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metrics.py
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metrics.py
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from subprocess import Popen, PIPE, CalledProcessError
import json
from text.torchtext.datasets.generic import Query
import logging
import os
import re
import string
import numpy as np
import collections
from multiprocessing import Pool, cpu_count
from contextlib import closing
from sklearn import metrics
from pyrouge import Rouge155
from sacrebleu import corpus_bleu
import pdb
def to_lf(s, table):
aggs = [y.lower() for y in Query.agg_ops]
agg_to_idx = {x: i for i, x in enumerate(aggs)}
conditionals = [y.lower() for y in Query.cond_ops]
headers_unsorted = [(y.lower(), i) for i, y in enumerate(table['header'])]
headers = [(y.lower(), i) for i, y in enumerate(table['header'])]
headers.sort(reverse=True, key=lambda x: len(x[0]))
condition_s, conds = None, []
if 'where' in s:
s, condition_s = s.split('where', 1)
s = ' '.join(s.split()[1:-2])
sel, agg = None, 0
for col, idx in headers:
if col == s:
sel = idx
if sel is None:
s = s.split()
agg = agg_to_idx[s[0]]
s = ' '.join(s[1:])
for col, idx in headers:
if col == s:
sel = idx
full_conditions = []
if not condition_s is None:
condition_s = ' ' + condition_s + ' '
for idx, col in enumerate(headers):
condition_s = condition_s.replace(' ' + col[0] + ' ', ' Col{} '.format(col[1]))
condition_s = condition_s.strip()
for idx, col in enumerate(conditionals):
new_s = []
for t in condition_s.split():
if t == col:
new_s.append('Cond{}'.format(idx))
else:
new_s.append(t)
condition_s = ' '.join(new_s)
s = condition_s
conds = re.split('(Col\d+ Cond\d+)', s)
if len(conds) == 0:
conds = [s]
conds = [x for x in conds if len(x.strip()) > 0]
full_conditions = []
for i, x in enumerate(conds):
if i % 2 == 0:
x = x.split()
col_num = int(x[0].replace('Col', ''))
opp_num = int(x[1].replace('Cond', ''))
full_conditions.append([col_num, opp_num])
else:
x = x.split()
if x[-1] == 'and':
x = x[:-1]
x = ' '.join(x)
if 'Col' in x:
new_x = []
for t in x.split():
if 'Col' in t:
idx = int(t.replace('Col', ''))
t = headers_unsorted[idx][0]
new_x.append(t)
x = new_x
x = ' '.join(x)
if 'Cond' in x:
new_x = []
for t in x.split():
if 'Cond' in t:
idx = int(t.replace('Cond', ''))
t = conditionals[idx]
new_x.append(t)
x = new_x
x = ' '.join(x)
full_conditions[-1].append(x)
logical_form = {'sel': sel, 'conds': full_conditions, 'agg': agg}
return logical_form
def computeLFEM(greedy, answer, args):
answer = [x[0] for x in answer]
count = 0
correct = 0
text_answers = []
for idx, (g, ex) in enumerate(zip(greedy, answer)):
count += 1
text_answers.append([ex['answer'].lower()])
try:
lf = to_lf(g, ex['table'])
gt = ex['sql']
conds = gt['conds']
lower_conds = []
for c in conds:
lc = c
lc[2] = str(lc[2]).lower()
lower_conds.append(lc)
gt['conds'] = lower_conds
correct += lf == gt
except Exception as e:
continue
return correct / count * 100, text_answers
def score(answer, gold):
if len(gold) > 0:
gold = set.union(*[simplify(g) for g in gold])
