def process_item(self, item):
"""
Run correlation calculation on a pair of properties using the specified function.
Args:
item: (dict) input provided by get_items() (see get_items() for structure)
Returns: (tuple<str, str, float, str, int>) output of calculation with necessary
information about calculation included. Format in tuple:
independent property (x-axis) name,
dependent property (y-axis) name,
correlation value,
correlation function name,
number of data points used for correlation
length of shortest path between properties on propnet graph where x-axis property
is starting property and y-axis property is ending property.
Note: if no (forward) connection exists, the path length will be None. This does
not preclude y->x having a forward path.
"""
prop_x, prop_y = item['x_name'], item['y_name']
data_x, data_y = item['x_data'], item['y_data']
func_name, func = item['func']
n_points = len(data_x)
g = Graph()
try:
path_length_xy = g.get_degree_of_separation(prop_x, prop_y)
path_length_yx = g.get_degree_of_separation(prop_y, prop_x)
except ValueError:
# This shouldn't happen...but just in case
path_length_xy = None
path_length_yx = None
try:
path_length = min(path_length_xy, path_length_yx)
except TypeError:
path_length = path_length_xy or path_length_yx
if n_points < 2:
result = 0.0
else:
try:
result = func(data_x, data_y)
except Exception as ex:
# If correlation fails, catch the error, save it, and move on
result = ex
return prop_x, prop_y, result, func_name, n_points, path_length
def process_item(self, item):
"""
Run correlation calculation on a pair of properties using the specified function.
Args:
item: (dict) input provided by get_items() (see get_items() for structure)
Returns: (tuple<str, str, float, str, int>) output of calculation with necessary
information about calculation included. Format in tuple:
independent property (x-axis) name,
dependent property (y-axis) name,
correlation value,
correlation function name,
number of data points used for correlation
length of shortest path between properties on propnet graph where x-axis property
is starting property and y-axis property is ending property.
Note: if no (forward) connection exists, the path length will be None. This does
not preclude y->x having a forward path.
"""
prop_x, prop_y = item['x_name'], item['y_name']
data_x, data_y = item['x_data'], item['y_data']
func_name, func = item['func']
n_points = len(data_x)
g = Graph()
try:
path_length = g.get_degree_of_separation(prop_x, prop_y)
except ValueError:
path_length = None
if n_points < 2:
correlation = 0.0
else:
correlation = func(data_x, data_y)
return prop_x, prop_y, correlation, func_name, n_points, path_length
