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The bindings offer methods to create and execute WhizzML scripts in the platform. WhizzML is the DSL that allows you to automate tasks in BigML.
These code snippets show examples to illustrate how to create and execute simple scripts:
This is the code to create a simple script that creates a source from an existing CSV file that is available in a remote URL:
In this example. the url used is always the same, so no inputs are provided to the script. This is not a realistic situation, because usually scripts need user-provided inputs. The next example shows how to add two variables, whose values will be provided as inputs.
Basic script with inputs¶
Scripts usually need some inputs to work. When defining the script, you need to provide booth the code and the description of the inputs that it will accept.
from bigml.api import BigML # step 0: creating a connection to the service (default credentials) api = BigML() # step 1: creating a script that adds two numbers script = api.create_script( \ "(+ a b)", {"inputs": [{"name": "a", "type": "number"}, {"name": "b", "type": "number"}]}) # waiting for the script to be finished. api.ok(script) # step 2: executing the script with some particular inputs: a=1, b=2 execution = api.create_execution( \ script, {"inputs": [["a", 1], ["b", 2]]}) # waiting for the execution to be finished api.ok(execution) # step 3: retrieving the result (e.g. 3) result = execution['object']['execution']['result']
And of course, you will usually store your code, inputs and outputs in files. The create_script method can receive as first argument the path to a file that contains the source code and the rest of arguments can be retrieved from a JSON file using the standard tools available in Python. The previous example could also be created from a file that contains the WhizzML code and a metadata file that contains the inputs and outputs description as a JSON.
import json from bigml.api import BigML # step 0: creating a connection to the service (default credentials) api = BigML() # step 1: creating a script from the code stored in `my_script.whizzml` # and the inputs and outputs metadata stored in `metadata.json` with open('./metadata.json') as json_file: metadata = json.load(json_file) script = api.create_script("./my_script.whizzml", metadata) # waiting for the script to be finished. api.ok(script)
Or load the files from a gist url:
import json from bigml.api import BigML # step 0: creating a connection to the service (default credentials) api = BigML() # step 1: creating a script from a gist gist_url = "https://gist.github.com/mmerce/49e0a69cab117b6a11fb490140326020" script = api.create_script(gist_url) # waiting for the script to be finished. api.ok(script)
In a full-fledged script, you will also produce some outputs that can be used in other scripts. This is an example of a script creating a dataset from a source that was generated from a remote URL. Both the URL and the source name are provided by the user. Once the script has been created, we run it by creating an execution from it and placing the particular input values that we want to apply it to.
from bigml.api import BigML # step 0: creating a connection to the service (default credentials) api = BigML() # step 1: creating a script that creates a `source` and a dataset from # a user-given remote file script = api.create_script( \ "(define my-dataset (create-dataset (create-source {\"remote\" url \"name\" source-name})))", {"inputs": [{"name": "url", "type": "string"}, {"name": "source-name", "type": "string"}], "outputs": [{"name": "my-dataset", "type": "dataset"}]}) # waiting for the script to be finished. api.ok(script) # step 2: executing the script with some particular inputs execution = api.create_execution( \ script, {"inputs": [["url", "https://static.bigml.com/csv/iris.csv"], ["source-name", "my source"]]}) # waiting for the dataset to be finished api.ok(execution) # step 3: retrieving the result (e.g. "dataset/5cae5ad4b72c6609d9000356") result = execution['object']['execution']['result']
You can also use the Execution class to easily access the results, outputs and output resources of an existing execution. Just instantiate the class with the execution resource or ID:
from bigml.execution import Execution execution = Execution("execution/5cae5ad4b72c6609d9000468") print "The result of the execution is %s" % execution.result print " and the output for variable 'my_variable': %s" % \ execution.outputs["my_variable"]
Local and remote scripting¶
Any operation in BigML can be scripted by using the bindings locally to call the API. However, the highest efficiency, scalability and reproducibility will come only by using WhizzML scripts in the platform to handle the Machine Learning workflow that you need. Thus, in most situations, the bindings are used merely to upload the data to the platform and create an execution that uses that data to reproduce the same operations. Let’s say that you have a WhizzML script that generates a batch prediction based on an existing model. The only input for the script will be the source ID that will be used to predict, and the rest of steps will be handled by the WhizzML script. Therefore, in order to use that on new data you’ll need to upload that data to the platform and use the resulting ID as input.
from bigml.api import BigML # step 0: creating a connection to the service (default credentials) api = BigML() # step 1: creating a script that uploads local data to create a `source` source = api.create_source("my_local_file") # waiting for the source to be finished. api.ok(source) # step 2: executing the script to do a batch prediction with the new # source as input script = "script/5cae5ad4b72c6609d9000235" execution = api.create_execution( \ script, {"inputs": [["source", source["resource"]]]}) # waiting for the workflow to be finished api.ok(execution) # step 3: retrieving the result (e.g. "dataset/5cae5ad4b72c6609d9000356") result = execution['object']['execution']['result'] # step 4: maybe storing the result as a CSV api.download_dataset(result, "my_predictions.csv")