def runner(config):
generate_args, analyze_args, inf_seed = _munge_config(config)
# generate synthetic data
T, M_c, M_r, X_L, X_D = du.generate_clean_state(max_mean=10, max_std=1,
**generate_args)
table_shape = map(len, (T, T[0]))
start_dims = du.get_state_shape(X_L)
# run engine with do_timing = True
engine = LocalEngine(inf_seed)
X_L, X_D, (elapsed_secs,) = engine.analyze(M_c, T, X_L, X_D,
do_timing=True,
**analyze_args
)
#
end_dims = du.get_state_shape(X_L)
same_shape = start_dims == end_dims
summary = dict(
elapsed_secs=elapsed_secs,
same_shape=same_shape,
)
ret_dict = dict(
config=config,
summary=summary,
table_shape=table_shape,
start_dims=start_dims,
end_dims=end_dims,
)
return ret_dict
def time_analyze_helper(table_data, data_dict, command_dict):
# FIXME: this is a kludge
command_dict.update(data_dict)
#
gen_seed = data_dict['SEED']
num_clusters = data_dict['num_clusters']
num_cols = data_dict['num_cols']
num_rows = data_dict['num_rows']
num_views = data_dict['num_views']
T, M_c, M_r, X_L, X_D = ttu.generate_clean_state(gen_seed,
num_clusters,
num_cols, num_rows,
num_views,
max_mean=10, max_std=1)
table_data = dict(T=T,M_c=M_c)
data_dict['X_L'] = X_L
data_dict['X_D'] = X_D
start_dims = du.get_state_shape(X_L)
with gu.Timer('time_analyze_helper', verbose=False) as timer:
inner_ret_dict = analyze_helper(table_data, data_dict, command_dict)
end_dims = du.get_state_shape(inner_ret_dict['X_L'])
T = table_data['T']
table_shape = (len(T), len(T[0]))
ret_dict = dict(
table_shape=table_shape,
start_dims=start_dims,
end_dims=end_dims,
elapsed_secs=timer.elapsed_secs,
kernel_list=command_dict['kernel_list'],
n_steps=command_dict['n_steps'],
)
return ret_dict
def time_analyze_helper(table_data, data_dict, command_dict):
# FIXME: this is a kludge
command_dict.update(data_dict)
#
gen_seed = data_dict['SEED']
num_clusters = data_dict['num_clusters']
num_cols = data_dict['num_cols']
num_rows = data_dict['num_rows']
num_views = data_dict['num_views']
T, M_c, M_r, X_L, X_D = ttu.generate_clean_state(gen_seed,
num_clusters,
num_cols, num_rows,
num_views,
max_mean=10, max_std=1)
table_data = dict(T=T,M_c=M_c)
data_dict['X_L'] = X_L
data_dict['X_D'] = X_D
start_dims = du.get_state_shape(X_L)
with gu.Timer('time_analyze_helper', verbose=False) as timer:
inner_ret_dict = analyze_helper(table_data, data_dict, command_dict)
end_dims = du.get_state_shape(inner_ret_dict['X_L'])
T = table_data['T']
table_shape = (len(T), len(T[0]))
ret_dict = dict(
table_shape=table_shape,
start_dims=start_dims,
end_dims=end_dims,
elapsed_secs=timer.elapsed_secs,
kernel_list=command_dict['kernel_list'],
n_steps=command_dict['n_steps'],
)
return ret_dict
def time_analyze_helper(table_data, dict_in):
start_dims = du.get_state_shape(dict_in['X_L'])
with gu.Timer('time_analyze_helper', verbose=False) as timer:
inner_ret_dict = analyze_helper(table_data, dict_in)
end_dims = du.get_state_shape(inner_ret_dict['X_L'])
T = table_data['T']
table_shape = (len(T), len(T[0]))
ret_dict = dict(
table_shape=table_shape,
start_dims=start_dims,
end_dims=end_dims,
elapsed_secs=timer.elapsed_secs,
kernel_list=dict_in['kernel_list'],
n_steps=dict_in['n_steps'],
)
return ret_dict
def time_analyze_helper(table_data, dict_in):
start_dims = du.get_state_shape(dict_in['X_L'])
with gu.Timer('time_analyze_helper', verbose=False) as timer:
inner_ret_dict = analyze_helper(table_data, dict_in)
end_dims = du.get_state_shape(inner_ret_dict['X_L'])
T = table_data['T']
table_shape = (len(T), len(T[0]))
ret_dict = dict(
table_shape=table_shape,
start_dims=start_dims,
end_dims=end_dims,
elapsed_secs=timer.elapsed_secs,
kernel_list=dict_in['kernel_list'],
n_steps=dict_in['n_steps'],
)
return ret_dict