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README.md

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+---
+title: accuracy_score
+emoji: 🤗 
+colorFrom: blue
+colorTo: orange
+tags:
+- evaluate
+- metric
+- sklearn
+description: >-
+  "Accuracy classification score."
+sdk: gradio
+sdk_version: 3.12.0
+app_file: app.py
+pinned: false
+---
+
+This metric is part of the Scikit-learn integration into 🤗 Evaluate. You can find all available metrics in the [Scikit-learn organization](https://huggingface.co/scikit-learn) on the Hugging Face Hub.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/1280px-scikit-learn-logo.png" width="400"/>
+</p>
+
+# Metric Card for `sklearn.metrics.accuracy_score`
+
+
+
+
+## Input Convention
+
+To be consistent with the `evaluate` input conventions the scikit-learn inputs are renamed:
+
+- `y_true`: `references`
+- `y_pred`: `predictions`
+
+
+## Usage
+
+```python
+import evaluate
+
+metric = evaluate.load("sklearn/accuracy_score")
+results = metric.compute(references=references, predictions=predictions)
+```
+
+## Description
+
+Accuracy classification score.
+
+    In multilabel classification, this function computes subset accuracy:
+    the set of labels predicted for a sample must *exactly* match the
+    corresponding set of labels in y_true.
+
+    Read more in the :ref:`User Guide <accuracy_score>`.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) labels.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Predicted labels, as returned by a classifier.
+
+    normalize : bool, default=True
+        If ``False``, return the number of correctly classified samples.
+        Otherwise, return the fraction of correctly classified samples.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    score : float
+        If ``normalize == True``, return the fraction of correctly
+        classified samples (float), else returns the number of correctly
+        classified samples (int).
+
+        The best performance is 1 with ``normalize == True`` and the number
+        of samples with ``normalize == False``.
+
+    See Also
+    --------
+    balanced_accuracy_score : Compute the balanced accuracy to deal with
+        imbalanced datasets.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    hamming_loss : Compute the average Hamming loss or Hamming distance between
+        two sets of samples.
+    zero_one_loss : Compute the Zero-one classification loss. By default, the
+        function will return the percentage of imperfectly predicted subsets.
+
+    Notes
+    -----
+    In binary classification, this function is equal to the `jaccard_score`
+    function.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import accuracy_score
+    >>> y_pred = [0, 2, 1, 3]
+    >>> y_true = [0, 1, 2, 3]
+    >>> accuracy_score(y_true, y_pred)
+    0.5
+    >>> accuracy_score(y_true, y_pred, normalize=False)
+    2
+
+    In the multilabel case with binary label indicators:
+
+    >>> import numpy as np
+    >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
+    0.5
+    
+
+## Citation
+```bibtex
+@article{scikit-learn,
+ title={Scikit-learn: Machine Learning in {P}ython},
+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+ journal={Journal of Machine Learning Research},
+ volume={12},
+ pages={2825--2830},
+ year={2011}
+}
+```
+## Further References
+- Docs: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html

accuracy_score.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.
+"""TODO: Add a description here."""
+
+import datasets
+from sklearn.metrics import accuracy_score
+
+import evaluate
+
+
+_CITATION = """\
+@article{scikit-learn,
+ title={Scikit-learn: Machine Learning in {P}ython},
+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+ journal={Journal of Machine Learning Research},
+ volume={12},
+ pages={2825--2830},
+ year={2011}
+}
+"""
+
+_DESCRIPTION = """\
+Accuracy classification score.
+"""
+
+
+_KWARGS_DESCRIPTION = """
+    Note: To be consistent with the `evaluate` input conventions the scikit-learn inputs are renamed:
+    - `y_true`: `references`
+    - `y_pred`: `predictions`
+    
+    Scikit-learn docstring:
+    Accuracy classification score.
+
+    In multilabel classification, this function computes subset accuracy:
+    the set of labels predicted for a sample must *exactly* match the
+    corresponding set of labels in y_true.
+
+    Read more in the :ref:`User Guide <accuracy_score>`.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) labels.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Predicted labels, as returned by a classifier.
+
+    normalize : bool, default=True
+        If ``False``, return the number of correctly classified samples.
+        Otherwise, return the fraction of correctly classified samples.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    score : float
+        If ``normalize == True``, return the fraction of correctly
+        classified samples (float), else returns the number of correctly
+        classified samples (int).
+
+        The best performance is 1 with ``normalize == True`` and the number
+        of samples with ``normalize == False``.
+
+    See Also
+    --------
+    balanced_accuracy_score : Compute the balanced accuracy to deal with
+        imbalanced datasets.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    hamming_loss : Compute the average Hamming loss or Hamming distance between
+        two sets of samples.
+    zero_one_loss : Compute the Zero-one classification loss. By default, the
+        function will return the percentage of imperfectly predicted subsets.
+
+    Notes
+    -----
+    In binary classification, this function is equal to the `jaccard_score`
+    function.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import accuracy_score
+    >>> y_pred = [0, 2, 1, 3]
+    >>> y_true = [0, 1, 2, 3]
+    >>> accuracy_score(y_true, y_pred)
+    0.5
+    >>> accuracy_score(y_true, y_pred, normalize=False)
+    2
+
+    In the multilabel case with binary label indicators:
+
+    >>> import numpy as np
+    >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
+    0.5
+    
+
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class AccuracyScore(evaluate.Metric):
+    """Accuracy classification score."""
+
+    def _info(self):
+        return evaluate.MetricInfo(
+            # This is the description that will appear on the modules page.
+            module_type="metric",
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            # This defines the format of each prediction and reference
+            features=[
+                datasets.Features(
+                    {
+                        "predictions": datasets.Sequence(datasets.Value("int32")),
+                        "references": datasets.Sequence(datasets.Value("int32")),
+                    }
+                ),
+                datasets.Features(
+                    {"predictions": datasets.Sequence(datasets.Value("int32")), "references": datasets.Value("int32")}
+                ),
+                datasets.Features(
+                    {"predictions": datasets.Value("int32"), "references": datasets.Sequence(datasets.Value("int32"))}
+                ),
+                datasets.Features({"predictions": datasets.Value("int32"), "references": datasets.Value("int32")}),
+            ],
+            # Homepage of the module for documentation
+            homepage="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html",
+            # Additional links to the codebase or references
+            codebase_urls=["https://github.com/scikit-learn/scikit-learn"],
+            reference_urls=["https://scikit-learn.org/stable/index.html"],
+        )
+
+    def _compute(self, predictions, references, normalize=True, sample_weight=None):
+        """Returns the scores"""
+
+        score = accuracy_score(y_true=references, y_pred=predictions, normalize=normalize, sample_weight=sample_weight)
+
+        return {"accuracy_score": score}

app.py

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+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("sklearn/accuracy_score")
+launch_gradio_widget(module)

requirements.txt

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+evaluate
+sklearn