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################### Select Models ##################
models = [
['basicFM','FM','Basic',['2']],
#['basicMovTag','FM','BasicMovieTag',['2']],
#['nearNeib', 'FM', 'NearestNeighbor',['2']],
#['rmtThresh5t','FM','RelatedMovieTagThreshold',['2']],
#['rmtThresh2','FM','RelatedMovieTagThreshold2',['2']],
#['userHist','FM','UserHistory',['2']],
#['userSocial','FM','UserSocial',['2']],
['basicSVD','SVD','Basic',[]]
#['ImplicitFeedbackSVD','SVD','ImplicitFeedback',[]],
#['NeighborhoodMovieTag', 'SVD' , 'Neighborhood' , ['MovieTag']]
]
# Defining models:
# Each element is a list:
# [tag,program,setting,misc]
# tag is the name of the model
# program specifies which program to use
# setting defines which features will be used
# Misc depends on the program:
# For FM: [dims]
# For SVDFeature: []
ensembleModels = [['OLSR', 'OLS', []],
['OLSI','OLSI',['2']],
['RR' ,'RR',['2']],
['BRT','BRT',[]],
['BMAR','BMAR',[]],
#['RFR' ,'RFR' ,[]], # Large memory requirement
['Lasso', 'Lasso', []],
['GBRT','GBRT',['200']]
]
# Defining ensemble models:
# Each element is a list:
# [tag,modelType,misc]
# tag is the name of the model
# modelType defines what model to use
# misc is the arguments to the program
synthModel = ['GBRT','GBRT',['200']]
################### Select Parts ##################
TRIALS = 1
PRE_PROCESS = True
# ---- ---- PreProcess Selection ---- ---- #
TEST_SUBSET = True # uses small data set
### Baidu Specific Preprocess ###
PROCESS_TAGS = False # generates new file for movie tag feature
PROCESS_SOCIAL = False # cuts out all the extra social users not in data set
PROCESS_HISTORY = False
### End Baidu Specific ####
DE_EFFECT = False # If De-effect is false, intermittent predictions are correct
# ---- ---- ---- ---- ----- ---- ---- ---- #
SETUP_MODELS = True
RUN_MODELS = True
SETUP_HYBRID = True
RUN_HYBRID = True
SETUP_SYNTHESIZE = True
RUN_SYNTHESIZE = True
POST_PROCESS = True
################## Select Bootstrap Parameters ##################
BOOTSTRAP_SPLITS = [.8, .8, .8]
################## Timer ##############
TIME_RUN = False
################## Factorization Machines ##########
FM_ITER = 100
FM_STR_ITER = str(FM_ITER)
FM_INIT_STD = '.3'
################## SVD Feature #####################
SVD_LEARNING_RATE = '.005'
SVD_REGULARIZATION_ITEM = '.004'
SVD_REGULARIZATION_USER = '.004'
SVD_REGULARIZATION_GLOBAL = '.001'
SVD_REGULARIZATION_FEEDBACK = '.004'
SVD_NUM_FACTOR = '64'
SVD_ACTIVE_TYPE = '0'
SVD_NUM_ITER = '40'
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