def eval():
print('Loading association matrix')
matrix = Matrix()
matrix.read_matrix_from_file(MATRIX_FILE)
print('Evaluating')
scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.GAME_SET_100_FILE,
EVAL_WORD_GAME100_FILE)
def eval():
from convert_xml import output_results
print('Loading association matrix')
#matrix = Matrix_Split(MATRIX_FILE_SPLIT_DIR, LOWEST_SCORE)
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_FILE)
#matrix.read_matrix_from_file(MATRIX_REVERSED_FILE)
print('Number of rows: {}'.format(matrix.size()))
print('Evaluating')
# 100 GAMES
#scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.GAME_SET_100_FILE, EVAL_WORD_GAME100_FILE)
# DEV SET
#scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.NLP4FUN_DEV_TSV_v2_ALL_FILE, EVAL_NLP4FUN_DEV_ALL_FILE)
# scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.NLP4FUN_DEV_TSV_v2_tv_FILE, EVAL_NLP4FUN_DEV_TV_FILE)
# scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.NLP4FUN_DEV_TSV_v2_bg_FILE, EVAL_NLP4FUN_DEV_BG_FILE)
'''
# GOLD SET (BLIND)
scorer.batch_solver(matrix, corpora.NLP4FUN_TEST_TSV_v2_ALL_FILE, TEST_RESULTS_TSV_1, extra_search=False)
scorer.batch_solver(matrix, corpora.NLP4FUN_TEST_TSV_v2_ALL_FILE, TEST_RESULTS_TSV_2, extra_search=True)
output_results(corpora.NLP4FUN_TEST_XML_v2_FILE, TEST_RESULTS_TSV_1, TEST_RESULTS_SUBMIT_1)
output_results(corpora.NLP4FUN_TEST_XML_v2_FILE, TEST_RESULTS_TSV_2, TEST_RESULTS_SUBMIT_2)
'''
# GOLD SET
#scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.NLP4FUN_GOLD_TSV_v2_tv_FILE, EVAL_NLP4FUN_TEST_TV_FILE)
#scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.NLP4FUN_GOLD_TSV_v2_bg_FILE, EVAL_NLP4FUN_TEST_BG_FILE)
scorer.evaluate_kbest_MeanReciprocalRank(
matrix, corpora.NLP4FUN_GOLD_TSV_v2_ALL_FILE,
EVAL_NLP4FUN_TEST_ALL_FILE)
def correlation_score_match():
from convert_xml import output_results
print('Loading association matrix')
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_FILE)
print('Computing Correlation')
input_file = corpora.NLP4FUN_DEV_GOLD_TSV_ALL_FILE
output_file_clues_matched = OUTPUT_DIR + 'nlp4fun_dev+test_all_scores_clues_matched.txt'
output_file_solutions_matched = OUTPUT_DIR + 'nlp4fun_dev+test_all_scores_solutions_guessed.txt'
scorer.compute_correlation_score_match(matrix, input_file,
output_file_clues_matched,
output_file_solutions_matched)
def split_matrix():
print('Loading association matrix')
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_FILE)
print('Lowest score: {}'.format(matrix.get_min_association_score()))
print('Splitting matrix in {} files in {}'.format(matrix.size(),
MATRIX_FILE_SPLIT_DIR))
matrix.split_matrix_dict(MATRIX_FILE_SPLIT_DIR)
def build_and_eval():
utility.make_dir(OUTPUT_DIR)
print('\nBuilding lexicon')
lex_set = lexicon.loadLexiconFromFile(corpora.DIZ_POLI_WORD_SORTED_FILE)
lex_solution_set = lex_set
'''
poli_lexicon = list(lexicon.loadLexiconFromFile(corpora.DIZ_POLI_WORD_SORTED_FILE))
sost_lexicon = list(corpora.getSostantiviSetFromPaisa(min_freq=100, inflected=True))
print('\nSize of sostantivi lex: {}'.format(len(sost_lexicon)))
agg_lexicon = list(corpora.getAggettiviSetFromPaisa(min_freq=100, inflected=True))
print('\nSize of agg lex: {}'.format(len(agg_lexicon)))
lex_set = set(poli_lexicon + sost_lexicon + agg_lexicon)
lex_solution_set = set(sost_lexicon+agg_lexicon)
#lex_solution_set = lex_set
'''
print('\nComputing lex coverage')
scorer.computeCoverageOfGameWordLex(lex_set, lex_solution_set,
corpora.GAME_SET_100_FILE,
COVERAGE_WORD_GAME100_FILE)
print('\nBuilding association matrix')
matrix = Matrix(lex_set, lex_solution_set)
matrix.add_patterns_from_corpus(corpora.DE_MAURO_POLIREMATICHE_INFO)
corpora.addBigramFromPolirematicheInMatrix(matrix, weight=1)
#corpora.addBigramFromCompunds(matrix, lex_set, min_len=4, weight=10)
matrix.compute_association_scores()
matrix.write_matrix_to_file(MATRIX_FILE)
print('\nEval')
scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.GAME_SET_100_FILE,
EVAL_WORD_GAME100_FILE)
def print_row_column_sets():
print('Loading association matrix')
row_file = OUTPUT_DIR + 'set_row.txt'
col_file = OUTPUT_DIR + 'set_col.txt'
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_FILE)
matrix.print_row_column_sets(row_file, col_file)
def interactive_solver():
print('Loading association matrix')
matrix = Matrix()
matrix.read_matrix_from_file(MATRIX_FILE)
scorer.interactive_solver(matrix)
print('\n'.join(clues))
text = input('\nProva a indovinare --> ')
guess = text.strip().lower()
if not guess:
print('exitig')
return
if guess == solution:
print('Indovinato!')
