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f-string formatting #3277

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merged 6 commits into from
Nov 17, 2021
Merged

f-string formatting #3277

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d375db9
f-string formatting
Mehdi2402 Nov 15, 2021
e03f05b
f-string formatting on src/
Mehdi2402 Nov 15, 2021
127c51b
f-string formatting last modules
Mehdi2402 Nov 16, 2021
1751d2f
Merge branch 'huggingface:master' into f-string-formatting-2
Mehdi2402 Nov 16, 2021
880f7b0
make style on edited files
Mehdi2402 Nov 16, 2021
36f2ca7
Update tests/test_dataset_dict.py
lhoestq Nov 17, 2021
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6 changes: 3 additions & 3 deletions benchmarks/format.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,12 +24,12 @@ def format_json_to_md(input_json_file, output_md_file):
old_val = metric_vals.get("old", None)
dif_val = metric_vals.get("diff", None)

val_str = " {:f}".format(new_val) if isinstance(new_val, (int, float)) else "None"
val_str = f" {new_val:f}" if isinstance(new_val, (int, float)) else "None"

if old_val is not None:
val_str += " / {:f}".format(old_val) if isinstance(old_val, (int, float)) else "None"
val_str += f" / {new_val:f}" if isinstance(old_val, (int, float)) else "None"
if dif_val is not None:
val_str += " ({:f})".format(dif_val) if isinstance(dif_val, (int, float)) else "None"
val_str += f" ({dif_val:f})" if isinstance(dif_val, (int, float)) else "None"

title += " " + metric_name + " |"
lines += "---|"
Expand Down
16 changes: 8 additions & 8 deletions metrics/coval/coval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,17 +210,17 @@ def get_coref_infos(
if remove_nested:
logger.info(
"Number of removed nested coreferring mentions in the key "
"annotation: %s; and system annotation: %s" % (key_nested_coref_num, sys_nested_coref_num)
f"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"
)
logger.info(
"Number of resulting singleton clusters in the key "
"annotation: %s; and system annotation: %s" % (key_removed_nested_clusters, sys_removed_nested_clusters)
f"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"
)

if not keep_singletons:
logger.info(
"%d and %d singletons are removed from the key and system "
"files, respectively" % (key_singletons_num, sys_singletons_num)
f"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
"files, respectively"
)

return doc_coref_infos
Expand All @@ -242,14 +242,14 @@ def evaluate(key_lines, sys_lines, metrics, NP_only, remove_nested, keep_singlet

logger.info(
name.ljust(10),
"Recall: %.2f" % (recall * 100),
" Precision: %.2f" % (precision * 100),
" F1: %.2f" % (f1 * 100),
f"Recall: {recall * 100:.2f}",
f" Precision: {precision * 100:.2f}",
f" F1: {f1 * 100:.2f}",
)

if conll_subparts_num == 3:
conll = (conll / 3) * 100
logger.info("CoNLL score: %.2f" % conll)
logger.info(f"CoNLL score: {conll:.2f}")
output_scores.update({"conll_score": conll})

return output_scores
Expand Down
8 changes: 4 additions & 4 deletions metrics/squad_v2/evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,7 @@ def get_raw_scores(dataset, preds):
# For unanswerable questions, only correct answer is empty string
gold_answers = [""]
if qid not in preds:
print("Missing prediction for %s" % qid)
print(f"Missing prediction for {qid}")
continue
a_pred = preds[qid]
# Take max over all gold answers
Expand Down Expand Up @@ -156,7 +156,7 @@ def make_eval_dict(exact_scores, f1_scores, qid_list=None):

def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval["%s_%s" % (prefix, k)] = new_eval[k]
main_eval[f"{prefix}_{k}"] = new_eval[k]


def plot_pr_curve(precisions, recalls, out_image, title):
Expand Down Expand Up @@ -238,8 +238,8 @@ def histogram_na_prob(na_probs, qid_list, image_dir, name):
plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
plt.xlabel("Model probability of no-answer")
plt.ylabel("Proportion of dataset")
plt.title("Histogram of no-answer probability: %s" % name)
plt.savefig(os.path.join(image_dir, "na_prob_hist_%s.png" % name))
plt.title(f"Histogram of no-answer probability: {name}")
plt.savefig(os.path.join(image_dir, f"na_prob_hist_{name}.png"))
plt.clf()


