def process_associations(self, features_to_process: List[FeatureToProcess],
source_target_series, compare_target_series):
def mirror_association(association_dict, feature_name, other_name,
value):
if other_name not in association_dict.keys():
association_dict[other_name] = dict()
other_dict = association_dict[other_name]
if feature_name not in other_dict.keys():
other_dict[feature_name] = value
for feature in features_to_process:
feature_name = feature.source.name
if feature_name not in self._associations.keys():
self._associations[feature_name] = dict()
cur_associations = self._associations[feature_name]
if feature.compare is not None:
if feature_name not in self._associations_compare.keys():
self._associations_compare[feature_name] = dict()
cur_associations_compare = self._associations_compare[
feature_name]
else:
cur_associations_compare = None
for other in features_to_process:
# for other in [of for of in features_to_process if of.source.name != feature_name]:
process_compare = cur_associations_compare is not None and other.compare is not None
# if other.source.name in cur_associations.keys():
# print(f"Skipping {feature_name} {other.source.name}")
# continue
if other.source.name == feature_name:
cur_associations[other.source.name] = 0.0
mirror_association(self._associations, feature_name,
other.source.name, 0.0)
if process_compare:
cur_associations_compare[other.source.name] = 0.0
mirror_association(self._associations_compare,
feature_name, other.source.name,
0.0)
continue
if self[feature_name]["type"] == FeatureType.TYPE_CAT or \
self[feature_name]["type"] == FeatureType.TYPE_BOOL:
# CAT/BOOL source
# ------------------------------------
if self[other.source.name]["type"] == FeatureType.TYPE_CAT or \
self[other.source.name]["type"] == FeatureType.TYPE_BOOL:
# CAT-CAT
cur_associations[other.source.name] = \
associations.theils_u(feature.source, other.source)
if process_compare:
cur_associations_compare[other.source.name] = \
associations.theils_u(feature.compare, other.compare)
elif self[
other.source.name]["type"] == FeatureType.TYPE_NUM:
# CAT-NUM
cur_associations[other.source.name] = \
associations.correlation_ratio(feature.source, other.source)
mirror_association(self._associations, feature_name, other.source.name, \
cur_associations[other.source.name])
if process_compare:
cur_associations_compare[other.source.name] = \
associations.correlation_ratio(feature.compare, other.compare)
mirror_association(self._associations_compare, feature_name, other.source.name, \
cur_associations_compare[other.source.name])
elif self[feature_name]["type"] == FeatureType.TYPE_NUM:
# NUM source
# ------------------------------------
if self[other.source.name]["type"] == FeatureType.TYPE_NUM:
# NUM-NUM
cur_associations[other.source.name] = \
feature.source.corr(other.source, method='pearson')
mirror_association(self._associations, feature_name, other.source.name, \
cur_associations[other.source.name])
if process_compare:
cur_associations_compare[other.source.name] = \
feature.compare.corr(other.compare, method='pearson')
mirror_association(self._associations_compare, feature_name, other.source.name, \
cur_associations_compare[other.source.name])
self.progress_bar.update(1)
def process_associations(self, features_to_process: List[FeatureToProcess], source_target_series,
compare_target_series):
def mirror_association(association_dict, feature_name, other_name, value):
if other_name not in association_dict.keys():
association_dict[other_name] = dict()
other_dict = association_dict[other_name]
if feature_name not in other_dict.keys():
other_dict[feature_name] = value
for feature in features_to_process:
feature_name = feature.source.name
if feature_name not in self._associations.keys():
self._associations[feature_name] = dict()
cur_associations = self._associations[feature_name]
if feature.compare is not None:
if feature_name not in self._associations_compare.keys():
self._associations_compare[feature_name] = dict()
cur_associations_compare = self._associations_compare[feature_name]
else:
cur_associations_compare = None
for other in features_to_process:
# for other in [of for of in features_to_process if of.source.name != feature_name]:
process_compare = cur_associations_compare is not None and other.compare is not None
# if other.source.name in cur_associations.keys():
# print(f"Skipping {feature_name} {other.source.name}")
# continue
if other.source.name == feature_name:
cur_associations[other.source.name] = 0.0
mirror_association(self._associations, feature_name, other.source.name, 0.0)
if process_compare:
cur_associations_compare[other.source.name] = 0.0
mirror_association(self._associations_compare, feature_name, other.source.name, 0.0)
continue
if self[feature_name]["type"] == FeatureType.TYPE_CAT or \
self[feature_name]["type"] == FeatureType.TYPE_BOOL:
# CAT/BOOL source
# ------------------------------------
if self[other.source.name]["type"] == FeatureType.TYPE_CAT or \
self[other.source.name]["type"] == FeatureType.TYPE_BOOL:
# CAT-CAT
cur_associations[other.source.name] = \
associations.theils_u(feature.source, other.source)
if process_compare:
cur_associations_compare[other.source.name] = \
associations.theils_u(feature.compare, other.compare)
elif self[other.source.name]["type"] == FeatureType.TYPE_NUM:
# CAT-NUM
# This handles cat-num, then mirrors so no need to process num-cat separately
# (symmetrical relationship)
cur_associations[other.source.name] = \
associations.correlation_ratio(feature.source, other.source)
mirror_association(self._associations, feature_name, other.source.name, \
cur_associations[other.source.name])
if process_compare:
cur_associations_compare[other.source.name] = \
associations.correlation_ratio(feature.compare, other.compare)
mirror_association(self._associations_compare, feature_name, other.source.name, \
cur_associations_compare[other.source.name])
elif self[feature_name]["type"] == FeatureType.TYPE_NUM:
# NUM source
# ------------------------------------
if self[other.source.name]["type"] == FeatureType.TYPE_NUM:
# NUM-NUM
cur_associations[other.source.name] = \
feature.source.corr(other.source, method='pearson')
