BigML Bindings: 101 - Using an anomaly detector

Following the schema described in the prediction workflow, document, this is the code snippet that shows the minimal workflow to create an anomaly detector to produce a single anomaly score.

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`
# step 3: creating a dataset from the previously created `source`
dataset = api.create_dataset(source)
# waiting for the dataset to be finished
# step 5: creating an anomaly detector
anomaly = api.create_anomaly(dataset)
# waiting for the anomaly detector to be finished
# the input data to score
input_data = {"petal length": 4, "sepal length": 2, "petal width": 1,
              "sepal witdh": 3}
# assigning an anomaly score to it
anomaly_score = api.create_anomaly_score(anomaly, input_data)

If you want to assign scores to the original dataset (or a different dataset), you can do so by creating a batch_anomaly_score resource. In the example, we’ll be assuming you already created an anomaly following the steps 0 to 5 in the previous snippet and that you want to score the same data you used in the anomaly detector.

test_dataset = dataset
# step 10: creating a batch anomaly score
batch_anomaly_score = api.create_batch_anomaly_score(anomaly, test_dataset)
# waiting for the batch_anomaly_score to be finished
# downloading the results to your computer

The batch anomaly score 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_anomaly_score = api.create_batch_anomaly_score(anomaly, test_dataset,
                                                     {"all_fields": True})

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

You can also score your data locally using the Anomaly class in the anomaly module. A simple example of that is:

from bigml.anomaly import Anomaly
local_anomaly = Anomaly("anomaly/5968ec46983efc21b000001b")
# assigning the anomaly score to some input data
local_anomaly.anomaly_score({"petal length": 4, "sepal length": 2,
                             "petal width": 1, "sepal witdh": 3})

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

# downloading the anomaly detector JSON to a local file
from bigml.api import BigML
api = BigML()
           "filename": "my_anomaly.json")
# creating an anomaly object using the information in the file
from bigml.anomaly import Anomaly
local_anomaly = Anomaly("my_anomaly.json")
# assigning the anomaly score to some input data
local_anomaly.anomaly_score({"petal length": 4, "sepal length": 2,
                             "petal width": 1, "sepal witdh": 3})

If you want to assign the anomaly score locally for all the rows in a CSV file (first line should contain the field headers):

import csv
from bigml.anomaly import Anomaly
local_anomaly = Anomaly("anomaly/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_anomaly.anomaly_score(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.