Updated README

README.md

@@ -1,13 +1,114 @@
 ---
-title: NDCG
+title: nDCG
 emoji: 👁
-colorFrom: purple
+colorFrom: orange
 colorTo: red
 sdk: gradio
 sdk_version: 3.9.1
 app_file: app.py
 pinned: false
 license: mit
+tags:
+- evaluate
+- metric
+- 
+description: >- 
+The Discounted Cumulative Gain is a measure of ranking quality. 
+It is used to evaluate Information Retrieval Systems under the following 2 assumptions:
+1. Highly relevant documents/Labels are more useful when appearing earlier in the results
+2. Documents/Labels are relevant to different degrees  
+
+It is defined as the Sum over all relevances of the retrieved documents reduced logarithmically proportional to 
+the position in which they were retrieved.
+The Normalized DCG (nDCG) divides the resulting value by the optimal value, that can be achieved, to get a value between 
+0 and 1 s.t. a perfect retrieval achieves a nDCG of 1.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Metric Card for nDCG
+
+## Metric Description
+The Discounted Cumulative Gain is a measure of ranking quality. 
+It is used to evaluate Information Retrieval Systems under the 2 assumptions:
+1. Highly relevant documents/Labels are more useful when appearing earlier in the results
+2. Documents/Labels are relevant to different degrees  
+
+It is defined as the sum over all relevances of the retrieved documents reduced logarithmically proportional to 
+the position in which they were retrieved.
+The Normalized DCG (nDCG) divides the resulting value by the optimal value that can be achieved to get a value between 
+0 and 1 s.t. a perfect retrieval achieves a nDCG of 1.0
+
+## How to Use
+
+At minimum, this metric takes as input two `list`s of `list`s, each containing `float`s: predictions and references.
+
+```python
+import evaluate
+nDCG_metric = evaluate.load('JP-SystemsX/nDCG')
+results = nDCG_metric.compute(references=[[0, 1]], predictions=[[0, 1]])
+print(results)
+["{'nDCG@2': 1.0}"]
+```
+
+### Inputs:
+**references** ('list' of 'float'): True relevance 
+
+**predictions** ('list' of 'float'): Either predicted relevance, probability estimates or confidence values 
+
+**k** (int): If set to a value only the k highest scores in the ranking will be considered, else considers all outputs.
+        Defaults to None.
+
+**sample_weight** (`list` of `float`): Sample weights Defaults to None.
+
+**ignore_ties** ('boolean'): If set to true, assumes that there are no ties (this is likely if predictions are continuous)
+        for efficiency gains. Defaults to False.
+
+### Output:
+**normalized_discounted_cumulative_gain** ('float'): The averaged nDCG scores for all samples. 
+        Minimum possible value is 0.0 Maximum possible value is 1.0
+
+Output Example(s):
+```python
+{'nDCG@5': 1.0}
+```
+This metric outputs a dictionary, containing the nDCG score   
+
+     
+### Examples:
+    Example 1-A simple example
+        >>> nDCG_metric = evaluate.load("JP-SystemsX/nDCG")
+        >>> results = nDCG_metric.compute(references=[[10, 0, 0, 1, 5]], predictions=[[.1, .2, .3, 4, 70]])
+        >>> print(results)
+        {'nDCG': 0.6956940443813076}
+    Example 2-The same as Example 1, except with k set to 3.
+        >>> nDCG_metric = evaluate.load("JP-SystemsX/nDCG")
+        >>> results = nDCG_metric.compute(references=[[10, 0, 0, 1, 5]], predictions=[[.1, .2, .3, 4, 70]], k=3)
+        >>> print(results)
+        {'nDCG@3': 0.4123818817534531}
+    Example 3-There is only one relevant label, but there is a tie and the model can't decide which one is the one.
+        >>> accuracy_metric = evaluate.load("accuracy")
+        >>> results = nDCG_metric.compute(references=[[1, 0, 0, 0, 0]], predictions=[[1, 1, 0, 0, 0]], k=1)
+        >>> print(results)
+        {'nDCG@1': 0.5}
+        >>> #That is it calculates both and returns the average of both
+    Example 4-The Same as 3, except ignore_ties is set to True.
+        >>> accuracy_metric = evaluate.load("accuracy")
+        >>> results = nDCG_metric.compute(references=[[1, 0, 0, 0, 0]], predictions=[[1, 1, 0, 0, 0]], k=1, ignore_ties=True)
+        >>> print(results)
+        {'nDCG@1': 0.0}
+        >>> # Alternative Result: {'nDCG@1': 1.0}
+        >>> # That is it chooses one of the 2 candidates and calculates the score only for this one
+        >>> # That means the score may vary depending on which one was chosen
+
+## 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}
+}

nDCG.py

@@ -46,7 +46,7 @@ Args:
 
     sample_weight (`list` of `float`): Sample weights Defaults to None.
 
-    ignore_ties ('boolean'): If set to true asumes that there are no ties (this is likely if predictions are continuous)
+    ignore_ties ('boolean'): If set to true assumes that there are no ties (this is likely if predictions are continuous)
         for efficiency gains. Defaults to False.
 
 Returns: