我希望class=0的数据值都显示在y轴左侧

要将类0的数据值显示在y轴的左侧，你可以使用`shap.summary_plot`函数的`plot_type='bar'`参数，并设置`plot=False`来手动绘制摘要图。然后，你可以自定义绘图的方式，将类0的数据值放在y轴的左侧。下面是修改后的代码示例：

from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import shap

# 读取数据
data = pd.read_excel(r"D:桌面/CESHIJIANGWEI 1.xlsx", sheet_name="Ag2", index_col=0)

# 提取特征和目标变量
X = data.loc[1:440, ['shape', 'bacterial species', 'Detection method', 'concentration', 'size', 'response time']].astype(str).values
Y = data.loc[1:440, 'MIC performance rate'].values

# 划分训练集和测试集
train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.2, random_state=258)

# 设置决策树参数
parameters = {
    'splitter': 'best',
    'criterion': "gini",
    "max_depth": 6,
    'min_samples_leaf': 1,
    "min_samples_split": 4,
}

feature_names = ['shape', 'bacterial species', 'Detection method', 'concentration', 'size', 'response time']
target_names = data['MIC performance rate'].unique().tolist()

# 模型训练
clf = DecisionTreeClassifier(**parameters)
clf.fit(train_x, train_y)

# SHAP解释器初始化
explainer = shap.TreeExplainer(clf)

# 计算SHAP值
shap_values = explainer.shap_values(test_x)

# 计算摘要统计值
expected_value = explainer.expected_value
feature_importances = np.abs(shap_values).mean(axis=0)

# 绘制摘要图
fig, ax = plt.subplots()
pos = np.arange(len(feature_names))

# 在y轴左侧绘制类0的数据值
ax.barh(pos, feature_importances, align='center', color='b')
ax.barh(pos, -feature_importances[test_y == 0], align='center', color='r')

# 设置y轴标签
ax.set_yticks(pos)
ax.set_yticklabels(feature_names)

# 设置x轴标签
ax.set_xlabel('Feature Importance')

# 设置标题
ax.set_title('SHAP Summary Plot')

# 显示图形
plt.tight_layout()
plt.savefig('shap_summary.pdf')
plt.show()

这样修改后，类0的数据值将显示在y轴的左侧。请记得适当调整图形的大小和布局，以便显示完整的图形和标签。