from datetime import datetime
from random import randrange

import recordlinkage as rl
import recordlinkage.algorithms.conflict_resolution as cr
from recordlinkage.datasets import load_febrl4

dfA, dfB = load_febrl4()

# Adapt dataset for example
dfA['date_of_birth'] = dfA['date_of_birth'].apply(float)
dfB['date_of_birth'] = dfB['date_of_birth'].apply(float)

dfA['dates_updated'] = [datetime(randrange(2000, 2017), randrange(1, 12), randrange(1, 28)) for _ in range(len(dfA))]
dfB['dates_updated'] = [datetime(randrange(2000, 2017), randrange(1, 12), randrange(1, 28)) for _ in range(len(dfB))]

dfA['salary'] = [randrange(40000, 120000) for _ in range(len(dfA))]
dfB['salary'] = [randrange(40000, 120000) for _ in range(len(dfB))]

dfA['min'] = [randrange(10, 20) for _ in range(len(dfA))]
dfB['min'] = [randrange(10, 20) for _ in range(len(dfB))]

dfA['max'] = [randrange(20, 30) for _ in range(len(dfA))]
dfB['max'] = [randrange(20, 30) for _ in range(len(dfB))]

# Sample data subsets
dfA = dfA.sample(200)
dfB = dfB.sample(200)

# Indexation step
indexer = rl.BlockIndex(on='given_name')
pairs = indexer.index(dfA, dfB)

# Comparison step
compare_cl = rl.Compare(pairs=pairs, df_a=dfA, df_b=dfB)

compare_cl.exact('given_name', 'given_name')
compare_cl.string('surname', 'surname', method='jarowinkler', threshold=0.85)
compare_cl.exact('date_of_birth', 'date_of_birth')
compare_cl.exact('suburb', 'suburb')
compare_cl.exact('state', 'state')
compare_cl.string('address_1', 'address_1', threshold=0.85)

features = compare_cl.vectors

# Classification step
matches = features.sum(axis=1) > 3

# Fusion step
fuse = rl.FuseLinks()

# Prefer values in dataframe a
fuse.trust_your_friends('given_name', 'given_name', trusted='a', name='given_name')

# Choose values from the row that was updated most recently
fuse.keep_up_to_date('surname', 'surname', 'dates_updated', 'dates_updated', name='surname')

# Take the average of salary values
fuse.meet_in_the_middle('salary', 'salary', metric='mean', name='salary')

# Choose randomly between street numbers
fuse.roll_the_dice('street_number', 'street_number', name='street_number')

# Keep all social security id values for future processing.
fuse.pass_it_on('soc_sec_id', 'soc_sec_id', name='soc_sec_id')

# Handle data conflicts between multiple columns in each data frame
fuse.meet_in_the_middle(['min', 'max'], ['min', 'max'], metric='stdev', name='spread')

# Create custom conflict handling strategies with the resolve method
fuse.resolve(
    cr.choose_longest,
    ['address_1', 'address_2'],
    ['address_1', 'address_2'],
    tie_break=cr.choose_random,
    name='longest_address'
)

# Execute the scheduled conflict resolution jobs for the given
#   candidate links, data, and classifications.
fused = fuse.fuse(pairs, dfA, dfB, matches)