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Unverified Commit bffa6ca5 authored by NovusEdge's avatar NovusEdge Committed by GitHub
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[python] added f-string to python-package/lightgbm/basic.py (#4143) 



* added f-string

* fix missing parentheses and other string formatting

* remove extra trailing parenthesis

* one more missing parenthesis

* fix pandas categoricals

* update uses of +

* Apply suggestions from code review
Co-authored-by: default avatarNikita Titov <nekit94-08@mail.ru>
Co-authored-by: default avatarJames Lamb <jaylamb20@gmail.com>
Co-authored-by: default avatarNikita Titov <nekit94-08@mail.ru>
parent 3a657c8b
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Showing with 49 additions and 65 deletions
python-package/lightgbm/basic.py
View file @ bffa6ca5
......@@ -75,7 +75,7 @@ def _log_native(msg):
def _log_callback(msg):
"""Redirect logs from native library into Python."""
_log_native("{0:s}".format(msg.decode('utf-8')))
_log_native(str(msg.decode('utf-8')))
def _load_lib():
......@@ -161,8 +161,8 @@ def list_to_1d_numpy(data, dtype=np.float32, name='list'):
raise ValueError('Series.dtypes must be int, float or bool')
return np.array(data, dtype=dtype, copy=False) # SparseArray should be supported as well
else:
raise TypeError("Wrong type({0}) for {1}.\n"
"It should be list, numpy 1-D array or pandas Series".format(type(data).__name__, name))
raise TypeError(f"Wrong type({type(data).__name__}) for {name}.\n"
"It should be list, numpy 1-D array or pandas Series")
def cfloat32_array_to_numpy(cptr, length):
......@@ -226,15 +226,14 @@ def param_dict_to_str(data):
if isinstance(val, (list, tuple, set)) or is_numpy_1d_array(val):
def to_string(x):
if isinstance(x, list):
return "[{}]".format(','.join(map(str, x)))
return f"[{','.join(map(str, x))}]"
else:
return str(x)
pairs.append(str(key) + '=' + ','.join(map(to_string, val)))
pairs.append(f"{key}={','.join(map(to_string, val))}")
elif isinstance(val, (str, NUMERIC_TYPES)) or is_numeric(val):
pairs.append(str(key) + '=' + str(val))
pairs.append(f"{key}={val}")
elif val is not None:
raise TypeError('Unknown type of parameter:%s, got:%s'
% (key, type(val).__name__))
raise TypeError(f'Unknown type of parameter:{key}, got:{type(val).__name__}')
return ' '.join(pairs)
......@@ -465,10 +464,9 @@ def c_float_array(data):
ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
type_data = C_API_DTYPE_FLOAT64
else:
raise TypeError("Expected np.float32 or np.float64, met type({})"
.format(data.dtype))
raise TypeError(f"Expected np.float32 or np.float64, met type({data.dtype})")
else:
raise TypeError("Unknown type({})".format(type(data).__name__))
raise TypeError(f"Unknown type({type(data).__name__})")
return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed
......@@ -486,10 +484,9 @@ def c_int_array(data):
ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int64))
type_data = C_API_DTYPE_INT64
else:
raise TypeError("Expected np.int32 or np.int64, met type({})"
.format(data.dtype))
raise TypeError(f"Expected np.int32 or np.int64, met type({data.dtype})")
else:
raise TypeError("Unknown type({})".format(type(data).__name__))
raise TypeError(f"Unknown type({type(data).__name__})")
return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed
......@@ -534,9 +531,10 @@ def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorica
feature_name = list(data.columns)
bad_indices = _get_bad_pandas_dtypes(data.dtypes)
if bad_indices:
bad_index_cols_str = ', '.join(data.columns[bad_indices])
raise ValueError("DataFrame.dtypes for data must be int, float or bool.\n"
"Did not expect the data types in the following fields: "
+ ', '.join(data.columns[bad_indices]))
f"{bad_index_cols_str}")
data = data.values
if data.dtype != np.float32 and data.dtype != np.float64:
data = data.astype(np.float32)
......@@ -559,9 +557,8 @@ def _label_from_pandas(label):
def _dump_pandas_categorical(pandas_categorical, file_name=None):
pandas_str = ('\npandas_categorical:'
+ json.dumps(pandas_categorical, default=json_default_with_numpy)
+ '\n')
categorical_json = json.dumps(pandas_categorical, default=json_default_with_numpy)
