Sourcery refactored master branch #1
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| htmlhelp_basename = project + "-doc" | ||
| htmlhelp_basename = f"{project}-doc" |
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Lines 178-221 refactored with the following changes:
- Use f-string instead of string concatenation [×5] (
use-fstring-for-concatenation)
| transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) | ||
| return transformer_encoder | ||
| return nn.TransformerEncoder(encoder_layer, num_layers=num_layers) |
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Function get_user_model_encoder refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| output_text = re.sub(self.pattern, " ", text.lower()) | ||
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| return output_text | ||
| return re.sub(self.pattern, " ", text.lower()) |
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Function UserNormalizer.__call__ refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| output_tokens = re.split(self.pattern, text) | ||
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| return output_tokens | ||
| return re.split(self.pattern, text) |
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Function UserTokenizer.__call__ refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| self._groups = {} | ||
| for idx, values in enumerate(temp.values()): | ||
| self._groups[idx] = values | ||
| self._groups = dict(enumerate(temp.values())) |
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Function MetricCollection._merge_compute_groups refactored with the following changes:
- Convert for loop into dictionary comprehension (
dict-comprehension) - Replace identity comprehension with call to collection constructor (
identity-comprehension)
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| if len(gt) == 0 and len(det) == 0: | ||
| if not gt and not det: |
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Function MeanAveragePrecision._evaluate_image refactored with the following changes:
- Simplify sequence length comparison [×2] (
simplify-len-comparison)
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| mean_prec = torch.tensor([-1.0]) if len(prec[prec > -1]) == 0 else torch.mean(prec[prec > -1]) | ||
| return mean_prec | ||
| return ( | ||
| torch.tensor([-1.0]) | ||
| if len(prec[prec > -1]) == 0 | ||
| else torch.mean(prec[prec > -1]) | ||
| ) |
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Function MeanAveragePrecision._summarize refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| if preds.shape[0:2] != target.shape[0:2]: | ||
| if preds.shape[:2] != target.shape[:2]: |
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Function permutation_invariant_training refactored with the following changes:
- Replace a[0:x] with a[:x] and a[x:len(a)] with a[x:] [×3] (
remove-redundant-slice-index) - Remove redundant conditional (
remove-redundant-if)
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| preds_pmted = torch.stack([torch.index_select(pred, 0, p) for pred, p in zip(preds, perm)]) | ||
| return preds_pmted | ||
| return torch.stack( | ||
| [torch.index_select(pred, 0, p) for pred, p in zip(preds, perm)] | ||
| ) |
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Function pit_permutate refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| elif not _TORCH_GREATER_EQUAL_1_8: | ||
| else: |
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Function signal_distortion_ratio refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if) - Replace if statement with if expression (
assign-if-exp)
| mode = _check_classification_inputs( | ||
| return _check_classification_inputs( |
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Function _mode refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1): | ||
| if average in {"macro", "weighted", "none", None} and ( | ||
| (not num_classes or num_classes < 1) | ||
| ): |
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Function accuracy refactored with the following changes:
- Use set when checking membership of a collection of literals [×2] (
collection-into-set)
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| res = [] | ||
| for p, r in zip(precision, recall): | ||
| res.append(-torch.sum((r[1:] - r[:-1]) * p[:-1])) | ||
| res = [ | ||
| -torch.sum((r[1:] - r[:-1]) * p[:-1]) | ||
| for p, r in zip(precision, recall) | ||
| ] |
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Function _average_precision_compute_with_precision_recall refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension)
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| if not ((0 <= preds) * (preds <= 1)).all(): | ||
| if not ((preds >= 0) * (preds <= 1)).all(): | ||
| preds = preds.sigmoid() | ||
| confidences, accuracies = preds, target | ||
| elif mode == DataType.MULTICLASS: | ||
| if not ((0 <= preds) * (preds <= 1)).all(): | ||
| if not ((preds >= 0) * (preds <= 1)).all(): |
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Function _ce_update refactored with the following changes:
- Ensure constant in comparison is on the right [×2] (
flip-comparison)
| confmat = confmat.float() if not confmat.is_floating_point() else confmat | ||
| confmat = confmat if confmat.is_floating_point() else confmat.float() |
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Function _cohen_kappa_compute refactored with the following changes:
- Swap if/else branches of if expression to remove negation (
swap-if-expression)
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| if sample_weight is not None: | ||
| if sample_weight.ndim != 1 or sample_weight.shape[0] != preds.shape[0]: | ||
| raise ValueError( | ||
| "Expected sample weights to be 1 dimensional and have same size" | ||
| f" as the first dimension of preds and target but got {sample_weight.shape}" | ||
| ) | ||
| if sample_weight is not None and ( | ||
| sample_weight.ndim != 1 or sample_weight.shape[0] != preds.shape[0] | ||
| ): | ||
| raise ValueError( | ||
| "Expected sample weights to be 1 dimensional and have same size" | ||
| f" as the first dimension of preds and target but got {sample_weight.shape}" | ||
| ) |
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Function _check_ranking_input refactored with the following changes:
- Merge nested if conditions (
merge-nested-ifs)
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| if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1): | ||
| if average in {"macro", "weighted", "none", None} and ( | ||
| (not num_classes or num_classes < 1) | ||
| ): | ||
| raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.") | ||
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| if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1): | ||
| raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes") | ||
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| reduce = "macro" if average in ["weighted", "none", None] else average | ||
| reduce = "macro" if average in {"weighted", "none", None} else average |
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Function specificity refactored with the following changes:
