information retrieval evaluation python precision, recall, f score, AP,MAP
Evaluation has two essentials. First one is a test resource with the ranking of documents or their relevancy tag (relevant or not-relevant) for specific queries, which is made with an experiment (like user click, etc. and is mostly used when you have a running IR system), or made through crowd-sourcing. The second essential part of evaluation is which formula to use for evaluating an IR system with the test collection. 
So based on what you said, if you don't have a labeled test collection you cant evaluate your system.
i wrote one program to do the information retrieval and extraction. user enter the query in the search bar, the program can show the relevant txt result such as the relevant sentence and the article which consists the sentence.
I did some research for how to evaluate the result. I might need to calculate the precision, recall, AP, MAP....
However, I am new to that. How to calculate the result. Since my dataset is not labeled and i did not do the classification. The dataset I used was the article from BBC news. there were 200 articles. i named it as 001.txt, 002.txt ...... 200.txt 
It would be good if u have any ideas how to do the evaluation in python. Thanks.