forked from alexa/dialoglue
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evaluate.py
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evaluate.py
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import json
import numpy as np
import re
def evaluate(test_annotation_file, user_annotation_file, phase_codename, **kwargs):
"""
DialoGLUE evaluation function for eval.ai
See documentation: https://evalai.readthedocs.io/en/latest/evaluation_scripts.html
See test_annotation_file: gt_test.json
The same annotation file and function will be used regardless of mode (e.g., full data, few shot, few shot + unlabeled)
"""
gt_outputs = json.load(open(test_annotation_file))
gen_outputs = json.load(open(user_annotation_file))
# Iterate over the tasks/datasets
dataset_to_task = {
"hwu": "intent",
"clinc": "intent",
"banking": "intent",
"restaurant8k": "slot",
"dstc8": "slot",
"multiwoz": "dst",
"top": "top",
}
assert len(gen_outputs.keys()) == 7
assert all(key in gen_outputs for key in dataset_to_task.keys())
results = {}
for dataset, gt_outputs in gt_outputs.items():
# Calculate score differently depending on the dataset/task
task = dataset_to_task[dataset]
if phase_codename == "few":
dataset_results = []
for i in range(5):
# Accuracy and exact match for intent classification & TOP
if task == "intent" or task == "top":
# Calculate accuracy between generated and ground-truth
dataset_results.append(sum(p == t for p,t in zip(gen_outputs.get(dataset)[i], gt_outputs))/len(gt_outputs))
elif task == "slot":
dataset_results.append(evaluate_slot(gen_outputs.get(dataset)[i], gt_outputs))
elif task == "dst":
dataset_results.append(evaluate_mwoz(gen_outputs.get(dataset)[i]))
results[dataset] = np.mean(dataset_results)
else:
# Accuracy and exact match for intent classification & TOP
if task == "intent" or task == "top":
# Calculate accuracy between generated and ground-truth
results[dataset] = sum(p == t for p,t in zip(gen_outputs.get(dataset), gt_outputs))/len(gt_outputs)
elif task == "slot":
results[dataset] = evaluate_slot(gen_outputs.get(dataset), gt_outputs)
elif task == "dst":
results[dataset] = evaluate_mwoz(gen_outputs.get(dataset))
rename = {
"hwu": "HWU64 (Acc)",
"clinc": "CLINC150 (Acc)",
"banking": "Banking77 (Acc)",
"restaurant8k": "Restaurant8k (F-1)",
"dstc8": "DSTC8 (F-1)",
"top": "TOP (EM)",
"multiwoz": "MultiWOZ (Joint Goal Acc)",
}
results = {
rename.get(key): value*100 for key,value in results.items()
}
# Add average
results["Average"] = np.mean(list(results.values()))
labels = ["Average", "Banking77 (Acc)", "CLINC150 (Acc)", "HWU64 (Acc)", "Restaurant8k (F-1)", "DSTC8 (F-1)", "TOP (EM)", "MultiWOZ (Joint Goal Acc)"]
output = {"result": [{"test_split": results}], "submission_result": [results.get(e) for e in labels]}
print(output)
return output
def evaluate_slot(pred_slots, true_slots):
slot_types = set([slot.split(":")[0] for row in true_slots for slot in row])
slot_type_f1_scores = []
for slot_type in slot_types:
predictions_for_slot = [
[p for p in prediction if slot_type in p] for prediction in pred_slots
]
labels_for_slot = [
[l for l in label if slot_type in l] for label in true_slots
]
proposal_made = [len(p) > 0 for p in predictions_for_slot]
has_label = [len(l) > 0 for l in labels_for_slot]
prediction_correct = [
prediction == label for prediction, label in zip(predictions_for_slot, labels_for_slot)
]
true_positives = sum([
int(proposed and correct)
for proposed, correct in zip(proposal_made, prediction_correct)
])