answer = simplify(answer)
tp, tn, sys_pos, real_pos = 0, 0, 0, 0
if answer == gold:
if not ('unanswerable' in gold and len(gold) == 1):
tp += 1
else:
tn += 1
if not ('unanswerable' in answer and len(answer) == 1):
sys_pos += 1
if not ('unanswerable' in gold and len(gold) == 1):
real_pos += 1
return np.array([tp, tn, sys_pos, real_pos])
def simplify(answer):
return set(''.join(c for c in t if c not in string.punctuation) for t in answer.strip().lower().split()) - {'the',
'a',
'an',
'and',
''}
# http://nlp.cs.washington.edu/zeroshot/evaluate.py
def computeCF1(greedy, answer):
scores = np.zeros(4)
for g, a in zip(greedy, answer):
scores += score(g, a)
tp, tn, sys_pos, real_pos = scores.tolist()
total = len(answer)
if tp == 0:
p = r = f = 0.0
else:
p = tp / float(sys_pos)
r = tp / float(real_pos)
f = 2 * p * r / (p + r)
return f * 100, p * 100, r * 100
def normalize_text(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
# TODO: Modify the metric: deleting repeated words
# def remove_repeat(text):
# pass
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def compute_multi_ones_hot_vector(y, args):
val_task2dict = {
'AAPD': {'csir': 0, 'statme': 1, 'quantph': 2, 'csit': 3, 'mathit': 4, 'statap': 5, 'cscv': 6, 'cscl': 7,
'csai': 8,
'mathna': 9, 'csms': 10, 'cscr': 11, 'csse': 12, 'cslg': 13, 'csni': 14, 'cssy': 15, 'csds': 16,
'cscc': 17,
'csfl': 18, 'csro': 19, 'mathoc': 20, 'csma': 21, 'cspf': 22, 'cssi': 23, 'physicssocph': 24,
'csdc': 25,
'csdb': 26, 'mathco': 27, 'statml': 28, 'mathpr': 29, 'csne': 30, 'csdm': 31, 'condmatstatmech': 32,
'cslo': 33,
'cscy': 34, 'condmatdisnn': 35, 'csna': 36, 'csce': 37, 'cssc': 38, 'csgt': 39, 'cshc': 40,
'qbioqm': 41,
'csdl': 42, 'qbionc': 43, 'cscg': 44, 'cmplg': 45, 'cspl': 46, 'mathlo': 47, 'mathnt': 48, 'csmm': 49,
'mathst': 50, 'statth': 51, 'nlinao': 52, 'physicsdataan': 53},
'AAPDEnhance': {'csir': 0, 'statme': 1, 'quantph': 2, 'csit': 3, 'mathit': 4, 'statap': 5, 'cscv': 6, 'cscl': 7,
'csai': 8,
'mathna': 9, 'csms': 10, 'cscr': 11, 'csse': 12, 'cslg': 13, 'csni': 14, 'cssy': 15, 'csds': 16,
'cscc': 17,
'csfl': 18, 'csro': 19, 'mathoc': 20, 'csma': 21, 'cspf': 22, 'cssi': 23, 'physicssocph': 24,
'csdc': 25,
'csdb': 26, 'mathco': 27, 'statml': 28, 'mathpr': 29, 'csne': 30, 'csdm': 31,
'condmatstatmech': 32,
'cslo': 33,
'cscy': 34, 'condmatdisnn': 35, 'csna': 36, 'csce': 37, 'cssc': 38, 'csgt': 39, 'cshc': 40,
'qbioqm': 41,
'csdl': 42, 'qbionc': 43, 'cscg': 44, 'cmplg': 45, 'cspl': 46, 'mathlo': 47, 'mathnt': 48,
'csmm': 49,
'mathst': 50, 'statth': 51, 'nlinao': 52, 'physicsdataan': 53},
'AAPDOri': {'csir': 0, 'statme': 1, 'quantph': 2, 'csit': 3, 'mathit': 4, 'statap': 5, 'cscv': 6, 'cscl': 7,
'csai': 8,
'mathna': 9, 'csms': 10, 'cscr': 11, 'csse': 12, 'cslg': 13, 'csni': 14, 'cssy': 15, 'csds': 16,
'cscc': 17,
'csfl': 18, 'csro': 19, 'mathoc': 20, 'csma': 21, 'cspf': 22, 'cssi': 23, 'physicssocph': 24,
'csdc': 25,
'csdb': 26, 'mathco': 27, 'statml': 28, 'mathpr': 29, 'csne': 30, 'csdm': 31, 'condmatstatmech': 32,
'cslo': 33,
'cscy': 34, 'condmatdisnn': 35, 'csna': 36, 'csce': 37, 'cssc': 38, 'csgt': 39, 'cshc': 40,
