def test_load_ytrue():
ix_all = createmultiindex(X=getXst())
y = getytrue()
assert y.shape[0] == ix_all.shape[0]
assert unique(y).shape[0] == 2
print(y.sample(10))
assert isinstance(y, pd.Series)
def test_pruningpipe():
print('start', pd.datetime.now())
n_rows = 500
n_cluster = 25
n_simplequestions = 50
n_pointedquestions = 50
Xst = getXst(nrows=n_rows)
ixc = createmultiindex(X=Xst)
y_true = getytrue()
y_true = y_true.loc[ixc]
print(pd.datetime.now(), 'data loaded')
pipe = PruningPipe(
connector=DfConnector(
scorer=Pipeline(steps=[
('scores', FeatureUnion(_lr_score_list)),
('imputer', SimpleImputer(strategy='constant', fill_value=0))]
)
),
pruningclf=Explorer(clustermixin=KBinsCluster(n_clusters=n_cluster)),
sbsmodel=FeatureUnion(transformer_list=_sbs_score_list),
classifier=LogisticRegressionCV()
)
pipe.fit(X=Xst, y=y_true)
y_pred = pipe.predict(X=Xst)
precision = precision_score(y_true=y_true, y_pred=y_pred)
recall = recall_score(y_true=y_true, y_pred=y_pred)
accuracy = balanced_accuracy_score(y_true=y_true, y_pred=y_pred)
print('***\nscores:\n')
print('precision score:{}\n recall score:{}\n balanced accuracy score:{}'.format(
precision, recall, accuracy))
def test_build_questions_cluster_score(fixture_data, fixture_scores):
X_lr = fixture_data
scorer = fixture_scores
cluster = KMeans(n_clusters=10)
X_sbs = DfVisualSbs().fit_transform(X=X_lr)
X_score = scorer.fit_transform(X=X_lr)
y_cluster = cluster.fit_predict(X=X_score)
y_score = np.mean(X_score, axis=1)
X_info = pd.DataFrame(data=np.column_stack([y_cluster, y_score]),
index=createmultiindex(X=X_lr),
columns=['y_cluster', 'y_score'])
X_all = pd.concat([X_info, X_sbs], ignore_index=False, axis=1)
X_all.sort_values(by=['y_score', 'y_cluster'], inplace=True)
questions = SimpleQuestions(n_questions=10)
ix_questions = questions.fit_transform(X=pd.Series(data=y_cluster, index=createmultiindex(X=X_lr)))
X_questions = X_all.loc[ix_questions]
print(X_questions.sample(5))
assert True
def test_esconnector():
print('start', pd.datetime.now())
n_rows = 500
n_cluster = 25
Xst = getXst(nrows=n_rows)
left = Xst[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=20
)
ixc = createmultiindex(X=Xst)
y_true = getytrue()
y_true = y_true.loc[ixc]
print(pd.datetime.now(), 'data loaded')
pipe = PruningPipe(
connector=escon,
pruningclf=Explorer(clustermixin=KBinsCluster(n_clusters=n_cluster)),
sbsmodel=FeatureUnion(transformer_list=_sbs_score_list),
classifier=LogisticRegressionCV()
)
pipe.fit(X=left, y=y_true)
y_pred = pipe.predict(X=left)
scores = get_commonscores(y_pred=y_pred, y_true=y_true)
precision = scores['precision']
recall = scores['recall']
accuracy = scores['balanced_accuracy']
print('***\nscores:\n')
print('precision score:{}\n recall score:{}\n balanced accuracy score:{}'.format(
precision, recall, accuracy))
def _getindex(self, X, y=None):
"""
Return the cartesian product index of both dataframes
Args:
X (list): [df_source, df_target]
y (pd.Series/pd.DataFrame/pd.MultiIndex): dummy, not used
Returns:
pd.MultiIndex
"""
ix = createmultiindex(X=X, names=self.ixnamepairs)
return ix
def fit(self, X, y=None):
"""
Args:
X (list): [df_source, df_target]
y: dummy, not used
Returns:
self
"""
self.index = createmultiindex(X=X, names=self.ixnamepairs)
self.dfnum = pd.Series(index=np.arange(0, len(self.index)),
data=self.index.values,
name='ix')
self.dfix = pd.Series(index=self.index,
data=np.arange(0, len(self.index)),
name='ixnum')
return self
def getytrue(Xst=None):
"""
Args:
Xst: source and target dataframe for which to get the labelling
Returns:
pd.Series: supervised training data
"""
if Xst is None:
Xst = getXst()
ix_all = createmultiindex(X=Xst)
y_true = pd.Series(data=zeros(shape=(ix_all.shape[0], )),
index=ix_all,
name='y_true').fillna(0)
