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String clustering tests #1201

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7122da2
Add string clustering tests
joseangelhernao Sep 17, 2021
c07bd77
Merge branch 'develop-21.9' into feature/stringclustering-tests
joseangelhernao Oct 15, 2021
4fcc370
Merge remote-tracking branch 'origin/develop-21.9' into feature/strin…
joseangelhernao Nov 1, 2021
87c7b31
Merge branch 'develop-21.11' into feature/stringclustering-tests
joseangelhernao Dec 3, 2021
c70cbca
Merge remote-tracking branch 'origin/develop-21.11' into feature/stri…
joseangelhernao Dec 6, 2021
61fc379
Add string clustering tests
joseangelhernao Dec 6, 2021
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115 changes: 115 additions & 0 deletions tests/creators/creator_stringclustering.py
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import datetime
import sys
sys.path.append("../..")


def create():
from optimus import Optimus
from optimus.tests.creator import TestCreator, default_configs

op = Optimus("pandas")
df = op.create.dataframe({
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@luis11011 luis11011 Sep 20, 2021

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I think you should include at least one column with similar strings to properly test this functions.

'NullType': [None, None, None, None, None, None],
'date arrival': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10'],
'height(ft)': [-28, 17, 26, 13, None, 300],
('last date seen', 'date'): ['2016/09/10', '2015/08/10', '2014/07/10', '2013/06/10', '2012/05/10', '2011/04/10'],
'last position seen': ['19.442735,-99.201111', '10.642707,-71.612534', '37.789563,-122.400356', '33.670666,-117.841553', None, None],
'rank': [10, 7, 7, 8, 10, 8],
('Cybertronian', 'bool'): [True, True, True, True, True, False],
('Date Type'): [datetime.datetime(2016, 9, 10), datetime.datetime(2015, 8, 10), datetime.datetime(2014, 6, 24), datetime.datetime(2013, 6, 24), datetime.datetime(2012, 5, 10), datetime.datetime(2011, 4, 10)],
('age', 'int'): [5000000, 5000000, 5000000, 5000000, 5000000, 5000000],
('function', 'string'): ['Leader', 'Espionage', 'Security', 'First Lieutenant', None, 'Battle Station'],
('names', 'str'): ['Optimus', 'bumbl#ebéé ', 'Metroplex', 'bumblebee', 'métrop´le-x', 'Metroplex_)^$'],
('timestamp', 'time'): [datetime.datetime(2014, 6, 24, 0, 0), datetime.datetime(2014, 6, 24, 0, 0), datetime.datetime(2014, 6, 24, 0, 0), datetime.datetime(2014, 6, 24, 0, 0), datetime.datetime(2014, 6, 24, 0, 0), datetime.datetime(2014, 6, 24, 0, 0)],
('weight(t)', 'float'): [4.3, 2.0, 4.0, 1.8, 5.7, None]
})

t = TestCreator(op, df, name="stringclustering", configs=default_configs)

t.create(method="string_clustering", variant="all_fingerprint", cols="*", algorithm="fingerprint")
t.create(method="string_clustering", variant="all_ngram_fingerprint", cols="*", algorithm="ngram_fingerprint")
t.create(method="string_clustering", variant="all_metaphone", cols="*", algorithm="metaphone")
t.create(method="string_clustering", variant="all_nysiis", cols="*", algorithm="nysiis")
t.create(method="string_clustering", variant="all_match_rating_codex", cols="*", algorithm="match_rating_codex")
t.create(method="string_clustering", variant="all_double_metaphone", cols="*", algorithm="double_metaphone")
t.create(method="string_clustering", variant="all_soundex", cols="*", algorithm="soundex")
t.create(method="string_clustering", variant="all_levenshtein", cols="*", algorithm="levenshtein")

t.create(method="string_clustering", variant="numeric_fingerprint", cols=["rank"], algorithm="fingerprint")
t.create(method="string_clustering", variant="numeric_ngram_fingerprint", cols=["rank"], algorithm="ngram_fingerprint")
t.create(method="string_clustering", variant="numeric_metaphone", cols=["rank"], algorithm="metaphone")
t.create(method="string_clustering", variant="numeric_nysiis", cols=["rank"], algorithm="nysiis")
t.create(method="string_clustering", variant="numeric_match_rating_codex", cols=["rank"], algorithm="match_rating_codex")
t.create(method="string_clustering", variant="numeric_double_metaphone", cols=["rank"], algorithm="double_metaphone")
t.create(method="string_clustering", variant="numeric_soundex", cols=["rank"], algorithm="soundex")
t.create(method="string_clustering", variant="numeric_levenshtein", cols=["rank"], algorithm="levenshtein")

t.create(method="string_clustering", variant="string_fingerprint", cols=["names"], algorithm="fingerprint")
t.create(method="string_clustering", variant="string_ngram_fingerprint", cols=["names"], algorithm="ngram_fingerprint")
t.create(method="string_clustering", variant="string_metaphone", cols=["names"], algorithm="metaphone")
t.create(method="string_clustering", variant="string_nysiis", cols=["names"], algorithm="nysiis")
t.create(method="string_clustering", variant="string_match_rating_codex", cols=["names"], algorithm="match_rating_codex")
t.create(method="string_clustering", variant="string_double_metaphone", cols=["names"], algorithm="double_metaphone")
t.create(method="string_clustering", variant="string_soundex", cols=["names"], algorithm="soundex")
t.create(method="string_clustering", variant="string_levenshtein", cols=["names"], algorithm="levenshtein")

