SHAP总结图中的颜色地图条未正确显示。

Question

更新：我发现了color_bar和color_bar_label参数，但它们没有影响。我还发现，如果显示26个或更多的特征，条形图将出现，但会像下面的LoL示例中一样变得小而薄。我也尝试改变绘图的大小和特征名称之间的空格，但没有成功。

我正在努力创建SHAP摘要图，虽然绘图已经出现，但是y轴上的垂直“特征值”颜色条根本不会出现。 力量图和决策图都能正常工作。我尝试更改最大特征数量，看看是否需要扩展轴，但没有解决任何问题。我正在使用Python 3.9.7（因为3.10和一些arches软件包存在问题），在jupyter笔记本中使用SHAP 0.39.0。我尝试通过conda更新/卸载/重新安装SHAP（4.10.3）。我甚至去了SHAP演示文稿here，按照这个演示文稿，一个垂直的特征值条确实出现了，但它非常小。 SHAP测试图 供参考，这是演示文稿中应该看起来的样子。

我无法确定酒吧本身的名称或需要更改什么以尝试使其出现。没有错误消息或警告，它在我的实际使用情况下根本不显示或者只是在示例代码中显示得非常小，我不确定要操纵哪些设置来更改它。

演练的数据集来自kaggle，这里，生成示例图的演练代码在此处：

import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split
import shap
import matplotlib.pyplot as pl

shap.initjs()

# read in the data
prefix = "local_scratch/data/league-of-legends-ranked-matches/"
matches = pd.read_csv(prefix+"matches.csv")
participants = pd.read_csv(prefix+"participants.csv")
stats1 = pd.read_csv(prefix+"stats1.csv", low_memory=False)
stats2 = pd.read_csv(prefix+"stats2.csv", low_memory=False)
stats = pd.concat([stats1,stats2])

# merge into a single DataFrame
a = pd.merge(participants, matches, left_on="matchid", right_on="id")
allstats_orig = pd.merge(a, stats, left_on="matchid", right_on="id")
allstats = allstats_orig.copy()

# drop games that lasted less than 10 minutes
allstats = allstats.loc[allstats["duration"] >= 10*60,:]

# Convert string-based categories to numeric values
cat_cols = ["role", "position", "version", "platformid"]
for c in cat_cols:
    allstats[c] = allstats[c].astype('category')
    allstats[c] = allstats[c].cat.codes
allstats["wardsbought"] = allstats["wardsbought"].astype(np.int32)

X = allstats.drop(["win"], axis=1)
y = allstats["win"]

# convert all features we want to consider as rates
rate_features = [
    "kills", "deaths", "assists", "killingsprees", "doublekills",
    "triplekills", "quadrakills", "pentakills", "legendarykills",
    "totdmgdealt", "magicdmgdealt", "physicaldmgdealt", "truedmgdealt",
    "totdmgtochamp", "magicdmgtochamp", "physdmgtochamp", "truedmgtochamp",
    "totheal", "totunitshealed", "dmgtoobj", "timecc", "totdmgtaken",
    "magicdmgtaken" , "physdmgtaken", "truedmgtaken", "goldearned", "goldspent",
    "totminionskilled", "neutralminionskilled", "ownjunglekills",
    "enemyjunglekills", "totcctimedealt", "pinksbought", "wardsbought",
    "wardsplaced", "wardskilled"
]
for feature_name in rate_features:
    X[feature_name] /= X["duration"] / 60 # per minute rate

