systems/evaluate.py

from lib.CoNLL_Annotation import *
from collections import Counter, defaultdict
import pandas as pd
import numpy as np
from sklearn.metrics import precision_recall_fscore_support as eval_f1
from tabulate import tabulate
import logging, argparse, sys
from datetime import datetime


tree_tagger_fixes = {
    "die": "der", 
    "eine": "ein", 
    "dass": "daß", 
    "keine": "kein",
    "dies": "dieser",
    "erst": "erster",
    "andere": "anderer",
    "alle": "aller",
    "Sie": "sie",
    "wir": "uns", 
    "alle": "aller",
    "wenige": "wenig"
}


def save_evaluated(all_sys, all_gld, out_path, print_gold=True):
    with open(out_path, "w") as out:
        if print_gold:
            out.write(f"ORIGINAL_CORPUS_TAGS\n\nTAG\tGLD_COUNT\tSYS_COUNT\n")
            for g_tag,g_count in sorted(all_gld.items()):
                s_count = all_sys.get(g_tag, 0)
                out.write(f"{g_tag}\t{g_count}\t{s_count}\n")
        
        out.write("\n\nSYSTEM_ONLY_TAGS\n\nTAG\tG_COUNT\tSYS_COUNT\n")
        for s_tag,s_count in sorted(all_sys.items()):
            g_count = all_gld.get(s_tag, 0)
            if g_count == 0:
                out.write(f"{s_tag}\t{g_count}\t{s_count}\n")
    


def eval_lemma(sys, gld):
    match, err, symbol = 0, 0, []
    y_gld, y_pred, mistakes = [], [], []
    for i, gld_tok in enumerate(gld.tokens):
        # sys_lemma = tree_tagger_fixes.get(sys.tokens[i].lemma, sys.tokens[i].lemma)  # Omit TreeTagger "errors" because of article lemma disagreement 
        sys_lemma = sys.tokens[i].lemma
        y_gld.append(gld_tok.pos_tag)
        y_pred.append(sys_lemma)
        if gld_tok.lemma == sys_lemma:
            match += 1
        elif not sys.tokens[i].lemma.isalnum(): # Turku does not lemmatize symbols (it only copies them) => ERR ((',', '--', ','), 43642)
            symbol.append(sys.tokens[i].lemma)
            if sys.tokens[i].word == sys.tokens[i].lemma:
                match += 1
            else:
                err += 1
        else:
            err += 1
            mistakes.append((gld_tok.word, gld_tok.lemma, sys.tokens[i].lemma))
    return y_gld, y_pred, match, err, symbol, mistakes
    

def eval_pos(sys, gld):
    match, mistakes = 0, []
    y_gld, y_pred = [], []
    for i, gld_tok in enumerate(gld.tokens):
        y_gld.append(gld_tok.pos_tag)
        y_pred.append(sys.tokens[i].pos_tag)
        # pos_all_pred[gld_tok.pos_tag] += 1
        # pos_all_gold[sys.tokens[i].pos_tag] += 1
        if gld_tok.pos_tag == sys.tokens[i].pos_tag:
            match += 1
        elif gld_tok.pos_tag == "$." and sys.tokens[i].pos_tag == "$":
            match += 1
            y_pred = y_pred[:-1] + ["$."]
        else:
            mistakes.append((gld_tok.word, gld_tok.pos_tag, sys.tokens[i].pos_tag))
    return y_gld, y_pred, match, mistakes



if __name__ == "__main__":
    """
        EVALUATIONS: 
        
