def __init__(self, pgenerator, project_name, experiment_name):
jobsDB = JobsDB(project_name, experiment_name)
self.params = []
self.pgenerator = pgenerator
# Go through all the parameters that pgenerator would make
# and take out the ones which are already in JobsDB.
for params in pgenerator:
if jobsDB.search_by_param(params) is None:
self.params.append(params)
def __init__(self, pgenerator, project_name, experiment_name, n_best=0.1, n_samples=None, param_space=None):
jobsDB = JobsDB(project_name, experiment_name)
param_values = jobsDB.get_param_values(encode_labels=True)
df = pd.DataFrame(param_values)
gb_up = GB(n_estimators=500, learning_rate=0.1,
loss='quantile', alpha=0.95,
max_depth=3, max_features=None,
min_samples_leaf=9, min_samples_split=9)
gb_up.fit(df, losses)
gb_dn = GB(n_estimators=500, learning_rate=0.1,
loss='quantile', alpha=0.05,
max_depth=3, max_features=None,
min_samples_leaf=9, min_samples_split=9)
gb_dn.fit(df, losses)
gb = GB(n_estimators=500, learning_rate=0.1,
loss='ls',
max_depth=3, max_features=None,
min_samples_leaf=9, min_samples_split=9)
gb.fit(df, losses)
if hasattr(pgenerator, "__name__"):
self.pgenerator = pgenerator(param_space, n_samples)
else:
self.pgenerator = pgenerator
trial_params_list = list(self.pgenerator)
trial_params_df = pd.DataFrame( trial_params_list )
predicted_loss = gb.predict(trial_params_df)
predicted_loss_up = gb_up.predict(trial_params_df)
predicted_loss_dn = gb_dn.predict(trial_params_df)
idx = np.argsort(predicted_loss_dn)
sorted_trial_params = np.array(trial_params_list)[idx]
if n_best >= 1: # actual
thresh_idx = int(np.round(n_best))
else: # fraction
thresh_idx = int( np.round(n_best * len(trial_params_list)) )
self.params = list(trial_params_list)[:thresh_idx]
