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"""Computes the mean profit per outbound marketing letter, given a fraction of the population addressed, and fixed cost and reward"""
import typing
import numpy as np
from h2oaicore.metrics import CustomScorer
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix
class MarketingCampaign(CustomScorer):
_description = "Calculates mean profit per letter sent for a marketing campaign"
_binary = True
_maximize = True
_perfect_score = 1e20
_display_name = "Campaign"
_supports_sample_weight = False
# Configure these for your problem
_cost = 1.0 # cost to send letter
_reward = 100.0 # reward if get response back
_quantile = 0.9 # fraction of population to discard (here, only 10% of population gets a letter)
def score(self,
actual: np.array,
predicted: np.array,
sample_weight: typing.Optional[np.array] = None,
labels: typing.Optional[np.array] = None) -> float:
# label actuals as 1 or 0
lb = LabelEncoder()
labels = lb.fit_transform(labels)
actual = lb.transform(actual)
# probability of predicted response likelihood above which we'll send a letter
cutoff = np.quantile(predicted, self._quantile)
# print("cutoff: %f" % cutoff)
# whom we'll send letter to
selected = (predicted >= cutoff).ravel()
num_letters = np.count_nonzero(selected)
# print("number of letters: %d" % num_letters)
# compute cost and reward
cost = num_letters * self._cost # each letter costs _cost
reward = np.count_nonzero(actual[selected] == 1) * self._reward # each true positive leads to _reward
# print("cost: %f" % cost)
# print("reward: %f" % reward)
# compute total net income
net_income = reward - cost
# print("net_income: %f" % net_income)
# return mean profit per letter sent
return 0 if num_letters == 0 else net_income / num_letters
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