def test_getsbs(esconnectornew):
df_source = getsource(nrows=50)
Xst = esconnectornew.fit_transform(X=df_source)
ix = Xst.index
X_sbs = esconnectornew.getsbs(X=df_source, on_ix=ix)
assert X_sbs.index.equals(ix)
for c in df_source.columns:
assert c + '_source' in X_sbs.columns
return True
def test_search_record(esconnectornew):
df_source = getsource(nrows=100)
record = df_source.sample().iloc[0]
res = esconnectornew.search_record(record=record)
score = unpack_allhits(res)
assert len(score) <= esconnectornew.size
assert isinstance(score, list)
assert isinstance(score[0], dict)
print(res)
print(score)
def test_scorecols_datacols(esconnectornew):
df_source = getsource(nrows=10)
for c in df_source.sample(1).index:
record = df_source.loc[c]
res = esconnectornew.search_record(record=record)
score = unpack_allhits(res)
df = pd.DataFrame.from_dict(score, orient='columns').rename(
columns={
'ix': 'ix_target'
})
scorecols = pd.Index(['es_rank', 'es_score'])
assert df.columns.contains(scorecols[0])
assert df.columns.contains(scorecols[1])
def test_pipe_df():
df_source = getsource(nrows=100)
df_target = gettarget(nrows=100)
assert df_source.columns.equals(df_target.columns)
print(pd.datetime.now(), ' | ',
'number of rows on left:{}'.format(df_source.shape[0]))
print(pd.datetime.now(), ' | ',
'number of rows on right:{}'.format(df_target.shape[0]))
scorer = FeatureUnion(transformer_list=[(
'name_char', VectorizerConnector(on='name', analyzer='char')
), ('street_char', VectorizerConnector(on='street', analyzer='char')
), ('countrycode_exact', ExactConnector(on='countrycode'))])
dfcon = DfConnector(scorer=scorer)
Xsm = dfcon.fit_transform(X=[df_source, df_target])
ix_con = Xsm.index
y_true = getytrue(Xst=[df_source, df_target]).loc[ix_con]
Xsbs = dfcon.getsbs(X=[df_source, df_target], on_ix=ix_con)
scores_further = scorer_sbs.fit_transform(X=Xsbs)
scores_further = pd.DataFrame(data=scores_further,
index=ix_con,
columns=[c[0] for c in _sbs_score_list])
scores_further = pd.concat([Xsm, scores_further],
axis=1,
ignore_index=False)
X = scores_further
scoring = ['precision', 'recall', 'accuracy']
print(pd.datetime.now(), ' | starting score')
pipe = Pipeline(
steps=[('Impute', SimpleImputer(strategy='constant', fill_value=0)
), ('Scaler', Normalizer()), ('PCA', PCA(n_components=4)),
('Predictor',
GradientBoostingClassifier(n_estimators=1000, max_depth=5))])
scores = cross_validate(estimator=pipe,
X=X,
y=y_true,
scoring=scoring,
cv=5)
for c in scoring:
print(pd.datetime.now(),
' | {} score1: {}'.format(c, np.average(scores['test_' + c])))
def test_pipe_es():
df_source = getsource(nrows=100)
df_target = gettarget(nrows=None)
assert df_source.columns.equals(df_target.columns)
print(pd.datetime.now(), ' | ',
'number of rows on left:{}'.format(df_source.shape[0]))
print(pd.datetime.now(), ' | ',
'number of rows on right:{}'.format(df_target.shape[0]))
esclient = elasticsearch.Elasticsearch()
scoreplan = {
'name': {
'type': 'FreeText'
},
'street': {
'type': 'FreeText'
},
'city': {
'type': 'FreeText'
},
'duns': {
'type': 'Exact'
},
'postalcode': {
'type': 'FreeText'
},
'countrycode': {
'type': 'Exact'
}
}
escon = EsConnector(client=esclient,
scoreplan=scoreplan,
index="right",
explain=False,
size=10)
#Xsm is the similarity matrix
Xsm = escon.fit_transform(X=df_source)
ix_con = Xsm.index
y_true = getytrue(Xst=[df_source, df_target]).loc[ix_con]
Xsbs = escon.getsbs(X=df_source, on_ix=ix_con)
scores_further = scorer_sbs.fit_transform(X=Xsbs)
scores_further = pd.DataFrame(data=scores_further,
index=ix_con,
columns=[c[0] for c in _sbs_score_list])