'qbioqm': 41,
'csdl': 42, 'qbionc': 43, 'cscg': 44, 'cmplg': 45, 'cspl': 46, 'mathlo': 47, 'mathnt': 48,
'csmm': 49,
'mathst': 50, 'statth': 51, 'nlinao': 52, 'physicsdataan': 53},
'AAPDDoc': {'er': 0, 'ed': 1, 'ey': 2, 'ew': 3, 'fl': 4, 'ee': 5, 'fj': 6, 'ft': 7, 'ec': 8, 'fr': 9, 'fd': 10,
'fg': 11, 'eq': 12, 'fv': 13, 'dy': 14, 'et': 15, 'fx': 16, 'fu': 17, 'ei': 18, 'eh': 19, 'eg': 20,
'dx': 21, 'fk': 22, 'eu': 23, 'dz': 24, 'ek': 25, 'fw': 26, 'ej': 27, 'ef': 28, 'fo': 29, 'fa': 30,
'fc': 31, 'dw': 32, 'en': 33, 'ea': 34, 'ex': 35, 'fp': 36, 'ff': 37, 'fn': 38, 'ep': 39, 'eb': 40,
'fq': 41, 'fh': 42, 'es': 43, 'ev': 44, 'fb': 45, 'el': 46, 'em': 47, 'ez': 48, 'fm': 49, 'fi': 50,
'fe': 51, 'eo': 52, 'fs': 53},
'WOS46985': {"111120": 0, "111104": 1, "11131": 2, "11181": 3, "11153": 4, "11143": 5, "11192": 6, "11142": 7,
"11190": 8, "111100": 9, "111126": 10, "1116": 11, "11149": 12, "111107": 13, "11144": 14,
"11126": 15,
"1115": 16, "111116": 17, "1119": 18, "111121": 19, "11164": 20, "11191": 21, "11151": 22,
"11125": 23,
"11180": 24, "11113": 25, "11186": 26, "11178": 27, "11199": 28, "111111": 29, "111108": 30,
"111119": 31, "11117": 32, "111128": 33, "111113": 34, "11185": 35, "111106": 36, "11189": 37,
"11146": 38, "111102": 39, "11130": 40, "11198": 41, "11127": 42, "11116": 43, "11134": 44,
"11183": 45,
"111130": 46, "11179": 47, "11158": 48, "111105": 49, "11111": 50, "11173": 51, "11177": 52,
"111133": 53, "11150": 54, "111103": 55, "11129": 56, "11162": 57, "11161": 58, "111110": 59,
"11136": 60, "11135": 61, "11137": 62, "11119": 63, "1117": 64, "11120": 65, "1113": 66,
"11115": 67,
"111112": 68, "11147": 69, "11154": 70, "11123": 71, "11169": 72, "11196": 73, "11160": 74,
"11159": 75,
"111122": 76, "11114": 77, "11157": 78, "11165": 79, "111101": 80, "11110": 81, "11118": 82,
"111131": 83, "11195": 84, "11139": 85, "11155": 86, "11168": 87, "1111": 88, "111129": 89,
"11148": 90,
"11182": 91, "11112": 92, "11188": 93, "11170": 94, "111123": 95, "11166": 96, "1114": 97,
"11175": 98,
"111117": 99, "11156": 100, "11138": 101, "111124": 102, "111118": 103, "1110": 104, "11128": 105,
"11197": 106, "11124": 107, "11121": 108, "111115": 109, "11171": 110, "11133": 111, "1112": 112,
"111127": 113, "11141": 114, "111109": 115, "11187": 116, "111114": 117, "11176": 118,
"11122": 119,
"11167": 120, "11174": 121, "11184": 122, "11145": 123, "11172": 124, "111132": 125, "11193": 126,
"111125": 127, "11152": 128, "11163": 129, "11194": 130, "1118": 131, "11132": 132, "11140": 133},
'WOS11967': {"11118": 0, "1113": 1, "11122": 2, "11121": 3, "11123": 4, "11113": 5, "1111": 6, "11111": 7,
"1114": 8, "11128": 9, "1115": 10, "1116": 11, "11131": 12, "11125": 13, "11110": 14, "1118": 15,
"11117": 16, "1110": 17, "11116": 18, "11127": 19, "11119": 20, "11114": 21, "11132": 22,
"11115": 23, "11126": 24, "1112": 25, "11130": 26, "11129": 27, "11124": 28, "1119": 29,
"11112": 30, "11120": 31, "1117": 32},
'WOS5736': {"1119": 0, "1117": 1, "1110": 2, "1115": 3, "1116": 4, "1118": 5, "11110": 6, "1114": 7, "1112": 8,
