from typing import Optional, Iterable, Dict, Any, Callable, Tuple, TYPE_CHECKING import numpy as np from .gold import Example from .tokens import Token, Doc, Span from .errors import Errors from .util import get_lang_class from .morphology import Morphology if TYPE_CHECKING: # This lets us add type hints for mypy etc. without causing circular imports from .language import Language # noqa: F401 DEFAULT_PIPELINE = ["senter", "tagger", "morphologizer", "parser", "ner", "textcat"] class PRFScore: """A precision / recall / F score.""" def __init__(self) -> None: self.tp = 0 self.fp = 0 self.fn = 0 def score_set(self, cand: set, gold: set) -> None: self.tp += len(cand.intersection(gold)) self.fp += len(cand - gold) self.fn += len(gold - cand) @property def precision(self) -> float: return self.tp / (self.tp + self.fp + 1e-100) @property def recall(self) -> float: return self.tp / (self.tp + self.fn + 1e-100) @property def fscore(self) -> float: p = self.precision r = self.recall return 2 * ((p * r) / (p + r + 1e-100)) def to_dict(self) -> Dict[str, float]: return {"p": self.precision, "r": self.recall, "f": self.fscore} class ROCAUCScore: """An AUC ROC score.""" def __init__(self) -> None: self.golds = [] self.cands = [] self.saved_score = 0.0 self.saved_score_at_len = 0 def score_set(self, cand, gold) -> None: self.cands.append(cand) self.golds.append(gold) @property def score(self): if len(self.golds) == self.saved_score_at_len: return self.saved_score try: self.saved_score = _roc_auc_score(self.golds, self.cands) # catch ValueError: Only one class present in y_true. # ROC AUC score is not defined in that case. except ValueError: self.saved_score = -float("inf") self.saved_score_at_len = len(self.golds) return self.saved_score class Scorer: """Compute evaluation scores.""" def __init__( self, nlp: Optional["Language"] = None, default_lang: str = "xx", default_pipeline=DEFAULT_PIPELINE, **cfg, ) -> None: """Initialize the Scorer. DOCS: https://spacy.io/api/scorer#init """ self.nlp = nlp self.cfg = cfg if not nlp: nlp = get_lang_class(default_lang)() for pipe in default_pipeline: nlp.add_pipe(pipe) self.nlp = nlp def score(self, examples: Iterable[Example]) -> Dict[str, Any]: """Evaluate a list of Examples. examples (Iterable[Example]): The predicted annotations + correct annotations. RETURNS (Dict): A dictionary of scores. DOCS: https://spacy.io/api/scorer#score """ scores = {} if hasattr(self.nlp.tokenizer, "score"): scores.update(self.nlp.tokenizer.score(examples, **self.cfg)) for name, component in self.nlp.pipeline: if hasattr(component, "score"): scores.update(component.score(examples, **self.cfg)) return scores @staticmethod def score_tokenization(examples: Iterable[Example], **cfg) -> Dict[str, float]: """Returns accuracy and PRF scores for tokenization. * token_acc: # correct tokens / # gold tokens * token_p/r/f: PRF for token character spans examples (Iterable[Example]): Examples to score RETURNS (Dict[str, float]): A dictionary containing the scores token_acc/p/r/f. DOCS: https://spacy.io/api/scorer#score_tokenization """ acc_score = PRFScore() prf_score = PRFScore() for example in examples: gold_doc = example.reference pred_doc = example.predicted align = example.alignment gold_spans = set() pred_spans = set() for token in gold_doc: if token.orth_.isspace(): continue gold_spans.add((token.idx, token.idx + len(token))) for token in pred_doc: if token.orth_.isspace(): continue pred_spans.add((token.idx, token.idx + len(token))) if align.x2y.lengths[token.i] != 1: acc_score.fp += 1 else: acc_score.tp += 1 prf_score.score_set(pred_spans, gold_spans) return { "token_acc": acc_score.fscore, "token_p": prf_score.precision, "token_r": prf_score.recall, "token_f": prf_score.fscore, } @staticmethod def score_token_attr( examples: Iterable[Example], attr: str, *, getter: