def main():
# These ranges likely contain the global maximum. Find it.
center = [50, 100, 150, 200, 250]
spread = np.arange(50, 1001, 50)
params = {'centers': center, 'spread': spread}
run_optimization(get_rbf_nn_prediction, params, 'optimal_rbf.shelf', 'RBF')
def main():
alphas = list(np.arange(0.01, 1.01, 0.01))
params = {'alpha': alphas}
run_optimization(get_lasso_prediction,
params,
'optimal_lasso.shelf',
'LASSO',
backend=PARALLEL_BACKEND)
def main():
alphas = list(np.arange(0.05, 1.00, 0.05))
l1_ratios = list(np.arange(0.05, 1.00, 0.05))
params = {'alpha': alphas, 'l1_ratio': l1_ratios}
run_optimization(get_en_prediction,
params,
'optimal_en.shelf',
'EN',
backend=PARALLEL_BACKEND)
def main():
nums = (1e10, 1e5, 1e1, 0, -1e1, -1e5, -1e10)
alpha_1 = nums
alpha_2 = nums
lambda_1 = nums
lambda_2 = nums
params = { 'alpha_1': alpha_1, 'alpha_2': alpha_2
, 'lambda_1': lambda_1, 'lambda_2': lambda_2
}
run_optimization(get_brr_prediction, params, 'optimal_brr.shelf', 'BRR', backend=PARALLEL_BACKEND)
def main():
params = { 'hidden': HIDDEN
, 'epochs' : (EPOCHS,)
}
backend = PARALLEL_BACKEND
if get_is_on_gpu() or get_is_time_stats() or get_should_plot():
backend = SINGLE_CORE_BACKEND
run_optimization( get_net_prediction, params, 'optimal_nn.shelf', 'N'
, sample_size_multiplier=DOUBLE_MULTIPLIER
, backend=backend, retry_nans=True)
def main():
params = { 'hidden': HIDDEN
, 'dropout_prob': DROPOUT
, 'weight_decay': WEIGHT_DECAY
, 'epochs': (EPOCHS,)
}
backend = PARALLEL_BACKEND
if get_is_on_gpu() or get_is_time_stats() or get_should_plot():
backend = SINGLE_CORE_BACKEND
run_optimization(get_net_prediction, params, 'optimal_wddonn.shelf', 'NWDDO', backend=backend, retry_nans=True)
def main():
nums = (1e10, 1e5, 1e1, 0, -1e1, -1e5, -1e10)
alpha_1 = nums
alpha_2 = nums
lambda_1 = nums
lambda_2 = nums
params = {
'alpha_1': alpha_1,
'alpha_2': alpha_2,
'lambda_1': lambda_1,
'lambda_2': lambda_2
}
run_optimization(get_brr_prediction,
params,
'optimal_brr.shelf',
'BRR',
backend=PARALLEL_BACKEND)
def main():
alphas = list(np.arange(0.05, 1.00, 0.05))
l1_ratios = list(np.arange(0.05, 1.00, 0.05))
params = {'alpha': alphas, 'l1_ratio': l1_ratios}
run_optimization(get_en_prediction, params, 'optimal_en.shelf', 'EN', backend=PARALLEL_BACKEND)
def main():
# alphas = list(np.logspace(-8., 8., base=10, num=17)) # Wide search
alphas = list(np.linspace(10, 1000, num=100)) # Narrow Search
params = {'alpha': alphas}
run_optimization(get_rr_prediction, params, 'optimal_rr.shelf',
'RR') # Already parallel.
def main():
params = {}
run_optimization(get_lr_prediction, params, 'optimal_lr.shelf', 'OLS')
def main():
alphas = list(np.arange(0.01, 1.01,0.01))
params = {'alpha': alphas}
run_optimization( get_lasso_prediction, params
, 'optimal_lasso.shelf', 'LASSO', backend=PARALLEL_BACKEND)
def main():
params = {}
run_optimization(get_lr_prediction, params, 'optimal_lr.shelf', 'OLS')
def main():
# alphas = list(np.logspace(-8., 8., base=10, num=17)) # Wide search
alphas = list(np.linspace(10, 1000, num=100)) # Narrow Search
params = {'alpha': alphas}
run_optimization(get_rr_prediction, params, 'optimal_rr.shelf', 'RR') # Already parallel.