num_predicted = sum([int(proposed) for proposed in proposal_made])
num_to_recall = sum([int(hl) for hl in has_label])
precision = true_positives / (1e-5 + num_predicted)
recall = true_positives / (1e-5 + num_to_recall)
f1_score = 2 * precision * recall / (1e-5 + precision + recall)
slot_type_f1_scores.append(f1_score)
return np.mean(slot_type_f1_scores)
def load_dataset_config(dataset_config):
raw_config = dataset_config
return raw_config['class_types'], raw_config['slots'], raw_config['label_maps']
def tokenize(text):
if "\u0120" in text:
text = re.sub(" ", "", text)
text = re.sub("\u0120", " ", text)
text = text.strip()
return ' '.join([tok for tok in map(str.strip, re.split("(\W+)", text)) if len(tok) > 0])
def is_in_list(tok, value):
found = False
tok_list = [item for item in map(str.strip, re.split("(\W+)", tok)) if len(item) > 0]
value_list = [item for item in map(str.strip, re.split("(\W+)", value)) if len(item) > 0]
tok_len = len(tok_list)
value_len = len(value_list)
for i in range(tok_len + 1 - value_len):
if tok_list[i:i + value_len] == value_list:
found = True
break
return found
def check_slot_inform(value_label, inform_label, label_maps):
value = inform_label
if value_label == inform_label:
value = value_label
elif is_in_list(inform_label, value_label):
value = value_label
elif is_in_list(value_label, inform_label):
value = value_label
elif inform_label in label_maps:
for inform_label_variant in label_maps[inform_label]:
if value_label == inform_label_variant:
value = value_label
break
elif is_in_list(inform_label_variant, value_label):
value = value_label
break
elif is_in_list(value_label, inform_label_variant):
value = value_label
break
elif value_label in label_maps:
for value_label_variant in label_maps[value_label]:
if value_label_variant == inform_label:
value = value_label
break
elif is_in_list(inform_label, value_label_variant):
value = value_label
break
elif is_in_list(value_label_variant, inform_label):
value = value_label
break
return value
def get_joint_slot_correctness(preds, class_types, label_maps,
key_class_label_id='class_label_id',
key_class_prediction='class_prediction',
key_start_pos='start_pos',
key_start_prediction='start_prediction',
key_end_pos='end_pos',
key_end_prediction='end_prediction',
key_refer_id='refer_id',
key_refer_prediction='refer_prediction',
key_slot_groundtruth='slot_groundtruth',
key_slot_prediction='slot_prediction'):
class_correctness = [[] for cl in range(len(class_types) + 1)]
confusion_matrix = [[[] for cl_b in range(len(class_types))] for cl_a in range(len(class_types))]
pos_correctness = []
refer_correctness = []
val_correctness = []
total_correctness = []
c_tp = {ct: 0 for ct in range(len(class_types))}
c_tn = {ct: 0 for ct in range(len(class_types))}
c_fp = {ct: 0 for ct in range(len(class_types))}
c_fn = {ct: 0 for ct in range(len(class_types))}
for pred in preds:
guid = pred['guid'] # List: set_type, dialogue_idx, turn_idx
turn_gt_class = pred[key_class_label_id]
turn_pd_class = pred[key_class_prediction]
gt_start_pos = pred[key_start_pos]
pd_start_pos = pred[key_start_prediction]
gt_end_pos = pred[key_end_pos]
pd_end_pos = pred[key_end_prediction]
gt_refer = pred[key_refer_id]
pd_refer = pred[key_refer_prediction]
gt_slot = pred[key_slot_groundtruth]
pd_slot = pred[key_slot_prediction]
gt_slot = tokenize(gt_slot)
pd_slot = tokenize(pd_slot)
# Make sure the true turn labels are contained in the prediction json file!
joint_gt_slot = gt_slot
if guid[-1] == '0': # First turn, reset the slots
joint_pd_slot = 'none'
