check typos & fixing some... (#2102)

.github/workflows/ci-checks.yml

@@ -5,6 +5,7 @@ on:
     branches: [master, "release/*"]
   pull_request:
     branches: [master, "release/*"]
+    types: [opened, reopened, ready_for_review, synchronize]
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref }}

.github/workflows/docs-build.yml

@@ -6,6 +6,7 @@ on:
     tags: ["*"]
   pull_request:
     branches: ["master", "release/*"]
+    types: [opened, reopened, ready_for_review, synchronize]
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref }}

.pre-commit-config.yaml

@@ -50,6 +50,15 @@ repos:
       - id: codespell
         additional_dependencies: [tomli]
         #args: ["--write-changes"]
+        exclude: pyproject.toml
+
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.16.12
+    hooks:
+      - id: typos
+        # empty to do not write fixes
+        args: []
+        exclude: pyproject.toml
 
   - repo: https://github.com/PyCQA/docformatter
     rev: v1.7.5

pyproject.toml

@@ -60,6 +60,25 @@ ignore-words-list = """
   archiv
 """
 
+[tool.typos.default]
+extend-ignore-identifiers-re = [
+    # *sigh* this just isn't worth the cost of fixing
+    "AttributeID.*Supress.*",
+]
+
+[tool.typos.default.extend-identifiers]
+# *sigh* this just isn't worth the cost of fixing
+MAPE = "MAPE"
+WIL = "WIL"
+Raison = "Raison"
+
+[tool.typos.default.extend-words]
+# Don't correct the surname "Teh"
+fpr = "fpr"
+mape = "mape"
+wil = "wil"
+
+
 
 [tool.ruff]
 line-length = 120

src/torchmetrics/classification/accuracy.py

@@ -45,7 +45,7 @@ class BinaryAccuracy(BinaryStatScores):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-        - ``ba`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, metric returns a scalar value.
+        - ``acc`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, metric returns a scalar value.
           If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar
           value per sample.
 

src/torchmetrics/functional/regression/tweedie_deviance.py

@@ -88,7 +88,7 @@ def _tweedie_deviance_score_compute(sum_deviance_score: Tensor, num_observations
     """Compute Deviance Score.
 
     Args:
-        sum_deviance_score: Sum of deviance scores accumalated until now.
+        sum_deviance_score: Sum of deviance scores accumulated until now.
         num_observations: Number of observations encountered until now.
 
     Example:

src/torchmetrics/functional/text/chrf.py

@@ -94,7 +94,7 @@ def _get_characters(sentence: str, whitespace: bool) -> List[str]:
     return list(sentence.strip().replace(" ", ""))
 
 
-def _separate_word_and_punctiation(word: str) -> List[str]:
+def _separate_word_and_punctuation(word: str) -> List[str]:
     """Separates out punctuations from beginning and end of words for chrF.
 
     Adapted from https://github.com/m-popovic/chrF and
@@ -117,7 +117,7 @@ def _separate_word_and_punctiation(word: str) -> List[str]:
     return [word]
 
 
-def _get_words_and_punctiation(sentence: str) -> List[str]:
+def _get_words_and_punctuation(sentence: str) -> List[str]:
     """Separates out punctuations from beginning and end of words for chrF for all words in the sentence.
 
     Args:
@@ -127,7 +127,7 @@ def _get_words_and_punctiation(sentence: str) -> List[str]:
         An aggregated list of separated words and punctuations.
 
     """
-    return sum((_separate_word_and_punctiation(word) for word in sentence.strip().split()), [])
+    return sum((_separate_word_and_punctuation(word) for word in sentence.strip().split()), [])
 
 
 def _ngram_counts(char_or_word_list: List[str], n_gram_order: int) -> Dict[int, Dict[Tuple[str, ...], Tensor]]:
@@ -180,7 +180,7 @@ def _char_and_word_ngrams_counts(
         if lowercase:
             sentence = sentence.lower()
         char_n_grams_counts = _ngram_counts(_get_characters(sentence, whitespace), n_char_order)
-        word_n_grams_counts = _ngram_counts(_get_words_and_punctiation(sentence), n_word_order)
+        word_n_grams_counts = _ngram_counts(_get_words_and_punctuation(sentence), n_word_order)
         return char_n_grams_counts, word_n_grams_counts
 
     def _get_total_ngrams(n_grams_counts: Dict[int, Dict[Tuple[str, ...], Tensor]]) -> Dict[int, Tensor]:

src/torchmetrics/functional/text/infolm.py

@@ -137,9 +137,9 @@ def __init__(
         self.alpha = alpha or 0
         self.beta = beta or 0
 
-    def __call__(self, preds_distribution: Tensor, target_distribtuion: Tensor) -> Tensor:
+    def __call__(self, preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
         information_measure_function = getattr(self, f"_calculate_{self.information_measure.value}")
-        return torch.nan_to_num(information_measure_function(preds_distribution, target_distribtuion))
+        return torch.nan_to_num(information_measure_function(preds_distribution, target_distribution))
 