pandas_str = f'\npandas_categorical:{categorical_json}\n'
if file_name is not None:
with open(file_name, 'a') as f:
f.write(pandas_str)
......@@ -739,7 +736,7 @@ class _InnerPredictor:
_log_warning('Converting data to scipy sparse matrix.')
csr = scipy.sparse.csr_matrix(data)
except BaseException:
raise TypeError('Cannot predict data for type {}'.format(type(data).__name__))
raise TypeError(f'Cannot predict data for type {type(data).__name__}')
preds, nrow = self.__pred_for_csr(csr, start_iteration, num_iteration, predict_type)
if pred_leaf:
preds = preds.astype(np.int32)
......@@ -748,17 +745,16 @@ class _InnerPredictor:
if preds.size % nrow == 0:
preds = preds.reshape(nrow, -1)
else:
raise ValueError('Length of predict result (%d) cannot be divide nrow (%d)'
% (preds.size, nrow))
raise ValueError(f'Length of predict result ({preds.size}) cannot be divide nrow ({nrow})')
return preds
def __get_num_preds(self, start_iteration, num_iteration, nrow, predict_type):
"""Get size of prediction result."""
if nrow > MAX_INT32:
raise LightGBMError('LightGBM cannot perform prediction for data'
'with number of rows greater than MAX_INT32 (%d).\n'
f'with number of rows greater than MAX_INT32 ({MAX_INT32}).\n'
'You can split your data into chunks'
'and then concatenate predictions for them' % MAX_INT32)
'and then concatenate predictions for them')
n_preds = ctypes.c_int64(0)
_safe_call(_LIB.LGBM_BoosterCalcNumPredict(
self.handle,
......@@ -1219,9 +1215,8 @@ class Dataset:
.co_varnames[:getattr(self.__class__, '_lazy_init').__code__.co_argcount])
for key, _ in params.items():
if key in args_names:
_log_warning('{0} keyword has been found in `params` and will be ignored.\n'
'Please use {0} argument of the Dataset constructor to pass this parameter.'
.format(key))
_log_warning(f'{key} keyword has been found in `params` and will be ignored.\n'
f'Please use {key} argument of the Dataset constructor to pass this parameter.')
# user can set verbose with params, it has higher priority
if not any(verbose_alias in params for verbose_alias in _ConfigAliases.get("verbosity")) and silent:
params["verbose"] = -1
......@@ -1237,12 +1232,11 @@ class Dataset:
elif isinstance(name, int):
categorical_indices.add(name)
else:
raise TypeError("Wrong type({}) or unknown name({}) in categorical_feature"
.format(type(name).__name__, name))
raise TypeError(f"Wrong type({type(name).__name__}) or unknown name({name}) in categorical_feature")
if categorical_indices:
for cat_alias in _ConfigAliases.get("categorical_feature"):
if cat_alias in params:
_log_warning('{} in param dict is overridden.'.format(cat_alias))
_log_warning(f'{cat_alias} in param dict is overridden.')
params.pop(cat_alias, None)
params['categorical_column'] = sorted(categorical_indices)
......@@ -1277,7 +1271,7 @@ class Dataset:
csr = scipy.sparse.csr_matrix(data)
self.__init_from_csr(csr, params_str, ref_dataset)
except BaseException:
raise TypeError('Cannot initialize Dataset from {}'.format(type(data).__name__))
raise TypeError(f'Cannot initialize Dataset from {type(data).__name__}')
if label is not None:
self.set_label(label)
if self.get_label() is None:
......@@ -1293,7 +1287,7 @@ class Dataset:
elif init_score is not None:
self.set_init_score(init_score)
elif predictor is not None:
raise TypeError('Wrong predictor type {}'.format(type(predictor).__name__))
raise TypeError(f'Wrong predictor type {type(predictor).__name__}')
# set feature names
return self.set_feature_name(feature_name)
......@@ -1369,7 +1363,7 @@ class Dataset:
def __init_from_csr(self, csr, params_str, ref_dataset):
"""Initialize data from a CSR matrix."""
if len(csr.indices) != len(csr.data):
raise ValueError('Length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
raise ValueError(f'Length mismatch: {len(csr.indices)} vs {len(csr.data)}')
self.handle = ctypes.c_void_p()
ptr_indptr, type_ptr_indptr, __ = c_int_array(csr.indptr)
......@@ -1395,7 +1389,7 @@ class Dataset:
def __init_from_csc(self, csc, params_str, ref_dataset):
"""Initialize data from a CSC matrix."""
if len(csc.indices) != len(csc.data):
raise ValueError('Length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
