Arizona-metrics is a Python framework for sequence labeling evaluation. Arizona-metrics can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
This is well-tested by using the Perl script conlleval, which can be used for measuring the performance of a system that has processed the CoNLL-2000 shared task data.
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Support features:
Arizona-metrics supports following schemes: - IOB1 - IOB2 - IOE1 - IOE2 - IOBES(only in strict mode) - BILOU(only in strict mode) and following metrics: | metrics | description | |---|---| | accuracy_score(y\_true, y\_pred) | Compute the accuracy. | | precision_score(y\_true, y\_pred) | Compute the precision. | | recall_score(y\_true, y\_pred) | Compute the recall. | | f1_score(y\_true, y\_pred) | Compute the F1 score, also known as balanced F-score or F-measure. | | classification_report(y\_true, y\_pred, digits=2) | Build a text report showing the main classification metrics. `digits` is number of digits for formatting output floating point values. Default value is `2`. |
>>> python setup.bdist_wheel
>>> pip install dist/arizona-0.0.1-py3-none-any.whl
Arizona-metrics supports the two evaluation modes. You can specify the following mode to each metrics:
- default
- strict
The default mode is compatible with conlleval. If you want to use the default mode, you don't need to specify it:
>>> from arizona.metrics import accuracy_score
>>> from arizona.metrics import classification_report
>>> from arizona.metrics import f1_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> f1_score(y_true, y_pred)
0.50
>>> classification_report(y_true, y_pred)
precision recall f1-score support
MISC 0.00 0.00 0.00 1
PER 1.00 1.00 1.00 1
micro avg 0.50 0.50 0.50 2
macro avg 0.50 0.50 0.50 2
weighted avg 0.50 0.50 0.50 2In strict mode, the inputs are evaluated according to the specified schema. The behavior of the strict mode is different from the default one which is designed to simulate conlleval. If you want to use the strict mode, please specify mode='strict' and scheme arguments at the same time:
>>> from arizona.scheme import IOB2
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
precision recall f1-score support
MISC 0.00 0.00 0.00 1
PER 1.00 1.00 1.00 1
micro avg 0.50 0.50 0.50 2
macro avg 0.50 0.50 0.50 2
weighted avg 0.50 0.50 0.50 2A minimum case to explain differences between the default and strict mode:
>>> from arizona.metrics import classification_report
>>> from arizona.scheme import IOB2
>>> y_true = [['B-NP', 'I-NP', 'O']]
>>> y_pred = [['I-NP', 'I-NP', 'O']]
>>> classification_report(y_true, y_pred)
precision recall f1-score support
NP 1.00 1.00 1.00 1
micro avg 1.00 1.00 1.00 1
macro avg 1.00 1.00 1.00 1
weighted avg 1.00 1.00 1.00 1
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
precision recall f1-score support
NP 0.00 0.00 0.00 1
micro avg 0.00 0.00 0.00 1
macro avg 0.00 0.00 0.00 1
weighted avg 0.00 0.00 0.00 1
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