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update nlc examples to use new method signatures #310

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ammardodin merged 2 commits into from
Nov 26, 2017
Merged

update nlc examples to use new method signatures #310

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203f131
:bug: update nlc examples to use new method signatures
Nov 25, 2017
9993f13
:bug: fix path to open()
ammardodin Nov 25, 2017
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12 changes: 8 additions & 4 deletions examples/natural_language_classifier_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,12 @@

# create a classifier
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@stevemar stevemar Nov 26, 2017

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is there a reason why these are commented out? are the examples run with some sort of CI?

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@ammardodin ammardodin Nov 26, 2017

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@germanattanasio ☝️

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@mkistler mkistler Nov 26, 2017 •
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Yes, the examples serve double-duty as they are used as integration tests. You can see this in the test/test_examples.py module.

# with open('../resources/weather_data_train.csv', 'rb') as training_data:
# print(json.dumps(natural_language_classifier.create(
# training_data=training_data, name='weather'), indent=2))
# metadata = json.dumps({'name': 'my-classifier', 'language': 'en'})
# classifier = natural_language_classifier.create_classifier(
# metadata=metadata,
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@stevemar stevemar Nov 26, 2017

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this should be training_metadata now, right?

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@ammardodin ammardodin Nov 26, 2017 •
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training_metadata is what we name the parameter when we send it to the service and what it expects, but the parameter is still called metadata for your purposes in invoking the method.

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@stevemar stevemar Nov 26, 2017

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gotcha, cool cool

# training_data=training_data
# )
# print(json.dumps(classifier, indent=2))

status = natural_language_classifier.get_classifier(classifier_id)
print(json.dumps(status, indent=2))
Expand All @@ -30,10 +34,10 @@
'tomorrow?')
print(json.dumps(classes, indent=2))

# delete = natural_language_classifier.remove('2374f9x68-nlc-2697')
# delete = natural_language_classifier.delete_classifier('2374f9x68-nlc-2697')
# print(json.dumps(delete, indent=2))

# example of raising a ValueError
# print(json.dumps(
# natural_language_classifier.create(training_data='', name='weather3'),
# natural_language_classifier.create_classifier(training_data='', name='weather3'),
# indent=2))