# If turn_pd_class or a value to be copied is "none", do not update the dialog state.
if turn_pd_class == class_types.index('none'):
pass
elif turn_pd_class == class_types.index('dontcare'):
joint_pd_slot = 'dontcare'
elif turn_pd_class == class_types.index('copy_value'):
joint_pd_slot = pd_slot
elif 'true' in class_types and turn_pd_class == class_types.index('true'):
joint_pd_slot = 'true'
elif 'false' in class_types and turn_pd_class == class_types.index('false'):
joint_pd_slot = 'false'
elif 'refer' in class_types and turn_pd_class == class_types.index('refer'):
if pd_slot[0:3] == "§§ ":
if pd_slot[3:] != 'none':
joint_pd_slot = check_slot_inform(joint_gt_slot, pd_slot[3:], label_maps)
elif pd_slot[0:2] == "§§":
if pd_slot[2:] != 'none':
joint_pd_slot = check_slot_inform(joint_gt_slot, pd_slot[2:], label_maps)
elif pd_slot != 'none':
joint_pd_slot = pd_slot
elif 'inform' in class_types and turn_pd_class == class_types.index('inform'):
if pd_slot[0:3] == "§§ ":
if pd_slot[3:] != 'none':
joint_pd_slot = check_slot_inform(joint_gt_slot, pd_slot[3:], label_maps)
elif pd_slot[0:2] == "§§":
if pd_slot[2:] != 'none':
joint_pd_slot = check_slot_inform(joint_gt_slot, pd_slot[2:], label_maps)
else:
print("ERROR: Unexpected slot value format. Aborting.")
exit()
else:
print("ERROR: Unexpected class_type. Aborting.")
exit()
total_correct = True
# Check the per turn correctness of the class_type prediction
if turn_gt_class == turn_pd_class:
class_correctness[turn_gt_class].append(1.0)
class_correctness[-1].append(1.0)
c_tp[turn_gt_class] += 1
# Only where there is a span, we check its per turn correctness
if turn_gt_class == class_types.index('copy_value'):
if gt_start_pos == pd_start_pos and gt_end_pos == pd_end_pos:
pos_correctness.append(1.0)
else:
pos_correctness.append(0.0)
# Only where there is a referral, we check its per turn correctness
if 'refer' in class_types and turn_gt_class == class_types.index('refer'):
if gt_refer == pd_refer:
refer_correctness.append(1.0)
else:
refer_correctness.append(0.0)
else:
if turn_gt_class == class_types.index('copy_value'):
pos_correctness.append(0.0)
if 'refer' in class_types and turn_gt_class == class_types.index('refer'):
refer_correctness.append(0.0)
class_correctness[turn_gt_class].append(0.0)
class_correctness[-1].append(0.0)
confusion_matrix[turn_gt_class][turn_pd_class].append(1.0)
c_fn[turn_gt_class] += 1
c_fp[turn_pd_class] += 1
for cc in range(len(class_types)):
if cc != turn_gt_class and cc != turn_pd_class:
c_tn[cc] += 1
# Check the joint slot correctness.
# If the value label is not none, then we need to have a value prediction.
# Even if the class_type is 'none', there can still be a value label,
# it might just not be pointable in the current turn. It might however
# be referrable and thus predicted correctly.
if joint_gt_slot == joint_pd_slot:
val_correctness.append(1.0)
elif joint_gt_slot != 'none' and joint_gt_slot != 'dontcare' and joint_gt_slot != 'true' and joint_gt_slot != 'false' and joint_gt_slot in label_maps:
no_match = True
for variant in label_maps[joint_gt_slot]:
if variant == joint_pd_slot:
no_match = False
break
if no_match:
val_correctness.append(0.0)
total_correct = False
else:
val_correctness.append(1.0)
else:
val_correctness.append(0.0)
total_correct = False
total_correctness.append(1.0 if total_correct else 0.0)
# Account for empty lists (due to no instances of spans or referrals being seen)
if pos_correctness == []:
pos_correctness.append(1.0)
if refer_correctness == []:
refer_correctness.append(1.0)
for ct in range(len(class_types)):
if c_tp[ct] + c_fp[ct] > 0:
precision = c_tp[ct] / (c_tp[ct] + c_fp[ct])
else:
precision = 1.0
if c_tp[ct] + c_fn[ct] > 0:
recall = c_tp[ct] / (c_tp[ct] + c_fn[ct])
else:
recall = 1.0
if precision + recall > 0:
f1 = 2 * ((precision * recall) / (precision + recall))
else:
f1 = 1.0
if c_tp[ct] + c_tn[ct] + c_fp[ct] + c_fn[ct] > 0:
acc = (c_tp[ct] + c_tn[ct]) / (c_tp[ct] + c_tn[ct] + c_fp[ct] + c_fn[ct])
else:
acc = 1.0
return np.asarray(total_correctness), np.asarray(val_correctness), np.asarray(class_correctness), np.asarray(pos_correctness), np.asarray(refer_correctness), np.asarray(confusion_matrix), c_tp, c_tn, c_fp, c_fn
def evaluate_mwoz(preds):
acc_list = []
key_class_label_id = 'class_label_id_%s'
key_class_prediction = 'class_prediction_%s'
key_start_pos = 'start_pos_%s'
key_start_prediction = 'start_prediction_%s'
key_end_pos = 'end_pos_%s'
key_end_prediction = 'end_prediction_%s'
key_refer_id = 'refer_id_%s'
key_refer_prediction = 'refer_prediction_%s'
key_slot_groundtruth = 'slot_groundtruth_%s'
key_slot_prediction = 'slot_prediction_%s'
dataset_config = mwoz_config
class_types, slots, label_maps = load_dataset_config(dataset_config)
# Prepare label_maps
label_maps_tmp = {}
for v in label_maps:
label_maps_tmp[tokenize(v)] = [tokenize(nv) for nv in label_maps[v]]
label_maps = label_maps_tmp
goal_correctness = 1.0
cls_acc = [[] for cl in range(len(class_types))]
cls_conf = [[[] for cl_b in range(len(class_types))] for cl_a in range(len(class_types))]
c_tp = {ct: 0 for ct in range(len(class_types))}
c_tn = {ct: 0 for ct in range(len(class_types))}
c_fp = {ct: 0 for ct in range(len(class_types))}
c_fn = {ct: 0 for ct in range(len(class_types))}
for slot in slots:
tot_cor, joint_val_cor, cls_cor, pos_cor, ref_cor, conf_mat, ctp, ctn, cfp, cfn = get_joint_slot_correctness(preds, class_types, label_maps,
key_class_label_id=(key_class_label_id % slot),
key_class_prediction=(key_class_prediction % slot),
key_start_pos=(key_start_pos % slot),
key_start_prediction=(key_start_prediction % slot),
key_end_pos=(key_end_pos % slot),
key_end_prediction=(key_end_prediction % slot),
key_refer_id=(key_refer_id % slot),
key_refer_prediction=(key_refer_prediction % slot),
key_slot_groundtruth=(key_slot_groundtruth % slot),
key_slot_prediction=(key_slot_prediction % slot)
)
goal_correctness *= tot_cor
for cl_a in range(len(class_types)):
cls_acc[cl_a] += cls_cor[cl_a]