"1111": 9, "1113": 10},
'20news': {'recsporthockey': 0, 'recmotorcycles': 1, 'talkreligionmisc': 2, 'socreligionchristian': 3,
'talkpoliticsmideast': 4, 'compsysmachardware': 5, 'scimed': 6, 'miscforsale': 7, 'altatheism': 8,
'compsysibmpchardware': 9, 'compgraphics': 10, 'composmswindowsmisc': 11, 'recautos': 12,
'recsportbaseball': 13, 'scicrypt': 14, 'scielectronics': 15, 'scispace': 16, 'compwindowsx': 17,
'talkpoliticsguns': 18, 'talkpoliticsmisc': 19},
'TREC6': {'abbr': 0, 'loc': 1, 'num': 2, 'hum': 3, 'enty': 4, 'desc': 5},
'TREC50': {'country': 0, 'animal': 1, 'ord': 2, 'abb': 3, 'state': 4, 'word': 5, 'volsize': 6, 'temp': 7,
'religion': 8, 'symbol': 9, 'substance': 10, 'manner': 11, 'city': 12, 'exp': 13, 'termeq': 14,
'letter': 15, 'weight': 16, 'code': 17, 'sport': 18, 'other': 19, 'money': 20, 'count': 21,
'food': 22, 'plant': 23, 'reason': 24, 'color': 25, 'product': 26, 'period': 27, 'gr': 28,
'currency': 29, 'title': 30, 'dismed': 31, 'speed': 32, 'cremat': 33, 'desc': 34, 'perc': 35,
'date': 36, 'body': 37, 'instru': 38, 'mount': 39, 'veh': 40, 'techmeth': 41, 'def': 42, 'event': 43,
'ind': 44, 'lang': 45, 'dist': 46},
'OhsumedSingle': {'11113': 0, '11112': 1, '11115': 2, '11117': 3, '11103': 4, '11118': 5, '11119': 6,
'11123': 7, '11120': 8, '11105': 9, '11109': 10, '11104': 11, '11121': 12, '11116': 13,
'11101': 14, '11107': 15, '11102': 16, '11108': 17, '11106': 18, '11111': 19, '11114': 20,
'11122': 21, '11110': 22},
'OhsumedMulti': {'11113': 0, '11112': 1, '11115': 2, '11117': 3, '11103': 4, '11118': 5, '11119': 6, '11123': 7,
'11120': 8, '11105': 9, '11109': 10, '11104': 11, '11121': 12, '11116': 13, '11101': 14,
'11107': 15, '11102': 16, '11108': 17, '11106': 18, '11111': 19, '11114': 20, '11122': 21,
'11110': 22},
'YahooAnswers': {'societyculture': 0, 'sciencemathematics': 1, 'health': 2, 'educationreference': 3,
'computersinternet': 4, 'sports': 5, 'businessfinance': 6, 'entertainmentmusic': 7,
'familyrelationships': 8, 'politicsgovernment': 9},
'Reuters90': {'sugar': 0, 'bop': 1, 'moneyfx': 2, 'dlr': 3, 'carcass': 4, 'reserves': 5, 'wheat': 6,
'interest': 7, 'income': 8, 'mealfeed': 9, 'nkr': 10, 'sunoil': 11, 'natgas': 12, 'petchem': 13,
'cottonoil': 14, 'fuel': 15, 'retail': 16, 'tea': 17, 'rye': 18, 'jet': 19, 'sunseed': 20,
'cocoa': 21, 'dmk': 22, 'jobs': 23, 'grain': 24, 'corn': 25, 'cpu': 26, 'hog': 27, 'soyoil': 28,
'coconut': 29, 'palmoil': 30, 'rand': 31, 'heat': 32, 'lei': 33, 'oilseed': 34, 'rapeseed': 35,
'lead': 36, 'orange': 37, 'trade': 38, 'ironsteel': 39, 'silver': 40, 'oat': 41, 'castoroil': 42,
'crude': 43, 'strategicmetal': 44, 'coconutoil': 45, 'instaldebt': 46, 'propane': 47, 'nzdlr': 48,
'coffee': 49, 'naphtha': 50, 'cpi': 51, 'gold': 52, 'palladium': 53, 'nickel': 54, 'dfl': 55,
'copper': 56, 'rice': 57, 'groundnutoil': 58, 'rubber': 59, 'platinum': 60, 'ipi': 61, 'zinc': 62,
'palmkernel': 63, 'linoil': 64, 'acq': 65, 'gas': 66, 'soymeal': 67, 'tin': 68, 'soybean': 69,
'ship': 70, 'potato': 71, 'unknown': 72, 'groundnut': 73, 'vegoil': 74, 'moneysupply': 75,
