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[python] Migrate to f-strings in python-package/lightgbm/sklearn.py #4188

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Apr 19, 2021
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[python] Migrate to f-strings in python-package/lightgbm/sklearn.py #4188

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Migrate to f-strings in python-package/lightgbm/sklearn.py
Apr 16, 2021
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akshitadixit Apr 16, 2021
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Update python-package/lightgbm/sklearn.py
akshitadixit Apr 17, 2021
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Apr 18, 2021
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Merge branch 'fstrings-patch-1' of https://github.com/akshitadixit/Li…
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akshitadixit Apr 19, 2021
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akshitadixit Apr 19, 2021
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18 changes: 9 additions & 9 deletions python-package/lightgbm/sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ def __call__(self, preds, dataset):
elif argc == 3:
grad, hess = self.func(labels, preds, dataset.get_group())
else:
raise TypeError("Self-defined objective function should have 2 or 3 arguments, got %d" % argc)
raise TypeError(f"Self-defined objective function should have 2 or 3 arguments, got {argc}")
"""weighted for objective"""
weight = dataset.get_weight()
if weight is not None:
Expand Down Expand Up @@ -171,7 +171,7 @@ def __call__(self, preds, dataset):
elif argc == 4:
return self.func(labels, preds, dataset.get_weight(), dataset.get_group())
else:
raise TypeError("Self-defined eval function should have 2, 3 or 4 arguments, got %d" % argc)
raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}")


# documentation templates for LGBMModel methods are shared between the classes in
Expand Down Expand Up @@ -719,10 +719,9 @@ def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
n_features = X.shape[1]
if self._n_features != n_features:
raise ValueError("Number of features of the model must "
"match the input. Model n_features_ is %s and "
"input n_features is %s "
% (self._n_features, n_features))
raise ValueError(f"Number of features of the model must " \
f"match the input. Model n_features_ is {self._n_features} and " \
f"input n_features is {n_features} ")
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return self._Booster.predict(X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration,
pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs)

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Please update the following lines within this PR as well

LightGBM/python-package/lightgbm/sklearn.py

Lines 535 to 536 in 211ef78

if hasattr(self, '_' + key):
setattr(self, '_' + key, value)

LightGBM/python-package/lightgbm/sklearn.py

Lines 997 to 998 in 211ef78

+ ' ' * 12 + 'The evaluation positions of the specified metric.\n'
+ ' ' * 8 + _early_stop + _after_early_stop)
(part about ' ' * x)

LightGBM/python-package/lightgbm/sklearn.py

Line 655 in 211ef78

raise TypeError('{} should be dict or list'.format(name))

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@StrikerRUS do I only change the ' '*x part or do I also modify the concatenation taking place there into an f-string as below?

space = " "
    fit.__doc__ = (f"{_before_early_stop}" \
                  f"eval_at : iterable of int, optional (default=(1, 2, 3, 4, 5))\n" \
                  f"{space*12} The evaluation positions of the specified metric.\n" \
                  f"{space*8} {_early_stop} {_after_early_stop}")

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Originally I thought about only ' '*x part, but just saw that @jameslamb would prefer modifying concatenation as well: #4144 (comment). So, please modify it with concatenation.

BTW, there are some other places where concatenation can be eliminated with f-strings:

LightGBM/python-package/lightgbm/sklearn.py

Lines 705 to 711 in 211ef78

fit.__doc__ = _lgbmmodel_doc_fit.format(
X_shape="array-like or sparse matrix of shape = [n_samples, n_features]",
y_shape="array-like of shape = [n_samples]",
sample_weight_shape="array-like of shape = [n_samples] or None, optional (default=None)",
init_score_shape="array-like of shape = [n_samples] or None, optional (default=None)",
group_shape="array-like or None, optional (default=None)"
) + "\n\n" + _lgbmmodel_doc_custom_eval_note

LightGBM/python-package/lightgbm/sklearn.py

Lines 825 to 831 in 211ef78

_base_doc = LGBMModel.fit.__doc__
_base_doc = (_base_doc[:_base_doc.find('group :')] # type: ignore
+ _base_doc[_base_doc.find('eval_set :'):]) # type: ignore
_base_doc = (_base_doc[:_base_doc.find('eval_class_weight :')]
+ _base_doc[_base_doc.find('eval_init_score :'):])
fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
+ _base_doc[_base_doc.find('eval_metric :'):])

LightGBM/python-package/lightgbm/sklearn.py

Lines 898 to 902 in 211ef78

_base_doc = LGBMModel.fit.__doc__
_base_doc = (_base_doc[:_base_doc.find('group :')] # type: ignore
+ _base_doc[_base_doc.find('eval_set :'):]) # type: ignore
fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
+ _base_doc[_base_doc.find('eval_metric :'):])

LightGBM/python-package/lightgbm/sklearn.py

Lines 990 to 992 in 211ef78

_base_doc = LGBMModel.fit.__doc__
fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')] # type: ignore
+ _base_doc[_base_doc.find('eval_init_score :'):]) # type: ignore

But I don't think we should replace concatenation with f-strings there because concatenation is more readable in those cases in my opinion. @jameslamb WDYT?

Also, please check the following comment about padding #4144 (comment).

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But I don't think we should replace concatenation with f-strings there because concatenation is more readable in those cases in my opinion

Yes I agree with you.

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So I infer that no concatenations be migrated then.

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yes, cases like the ones @StrikerRUS mentioned (where .format() was used to replace a shared template like _lgbmmodel_doc_fit or where there are just variables being added to each other but not string literals) do not need to be changed for this pull request

Expand Down Expand Up @@ -919,9 +918,10 @@ def predict_proba(self, X, raw_score=False, start_iteration=0, num_iteration=Non
"""Docstring is set after definition, using a template."""
result = super().predict(X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs)
if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib):
_log_warning("Cannot compute class probabilities or labels "
"due to the usage of customized objective function.\n"
"Returning raw scores instead.")
new_line = "\n"
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_log_warning(f"Cannot compute class probabilities or labels " \
f"due to the usage of customized objective function.{new_line}" \
f"Returning raw scores instead.")
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return result
elif self._n_classes > 2 or raw_score or pred_leaf or pred_contrib:
return result
Expand Down