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

# # Introduction

# 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[2]:


# 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[3]:


# 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')


# In[4]:


# Get features (for blocking)
feature_table = em.get_features_for_blocking(A, B)

# Get features (for matching)
# feature_table = em.get_features_for_blocking(A, B)


# # Adding Features Declaratively

# In[5]:


# Add a feature to do Jaccard on title + authors and add it to F

# Create a feature declaratively
sim = em.get_sim_funs_for_matching()
tok = em.get_tokenizers_for_matching()
feature_string = """jaccard(wspace((ltuple['name'] + ' ' + ltuple['address']).lower()), 
                            wspace((rtuple['name'] + ' ' + rtuple['address']).lower()))"""
feature = em.get_feature_fn(feature_string, sim, tok)

# Add feature to F
em.add_feature(feature_table, 'jac_ws_name_address', feature)


# In[6]:


feature_table.feature_name


# In[7]:


import fuzzywuzzy.StringMatcher as fz


# In[8]:


fz.ratio('xyz', 'ayz')


# In[9]:


# Create a feature declaratively
sim = em.get_sim_funs_for_matching()


# In[10]:


sim['fz_ratio'] = fz.ratio


# In[11]:


sim


# In[12]:


feature_string = """fz_ratio((ltuple['name'] + ' ' + ltuple['address']).lower(), 
                            (rtuple['name'] + ' ' + rtuple['address']).lower())"""
feature = em.get_feature_fn(feature_string, sim, tok)

# Add feature to F
em.add_feature(feature_table, 'fzratio_name_address', feature)


# In[13]:


feature_table.feature_name


# # Adding Blackbox Features

# In[14]:


import fuzzywuzzy.StringMatcher as fz


# In[15]:


def my_feature(ltuple, rtuple):
    return(ltuple['name'], rtuple['name'])


# In[16]:


feature_table = em.get_features_for_blocking(A, B)


# In[17]:


help(em.add_blackbox_feature)


# In[18]:


em.add_blackbox_feature(feature_table, 'blackbox_fz_ratio_name', my_feature)


# In[19]:


feature_table.feature_name