scores_further = pd.concat([Xsm[['es_score']], scores_further],
axis=1,
ignore_index=False)
X = scores_further
scoring = ['precision', 'recall', 'accuracy']
print(pd.datetime.now(), ' | starting score')
pipe = Pipeline(
steps=[('Impute', SimpleImputer(strategy='constant', fill_value=0)
), ('Scaler', Normalizer()), ('PCA', PCA(n_components=4)),
('Predictor',
GradientBoostingClassifier(n_estimators=1000, max_depth=5))])
scores = cross_validate(estimator=pipe,
X=X,
y=y_true,
scoring=scoring,
cv=5)
for c in scoring:
print(pd.datetime.now(),
' | {} score1: {}'.format(c, np.average(scores['test_' + c])))
def test_getresults(esconnectornew):
df_source = getsource(nrows=10)
record = df_source.sample().iloc[0]
q = esconnectornew._write_es_query(record)
r = esconnectornew.client.search(body=q, index="right")
print(r)
def test_create_query(esconnectornew):
df_source = getsource(nrows=10)
record = df_source.sample().iloc[0]
q = esconnectornew._write_es_query(record)
print(q)
def test_transform(esconnectornew):
df_source = getsource(nrows=100)
X = esconnectornew.fit_transform(X=df_source)
assert isinstance(X, pd.DataFrame)
assert X.shape[1] == 2
from tutorial.main.stepbystep.stepbysteputils.pgconnector import create_engine_ready
from suricate.data.companies import getsource, gettarget
import pandas as pd
import numpy as np
engine = create_engine_ready()
# filefolder = '~/'
# leftpath = 'source.csv'
# rightpath = 'target.csv'
# df_source = pd.read_csv(filefolder + leftpath, index_col=0, sep='|', encoding='utf-8')
# df_target = pd.read_csv(filefolder + rightpath, index_col=0, sep='|', encoding='utf-8')
df_source_raw = getsource(nrows=500)
df_target_raw = gettarget(nrows=None)
from sklearn.model_selection import train_test_split
def rebuild_ytrue(ix):
y_true_saved = pd.read_sql(
sql="SELECT * FROM y_true WHERE y_true.y_true = 1",
con=engine).set_index(['ix_source', 'ix_target'], drop=True)['y_true']
y = pd.Series(index=ix, data=np.zeros(shape=len(ix)), name='y_true')
ix_common = y_true_saved.index.intersection(ix)
y.loc[ix_common] = y_true_saved.loc[ix_common]
return y
def prepare_source(df):
"""
from sklearn.pipeline import make_union
from suricate.dftransformers.vectorizer import VectorizerConnector
from suricate.data.base import ix_names
from suricate.data.companies import getsource, gettarget, getXst, getytrue
left = getsource(nrows=100)
right = gettarget(nrows=100)
X_lr = getXst(nrows=100)
y_true = getytrue(Xst=X_lr)
def test_loaddata():
print(ix_names['ixname'])
print(left.shape[0])
print(right.shape[0])
assert True
def test_tfidf():
expected_shape = left.shape[0] * right.shape[0]
stages = [
VectorizerConnector(on='name', analyzer='char', pruning=False),
VectorizerConnector(on='street', analyzer='char', pruning=False),
]
scorer = make_union(*stages)
scorer.fit(X=X_lr)
X_score = scorer.transform(X=X_lr)
assert X_score.shape[0] == expected_shape
pass
import pandas as pd
from tutorial.main.stepbystep.stepbysteputils.pgconnector import create_engine_ready
engine = create_engine_ready()
# filefolder = '~/'
# leftpath = 'source.csv'
# rightpath = 'target.csv'
# df_source = pd.read_csv(filefolder + leftpath, index_col=0, sep='|', encoding='utf-8')
# df_target = pd.read_csv(filefolder + rightpath, index_col=0, sep='|', encoding='utf-8')
from suricate.data.companies import getsource, gettarget
df_source_raw=getsource(nrows=None)
df_target_raw = gettarget(nrows=None)
def prepare_source(df):
"""
Args:
df:
Returns:
pd.DataFrame
"""
df2 = df
return df2
def prepare_target(df):
"""
Args:
def test_load_source():
df = getsource()
print(df.sample(10))
assert isinstance(df, pd.DataFrame)
