hunting some more typos

CHANGELOG.md

@@ -430,7 +430,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed mAP calculation for areas with 0 predictions ([#1080](https://github.com/Lightning-AI/metrics/pull/1080))
 - Fixed bug where avg precision state and auroc state was not merge when using MetricCollections ([#1086](https://github.com/Lightning-AI/metrics/pull/1086))
 - Skip box conversion if no boxes are present in `MeanAveragePrecision` ([#1097](https://github.com/Lightning-AI/metrics/pull/1097))
-- Fixed inconsistency in docs and code when setting `average="none"` in `AvaragePrecision` metric ([#1116](https://github.com/Lightning-AI/metrics/pull/1116))
+- Fixed inconsistency in docs and code when setting `average="none"` in `AveragePrecision` metric ([#1116](https://github.com/Lightning-AI/metrics/pull/1116))
 
 
 ## [0.9.1] - 2022-06-08

src/torchmetrics/audio/__init__.py

@@ -23,10 +23,10 @@
     SignalNoiseRatio,
 )
 from torchmetrics.utilities.imports import (
-    _GAMMATONE_AVAILABEL,
+    _GAMMATONE_AVAILABLE,
     _PESQ_AVAILABLE,
     _PYSTOI_AVAILABLE,
-    _TORCHAUDIO_AVAILABEL,
+    _TORCHAUDIO_AVAILABLE,
     _TORCHAUDIO_GREATER_EQUAL_0_10,
 )
 
@@ -50,7 +50,7 @@
 
     __all__.append("ShortTimeObjectiveIntelligibility")
 
-if _GAMMATONE_AVAILABEL and _TORCHAUDIO_AVAILABEL and _TORCHAUDIO_GREATER_EQUAL_0_10:
+if _GAMMATONE_AVAILABLE and _TORCHAUDIO_AVAILABLE and _TORCHAUDIO_GREATER_EQUAL_0_10:
     from torchmetrics.audio.srmr import SpeechReverberationModulationEnergyRatio  # noqa: F401
 
     __all__.append("SpeechReverberationModulationEnergyRatio")

src/torchmetrics/audio/srmr.py

@@ -21,14 +21,14 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.imports import (
-    _GAMMATONE_AVAILABEL,
+    _GAMMATONE_AVAILABLE,
     _MATPLOTLIB_AVAILABLE,
-    _TORCHAUDIO_AVAILABEL,
+    _TORCHAUDIO_AVAILABLE,
     _TORCHAUDIO_GREATER_EQUAL_0_10,
 )
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
-if not all([_GAMMATONE_AVAILABEL, _TORCHAUDIO_AVAILABEL, _TORCHAUDIO_GREATER_EQUAL_0_10]):
+if not all([_GAMMATONE_AVAILABLE, _TORCHAUDIO_AVAILABLE, _TORCHAUDIO_GREATER_EQUAL_0_10]):
     __doctest_skip__ = ["SpeechReverberationModulationEnergyRatio", "SpeechReverberationModulationEnergyRatio.plot"]
 elif not _MATPLOTLIB_AVAILABLE:
     __doctest_skip__ = ["SpeechReverberationModulationEnergyRatio.plot"]
@@ -106,7 +106,7 @@ def __init__(
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
-        if not _TORCHAUDIO_AVAILABEL or not _TORCHAUDIO_GREATER_EQUAL_0_10 or not _GAMMATONE_AVAILABEL:
+        if not _TORCHAUDIO_AVAILABLE or not _TORCHAUDIO_GREATER_EQUAL_0_10 or not _GAMMATONE_AVAILABLE:
             raise ModuleNotFoundError(
                 "speech_reverberation_modulation_energy_ratio requires you to have `gammatone` and"
                 " `torchaudio>=0.10` installed. Either install as ``pip install torchmetrics[audio]`` or "

src/torchmetrics/functional/audio/__init__.py

@@ -23,10 +23,10 @@
     signal_noise_ratio,
 )
 from torchmetrics.utilities.imports import (
-    _GAMMATONE_AVAILABEL,
+    _GAMMATONE_AVAILABLE,
     _PESQ_AVAILABLE,
     _PYSTOI_AVAILABLE,
-    _TORCHAUDIO_AVAILABEL,
+    _TORCHAUDIO_AVAILABLE,
     _TORCHAUDIO_GREATER_EQUAL_0_10,
 )
 