     @staticmethod
     def _calculate_kl_divergence(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:

src/torchmetrics/functional/text/ter.py

@@ -134,7 +134,7 @@ def _normalize_general_and_western(sentence: str) -> str:
             (r">", ">"),
             # tokenize punctuation
             (r"([{-~[-` -&(-+:-@/])", r" \1 "),
-            # handle possesives
+            # handle possessive
             (r"'s ", r" 's "),
             (r"'s$", r" 's"),
             # tokenize period and comma unless preceded by a digit

src/torchmetrics/functional/text/wil.py

@@ -19,11 +19,11 @@
 from torchmetrics.functional.text.helper import _edit_distance
 
 
-def _wil_update(
+def _word_info_lost_update(
     preds: Union[str, List[str]],
     target: Union[str, List[str]],
 ) -> Tuple[Tensor, Tensor, Tensor]:
-    """Update the wil score with the current set of references and predictions.
+    """Update the WIL score with the current set of references and predictions.
 
     Args:
         preds: Transcription(s) to score as a string or list of strings
@@ -54,7 +54,7 @@ def _wil_update(
     return errors - total, target_total, preds_total
 
 
-def _wil_compute(errors: Tensor, target_total: Tensor, preds_total: Tensor) -> Tensor:
+def _word_info_lost_compute(errors: Tensor, target_total: Tensor, preds_total: Tensor) -> Tensor:
     """Compute the Word Information Lost.
 
     Args:
@@ -90,5 +90,5 @@ def word_information_lost(preds: Union[str, List[str]], target: Union[str, List[
         tensor(0.6528)
 
     """
-    errors, target_total, preds_total = _wil_update(preds, target)
-    return _wil_compute(errors, target_total, preds_total)
+    errors, target_total, preds_total = _word_info_lost_update(preds, target)
+    return _word_info_lost_compute(errors, target_total, preds_total)

src/torchmetrics/image/fid.py

@@ -313,13 +313,13 @@ def __init__(
             raise ValueError("Argument `normalize` expected to be a bool")
         self.normalize = normalize
 
-        mx_num_feets = (num_features, num_features)
+        mx_num_feats = (num_features, num_features)
         self.add_state("real_features_sum", torch.zeros(num_features).double(), dist_reduce_fx="sum")
-        self.add_state("real_features_cov_sum", torch.zeros(mx_num_feets).double(), dist_reduce_fx="sum")
+        self.add_state("real_features_cov_sum", torch.zeros(mx_num_feats).double(), dist_reduce_fx="sum")
         self.add_state("real_features_num_samples", torch.tensor(0).long(), dist_reduce_fx="sum")
 
         self.add_state("fake_features_sum", torch.zeros(num_features).double(), dist_reduce_fx="sum")
-        self.add_state("fake_features_cov_sum", torch.zeros(mx_num_feets).double(), dist_reduce_fx="sum")
+        self.add_state("fake_features_cov_sum", torch.zeros(mx_num_feats).double(), dist_reduce_fx="sum")
         self.add_state("fake_features_num_samples", torch.tensor(0).long(), dist_reduce_fx="sum")
 
     def update(self, imgs: Tensor, real: bool) -> None:

src/torchmetrics/text/wil.py

@@ -15,7 +15,7 @@
 
 from torch import Tensor, tensor
 
-from torchmetrics.functional.text.wil import _wil_compute, _wil_update
+from torchmetrics.functional.text.wil import _word_info_lost_compute, _word_info_lost_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
@@ -82,14 +82,14 @@ def __init__(
 
     def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) -> None:
         """Update state with predictions and targets."""
-        errors, target_total, preds_total = _wil_update(preds, target)
+        errors, target_total, preds_total = _word_info_lost_update(preds, target)
         self.errors += errors
         self.target_total += target_total
         self.preds_total += preds_total
 
     def compute(self) -> Tensor:
         """Calculate the Word Information Lost."""
-        return _wil_compute(self.errors, self.target_total, self.preds_total)
+        return _word_info_lost_compute(self.errors, self.target_total, self.preds_total)
 
     def plot(
         self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None

tests/unittests/bases/test_collections.py

@@ -557,7 +557,7 @@ def test_compute_on_different_dtype():
 
 
 def test_error_on_wrong_specified_compute_groups():
-    """Test that error is raised if user mis-specify the compute groups."""
+    """Test that error is raised if user miss-specify the compute groups."""
     with pytest.raises(ValueError, match="Input MulticlassAccuracy in `compute_groups`.*"):
         MetricCollection(
             MulticlassConfusionMatrix(3),

tests/unittests/pairwise/test_pairwise_distance.py

@@ -153,7 +153,7 @@ def test_error_on_wrong_shapes(metric):
         (partial(pairwise_minkowski_distance, exponent=3), partial(pairwise_distances, metric="minkowski", p=3)),
     ],
 )
-def test_precison_case(metric_functional, sk_fn):
+def test_precision_case(metric_functional, sk_fn):
     """Test that metrics are robust towars cases where high precision is needed."""
     x = torch.tensor([[772.0, 112.0], [772.20001, 112.0]])
     res1 = metric_functional(x, zero_diagonal=False)