# TODO: display correlation error better in graph!
if isnan(cur_associations[other.source.name]):
if feature.source.equals(other.source):
cur_associations[other.source.name] = CORRELATION_IDENTICAL
else:
# ERROR may occur if Nan's in one match values in other, and vice-versa
cur_associations[other.source.name] = CORRELATION_ERROR
mirror_association(self._associations, feature_name, other.source.name, \
cur_associations[other.source.name])
if process_compare:
cur_associations_compare[other.source.name] = \
feature.compare.corr(other.compare, method='pearson')
# TODO: display correlation error better in graph!
if isnan(cur_associations_compare[other.source.name]):
if feature.compare.equals(other.compare):
cur_associations_compare[other.source.name] = CORRELATION_IDENTICAL
else:
# ERROR may occur if Nan's in one match values in other, and vice-versa
cur_associations_compare[other.source.name] = CORRELATION_ERROR
mirror_association(self._associations_compare, feature_name, other.source.name, \
cur_associations_compare[other.source.name])
self.progress_bar.update(1)
def process_associations(self, features_to_process: List[FeatureToProcess],
source_target_series, compare_target_series):
def mirror_association(association_dict, feature_name, other_name,
value):
if other_name not in association_dict.keys():
association_dict[other_name] = dict()
other_dict = association_dict[other_name]
if feature_name not in other_dict.keys():
other_dict[feature_name] = value
for feature in features_to_process:
feature_name = feature.source.name
if feature_name not in self._associations.keys():
self._associations[feature_name] = dict()
cur_associations = self._associations[feature_name]
if feature.compare is not None:
if feature_name not in self._associations_compare.keys():
self._associations_compare[feature_name] = dict()
cur_associations_compare = self._associations_compare[
feature_name]
else:
cur_associations_compare = None
for other in features_to_process:
# for other in [of for of in features_to_process if of.source.name != feature_name]:
process_compare = cur_associations_compare is not None and other.compare is not None
# if other.source.name in cur_associations.keys():
# print(f"Skipping {feature_name} {other.source.name}")
# continue
if other.source.name == feature_name:
cur_associations[other.source.name] = 0.0
mirror_association(self._associations, feature_name,
other.source.name, 0.0)
if process_compare:
cur_associations_compare[other.source.name] = 0.0
mirror_association(self._associations_compare,
feature_name, other.source.name,
0.0)
continue
if self[feature_name]["type"] == FeatureType.TYPE_CAT or \
self[feature_name]["type"] == FeatureType.TYPE_BOOL:
# CAT/BOOL source
# ------------------------------------
if self[other.source.name]["type"] == FeatureType.TYPE_CAT or \
self[other.source.name]["type"] == FeatureType.TYPE_BOOL:
# CAT-CAT
cur_associations[other.source.name] = \
associations.theils_u(feature.source, other.source)
if process_compare:
cur_associations_compare[other.source.name] = \
associations.theils_u(feature.compare, other.compare)
elif self[
other.source.name]["type"] == FeatureType.TYPE_NUM:
# CAT-NUM
# This handles cat-num, then mirrors so no need to process num-cat separately
# (symmetrical relationship)
cur_associations[other.source.name] = \
associations.correlation_ratio(feature.source, other.source)
mirror_association(self._associations, feature_name, other.source.name, \
cur_associations[other.source.name])
if process_compare:
cur_associations_compare[other.source.name] = \
associations.correlation_ratio(feature.compare, other.compare)
mirror_association(self._associations_compare, feature_name, other.source.name, \
cur_associations_compare[other.source.name])
elif self[feature_name]["type"] == FeatureType.TYPE_NUM:
# NUM source
# ------------------------------------
if self[other.source.name]["type"] == FeatureType.TYPE_NUM:
# NUM-NUM
try:
cur_associations[other.source.name] = \
feature.source.corr(other.source, method='pearson')
except FloatingPointError:
# This usually happens when there is only 1 non-NaN value in each data series
# Assigning the value 1.0 as per
# https://stats.stackexchange.com/questions/94150/why-is-the-pearson-correlation-1-when-only-two-data-values-are-available
# -> Also showing a warning
cur_associations[other.source.name] = 1.0
self.corr_warning.append(feature_name + "/" +
other.source.name)
# TODO: display correlation error better in graph!
if isnan(cur_associations[other.source.name]):
if feature.source.equals(other.source):
cur_associations[
other.source.name] = CORRELATION_IDENTICAL
else:
# ERROR may occur if Nan's in one match values in other, and vice-versa
cur_associations[
other.source.name] = CORRELATION_ERROR
mirror_association(self._associations, feature_name, other.source.name, \
cur_associations[other.source.name])
if process_compare:
cur_associations_compare[other.source.name] = \
feature.compare.corr(other.compare, method='pearson')
# TODO: display correlation error better in graph!
if isnan(cur_associations_compare[
other.source.name]):
if feature.compare.equals(other.compare):
cur_associations_compare[
other.source.
name] = CORRELATION_IDENTICAL
else:
# ERROR may occur if Nan's in one match values in other, and vice-versa
cur_associations_compare[
other.source.name] = CORRELATION_ERROR
mirror_association(self._associations_compare, feature_name, other.source.name, \
cur_associations_compare[other.source.name])
self.progress_bar.update(1)