@@ -51,7 +51,7 @@
 
     __all__.append("short_time_objective_intelligibility")
 
-if _GAMMATONE_AVAILABEL and _TORCHAUDIO_AVAILABEL and _TORCHAUDIO_GREATER_EQUAL_0_10:
+if _GAMMATONE_AVAILABLE and _TORCHAUDIO_AVAILABLE and _TORCHAUDIO_GREATER_EQUAL_0_10:
     from torchmetrics.functional.audio.srmr import speech_reverberation_modulation_energy_ratio  # noqa: F401
 
     __all__.append("speech_reverberation_modulation_energy_ratio")

src/torchmetrics/functional/audio/srmr.py

@@ -25,18 +25,18 @@
 
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.imports import (
-    _GAMMATONE_AVAILABEL,
-    _TORCHAUDIO_AVAILABEL,
+    _GAMMATONE_AVAILABLE,
+    _TORCHAUDIO_AVAILABLE,
     _TORCHAUDIO_GREATER_EQUAL_0_10,
 )
 
-if _TORCHAUDIO_AVAILABEL and _TORCHAUDIO_GREATER_EQUAL_0_10:
+if _TORCHAUDIO_AVAILABLE and _TORCHAUDIO_GREATER_EQUAL_0_10:
     from torchaudio.functional.filtering import lfilter
 else:
     lfilter = None
     __doctest_skip__ = ["speech_reverberation_modulation_energy_ratio"]
 
-if _GAMMATONE_AVAILABEL:
+if _GAMMATONE_AVAILABLE:
     from gammatone.fftweight import fft_gtgram
     from gammatone.filters import centre_freqs, make_erb_filters
 else:
@@ -233,7 +233,7 @@ def speech_reverberation_modulation_energy_ratio(
         tensor([0.3354], dtype=torch.float64)
 
     """
-    if not _TORCHAUDIO_AVAILABEL or not _TORCHAUDIO_GREATER_EQUAL_0_10 or not _GAMMATONE_AVAILABEL:
+    if not _TORCHAUDIO_AVAILABLE or not _TORCHAUDIO_GREATER_EQUAL_0_10 or not _GAMMATONE_AVAILABLE:
         raise ModuleNotFoundError(
             "speech_reverberation_modulation_energy_ratio requires you to have `gammatone` and"
             " `torchaudio>=0.10` installed. Either install as ``pip install torchmetrics[audio]`` or "

src/torchmetrics/functional/classification/dice.py

@@ -50,9 +50,9 @@ def _dice_compute(
 
     if average == AverageMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
         # a class is not present if there exists no TPs, no FPs, and no FNs
-        meaningless_indeces = torch.nonzero((tp | fn | fp) == 0).cpu()
-        numerator[meaningless_indeces, ...] = -1
-        denominator[meaningless_indeces, ...] = -1
+        meaningless_indices = torch.nonzero((tp | fn | fp) == 0).cpu()
+        numerator[meaningless_indices, ...] = -1
+        denominator[meaningless_indices, ...] = -1
 
     return _reduce_stat_scores(
         numerator=numerator,

src/torchmetrics/functional/classification/specificity_sensitivity.py

@@ -422,8 +422,8 @@ def specicity_at_sensitivity(
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
     ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_specificity_at_sensitivity`,
-    :func:`~torchmetrics.functional.classification.multiclass_specicity_at_sensitivity` and
-    :func:`~torchmetrics.functional.classification.multilabel_specifity_at_sensitvity` for the specific details of
+    :func:`~torchmetrics.functional.classification.multiclass_specificity_at_sensitivity` and
+    :func:`~torchmetrics.functional.classification.multilabel_specificity_at_sensitivity` for the specific details of
     each argument influence and examples.
 