y_saved = open_csv(filename='ytrue.csv',
foldername=_folder_companydf,
index_col=[0, 1])['y_true']
y_true.loc[y_saved.index.intersection(ix_all)] = y_saved
return y_true
def test_ask_simple_questions_return_multiindex(fixture_data, fixture_scores):
n_clusters = 5
n_questions = 6
X_lr = fixture_data
scorer = fixture_scores
X_score = scorer.fit_transform(X=X_lr)
cluster = KMeans(n_clusters=n_clusters)
y_cluster = pd.Series(
data=cluster.fit_predict(X=X_score),
index=createmultiindex(X=X_lr)
)
questions = SimpleQuestions(n_questions=n_questions)
ix_questions = questions.fit_transform(X=y_cluster)
assert ix_questions.ndim == 1
assert ix_questions.shape[0] <= n_questions * n_clusters
assert ix_questions.shape[0] > 0
X_sbs = DfVisualSbs().fit_transform(X=X_lr)
X_questions = X_sbs.loc[ix_questions]
assert X_questions.shape[0] == ix_questions.shape[0]
def test_hardquestions(fixture_data, fixture_scores):
n_clusters = 5
n_questions = 6
X_lr = fixture_data
y_true = getytrue()
scorer = fixture_scores
X_score = scorer.fit_transform(X=X_lr)
cluster = KMeans(n_clusters=n_clusters)
y_cluster = pd.Series(
data=cluster.fit_predict(X=X_score),
index=createmultiindex(X=X_lr)
)
questions = HardQuestions(n_questions=n_questions)
ix_questions = questions.fit_transform(X=y_cluster, y=y_true)
assert ix_questions.ndim == 1
assert ix_questions.shape[0] <= n_questions * n_clusters
assert ix_questions.shape[0] > 0
X_sbs = DfVisualSbs().fit_transform(X=X_lr)
X_questions = X_sbs.loc[ix_questions]
assert X_questions.shape[0] == ix_questions.shape[0]
def test_clusterclassifier(fixture_scores, fixture_data):
n_clusters = 10
n_questions = 200
X_lr = fixture_data
y_true = getytrue(Xst=X_lr)
X_raw = fixture_scores.fit_transform(X=X_lr)
X_reduced = PCA(n_components=3).fit_transform(X_raw)
cluster = KMeans(n_clusters=n_clusters)
y_cluster = pd.Series(data=cluster.fit_predict(X=X_reduced),
index=createmultiindex(X=X_lr))
questions = SimpleQuestions(n_questions=n_questions)
ix_questions = questions.fit_transform(X=y_cluster)
y_true = y_true.loc[y_cluster.index.intersection(y_true.index)]
print('number of labellized rows found :{}'.format(len(y_true)))
clf = ClusterClassifier(cluster=cluster)
clf.fit(X=y_cluster, y=y_true)
print('all match: {}'.format(clf.allmatch))
print('no match: {}'.format(clf.nomatch))
print('mixed match: {}'.format(clf.mixedmatch))
print('not found: {}'.format(clf.notfound))
y_pred = clf.predict(X=y_cluster)
res = pd.Series(y_pred).value_counts()
print(res)
('street_token', SbsApplyComparator(on='street', comparator='token')),
('city_fuzzy', SbsApplyComparator(on='city', comparator='simple')),
('postalcode_fuzzy', SbsApplyComparator(on='postalcode', comparator='simple')),
('postalcode_contains', SbsApplyComparator(on='postalcode', comparator='contains')),
]
n_rows = 500 # Number of rows to compare in each datasets
n_cluster = 10 # Number of clusters used in the exploratory step
n_simplequestions = 100 # Number of questions per cluster
n_pointedquestions = 100 # Number of additional questions for clusters with mixed matches
##Load the data
print('start', pd.datetime.now())
Xst = getXst(nrows=n_rows)
ixc = createmultiindex(X=Xst)
# Load the vector corresponding to Xst
y_true = getytrue().loc[ixc]
print(y_true.value_counts())
print(pd.datetime.now(), 'data loaded')
## Explore the data:
connector = DfConnector(
scorer=Pipeline(steps=[
('scores', FeatureUnion(_lr_score_list)),
('imputer', SimpleImputer(strategy='constant', fill_value=0))]
)
)
### Fit the cluster non-supervizes
explorer = Explorer(clustermixin=KBinsCluster(n_clusters=n_cluster), n_simple=n_simplequestions, n_hard=n_pointedquestions)
Xst = connector.fit_transform(X=Xst)
def getindex(self, X):
return createmultiindex(X=X, names=self.ixnamepairs)
