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Main1.py
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Main1.py
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import Groundtruthratings as GR
import numpy as np
def main():
# Define the number of groups and their names
group_names = ['Group1', 'Group2', 'Group3','Group4','Group5','Group6','Group7','Group8','Group9','Group10','Group11','Group12']
# Sample user ratings for groups
user_ratings = {
'Group1': {'User1': [.34, .33, .54, .36, .67, .77], 'User2': [.51, .45, .55, .42, .66, .42], 'User3': [.47, .52, .49, .53, .47, .53], 'User4': [.71, .62, .57, .44, .35, .32], 'User5': [.35, .74, .41, .49, .55, .46]},
'Group2': {'User6': [.23, .79, .44, .53, .65, .66], 'User7': [.20, .52, .31, .55, .76, .52], 'User8': [.45, .48, .55, .64, .47, .41], 'User9': [.35, .34, .46, .57, .61, .69], 'User10': [.32, .60, .39, .75, .51, .44]},
'Group3': {'User11': [.38, .34, .47, .57, .63, .61], 'User12': [.48, .48, .52, .39, .67, .46],
'User13': [.43, .55, .46, .48, .52, .56], 'User14': [.68, .62, .53, .42, .38, .37],
'User15': [.37, .67, .43, .47, .79, .53]},
'Group4': {'User16': [.27, .47, .43, .54, .64, .65], 'User17': [.28, .54, .37, .56, .89, .51],
'User18': [.41, .51, .51, .60, .52, .47], 'User19': [.41, .37, .47, .58, .60, .66],
'User20': [.29, .55, .43, .67, .59, .47]},
'Group5': {
'User3': [.47, .52, .49, .53, .47, .53],
'User4': [.71, .62, .57, .44, .35, .32],
'User8': [.45, .48, .55, .64, .47, .41],
'User9': [.35, .34, .46, .57, .61, .69],
'User13': [.43, .55, .46, .48, .52, .56],
'User14': [.68, .62, .53, .42, .38, .37],
'User15': [.37, .67, .43, .47, .79, .53],
'User17': [.28, .54, .37, .56, .89, .51],
'User18': [.41, .51, .51, .60, .52, .47],
'User19': [.41, .37, .47, .58, .60, .66]},
'Group6': {'User1': [.34, .33, .54, .36, .67, .77],
'User10': [.32, .60, .39, .75, .51, .44],
'User12': [.48, .48, .52, .39, .67, .46],
'User16': [.27, .47, .43, .54, .64, .65],
'User20': [.29, .55, .43, .67, .59, .47]},
'Group7': {'User11': [.38, .34, .47, .57, .63, .61],
'User6': [.23, .79, .44, .53, .65, .66]},
'Group8': {'User19': [0.5491680391401026, 0.4091159881431616, 0.5238989337199339, 0.4253645624334623,
0.5179955334454852, 0.5744569431178544],
'User9': [0.5073736059037911, 0.5173342165588989, 0.487800914983563, 0.4907568092728453,
0.4863949930461826, 0.5103394602347191],
'User24': [0.57856469275859, 0.44488525775372345, 0.5111817029841895, 0.49244928784992575,
0.4227241540950725, 0.5501949045584988],
'User29': [0.6825017643028186, 0.38451661384219077, 0.6134287290633799, 0.5019716119424882,
0.26961398254570845, 0.5479672983034142],
'User20': [0.6484139140799251, 0.5961266731894571, 0.5190812087069546, 0.49257057834261136,
0.3023249689883916, 0.44148265669266024],
'User30': [0.5101538358494029, 0.5925846090445834, 0.4002018683286202, 0.7034593272468473,
0.3348622369388216, 0.4587381225917246],
'User13': [0.7079540480563146, 0.2526116870044225, 0.6490527139328618, 0.4349512261565043,
0.43765895313320796, 0.5177713717166889],
'User3': [0.6298934473338592, 0.3964527065681313, 0.56961282847719, 0.509634786431142,
0.3975567875967332, 0.4968494435929444],
'User4': [0.51044533366122, 0.5348922970382303, 0.5030626587374444, 0.41024996484860593,
0.5127910884865944, 0.5285586572279048],
'User14': [0.5478790071412487, 0.47230169885603535, 0.5188067125670089, 0.5119460699837155,
0.465408332965161, 0.4836581784868305],
'User17': [0.32224325916571833, 0.5228935971318422, 0.429571267704719, 0.6286967523603214,
0.6229375899007911, 0.4736575337366078],
'User23': [0.543114939660581, 0.4477826768605702, 0.47134676630670275, 0.5137910157686174,
0.4802398289896075, 0.5437247724139213]},
