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Update prior handling with positive variationals
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1. the hyperprior is gaussian
2. the prior is gaussian
3. the lower bound of the lognormal is lowered
4. handling is changed to make it clearer
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@kanodiaayush
kanodiaayush committed Jun 17, 2024
1 parent b07d90a commit 0e034de
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54 changes: 28 additions & 26 deletions bemb/model/bayesian_coefficient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,26 +89,28 @@ def __init__(self,
self.mean_clamp = (np.log(0.1), np.log(100.0))
self.logstd_clamp = (np.log(0.1), np.log(10000.0))
shape = prior_mean ** 2 / prior_variance
assert shape > np.exp(self.mean_clamp[0])**2 and shape < np.exp(self.mean_clamp[1])**2, f'Gamma shape {shape} is out of range, should be in ({np.exp(self.mean_clamp[0])**2}, {np.exp(self.mean_clamp[1])**2})'
rate = prior_mean / prior_variance
assert rate > np.exp(self.logstd_clamp[0]) and rate < np.exp(self.logstd_clamp[1]), f'Gamma rate {rate} is out of range, should be in ({np.exp(self.logstd_clamp[0])}, {np.exp(self.logstd_clamp[1])})'
# prior_mean stores ln(shape) for gamma
prior_mean = np.log(shape)
# prior_variance stores rate for gamma
prior_variance = rate
# prior_mean = np.log(prior_mean)
# prior_variance = prior_variance
if not obs2prior:
assert shape > np.exp(self.mean_clamp[0])**2 and shape < np.exp(self.mean_clamp[1])**2, f'Gamma shape {shape} is out of range, should be in ({np.exp(self.mean_clamp[0])**2}, {np.exp(self.mean_clamp[1])**2})'
assert rate > np.exp(self.logstd_clamp[0]) and rate < np.exp(self.logstd_clamp[1]), f'Gamma rate {rate} is out of range, should be in ({np.exp(self.logstd_clamp[0])}, {np.exp(self.logstd_clamp[1])})'
# prior_mean stores ln(shape) for gamma
prior_mean = np.log(shape)
# prior_variance stores rate for gamma
prior_variance = rate
# prior_mean = np.log(prior_mean)
# prior_variance = prior_variance

elif distribution == 'lognormal':
# mean is exp(mu + sigma^2/2), variance is (exp(sigma^2) - 1) * exp(2*mu + sigma^2)
# prior_mean in -2, exp(3)
self.mean_clamp = (-2.0, np.exp(1.5))
self.mean_clamp = (-10.0, np.exp(1.5))
# sigma sq clamp exp(-20), exp(1.5)
# therefore sigma in (exp(-10), exp(0.75))
# therefore log sigma in (-10, 0.75)
self.logstd_clamp = (-10.0, 0.75)
assert prior_mean > self.mean_clamp[0] and prior_mean < self.mean_clamp[1], f'Lognormal distribution requires prior_mean in {self.mean_clamp}, given {prior_mean}'
assert np.sqrt(prior_variance) > np.exp(self.logstd_clamp[0]) and np.sqrt(prior_variance) < np.exp(self.logstd_clamp[1]), f'Lognormal distribution requires prior_variance in {self.logstd_clamp}, given {prior_variance}'
if not obs2prior:
assert prior_mean > self.mean_clamp[0] and prior_mean < self.mean_clamp[1], f'Lognormal distribution requires prior_mean in {self.mean_clamp}, given {prior_mean}'
assert np.sqrt(prior_variance) > np.exp(self.logstd_clamp[0]) and np.sqrt(prior_variance) < np.exp(self.logstd_clamp[1]), f'Lognormal distribution requires prior_variance in {self.logstd_clamp}, given {prior_variance}'
# assert prior_mean > np.exp(-100.0) and prior_mean < np.exp(10.0), f'Lognormal distribution requires shape in (exp(-100), exp(10)), given {prior_mean}'
# assert prior_variance > np.exp(-100.0) and prior_variance < np.exp(2.0), f'Lognormal distribution requires rate in (exp(-100), exp(2)), given {prior_variance}'

