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Selecting Matcher

py_entitymatching.select_matcher(matchers, x=None, y=None, table=None, exclude_attrs=None, target_attr=None, metric='precision', k=5, random_state=None)

This function selects a matcher from a given list of matchers based on a given metric.

Specifically, this function internally uses scikit-learn’s cross validation function to select a matcher. There are two ways the user can call the fit method. First, interface similar to scikit-learn where the feature vectors and target attribute given as projected DataFrame. Second, give the DataFrame and explicitly specify the feature vectors (by specifying the attributes to be excluded) and the target attribute

A point to note is all the input parameters have a default value of None. This is done to support both the interfaces in a single function.

Parameters:
  • matchers (MLMatcher) – List of ML matchers to be selected from.
  • x (DataFrame) – Input feature vectors given as pandas DataFrame ( defaults to None).
  • y (DatFrame) – Input target attribute given as pandas DataFrame with a single column (defaults to None).
  • table (DataFrame) – Input pandas DataFrame containing feature vectors and target attribute (defaults to None).
  • exclude_attrs (list) – The list of attributes that should be excluded from the input table to get the feature vectors.
  • target_attr (string) – The target attribute in the input table (defaults to None).
  • metric (string) – The metric based on which the matchers must be selected. The string can be one of ‘precision’, ‘recall’, ‘f1’ (defaults to ‘precision’).
  • k (int) – The k value for cross-validation (defaults to 5).
  • random_state (object) – Pseudo random number generator that should be used for splitting the data into folds (defaults to None).
Returns:

A dictionary containing two keys - selected matcher and the cv_stats.

The selected matcher has a value that is a matcher (MLMatcher) object and cv_stats has a value that is a dictionary containing cross-validation statistics.

Examples

>>> dt = em.DTMatcher()
>>> rf = em.RFMatcher()
# train is the feature vector containing user labels
>>> result = em.select_matcher(matchers=[dt, rf], table=train, exclude_attrs=['_id', 'ltable_id', 'rtable_id'], target_attr='gold_labels', k=5)
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