def test_select_matcher_valid_multiple_metrics(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
target_attr='gold', k=7)
header = ['Name', 'Matcher', 'Num folds']
result_df_p = result['drill_down_cv_stats']['precision']
result_df_f = result['drill_down_cv_stats']['f1']
result_df_r = result['drill_down_cv_stats']['recall']
# Check header of precision dataframe
self.assertEqual(set(header) == set(list(result_df_p.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df_p.columns[len(result_df_p.columns) - 1])
# Check header of f1 dataframe
self.assertEqual(set(header) == set(list(result_df_f.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df_f.columns[len(result_df_f.columns) - 1])
# Check header of recall dataframe
self.assertEqual(set(header) == set(list(result_df_r.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df_p.columns[len(result_df_r.columns) - 1])
d = result_df_p.set_index('Name')
p_max = d.loc[result['selected_matcher'].name, 'Mean score']
a_max = pd.np.max(d['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_valid_cv_stats_3(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
metric_to_select_matcher='recall',
metrics_to_display='recall',
target_attr='gold', k=7)
header = ['Matcher', 'Average recall']
result_df = result['cv_stats']
result_df_r = result['drill_down_cv_stats']['recall']
self.assertEqual(set(header) == set(list(result_df.columns[[0, 1]])), True)
d = result_df.set_index('Matcher')
p_max = d.ix[result['selected_matcher'].name, 'Average recall']
a_max = pd.np.max(result_df_r['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_invalid_no_display_drill_down(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [
dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher,
logregmatcher
]
result = select_matcher(
matchers,
x=None,
y=None,
table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
metrics_to_display=['precision'],
target_attr='gold',
k=7)
result_df_p = result['drill_down_cv_stats']['recall']
def test_select_matcher_valid_1(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
# C = read_csv_metadata(path_c, ltable=A, rtable=B, fk_ltable='ltable.id',
# fk_rtable='rtable.id', key='_id')
# C['labels'] = labels
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
# xgmatcher = XGBoostMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher,
logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
target_attr='gold', k=7)
header = ['Name', 'Matcher', 'Num folds']
result_df = result['drill_down_cv_stats']['precision']
self.assertEqual(set(header) == set(list(result_df.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df.columns[len(result_df.columns) - 1])
d = result_df.set_index('Name')
p_max = d.loc[result['selected_matcher'].name, 'Mean score']
a_max = pd.np.max(d['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_valid_2(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
# C = read_csv_metadata(path_c, ltable=A, rtable=B, fk_ltable='ltable.id',
# fk_rtable='rtable.id', key='_id')
# labels = [0] * 7
# labels.extend([1] * 8)
# C['labels'] = labels
# feature_table = get_features_for_matching(A, B)
# feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='gold')
# feature_vectors.fillna(0, inplace=True)
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
col_list = list(feature_vectors.columns)
l = list_diff(col_list, [cm.get_key(feature_vectors), cm.get_fk_ltable(feature_vectors),
cm.get_fk_rtable(feature_vectors),
'gold'])
X = feature_vectors[l]
Y = feature_vectors['gold']
result = select_matcher(matchers, x=X, y=Y)
header = ['Name', 'Matcher', 'Num folds']
result_df = result['drill_down_cv_stats']['precision']
self.assertEqual(set(header) == set(list(result_df.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df.columns[len(result_df.columns) - 1])
d = result_df.set_index('Name')
p_max = d.ix[result['selected_matcher'].name, 'Mean score']
a_max = pd.np.max(d['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_valid_2(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
# C = read_csv_metadata(path_c, ltable=A, rtable=B, fk_ltable='ltable.id',
# fk_rtable='rtable.id', key='_id')
# labels = [0] * 7
# labels.extend([1] * 8)
# C['labels'] = labels
# feature_table = get_features_for_matching(A, B)
# feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='gold')
# feature_vectors.fillna(0, inplace=True)
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
col_list = list(feature_vectors.columns)
l = list_diff(col_list, [cm.get_key(feature_vectors), cm.get_fk_ltable(feature_vectors),
cm.get_fk_rtable(feature_vectors),
'gold'])
X = feature_vectors[l]
Y = feature_vectors['gold']
result = select_matcher(matchers, x=X, y=Y)
header = ['Name', 'Matcher', 'Num folds']
result_df = result['drill_down_cv_stats']['precision']
self.assertEqual(set(header) == set(list(result_df.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df.columns[len(result_df.columns) - 1])
d = result_df.set_index('Name')
p_max = d.loc[result['selected_matcher'].name, 'Mean score']
a_max = pd.np.max(d['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_target_attr_not_present(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
# C = read_csv_metadata(path_c, ltable=A, rtable=B, fk_ltable='ltable.id',
# fk_rtable='rtable.id', key='_id')
# labels = [0] * 7
# labels.extend([1] * 8)
# C['labels'] = labels
# feature_table = get_features_for_matching(A, B)
# feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='gold')
# feature_vectors.fillna(0, inplace=True)
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
col_list = list(feature_vectors.columns)
l = list_diff(col_list, [cm.get_fk_ltable(feature_vectors),
cm.get_fk_rtable(feature_vectors)
])
feature_vectors = feature_vectors[l]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs='_id',
target_attr='labels1', k=2)
