v_values[','.join(sorted(A))]=v_function(A,c_values)n=len(channels)#no.ofchannels shapley_values=defaultdict(int)forchannelinchannels:forAinv_values.keys():ifchannel notinA.split(","):cardinal_A=len(A.split(","))A_with_channel=A.split(",")A_with_channel.append(channel)A_with_c...
"r") as f: journeys = json.load(f) o = SimplifiedShapleyAttributionModel() result = o.attribute(journeys) print(f"Total value: {len(journeys)}") total = 0 for k, v in result.items(): print(f"Channel {k}: {v:.2f}") total += v print(f"Total of attributed values: {total:...
link='logit')输出在整个数据集中各个特征的贡献shap_values_all = explainer.shap_values(X_test)shap...
i+1)]returnPSdeffactorial(n):ifn==0:return1else:returnn*factorial(n-1)defv_function(A,C_values):'''This function computes the worth of eachcoalition.inputs:- A : a coalition of channels.- C_values : A dictionnary containing the number of conversions that each subset of channels ...
importmatplotlib.pyplotasplt# 可视化Shapley值plt.bar(results.keys(),results.values())plt.xlabel('特征')plt.ylabel('Shapley值')plt.title('各特征的Shapley值')plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 在这个部分,我们使用Matplotlib来绘制Shapley值的柱状图,让结果更加直观。
shap.summary_plot(shap_values[0][:, interested_feature], X[interested_feature]) 由于这是一个二分类问题,shap_values包含两个部分。我假设其中一个部分是类别0的,另一个部分是类别1的。如果我想知道一个特征的贡献,我必须绘制两个像下面这样的图。
import itertools players = ['A', 'B', 'C', 'D'] values = {'A': 20, 'B': 30, 'C': 25, 'D': 25} def powerSets(p): return itertools.chain.from_iterable(itertools.combinations(p, r) for r in range(len(p)+1)) def shapley_values(players, values): n = len(players) ...
# 输出渠道shapley value print(f"Total value: {len(journeys)}") total = 0 for k, v in result.items(): print(f"Channel {k}: {v:.2f}") total += v print(f"Total of attributed values: {total:.2f}") 代码语言:javascript 代码运行次数:0 运行 AI代码解释 Total value: 2392 Channel ...
others = tuple(x for x in subset if x != player) marginal_contribution = values[player] / m shapley[player] += marginal_contribution * (n - m + 1) return shapley shapley = shapley_values(players, values) print(shapley) 运行以上代码,将输出Shapley值的计算结果: {'A': 13.75, 'B': ...
D]values={A:20,B:30,1C:25/A:25}def powerSetsp:returnitertools.chain.from_iterableitertools.combinationsp,r forrinrangelenp+ldef shapley_valuesplayers,values:n=lenplayersshapley={player:0forplayerin players}for subsetin powerSetsplayers:m=lensubsetfor playerinsubset:others=tuplex forx in...