OpeNTF / param.py

Upload param.py

a1b3d87 about 3 years ago

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import random
import torch
import numpy as np

random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

np.random.seed(0)

settings = {
    'model':{
        'baseline': {
            'random': {
                'b': 128
            },
            'fnn':{
                'l': [128],  # list of number of nodes in each layer
                'lr': 0.1,  # learning rate
                'b': 128,  # batch size
                'e': 20,  # epoch
                'nns': 3,  # number of negative samples
                'ns': 'unigram_b',  # 'uniform', 'unigram', 'unigram_b'
            },
            'bnn':{
                'l': [128],  # list of number of nodes in each layer
                'lr': 0.1,  # learning rate
                'b': 128,  # batch size
                'e': 20,  # epoch
                'nns': 3,  # number of negative samples
                'ns': 'unigram_b',  # 'uniform', 'unigram', 'unigram_b'
                's': 1  # # sample_elbo for bnn
            },
            'nmt': {
                'base_config': './mdl/nmt_config.yaml'
            },
            'caser': {},
            'rrn': {
                'with_zero': True
            },
            'emb':{
                'd': 100,# embedding dimension
                'e': 100,# epoch
                'dm': 1,# training algorithm. 1: distributed memory (PV-DM), 0: distributed bag of words (PV-DBOW)
                'w': 1 #cooccurrence window
            }
        },
        'cmd': ['train', 'test', 'eval', 'fair'],  # 'train', 'test', 'eval', 'plot', 'agg', 'adila'
        'nfolds': 5,
        'train_test_split': 0.85,
        'step_ahead': 2,#for now, it means that whatever are in the last [step_ahead] time interval will be the test set!
    },
    'data':{
        'domain': {
            'dblp':{},
            'uspt':{},
            'imdb':{},
        },
        'location_type': 'country', #should be one of 'city', 'state', 'country' and represents the location of members in teams (not the location of teams)
        'filter': {
            'min_nteam': 75,
            'min_team_size': 3,
        },
        'parallel': 1,
        'ncore': 0,# <= 0 for all
        'bucket_size': 500
    },
    'fair': {'np_ratio': None,
              'fairness': ['det_greedy',],
              'k_max': None,
              'fairness_metrics': {'ndkl'},
              'utility_metrics': {'map_cut_2,5,10'},
              'eq_op': False,
              'mode': 0,
              'core': -1,
              'attribute': ['gender', 'popularity']},
}

	import random
	import torch
	import numpy as np

	random.seed(0)
	torch.manual_seed(0)
	torch.cuda.manual_seed_all(0)

	np.random.seed(0)

	settings = {
	'model':{
	'baseline': {
	'random': {
	'b': 128
	},
	'fnn':{
	'l': [128], # list of number of nodes in each layer
	'lr': 0.1, # learning rate
	'b': 128, # batch size
	'e': 20, # epoch
	'nns': 3, # number of negative samples
	'ns': 'unigram_b', # 'uniform', 'unigram', 'unigram_b'
	},
	'bnn':{
	'l': [128], # list of number of nodes in each layer
	'lr': 0.1, # learning rate
	'b': 128, # batch size
	'e': 20, # epoch
	'nns': 3, # number of negative samples
	'ns': 'unigram_b', # 'uniform', 'unigram', 'unigram_b'
	's': 1 # # sample_elbo for bnn
	},
	'nmt': {
	'base_config': './mdl/nmt_config.yaml'
	},
	'caser': {},
	'rrn': {
	'with_zero': True
	},
	'emb':{
	'd': 100,# embedding dimension
	'e': 100,# epoch
	'dm': 1,# training algorithm. 1: distributed memory (PV-DM), 0: distributed bag of words (PV-DBOW)
	'w': 1 #cooccurrence window
	}
	},
	'cmd': ['train', 'test', 'eval', 'fair'], # 'train', 'test', 'eval', 'plot', 'agg', 'adila'
	'nfolds': 5,
	'train_test_split': 0.85,
	'step_ahead': 2,#for now, it means that whatever are in the last [step_ahead] time interval will be the test set!
	},
	'data':{
	'domain': {
	'dblp':{},
	'uspt':{},
	'imdb':{},
	},
	'location_type': 'country', #should be one of 'city', 'state', 'country' and represents the location of members in teams (not the location of teams)
	'filter': {
	'min_nteam': 75,
	'min_team_size': 3,
	},
	'parallel': 1,
	'ncore': 0,# <= 0 for all
	'bucket_size': 500
	},
	'fair': {'np_ratio': None,
	'fairness': ['det_greedy',],
	'k_max': None,
	'fairness_metrics': {'ndkl'},
	'utility_metrics': {'map_cut_2,5,10'},
	'eq_op': False,
	'mode': 0,
	'core': -1,
	'attribute': ['gender', 'popularity']},
	}