Custom Evaluation Function based on F1 for use in xgboost - Python API - python-3.6

Question

I have written the following custom evaluation function to use with xgboost, in order to optimize F1. Umfortuantely it returns an exception when run with xgboost.
The evaluation function is the following:
def F1_eval(preds, labels):
t = np.arange(0, 1, 0.005)
f = np.repeat(0, 200)
Results = np.vstack([t, f]).T
P = sum(labels == 1)
for i in range(200):
m = (preds >= Results[i, 0])
TP = sum(labels[m] == 1)
FP = sum(labels[m] == 0)
if (FP + TP) > 0:
Precision = TP/(FP + TP)
Recall = TP/P
if (Precision + Recall >0) :
F1 = 2 * Precision * Recall / (Precision + Recall)
else:
F1 = 0
Results[i, 1] = F1
return(max(Results[:, 1]))
Below I provide a reproducible example along with the error message:
from sklearn import datasets
Wine = datasets.load_wine()
X_wine = Wine.data
y_wine = Wine.target
y_wine[y_wine == 2] = 1
X_wine_train, X_wine_test, y_wine_train, y_wine_test = train_test_split(X_wine, y_wine, test_size = 0.2)
clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', \
booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, \
subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
clf_wine.fit(X_wine_train, y_wine_train,\
eval_set=[(X_wine_train, y_wine_train), (X_wine_test, y_wine_test)], eval_metric=F1_eval, early_stopping_rounds=10, verbose=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-453-452852658dd8> in <module>()
12 clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
13
---> 14 clf_wine.fit(X_wine_train, y_wine_train,eval_set=[(X_wine_train, y_wine_train), (X_wine_test, y_wine_test)], eval_metric=F1_eval, early_stopping_rounds=10, verbose=True)
15
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set)
519 early_stopping_rounds=early_stopping_rounds,
520 evals_result=evals_result, obj=obj, feval=feval,
--> 521 verbose_eval=verbose, xgb_model=None)
522
523 self.objective = xgb_options["objective"]
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)
202 evals=evals,
203 obj=obj, feval=feval,
--> 204 xgb_model=xgb_model, callbacks=callbacks)
205
206
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
82 # check evaluation result.
83 if len(evals) != 0:
---> 84 bst_eval_set = bst.eval_set(evals, i, feval)
85 if isinstance(bst_eval_set, STRING_TYPES):
86 msg = bst_eval_set
C:\ProgramData\Anaconda3\lib\site-packages\xgboost\core.py in eval_set(self, evals, iteration, feval)
957 if feval is not None:
958 for dmat, evname in evals:
--> 959 feval_ret = feval(self.predict(dmat), dmat)
960 if isinstance(feval_ret, list):
961 for name, val in feval_ret:
<ipython-input-383-dfb8d5181b18> in F1_eval(preds, labels)
11
12
---> 13 P = sum(labels == 1)
14
15
TypeError: 'bool' object is not iterable
I do not understand why the function is not working. I have followed the examples here: https://github.com/dmlc/xgboost/blob/master/demo/guide-python/custom_objective.py
I would like to understand where I err.

Answer 1

When doing sum(labels == 1), Python evaluates labels == 1 as a Boolean object, thus you get TypeError: 'bool' object is not iterable
The function sum expecting an iterable object, like a list. Here's an example of your error:
In[32]: sum(True)
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-32-6eb8f80b7f2e>", line 1, in <module>
sum(True)
TypeError: 'bool' object is not iterable
If you want to use f1_score of scikit-learn you can implement the following wrapup:
from sklearn.metrics import f1_score
import numpy as np
def f1_eval(y_pred, dtrain):
y_true = dtrain.get_label()
err = 1-f1_score(y_true, np.round(y_pred))
return 'f1_err', err
params of the wrap up are list (of predictions) and DMatrix, and it returns a string, float
# Setting your classifier
clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', \
booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, \
subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
# When you fit, add eval_metric=f1_eval
# Please don't forget to insert all the .fit arguments required
clf_wine.fit(eval_metric=f1_eval)
Here you can see an example of how to implement custom objective function and custom evaluation metric
Example containing the following code:
# user defined evaluation function, return a pair metric_name, result
# NOTE: when you do customized loss function, the default prediction value is margin
# this may make builtin evaluation metric not function properly
# for example, we are doing logistic loss, the prediction is score before logistic transformation
# the builtin evaluation error assumes input is after logistic transformation
# Take this in mind when you use the customization, and maybe you need write customized evaluation function
def evalerror(preds, dtrain):
labels = dtrain.get_label()
# return a pair metric_name, result
# since preds are margin(before logistic transformation, cutoff at 0)
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
which specify that an evaluation function gets as arguments (predictions, dtrain) dtrain is of type DMatrix and returns a string, float which is the name of the metric and the error.
Adding working python code example
import numpy as np
def _F1_eval(preds, labels):
t = np.arange(0, 1, 0.005)
f = np.repeat(0, 200)
results = np.vstack([t, f]).T
# assuming labels only containing 0's and 1's
n_pos_examples = sum(labels)
if n_pos_examples == 0:
raise ValueError("labels not containing positive examples")
for i in range(200):
pred_indexes = (preds >= results[i, 0])
TP = sum(labels[pred_indexes])
FP = len(labels[pred_indexes]) - TP
precision = 0
recall = TP / n_pos_examples
if (FP + TP) > 0:
precision = TP / (FP + TP)
if (precision + recall > 0):
F1 = 2 * precision * recall / (precision + recall)
else:
F1 = 0
results[i, 1] = F1
return (max(results[:, 1]))
if __name__ == '__main__':
labels = np.random.binomial(1, 0.75, 100)
preds = np.random.random_sample(100)
print(_F1_eval(preds, labels))
And if you want to implement _F1_eval to work specifically for xgboost evaluation methods add this:
def F1_eval(preds, dtrain):
res = _F1_eval(preds, dtrain.get_label())
return 'f1_err', 1-res