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nl2bash_metric.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""Exact Match metric."""
+import re
+import string
+
+import numpy as np
+
+import datasets
+
+
+_DESCRIPTION = """
+Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
+"""
+
+_KWARGS_DESCRIPTION = """
+Args:
+    predictions: List of predicted texts.
+    references: List of reference texts.
+    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
+        ignore when calculating the exact matches. Note: these regexes are removed
+        from the input data before the changes based on the options below (e.g. ignore_case,
+        ignore_punctuation, ignore_numbers) are applied.
+    ignore_case: Boolean, defaults to False. If true, turns everything
+        to lowercase so that capitalization differences are ignored.
+    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
+        comparing predictions and references.
+    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
+        comparing predictions and references.
+Returns:
+    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
+Examples:
+    >>> exact_match = datasets.load_metric("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds)
+    >>> print(round(results["exact_match"], 1))
+    25.0
+    >>> exact_match = datasets.load_metric("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
+    >>> print(round(results["exact_match"], 1))
+    50.0
+    >>> exact_match = datasets.load_metric("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
+    >>> print(round(results["exact_match"], 1))
+    75.0
+    >>> exact_match = datasets.load_metric("exact_match")
+    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
+    >>> preds = ["cat?", "theater", "yelling", "agent"]
+    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
+    >>> print(round(results["exact_match"], 1))
+    100.0
+    >>> exact_match = datasets.load_metric("exact_match")
+    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It's like comparing oranges and apples."]
+    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It's like comparing apples and oranges."]
+    >>> results = exact_match.compute(references=refs, predictions=preds)
+    >>> print(round(results["exact_match"], 1))
+    33.3
+"""
+
+_CITATION = """
+"""
+
+
+@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class ExactMatch(datasets.Metric):
+    def _info(self):
+        return datasets.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Value("string", id="sequence"),
+                    "references": datasets.Value("string", id="sequence"),
+                }
+            ),
+            reference_urls=[],
+        )
+
+    def _compute(
+        self,
+        predictions,
+        references,
+        regexes_to_ignore=None,
+        ignore_case=False,
+        ignore_punctuation=False,
+        ignore_numbers=False,
+    ):
+
+        if regexes_to_ignore is not None:
+            for s in regexes_to_ignore:
+                predictions = np.array([re.sub(s, "", x) for x in predictions])
+                references = np.array([re.sub(s, "", x) for x in references])
+        else:
+            predictions = np.asarray(predictions)
+            references = np.asarray(references)
+
+        if ignore_case:
+            predictions = np.char.lower(predictions)
+            references = np.char.lower(references)
+
+        if ignore_punctuation:
+            repl_table = string.punctuation.maketrans("", "", string.punctuation)
+            predictions = np.char.translate(predictions, table=repl_table)
+            references = np.char.translate(references, table=repl_table)
+
+        if ignore_numbers:
+            repl_table = string.digits.maketrans("", "", string.digits)
+            predictions = np.char.translate(predictions, table=repl_table)
+            references = np.char.translate(references, table=repl_table)
+
+        score_list = predictions == references
+
+        return {"exact_match": np.mean(score_list) * 100}