:
topic_model = api.create_topic_model(dataset, { \ "name": "my topics", "number_of_topics": 32})
Again, the topic model is scheduled for creation, and you can retrieve its status at any time by means of api.status(topic_model).
Topic models can also be created from lists of datasets. Just use the list of ids as the first argument in the api call
topic_model = api.create_topic_model([dataset1, dataset2], { \ "name": "my topics", "number_of_topics": 32})
Existing topic models can also be cloned:
To forecast the behaviour of any numeric variable that depends on its historical records you can use a time series. The only required argument to create a time series is a dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Time Series section of the Developer’s documentation.
For example, to create a time series including a forecast of 10 points for the numeric values you can use the following invocation:
time_series = api.create_time_series(dataset, { \ "name": "my time series", "horizon": 10})
Again, the time series is scheduled for creation, and you can retrieve its status at any time by means of api.status(time_series).
Time series also be created from lists of datasets. Just use the list of ids as the first argument in the api call
time_series = api.create_time_series([dataset1, dataset2], { \ "name": "my time series", "horizon": 10})
Existing time series can also be cloned:
To create an OptiML, the only required argument is a dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the OptiML section of the Developer’s documentation.
For example, to create an OptiML which optimizes the accuracy of the model you can use the following method
optiml = api.create_optiml(dataset, { \ "name": "my optiml", "metric": "accuracy"})
The OptiML is then scheduled for creation, and you can retrieve its status at any time by means of api.status(optiml).
To create a Fusion, the only required argument is a list of models. You can also include in the request all the additional arguments accepted by BigML and documented in the Fusion section of the Developer’s documentation.
For example, to create a Fusion you can use this connection method:
fusion = api.create_fusion(["model/5af06df94e17277501000010", "model/5af06df84e17277502000019", "deepnet/5af06df84e17277502000016", "ensemble/5af06df74e1727750100000d"], {"name": "my fusion"})
The Fusion is then scheduled for creation, and you can retrieve its status at any time by means of api.status(fusion).
Fusions can also be created by assigning some weights to each model in the list. In this case, the argument for the create call will be a list of dictionaries that contain the id and weight keys:
fusion = api.create_fusion([{"id": "model/5af06df94e17277501000010", "weight": 10}, {"id": "model/5af06df84e17277502000019", "weight": 20}, {"id": "deepnet/5af06df84e17277502000016", "weight": 5}], {"name": "my weighted fusion"})
You can now use the model resource identifier together with some input parameters to ask for predictions, using the create_prediction method. You can also give the prediction a name:
prediction = api.create_prediction(model, {"sepal length": 5, "sepal width": 2.5}, {"name": "my prediction"})
To see the prediction you can use pprint:
Predictions can be created using any supervised model (model, ensemble, logistic regression, linear regression, deepnet and fusion) as first argument.
To obtain the centroid associated to new input data, you can now use the create_centroid method. Give the method a cluster identifier and the input data to obtain the centroid. You can also give the centroid predicition a name:
centroid = api.create_centroid(cluster, {"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"}, {"name": "my centroid"})
To obtain the anomaly score associated to new input data, you can now use the create_anomaly_score method. Give the method an anomaly detector identifier and the input data to obtain the score:
anomaly_score = api.create_anomaly_score(anomaly, {"src_bytes": 350}, args={"name": "my score"})
Using the association resource, you can obtain the consequent items associated by its rules to your input data. These association sets can be obtained calling the create_association_set method. The first argument is the association ID or object and the next one is the input data.
association_set = api.create_association_set( \ association, {"genres": "Action$Adventure"}, \ args={"name": "my association set"})
To obtain the topic distributions associated to new input data, you can now use the create_topic_distribution method. Give the method a topic model identifier and the input data to obtain the score:
topic_distribution = api.create_topic_distribution( \ topic_model, {"Message": "The bubble exploded in 2007."}, args={"name": "my topic distribution"})
To obtain the forecast associated to a numeric variable, you can now use the create_forecast method. Give the method a time series identifier and the input data to obtain the forecast:
forecast = api.create_forecast( \ time_series, {"Final": {"horizon": 10}})
You can now use the PCA resource identifier together with some input parameters to ask for the corresponding projections, using the create_projection method. You can also give the projection a name:
projection = api.create_projection(pca, {"sepal length": 5, "sepal width": 2.5}, {"name": "my projection"})
Once you have created a supervised learning model, you can measure its performance by running a dataset of test data through it and comparing its predictions to the objective field real values. Thus, the required arguments to create an evaluation are model id and a dataset id. You can also include in the request all the additional arguments accepted by BigML and documented in the Evaluations section of the Developer’s documentation.