for cl_b in range(len(class_types)):
cls_conf[cl_a][cl_b] += list(conf_mat[cl_a][cl_b])
c_tp[cl_a] += ctp[cl_a]
c_tn[cl_a] += ctn[cl_a]
c_fp[cl_a] += cfp[cl_a]
c_fn[cl_a] += cfn[cl_a]
for ct in range(len(class_types)):
if c_tp[ct] + c_fp[ct] > 0:
precision = c_tp[ct] / (c_tp[ct] + c_fp[ct])
else:
precision = 1.0
if c_tp[ct] + c_fn[ct] > 0:
recall = c_tp[ct] / (c_tp[ct] + c_fn[ct])
else:
recall = 1.0
if precision + recall > 0:
f1 = 2 * ((precision * recall) / (precision + recall))
else:
f1 = 1.0
if c_tp[ct] + c_tn[ct] + c_fp[ct] + c_fn[ct] > 0:
acc = (c_tp[ct] + c_tn[ct]) / (c_tp[ct] + c_tn[ct] + c_fp[ct] + c_fn[ct])
else:
acc = 1.0
acc = np.mean(goal_correctness)
return acc
# MWOZ CONFIG
mwoz_config = {
"class_types": [
"none",
"dontcare",
"copy_value",
"true",
"false",
"refer",
"inform"
],
"slots": [
"taxi-leaveAt",
"taxi-destination",
"taxi-departure",
"taxi-arriveBy",
"restaurant-book_people",
"restaurant-book_day",
"restaurant-book_time",
"restaurant-food",
"restaurant-pricerange",
"restaurant-name",
"restaurant-area",
"hotel-book_people",
"hotel-book_day",
"hotel-book_stay",
"hotel-name",
"hotel-area",
"hotel-parking",
"hotel-pricerange",
"hotel-stars",
"hotel-internet",
"hotel-type",
"attraction-type",
"attraction-name",
"attraction-area",
"train-book_people",
"train-leaveAt",
"train-destination",
"train-day",
"train-arriveBy",
"train-departure"
],
"label_maps": {
"guest house": [
"guest houses"
],
"hotel": [
"hotels"
],
"centre": [
"center",
"downtown"
],
"north": [
"northern",
"northside",
"northend"
],
"east": [
"eastern",
"eastside",
"eastend"
],
"west": [
"western",
"westside",
"westend"
],
"south": [
"southern",
"southside",
"southend"
],
"cheap": [
"inexpensive",
"lower price",
"lower range",
"cheaply",
"cheaper",
"cheapest",
"very affordable"
],
"moderate": [
"moderately",
"reasonable",
"reasonably",
"affordable",
"mid range",
"mid-range",
"priced moderately",
"decently priced",
"mid price",
"mid-price",
"mid priced",
"mid-priced",
"middle price",
"medium price",
"medium priced",
"not too expensive",
"not too cheap"
],
"expensive": [
"high end",
"high-end",
"high class",
"high-class",
"high scale",
"high-scale",
"high price",
"high priced",
"higher price",
"fancy",
"upscale",
"nice",
"expensively",
"luxury"
],
"0": [
"zero"
],
"1": [
"one",
"just me",
"for me",
"myself",
"alone",
"me"
],
"2": [
"two"
],
"3": [
"three"
],
"4": [
"four"
],
"5": [
"five"
],
"6": [
"six"
],
"7": [
"seven"
],
"8": [
"eight"
],
"9": [
"nine"
],
"10": [
"ten"
],
"11": [
"eleven"
],
"12": [
"twelve"
],
"architecture": [
"architectural",
"architecturally",
"architect"
],
"boat": [
"boating",
"boats",
"camboats"
],
"boating": [
"boat",
"boats",
"camboats"
],
"camboats": [
"boating",
"boat",
"boats"
],
"cinema": [
"cinemas",
"movie",
"films",
"film"
],
"college": [
"colleges"
],
"concert": [
"concert hall",
"concert halls",
"concerthall",
"concerthalls",
"concerts"
],
"concerthall": [
"concert hall",
"concert halls",
"concerthalls",
"concerts",
"concert"
],
"entertainment": [
"entertaining"
],
"gallery": [
"museum"
],
"gastropubs": [
"gastropub"
],
"multiple sports": [
"multiple sport",
"multi sport",
"multi sports",
"sports",
"sporting"
],
"museum": [
"museums",
"gallery",
"galleries"
],
"night club": [
"night clubs",
"nightclub",
"nightclubs",
"club",
"clubs"
],
"park": [
"parks"
],
"pool": [
"swimming pool",
"swimming",
"pools",
"swimmingpool",
"water",
"swim"
],
"sports": [
"multiple sport",
"multi sport",
"multi sports",
"multiple sports",
"sporting"
],