Expand Down
6 changes: 3 additions & 3 deletions metrics/super_glue/record_evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def evaluate(dataset, predictions):
for qa in passage["qas"]:
total += 1
if qa["id"] not in predictions:
message = "Unanswered question {} will receive score 0.".format(qa["id"])
message = f'Unanswered question {qa["id"]} will receive score 0.'
print(message, file=sys.stderr)
continue

Expand Down Expand Up @@ -95,7 +95,7 @@ def evaluate(dataset, predictions):
dataset_json = json.load(data_file)
if dataset_json["version"] != expected_version:
print(
"Evaluation expects v-{}, but got dataset with v-{}".format(expected_version, dataset_json["version"]),
f'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
file=sys.stderr,
)
dataset = dataset_json["data"]
Expand All @@ -106,6 +106,6 @@ def evaluate(dataset, predictions):
metrics, correct_ids = evaluate(dataset, predictions)

if args.output_correct_ids:
print("Output {} correctly answered question IDs.".format(len(correct_ids)))
print(f"Output {len(correct_ids)} correctly answered question IDs.")
with open("correct_ids.json", "w") as f:
json.dump(correct_ids, f)
2 changes: 1 addition & 1 deletion metrics/super_glue/super_glue.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,7 +125,7 @@ def evaluate_multirc(ids_preds, labels):
"""
question_map = {}
for id_pred, label in zip(ids_preds, labels):
question_id = "{}-{}".format(id_pred["idx"]["paragraph"], id_pred["idx"]["question"])
question_id = f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
pred = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label))
Expand Down
68 changes: 21 additions & 47 deletions src/datasets/arrow_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -621,9 +621,7 @@ def __init__(
assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
if self.info.features.type != inferred_features.type:
raise ValueError(
"External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
self.info.features, self.info.features.type, inferred_features, inferred_features.type
)
f"External features info don't match the dataset:\nGot\n{self.info.features}\nwith type\n{self.info.features.type}\n\nbut expected something like\n{inferred_features}\nwith type\n{inferred_features.type}"
)

if self._indices is not None:
Expand Down Expand Up @@ -735,9 +733,7 @@ def from_pandas(
"""
if info is not None and features is not None and info.features != features:
raise ValueError(
"Features specified in `features` and `info.features` can't be different:\n{}\n{}".format(
features, info.features
)
f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
)
features = features if features is not None else info.features if info is not None else None
if info is None:
Expand Down Expand Up @@ -768,9 +764,7 @@ def from_dict(
"""
if info is not None and features is not None and info.features != features:
raise ValueError(
"Features specified in `features` and `info.features` can't be different:\n{}\n{}".format(
features, info.features
)
f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
)
features = features if features is not None else info.features if info is not None else None
if info is None:
Expand Down Expand Up @@ -1008,7 +1002,7 @@ def save_to_disk(self, dataset_path: str, fs=None):
# Sort only the first level of keys, or we might shuffle fields of nested features if we use sort_keys=True
sorted_keys_dataset_info = {key: dataset_info[key] for key in sorted(dataset_info)}
json.dump(sorted_keys_dataset_info, dataset_info_file, indent=2)
logger.info("Dataset saved in {}".format(dataset_path))
logger.info(f"Dataset saved in {dataset_path}")

@staticmethod
def load_from_disk(dataset_path: str, fs=None, keep_in_memory: Optional[bool] = None) -> "Dataset":
Expand Down Expand Up @@ -1216,9 +1210,7 @@ def flatten_(self, max_depth=16):
break
self.info.features = Features.from_arrow_schema(self._data.schema)
self._data = update_metadata_with_features(self._data, self.features)
logger.info(
"Flattened dataset from depth {} to depth {}.".format(depth, 1 if depth + 1 < max_depth else "unknown")
)
logger.info(f'Flattened dataset from depth {depth} to depth { 1 if depth + 1 < max_depth else "unknown"}.')