Callable[[Token, str], Any] = getattr, **cfg, ) -> Dict[str, float]: """Returns an accuracy score for a token-level attribute. examples (Iterable[Example]): Examples to score attr (str): The attribute to score. getter (Callable[[Token, str], Any]): Defaults to getattr. If provided, getter(token, attr) should return the value of the attribute for an individual token. RETURNS (Dict[str, float]): A dictionary containing the accuracy score under the key attr_acc. DOCS: https://spacy.io/api/scorer#score_token_attr """ tag_score = PRFScore() for example in examples: gold_doc = example.reference pred_doc = example.predicted align = example.alignment gold_tags = set() for gold_i, token in enumerate(gold_doc): gold_tags.add((gold_i, getter(token, attr))) pred_tags = set() for token in pred_doc: if token.orth_.isspace(): continue if align.x2y.lengths[token.i] == 1: gold_i = align.x2y[token.i].dataXd[0, 0] pred_tags.add((gold_i, getter(token, attr))) tag_score.score_set(pred_tags, gold_tags) return {f"{attr}_acc": tag_score.fscore} @staticmethod def score_token_attr_per_feat( examples: Iterable[Example], attr: str, *, getter: Callable[[Token, str], Any] = getattr, **cfg, ): """Return PRF scores per feat for a token attribute in UFEATS format. examples (Iterable[Example]): Examples to score attr (str): The attribute to score. getter (Callable[[Token, str], Any]): Defaults to getattr. If provided, getter(token, attr) should return the value of the attribute for an individual token. RETURNS (dict): A dictionary containing the per-feat PRF scores unders the key attr_per_feat. """ per_feat = {} for example in examples: pred_doc = example.predicted gold_doc = example.reference align = example.alignment gold_per_feat = {} for gold_i, token in enumerate(gold_doc): morph = str(getter(token, attr)) if morph: for feat in morph.split(Morphology.FEATURE_SEP): field, values = feat.split(Morphology.FIELD_SEP) if field not in per_feat: per_feat[field] = PRFScore() if field not in gold_per_feat: gold_per_feat[field] = set() gold_per_feat[field].add((gold_i, feat)) pred_per_feat = {} for token in pred_doc: if token.orth_.isspace(): continue if align.x2y.lengths[token.i] == 1: gold_i = align.x2y[token.i].dataXd[0, 0] morph = str(getter(token, attr)) if morph: for feat in morph.split("|"): field, values = feat.split("=") if field not in per_feat: per_feat[field] = PRFScore() if field not in pred_per_feat: pred_per_feat[field] = set() pred_per_feat[field].add((gold_i, feat)) for field in per_feat: per_feat[field].score_set( pred_per_feat.get(field, set()), gold_per_feat.get(field, set()), ) result = {k: v.to_dict() for k, v in per_feat.items()} return {f"{attr}_per_feat": result} @staticmethod def score_spans( examples: Iterable[Example], attr: str, *, getter: Callable[[Doc, str], Iterable[Span]] = getattr, **cfg, ) -> Dict[str, Any]: """Returns PRF scores for labeled spans. examples (Iterable[Example]): Examples to score attr (str): The attribute to score. getter (Callable[[Doc, str], Iterable[Span]]): Defaults to getattr. If provided, getter(doc, attr) should return the spans for the individual doc. RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under the keys attr_p/r/f and the per-type PRF scores under attr_per_type. DOCS: https://spacy.io/api/scorer#score_spans """ score = PRFScore() score_per_type = dict() for example in examples: pred_doc = example.predicted gold_doc = example.reference # Find all labels in gold and doc labels = set( [k.label_ for k in getter(gold_doc, attr)] + [k.label_ for k in getter(pred_doc, attr)] ) # Set up all labels for per type scoring and prepare gold per type gold_per_type = {label: set() for label in labels} for label in labels: if label not in score_per_type: score_per_type[label] = PRFScore() # Find all predidate labels, for all and per type gold_spans = set() pred_spans = set() # Special