def corr(idadf, features=None,ignore_indexer=True):
"""
See IdaDataFrame.corr
"""
if isinstance(idadf, ibmdbpy.IdaSeries):
raise TypeError("corr() missing 1 required positional argument: 'other'")
# TODO: catch case n <= 1
numerical_columns = idadf._get_numerical_columns()
if not numerical_columns:
print(idadf.name + " has no numeric columns")
return
if ignore_indexer is True:
if idadf.indexer:
if idadf.indexer in numerical_columns:
numerical_columns.remove(idadf.indexer)
#print(features)
#target, features = ibmdbpy.utils._check_input(target, features)
if features is not None:
for feature in features:
if feature not in numerical_columns:
raise TypeError("Correlation-based measure not available for non-numerical columns %s"%feature)
else:
features = numerical_columns
#if target not in columns:
# raise ValueError("%s is not a column of numerical type in %s"%(target, idadf.name))
values = OrderedDict()
combinations = [x for x in itertools.combinations(features, 2)]
#columns_set = [{x[0], x[1]} for x in combinations]
if len(features) < 64: # the limit of variables for an SQL statement is 4096, i.e 64^2
agg_list = []
for column_pair in combinations:
agg = "CORRELATION(\"%s\",\"%s\")"%(column_pair[0], column_pair[1])
agg_list.append(agg)
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data = idadf.ida_query("SELECT %s FROM %s"%(agg_string, name), first_row_only = True)
for i, element in enumerate(combinations):
if element[0] not in values:
values[element[0]] = {}
if element[1] not in values:
values[element[1]] = {}
values[element[0]][element[1]] = data[i]
values[element[1]][element[0]] = data[i]
result = pd.DataFrame(values).fillna(1)
else:
chunkgen = chunklist(combinations, 100)
for chunk in chunkgen:
agg_list = []
for column_pair in chunk:
agg = "CORRELATION(\"%s\",\"%s\")"%(column_pair[0], column_pair[1])
agg_list.append(agg)
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data = idadf.ida_query("SELECT %s FROM %s"%(agg_string, name), first_row_only = True)
for i, element in enumerate(chunk):
if element[0] not in values:
values[element[0]] = OrderedDict()
if element[1] not in values:
values[element[1]] = OrderedDict()
values[element[0]][element[1]] = data[i]
values[element[1]][element[0]] = data[i]
result = pd.DataFrame(values).fillna(1)
result = result.reindex(result.columns)
if len(result) == 1:
result = result[0]
return result
def corr(idadf, features=None, ignore_indexer=True):
"""
See IdaDataFrame.corr
"""
if isinstance(idadf, ibmdbpy.IdaSeries):
raise TypeError(
"corr() missing 1 required positional argument: 'other'")
# TODO: catch case n <= 1
numerical_columns = idadf._get_numerical_columns()
if not numerical_columns:
print(idadf.name + " has no numeric columns")
return
if ignore_indexer is True:
if idadf.indexer:
if idadf.indexer in numerical_columns:
numerical_columns.remove(idadf.indexer)
#print(features)
#target, features = ibmdbpy.utils._check_input(target, features)
if features is not None:
for feature in features:
if feature not in numerical_columns:
raise TypeError(
"Correlation-based measure not available for non-numerical columns %s"
% feature)
else:
features = numerical_columns
#if target not in columns:
# raise ValueError("%s is not a column of numerical type in %s"%(target, idadf.name))
values = OrderedDict()
combinations = [x for x in itertools.combinations(features, 2)]
#columns_set = [{x[0], x[1]} for x in combinations]
if len(
features
) < 64: # the limit of variables for an SQL statement is 4096, i.e 64^2
agg_list = []
for column_pair in combinations:
agg = "CORRELATION(\"%s\",\"%s\")" % (column_pair[0],
column_pair[1])
agg_list.append(agg)
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data = idadf.ida_query("SELECT %s FROM %s" % (agg_string, name),
first_row_only=True)
for i, element in enumerate(combinations):
if element[0] not in values:
values[element[0]] = {}
if element[1] not in values:
values[element[1]] = {}
values[element[0]][element[1]] = data[i]
values[element[1]][element[0]] = data[i]
result = pd.DataFrame(values).fillna(1)
else:
chunkgen = chunklist(combinations, 100)
for chunk in chunkgen:
agg_list = []
for column_pair in chunk:
agg = "CORRELATION(\"%s\",\"%s\")" % (column_pair[0],
column_pair[1])
agg_list.append(agg)
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data = idadf.ida_query("SELECT %s FROM %s" % (agg_string, name),
first_row_only=True)
for i, element in enumerate(chunk):
if element[0] not in values:
values[element[0]] = OrderedDict()
if element[1] not in values:
values[element[1]] = OrderedDict()
values[element[0]][element[1]] = data[i]
values[element[1]][element[0]] = data[i]
result = pd.DataFrame(values).fillna(1)
result = result.reindex(result.columns)
if len(result) == 1:
result = result[0]
return result
def pearson(idadf, target=None, features=None, ignore_indexer=True):
"""
Compute the pearson correlation coefficients between a set of features and a
set of target in an IdaDataFrame. Provide more granualirity than
IdaDataFrame.corr
Parameters
----------
idadf : IdaDataFrame
target : str or list of str, optional
A column or list of columns against to be used as target. Per default,
consider all columns
features : str or list of str, optional
A column or list of columns to be used as features. Per default,
consider all columns.
ignore_indexer : bool, default: True
Per default, ignore the column declared as indexer in idadf
Returns
-------
Pandas.DataFrame or Pandas.Series if only one target
Notes
-----
Input columns as target and features should be numerical.