else:
print('Sbagliato. La soluzione era {}'.format(solution))
scores_str = ', '.join(['{0:.1f}'.format(s) for s in scores])
print('Scores: {}'.format(scores_str))
print('Sum: {0:.1f}'.format(sum(scores)))
text = input('\nVuoi continuare? (y/n)')
if not text or text != 'y':
print('exitig')
return
if __name__ == '__main__':
from matrix_dict import Matrix_Dict
import path
OUTPUT_DIR = path.GHIGLIOTTINA_BASE_FILE_PATH + "model_06_evalita_split/"
MATRIX_REVERSED_FILE = OUTPUT_DIR + 'matrix_reversed.pkl' #solution in keys
print('Loading matrix')
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_REVERSED_FILE)
print('Number of rows: {}'.format(matrix.size()))
#unfound_pair_score = matrix.get_min_association_score()
#print("Min association score: {0:.1f}".format(unfound_pair_score))
interactive_generator(matrix)
def build_and_eval():
utility.make_dir(OUTPUT_DIR)
print('Building lexicon')
poli_lexicon = list(
lexicon.loadLexiconFromFile(corpora.DIZ_POLI_WORD_SORTED_FILE))
sost_lexicon = list(
lexicon.loadLexiconFromFile(
corpora.DIZIONARIO_SOSTANTIVI_AUGMENTED_PAISA_FILE))
agg_lexicon = list(
lexicon.loadLexiconFromFile(
corpora.DIZIONARIO_AGGETTIVI_AUGMENTED_PAISA_FILE))
lex_set = set(poli_lexicon + sost_lexicon + agg_lexicon)
lex_solution_set = set(sost_lexicon + agg_lexicon)
'''
poli_lexicon = list(lexicon.loadLexiconFromFile(corpora.DIZ_POLI_WORD_SORTED_FILE))
sost_lexicon = list(corpora.getSostantiviSetFromPaisa(min_freq=1000, inflected=True))
print('\nSize of sostantivi lex: {}'.format(len(sost_lexicon)))
agg_lexicon = list(corpora.getAggettiviSetFromPaisa(min_freq=1000, inflected=True))
print('\nSize of agg lex: {}'.format(len(agg_lexicon)))
lex_set = set(poli_lexicon + sost_lexicon + agg_lexicon)
'''
lexicon.printLexiconToFile(lex_set, LEX_FREQ_FILE)
lexicon.printLexiconToFile(lex_solution_set, SOLUTION_LEX_FREQ_FILE)
print('Computing lex coverage')
scorer.computeCoverageOfGameWordLex(lex_set, lex_solution_set,
corpora.GAME_SET_100_FILE,
COVERAGE_WORD_GAME100_FILE)
print('Building association matrix')
matrix = Matrix_Dict(lex_set, lex_solution_set)
matrix.add_patterns_from_corpus(corpora.PAISA_RAW_INFO)
matrix.add_patterns_from_corpus(corpora.DE_MAURO_POLIREMATICHE_INFO,
weight=DE_MAURO_WEIGHT)
matrix.add_patterns_from_corpus(corpora.PROVERBI_INFO,
weight=PROVERBI_WEIGHT)
matrix.add_patterns_from_corpus(corpora.ITWAC_RAW_INFO, weight=1)
matrix.add_patterns_from_corpus(corpora.WIKI_IT_TITLES_INFO,
weight=WIKI_IT_WEIGHT)
#matrix.add_patterns_from_corpus(corpora.WIKI_IT_TEXT_INFO, weight=1)
corpora.addBigramFromPolirematicheInMatrix(matrix, DE_MAURO_WEIGHT)
corpora.addBigramFromCompunds(matrix,
lex_set,
min_len=4,
weight=COMPOUNDS_WEIGHT)
matrix.compute_association_scores()
matrix.write_matrix_to_file(MATRIX_FILE)
print('Eval')
scorer.evaluate_kbest_MeanReciprocalRank(matrix, corpora.GAME_SET_100_FILE,
EVAL_WORD_GAME100_FILE)
def reverse_matrix():
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_FILE)
reversed_matrix = matrix.reverse_matrix()
reversed_matrix.write_matrix_to_file(MATRIX_REVERSED_FILE)
def generate_games():
import game_generator
matrix = Matrix_Dict()
matrix.read_matrix_from_file(MATRIX_REVERSED_FILE)
print('Number of rows: {}'.format(matrix.size()))
game_generator.interactive_generator(matrix)