Metadata-Version: 1.0
Name: PYEVALB
Version: 0.1.0
Summary: Scoring tools for bracket tree banks.
Home-page: https://github.com/flyaway1217/PYEVALB
Author: Flyaway
Author-email: flyaway1217@gmail.com
License: UNKNOWN
Description: # PYEVALB
        
        EVEVALB is a python version of [Evalb][] which is used to score the bracket tree banks.
        
        # Examples
        
        ## Score two corpus
        
        ```python
        from PYEVALB import scorer
        
        gold_path = 'gold_corpus.txt'
        test_path = 'test_corpus.txt'
        result_path = 'result.txt'
        
        scorer.evalb(gold_path, test_path, result_path)
        ```
        
        And the result would be:
        ```Markdown
        
         ID | length | state | recall | prec | matched_brackets | gold_brackets | test_brackets | cross_brackets | words | correct_tags | tag_accracy 
        ---:|-------:|------:|-------:|-----:|-----------------:|--------------:|--------------:|---------------:|------:|-------------:|------------:
           0|      44|      0|    0.57|  0.61|                31|             54|             51|              16|     44|            43|         0.98
           1|      13|      0|    0.64|  0.60|                 9|             14|             15|               3|     13|            12|         0.92
           2|      29|      0|    0.97|  0.97|                29|             30|             30|               0|     29|            29|         1.00
           3|      20|      0|    0.80|  0.80|                20|             25|             25|               4|     20|            20|         1.00
           4|      19|      0|    0.91|  1.00|                21|             23|             21|               0|     19|            19|         1.00
           5|      71|      0|    0.67|  0.68|                52|             78|             77|              15|     71|            65|         0.92
           6|      16|      0|    0.61|  0.69|                11|             18|             16|               0|     16|            14|         0.88
           7|      27|      0|    0.92|  0.96|                24|             26|             25|               0|     27|            26|         0.96
           8|      19|      0|    1.00|  1.00|                20|             20|             20|               0|     19|            19|         1.00
           9|      41|      0|    0.80|  0.78|                32|             40|             41|               5|     41|            39|         0.95
        
        =================================================================================================================================================
        Number of sentence:	10.00
        Number of Error sentence:	0.00
        Number of Skip  sentence:	0.00
        Number of Valid sentence:	10.00
        Bracketing Recall:	75.91
        Bracketing Precision:	77.57
        Bracketing FMeasure:	76.73
        Complete match:	10.00
        Average crossing:	4.30
        No crossing:	50.00
        Tagging accuracy:	95.65
        ```
        
        ## Score two trees
        
        ```python
        from PYEVALB import scorer
        from PYEVALB import parser
        
        gold = '(IP (NP (PN 这里)) (VP (ADVP (AD 便)) (VP (VV 产生) (IP (NP (QP (CD 一) (CLP (M 个))) (DNP (NP (JJ 结构性)) (DEG 的)) (NP (NN 盲点))) (PU ：) (IP (VP (VV 臭味相投) (PU ，) (VV 物以类聚)))))) (PU 。))'
        
        test = '(IP (IP (NP (PN 这里)) (VP (ADVP (AD 便)) (VP (VV 产生) (NP (QP (CD 一) (CLP (M 个))) (DNP (ADJP (JJ 结构性)) (DEG 的)) (NP (NN 盲点)))))) (PU ：) (IP (NP (NN 臭味相投)) (PU ，) (VP (VV 物以类聚))) (PU 。))'
        
        gold_tree = parser.create_from_bracket_string(gold)
        test_tree = parser.create_from_bracket_string(test)
        
        result = scorer.score_trees(gold_tree, test_tree)
        
        print('Recall =' + str(result.recall))
        print('Precision =' + str(result.prec))
        ```
        
        And the result is:
        
        ```bash
        Recall = 64.29
        Precision =  56.25
        ```
        
        
        [Evalb]: http://nlp.cs.nyu.edu/evalb/
        
Platform: UNKNOWN
