Create seqeval.py

seqeval.py

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+# Copyright 2020 The HuggingFace Evaluate Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" seqeval metric. """
+
+import importlib
+from typing import List, Optional, Union
+
+import datasets
+from seqeval.metrics import accuracy_score, classification_report
+
+import evaluate
+
+
+_CITATION = """\
+@inproceedings{ramshaw-marcus-1995-text,
+    title = "Text Chunking using Transformation-Based Learning",
+    author = "Ramshaw, Lance  and
+      Marcus, Mitch",
+    booktitle = "Third Workshop on Very Large Corpora",
+    year = "1995",
+    url = "https://www.aclweb.org/anthology/W95-0107",
+}
+@misc{seqeval,
+  title={{seqeval}: A Python framework for sequence labeling evaluation},
+  url={https://github.com/chakki-works/seqeval},
+  note={Software available from https://github.com/chakki-works/seqeval},
+  author={Hiroki Nakayama},
+  year={2018},
+}
+"""
+
+_DESCRIPTION = """\
+seqeval is a Python framework for sequence labeling evaluation.
+seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
+
+This is well-tested by using the Perl script conlleval, which can be used for
+measuring the performance of a system that has processed the CoNLL-2000 shared task data.
+
+seqeval supports following formats:
+IOB1
+IOB2
+IOE1
+IOE2
+IOBES
+
+See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
+"""
+
+_KWARGS_DESCRIPTION = """
+Produces labelling scores along with its sufficient statistics
+from a source against one or more references.
+
+Args:
+    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
+    references: List of List of reference labels (Ground truth (correct) target values)
+    suffix: True if the IOB prefix is after type, False otherwise. default: False
+    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
+        default: None
+    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
+        If you want to only count exact matches, pass mode="strict". default: None.
+    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
+    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
+        "warn". "warn" acts as 0, but the warning is raised.
+
+Returns:
+    'scores': dict. Summary of the scores for mirco, macro, weighed average and per type
+        Micro-averaged
+            'accuracy': accuracy,
+            'precision': precision,
+            'recall': recall,
+            'f1': F1 score, also known as balanced F-score or F-measure,
+        Macro-averaged:
+            'accuracy': accuracy,
+            'precision': precision,
+            'recall': recall,
+            'f1': F1 score, also known as balanced F-score or F-measure,
+        Weighted-averaged:
+            'accuracy': accuracy,
+            'precision': precision,
+            'recall': recall,
+            'f1': F1 score, also known as balanced F-score or F-measure,
+        Per type:
+            'precision': precision,
+            'recall': recall,
+            'f1': F1 score, also known as balanced F-score or F-measure
+Examples:
+
+    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
+    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
+    >>> seqeval = evaluate.load("seqeval")
+    >>> results = seqeval.compute(predictions=predictions, references=references)
+    >>> print(list(results.keys()))
+    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
+    >>> print(results["overall_f1"])
+    0.5
+    >>> print(results["PER"]["f1"])
+    1.0
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class Seqeval(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            homepage="https://github.com/chakki-works/seqeval",
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Sequence(datasets.Value("string", id="label"), id="sequence"),
+                    "references": datasets.Sequence(datasets.Value("string", id="label"), id="sequence"),
+                }
+            ),
+            codebase_urls=["https://github.com/chakki-works/seqeval"],
+            reference_urls=["https://github.com/chakki-works/seqeval"],
+        )
+
+    def _compute(
+        self,
+        predictions,
+        references,
+        suffix: bool = False,
+        scheme: Optional[str] = None,
+        mode: Optional[str] = None,
+        sample_weight: Optional[List[int]] = None,
+        zero_division: Union[str, int] = "warn",
+    ):
+        if scheme is not None:
+            try:
+                scheme_module = importlib.import_module("seqeval.scheme")
+                scheme = getattr(scheme_module, scheme)
+            except AttributeError:
+                raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}")
+        report = classification_report(
+            y_true=references,
+            y_pred=predictions,
+            suffix=suffix,
+            output_dict=True,
+            scheme=scheme,
+            mode=mode,
+            sample_weight=sample_weight,
+            zero_division=zero_division,
+        )
+        macro_score = report.pop("macro avg")
+        weighted_score = report.pop("weighted avg")
+        micro_score = report.pop("micro avg")
+
+        scores = {
+            type_name: {
+                "precision": score["precision"],
+                "recall": score["recall"],
+                "f1": score["f1-score"],
+                "number": score["support"],
+            }
+            for type_name, score in report.items()
+        }
+        scores["micro_precision"] = micro_score["precision"]
+        scores["micro_recall"] = micro_score["recall"]
+        scores["micro_f1"] = micro_score["f1-score"]
+
+        scores["weighted_precision"] = weighted_score["precision"]
+        scores["weighted_recall"] = weighted_score["recall"]
+        scores["weighted_f1"] = weighted_score["f1-score"]
+
+        scores["macro_precision"] = macro_score["precision"]
+        scores["macro_recall"] = macro_score["recall"]
+        scores["macro_f1"] = macro_score["f1-score"]
+
+        scores["overall_accuracy"] = accuracy_score(y_true=references, y_pred=predictions)
+
+        return scores