BigML Bindings: 101 - Using a Cluster

Following the schema described in the prediction workflow, document, this is the code snippet that shows the minimal workflow to create a cluster and find the centroid associated to a single instance.

from bigml.api import BigML
# step 0: creating a connection to the service (default credentials)
# check how to set your credentials in the Authentication section
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 cluster
cluster = api.create_cluster(dataset)
# waiting for the cluster to be finished
api.ok(cluster)
# the new input data to find the centroid. All numeric fields are to be
# provided.
input_data = {"petal length": 4, "sepal length": 2, "petal width": 3,
              "sepal width": 1, "species": "Iris-setosa"}
# getting the associated centroid
centroid = api.create_centroid(cluster, input_data)

If you want to find the centroids for many inputs at once, you can do so by creating a batch_centroid resource. You can create a batch_centroid using the same dataset that you used to built the cluster and this will produce a new dataset with a new column that contains the name of the cluster each instance has been assigned to.

Of course, you can also apply the cluster to new data to find the associated centroids. Then, you will first need to upload to the platform all the input data that you want to use and create the corresponding source and dataset resources. In the example, we’ll be assuming you already created a cluster following the steps 0 to 5 in the previous snippet. In the next example, steps 6 and 8 will only be necessary if you want to use new data to be clustered. If you just want the information about the cluster assigned to each instance in the clustering algorithm, you can go to step 10 and use the dataset created in step 3 as test_dataset.

# 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 centroid
batch_centroid = api.create_batch_centroid(cluster, test_dataset)
# waiting for the batch_centroid to be finished
api.ok(batch_centroid)
# downloading the results to your computer
api.download_batch_centroid(batch_centroid,
                            filename='my_dir/my_centroids.csv')

The batch centroid 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:

bach_centroid = api.create_batch_centroid(cluster, test_dataset,
                                          {"all_fields": True})

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

You can also associate centroids locally using the Cluster class in the cluster module. A simple example of that is:

from bigml.cluster import Cluster
local_cluster = Cluster("cluster/5968ec46983efc21b000001b")
# associated centroid for some input data
local_cluster.centroid({"petal length": 4, "sepal length": 2,
                        "petal width": 1, "sepal witdh": 3})

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

# downloading the cluster JSON to a local file
from bigml.api import BigML
api = BigML()
api.export("cluster/5968ec46983efc21b000001b",
           "filename": "my_cluster.json")
# creating the cluster from the file
from bigml.cluster import Cluster
local_cluster = Cluster("my_cluster.json")
# associated centroid for some input data
local_cluster.centroid({"petal length": 4, "sepal length": 2,
                        "petal width": 1, "sepal witdh": 3})

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

import csv
from bigml.cluster import Cluster
local_cluster = Cluster("cluster/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_cluster.centroid(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.