- Use set when checking membership of a collection of literals [×2] (
collection-into-set)
| if weights is None: | ||
| weights = torch.ones_like(denominator) | ||
| else: | ||
| weights = weights.float() | ||
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| weights = torch.ones_like(denominator) if weights is None else weights.float() |
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Function _reduce_stat_scores refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
| if isinstance(dim, int): | ||
| dim_list = [dim] | ||
| else: | ||
| dim_list = list(dim) | ||
| dim_list = [dim] if isinstance(dim, int) else list(dim) |
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Function _psnr_update refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
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| if y is not None: | ||
| if y.ndim != 2 or y.shape[1] != x.shape[1]: | ||
| raise ValueError( | ||
| "Expected argument `y` to be a 2D tensor of shape `[M, d]` where" | ||
| " `d` should be same as the last dimension of `x`" | ||
| ) | ||
| zero_diagonal = False if zero_diagonal is None else zero_diagonal | ||
| else: | ||
| if y is None: | ||
| y = x.clone() | ||
| zero_diagonal = True if zero_diagonal is None else zero_diagonal | ||
| elif y.ndim != 2 or y.shape[1] != x.shape[1]: | ||
| raise ValueError( | ||
| "Expected argument `y` to be a 2D tensor of shape `[M, d]` where" | ||
| " `d` should be same as the last dimension of `x`" | ||
| ) | ||
| else: | ||
| zero_diagonal = False if zero_diagonal is None else zero_diagonal |
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Function _check_input refactored with the following changes:
- Swap if/else branches (
swap-if-else-branches) - Merge else clause's nested if statement into elif (
merge-else-if-into-elif) - Lift code into else after jump in control flow (
reintroduce-else)
| mean_ape = _mean_absolute_percentage_error_compute(sum_abs_per_error, num_obs) | ||
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| return mean_ape | ||
| return _mean_absolute_percentage_error_compute(sum_abs_per_error, num_obs) |
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Function mean_absolute_percentage_error refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| mean_ape = _symmetric_mean_absolute_percentage_error_compute( | ||
| return _symmetric_mean_absolute_percentage_error_compute( | ||
| sum_abs_per_error, | ||
| num_obs, | ||
| ) | ||
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| return mean_ape |
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Function symmetric_mean_absolute_percentage_error refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| deviance_score = 2 * (_safe_xlogy(targets, targets / preds) + preds - targets) | ||
| else: | ||
| deviance_score = 2 * (_safe_xlogy(targets, targets / preds) + preds - targets) | ||
| elif power == 2: | ||
| # Gamma distribution | ||
| if torch.any(preds <= 0) or torch.any(targets <= 0): | ||
| raise ValueError(f"For power={power}, both 'preds' and 'targets' have to be strictly positive.") | ||
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| deviance_score = 2 * (torch.log(preds / targets) + (targets / preds) - 1) | ||
| else: | ||
| deviance_score = 2 * (torch.log(preds / targets) + (targets / preds) - 1) |
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Function _tweedie_deviance_score_update refactored with the following changes:
- Simplify conditional into switch-like form [×3] (
switch) - Lift code into else after jump in control flow [×2] (
reintroduce-else) - Merge else clause's nested if statement into elif (
merge-else-if-into-elif)
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| weighted_ape = _weighted_mean_absolute_percentage_error_compute( | ||
| return _weighted_mean_absolute_percentage_error_compute( | ||
| sum_abs_error, | ||
| sum_scale, | ||
| ) | ||
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| return weighted_ape |
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Function weighted_mean_absolute_percentage_error refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| res = torch.div((torch.arange(len(positions), device=positions.device, dtype=torch.float32) + 1), positions).mean() | ||
| return res | ||
| return torch.div( | ||
| ( | ||
| torch.arange( | ||
| len(positions), device=positions.device, dtype=torch.float32 | ||
| ) | ||
| + 1 | ||
| ), | ||
| positions, | ||
| ).mean() |
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Function retrieval_average_precision refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| for i in range(0, len(hyp) + 1): | ||
| for i in range(len(hyp) + 1): |
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Function _eed_function refactored with the following changes:
- Replace range(0, x) with range(x) (
remove-zero-from-range)
| sentence = " " + sentence + " " | ||
| sentence = f" {sentence} " |
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Function _preprocess_en refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| if len(sentence_level_scores) == 0: | ||
| return tensor(0.0) | ||
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| average = sum(sentence_level_scores) / tensor(len(sentence_level_scores)) | ||
| return average | ||
| return ( | ||
| sum(sentence_level_scores) / tensor(len(sentence_level_scores)) | ||
| if sentence_level_scores | ||
| else tensor(0.0) | ||
| ) |
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Function _eed_compute refactored with the following changes:
- Swap if/else branches of if expression to remove negation (
swap-if-expression) - Lift code into else after jump in control flow (
reintroduce-else) - Replace if statement with if expression (
assign-if-exp) - Simplify sequence length comparison (
simplify-len-comparison) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
| if not isinstance(param, float) or isinstance(param, float) and param < 0: | ||
| if not isinstance(param, float) or param < 0: |
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Function extended_edit_distance refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if)
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| empty_row = [(int(_EDIT_OPERATIONS_COST.OP_UNDEFINED), _EDIT_OPERATIONS.OP_UNDEFINED)] * (length + 1) | ||
| return empty_row | ||
| return [ | ||
| ( | ||
| int(_EDIT_OPERATIONS_COST.OP_UNDEFINED), | ||
| _EDIT_OPERATIONS.OP_UNDEFINED, | ||
| ) | ||
| ] * (length + 1) |
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Function _LevenshteinEditDistance._get_empty_row refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
Sourcery Code Quality Report✅ Merging this PR will increase code quality in the affected files by 0.08%.
Here are some functions in these files that still need a tune-up:
Legend and ExplanationThe emojis denote the absolute quality of the code:
The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request. Please see our documentation here for details on how these metrics are calculated. We are actively working on this report - lots more documentation and extra metrics to come! Help us improve this quality report! |
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