     """

src/torchmetrics/functional/clustering/adjusted_rand_score.py

@@ -15,8 +15,8 @@
 from torch import Tensor
 
 from torchmetrics.functional.clustering.utils import (
-    calcualte_pair_cluster_confusion_matrix,
     calculate_contingency_matrix,
+    calculate_pair_cluster_confusion_matrix,
     check_cluster_labels,
 )
 
@@ -46,7 +46,7 @@ def _adjusted_rand_score_compute(contingency: Tensor) -> Tensor:
         rand_score: rand score
 
     """
-    (tn, fp), (fn, tp) = calcualte_pair_cluster_confusion_matrix(contingency=contingency)
+    (tn, fp), (fn, tp) = calculate_pair_cluster_confusion_matrix(contingency=contingency)
     if fn == 0 and fp == 0:
         return torch.ones_like(tn, dtype=torch.float32)
     return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn))

src/torchmetrics/functional/clustering/rand_score.py

@@ -15,8 +15,8 @@
 from torch import Tensor
 
 from torchmetrics.functional.clustering.utils import (
-    calcualte_pair_cluster_confusion_matrix,
     calculate_contingency_matrix,
+    calculate_pair_cluster_confusion_matrix,
     check_cluster_labels,
 )
 
@@ -46,7 +46,7 @@ def _rand_score_compute(contingency: Tensor) -> Tensor:
         rand_score: rand score
 
     """
-    pair_matrix = calcualte_pair_cluster_confusion_matrix(contingency=contingency)
+    pair_matrix = calculate_pair_cluster_confusion_matrix(contingency=contingency)
 
     numerator = pair_matrix.diagonal().sum()
     denominator = pair_matrix.sum()

src/torchmetrics/functional/clustering/utils.py

@@ -214,7 +214,7 @@ def _validate_intrinsic_labels_to_samples(num_labels: int, num_samples: int) ->
         )
 
 
-def calcualte_pair_cluster_confusion_matrix(
+def calculate_pair_cluster_confusion_matrix(
     preds: Optional[Tensor] = None,
     target: Optional[Tensor] = None,
     contingency: Optional[Tensor] = None,
@@ -247,15 +247,15 @@ def calcualte_pair_cluster_confusion_matrix(
 
     Example:
         >>> import torch
-        >>> from torchmetrics.functional.clustering.utils import calcualte_pair_cluster_confusion_matrix
+        >>> from torchmetrics.functional.clustering.utils import calculate_pair_cluster_confusion_matrix
         >>> preds = torch.tensor([0, 0, 1, 1])
         >>> target = torch.tensor([1, 1, 0, 0])
-        >>> calcualte_pair_cluster_confusion_matrix(preds, target)
+        >>> calculate_pair_cluster_confusion_matrix(preds, target)
         tensor([[8, 0],
                 [0, 4]])
         >>> preds = torch.tensor([0, 0, 1, 2])
         >>> target = torch.tensor([0, 0, 1, 1])
-        >>> calcualte_pair_cluster_confusion_matrix(preds, target)
+        >>> calculate_pair_cluster_confusion_matrix(preds, target)
         tensor([[8, 2],
                 [0, 2]])
 

src/torchmetrics/functional/regression/kl_divergence.py

@@ -89,7 +89,7 @@ def kl_divergence(
 
     Where :math:`P` and :math:`Q` are probability distributions where :math:`P` usually represents a distribution
     over data and :math:`Q` is often a prior or approximation of :math:`P`. It should be noted that the KL divergence
-    is a non-symetrical metric i.e. :math:`D_{KL}(P||Q) \neq D_{KL}(Q||P)`.
+    is a non-symmetrical metric i.e. :math:`D_{KL}(P||Q) \neq D_{KL}(Q||P)`.
 
     Args:
         p: data distribution with shape ``[N, d]``

src/torchmetrics/functional/text/chrf.py

@@ -559,7 +559,7 @@ def chrf_score(
             metric is equivalent to the original chrF.
         beta:
             A parameter determining an importance of recall w.r.t. precision. If `beta=1`, their importance is equal.
-        lowercase: An indication whether to enable case-insesitivity.
+        lowercase: An indication whether to enable case-insensitivity.
         whitespace: An indication whether to keep whitespaces during character n-gram extraction.
         return_sentence_level_score: An indication whether a sentence-level chrF/chrF++ score to be returned.
 

src/torchmetrics/functional/text/ter.py

@@ -80,7 +80,7 @@ def __init__(
         Args:
             normalize: An indication whether a general tokenization to be applied.
             no_punctuation: An indication whteher a punctuation to be removed from the sentences.
-            lowercase: An indication whether to enable case-insesitivity.
+            lowercase: An indication whether to enable case-insensitivity.
             asian_support: An indication whether asian characters to be processed.
 