case for ents: # If we have missing values in the gold, we can't easily tell # whether our NER predictions are true. # It seems bad but it's what we've always done. if attr == "ents" and not all(token.ent_iob != 0 for token in gold_doc): continue for span in getter(gold_doc, attr): gold_span = (span.label_, span.start, span.end - 1) gold_spans.add(gold_span) gold_per_type[span.label_].add((span.label_, span.start, span.end - 1)) pred_per_type = {label: set() for label in labels} for span in example.get_aligned_spans_x2y(getter(pred_doc, attr)): pred_spans.add((span.label_, span.start, span.end - 1)) pred_per_type[span.label_].add((span.label_, span.start, span.end - 1)) # Scores per label for k, v in score_per_type.items(): if k in pred_per_type: v.score_set(pred_per_type[k], gold_per_type[k]) # Score for all labels score.score_set(pred_spans, gold_spans) results = { f"{attr}_p": score.precision, f"{attr}_r": score.recall, f"{attr}_f": score.fscore, f"{attr}_per_type": {k: v.to_dict() for k, v in score_per_type.items()}, } return results @staticmethod def score_cats( examples: Iterable[Example], attr: str, *, getter: Callable[[Doc, str], Any] = getattr, labels: Iterable[str] = tuple(), multi_label: bool = True, positive_label: Optional[str] = None, threshold: Optional[float] = None, **cfg, ) -> Dict[str, Any]: """Returns PRF and ROC AUC scores for a doc-level attribute with a dict with scores for each label like Doc.cats. The reported overall score depends on the scorer settings. examples (Iterable[Example]): Examples to score attr (str): The attribute to score. getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided, getter(doc, attr) should return the values for the individual doc. labels (Iterable[str]): The set of possible labels. Defaults to []. multi_label (bool): Whether the attribute allows multiple labels. Defaults to True. positive_label (str): The positive label for a binary task with exclusive classes. Defaults to None. threshold (float): Cutoff to consider a prediction "positive". Defaults to 0.5 for multi-label, and 0.0 (i.e. whatever's highest scoring) otherwise. RETURNS (Dict[str, Any]): A dictionary containing the scores, with inapplicable scores as None: for all: attr_score (one of attr_micro_f / attr_macro_f / attr_macro_auc), attr_score_desc (text description of the overall score), attr_micro_f, attr_macro_f, attr_auc, attr_f_per_type, attr_auc_per_type DOCS: https://spacy.io/api/scorer#score_cats """ if threshold is None: threshold = 0.5 if multi_label else 0.0 f_per_type = {label: PRFScore() for label in labels} auc_per_type = {label: ROCAUCScore() for label in labels} labels = set(labels) if labels: for eg in examples: labels.update(eg.predicted.cats.keys()) labels.update(eg.reference.cats.keys()) for example in examples: # Through this loop, None in the gold_cats indicates missing label. pred_cats = getter(example.predicted, attr) gold_cats = getter(example.reference, attr) # I think the AUC metric is applicable regardless of whether we're # doing multi-label classification? Unsure. If not, move this into # the elif pred_cats and gold_cats block below. for label in labels: pred_score = pred_cats.get(label, 0.0) gold_score = gold_cats.get(label, 0.0) if gold_score is not None: auc_per_type[label].score_set(pred_score, gold_score) if multi_label: for label in labels: pred_score = pred_cats.get(label, 0.0) gold_score = gold_cats.get(label, 0.0) if gold_score is not None: if pred_score >= threshold and gold_score > 0: f_per_type[label].tp += 1 elif pred_score >= threshold and gold_score == 0: f_per_type[label].fp += 1 elif pred_score < threshold and gold_score > 0: f_per_type[label].fn += 1 elif pred_cats and gold_cats: # Get the highest-scoring for each. pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1]) gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1]) if