'cotton': 76, 'livestock': 77, 'yen': 78, 'alum': 79, 'sunmeal': 80, 'earn': 81, 'sorghum': 82,
'lumber': 83, 'barley': 84, 'gnp': 85, 'lcattle': 86, 'wpi': 87, 'housing': 88, 'copracake': 89,
'rapeoil': 90},
'Reuters115': {'dfl': 0, 'rice': 1, 'silver': 2, 'nkr': 3, 'sunseed': 4, 'nickel': 5, 'dkr': 6, 'oat': 7,
'plywood': 8, 'lead': 9, 'rubber': 10, 'fuel': 11, 'naphtha': 12, 'rye': 13, 'linmeal': 14,
'housing': 15, 'wpi': 16, 'instaldebt': 17, 'palmkernel': 18, 'ringgit': 19, 'tea': 20,
'heat': 21, 'inventories': 22, 'unknown': 23, 'alum': 24, 'sunmeal': 25, 'orange': 26,
'potato': 27, 'soyoil': 28, 'ship': 29, 'coconutoil': 30, 'jet': 31, 'copper': 32, 'grain': 33,
'crude': 34, 'palmoil': 35, 'cocoa': 36, 'copracake': 37, 'castoroil': 38, 'propane': 39,
'saudriyal': 40, 'gas': 41, 'moneysupply': 42, 'cornglutenfeed': 43, 'cpi': 44, 'sorghum': 45,
'earn': 46, 'platinum': 47, 'soybean': 48, 'carcass': 49, 'reserves': 50, 'livestock': 51,
'natgas': 52, 'soymeal': 53, 'zinc': 54, 'corn': 55, 'palladium': 56, 'cruzado': 57, 'cpu': 58,
'bop': 59, 'gold': 60, 'ironsteel': 61, 'income': 62, 'acq': 63, 'ipi': 64, 'gnp': 65,
'cottonoil': 66, 'yen': 67, 'linoil': 68, 'petchem': 69, 'lcattle': 70, 'sunoil': 71, 'stg': 72,
'tapioca': 73, 'rapeoil': 74, 'fishmeal': 75, 'groundnutoil': 76, 'oilseed': 77, 'skr': 78,
'dlr': 79, 'hog': 80, 'linseed': 81, 'dmk': 82, 'can': 83, 'castorseed': 84, 'sugar': 85,
'rupiah': 86, 'rapemeal': 87, 'retail': 88, 'lit': 89, 'peseta': 90, 'wool': 91, 'groundnut': 92,
'lei': 93, 'rapeseed': 94, 'vegoil': 95, 'mealfeed': 96, 'citruspulp': 97, 'interest': 98,
'porkbelly': 99, 'coconut': 100, 'tin': 101, 'lumber': 102, 'trade': 103, 'austdlr': 104,
'cornoil': 105, 'rand': 106, 'coffee': 107, 'cotton': 108, 'jobs': 109, 'nzdlr': 110,
'wheat': 111, 'moneyfx': 112, 'strategicmetal': 113, 'barley': 114, 'redbean': 115},
'20newsSingle': {'scimed': 0, 'socreligionchristian': 1, 'scielectronics': 2, 'recautos': 3,
'talkreligionmisc': 4, 'miscforsale': 5, 'compgraphics': 6, 'composmswindowsmisc': 7,
'recmotorcycles': 8, 'compsysibmpchardware': 9, 'altatheism': 10, 'recsporthockey': 11,
'talkpoliticsguns': 12, 'recsportbaseball': 13, 'talkpoliticsmideast': 14,
'talkpoliticsmisc': 15, 'compwindowsx': 16, 'scispace': 17, 'compsysmachardware': 18,
'scicrypt': 19},
'20newsMulti': {'scimed': 0, 'socreligionchristian': 1, 'scielectronics': 2, 'recautos': 3,
'talkreligionmisc': 4, 'miscforsale': 5, 'compgraphics': 6, 'composmswindowsmisc': 7,
'recmotorcycles': 8, 'compsysibmpchardware': 9, 'altatheism': 10, 'recsporthockey': 11,
'talkpoliticsguns': 12, 'recsportbaseball': 13, 'talkpoliticsmideast': 14,
'talkpoliticsmisc': 15, 'compwindowsx': 16, 'scispace': 17, 'compsysmachardware': 18,
'scicrypt': 19},
'AmazonFullReview': {'clsone': 0, 'clstwo': 1, 'clsthree': 2, 'clsfour': 3, 'clsfive': 4},
'R8': {'acq': 0, 'crude': 1, 'earn': 2, 'grain': 3, 'interest': 4, 'moneyfx': 5, 'ship': 6, 'trade': 7},
'R52': {'acq': 0, 'alum': 1, 'bop': 2, 'carcass': 3, 'cocoa': 4, 'coffee': 5, 'copper': 6, 'cotton': 7,
