eval
Evaluation
Class used for evaluation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks |
KlinkerBlockManager
|
KlinkerBlockManager: Blocking result |
required |
gold |
DataFrame
|
pd.DataFrame: Gold standard pairs as two column dataframe |
required |
left_data_len |
int
|
int: number of entities in left dataset |
required |
right_data_len |
int
|
int: number of entities in right dataset |
required |
Source code in klinker/eval.py
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from_dataset(blocks, dataset)
classmethod
Helper function to initialise evaluation with dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks |
KlinkerBlockManager
|
KlinkerBlockManager: Calculated blocks |
required |
dataset |
KlinkerDataset
|
KlinkerDataset: Dataset that was used for blocking |
required |
Returns:
Type | Description |
---|---|
Evaluation
|
eval instance |
Examples:
>>> # doctest: +SKIP
>>> from sylloge import MovieGraphBenchmark
>>> from klinker.data import KlinkerDataset
>>> ds = KlinkerDataset.from_sylloge(MovieGraphBenchmark(),clean=True)
>>> from klinker.blockers import TokenBlocker
>>> blocks = TokenBlocker().assign(left=ds.left, right=ds.right)
>>> from klinker.eval import Evaluation
>>> ev = Evaluation.from_dataset(blocks, ds)
>>> ev.to_dict()
{'recall': 0.993933265925177, 'precision': 0.002804877004859314, 'f_measure': 0.005593967847488974, 'reduction_ratio': 0.9985747694185365, 'h3r': 0.9962486115318822, 'mean_block_size': 10.160596863935256}
Source code in klinker/eval.py
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compare_blocks(blocks_a, blocks_b, dataset, improvement_metric='h3r')
Compare similarity between blocks using calculated eval.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks_a |
KlinkerBlockManager
|
KlinkerBlockManager: one blocking result |
required |
blocks_b |
KlinkerBlockManager
|
KlinkerBlockManager: other blocking result |
required |
dataset |
KlinkerDataset
|
KlinkerDataset: dataset from which blocks where calculated |
required |
improvement_metric |
str
|
str: used to calculate improvement |
'h3r'
|
Returns:
Type | Description |
---|---|
Dict
|
Dictionary with improvement metrics. |
Source code in klinker/eval.py
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compare_blocks_from_eval(blocks_a, blocks_b, eval_a, eval_b, dataset, improvement_metric='h3r')
Compare similarity between blocks using calculated eval.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks_a |
KlinkerBlockManager
|
KlinkerBlockManager: one blocking result |
required |
blocks_b |
KlinkerBlockManager
|
KlinkerBlockManager: other blocking result |
required |
eval_a |
Evaluation
|
Evaluation: eval of a |
required |
eval_b |
Evaluation
|
Evaluation: eval of b |
required |
dataset |
KlinkerDataset
|
KlinkerDataset: dataset from which blocks where calculated |
required |
improvement_metric |
str
|
str: used to calculate improvement |
'h3r'
|
Returns:
Type | Description |
---|---|
Dict
|
Dictionary with improvement metrics. |
Source code in klinker/eval.py
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dice(a, b)
Calculate Soerensen-Dice Coefficient.
Source code in klinker/eval.py
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harmonic_mean(a, b)
Calculate harmonic mean between a and b.
Source code in klinker/eval.py
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multiple_block_comparison(blocks, dataset, improvement_metric='h3r')
Compare multiple blocking strategies.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
blocks |
Dict[str, KlinkerBlockManager]
|
Dict[str, KlinkerBlockManager]: Blocking results |
required |
dataset |
KlinkerDataset
|
KlinkerDataset: Dataset that was used for blocking |
required |
improvement_metric |
str
|
str: Metric used for calculating improvement |
'h3r'
|
Returns:
Type | Description |
---|---|
DataFrame
|
DataFrame with improvement values. |
Source code in klinker/eval.py
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