Examples
--------
>>> idadf = IdaDataFrame(idadb, "IRIS")
>>> pearson(idadf)
"""
numerical_columns = idadf._get_numerical_columns()
if features is None:
features = numerical_columns
target, features = _check_input(idadf, target, features, ignore_indexer)
value_dict = OrderedDict()
for feature in features:
if feature not in numerical_columns:
raise TypeError(
"Correlation-based measure not available for non-numerical column %s"
% feature)
if target == features:
return idadf.corr(features=features, ignore_indexer=ignore_indexer)
else:
for t in target:
if feature not in numerical_columns:
raise TypeError(
"Correlation-based measure not available for non-numerical column %s"
% t)
for t in target:
value_dict[t] = OrderedDict()
features_notarget = [x for x in features if x != t]
if len(features_notarget) < 64:
agg_list = [
"CORRELATION(\"%s\",\"%s\")" % (x, t)
for x in features_notarget
]
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data = idadf.ida_query("SELECT %s FROM %s" %
(agg_string, name),
first_row_only=True)
else:
chunkgen = chunklist(features_notarget, 100)
data = ()
for chunk in chunkgen:
agg_list = [
"CORRELATION(\"%s\",\"%s\")" % (x, t) for x in chunk
]
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data += idadf.ida_query("SELECT %s FROM %s" %
(agg_string, name),
first_row_only=True)
for i, feature in enumerate(features_notarget):
value_dict[t][feature] = data[i]
### Fill the matrix
result = pd.DataFrame(value_dict).fillna(1)
if len(result.columns) == 1:
if len(result) == 1:
result = result.iloc[0, 0]
else:
result = result[result.columns[0]].copy()
result.sort_values(inplace=True, ascending=False)
else:
order = [x for x in result.columns if x in features
] + [x for x in features if x not in result.columns]
result = result.reindex(order)
return result
def pearson(idadf, target=None, features=None, ignore_indexer=True):
"""
Compute the pearson correlation coefficients between a set of features and a
set of target in an IdaDataFrame. Provide more granualirity than
IdaDataFrame.corr
Parameters
----------
idadf : IdaDataFrame
target : str or list of str, optional
A column or list of columns against to be used as target. Per default,
consider all columns
features : str or list of str, optional
A column or list of columns to be used as features. Per default,
consider all columns.
ignore_indexer : bool, default: True
Per default, ignore the column declared as indexer in idadf
Returns
-------
Pandas.DataFrame or Pandas.Series if only one target
Notes
-----
Input columns as target and features should be numerical.
Examples
--------
>>> idadf = IdaDataFrame(idadb, "IRIS")
>>> pearson(idadf)
"""
numerical_columns = idadf._get_numerical_columns()
if features is None:
features = numerical_columns
target, features = _check_input(idadf, target, features, ignore_indexer)
value_dict = OrderedDict()
for feature in features:
if feature not in numerical_columns:
raise TypeError("Correlation-based measure not available for non-numerical column %s"%feature)
if target == features:
return idadf.corr(features = features, ignore_indexer=ignore_indexer)
else:
for t in target:
if feature not in numerical_columns:
raise TypeError("Correlation-based measure not available for non-numerical column %s"%t)
for t in target:
value_dict[t] = OrderedDict()
features_notarget = [x for x in features if x != t]
if len(features_notarget) < 64:
agg_list = ["CORRELATION(\"%s\",\"%s\")"%(x, t) for x in features_notarget]
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data = idadf.ida_query("SELECT %s FROM %s"%(agg_string, name), first_row_only = True)
else:
chunkgen = chunklist(features_notarget, 100)
data = ()
for chunk in chunkgen:
agg_list = ["CORRELATION(\"%s\",\"%s\")"%(x, t) for x in chunk]
agg_string = ', '.join(agg_list)
name = idadf.internal_state.current_state
data += idadf.ida_query("SELECT %s FROM %s"%(agg_string, name), first_row_only = True)
for i, feature in enumerate(features_notarget):
value_dict[t][feature] = data[i]
### Fill the matrix
result = pd.DataFrame(value_dict).fillna(1)
if len(result.columns) == 1:
if len(result) == 1:
result = result.iloc[0,0]
else:
result = result[result.columns[0]].copy()
result.sort(ascending = False)
else:
order = [x for x in result.columns if x in features] + [x for x in features if x not in result.columns]
result = result.reindex(order)
return result