'cpi': 8, 'cpu': 9, 'crude': 10, 'dlr': 11, 'earn': 12, 'fuel': 13, 'gas': 14, 'gnp': 15, 'gold': 16,
'grain': 17, 'heat': 18, 'housing': 19, 'income': 20, 'instaldebt': 21, 'interest': 22, 'ipi': 23,
'ironsteel': 24, 'jet': 25, 'jobs': 26, 'lead': 27, 'lei': 28, 'livestock': 29, 'lumber': 30,
'mealfeed': 31, 'moneyfx': 32, 'moneysupply': 33, 'natgas': 34, 'nickel': 35, 'orange': 36,
'petchem': 37, 'platinum': 38, 'potato': 39, 'reserves': 40, 'retail': 41, 'rubber': 42, 'ship': 43,
'strategicmetal': 44, 'sugar': 45, 'tea': 46, 'tin': 47, 'trade': 48, 'vegoil': 49, 'wpi': 50,
'zinc': 51},
'YelpFullReview': {'clsone': 0, 'clstwo': 1, 'clsthree': 2, 'clsfour': 3, 'clsfive': 4},
}
assert len(args.val_tasks) == 1, 'for now only support single task micro f1 metric'
l2i_dict = val_task2dict[args.val_tasks[0]]
y = y.split()
# ground_truth_tokens = ground_truth.split()
total_length = len(l2i_dict)
ones_hot = np.zeros(total_length, dtype=np.int)
# true_ones_hot = np.zeros(total_length, dtype=np.int)
hot_indices = [l2i_dict[token] for token in y if token in l2i_dict.keys()]
# true_hot_indices = [l2i_dict[token] for token in ground_truth_tokens]
ones_hot[hot_indices] = 1
# true_ones_hot[true_hot_indices] = 1
return ones_hot
def f1_score(prediction, ground_truth):
prediction_tokens = prediction.split()
ground_truth_tokens = ground_truth.split()
common = collections.Counter(prediction_tokens) & collections.Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match(prediction, ground_truth):
return prediction == ground_truth
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
scores_for_ground_truths = []
for idx, ground_truth in enumerate(ground_truths):
score = metric_fn(prediction, ground_truth)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def computeMicroF1(outputs, targets, args):
preds = np.array([compute_multi_ones_hot_vector(output, args) for output in outputs], dtype=np.int)
tgts = np.array([compute_multi_ones_hot_vector(tgt[0], args) for tgt in targets], dtype=np.int)
micro_f1 = metrics.f1_score(tgts, preds, average='micro') * 100.0
precision = metrics.precision_score(tgts, preds, average='micro') * 100.0
recall = metrics.recall_score(tgts, preds, average='micro') * 100.0
return micro_f1, precision, recall
def computeF1(outputs, targets):
# Origin
return sum([metric_max_over_ground_truths(f1_score, o, t) for o, t in zip(outputs, targets)]) / len(outputs) * 100
# preds = np.array([compute_multi_ones_hot_vector(output) for output in outputs], dtype=np.int)
# tgts = np.array([compute_multi_ones_hot_vector(tgt[0]) for tgt in targets], dtype=np.int)
# micro_f1 = metrics.f1_score(tgts, preds, average='micro') * 100.0
# return micro_f1
def computeEM(outputs, targets):
outs = [metric_max_over_ground_truths(exact_match, o, t) for o, t in zip(outputs, targets)]
return sum(outs) / len(outputs) * 100
def computeBLEU(outputs, targets):
targets = [[t[i] for t in targets] for i in range(len(targets[0]))]
return corpus_bleu(outputs, targets, lowercase=True).score
class Rouge(Rouge155):
"""Rouge calculator class with custom command-line options."""