'Group9': {'User13': [0.7079540480563146, 0.2526116870044225, 0.6490527139328618, 0.4349512261565043,
0.43765895313320796, 0.5177713717166889],
'User17': [0.32224325916571833, 0.5228935971318422, 0.429571267704719, 0.6286967523603214,
0.6229375899007911, 0.4736575337366078],
'User14': [0.5478790071412487, 0.47230169885603535, 0.5188067125670089, 0.5119460699837155,
0.465408332965161, 0.4836581784868305],
'User28': [0.5394440187444208, 0.4705764940039812, 0.4829233799776344, 0.5189443360502198,
0.46408122962395787, 0.5240305415997857],
'User26': [0.4987837121546044, 0.46128688930234985, 0.5052310419789682, 0.481649125482996,
0.49789216846407963, 0.555157062617002],
'User18': [0.6030973254896549, 0.48887630443079827, 0.5198043060095414, 0.5389307664963858,
0.40588245053463584, 0.4434088470389838]}, 'Group10': {
'User10': [0.4416748851621039, 0.4808340871184649, 0.4772634280547472, 0.5484929669053865,
0.5026392455059032, 0.5490953872533941],
'User6': [0.5293631530302498, 0.47084038736821515, 0.4974465103332209, 0.45045009091004634,
0.46975708086787954, 0.5821427774903883],
'User3': [0.6298934473338592, 0.3964527065681313, 0.56961282847719, 0.509634786431142, 0.3975567875967332,
0.4968494435929444],
'User23': [0.543114939660581, 0.4477826768605702, 0.47134676630670275, 0.5137910157686174,
0.4802398289896075, 0.5437247724139213],
'User1': [0.5364127124157784, 0.4872918672266379, 0.4976566906486129, 0.503292207210146, 0.4624961193816854,
0.5128504031171394]}, 'Group11': {
'User18': [0.6030973254896549, 0.48887630443079827, 0.5198043060095414, 0.5389307664963858,
0.40588245053463584, 0.4434088470389838],
'User6': [0.5293631530302498, 0.47084038736821515, 0.4974465103332209, 0.45045009091004634,
0.46975708086787954, 0.5821427774903883],
'User28': [0.5394440187444208, 0.4705764940039812, 0.4829233799776344, 0.5189443360502198,
0.46408122962395787, 0.5240305415997857],
'User5': [0.620899541849531, 0.7242200374023963, 0.36034684000147227, 0.5150608555782032,
0.17104251251589664, 0.6084302126525005],
'User10': [0.4416748851621039, 0.4808340871184649, 0.4772634280547472, 0.5484929669053865,
0.5026392455059032, 0.5490953872533941],
'User1': [0.5364127124157784, 0.4872918672266379, 0.4976566906486129, 0.503292207210146, 0.4624961193816854,
0.5128504031171394],
'User26': [0.4987837121546044, 0.46128688930234985, 0.5052310419789682, 0.481649125482996,
0.49789216846407963, 0.555157062617002],
'User4': [0.51044533366122, 0.5348922970382303, 0.5030626587374444, 0.41024996484860593, 0.5127910884865944,
0.5285586572279048],
'User9': [0.5073736059037911, 0.5173342165588989, 0.487800914983563, 0.4907568092728453, 0.4863949930461826,
0.5103394602347191],
'User22': [0.6395872987060615, 0.5646070351595447, 0.5024542335780562, 0.4604219427452965,
0.40419548835392544, 0.42873400145711554],
'User14': [0.5478790071412487, 0.47230169885603535, 0.5188067125670089, 0.5119460699837155,
0.465408332965161, 0.4836581784868305],
'User19': [0.5491680391401026, 0.4091159881431616, 0.5238989337199339, 0.4253645624334623,
0.5179955334454852, 0.5744569431178544],
'User16': [0.6477057086556136, 0.5364834428466186, 0.5883956146042951, 0.47024988503773096,
0.38344833726712907, 0.37371701158861265],
'User20': [0.6484139140799251, 0.5961266731894571, 0.5190812087069546, 0.49257057834261136,
0.3023249689883916, 0.44148265669266024],
'User7': [0.5824837743313326, 0.5100795734149429, 0.5263584371057798, 0.43652659864724797,
0.47573804383850704, 0.4688135726621898]}, 'Group12': {
'User25': [0.5272610227773545, 0.4753575060749822, 0.4897669815812587, 0.5081430583039808,
0.4616813440900173, 0.5377900871724065],
'User29': [0.6825017643028186, 0.38451661384219077, 0.6134287290633799, 0.5019716119424882,
0.26961398254570845, 0.5479672983034142],
