Add confusion matrix

CONTRIBUTING.md

@@ -145,7 +145,7 @@ unsure, it is always a good idea to open an issue to get some feedback.
    that can't be automated in one go with:
 
    ```bash
-   $ make fixup
+   $ make quality
    ```
 
    This target is also optimized to only work with files modified by the PR you're working on.

metrics/accuracy/README.md

@@ -54,7 +54,7 @@ At minimum, this metric requires predictions and references as inputs.
 
 
 ### Output Values
-- **accuracy**(`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
+- **accuracy**(`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`. A higher score means higher accuracy.
 
 Output Example(s):
 ```python

metrics/confusion_matrix/README.md

@@ -0,0 +1,101 @@
+---
+title: Confusion Matrix
+emoji: 🤗 
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+tags:
+- evaluate
+- metric
+description: >-
+  The confusion matrix evaluates classification accuracy. 
+
+  Each row in a confusion matrix represents a true class and each column represents the instances in a predicted class.
+---
+
+# Metric Card for Confusion Matrix
+
+
+## Metric Description
+
+The confusion matrix evaluates classification accuracy. Each row in a confusion matrix represents a true class and each column represents the instances in a predicted class. Let's look at an example:
+
+|            | setosa | versicolor | virginica |
+| ---------- | ------ | ---------- | --------- |
+| setosa     | 13     | 0          | 0         |
+| versicolor | 0      | 10         | 6         |
+| virginica  | 0      | 0          | 9         |
+
+What information does this confusion matrix provide?
+
+* All setosa instances were properly predicted as such (true positives).
+* The model always correctly classifies the setosa class (there are no false positives).
+* 10 versicolor instances were properly classified, but 6 instances were misclassified as virginica.
+* All virginica insances were properly classified as such.
+
+
+## How to Use
+
+At minimum, this metric requires predictions and references as inputs.
+
+```python
+>>> confusion_metric = evaluate.load("confusion_matrix")
+>>> results = confusion_metric.compute(references=[0, 1, 1, 2, 0, 2, 2], predictions=[0, 2, 1, 1, 0, 2, 0])
+>>> print(results)
+{'confusion_matrix': [[2, 0, 0], [0, 1, 1], [1, 1, 1]]}
+```
+
+
+### Inputs
+- **predictions** (`list` of `int`): Predicted labels.
+- **references** (`list` of `int`): Ground truth labels.
+- **labels** (`list` of `int`): List of labels to index the matrix. This may be used to reorder or select a subset of labels.
+- **sample_weight** (`list` of `float`): Sample weights.
+- **normalize** (`str`): Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. 
+
+
+### Output Values
+- **confusion_matrix**(`list` of `list` of `str`): Confusion matrix. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.
+
+Output Example(s):
+```python
+{'confusion_matrix': [[2, 0, 0], [0, 1, 1], [1, 1, 1]]}
+```
+
+This metric outputs a dictionary, containing the confusion matrix.
+
+
+### Examples
+
+Example 1 - A simple example
+
+```python
+>>> confusion_metric = evaluate.load("confusion_matrix")
+>>> results = confusion_metric.compute(references=[0, 1, 1, 2, 0, 2, 2], predictions=[0, 2, 1, 1, 0, 2, 0])
+>>> print(results)
+{'confusion_matrix': [[2, 0, 0], [0, 1, 1], [1, 1, 1]]}
+```
+
+## Citation(s)
+```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
+
+* https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
+* https://en.wikipedia.org/wiki/Confusion_matrix

metrics/confusion_matrix/app.py

@@ -0,0 +1,6 @@
+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("confusion_matrix")
+launch_gradio_widget(module)

metrics/confusion_matrix/confusion_matrix.py

@@ -0,0 +1,88 @@
+# 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.
+"""Confusion Matrix."""
+
+import datasets
+from sklearn.metrics import confusion_matrix
+
+import evaluate
+
+
+_DESCRIPTION = """
+The confusion matrix evaluates classification accuracy. Each row in a confusion matrix represents a true class and each column represents the instances in a predicted class
+"""
+
+_KWARGS_DESCRIPTION = """
+Args:
+    predictions (`list` of `int`): Predicted labels.
+    references (`list` of `int`): Ground truth labels.
+    labels (`list` of `int`): List of labels to index the matrix. This may be used to reorder or select a subset of labels.
+    sample_weight (`list` of `float`): Sample weights.
+    normalize (`str`): Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population.
+
+Returns:
+    confusion_matrix (`list` of `list` of `int`): Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and predicted label being j-th class.
+
+Examples:
+
+    Example 1-A simple example
+        >>> confusion_matrix_metric = evaluate.load("confusion_matrix")
+        >>> results = confusion_matrix_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
+        >>> print(results)
+        {'confusion_matrix': [[1, 0, 1], [0, 2, 0], [1, 0, 1]]}
+"""
+
+
+_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}
+}
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class ConfusionMatrix(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Sequence(datasets.Value("int32")),
+                    "references": datasets.Sequence(datasets.Value("int32")),
+                }
+                if self.config_name == "multilabel"
+                else {
+                    "predictions": datasets.Value("int32"),
+                    "references": datasets.Value("int32"),
+                }
+            ),
+            reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html"],
+        )
+
+    def _compute(self, predictions, references, labels=None, sample_weight=None, normalize=None):
+        return {
+            "confusion_matrix": confusion_matrix(
+                references, predictions, labels=labels, sample_weight=sample_weight, normalize=normalize
+            )
+        }

metrics/confusion_matrix/requirements.txt

@@ -0,0 +1,2 @@
+git+https://github.com/huggingface/evaluate@{COMMIT_PLACEHOLDER}
+scikit-learn

src/evaluate/evaluator/base.py

@@ -270,7 +270,7 @@ def compute(
         # TODO: To clarify why `wer` and `cer` return float
         # even though metric.compute contract says that it
         # returns Optional[dict].
-        if type(metric_results) == float:
+        if type(metric_results) is float:
             metric_results = {metric.name: metric_results}
 
         result.update(metric_results)

src/evaluate/module.py

@@ -66,7 +66,7 @@ def _release(self):
 # lists - summarize long lists similarly to NumPy
 # arrays/tensors - let the frameworks control formatting
 def summarize_if_long_list(obj):
-    if not type(obj) == list or len(obj) <= 6:
+    if type(obj) is not list or len(obj) <= 6:
         return f"{obj}"
 
     def format_chunk(chunk):