         """
@@ -551,7 +551,7 @@ def translation_edit_rate(
         target: An iterable of iterables of reference corpus.
         normalize: An indication whether a general tokenization to be applied.
         no_punctuation: An indication whteher a punctuation to be removed from the sentences.
-        lowercase: An indication whether to enable case-insesitivity.
+        lowercase: An indication whether to enable case-insensitivity.
         asian_support: An indication whether asian characters to be processed.
         return_sentence_level_score: An indication whether a sentence-level TER to be returned.
 

src/torchmetrics/regression/kl_divergence.py

@@ -35,7 +35,7 @@ class KLDivergence(Metric):
 
     Where :math:`P` and :math:`Q` are probability distributions where :math:`P` usually represents a distribution
     over data and :math:`Q` is often a prior or approximation of :math:`P`. It should be noted that the KL divergence
-    is a non-symetrical metric i.e. :math:`D_{KL}(P||Q) \neq D_{KL}(Q||P)`.
+    is a non-symmetrical metric i.e. :math:`D_{KL}(P||Q) \neq D_{KL}(Q||P)`.
 
     As input to ``forward`` and ``update`` the metric accepts the following input:
 

src/torchmetrics/text/chrf.py

@@ -71,7 +71,7 @@ class CHRFScore(Metric):
         n_word_order: A word n-gram order. If ``n_word_order=2``, the metric refers to the official chrF++.
             If ``n_word_order=0``, the metric is equivalent to the original ChrF.
         beta: parameter determining an importance of recall w.r.t. precision. If ``beta=1``, their importance is equal.
-        lowercase: An indication whether to enable case-insesitivity.
+        lowercase: An indication whether to enable case-insensitivity.
         whitespace: An indication whether keep whitespaces during n-gram extraction.
         return_sentence_level_score: An indication whether a sentence-level chrF/chrF++ score to be returned.
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

src/torchmetrics/text/ter.py

@@ -45,7 +45,7 @@ class TranslationEditRate(Metric):
     Args:
         normalize: An indication whether a general tokenization to be applied.
         no_punctuation: An indication whteher a punctuation to be removed from the sentences.
-        lowercase: An indication whether to enable case-insesitivity.
+        lowercase: An indication whether to enable case-insensitivity.
         asian_support: An indication whether asian characters to be processed.
         return_sentence_level_score: An indication whether a sentence-level TER to be returned.
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

src/torchmetrics/utilities/imports.py

@@ -45,8 +45,8 @@
 _TRANSFORMERS_GREATER_EQUAL_4_4: Optional[bool] = compare_version("transformers", operator.ge, "4.4.0")
 _TRANSFORMERS_GREATER_EQUAL_4_10: Optional[bool] = compare_version("transformers", operator.ge, "4.10.0")
 _PESQ_AVAILABLE: bool = package_available("pesq")
-_GAMMATONE_AVAILABEL: bool = package_available("gammatone")
-_TORCHAUDIO_AVAILABEL: bool = package_available("torchaudio")
+_GAMMATONE_AVAILABLE: bool = package_available("gammatone")
+_TORCHAUDIO_AVAILABLE: bool = package_available("torchaudio")
 _TORCHAUDIO_GREATER_EQUAL_0_10: Optional[bool] = compare_version("torchaudio", operator.ge, "0.10.0")
 _SACREBLEU_AVAILABLE: bool = package_available("sacrebleu")
 _REGEX_AVAILABLE: bool = package_available("regex")

tests/unittests/audio/test_sdr.py

@@ -75,7 +75,7 @@ def _average_metric(preds: Tensor, target: Tensor, metric_func: Callable) -> Ten
     ],
 )
 class TestSDR(MetricTester):
-    """Test class for `SingalDistortionRatio` metric."""
+    """Test class for `SignalDistortionRatio` metric."""
 