# convert to fraction of game
X["longesttimespentliving"] /= X["duration"]

# define friendly names for the features
full_names = {
    "kills": "Kills per min.",
    "deaths": "Deaths per min.",
    "assists": "Assists per min.",
    "killingsprees": "Killing sprees per min.",
    "longesttimespentliving": "Longest time living as % of game",
    "doublekills": "Double kills per min.",
    "triplekills": "Triple kills per min.",
    "quadrakills": "Quadra kills per min.",
    "pentakills": "Penta kills per min.",
    "legendarykills": "Legendary kills per min.",
    "totdmgdealt": "Total damage dealt per min.",
    "magicdmgdealt": "Magic damage dealt per min.",
    "physicaldmgdealt": "Physical damage dealt per min.",
    "truedmgdealt": "True damage dealt per min.",
    "totdmgtochamp": "Total damage to champions per min.",
    "magicdmgtochamp": "Magic damage to champions per min.",
    "physdmgtochamp": "Physical damage to champions per min.",
    "truedmgtochamp": "True damage to champions per min.",
    "totheal": "Total healing per min.",
    "totunitshealed": "Total units healed per min.",
    "dmgtoobj": "Damage to objects per min.",
    "timecc": "Time spent with crown control per min.",
    "totdmgtaken": "Total damage taken per min.",
    "magicdmgtaken": "Magic damage taken per min.",
    "physdmgtaken": "Physical damage taken per min.",
    "truedmgtaken": "True damage taken per min.",
    "goldearned": "Gold earned per min.",
    "goldspent": "Gold spent per min.",
    "totminionskilled": "Total minions killed per min.",
    "neutralminionskilled": "Neutral minions killed per min.",
    "ownjunglekills": "Own jungle kills per min.",
    "enemyjunglekills": "Enemy jungle kills per min.",
    "totcctimedealt": "Total crown control time dealt per min.",
    "pinksbought": "Pink wards bought per min.",
    "wardsbought": "Wards bought per min.",
    "wardsplaced": "Wards placed per min.",
    "turretkills": "# of turret kills",
    "inhibkills": "# of inhibitor kills",
    "dmgtoturrets": "Damage to turrets"
}
feature_names = [full_names.get(n, n) for n in X.columns]
X.columns = feature_names

# create train/validation split
Xt, Xv, yt, yv = train_test_split(X,y, test_size=0.2, random_state=10)
dt = xgb.DMatrix(Xt, label=yt.values)
dv = xgb.DMatrix(Xv, label=yv.values)

params = {
    "eta": 0.5,
    "max_depth": 4,
    "objective": "binary:logistic",
    "silent": 1,
    "base_score": np.mean(yt),
    "eval_metric": "logloss"
}
model = xgb.train(params, dt, 300, [(dt, "train"),(dv, "valid")], early_stopping_rounds=5, verbose_eval=25)

# compute the SHAP values for every prediction in the validation dataset
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(Xv)

shap.summary_plot(shap_values, Xv)

- semanning

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这个回答解决了你的问题吗？Shap - 总结图中的颜色条未显示 - Jeremy Caney

Answer 1

看起来其他人和我有相同的问题，在我发出问题几周后，有一个解决方案是将matplotlib版本降级到3.4.3。我降级到了3.4.3版本，并解决了这个问题。

- semanning

Answer 2

如上所述，似乎在matplotlib.pyplot版本3.5中改变了colorbar或box的纵横比处理方式。但是，您可以进行更正。

• 使用shap.summary_plot(..., show=False)允许修改图表
• 使用plt.gcf().axes[-1].set_aspect(1000)设置colorbar的纵横比
• 然后还要设置color bar的盒子的纵横比 plt.gcf().axes[-1].set_box_aspect(1000)

这将使您恢复旧结果。如果您想使colorbar更粗，请将其纵横比设置为100。

- NegatedObjectIdentity

Answer 3

我也遇到了同样的问题。 奇怪的是，当我使用Python 3.7.9时，我遇到了这个问题，但当我切换到3.6.8时，它就正常工作了。 不确定SHAP的某些实现是否对不同版本的Python敏感。

- Yu-Sheng Li

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谢谢你的建议，我之前在3.9.7版本上工作时也遇到了这个问题。我还尝试过3.8.12和3.7版本。虽然我按照你的建议尝试回到了3.6版本，但是我现在遇到了一些依赖numpy的问题，需要先解决这些问题才能真正尝试。 - semanning