"swimming pool": [
"swimming",
"pool",
"pools",
"swimmingpool",
"water",
"swim"
],
"theater": [
"theatre",
"theatres",
"theaters"
],
"theatre": [
"theater",
"theatres",
"theaters"
],
"abbey pool and astroturf pitch": [
"abbey pool and astroturf",
"abbey pool"
],
"abbey pool and astroturf": [
"abbey pool and astroturf pitch",
"abbey pool"
],
"abbey pool": [
"abbey pool and astroturf pitch",
"abbey pool and astroturf"
],
"adc theatre": [
"adc theater",
"adc"
],
"adc": [
"adc theatre",
"adc theater"
],
"addenbrookes hospital": [
"addenbrooke's hospital"
],
"cafe jello gallery": [
"cafe jello"
],
"cambridge and county folk museum": [
"cambridge and country folk museum",
"county folk museum"
],
"cambridge and country folk museum": [
"cambridge and county folk museum",
"county folk museum"
],
"county folk museum": [
"cambridge and county folk museum",
"cambridge and country folk museum"
],
"cambridge arts theatre": [
"cambridge arts theater"
],
"cambridge book and print gallery": [
"book and print gallery"
],
"cambridge contemporary art": [
"cambridge contemporary art museum",
"contemporary art museum"
],
"cambridge contemporary art museum": [
"cambridge contemporary art",
"contemporary art museum"
],
"cambridge corn exchange": [
"the cambridge corn exchange"
],
"the cambridge corn exchange": [
"cambridge corn exchange"
],
"cambridge museum of technology": [
"museum of technology"
],
"cambridge punter": [
"the cambridge punter",
"cambridge punters"
],
"cambridge punters": [
"the cambridge punter",
"cambridge punter"
],
"the cambridge punter": [
"cambridge punter",
"cambridge punters"
],
"cambridge university botanic gardens": [
"cambridge university botanical gardens",
"cambridge university botanical garden",
"cambridge university botanic garden",
"cambridge botanic gardens",
"cambridge botanical gardens",
"cambridge botanic garden",
"cambridge botanical garden",
"botanic gardens",
"botanical gardens",
"botanic garden",
"botanical garden"
],
"cambridge botanic gardens": [
"cambridge university botanic gardens",
"cambridge university botanical gardens",
"cambridge university botanical garden",
"cambridge university botanic garden",
"cambridge botanical gardens",
"cambridge botanic garden",
"cambridge botanical garden",
"botanic gardens",
"botanical gardens",
"botanic garden",
"botanical garden"
],
"botanic gardens": [
"cambridge university botanic gardens",
"cambridge university botanical gardens",
"cambridge university botanical garden",
"cambridge university botanic garden",
"cambridge botanic gardens",
"cambridge botanical gardens",
"cambridge botanic garden",
"cambridge botanical garden",
"botanical gardens",
"botanic garden",
"botanical garden"
],
"cherry hinton village centre": [
"cherry hinton village center"
],
"cherry hinton village center": [
"cherry hinton village centre"
],
"cherry hinton hall and grounds": [
"cherry hinton hall"
],
"cherry hinton hall": [
"cherry hinton hall and grounds"
],
"cherry hinton water play": [
"cherry hinton water play park"
],
"cherry hinton water play park": [
"cherry hinton water play"
],
"christ college": [
"christ's college",
"christs college"
],
"christs college": [
"christ college",
"christ's college"
],
"churchills college": [
"churchill's college",
"churchill college"
],
"cineworld cinema": [
"cineworld"
],
"clair hall": [
"clare hall"
],
"clare hall": [
"clair hall"
],
"the fez club": [
"fez club"
],
"great saint marys church": [
"great saint mary's church",
"great saint mary's",
