Python APIs for using Services that are part of the Microsoft Azure Cortana Analytics Suite
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This a Python library for using Microsoft Azure Datamarket and Cortana Analytics Services.


To install, use pip:

pip install cortanaanalytics

You can also get the development versions directly from the GitHub repo:

Getting Started

Cortana Analytics has many different packages. Please look at each section for the library you are interested in.

Also, you will need obtain an access key from the Azure Datamarket and subscribe to the service you wish to use.

Text Analytics

from cortanaanalytics.textanalytics import TextAnalytics

key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='
ta = TextAnalytics(key)

score = ta.get_sentiment("hello world")

scores = ta.get_sentiment_batch([{"Text":"hello world", "Id":0}, {"Text":"hello world again", "Id":2}])


from cortanaanalytics.recommendations import Recommendations

email = ''
key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='
rs = Recommendations(email, key)

# create model
model_id = rs.create_model('groceries' +'%Y%m%d%H%M%S'))

# import item catalog
catalog_path = os.path.join('app', 'management', 'commands', 'catalog.csv')
rs.import_file(model_id, catalog_path, Uris.import_catalog)

# import usage information
transactions_path = os.path.join('app', 'management', 'commands', 'transactions.csv')
rs.import_file(model_id, transactions_path, Uris.import_usage)

# build model
build_id = rs.build_fbt_model(model_id)
status = rs.wait_for_build(model_id, build_id)

if status != BuildStatus.success:
    print('Unsuccessful in building the model, failing now.')

# update model active build (not needed unless you are rebuilding)
rs.update_model(model_id, None, build_id)

print('Built a model. Model ID:{} Build ID:{}'.format(model_id, build_id))

Anomaly Detection

from cortanaanalytics.anomalydetection import AnomalyDetection  

key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='
ad = AnomalyDetection(key)

data = [
            (datetime(2014, 9, 21, 11, 5, 0), 3),
            (datetime(2014, 9, 21, 11, 10, 0), 9.09),
            (datetime(2014, 9, 21, 11, 15, 0), 0)
result = ad.score(test_data)

or you can also use strings

from cortanaanalytics.anomalydetection import AnomalyDetection  

key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='
ad = AnomalyDetection(key)

data = "9/21/2014 11:05:00 AM=3;9/21/2014 11:10:00 AM=9.09;9/21/2014 11:15:00 AM=0;"
params = "SpikeDetector.TukeyThresh=3; SpikeDetector.ZscoreThresh=3"
result = ad.score_raw(data, params)