@fingerprint_transform(inplace=False)
def flatten(self, new_fingerprint, max_depth=16) -> "Dataset":
Expand All @@ -1237,9 +1229,7 @@ def flatten(self, new_fingerprint, max_depth=16) -> "Dataset":
break
dataset.info.features = Features.from_arrow_schema(dataset._data.schema)
dataset._data = update_metadata_with_features(dataset._data, dataset.features)
logger.info(
"Flattened dataset from depth {} to depth {}.".format(depth, 1 if depth + 1 < max_depth else "unknown")
)
logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else "unknown"}.')
dataset._fingerprint = new_fingerprint
return dataset

Expand Down Expand Up @@ -1688,9 +1678,7 @@ def set_format(
columns = list(columns)
if columns is not None and any(col not in self._data.column_names for col in columns):
raise ValueError(
"Columns {} not in the dataset. Current columns in the dataset: {}".format(
list(filter(lambda col: col not in self._data.column_names, columns)), self._data.column_names
)
f"Columns {list(filter(lambda col: col not in self._data.column_names, columns))} not in the dataset. Current columns in the dataset: {self._data.column_names}"
)
if columns is not None:
columns = columns.copy() # Ensures modifications made to the list after this call don't cause bugs
Expand Down Expand Up @@ -2002,20 +1990,15 @@ def decorated(item, *args, **kwargs):
for input_column in input_columns:
if input_column not in self._data.column_names:
raise ValueError(
"Input column {} not in the dataset. Current columns in the dataset: {}".format(
input_column, self._data.column_names
)
f"Input column {input_column} not in the dataset. Current columns in the dataset: {self._data.column_names}"
)

if isinstance(remove_columns, str):
remove_columns = [remove_columns]

if remove_columns is not None and any(col not in self._data.column_names for col in remove_columns):
raise ValueError(
"Column to remove {} not in the dataset. Current columns in the dataset: {}".format(
list(filter(lambda col: col not in self._data.column_names, remove_columns)),
self._data.column_names,
)
f"Column to remove {list(filter(lambda col: col not in self._data.column_names, remove_columns))} not in the dataset. Current columns in the dataset: {self._data.column_names}"
)

load_from_cache_file = load_from_cache_file if load_from_cache_file is not None else is_caching_enabled()
Expand Down Expand Up @@ -2057,7 +2040,7 @@ def format_cache_file_name(cache_file_name, rank):
sep = cache_file_name.rindex(".")
base_name, extension = cache_file_name[:sep], cache_file_name[sep:]
cache_file_name = base_name + suffix_template.format(rank=rank, num_proc=num_proc) + extension
logger.info("Process #{} will write at {}".format(rank, cache_file_name))
logger.info(f"Process #{rank} will write at {cache_file_name}")
return cache_file_name

prev_env = deepcopy(os.environ)
Expand Down Expand Up @@ -2126,7 +2109,7 @@ def catch_non_existent_error(func, kwargs):
if nb_of_missing_shards > 0:
with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool:
os.environ = prev_env
logger.info("Spawning {} processes".format(num_proc))
logger.info(f"Spawning {num_proc} processes")
results = {
i: pool.apply_async(self.__class__._map_single, kwds=kwds)
for i, (kwds, cached_shard) in enumerate(zip(kwds_per_shard, transformed_shards))
Expand All @@ -2143,7 +2126,7 @@ def catch_non_existent_error(func, kwargs):
transformed_shards.count(None) == 0
), "All shards have to be defined Datasets, none should still be missing."