Expand Down Expand Up @@ -254,6 +256,7 @@ def variational_mean(self) -> torch.Tensor:

elif self.distribution == 'lognormal':
M = (torch.minimum((M.exp() + 0.000001), torch.tensor(1e3)))
# M = torch.minimum(M + 0.000001, torch.tensor(1e3))

if self.is_H and (self.H_zero_mask is not None):
# a H-variable with zero-entry restriction.
Expand Down Expand Up @@ -314,24 +317,23 @@ def log_prior(self,
cov_diag=self.prior_cov_diag).log_prob(sample)
elif self.distribution == 'lognormal':
mu = torch.clamp(mu, min=-100.0, max=10.0)
out = LogNormal(loc=mu, scale=np.sqrt(self.prior_variance)).log_prob(sample)
out = torch.sum(out, dim=-1)
out = LowRankMultivariateNormal(loc=mu,
cov_factor=self.prior_cov_factor,
cov_diag=self.prior_cov_diag).log_prob(sample)
# out = LogNormal(loc=mu, scale=np.sqrt(self.prior_variance)).log_prob(sample)
# out = torch.sum(out, dim=-1)
# out = torch.zeros((num_seeds, num_classes), device=sample.device)

elif self.distribution == 'gamma':
mu = torch.clamp(mu, min=-100.0, max=4.0)
concentration = torch.exp(mu)
rate = self.prior_variance
'''
print(concentration)
print('concentration')
print(rate)
print('rate')
'''
out = Gamma(concentration=concentration,
rate=rate).log_prob(sample)
# sum over the last dimension
out = torch.sum(out, dim=-1)
out = LowRankMultivariateNormal(loc=mu,
cov_factor=self.prior_cov_factor,
cov_diag=self.prior_cov_diag).log_prob(sample)
# concentration = torch.exp(mu)
# rate = self.prior_variance
# out = Gamma(concentration=concentration,
# rate=rate).log_prob(sample)
# out = torch.sum(out, dim=-1)

# out = torch.zeros((num_seeds, num_classes), device=sample.device)
assert out.shape == (num_seeds, num_classes)
Expand Down Expand Up @@ -443,7 +445,7 @@ def clamp_params(self) -> None:
self.variational_mean_flexible.data = torch.clamp(
self.variational_mean_flexible.data, min=self.mean_clamp[0], max=self.mean_clamp[1])
self.variational_logstd.data = torch.clamp(
self.variational_logstd.data, min=self.mean_clamp[0], max=self.logstd_clamp[1])
self.variational_logstd.data, min=self.logstd_clamp[0], max=self.logstd_clamp[1])
# elif self.distribution == 'gamma':
# self.variational_mean_flexible.data = torch.clamp(
# self.variational_mean_flexible.data, min=-2.0, max=4)
Expand Down
19 changes: 10 additions & 9 deletions tutorials/supermarket/configs3_1.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,19 +3,19 @@ device: cuda
# data_dir: /home/tianyudu/Data/MoreSupermarket/20180101-20191231_13/tsv/
# data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/m_9999/20180101-20191231_42/tsv
# data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/m_9999/2
data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/m_1904-1887-1974-2012-1992/20180101-20191231_44/tsv/
# data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/m_1904-1887-1974-2012-1992/20180101-20191231_44/tsv/
# data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/m_1887/chunked_1
# data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/sims34/1
data_dir: /oak/stanford/groups/athey/MoreSupermarkets/csv/new_data/nf_runs/sims34/1
# utility: lambda_item
# utility: lambda_item + theta_user * alpha_item
# utility: lambda_item + theta_user * alpha_item + zeta_user * item_obs
# utility: lambda_item + theta_user * alpha_item + gamma_user * beta_item * price_obs
# utility: lambda_item + theta_user * alpha_item + gamma_user * price_obs
# utility: lambda_item + theta_user * alpha_item - nfact_category * gamma_user * price_obs
utility: theta_user * alpha_item - nfact_category * gamma_user * price_obs
# utility: theta_user * alpha_item + nfact_category * gamma_user * price_obs
# utility: -nfact_category * gamma_user * price_obs
# utility: -nfact_category * gamma_user * price_obs
# utility: -gamma_user * price_obs
utility: -gamma_user * price_obs
# utility: lambda_item
out_dir: ./output/
# model configuration.
Expand All @@ -39,8 +39,9 @@ coef_dist_dict:
default: 'gaussian'
# gamma_user: 'gamma'
# nfact_category: 'gamma'
gamma_user: 'gaussian'
nfact_category: 'lognormal'
# gamma_user: 'gamma'
gamma_user: 'lognormal'
# nfact_category: 'gamma'
prior_mean:
default: 0.0
# mean is shape for gamma variable
Expand All @@ -53,8 +54,8 @@ prior_variance:
default: 100000.0
# variance is rate for gamma variable
# shape is mean / var
gamma_user: 10.0
nfact_category: 10.0
gamma_user: 2.0
nfact_category: 2.0
#### optimization.
trace_log_q: False
shuffle: False
Expand All @@ -63,7 +64,7 @@ num_epochs: 200
learning_rate: 0.03
num_mc_seeds: 1
num_price_obs: 1
obs_user: True
obs_user: False
obs_item: False
patience: 10
complete_availability: True

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