For instance, to evaluate a previously created model using an existing dataset you can use the following call:
evaluation = api.create_evaluation(model, dataset, { "name": "my model"})
Again, the evaluation is scheduled for creation and api.status(evaluation) will show its state.
Evaluations can also check the ensembles’ performance. To evaluate an ensemble you can do exactly what we just did for the model case, using the ensemble object instead of the model as first argument:
evaluation = api.create_evaluation(ensemble, dataset)
Evaluations can be created using any supervised model (including time series) as first argument.
To improve the performance of your predictions, you can create an ensemble of models and combine their individual predictions. The only required argument to create an ensemble is the dataset id:
BigML offers three kinds of ensembles. Two of them are known as Decision Forests because they are built as collections of Decision trees whose predictions are aggregated using different combiners (plurality, confidence weighted, probability weighted) or setting a threshold to issue the ensemble’s prediction. All Decision Forests use bagging to sample the data used to build the underlying models.
As an example of how to create a Decision Forest with 20 models, you only need to provide the dataset ID that you want to build the ensemble from and the number of models:
args = {'number_of_models': 20} ensemble = api.create_ensemble('dataset/5143a51a37203f2cf7000972', args)
If no number_of_models is provided, the ensemble will contain 10 models.
Random Decision Forests fall also into the Decision Forest category, but they only use a subset of the fields chosen at random at each split. To create this kind of ensemble, just use the randomize option:
args = {'number_of_models': 20, 'randomize': True} ensemble = api.create_ensemble('dataset/5143a51a37203f2cf7000972', args)
The third kind of ensemble is Boosted Trees. This type of ensemble uses quite a different algorithm. The trees used in the ensemble don’t have as objective field the one you want to predict, and they don’t aggregate the underlying models’ votes. Instead, the goal is adjusting the coefficients of a function that will be used to predict. The models’ objective is, therefore, the gradient that minimizes the error of the predicting function (when comparing its output with the real values). The process starts with some initial values and computes these gradients. Next step uses the previous fields plus the last computed gradient field as the new initial state for the next iteration. Finally, it stops when the error is smaller than a certain threshold or iterations reach a user-defined limit. In classification problems, every category in the ensemble’s objective field would be associated with a subset of the Boosted Trees. The objective of each subset of trees is adjustig the function to the probability of belonging to this particular category.
In order to build an ensemble of Boosted Trees you need to provide the boosting attributes. You can learn about the existing attributes in the ensembles’ section of the API documentation, but a typical attribute to be set would be the maximum number of iterations:
args = {'boosting': {'iterations': 20}} ensemble = api.create_ensemble('dataset/5143a51a37203f2cf7000972', args)
Existing ensembles can also be cloned:
For regression problems, you can choose also linear regressions to model your data. Linear regressions expect the predicted value for the objective field to be computable as a linear combination of the predictors.
As the rest of models, linear regressions can be created from a dataset by calling the corresponding create method:
linear_regression = api.create_linear_regression( \ 'dataset/5143a51a37203f2cf7000972', {"name": "my linear regression", "objective_field": "my_objective_field"})
In this example, we created a linear regression named my linear regression and set the objective field to be my_objective_field. Other arguments, like bias, can also be specified as attributes in arguments dictionary at creation time. Particularly for categorical fields, there are three different available field_codings` options (contrast, other or the dummy default coding). For a more detailed description of the field_codings attribute and its syntax, please see the Developers API Documentation.
Existing linear regressions can also be cloned:
For classification problems, you can choose also logistic regressions to model your data. Logistic regressions compute a probability associated to each class in the objective field. The probability is obtained using a logistic function, whose argument is a linear combination of the field values.
As the rest of models, logistic regressions can be created from a dataset by calling the corresponding create method:
logistic_regression = api.create_logistic_regression( \ 'dataset/5143a51a37203f2cf7000972', {"name": "my logistic regression", "objective_field": "my_objective_field"})
In this example, we created a logistic regression named my logistic regression and set the objective field to be my_objective_field. Other arguments, like bias, missing_numerics and c can also be specified as attributes in arguments dictionary at creation time. Particularly for categorical fields, there are four different available field_codings` options (dummy, contrast, other or the one-hot default coding). For a more detailed description of the field_codings attribute and its syntax, please see the Developers API Documentation.
