def get_features_for_matching(A, B):
"""
Get features with minimal input
Parameters
----------
A, B : MTable,
Input tables
Returns
-------
feature_table : pandas DataFrame
Consists of following columns
* feature_name - string, feature name
* left_attribute - string, attribute name
* right_attribute - string, attribute name
* left_attr_tokenizer - string, tokenizer name
* right_attr_tokenizer - string, tokenizer name
* simfunction - string, sumilarity function name
* function - function object
* function_source - string, containing source code
Notes
-----
The function also exports the important variables such as: _match_t, _match_s, _atypes1, _atypes2, _match_c;
to global name space so if a user want to examine they can do so.
"""
sim = mg.get_sim_funs()
tok = mg.get_single_arg_tokenizers()
t_A = mg.get_attr_types(A)
t_B = mg.get_attr_types(B)
attr_corres = mg.get_attr_corres(A, B)
feat_table = get_features(A, B, t_A, t_B, attr_corres, tok, sim)
# export important variables to global name space
#_m_current_tokenizers, _m_current_sim_funs, _m_current_attr_types_ltable, _m_current_attr_types_rtable_m_current_corres
mg._match_t = tok
mg._match_s = sim
mg._atypes1 = t_A
mg._atypes2 = t_B
mg._match_c = attr_corres
return feat_table
def get_features_for_matching(A, B):
"""
Get features with minimal input
Parameters
----------
A, B : MTable,
Input tables
Returns
-------
feature_table : pandas DataFrame
Consists of following columns
* feature_name - string, feature name
* left_attribute - string, attribute name
* right_attribute - string, attribute name
* left_attr_tokenizer - string, tokenizer name
* right_attr_tokenizer - string, tokenizer name
* simfunction - string, sumilarity function name
* function - function object
* function_source - string, containing source code
Notes
-----
The function also exports the important variables such as: _match_t, _match_s, _atypes1, _atypes2, _match_c;
to global name space so if a user want to examine they can do so.
"""
sim = mg.get_sim_funs()
tok = mg.get_single_arg_tokenizers()
t_A = mg.get_attr_types(A)
t_B = mg.get_attr_types(B)
attr_corres = mg.get_attr_corres(A, B)
feat_table = get_features(A, B, t_A, t_B, attr_corres, tok, sim)
# export important variables to global name space
#_m_current_tokenizers, _m_current_sim_funs, _m_current_attr_types_ltable, _m_current_attr_types_rtable_m_current_corres
mg._match_t = tok
mg._match_s = sim
mg._atypes1 = t_A
mg._atypes2 = t_B
mg._match_c = attr_corres
return feat_table
import sys
sys.path.append('C:\Pradap\Research\Python-work\Saranam\magellan')
A = mg.read_csv('../magellan/data/toy/table_A.csv', key='ID')
B = mg.read_csv('../magellan/data/toy/table_B.csv', key='ID')
blocker = mg.AttrEquivalenceBlocker()
C = blocker.block_tables(A, B, 'zipcode', 'zipcode', ['name', 'address', 'hourly_wage'], ['name', 'address', 'hourly_wage'])
D = mg.block_union_combine([C, C])
S = mg.sample_one_table(D, 10)
#L = mg.label(S, 'gold_label')
#print mg._m_global_tokenizers
#print mg._m_global_sim_fns
t = mg.get_single_arg_tokenizers()
print t
s = mg.get_sim_funs()
print s
corres = mg.get_attr_corres(A, B)
print corres['corres']
t_1 = mg.get_attr_types(A)
print t_1
t_2 = mg.get_attr_types(B)
print t_2