gold_score is not None: if pred_label == gold_label and pred_score >= threshold: f_per_type[pred_label].tp += 1 else: f_per_type[gold_label].fn += 1 if pred_score >= threshold: f_per_type[pred_label].fp += 1 elif gold_cats: gold_label, gold_score = max(gold_cats, key=lambda it: it[1]) if gold_score is not None and gold_score > 0: f_per_type[gold_label].fn += 1 else: pred_label, pred_score = max(pred_cats, key=lambda it: it[1]) if pred_score >= threshold: f_per_type[pred_label].fp += 1 micro_prf = PRFScore() for label_prf in f_per_type.values(): micro_prf.tp = label_prf.tp micro_prf.fn = label_prf.fn micro_prf.fp = label_prf.fp n_cats = len(f_per_type) + 1e-100 macro_p = sum(prf.precision for prf in f_per_type.values()) / n_cats macro_r = sum(prf.recall for prf in f_per_type.values()) / n_cats macro_f = sum(prf.fscore for prf in f_per_type.values()) / n_cats results = { f"{attr}_score": None, f"{attr}_score_desc": None, f"{attr}_micro_p": micro_prf.precision, f"{attr}_micro_r": micro_prf.recall, f"{attr}_micro_f": micro_prf.fscore, f"{attr}_macro_p": macro_p, f"{attr}_macro_r": macro_r, f"{attr}_macro_f": macro_f, f"{attr}_macro_auc": None, f"{attr}_f_per_type": {k: v.to_dict() for k, v in f_per_type.items()}, f"{attr}_auc_per_type": {k: v.score for k, v in auc_per_type.items()}, } if len(labels) == 2 and not multi_label and positive_label: positive_label_f = results[f"{attr}_f_per_type"][positive_label]["f"] results[f"{attr}_score"] = positive_label_f results[f"{attr}_score_desc"] = f"F ({positive_label})" elif not multi_label: results[f"{attr}_score"] = results[f"{attr}_macro_f"] results[f"{attr}_score_desc"] = "macro F" else: results[f"{attr}_score"] = results[f"{attr}_macro_auc"] results[f"{attr}_score_desc"] = "macro AUC" return results @staticmethod def score_deps( examples: Iterable[Example], attr: str, *, getter: Callable[[Token, str], Any] = getattr, head_attr: str = "head", head_getter: Callable[[Token, str], Token] = getattr, ignore_labels: Tuple[str] = tuple(), **cfg, ) -> Dict[str, Any]: """Returns the UAS, LAS, and LAS per type scores for dependency parses. examples (Iterable[Example]): Examples to score attr (str): The attribute containing the dependency label. getter (Callable[[Token, str], Any]): Defaults to getattr. If provided, getter(token, attr) should return the value of the attribute for an individual token. head_attr (str): The attribute containing the head token. Defaults to 'head'. head_getter (Callable[[Token, str], Token]): Defaults to getattr. If provided, head_getter(token, attr) should return the value of the head for an individual token. ignore_labels (Tuple): Labels to ignore while scoring (e.g., punct). RETURNS (Dict[str, Any]): A dictionary containing the scores: attr_uas, attr_las, and attr_las_per_type. DOCS: https://spacy.io/api/scorer#score_deps """ unlabelled = PRFScore() labelled = PRFScore() labelled_per_dep = dict() for example in examples: gold_doc = example.reference pred_doc = example.predicted align = example.alignment gold_deps = set() gold_deps_per_dep = {} for gold_i, token in enumerate(gold_doc): dep = getter(token, attr) head = head_getter(token, head_attr) if dep not in ignore_labels: gold_deps.add((gold_i, head.i, dep)) if dep not in labelled_per_dep: labelled_per_dep[dep] = PRFScore() if dep not in gold_deps_per_dep: gold_deps_per_dep[dep] = set() gold_deps_per_dep[dep].add((gold_i, head.i, dep)) pred_deps = set() pred_deps_per_dep = {} for token in pred_doc: if token.orth_.isspace(): continue if align.x2y.lengths[token.i] != 1: gold_i = None else: gold_i = align.x2y[token.i].dataXd[0, 0] dep = getter(token, attr) head = head_getter(token, head_attr) if dep not in ignore_labels and token.orth_.strip(): if align.x2y.lengths[head.i] == 1: gold_head = align.x2y[head.i].dataXd[0, 0] else: gold_head = None # None is indistinct, so we can't just add it to the set # Multiple (None, None) deps are possible if gold_i is None or gold_head is None: unlabelled.fp += 1 labelled.fp += 1 else: pred_deps.add((gold_i, gold_head, dep)) if dep not in labelled_per_dep: labelled_per_dep[dep] = PRFScore() if dep not in pred_deps_per_dep: pred_deps_per_dep[dep] = set() pred_deps_per_dep[dep].add((gold_i, gold_head, dep)) labelled.score_set(pred_deps, gold_deps) for dep in labelled_per_dep: labelled_per_dep[dep].score_set( pred_deps_per_dep.get(dep, set()), gold_deps_per_dep.get(dep, set()) ) unlabelled.score_set( set(item[:2] for item in pred_deps), set(item[:2] for item in gold_deps) ) return { f"{attr}_uas": unlabelled.fscore, f"{attr}_las": labelled.fscore, f"{attr}_las_per_type": { k: v.to_dict() for k, v in labelled_per_dep.items() }, } ############################################################################# # # The following implementation of roc_auc_score() is adapted from # scikit-learn, which is distributed under the following license: # # New BSD License # # Copyright (c) 2007–2019 The scikit-learn developers. # All rights reserved. # # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # a. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # b. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # c. Neither the name of the Scikit-learn Developers nor the names of # its contributors may be used to endorse or promote products # derived from this software without specific prior written # permission. # # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY # OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH # DAMAGE. def _roc_auc_score(y_true, y_score): """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. Note: this implementation is restricted to the binary classification task Parameters ---------- y_true : array, shape = [n_samples] or [n_samples, n_classes] True binary labels or binary label indicators. The multiclass case expects shape = [n_samples] and labels with values in ``range(n_classes)``. y_score : array, shape = [n_samples] or [n_samples, n_classes] Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by "decision_function" on some classifiers). For binary y_true, y_score is supposed to be the score of the class with greater label. The multiclass case expects shape = [n_samples, n_classes] where the scores correspond to probability estimates. Returns ------- auc : float References ---------- .. [1] `Wikipedia entry for the Receiver operating characteristic `_ .. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874. .. [3] `Analyzing a portion of the ROC curve. McClish, 1989 `_ """ if len(np.unique(y_true)) != 2: raise ValueError(Errors.E165) fpr, tpr, _ = _roc_curve(y_true, y_score) return _auc(fpr, tpr) def _roc_curve(y_true, y_score): """Compute Receiver operating characteristic (ROC) Note: this implementation is restricted to the binary classification task. Parameters ---------- y_true : array, shape = [n_samples] True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given. y_score : array, shape = [n_samples] Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by "decision_function" on some classifiers). Returns ------- fpr : array, shape = [>2] Increasing false positive rates such that element i is the false positive rate of predictions with score >= thresholds[i]. tpr : array, shape = [>2] Increasing true positive rates such that element i is the true positive rate of predictions with score >= thresholds[i]. thresholds : array, shape = [n_thresholds] Decreasing thresholds on the decision function used to compute fpr and tpr. `thresholds[0]` represents no instances being predicted and is arbitrarily set to `max(y_score) + 1`. Notes ----- Since the thresholds are sorted from low to high values, they are reversed upon returning them to ensure they correspond to both ``fpr`` and ``tpr``, which are sorted in reversed order during their calculation. References ---------- .. [1] `Wikipedia entry for the Receiver operating characteristic `_ .. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874. """ fps, tps, thresholds = _binary_clf_curve(y_true, y_score) # Add an extra threshold position # to make sure that the curve starts at (0, 0) tps = np.r_[0, tps] fps = np.r_[0, fps] thresholds = np.r_[thresholds[0] + 1, thresholds] if fps[-1] <= 0: fpr = np.repeat(np.nan, fps.shape) else: fpr = fps / fps[-1] if tps[-1] <= 0: tpr = np.repeat(np.nan, tps.shape) else: tpr = tps / tps[-1] return fpr, tpr, thresholds def _binary_clf_curve(y_true, y_score): """Calculate true and false positives per binary classification threshold. Parameters ---------- y_true : array, shape = [n_samples] True targets of binary classification y_score : array, shape = [n_samples] Estimated probabilities or decision function Returns ------- fps : array, shape = [n_thresholds] A count of false positives, at index i being the number of negative samples assigned a score >= thresholds[i]. The total number of negative samples is equal to fps[-1] (thus true negatives are given by fps[-1] - fps). tps : array, shape = [n_thresholds <= len(np.unique(y_score))] An increasing count of true positives, at index i being the number of positive samples assigned a score >= thresholds[i]. The total number of positive samples is equal to tps[-1] (thus false negatives are given by tps[-1] - tps). thresholds : array, shape = [n_thresholds] Decreasing score values. """ pos_label = 1.0 y_true = np.ravel(y_true) y_score = np.ravel(y_score) # make y_true a boolean vector y_true = y_true == pos_label # sort scores and corresponding truth values desc_score_indices = np.argsort(y_score, kind="mergesort")[::-1] y_score = y_score[desc_score_indices] y_true = y_true[desc_score_indices] weight = 1.0 # y_score typically has many tied values. Here we extract # the indices associated with the distinct values. We also # concatenate a value for the end of the curve. distinct_value_indices = np.where(np.diff(y_score))[0] threshold_idxs = np.r_[distinct_value_indices, y_true.size - 1] # accumulate the true positives with decreasing threshold tps = _stable_cumsum(y_true * weight)[threshold_idxs] fps = 1 + threshold_idxs - tps return fps, tps, y_score[threshold_idxs] def _stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08): """Use high precision for cumsum and check that final value matches sum Parameters ---------- arr : array-like To be cumulatively summed as flat axis : int, optional Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. rtol : float Relative tolerance, see ``np.allclose`` atol : float Absolute tolerance, see ``np.allclose`` """ out = np.cumsum(arr, axis=axis, dtype=np.float64) expected = np.sum(arr, axis=axis, dtype=np.float64) if not np.all( np.isclose( out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True ) ): raise ValueError(Errors.E163) return out def _auc(x, y): """Compute Area Under the Curve (AUC) using the trapezoidal rule This is a general function, given points on a curve. For computing the area under the ROC-curve, see :func:`roc_auc_score`. Parameters ---------- x : array, shape = [n] x coordinates. These must be either monotonic increasing or monotonic decreasing. y : array, shape = [n] y coordinates. Returns ------- auc : float """ x = np.ravel(x) y = np.ravel(y) direction = 1 dx = np.diff(x) if np.any(dx < 0): if np.all(dx <= 0): direction = -1 else: raise ValueError(Errors.E164.format(x)) area = direction * np.trapz(y, x) if isinstance(area, np.memmap): # Reductions such as .sum used internally in np.trapz do not return a # scalar by default for numpy.memmap instances contrary to # regular numpy.ndarray instances. area = area.dtype.type(area) return area