        ********** TIGER CORPUS ALL ************
        
            python systems/evaluate.py -t Turku --corpus_name Tiger\
                --sys_file /home/daza/datasets/TIGER_conll/tiger_turku_parsed.conllu \
                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
                
            python systems/evaluate.py -t SpaCy --corpus_name Tiger\
                --sys_file /home/daza/datasets/TIGER_conll/tiger_spacy_parsed.conllu \
                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
                
            python systems/evaluate.py -t RNNTagger --corpus_name Tiger\
                --sys_file /home/daza/datasets/TIGER_conll/tiger_all.parsed.RNNTagger.conll \
                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
            
            python systems/evaluate.py -t TreeTagger --corpus_name Tiger\
                --sys_file /home/daza/datasets/TIGER_conll/tiger_all.parsed.TreeTagger.conll \
                --gld_file /home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09
            
        ********** UNIVERSAL DEPENDENCIES TEST-SET ************

            python systems/evaluate.py -t Turku --gld_token_type CoNLLUP_Token --corpus_name DE_GSD\
            --sys_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.conllu.parsed.0.conllu \
            --gld_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.conllu    
            
            python systems/evaluate.py -t SpaCyGL --gld_token_type CoNLLUP_Token --corpus_name DE_GSD\
                --sys_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.parsed.germalemma.conllu \
                --gld_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.conllu    
            
            python systems/evaluate.py -t SpaCy --gld_token_type CoNLLUP_Token --corpus_name DE_GSD\
                --sys_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.parsed.conllu \
                --gld_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.conllu 
                
            python systems/evaluate.py -t RNNTagger --gld_token_type CoNLLUP_Token --corpus_name DE_GSD\
                --sys_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.RNNtagger.parsed.conll \
                --gld_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.conllu
            
            python systems/evaluate.py -t TreeTagger --gld_token_type CoNLLUP_Token --corpus_name DE_GSD\
                --sys_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.treetagger.parsed.conll \
                --gld_file /home/daza/datasets/ud-treebanks-v2.2/UD_German-GSD/de_gsd-ud-test.conllu
                
    """
    
    # =====================================================================================
    #                    INPUT PARAMS 
    # =====================================================================================
    parser = argparse.ArgumentParser()
    parser.add_argument("-s", "--sys_file", help="System output in CoNLL-U Format", required=True)
    parser.add_argument("-g", "--gld_file", help="Gold Labels to evaluate in CoNLL-U Format", required=True)
    parser.add_argument("-t", "--type_sys", help="Which system produced the outputs", default="system")
    parser.add_argument("-c", "--corpus_name", help="Corpus Name for Gold Labels", required=True)
    parser.add_argument("-gtt", "--gld_token_type", help="CoNLL Format of the Gold Data", default="CoNLL09_Token")
    parser.add_argument("-cs", "--comment_str", help="CoNLL Format of comentaries inside the file", default="#")
    args = parser.parse_args()
    
    # =====================================================================================
    #                    LOGGING INFO ...
    # =====================================================================================
    logger = logging.getLogger(__name__)
    console_hdlr = logging.StreamHandler(sys.stdout)
    file_hdlr = logging.FileHandler(filename=f"logs/Eval_{args.corpus_name}.{args.type_sys}.log")
    logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
    now_is = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    logger.info(f"\n\nEvaluating {args.corpus_name} Corpus {now_is}")

    # Read the Original GOLD Annotations [CoNLL09, CoNLLUP]
    gld_generator = read_conll_generator(args.gld_file, token_class=get_token_type(args.gld_token_type), comment_str=args.comment_str)
    # Read the Annotations Generated by the Automatic Parser [Turku, SpaCy, RNNTagger] 
    if args.type_sys == "RNNTagger":
        sys_generator = read_conll_generator(args.sys_file, token_class=RNNTagger_Token, comment_str="#")
    elif args.type_sys == "TreeTagger":
        sys_generator = read_conll_generator(args.sys_file, token_class=RNNTagger_Token, sent_sep="</S>", comment_str="#")
    else:
        sys_generator = read_conll_generator(args.sys_file, token_class=CoNLLUP_Token, comment_str="#")
    
