This IPython notebook illustrates how to sample and label a table (candidate set). First, we need to import py_entitymatching package and other libraries as follows:
In [1]:
# Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
In [2]:
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
path_A = datasets_dir + os.sep + 'DBLP.csv'
path_B = datasets_dir + os.sep + 'ACM.csv'
path_C = datasets_dir + os.sep + 'tableC.csv'
In [3]:
A = em.read_csv_metadata(path_A, key='id')
B = em.read_csv_metadata(path_B, key='id')
C = em.read_csv_metadata(path_C, key='_id',
fk_ltable='ltable_id', fk_rtable='rtable_id',
ltable=A, rtable=B)
In [4]:
C.head()
Out[4]:
In [5]:
len(C)
Out[5]:
From the candidate set, a sample (for labeling purposes) can be obtained like this:
In [6]:
S = em.sample_table(C, 450)
In [7]:
# Label the sampled set
# Specify the name for the label column
G = em.label_table(S, 'gold_label')
The user must specify 0 for non-match and 1 for match. Typically, the sampling and the labeling step is done in iterations (till we get sufficient density of matches). Once labeled, the labeled data set will look like this:
In [8]:
# Assume that we have labeled the data and stored it in
# labeled_data_demo.csv
path_labeled_data = datasets_dir + os.sep + 'labeled_data_demo.csv'
G = em.read_csv_metadata(path_labeled_data, key='_id',
fk_ltable='ltable_id', fk_rtable='rtable_id',
ltable=A, rtable=B)
In [9]:
G.head()
Out[9]: