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Calculate the optimal Ad Stock
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import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
def broadbent_adstock(grp, r): | |
adstock = np.zeros(len(grp)) | |
adstock[0] = grp[0] | |
for i in range(1,len(grp)): | |
adstock[i] = grp[i] + r*adstock[i-1] | |
return adstock | |
def optimize_adstock(grp, sales): | |
corrs = [] | |
rates = np.arange(0, 1.01, 0.01) | |
rmax = 0 | |
corrmax = -1 | |
for r in rates: | |
x = broadbent_adstock(grp.copy(), r) | |
y = sales | |
corr = np.corrcoef(x, y)[0][1] | |
corrs.append(corr) | |
if corr > corrmax: | |
rmax = r | |
corrmax = corr | |
plt.title("Correlation by Decay Rate") | |
plt.plot(rates, corrs, label="Correlation") | |
plt.xlabel("Decay Rate") | |
plt.ylabel("Correlation") | |
plt.legend() | |
plt.show() | |
print('忘却率が{0}のとき相関係数は最大値{1}'.format(rmax, corrmax)) |
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Thank you so much, this is exactly what I was looking for. Great job, thanks for helping out