# See full list of options here:
# https://github.com/andersjo/pyrouge/blob/master/tools/ROUGE-1.5.5/README.txt#L82
DEFAULT_OPTIONS = [
'-a', # evaluate all systems
'-n', 4, # max-ngram
'-x', # do not calculate ROUGE-L
'-2', 4, # max-gap-length
'-u', # include unigram in skip-bigram
'-c', 95, # confidence interval
'-r', 1000, # number-of-samples (for resampling)
'-f', 'A', # scoring formula
'-p', 0.5, # 0 <= alpha <=1
'-t', 0, # count by token instead of sentence
'-d', # print per evaluation scores
]
def __init__(self, n_words=None,
keep_files=False, options=None):
if options is None:
self.options = self.DEFAULT_OPTIONS.copy()
else:
self.options = options
if n_words:
options.extend(["-l", n_words])
stem = "-m" in self.options
super(Rouge, self).__init__(
n_words=n_words, stem=stem,
keep_files=keep_files)
def _run_rouge(self):
# Get full options
options = (
['-e', self._rouge_data] +
list(map(str, self.options)) +
[os.path.join(self._config_dir, "settings.xml")])
logging.info("Running ROUGE with options {}".format(" ".join(options)))
# print([self._rouge_bin] + list(options))
pipes = Popen([self._rouge_bin] + options, stdout=PIPE, stderr=PIPE)
std_out, std_err = pipes.communicate()
div_by_zero_error = std_err.decode("utf-8"). \
startswith("Illegal division by zero")
if pipes.returncode == 0 or div_by_zero_error:
# Still returns the correct output even with div by zero
return std_out
else:
raise ValueError(
std_out.decode("utf-8") + "\n" + std_err.decode("utf-8"))
def computeROUGE(greedy, answer):
rouges = compute_rouge_scores(greedy, answer)
if len(rouges) > 0:
avg_rouges = {}
for key in rouges[0].keys():
avg_rouges[key] = sum(
[r.get(key, 0.0) for r in rouges]) / len(rouges) * 100
else:
avg_rouges = None
return avg_rouges
def split_sentences(txt, splitchar=".", include_splitchar=False):
"""Split sentences of a text based on a given EOS char."""
