BigML Bindings: 101 - Using a Deepnet Model

Following the schema described in the prediction workflow, document, this is the code snippet that shows the minimal workflow to create a deepnet and produce a single prediction.

from bigml.api import BigML
# step 0: creating a connection to the service (default credentials)
api = BigML()
# step 1: creating a source from the data in your local "data/iris.csv" file
source = api.create_source("data/iris.csv")
# waiting for the source to be finished. Results will be stored in `source`
api.ok(source)
# step 3: creating a dataset from the previously created `source`
dataset = api.create_dataset(source)
# waiting for the dataset to be finished
api.ok(dataset)
# step 5: creating a deepnet
deepnet = api.create_deepnet(dataset)
# waiting for the deepnet to be finished
api.ok(deepnet)
# the new input data to predict for
input_data = {"petal width": 1.75, "petal length": 2.45}
# creating a single prediction
prediction = api.create_prediction(deepnet, input_data)

If you want to create predictions for many new inputs, you can do so by creating a batch_prediction resource. First, you will need to upload to the platform all the input data that you want to predict for and create the corresponding source and dataset resources. In the example, we’ll be assuming you already created a deepnet following the steps 0 to 5 in the previous snippet.

# step 6: creating a source from the data in your local "data/test_iris.csv" file
test_source = api.create_source("data/test_iris.csv")
# waiting for the source to be finished. Results will be stored in `source`
api.ok(test_source)
# step 8: creating a dataset from the previously created `source`
test_dataset = api.create_dataset(test_source)
# waiting for the dataset to be finished
api.ok(test_dataset)
# step 10: creating a batch prediction
batch_prediction = api.create_batch_prediction(deepnet, test_dataset)
# waiting for the batch_prediction to be finished
api.ok(batch_prediction)
# downloading the results to your computer
api.download_batch_prediction(batch_prediction,
                              filename='my_dir/my_predictions.csv')

The batch prediction output (as well as any of the resources created) can be configured using additional arguments in the corresponding create calls. For instance, to include all the information in the original dataset in the output you would change step 10 to:

batch_prediction = api.create_batch_prediction(deepnet, test_dataset,
                                               {"all_fields": True})

Check the API documentation to learn about the available configuration options for any BigML resource.

You can also predict locally using the Deepnet class in the deepnet module. A simple example of that is:

from bigml.deepnet import Deepnet
local_deepnet = Deepnet("deepnet/5968ec46983efc21b000001c")
# predicting for some input data
local_deepnet.predict({"petal length": 2.45, "sepal length": 2,
                       "petal width": 1.75, "sepal witdh": 3})

Or you could store first your deepnet information in a file and use that file to create the local Deepnet object:

# downloading the deepnet JSON to a local file
from bigml.api import BigML
api = BigML()
api.export("deepnet/5968ec46983efc21b000001b",
           "filename": "my_deepnet.json")
# creating the deepnet from the file
from bigml.deepnet import Deepnet
local_deepnet = Deepnet("my_deepnet.json")
# predicting for some input data
local_deepnet.predict({"petal length": 2.45, "sepal length": 2,
                       "petal width": 1.75, "sepal witdh": 3})

And if you want to predict locally for all the rows in a CSV file (first line should contain the field headers):

import csv
from bigml.deepnet import Deepnet
local_deepnet = Deepnet("deepnet/5a414c667811dd5057000ab5")
with open("test_data.csv") as test_handler:
    reader = csv.DictReader(test_handler)
    for input_data in reader:
    # predicting for all rows
        print local_deepnet.predict(input_data)

Every modeling resource in BigML has its corresponding local class. Check the Local resources section of the documentation to learn more about them.