How do you implement a weighted sum transform primitive in Featuretools? - featuretools

Question

I'm trying to figure how to implement a weighted cum sum primitive for Featuretools. The weighting shall depend on time_since_last like
cum_sum (amount) = sum_{i} exp( -a_{i} ) * amount_{i}
where i are rolling 6 Month periods....
above you find the original question. after a while of try and error I came up with this code for my purpose:
using the data and initial setup for entity and relation from here
def weight_time_until(array, time):
diff = pd.DatetimeIndex(array) - time
s = np.floor(diff.days/365/0.5)
aWidth = 9
a = math.log(0.1) / ( -(aWidth -1) )
w = np.exp(-a*s)
return w
WeightTimeUntil = make_trans_primitive(function=weight_time_until,
input_types=[Datetime],
return_type=Numeric,
uses_calc_time=True,
description="Calc weight using time until the cutoff time",
name="weight_time_until")
features, feature_names = ft.dfs(entityset = es, target_entity = 'clients',
agg_primitives = ['sum'],
trans_primitives = [WeightTimeUntil, MultiplyNumeric])
when I does above I came close to the feature I want but at the end I did not get it right which I do not understand. So I got feature
SUM(loans.WEIGHT_TIME_UNTIL(loan_start))
but not
SUM(loans.loan_amount * loans.WEIGHT_TIME_UNTIL(loan_start))
What did I miss here???
I tried further....
My guess was a type miss match! but the "types" are the same. Anyway I tried the following:
1) es["loans"].convert_variable_type("loan_amount",ft.variable_types.Numeric)
2) loans["loan_amount_"] = loans["loan_amount"]*1.0
For (1) as well for (2) I get the more promising resulting feature:
loan_amount_ * WEIGHT_TIME_UNTIL(loan_start)
and also
loan_amount * WEIGHT_TIME_UNTIL(loan_start)
but only when I have the target value = loans instead of clients which actually was not my intention.

Answer 1

This primitive doesn't currently exist. However, you can create your own custom primitive to accomplish this calculation.
Here is an example calculating the rolling sum, which can be updated to do a weighted sum using the appropriate pandas or python method
from featuretools.primitives import TransformPrimitive
from featuretools.variable_types import Numeric
class RollingSum(TransformPrimitive):
"""Calculates the rolling sum.
Description:
Given a list of values, return the rolling sum.
"""
name = "rolling_sum"
input_types = [Numeric]
return_type = Numeric
uses_full_entity = True
def __init__(self, window=1, min_periods=None):
self.window = window
self.min_periods = min_periods
def get_function(self):
def rolling_sum(values):
"""method is passed a pandas series"""
return values.rolling(window=self.window, min_periods=self.min_periods).sum()
return rolling_sum