Updating Readme.md

README.md

@@ -14,37 +14,65 @@ pinned: false
 
 # Metric Card for Path_Planning_evaluate
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
+This metric is used to evaluate path planning tasks where an LM as to generate a valid path going from a starting point to one or multiple end points in a grid and by avoiding all the obstacles.
 
 ## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
+
+This metric is used to evaluate path planning tasks where an LM as to generate a valid path going from a starting point to one or multiple end points in a grid and by avoiding all the obstacles.
+
 
 ## How to Use
-*Give general statement of how to use the metric*
 
-*Provide simplest possible example for using the metric*
+This metric takes 5 mandatory arguments : `generations` (a list of string), `golds` (a list of list of integers corresponding to the gold paths in a list format), `obstacles` (a list of list of integers corresponding to the coordinates of the obstacles for each question), `ends` (a list of list of integers corresponding to the coordinates of the ending points for each question) and `n` (a list of integers corresponding to the size pf the grid).
+
+```python
+import evaluate
+pp_eval = evaluate.load("rfr2003/path_planning_evaluate")
+results = pp_eval.compute(
+    generations=['[(0,0), (0,1), (1,1)]', '[(0,0), (1,0), (1,1)]', '[(0,0), (1,0), (1,1), (0,1)]', '(0,0'], 
+    golds=[[(0,0), (0,1), (1,1)], [(0,0), (0,1), (1,1)], [(0,0), (0,1)], []], 
+    obstacles=[[(1,0)], [(1,0)], [], []], 
+    ends=[[(1,1)], [(1,1)], [(0,1)], [(0,1)]], 
+    n=[2, 2, 2, 2]
+)
+print(results)
+{'compliance_ratio': 0.75, 'success_ratio': 0.6666666666666666,'optimal_ratio': 0.3333333333333333, 'feasible_ratio': 0.6666666666666666, 'distance': 0, 'unreachable_acc': 1.0}
+```
 
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+This metric doesn't take any optionnal arguments.
 
 ### Output Values
 
-*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
+This metric outputs a dictionary with the following values:
+
+`compliance_ratio`: The ratio of `generations` that complied to a list format across all questions, which ranges from 0.0 to 1.0. 
+`feasible_ratio`: The ratio of `generations` that are feasable among all reachable questions, which ranges from 0.0 to 1.0. 
+`sucess_ratio`: The ratio of `generations` that are correct among all reachable questions, which ranges from 0.0 to 1.0. 
+`optimal_ratio`: The ratio of `generations` that are optimal among all reachable questions, which ranges from 0.0 to 1.0. 
+`distance`: The mean distance to the end point for feasable paths that were not correct, it's a positive real. 
+`unreachable_acc`: The ratio of detected unreachable paths among all unreachable paths, which ranges from 0.0 to 1.0.
 
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
 
 #### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
 
 ### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
+
+```python
+import evaluate
+pp_eval = evaluate.load("rfr2003/path_planning_evaluate")
+results = pp_eval.compute(
+    generations=['[(0,0), (0,1), (1,1)]', '[(0,0), (1,0), (1,1)]', '[(0,0), (1,0), (1,1), (0,1)]', '(0,0'], 
+    golds=[[(0,0), (0,1), (1,1)], [(0,0), (0,1), (1,1)], [(0,0), (0,1)], []], 
+    obstacles=[[(1,0)], [(1,0)], [], []], 
+    ends=[[(1,1)], [(1,1)], [(0,1)], [(0,1)]], 
+    n=[2, 2, 2, 2]
+)
+print(results)
+{'compliance_ratio': 0.75, 'success_ratio': 0.6666666666666666,'optimal_ratio': 0.3333333333333333, 'feasible_ratio': 0.6666666666666666, 'distance': 0, 'unreachable_acc': 1.0}
+```
 
 ## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
 
 ## Citation
-*Cite the source where this metric was introduced.*
 
 ## Further References
-*Add any useful further references.*