out = [s.split() for s in txt.strip().split(splitchar) if len(s) > 0]
return out
def compute_rouge_scores(summs, refs, splitchar='.', options=None, parallel=True):
assert len(summs) == len(refs)
options = [
'-a', # evaluate all systems
'-c', 95, # confidence interval
'-m', # use Porter stemmer
'-n', 2, # max-ngram
'-w', 1.3, # weight (weighting factor for WLCS)
]
rr = Rouge(options=options)
rouge_args = []
for summ, ref in zip(summs, refs):
letter = "A"
ref_dict = {}
for r in ref:
ref_dict[letter] = [x for x in split_sentences(r, splitchar) if len(x) > 0]
letter = chr(ord(letter) + 1)
s = [x for x in split_sentences(summ, splitchar) if len(x) > 0]
rouge_args.append((s, ref_dict))
if parallel:
with closing(Pool(cpu_count() // 2)) as pool:
rouge_scores = pool.starmap(rr.score_summary, rouge_args)
else:
rouge_scores = []
for s, a in rouge_args:
rouge_scores.append(rr.score_summary(s, ref_dict))
return rouge_scores
def to_delta_state(line):
delta_state = {'inform': {}, 'request': {}}
try:
if line == 'None' or line.strip() == '' or line.strip() == ';':
return delta_state
inform, request = [[y.strip() for y in x.strip().split(',')] for x in line.split(';')]
inform_pairs = {}
for i in inform:
try:
k, v = i.split(':')
inform_pairs[k.strip()] = v.strip()
except:
pass
delta_state = {'inform': inform_pairs, 'request': request}
except:
pass
finally:
return delta_state
def update_state(state, delta):
for act, slot in delta.items():
state[act] = slot
return state
def dict_cmp(d1, d2):
def cmp(a, b):
for k1, v1 in a.items():
if k1 not in b:
return False
else:
if v1 != b[k1]:
return False
return True
return cmp(d1, d2) and cmp(d2, d1)
def computeDialogue(greedy, answer):
examples = []
for idx, (g, a) in enumerate(zip(greedy, answer)):
examples.append((a[0][0], g, a[0][1], idx))
examples.sort()
turn_request_positives = 0
turn_goal_positives = 0
joint_goal_positives = 0
ldt = None
for ex in examples:
if ldt is None or ldt.split('_')[:-1] != ex[0].split('_')[:-1]:
state, answer_state = {}, {}
ldt = ex[0]
delta_state = to_delta_state(ex[1])
answer_delta_state = to_delta_state(ex[2])
state = update_state(state, delta_state['inform'])
answer_state = update_state(answer_state, answer_delta_state['inform'])
if dict_cmp(state, answer_state):
joint_goal_positives += 1
if delta_state['request'] == answer_delta_state['request']:
turn_request_positives += 1
if dict_cmp(delta_state['inform'], answer_delta_state['inform']):
turn_goal_positives += 1
joint_goal_em = joint_goal_positives / len(examples) * 100
turn_request_em = turn_request_positives / len(examples) * 100
turn_goal_em = turn_goal_positives / len(examples) * 100
answer = [(x[-1], x[-2]) for x in examples]
answer.sort()
answer = [[x[1]] for x in answer]
return joint_goal_em, turn_request_em, turn_goal_em, answer
def compute_metrics(greedy, answer, rouge=False, bleu=False, corpus_f1=False, logical_form=False, args=None,
dialogue=False):
metric_keys = []
metric_values = []
if not isinstance(answer[0], list):
answer = [[a] for a in answer]
# if logical_form:
# lfem, answer = computeLFEM(greedy, answer, args)
# metric_keys += ['lfem']
# metric_values += [lfem]
# if dialogue:
# joint_goal_em, request_em, turn_goal_em, answer = computeDialogue(greedy, answer)
# avg_dialogue = (joint_goal_em + request_em) / 2
# metric_keys += ['joint_goal_em', 'turn_request_em', 'turn_goal_em', 'avg_dialogue']
# metric_values += [joint_goal_em, request_em, turn_goal_em, avg_dialogue]
# em = computeEM(greedy, answer)
# metric_keys += ['em']
# metric_values += [em]
# if bleu:
# bleu = computeBLEU(greedy, answer)
# metric_keys.append('bleu')
# metric_values.append(bleu)
# if rouge:
# rouge = computeROUGE(greedy, answer)
# metric_keys += ['rouge1', 'rouge2', 'rougeL', 'avg_rouge']
# avg_rouge = (rouge['rouge_1_f_score'] + rouge['rouge_2_f_score'] + rouge['rouge_l_f_score']) / 3
# metric_values += [rouge['rouge_1_f_score'], rouge['rouge_2_f_score'], rouge['rouge_l_f_score'], avg_rouge]
norm_greedy = [normalize_text(g) for g in greedy]
norm_answer = [[normalize_text(a) for a in al] for al in answer]
mf1, precision, recall = computeMicroF1(norm_greedy, norm_answer, args)
# nem = computeEM(norm_greedy, norm_answer)
metric_keys.extend(['micro-f1', 'precision', 'recall'])
metric_values.extend([mf1, precision, recall])
# if corpus_f1:
# corpus_f1, precision, recall = computeCF1(norm_greedy, norm_answer)
# metric_keys += ['corpus_f1', 'precision', 'recall']
# metric_values += [corpus_f1, precision, recall]
metric_dict = collections.OrderedDict(list(zip(metric_keys, metric_values)))
return metric_dict, answer