"great saint marys"
],
"jesus green outdoor pool": [
"jesus green"
],
"jesus green": [
"jesus green outdoor pool"
],
"kettles yard": [
"kettle's yard"
],
"kings college": [
"king's college"
],
"kings hedges learner pool": [
"king's hedges learner pool",
"king hedges learner pool"
],
"king hedges learner pool": [
"king's hedges learner pool",
"kings hedges learner pool"
],
"little saint marys church": [
"little saint mary's church",
"little saint mary's",
"little saint marys"
],
"mumford theatre": [
"mumford theater"
],
"museum of archaelogy": [
"museum of archaeology"
],
"museum of archaelogy and anthropology": [
"museum of archaeology and anthropology"
],
"peoples portraits exhibition": [
"people's portraits exhibition at girton college",
"peoples portraits exhibition at girton college",
"people's portraits exhibition"
],
"peoples portraits exhibition at girton college": [
"people's portraits exhibition at girton college",
"people's portraits exhibition",
"peoples portraits exhibition"
],
"queens college": [
"queens' college",
"queen's college"
],
"riverboat georgina": [
"riverboat"
],
"saint barnabas": [
"saint barbabas press gallery"
],
"saint barnabas press gallery": [
"saint barbabas"
],
"saint catharines college": [
"saint catharine's college",
"saint catharine's"
],
"saint johns college": [
"saint john's college",
"st john's college",
"st johns college"
],
"scott polar": [
"scott polar museum"
],
"scott polar museum": [
"scott polar"
],
"scudamores punting co": [
"scudamore's punting co",
"scudamores punting",
"scudamore's punting",
"scudamores",
"scudamore's",
"scudamore"
],
"scudamore": [
"scudamore's punting co",
"scudamores punting co",
"scudamores punting",
"scudamore's punting",
"scudamores",
"scudamore's"
],
"sheeps green and lammas land park fen causeway": [
"sheep's green and lammas land park fen causeway",
"sheep's green and lammas land park",
"sheeps green and lammas land park",
"lammas land park",
"sheep's green",
"sheeps green"
],
"sheeps green and lammas land park": [
"sheep's green and lammas land park fen causeway",
"sheeps green and lammas land park fen causeway",
"sheep's green and lammas land park",
"lammas land park",
"sheep's green",
"sheeps green"
],
"lammas land park": [
"sheep's green and lammas land park fen causeway",
"sheeps green and lammas land park fen causeway",
"sheep's green and lammas land park",
"sheeps green and lammas land park",
"sheep's green",
"sheeps green"
],
"sheeps green": [
"sheep's green and lammas land park fen causeway",
"sheeps green and lammas land park fen causeway",
"sheep's green and lammas land park",
"sheeps green and lammas land park",
"lammas land park",
"sheep's green"
],
"soul tree nightclub": [
"soul tree night club",
"soul tree",
"soultree"
],
"soultree": [
"soul tree nightclub",
"soul tree night club",
"soul tree"
],
"the man on the moon": [
"man on the moon"
],
"man on the moon": [
"the man on the moon"
],
"the junction": [
"junction theatre",
"junction theater"
],
"junction theatre": [
"the junction",
"junction theater"
],
"old schools": [
"old school"
],
"vue cinema": [
"vue"
],
"wandlebury country park": [
"the wandlebury"
],
"the wandlebury": [
"wandlebury country park"
],
"whipple museum of the history of science": [
"whipple museum",
"history of science museum"
],
"history of science museum": [
"whipple museum of the history of science",
"whipple museum"
],
"williams art and antique": [
"william's art and antique"
],
"alimentum": [
"restaurant alimentum"