CODEWITHSUNDEEP
pythonmachine-learningxgboost
kms
kms

Reputation: 2024

xgboost : TypeError: predict() got an unexpected keyword argument 'pred_contribs'

I trained a model using xgboost v0.90 to be compatible with AWS SageMaker ML Engine. I am doing the usual encoding and hyper-parameter tuning. Some code below:

import pandas as pd
import pickle
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder 


# split df into train and test
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:21], df.iloc[:,-1], test_size=0.1)

X_train.shape
(1000,21)

# Encode categorical variables  
cat_vars = ['cat1','cat2','cat3']
cat_transform = ColumnTransformer([('cat', OneHotEncoder(handle_unknown='ignore'), cat_vars)], remainder='passthrough')

encoder = cat_transform.fit(X_train)
X_train = encoder.transform(X_train)
X_test = encoder.transform(X_test)

X_train.shape
(1000,420)

# Define a xgboost regression model
model = XGBRegressor()

# Do hyper-parameter tuning
.....

# Fit model
model.fit(X_train, y_train)


# Forecast on test data
y_pred = model.predict(X_test, pred_contribs=True)
y_pred

I have installed SHAP and based on the documentation [1], .predict() takes pred_contribs argument. Traceback:

    TypeError                                 Traceback (most recent call last)
<ipython-input-1119-37e607e853fd> in <module>
      1 # Forecast on test data
----> 2 y_pred = model.predict(X_test, pred_contribs=True)
      3 y_pred

TypeError: predict() got an unexpected keyword argument 'pred_contribs'

[1]https://shap.readthedocs.io/en/latest/example_notebooks/tabular_examples/tree_based_models/Python%20Version%20of%20Tree%20SHAP.html?highlight=pred_contribs

[2]https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.predict

Upvotes: 2

Views: 6421

Answers (1)

Adarsh Wase
Adarsh Wase

Reputation: 1890

XGBRegressor's predict does not have pred_contribs parameter, it only has these parameters:

predict(self, X, output_margin=False, ntree_limit=None,
        validate_features=True, base_margin=None, iteration_range=None)

The parameter pred_contribs, that you are talking about is in xgboost.Booster.predict(), which is a low level XGBoost, it has these parameter:

predict(self, data: xgboost.core.DMatrix, output_margin: bool = False,
        ntree_limit: int = 0, pred_leaf: bool = False, pred_contribs: bool = False,
        approx_contribs: bool = False, pred_interactions: bool = False,
        validate_features: bool = True, training: bool = False,
        iteration_range: Tuple[int, int] = (0, 0), strict_shape: bool = False)

So you have to use that:

data =  xgboost.DMatrix(X, label = y)
model = xgboost.train({"learning_rate": 0.01, "max_depth": 4}, data)
model.predict(data, pred_contribs = True)

Upvotes: 1

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