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Evaluating the Matching Output

py_entitymatching.eval_matches(data_frame, gold_label_attr, predicted_label_attr)

Evaluates the matches from the matcher.

Specifically, given a DataFrame containing golden labels and predicted labels, this function would evaluate the matches and return the accuracy results such as precision, recall and F1.

Parameters:
  • data_frame (DataFrame) – The input pandas DataFrame containing “gold” labels and “predicted” labels.
  • gold_label_attr (string) – An attribute in the input DataFrame containing “gold” labels.
  • predicted_label_attr (string) – An attribute in the input DataFrame containing “predicted” labels.
Returns:

A Python dictionary containing the accuracy measures such as precision, recall, F1.
Raises:
  • AssertionError – If data_frame is not of type pandas DataFrame.
  • AssertionError – If gold_label_attr is not of type string.
  • AssertionError – If predicted_label_attr is not of type string.
  • AssertionError – If the gold_label_attr is not in the input dataFrame.
  • AssertionError – If the predicted_label_attr is not in the input dataFrame.

Examples

>>> import py_entitymatching as em
>>> # G is the labeled data used for development purposes, match_f is the feature table
>>> H = em.extract_feat_vecs(G, feat_table=match_f, attrs_after='gold_labels')
>>> dt = em.DTMatcher()
>>> dt.fit(table=H, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'], target_attr='gold_labels')
>>> pred_table = dt.predict(table=H,  exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'],  append=True, target_attr='predicted_labels')
>>> eval_summary = em.eval_matches(pred_table, 'gold_labels', 'predicted_labels')
py_entitymatching.print_eval_summary(eval_summary)

Prints a summary of evaluation results.

Parameters:eval_summary (dictionary) – Dictionary containing evaluation results, typically from ‘eval_matches’ function.

Examples

>>> import py_entitymatching as em
>>> # G is the labeled data used for development purposes, match_f is the feature table
>>> H = em.extract_feat_vecs(G, feat_table=match_f, attrs_after='gold_labels')
>>> dt = em.DTMatcher()
>>> dt.fit(table=H, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'], target_attr='gold_labels')
>>> pred_table = dt.predict(table=H,  exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'],  append=True, target_attr='predicted_labels')
>>> eval_summary = em.eval_matches(pred_table, 'gold_labels', 'predicted_labels')
>>> em.print_eval_summary(eval_summary)
py_entitymatching.get_false_positives_as_df(table, eval_summary, verbose=False)

Select only the false positives from the input table and return as a DataFrame based on the evaluation results.

Parameters:
  • table (DataFrame) – The input table (pandas DataFrame) that was used for evaluation.
  • eval_summary (dictionary) – A Python dictionary containing evaluation results, typically from ‘eval_matches’ command.
Returns:

A pandas DataFrame containing only the False positives from the input table.

Further, this function sets the output DataFrame’s properties same as input DataFrame.

Examples

>>> import py_entitymatching as em
>>> # G is the labeled data used for development purposes, match_f is the feature table
>>> H = em.extract_feat_vecs(G, feat_table=match_f, attrs_after='gold_labels')
>>> dt = em.DTMatcher()
>>> dt.fit(table=H, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'], target_attr='gold_labels')
>>> pred_table = dt.predict(table=H,  exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'],  append=True, target_attr='predicted_labels')
>>> eval_summary = em.eval_matches(pred_table, 'gold_labels', 'predicted_labels')
>>> false_pos_df = em.get_false_positives_as_df(H, eval_summary)
py_entitymatching.get_false_negatives_as_df(table, eval_summary, verbose=False)

Select only the false negatives from the input table and return as a DataFrame based on the evaluation results.

Parameters:
  • table (DataFrame) – The input table (pandas DataFrame) that was used for evaluation.
  • eval_summary (dictionary) – A Python dictionary containing evaluation results, typically from ‘eval_matches’ command.
Returns:

A pandas DataFrame containing only the false negatives from the input table.

Further, this function sets the output DataFrame’s properties same as input DataFrame.

Examples

>>> import py_entitymatching as em
>>> # G is the labeled data used for development purposes, match_f is the feature table
>>> H = em.extract_feat_vecs(G, feat_table=match_f, attrs_after='gold_labels')
>>> dt = em.DTMatcher()
>>> dt.fit(table=H, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'], target_attr='gold_labels')
>>> pred_table = dt.predict(table=H,  exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold_labels'],  append=True, target_attr='predicted_labels')
>>> eval_summary = em.eval_matches(pred_table, 'gold_labels', 'predicted_labels')
>>> false_neg_df = em.get_false_negatives_as_df(H, eval_summary)
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