t.create(method="string_clustering", variant="multiple_fingerprint", cols=["NullType","Cybertronian","timestamp"], algorithm="fingerprint")
t.create(method="string_clustering", variant="multiple_ngram_fingerprint", cols=["NullType","Cybertronian","timestamp"], algorithm="ngram_fingerprint")
t.create(method="string_clustering", variant="multiple_metaphone", cols=["NullType","Cybertronian","timestamp"], algorithm="metaphone")
t.create(method="string_clustering", variant="multiple_nysiis", cols=["NullType","Cybertronian","timestamp"], algorithm="nysiis")
t.create(method="string_clustering", variant="multiple_match_rating_codex", cols=["NullType","Cybertronian","timestamp"], algorithm="match_rating_codex")
t.create(method="string_clustering", variant="multiple_double_metaphone", cols=["NullType","Cybertronian","timestamp"], algorithm="double_metaphone")
t.create(method="string_clustering", variant="multiple_soundex", cols=["NullType","Cybertronian","timestamp"], algorithm="soundex")
t.create(method="string_clustering", variant="multiple_levenshtein", cols=["NullType","Cybertronian","timestamp"], algorithm="levenshtein")

t.create(method="cols.fingerprint", variant="all", cols="*")
t.create(method="cols.fingerprint", variant="string", cols=["names"])
t.create(method="cols.fingerprint", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.fingerprint", variant="multiple", cols=["NullType","Cybertronian","timestamp"], output_cols=["nt","ct","ts"])

t.create(method="cols.pos", variant="single", cols=["names"])
t.create(method="cols.pos", variant="multiple", cols=["date arrival","japanese name","last date seen"], output_cols=["da","jn","lds"])

t.create(method="cols.ngrams", variant="single", cols=["names"])
t.create(method="cols.ngrams", variant="multiple", cols=["date arrival","japanese name","last date seen"], n_size=1, output_cols=["da","jn","lds"])

t.create(method="cols.ngram_fingerprint", variant="all", cols="*")
t.create(method="cols.ngram_fingerprint", variant="string", cols=["function(binary)"], n_size=25)
t.create(method="cols.ngram_fingerprint", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.ngram_fingerprint", variant="multiple", cols=["NullType","Cybertronian","timestamp"], n_size=4, output_cols=["nt","ct","ts"])

t.create(method="cols.metaphone", variant="all", cols="*")
t.create(method="cols.metaphone", variant="string", cols=["names"])
t.create(method="cols.metaphone", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.metaphone", variant="multiple", cols=["NullType","Cybertronian","timestamp"], output_cols=["nt","ct","ts"])

t.create(method="cols.nysiis", variant="all", cols="*")
t.create(method="cols.nysiis", variant="string", cols=["names"])
t.create(method="cols.nysiis", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.nysiis", variant="multiple", cols=["NullType","Cybertronian","timestamp"], output_cols=["nt","ct","ts"])

t.create(method="cols.match_rating_codex", variant="all", cols="*")
t.create(method="cols.match_rating_codex", variant="string", cols=["names"])
t.create(method="cols.match_rating_codex", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.match_rating_codex", variant="multiple", cols=["NullType","Cybertronian","timestamp"], output_cols=["nt","ct","ts"])

t.create(method="cols.double_metaphone", variant="all", cols="*")
t.create(method="cols.double_metaphone", variant="string", cols=["names"])
t.create(method="cols.double_metaphone", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.double_metaphone", variant="multiple", cols=["NullType","Cybertronian","timestamp"], output_cols=["nt","ct","ts"])

t.create(method="cols.soundex", variant="all", cols="*")
t.create(method="cols.soundex", variant="string", cols=["names"])
t.create(method="cols.soundex", variant="numeric", cols=["rank"], output_cols=["rk"])
t.create(method="cols.soundex", variant="multiple", cols=["NullType","Cybertronian","timestamp"], output_cols=["nt","ct","ts"])

t.create(method="cols.levenshtein", variant="all_value", cols="*", value=["1a#-s","ERR","d2e","0","[]","''","","1","lu","2016","5000000","aeiou","abc#&^","2014-06-23","nan."])
t.create(method="cols.levenshtein", variant="all_col", cols="*", other_cols=['date arrival','weight(t)','age','height(ft)','japanese name','rank','last date seen','names','last position seen','Cybertronian','NullType','Date Type','function(binary)','function','timestamp'])
t.create(method="cols.levenshtein", variant="single_value", cols=["names"], value="prime", output_cols="nms")
t.create(method="cols.levenshtein", variant="single_col", cols=["rank"], other_cols=["weight(t)"])
t.create(method="cols.levenshtein", variant="multiple_value", cols=["last position seen","age","japanese name"], value=["10005","000","['Bumble']"])
t.create(method="cols.levenshtein", variant="multiple_col", cols=["NullType","Cybertronian","timestamp"], other_cols=["height(ft)","function","Date Type"], output_cols=["nt-ht","ct-ft","ts-dt"])

t.run()

create()
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