Problems with running xgboost in Python

Question

In running xgboost with Python I run into exceptions:

For a reproducible example:

import sklearn
from sklearn import datasets
data, labels = sklearn.datasets.load_diabetes(return_X_y= True)
labels = (labels > 100) * 1
X_train = data[:300, :]
y_train = labels[:300]
X_test = data[300:,:]
y_test = labels[300:]
dtrain = xgb.DMatrix(data = X_train, label = np.array(y_train), feature_names =['age',
  'sex',
  'bmi',
  'bp',
  's1',
  's2',
  's3',
  's4',
  's5',
  's6'])
dtest = xgb.DMatrix(data = X_test, label = np.array(y_test), feature_names = ['age',
  'sex',
  'bmi',
  'bp',
  's1',
  's2',
  's3',
  's4',
  's5',
  's6'])
param = {'colsample_bytree': 0.7,
 'eta': 0.3,
 'gamma': 3.0,
 'max_depth': 6,
 'min_child_weight': 4.0,
 'objective': 'binary:logistic',
 'reg_alpha': 0.3,
 'reg_lambda': 0.3,
 'silent': 0,
 'subsample': 0.8}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_boost_round = 100
bst = xgb.train(params = param, watchlist = watchlist , dtrain = dtrain, num_boost_round = num_boost_round,   \
                    early_stopping_rounds = True)

This results in the exception:

TypeError: train() got an unexpected keyword argument 'watchlist'

If I remove watchlist I get another exception:

bst = xgb.train(params = param, dtrain = dtrain, num_boost_round = num_boost_round,   \
                    early_stopping_rounds = True)
IndexError: list index out of range

Frayal · Accepted Answer · 2018-09-06 13:43:12Z

from doc

xgboost.train(params, dtrain, num_boost_round=10, evals=(), obj=None, 
feval=None, maximize=False, early_stopping_rounds=None, evals_result=None, 
verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None)

Train a booster with given parameters.

Parameters:

params (dict) – Booster params.

dtrain (DMatrix) – Data to be trained.

num_boost_round (int) – Number of boosting iterations.

evals (list of pairs (DMatrix, string)) – List of items to be evaluated during 
training, this allows user to watch performance on the validation set.

the parameter is not called watchlist but evals.

just changing that got me the following:

[0] eval-error:0.274648 train-error:0.253333
Multiple eval metrics have been passed: 'train-error' will be used for 
early stopping.

Will train until train-error hasn't improved in True rounds.
[1] eval-error:0.211268 train-error:0.223333
[2] eval-error:0.204225 train-error:0.203333
[3] eval-error:0.225352 train-error:0.203333
Stopping. Best iteration:
[2] eval-error:0.204225 train-error:0.203333

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