def benchmark(dataset='100k',
random_state=0,
n_jobs=1):
results = copy.deepcopy(estimator_grid)
hyperparams = _get_hyperparams()
cvparams = _get_cvparams()
X_tr, X_te = get_recsys_data(dataset, random_state)
subdir = 'benches'
output_dir = expanduser(join(trace_dir, subdir))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
indices = sorted(results.keys())
for idx in indices:
mf = results[idx]['estimator']
mf.set_params(**hyperparams[idx][dataset])
mf.set_params(**cvparams[idx][dataset])
mf.set_params(random_state=random_state)
res = Parallel(n_jobs=n_jobs,
max_nbytes=None)(
delayed(single_fit_bench)(results[idx]['estimator'],
X_tr, X_te)
for idx in indices)
times, rmses = zip(*res)
for time, rmse, idx in zip(times, rmses, indices):
results[idx]['timings'] = time
results[idx]['rmse'] = rmse
results[idx].pop('estimator')
with open(join(output_dir, 'results_%s.json' % dataset), 'w+') as f:
json.dump(results, f)
def benchmark(dataset='100k', random_state=0, n_jobs=1):
results = copy.deepcopy(estimator_grid)
hyperparams = _get_hyperparams()
cvparams = _get_cvparams()
X_tr, X_te = get_recsys_data(dataset, random_state)
subdir = 'benches'
output_dir = expanduser(join(trace_dir, subdir))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
indices = sorted(results.keys())
for idx in indices:
mf = results[idx]['estimator']
mf.set_params(**hyperparams[idx][dataset])
mf.set_params(**cvparams[idx][dataset])
mf.set_params(random_state=random_state)
res = Parallel(n_jobs=n_jobs, max_nbytes=None)(
delayed(single_fit_bench)(results[idx]['estimator'], X_tr, X_te)
for idx in indices)
times, rmses = zip(*res)
for time, rmse, idx in zip(times, rmses, indices):
results[idx]['timings'] = time
results[idx]['rmse'] = rmse
results[idx].pop('estimator')
with open(join(output_dir, 'results_%s.json' % dataset), 'w+') as f:
json.dump(results, f)
def cross_val(dataset='100k', random_state=0, n_jobs=1):
results = copy.deepcopy(estimator_grid)
X_tr, X_te = get_recsys_data(dataset, random_state)
subdir = 'cross_val'
output_dir = expanduser(join(trace_dir, subdir))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
hyperparams = _get_hyperparams()
for idx in results.keys():
mf = results[idx]['estimator']
mf.set_params(**hyperparams[idx][dataset])
if idx in ['dl', 'dl_partial']:
mf.set_params(n_epochs=5)
else:
mf.set_params(max_iter=40)
mf.set_params(random_state=random_state)
param_grid = [
dict(alpha=alpha, beta=beta) for alpha in alphas[dataset]
for beta in betas
]
mf.verbose = 0
mf.alpha = 0
mf.beta = 0
if dataset == 'netflix':
# We don't perform nested cross val here
res = Parallel(n_jobs=n_jobs, verbose=10, max_nbytes=None)(
delayed(single_fit)(mf, X_tr, X_te, params)
for params in param_grid)
else:
cv = ShuffleSplit(n_iter=3, train_size=0.66, random_state=0)
res = Parallel(n_jobs=n_jobs, verbose=10, max_nbytes=None)(
delayed(single_fit_nested)(mf, X_tr, cv, params)
for params in param_grid)
scores, params = zip(*res)
scores = np.array(scores).mean(axis=1)
best_score_arg = scores.argmin()
best_param = params[best_score_arg]
best_score = scores[best_score_arg]
results[idx]['params'] = params
results[idx]['scores'] = scores.tolist()
results[idx]['best_param'] = best_param
results[idx]['best_score'] = best_score
results[idx].pop('estimator')
with open(join(output_dir, 'results_%s.json' % dataset), 'w+') as f:
json.dump(results, f)
def compare_learning_rate(version='100k', n_jobs=1, random_state=0):
X_tr, X_te = get_recsys_data(version, random_state)
mf = copy.deepcopy(estimator_grid['dl_partial']['estimator'])
hyperparams = _get_hyperparams()
cvparams = _get_cvparams()
mf.set_params(**hyperparams['dl_partial'][version])
mf.set_params(**cvparams['dl_partial'][version])
mf.set_params(random_state=random_state)
output_dir = join(trace_dir, 'learning_rate')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
results = {}
res = Parallel(n_jobs=n_jobs, max_nbytes=None)(
delayed(single_learning_rate)(mf, learning_rate, X_tr, X_te) for
learning_rate in learning_rates)
for i, learning_rate in enumerate(learning_rates):
results[learning_rate] = res[i]
with open(join(output_dir, 'results_%s.json' % version), 'w+') as f:
json.dump(results, f)
def compare_learning_rate(version='100k', n_jobs=1, random_state=0):
X_tr, X_te = get_recsys_data(version, random_state)
mf = copy.deepcopy(estimator_grid['dl_partial']['estimator'])
hyperparams = _get_hyperparams()
cvparams = _get_cvparams()
mf.set_params(**hyperparams['dl_partial'][version])
mf.set_params(**cvparams['dl_partial'][version])
mf.set_params(random_state=random_state)
output_dir = join(trace_dir, 'learning_rate')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
results = {}
res = Parallel(n_jobs=n_jobs, max_nbytes=None)(
delayed(single_learning_rate)(mf, learning_rate, X_tr, X_te)
for learning_rate in learning_rates)
for i, learning_rate in enumerate(learning_rates):
results[learning_rate] = res[i]
with open(join(output_dir, 'results_%s.json' % version), 'w+') as f:
json.dump(results, f)
def cross_val(dataset='100k',
random_state=0,
n_jobs=1):
results = copy.deepcopy(estimator_grid)
X_tr, X_te = get_recsys_data(dataset, random_state)
subdir = 'cross_val'
output_dir = expanduser(join(trace_dir, subdir))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
hyperparams = _get_hyperparams()
for idx in results.keys():
mf = results[idx]['estimator']
mf.set_params(**hyperparams[idx][dataset])
if idx in ['dl', 'dl_partial']:
mf.set_params(n_epochs=5)
else:
mf.set_params(max_iter=40)
mf.set_params(random_state=random_state)
param_grid = [dict(alpha=alpha, beta=beta) for alpha in alphas[dataset]
for beta in betas]
mf.verbose = 0
mf.alpha = 0
mf.beta = 0
if dataset == 'netflix':
# We don't perform nested cross val here
res = Parallel(n_jobs=n_jobs,
verbose=10,
max_nbytes=None)(
delayed(single_fit)(mf, X_tr, X_te,
params) for params in param_grid)
else:
cv = ShuffleSplit(n_iter=3,
train_size=0.66,
random_state=0)
res = Parallel(n_jobs=n_jobs,
verbose=10,
max_nbytes=None)(
delayed(single_fit_nested)(mf, X_tr, cv, params)
for params in
param_grid)
scores, params = zip(*res)
scores = np.array(scores).mean(axis=1)
best_score_arg = scores.argmin()
best_param = params[best_score_arg]
best_score = scores[best_score_arg]
results[idx]['params'] = params
results[idx]['scores'] = scores.tolist()
results[idx]['best_param'] = best_param
results[idx]['best_score'] = best_score
results[idx].pop('estimator')
with open(join(output_dir, 'results_%s.json' % dataset), 'w+') as f:
json.dump(results, f)
