modified: average_precision.py

README.md

@@ -3,7 +3,7 @@ title: Average Precision
 tags:
 - evaluate
 - metric
-description: "TODO: add a description here"
+description: "Average precision score."
 sdk: gradio
 sdk_version: 3.19.1
 app_file: app.py
@@ -12,19 +12,38 @@ pinned: false
 
 # Metric Card for Average Precision
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
-
-## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
-
 ## How to Use
-*Give general statement of how to use the metric*
+```python
+import evaluate
 
-*Provide simplest possible example for using the metric*
+metric = evaluate.load("chanelcolgate/average_precision")
+results = metric.compute(references=references, prediction_scores=prediction_scores)
+```
 
 ### Inputs
-*List all input arguments in the format below*
 - **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+- **y_true** *(`ndarray` of shape (n_samples,) or (n_samples, n_classes)): True binary labels or binary label indicators.
+- **y_score** *(`ndarray` of 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 :term:`decision_function` on some classifiers).
+- **average**: {'micro', 'samples', 'weighted', 'macro'} or None, default='macro`
+
+    If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
+
+    ``'micro'``:
+        Calculate metrics globally by considering each element of the label indicator matrix as a label.
+
+    ``'macro'``:
+        Calculate metrics for each label, and find their unweighted mean This does not take label imbalance into account.
+        
+    ``'weighted'``:
+        Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).
+
+    ``'samples'``:
+        Calculate metrics for each label, and find their average
+    Will be ignored when ``y_true`` is binary.
+- **pos_label** *(`int` or `str`, default=1): The label of the positive class. Only applied to binary ``y_true``. For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.
+- **sample_weight** *(`array-like` of shape (n_samples,), default=None): Sample weights.
+    
 
 ### Output Values
 

average_precision.py

@@ -56,10 +56,33 @@ Note: this implementation is restricted to the binary classification task or
 multilabel classification task.
 Read more in the :ref:`User Guide <precision_recall_f_measure_metrics`.
 Args:
-    predictions: list of predictions to score. Each predictions
-        should be a string with tokens separated by spaces.
-    references: list of reference for each prediction. Each
-        reference should be a string with tokens separated by spaces.
+    y_true: ndarray of shape (n_samples,) or (n_samples, n_classes)
+        True binary labels or binary label indicators.
+    y_score: ndarray of 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 :term:`decision_function` on some classifiers).
+    average: {'micro', 'samples', 'weighted', 'macro'} or None, \
+            default='macro'
+        If ``None``, the scores for each class are retruned. Otherwise,
+        this determines the type of averaging performed on the data:
+        ``'micro'``:
+            Calculate metrics globally be considering each element of the label
+            indicator matrix as a label.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean. This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average, weighted
+            by support (the number of true instances for each label).
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average.
+        Will be ignored when ``y_true`` is binary.
+    pos_label: int or str, default=1
+        The label of the positive class. Only applied to binary ``y_true``.
+        For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.
+    sample_weight: array_like of shape (n_samples,), default=None
+        Sample weights.
 Returns:
     accuracy: description of the first score,
     another_score: description of the second score,