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.

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.

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.

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.