'User2': [0.5575603170327784, 0.42263181822082396, 0.5090018457953226, 0.43905166409395485,
0.500141325504767, 0.571613029352353]},
}
# Ground truth group ratings for groups
ground_truth_group_ratings = {
'Group1': [.48, .53, .51, .45, .54, .50],
'Group2': [.31, .55, .43, .61, .60, .55],
'Group3': [.47, .53, .48, .47, .60, .51],
'Group4': [.33, .49, .44, .59, .65, .55],
'Group5': [0.45600000000000007, 0.522, 0.4840000000000001, 0.5289999999999999, 0.5599999999999999, 0.505],
'Group6': [0.34, 0.48599999999999993, 0.462, 0.542, 0.616, 0.558],
'Group7': [0.305, 0.5650000000000001, 0.45499999999999996, 0.55, 0.64, 0.635],
'Group8': (0.5614754905877977, 0.4642915018326039, 0.5164205254593807, 0.5096534993864239, 0.4375423708443131,
0.5106166118894808),
'Group9': (
0.536566895125327, 0.4447577784549049, 0.5175649036951223, 0.5191863794216904, 0.48231012077030555,
0.49961392253264975),
'Group10': (
0.5360918275205144, 0.4566403450284039, 0.5026652447640948, 0.5051322134450676, 0.4625378124683618,
0.5369325567735574),
'Group11': (
0.560182135364376, 0.5149911328039821, 0.5007020607338156, 0.48366045066299335, 0.43480583955236096,
0.5056583808824721),
'Group12': (
0.5891077013709838, 0.42750197937933226, 0.5373991854799871, 0.48305544478014123, 0.4104788840468309,
0.5524568049427246)
}
#real_top2 = {[Item5,Item2],[Item5,Item4],[Item5,Item2],[Item5,Item4],[Item5,Item4],[Item5,Item6],[Item6,Item5]}
real_top2 = {'Group1':[5,2],'Group2':[5,4],'Group3':[5,2],'Group4':[5,4],'Group5':[5,4],'Group6':[5,6],'Group7':[5,6]}
num_top_items = 2
# Calculate average and ground truth ratings
average_ratings_vectors, ground_truth_vectors = GR.calculate_average_and_ground_truth_ratings(user_ratings, ground_truth_group_ratings)
# Calculate cosine similarity
cosine_similarity_scores = GR.calculate_cosine_similarity(average_ratings_vectors, ground_truth_vectors)
# Calculate cosine similarity without the current user
cosine_similarity_scores_without_user = GR.calculate_cosine_similarity_without_user(user_ratings, ground_truth_group_ratings)
# Calculate Shapley values
shapley_values = GR.calculate_shapley_values(cosine_similarity_scores, cosine_similarity_scores_without_user, group_names,user_ratings)
# Calculate group ratings
group_ratings = GR.calculate_group_ratings(user_ratings, shapley_values,group_names,ground_truth_group_ratings)
# Find the top items
top_items = GR.find_top_items(group_ratings, num_top_items)
# Print the top items for each group
for group_name, items in top_items.items():
print(f"Top {num_top_items} Priority Items for {group_name}:")
for item in items:
print(f"Item {item + 1}")
# Calculate and print user satisfaction for each group
threshold = 0.4 # Set your desired threshold value
for group_name in group_names:
satisfaction_values = GR.calculate_user_satisfaction(group_name, top_items, user_ratings, threshold)
total_satisfaction = sum(satisfaction_values) / len(top_items[group_name])
print(f"Total User Satisfaction for Group {group_name}: {total_satisfaction * 100:.2f}%")
# print(f"User Satisfaction for Group {group_name}:")
# for i, item_idx in enumerate(top_items[group_name]):
# print(f"Item {item_idx + 1}: {satisfaction_values[i] * 100:.2f}% users satisfied")
# Call this function after calculating the top_items
precision_result = GR.calculate_precision(group_name, top_items, num_top_items, user_ratings, threshold)
print(f"Precision for Group {group_name}: {precision_result * 100:.2f}%")
# precision_dict = GR.calculate_precision1(ground_truth_group_ratings, top_items, group_names, num_top_items=2, threshold=0.6)
precision = GR.calculate_precision1(ground_truth_group_ratings, top_items, group_names)
print("Precision:", precision)
if __name__ == "__main__":
main()