LimeTabular TypeError：__init__() 缺少 1 个必需的位置参数：'model'

我已经开始学习Lime了。当我使用LimeTabular形式的Interpret时，我遇到一个问题，我按照标准完成了功能，但它不起作用。 错误是 TypeError: init () 缺少 1 个必需的位置参数：'model'

# %% Imports
from utils import DataLoader
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score
from interpret.blackbox import LimeTabular
from interpret import show

# %% Load and preprocess data
data_loader = DataLoader()
data_loader.load_dataset()
data_loader.preprocess_data()
# Split the data for evaluation
X_train, X_test, y_train, y_test = data_loader.get_data_split()
# Oversample the train data
X_train, y_train = data_loader.oversample(X_train, y_train)
print(X_train.shape)
print(X_test.shape)

# %% Fit blackbox model
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
print(f"F1 Score {f1_score(y_test, y_pred, average='macro')}")
print(f"Accuracy {accuracy_score(y_test, y_pred)}")

# %% Apply lime
# Initilize Lime for Tabular data
lime = LimeTabular(predict_fn=rf.predict_proba,
                   data=X_train, 
                   random_state=1)
# Get local explanations
lime_local = lime.explain_local(X_test[-20:], 
                                y_test[-20:], 
                                name='LIME')

show(lime_local)

# %%

错误消息表明你正在使用 LimeTabular 类的一个旧版本，该版本没有将 model 作为 __init__ 方法中的必需参数。要解决此问题，你需要 将预测函数 ( rf.predict_proba ) 直接传递给 explain_local 方法 ，而不是在初始化 LimeTabular 时传递。

以下是如何修改代码的方法：

# %% Imports
from utils import DataLoader
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score
from interpret.blackbox import LimeTabular
from interpret import show

# %% Load and preprocess data
data_loader = DataLoader()
data_loader.load_dataset()
data_loader.preprocess_data()
# Split the data for evaluation
X_train, X_test, y_train, y_test = data_loader.get_data_split()
# Oversample the train data
X_train, y_train = data_loader.oversample(X_train, y_train)
print(X_train.shape)
print(X_test.shape)

# %% Fit blackbox model
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
print(f"F1 Score {f1_score(y_test, y_pred, average='macro')}")
print(f"Accuracy {accuracy_score(y_test, y_pred)}")

# %% Apply lime
# Initilize Lime for Tabular data
lime = LimeTabular(data=X_train, 
                   random_state=1)

# Get local explanations
# 将 predict_fn 传递给 explain_local
lime_local = lime.explain_local(X_test[-20:], 
                                y_test[-20:], 
                                name='LIME',
                                predict_fn=rf.predict_proba)

show(lime_local)

# %%

通过此更改，你应该能够在不出现 TypeError 的情况下运行代码。