#!/usr/bin/env python
# coding: utf-8

# # Introduction
# 
# This IPython notebook illustrates how to perform blocking using rule-based blocker.
# 
# 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


# Then, read the (sample) input tables for blocking purposes.
# 

# In[5]:


# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'

# Get the paths of the input tables
path_A = datasets_dir + os.sep + 'person_table_A.csv'
path_B = datasets_dir + os.sep + 'person_table_B.csv'


# In[6]:


# Read the CSV files and set 'ID' as the key attribute
A = em.read_csv_metadata(path_A, key='ID')
B = em.read_csv_metadata(path_B, key='ID')


# # Different Ways to Block Using Blackbox Based Blocker
# 
# There are three different ways to do overlap blocking:
# 
# 1. Block two tables to produce a `candidate set` of tuple pairs.
# 2. Block a `candidate set` of tuple pairs to typically produce a reduced candidate set of tuple pairs.
# 3. Block two tuples to check if a tuple pair would get blocked.

# ## Block Tables to Produce a Candidate Set of Tuple Pairs

# First, define a blackbox function

# In[18]:


def address_address_function(x, y):
    # x, y will be of type pandas series
    
    # get name attribute
    x_address = x['address']
    y_address = y['address']
    # get the city
    x_split, y_split = x_address.split(','), y_address.split(',')
    x_city = x_split[len(x_split) - 1]
    y_city = y_split[len(y_split) - 1]
    # check if the cities match
    if x_city != y_city:
        return True
    else:
        return False


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# Instantiate blackbox blocker
bb = em.BlackBoxBlocker()
# Set the black box function
bb.set_black_box_function(address_address_function)


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C = bb.block_tables(A, B, l_output_attrs=['name', 'address'], r_output_attrs=['name', 'address'])


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C


# ## Block Candidate Set
# 
# First, define a blackbox function

# In[25]:


def name_name_function(x, y):
    # x, y will be of type pandas series
    
    # get name attribute
    x_name = x['name']
    y_name = y['name']
    # get last names
    x_name = x_name.split(' ')[1]
    y_name = y_name.split(' ')[1]
    # check if last names match
    if x_name != y_name:
        return True
    else:
        return False


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# Instantiate blackbox blocker
bb = em.BlackBoxBlocker()
# Set the black box function
bb.set_black_box_function(name_name_function)


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D = bb.block_candset(C)


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D


# ## Block Two tuples To Check If a Tuple Pair Would Get Blocked

# First, define the black box function first

# In[33]:


def address_address_function(x, y):
    # x, y will be of type pandas series
    
    # get name attribute
    x_address = x['address']
    y_address = y['address']
    # get the city
    x_split, y_split = x_address.split(','), y_address.split(',')
    x_city = x_split[len(x_split) - 1]
    y_city = y_split[len(y_split) - 1]
    # check if the cities match
    if x_city != y_city:
        return True
    else:
        return False


# In[34]:


# Instantiate blackabox blocker
bb = em.BlackBoxBlocker()
# Set the blackbox function 
bb.set_black_box_function(address_address_function)


# In[35]:


A.ix[[0]]


# In[36]:


B.ix[[0]]


# In[38]:


status = bb.block_tuples(A.ix[0], B.ix[0])

print(status)