logger.info("Concatenating {} shards".format(num_proc))
logger.info(f"Concatenating {num_proc} shards")
result = concatenate_datasets(transformed_shards)
if new_fingerprint is not None:
result._fingerprint = new_fingerprint
Expand Down Expand Up @@ -2240,7 +2223,7 @@ def _map_single(
# current dataset file and the mapping args
cache_file_name = self._get_cache_file_path(new_fingerprint)
if os.path.exists(cache_file_name) and load_from_cache_file:
logger.warning("Loading cached processed dataset at %s", cache_file_name)
logger.warning(f"Loading cached processed dataset at {cache_file_name}")
info = self.info.copy()
info.features = features
info.task_templates = None
Expand All @@ -2262,9 +2245,7 @@ def validate_function_output(processed_inputs, indices):
"""Validate output of the map function."""
if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)):
raise TypeError(
"Provided `function` which is applied to all elements of table returns a variable of type {}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.".format(
type(processed_inputs)
)
f"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects."
)
elif isinstance(indices, list) and isinstance(processed_inputs, Mapping):
allowed_batch_return_types = (list, np.ndarray)
Expand All @@ -2273,9 +2254,7 @@ def validate_function_output(processed_inputs, indices):
)
if all_dict_values_are_lists is False:
raise TypeError(
"Provided `function` which is applied to all elements of table returns a `dict` of types {}. When using `batched=True`, make sure provided `function` returns a `dict` of types like `{}`.".format(
[type(x) for x in processed_inputs.values()], allowed_batch_return_types
)
"Provided `function` which is applied to all elements of table returns a `dict` of types {[type(x) for x in processed_inputs.values()]}. When using `batched=True`, make sure provided `function` returns a `dict` of types like `{allowed_batch_return_types}`."
)

def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):
Expand Down Expand Up @@ -2338,7 +2317,7 @@ def init_buffer_and_writer():
)
else:
buf_writer = None
logger.info("Caching processed dataset at %s", cache_file_name)
logger.info(f"Caching processed dataset at {cache_file_name}")
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
writer = ArrowWriter(
features=writer_features,
Expand Down Expand Up @@ -2653,7 +2632,7 @@ def select(
)
else:
buf_writer = None
logger.info("Caching indices mapping at %s", indices_cache_file_name)
logger.info(f"Caching indices mapping at {indices_cache_file_name}")
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
writer = ArrowWriter(
path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
Expand Down Expand Up @@ -2738,10 +2717,7 @@ def sort(
# Check the column name
if not isinstance(column, str) or column not in self._data.column_names:
raise ValueError(
"Column '{}' not found in the dataset. Please provide a column selected in: {}".format(
column,
self._data.column_names,
)
f"Column '{column}' not found in the dataset. Please provide a column selected in: {self._data.column_names}"
)

# Check if we've already cached this computation (indexed by a hash)
Expand All @@ -2750,7 +2726,7 @@ def sort(
# we create a unique hash from the function, current dataset file and the mapping args
indices_cache_file_name = self._get_cache_file_path(new_fingerprint)
if os.path.exists(indices_cache_file_name) and load_from_cache_file:
logger.warning("Loading cached sorted indices for dataset at %s", indices_cache_file_name)
logger.warning(f"Loading cached sorted indices for dataset at {indices_cache_file_name}")
return self._new_dataset_with_indices(
fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
)
Expand Down Expand Up @@ -2833,7 +2809,7 @@ def shuffle(
# we create a unique hash from the function, current dataset file and the mapping args
indices_cache_file_name = self._get_cache_file_path(new_fingerprint)
if os.path.exists(indices_cache_file_name) and load_from_cache_file:
logger.warning("Loading cached shuffled indices for dataset at %s", indices_cache_file_name)
logger.warning(f"Loading cached shuffled indices for dataset at {indices_cache_file_name}")
return self._new_dataset_with_indices(
fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
)
Expand Down Expand Up @@ -3008,9 +2984,7 @@ def train_test_split(
and load_from_cache_file
):
logger.warning(
"Loading cached split indices for dataset at %s and %s",
train_indices_cache_file_name,
test_indices_cache_file_name,
f"Loading cached split indices for dataset at {train_indices_cache_file_name} and {test_indices_cache_file_name}"
)
return DatasetDict(
{
Expand Down