raise ValueError(f'Length mismatch: {len(csc.indices)} vs {len(csc.data)}')
self.handle = ctypes.c_void_p()
ptr_indptr, type_ptr_indptr, __ = c_int_array(csc.indptr)
......@@ -1609,7 +1603,7 @@ class Dataset:
Dataset with set property.
"""
if self.handle is None:
raise Exception("Cannot set %s before construct dataset" % field_name)
raise Exception(f"Cannot set {field_name} before construct dataset")
if data is None:
# set to None
_safe_call(_LIB.LGBM_DatasetSetField(
......@@ -1630,7 +1624,7 @@ class Dataset:
elif data.dtype == np.int32:
ptr_data, type_data, _ = c_int_array(data)
else:
raise TypeError("Expected np.float32/64 or np.int32, met type({})".format(data.dtype))
raise TypeError(f"Expected np.float32/64 or np.int32, met type({data.dtype})")
if type_data != FIELD_TYPE_MAPPER[field_name]:
raise TypeError("Input type error for set_field")
_safe_call(_LIB.LGBM_DatasetSetField(
......@@ -1656,7 +1650,7 @@ class Dataset:
A numpy array with information from the Dataset.
"""
if self.handle is None:
raise Exception("Cannot get %s before construct Dataset" % field_name)
raise Exception(f"Cannot get {field_name} before construct Dataset")
tmp_out_len = ctypes.c_int()
out_type = ctypes.c_int()
ret = ctypes.POINTER(ctypes.c_void_p)()
......@@ -1703,7 +1697,7 @@ class Dataset:
return self
else:
_log_warning('categorical_feature in Dataset is overridden.\n'
'New categorical_feature is {}'.format(sorted(list(categorical_feature))))
f'New categorical_feature is {sorted(list(categorical_feature))}')
self.categorical_feature = categorical_feature
return self._free_handle()
else:
......@@ -1774,8 +1768,7 @@ class Dataset:
self.feature_name = feature_name
if self.handle is not None and feature_name is not None and feature_name != 'auto':
if len(feature_name) != self.num_feature():
raise ValueError("Length of feature_name({}) and num_feature({}) don't match"
.format(len(feature_name), self.num_feature()))
raise ValueError(f"Length of feature_name({len(feature_name)}) and num_feature({self.num_feature()}) don't match")
c_feature_name = [c_str(name) for name in feature_name]
_safe_call(_LIB.LGBM_DatasetSetFeatureNames(
self.handle,
......@@ -1895,8 +1888,8 @@ class Dataset:
raise ValueError("Length of feature names doesn't equal with num_feature")
if reserved_string_buffer_size < required_string_buffer_size.value:
raise BufferError(
"Allocated feature name buffer size ({}) was inferior to the needed size ({})."
.format(reserved_string_buffer_size, required_string_buffer_size.value)
f"Allocated feature name buffer size ({reserved_string_buffer_size}) was"
f"inferior to the needed size ({required_string_buffer_size.value})."
)
return [string_buffers[i].value.decode('utf-8') for i in range(num_feature)]
......@@ -1956,8 +1949,8 @@ class Dataset:
elif isinstance(self.data, dt_DataTable):
self.data = self.data[self.used_indices, :]
else:
_log_warning("Cannot subset {} type of raw data.\n"
"Returning original raw data".format(type(self.data).__name__))
_log_warning(f"Cannot subset {type(self.data).__name__} type of raw data.\n"
"Returning original raw data")
self.need_slice = False
if self.data is None:
raise LightGBMError("Cannot call `get_data` after freed raw data, "
......@@ -2121,9 +2114,8 @@ class Dataset:
else:
self.data = None
if self.data is None:
err_msg = ("Cannot add features from {} type of raw data to "
"{} type of raw data.\n").format(type(other.data).__name__,
old_self_data_type)
err_msg = (f"Cannot add features from {type(other.data).__name__} type of raw data to "
f"{old_self_data_type} type of raw data.\n")
err_msg += ("Set free_raw_data=False when construct Dataset to avoid this"
if was_none else "Freeing raw data")
_log_warning(err_msg)
......@@ -2189,8 +2181,7 @@ class Booster:
if train_set is not None:
# Training task
if not isinstance(train_set, Dataset):
raise TypeError('Training data should be Dataset instance, met {}'
.format(type(train_set).__name__))
raise TypeError(f'Training data should be Dataset instance, met {type(train_set).__name__}')
params = _choose_param_value(
main_param_name="machines",
params=params,
......@@ -2423,12 +2414,12 @@ class Booster:
feature_names=None, parent_node=None):