def test_select_matcher_valid_multiple_metrics(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
target_attr='gold', k=7)
header = ['Name', 'Matcher', 'Num folds']
result_df_p = result['drill_down_cv_stats']['precision']
result_df_f = result['drill_down_cv_stats']['f1']
result_df_r = result['drill_down_cv_stats']['recall']
# Check header of precision dataframe
self.assertEqual(set(header) == set(list(result_df_p.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df_p.columns[len(result_df_p.columns) - 1])
# Check header of f1 dataframe
self.assertEqual(set(header) == set(list(result_df_f.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df_f.columns[len(result_df_f.columns) - 1])
# Check header of recall dataframe
self.assertEqual(set(header) == set(list(result_df_r.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df_p.columns[len(result_df_r.columns) - 1])
d = result_df_p.set_index('Name')
p_max = d.ix[result['selected_matcher'].name, 'Mean score']
a_max = pd.np.max(d['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_valid_1(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
# C = read_csv_metadata(path_c, ltable=A, rtable=B, fk_ltable='ltable.id',
# fk_rtable='rtable.id', key='_id')
# C['labels'] = labels
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
# xgmatcher = XGBoostMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher,
logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
target_attr='gold', k=7)
header = ['Name', 'Matcher', 'Num folds']
result_df = result['drill_down_cv_stats']['precision']
self.assertEqual(set(header) == set(list(result_df.columns[[0, 1, 2]])), True)
self.assertEqual('Mean score', result_df.columns[len(result_df.columns) - 1])
d = result_df.set_index('Name')
p_max = d.ix[result['selected_matcher'].name, 'Mean score']
a_max = pd.np.max(d['Mean score'])
self.assertEqual(p_max, a_max)
def test_select_matcher_target_attr_not_series(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
# C = read_csv_metadata(path_c, ltable=A, rtable=B, fk_ltable='ltable.id',
# fk_rtable='rtable.id', key='_id')
# labels = [0] * 7
# labels.extend([1] * 8)
# C['labels'] = labels
# feature_table = get_features_for_matching(A, B)
# feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='gold')
# feature_vectors.fillna(0, inplace=True)
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [
dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher,
logregmatcher
]
col_list = list(feature_vectors.columns)
l = list_diff(col_list, [
cm.get_fk_ltable(feature_vectors),
cm.get_fk_rtable(feature_vectors), 'gold'
])
X = feature_vectors[l]
Y = feature_vectors[['gold']]
result = select_matcher(matchers, x=X, y=Y)
def test_select_matcher_valid_cv_stats_3(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [
dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher,
logregmatcher
]
result = select_matcher(
matchers,
x=None,
y=None,
table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
metric_to_select_matcher='recall',
metrics_to_display='recall',
target_attr='gold',
k=7)
header = ['Matcher', 'Average recall']
result_df = result['cv_stats']
result_df_r = result['drill_down_cv_stats']['recall']
self.assertEqual(
set(header) == set(list(result_df.columns[[0, 1]])), True)
d = result_df.set_index('Matcher')
p_max = d.loc[result['selected_matcher'].name, 'Average recall']
a_max = np.max(result_df_r['Mean score'])
self.assertEqual(p_max, a_max)
def selector_matcher_combiner(matchers, combiners, x=None, y=None, table=None, exclude_attrs=None, target_attr=None,
weights=None, threshold=None, k=5):
if not isinstance(matchers, list):
matchers = [matchers]
if not isinstance(combiners, list):
combiners = [combiners]
matcher_list = get_matcher_list(matchers, combiners, weights, threshold)
return select_matcher(matcher_list, x=x, y=y, table=table, exclude_attrs=exclude_attrs, target_attr=target_attr,
k=k)
def test_select_matcher_invalid_metric_to_select_matcher(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
metric_to_select_matcher='test',
target_attr='gold', k=7)
def test_select_matcher_invalid_metric_to_select_matcher(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
metric_to_select_matcher='test',
target_attr='gold', k=7)
def test_select_matcher_invalid_no_display_drill_down(self):
A = read_csv_metadata(path_a, key='id')
B = read_csv_metadata(path_b, key='id')
feature_vectors = read_csv_metadata(path_f, ltable=A, rtable=B)
dtmatcher = DTMatcher()
nbmatcher = NBMatcher()
rfmatcher = RFMatcher()
svmmatcher = SVMMatcher()
linregmatcher = LinRegMatcher()
logregmatcher = LogRegMatcher()
matchers = [dtmatcher, nbmatcher, rfmatcher, svmmatcher, linregmatcher, logregmatcher]
result = select_matcher(matchers, x=None, y=None, table=feature_vectors,
exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'],
metrics_to_display=['precision'],
target_attr='gold', k=7)
result_df_p = result['drill_down_cv_stats']['recall']
def selector_matcher_combiner(matchers,
combiners,
x=None,
y=None,
table=None,
exclude_attrs=None,
target_attr=None,
weights=None,
threshold=None,
k=5):
if not isinstance(matchers, list):
matchers = [matchers]
if not isinstance(combiners, list):
combiners = [combiners]
matcher_list = get_matcher_list(matchers, combiners, weights, threshold)
return select_matcher(matcher_list,
x=x,
y=y,
table=table,
exclude_attrs=exclude_attrs,
target_attr=target_attr,
k=k)
def test_select_matcher_invalid_df(self):
select_matcher(matchers=[], table="", exclude_attrs=[], target_attr="")
def test_select_matcher_invalid_args(self):
select_matcher(matchers=[], table="", exclude_attrs=[])
def test_select_matcher_invalid_args(self):
select_matcher(matchers=[], table="", exclude_attrs=[])
def test_select_matcher_invalid_df(self):
select_matcher(matchers=[], table="", exclude_attrs=[], target_attr="")
