if __name__ == '__main__':
import os
from sklearn.externals import joblib
from slda_utils__param_manager import (
flatten_to_differentiable_param_vec,
unflatten_to_common_param_dict,
)
import slda_loss__autograd
# Simplest possible test
# Load the toy bars dataset
# Load "true" bars topics
# Compute the loss
dataset_path = os.path.expandvars("$PC_REPO_DIR/datasets/toy_bars_3x3/")
dataset = slda_utils__dataset_manager.load_dataset(dataset_path, split_name='train')
n_batches = 100
# Load "true" 4 bars
dim_P = dict(n_states=4, n_labels=1, n_vocabs=9)
model_hyper_P = dict(alpha=1.1, tau=1.1, lambda_w=0.001, weight_x=1.0, weight_y=1.0)
GP = joblib.load(
os.path.join(dataset_path, "good_loss_x_K4_param_dict.dump"))
for key in GP.keys():
if key not in ['topics_KV', 'w_CK']:
del GP[key]
param_vec = flatten_to_differentiable_param_vec(**GP)
GPA = unflatten_to_common_param_dict(param_vec, **dim_P)
GPB = unflatten_to_common_param_dict__tf(param_vec, **dim_P)
return ans_dict
else:
return loss_ttl
if __name__ == '__main__':
import os
from sklearn.externals import joblib
from slda_utils__dataset_manager import load_dataset
# Simplest possible test
# Load the toy bars dataset
# Load "true" bars topics
# Compute the loss
dataset_path = os.path.expandvars("$PC_REPO_DIR/datasets/toy_bars_3x3/")
dataset = load_dataset(dataset_path, split_name='train')
# Load "true" 4 bars
GP = joblib.load(
os.path.join(dataset_path, "good_loss_x_K4_param_dict.dump"))
topics_KV = GP['topics_KV']
w_CK = GP['w_CK']
loss_dict = calc_loss__slda(dataset=dataset,
topics_KV=topics_KV,
w_CK=w_CK,
nef_alpha=1.1,
tau=1.1,
lambda_w=0.001,
return_dict=True)
print(loss_dict['summary_msg'])