    lemma_all_match, lemma_all_err, lemma_all_mistakes = 0, 0, []
    lemma_all_symbols, sys_only_lemmas = [], []
    pos_all_match, pos_all_err, pos_all_mistakes = 0, 0, []
    pos_all_pred, pos_all_gld = [], []
    lemma_all_pred, lemma_all_gld = [], []
    n_sents = 0
    
    for i, (s,g) in enumerate(zip(sys_generator, gld_generator)):
        # print([x.word for x in s.tokens])
        # print([x.word for x in g.tokens])
        assert len(s.tokens) == len(g.tokens), f"Token Mismatch! S={len(s.tokens)} G={len(g.tokens)} IX={i+1}"
        n_sents += 1
        # Lemmas ...
        lemma_gld, lemma_pred, lemma_match, lemma_err, lemma_sym, mistakes = eval_lemma(s,g)
        lemma_all_match += lemma_match
        lemma_all_err += lemma_err
        lemma_all_mistakes += mistakes
        lemma_all_symbols += lemma_sym
        lemma_all_pred += lemma_pred
        lemma_all_gld += lemma_gld
        # POS Tags ...
        pos_gld, pos_pred, pos_match, pos_mistakes = eval_pos(s, g)
        pos_all_pred += pos_pred 
        pos_all_gld += pos_gld
        pos_all_match +=  pos_match
        pos_all_err += len(pos_mistakes)
        pos_all_mistakes += pos_mistakes
    
    logger.info(f"A total of {n_sents} sentences were analyzed")
    
    # Lemmas ...
    logger.info(f"Lemma Matches = {lemma_all_match} || Errors = {lemma_all_err} || Symbol Chars = {len(lemma_all_symbols)}")
    logger.info(f"Lemma Accuracy = {(lemma_all_match*100/(lemma_all_match + lemma_all_err)):.2f}%\n")
    lemma_miss_df = pd.DataFrame(lemma_all_mistakes, columns =['Gold_Word', 'Gold_Lemma', 'Sys_Lemma']).value_counts()
    lemma_miss_df.to_csv(path_or_buf=f"outputs/LemmaErrors.{args.corpus_name}.{args.type_sys}.tsv", sep="\t")
    save_evaluated(Counter(lemma_all_pred), Counter(lemma_all_gld), 
                            f"outputs/Lemma-Catalogue.{args.corpus_name}.{args.type_sys}.txt", print_gold=False)
    
    # POS Tags ...
    logger.info(f"POS Matches = {pos_all_match} || Errors = {pos_all_err}")
    logger.info(f"POS Tagging Accuracy = {(pos_all_match*100/(pos_all_match + pos_all_err)):.2f}%\n")
    pos_miss_df = pd.DataFrame(pos_all_mistakes, columns =['Gold_Word', 'Gold_POS', 'Sys_POS']).value_counts()
    pos_miss_df.to_csv(path_or_buf=f"outputs/POS-Errors.{args.corpus_name}.{args.type_sys}.tsv", sep="\t")
    save_evaluated(Counter(pos_all_pred), Counter(pos_all_gld), f"outputs/POS-Catalogue.{args.corpus_name}.{args.type_sys}.txt")
    
    ordered_labels = sorted(set(pos_all_gld))
    p_labels, r_labels, f_labels, support = eval_f1(y_true=pos_all_gld, y_pred=pos_all_pred, labels=ordered_labels , average=None)
    scores_per_label = zip(ordered_labels, [x*100 for x in p_labels], [x*100 for x in r_labels], [x*100 for x in f_labels])
    logger.info("\n\n")
    logger.info(tabulate(scores_per_label, headers=["POS Tag","Precision", "Recall", "F1"], floatfmt=".2f"))
    p_labels, r_labels, f_labels, support = eval_f1(y_true=np.array(pos_all_gld), y_pred=np.array(pos_all_pred), average='macro', zero_division=0)
    logger.info(f"Total Prec = {p_labels*100}\tRec = {r_labels*100}\tF1 = {f_labels*100}")