Existing logistic regressions can also be cloned:
Deepnets can also solve classification and regression problems. Deepnets are an optimized version of Deep Neural Networks, a class of machine-learned models inspired by the neural circuitry of the human brain. In these classifiers, the input features are fed to a group of “nodes” called a “layer”. Each node is essentially a function on the input that transforms the input features into another value or collection of values. Then the entire layer transforms an input vector into a new “intermediate” feature vector. This new vector is fed as input to another layer of nodes. This process continues layer by layer, until we reach the final “output” layer of nodes, where the output is the network’s prediction: an array of per-class probabilities for classification problems or a single, real value for regression problems.
Deepnets predictions compute a probability associated to each class in the objective field for classification problems. As the rest of models, deepnets can be created from a dataset by calling the corresponding create method:
deepnet = api.create_deepnet( \ 'dataset/5143a51a37203f2cf7000972', {"name": "my deepnet", "objective_field": "my_objective_field"})
In this example, we created a deepnet named my deepnet and set the objective field to be my_objective_field. Other arguments, like number_of_hidden_layers, learning_rate and missing_numerics can also be specified as attributes in an arguments dictionary at creation time. For a more detailed description of the available attributes and its syntax, please see the Developers API Documentation.
Existing deepnets can also be cloned:
In order to reduce the number of features used in the modeling steps, you can use a PCA (Principal Component Analysis) to find out the best combination of features that describe the variance of your data. As the rest of models, PCAs can be created from a dataset by calling the corresponding create method:
pca = api.create_pca( \ 'dataset/5143a51a37203f2cf7000972', {"name": "my PCA"})
In this example, we created a PCA named my PCA. Other arguments, like standardized can also be specified as attributes in arguments dictionary at creation time. Please see the Developers API Documentation.
We have shown how to create predictions individually, but when the amount of predictions to make increases, this procedure is far from optimal. In this case, the more efficient way of predicting remotely is to create a dataset containing the input data you want your model to predict from and to give its id and the one of the model to the create_batch_prediction api call:
batch_prediction = api.create_batch_prediction(model, dataset, { "name": "my batch prediction", "all_fields": True, "header": True, "confidence": True})
In this example, setting all_fields to true causes the input data to be included in the prediction output, header controls whether a headers line is included in the file or not and confidence set to true causes the confidence of the prediction to be appended. If none of these arguments is given, the resulting file will contain the name of the objective field as a header row followed by the predictions.
As for the rest of resources, the create method will return an incomplete object, that can be updated by issuing the corresponding api.get_batch_prediction call until it reaches a FINISHED status. Then you can download the created predictions file using:
that will copy the output predictions to the local file given in filename. If no filename is provided, the method returns a file-like object that can be read as a stream:
CHUNK_SIZE = 1024 response = api.download_batch_prediction( 'batchprediction/526fc344035d071ea3031d70') chunk = response.read(CHUNK_SIZE) if chunk: print chunk
The output of a batch prediction can also be transformed to a source object using the source_from_batch_prediction method in the api:
api.source_from_batch_prediction( 'batchprediction/526fc344035d071ea3031d70', args={'name': 'my_batch_prediction_source'})
This code will create a new source object, that can be used again as starting point to generate datasets.
As described in the previous section, it is also possible to make centroids’ predictions in batch. First you create a dataset containing the input data you want your cluster to relate to a centroid. The create_batch_centroid call will need the id of the input data dataset and the cluster used to assign a centroid to each instance:
batch_centroid = api.create_batch_centroid(cluster, dataset, { "name": "my batch centroid", "all_fields": True, "header": True})
Creating Batch Anomaly Scores¶
Input data can also be assigned an anomaly score in batch. You train an anomaly detector with your training data and then build a dataset from your input data. The create_batch_anomaly_score call will need the id of the dataset and of the anomaly detector to assign an anomaly score to each input data instance:
batch_anomaly_score = api.create_batch_anomaly_score(anomaly, dataset, { "name": "my batch anomaly score", "all_fields": True, "header": True})
Creating Batch Topic Distributions¶
Input data can also be assigned a topic distribution in batch. You train a topic model with your training data and then build a dataset from your input data. The create_batch_topic_distribution call will need the id of the dataset and of the topic model to assign a topic distribution to each input data instance:
batch_topic_distribution = api.create_batch_topic_distribution( \ topic_model, dataset, { "name": "my batch topic distribution", "all_fields": True, "header": True})
Input data can also be assigned a projection in batch. You train a PCA with your training data and then build a dataset from your input data. The create_batch_projection call will need the id of the input data dataset and of the PCA to compute the projection that corresponds to each input data instance:
batch_projection = api.create_batch_projection( \ pca, dataset, { "name": "my batch pca", "all_fields": True, "header": True})