def _get_node_index(tree, tree_index):
tree_num = str(tree_index) + '-' if tree_index is not None else ''
tree_num = f'{tree_index}-' if tree_index is not None else ''
is_split = _is_split_node(tree)
node_type = 'S' if is_split else 'L'
# if a single node tree it won't have `leaf_index` so return 0
node_num = str(tree.get('split_index' if is_split else 'leaf_index', 0))
return tree_num + node_type + node_num
node_num = tree.get('split_index' if is_split else 'leaf_index', 0)
return f"{tree_num}{node_type}{node_num}"
def _get_split_feature(tree, feature_names):
if _is_split_node(tree):
......@@ -2549,8 +2540,7 @@ class Booster:
Booster with set validation data.
"""
if not isinstance(data, Dataset):
raise TypeError('Validation data should be Dataset instance, met {}'
.format(type(data).__name__))
raise TypeError(f'Validation data should be Dataset instance, met {type(data).__name__}')
if data._predictor is not self.__init_predictor:
raise LightGBMError("Add validation data failed, "
"you should use same predictor for these data")
......@@ -2628,8 +2618,7 @@ class Booster:
is_the_same_train_set = train_set is self.train_set and self.train_set_version == train_set.version
if train_set is not None and not is_the_same_train_set:
if not isinstance(train_set, Dataset):
raise TypeError('Training data should be Dataset instance, met {}'
.format(type(train_set).__name__))
raise TypeError(f'Training data should be Dataset instance, met {type(train_set).__name__}')
if train_set._predictor is not self.__init_predictor:
raise LightGBMError("Replace training data failed, "
"you should use same predictor for these data")
......@@ -2684,8 +2673,7 @@ class Booster:
assert grad.flags.c_contiguous
assert hess.flags.c_contiguous
if len(grad) != len(hess):
raise ValueError("Lengths of gradient({}) and hessian({}) don't match"
.format(len(grad), len(hess)))
raise ValueError(f"Lengths of gradient({len(grad)}) and hessian({len(hess)}) don't match")
is_finished = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterUpdateOneIterCustom(
self.handle,
......@@ -2983,7 +2971,7 @@ class Booster:
self.handle,
ctypes.byref(out_num_class)))
if verbose:
_log_info('Finished loading model, total used %d iterations' % int(out_num_iterations.value))
_log_info(f'Finished loading model, total used {int(out_num_iterations.value)} iterations')
self.__num_class = out_num_class.value
self.pandas_categorical = _load_pandas_categorical(model_str=model_str)
return self
......@@ -3272,9 +3260,7 @@ class Booster:
raise ValueError("Length of feature names doesn't equal with num_feature")
if reserved_string_buffer_size < required_string_buffer_size.value:
raise BufferError(
"Allocated feature name buffer size ({}) was inferior to the needed size ({})."
.format(reserved_string_buffer_size, required_string_buffer_size.value)
)
f"Allocated feature name buffer size ({reserved_string_buffer_size}) was inferior to the needed size ({required_string_buffer_size.value}).")
return [string_buffers[i].value.decode('utf-8') for i in range(num_feature)]
def feature_importance(self, importance_type='split', iteration=None):
......@@ -3437,7 +3423,7 @@ class Booster:
ctypes.byref(tmp_out_len),
data_ptr))
if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]):
raise ValueError("Wrong length of predict results for data %d" % (data_idx))
raise ValueError(f"Wrong length of predict results for data {data_idx}")
self.__is_predicted_cur_iter[data_idx] = True
return self.__inner_predict_buffer[data_idx]
......@@ -3471,9 +3457,7 @@ class Booster:
raise ValueError("Length of eval names doesn't equal with num_evals")
if reserved_string_buffer_size < required_string_buffer_size.value:
raise BufferError(
"Allocated eval name buffer size ({}) was inferior to the needed size ({})."
.format(reserved_string_buffer_size, required_string_buffer_size.value)
)
f"Allocated eval name buffer size ({reserved_string_buffer_size}) was inferior to the needed size ({required_string_vuffer_size.value}).")
self.__name_inner_eval = \
[string_buffers[i].value.decode('utf-8') for i in range(self.__num_inner_eval)]
self.__higher_better_inner_eval = \
......
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