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[python-package] use f-strings for concatenation in examples/python-guide/logistic_regression.py #4355

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[python-package] use f-strings for concatenation in examples/python-guide/logistic_regression.py #4355

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064e486
updated with f-string migration
sagnik1511 Jun 8, 2021
01380fb
Update logistic_regression.py
sagnik1511 Jun 8, 2021
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12 changes: 4 additions & 8 deletions examples/python-guide/logistic_regression.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,7 @@
# coding: utf-8
"""Comparison of `binary` and `xentropy` objectives.

BLUF: The `xentropy` objective does logistic regression and generalizes
to the case where labels are probabilistic (i.e. numbers between 0 and 1).

Details: Both `binary` and `xentropy` minimize the log loss and use
`boost_from_average = TRUE` by default. Possibly the only difference
between them with default settings is that `binary` may achieve a slight
Expand Down Expand Up @@ -54,7 +52,6 @@ def log_loss(preds, labels):

def experiment(objective, label_type, data):
"""Measure performance of an objective.

Parameters
----------
objective : string 'binary' or 'xentropy'
Expand All @@ -63,15 +60,14 @@ def experiment(objective, label_type, data):
Type of the label.
data : dict
Data for training.

Returns
-------
result : dict
Experiment summary stats.
"""
np.random.seed(0)
nrounds = 5
lgb_data = data['lgb_with_' + label_type + '_labels']
lgb_data = data[f"lgb_with_{label_type}_labels"]
params = {
'objective': objective,
'feature_fraction': 1,
Expand All @@ -81,7 +77,7 @@ def experiment(objective, label_type, data):
time_zero = time.time()
gbm = lgb.train(params, lgb_data, num_boost_round=nrounds)
y_fitted = gbm.predict(data['X'])
y_true = data[label_type + '_labels']
y_true = data[f"{label_type}_labels"]
duration = time.time() - time_zero
return {
'time': duration,
Expand Down Expand Up @@ -113,5 +109,5 @@ def experiment(objective, label_type, data):
for k in range(K)]
B = [experiment('xentropy', label_type='binary', data=DATA)['time']
for k in range(K)]
print('Best `binary` time: ' + str(min(A)))
print('Best `xentropy` time: ' + str(min(B)))
print(f"Best `binary` time: {str(min(A))}")
print(f"Best `xentropy` time: {str(min(B))}")