     atol = 1e-2
 

tests/unittests/clustering/test_utils.py

@@ -21,10 +21,10 @@
 from sklearn.metrics.cluster import pair_confusion_matrix as sklearn_pair_confusion_matrix
 from sklearn.metrics.cluster._supervised import _generalized_average as sklearn_generalized_average
 from torchmetrics.functional.clustering.utils import (
-    calcualte_pair_cluster_confusion_matrix,
     calculate_contingency_matrix,
     calculate_entropy,
     calculate_generalized_mean,
+    calculate_pair_cluster_confusion_matrix,
 )
 
 from unittests import BATCH_SIZE, NUM_BATCHES
@@ -113,6 +113,6 @@ class TestPairClusterConfusionMatrix:
 
     def test_pair_cluster_confusion_matrix(self, preds, target):
         """Check that pair cluster confusion matrix is calculated correctly."""
-        tm_res = calcualte_pair_cluster_confusion_matrix(preds, target)
+        tm_res = calculate_pair_cluster_confusion_matrix(preds, target)
         sklearn_res = sklearn_pair_confusion_matrix(preds, target)
         assert np.allclose(tm_res, sklearn_res, atol=self.atol)

tests/unittests/retrieval/helpers.py

@@ -29,8 +29,8 @@
 from unittests.retrieval.inputs import _input_retrieval_scores_float_target as _irs_float_tgt
 from unittests.retrieval.inputs import _input_retrieval_scores_for_adaptive_k as _irs_adpt_k
 from unittests.retrieval.inputs import _input_retrieval_scores_int_target as _irs_int_tgt
-from unittests.retrieval.inputs import _input_retrieval_scores_mismatching_sizes as _irs_mis_sz
-from unittests.retrieval.inputs import _input_retrieval_scores_mismatching_sizes_func as _irs_mis_sz_fn
+from unittests.retrieval.inputs import _input_retrieval_scores_mismatching_sizes as _irs_bad_sz
+from unittests.retrieval.inputs import _input_retrieval_scores_mismatching_sizes_func as _irs_bad_sz_fn
 from unittests.retrieval.inputs import _input_retrieval_scores_no_target as _irs_no_tgt
 from unittests.retrieval.inputs import _input_retrieval_scores_with_ignore_index as _irs_ii
 from unittests.retrieval.inputs import _input_retrieval_scores_wrong_targets as _irs_bad_tgt
@@ -136,7 +136,7 @@ def _concat_tests(*tests: Tuple[Dict]) -> Dict:
     "argnames": "preds,target,message,metric_args",
     "argvalues": [
         # check input shapes are consistent (func)
-        (_irs_mis_sz_fn.preds, _irs_mis_sz_fn.target, "`preds` and `target` must be of the same shape", {}),
+        (_irs_bad_sz_fn.preds, _irs_bad_sz_fn.target, "`preds` and `target` must be of the same shape", {}),
         # check input tensors are not empty
         (_irs_empty.preds, _irs_empty.target, "`preds` and `target` must be non-empty and non-scalar tensors", {}),
         # check on input dtypes
@@ -150,7 +150,7 @@ def _concat_tests(*tests: Tuple[Dict]) -> Dict:
     "argnames": "preds,target,message,metric_args",
     "argvalues": [
         # check input shapes are consistent (func)
-        (_irs_mis_sz_fn.preds, _irs_mis_sz_fn.target, "`preds` and `target` must be of the same shape", {}),
+        (_irs_bad_sz_fn.preds, _irs_bad_sz_fn.target, "`preds` and `target` must be of the same shape", {}),
         # check input tensors are not empty
         (_irs_empty.preds, _irs_empty.target, "`preds` and `target` must be non-empty and non-scalar tensors", {}),
         # check on input dtypes
@@ -224,9 +224,9 @@ def _concat_tests(*tests: Tuple[Dict]) -> Dict:
         ),
         # check input shapes are consistent
         (
-            _irs_mis_sz.indexes,
-            _irs_mis_sz.preds,
-            _irs_mis_sz.target,
+            _irs_bad_sz.indexes,
+            _irs_bad_sz.preds,
+            _irs_bad_sz.target,
             "`indexes`, `preds` and `target` must be of the same shape",
             {"empty_target_action": "skip"},
         ),
@@ -278,9 +278,9 @@ def _concat_tests(*tests: Tuple[Dict]) -> Dict:
         ),
         # check input shapes are consistent
         (
-            _irs_mis_sz.indexes,
-            _irs_mis_sz.preds,
-            _irs_mis_sz.target,
+            _irs_bad_sz.indexes,
+            _irs_bad_sz.preds,
+            _irs_bad_sz.target,
             "`indexes`, `preds` and `target` must be of the same shape",
             {"empty_target_action": "skip"},
         ),