def tiny_robot(x_train, y_train, x_valid, rf_rp, ext_rp, xgb_rp,
**robot_kwargs):
robot_cv_stack = robot_kwargs.get('robot_cv_stack', 5)
robot_nb_auto_max = robot_kwargs.get('robot_nb_auto_max', -1)
res = []
nb_auto = 0
logging.info(" >>> DGH >>> Feature cleaning")
x_train_num, x_valid_num = numerize(x_train, x_valid, **robot_kwargs)
stack_res = []
logging.info(" >>> DGH >>> Cross-val-stacking")
for train1_idx, stack_idx in tqdm(KFold(len(x_train_num),
n_folds=robot_cv_stack,
shuffle=True),
nested=True,
desc='cv2'):
y_probas = Cvs.stack_that(x_train_num, y_train, x_valid_num,
train1_idx, stack_idx, rf_rp, ext_rp, xgb_rp)
stack_res.append(y_probas)
nb_auto += 1
if nb_auto == robot_nb_auto_max:
return res
res.append(stack_res)
return res
def robot(x_train, y_train, x_valid, rf_ip, ext_ip, xgb_ip, **robot_kwargs):
robot_cv_feat = robot_kwargs.get('robot_cv_feat', 6)
robot_cv_hopt = robot_kwargs.get('robot_cv_hopt', 6)
robot_cv_stack = robot_kwargs.get('robot_cv_stack', 5)
robot_nb_auto_max = robot_kwargs.get('robot_nb_auto_max', -1)
robot_rand_state = robot_kwargs.get('robot_rand_state', 42)
res = []
nb_auto = 0
nb_samples = len(x_train)
for train1_idx, feat_idx in KFold(nb_samples,
n_folds=robot_cv_feat,
shuffle=True,
random_state=robot_rand_state):
x_train1 = x_train.iloc[train1_idx]
y_train1 = y_train.iloc[train1_idx]
x_feat = x_train.iloc[feat_idx]
y_feat = y_train.iloc[feat_idx]
logging.info(" >>> DGH >>> Chaos feature generation")
x_train1, x_valid = chaosize(x_feat, x_train1, x_valid, y_feat,
**robot_kwargs)
logging.info(" >>> DGH >>> Feature cleaning")
x_train_num, x_valid_num = numerize(x_train1, x_valid, **robot_kwargs)
for train2_idx, hopt_idx in KFold(len(x_train_num),
n_folds=robot_cv_hopt,
shuffle=True,
random_state=robot_rand_state):
x_train2 = x_train_num.iloc[train2_idx]
y_train2 = y_train1.iloc[train2_idx]
x_hopt = x_train_num.iloc[hopt_idx]
y_hopt = y_train1.iloc[hopt_idx]
logging.info(" >>> DGH >>> Looking for hopt parameters")
rf_rp = Misc.enhance_param(
Cvs.get_best_sklopt(x_hopt, y_hopt, rf_ip), **robot_kwargs)
ext_rp = Misc.enhance_param(
Cvs.get_best_etopt(x_hopt, y_hopt, ext_ip), **robot_kwargs)
xgb_rp = Misc.enhance_param(
Cvs.get_best_xgbopt(x_hopt, y_hopt, xgb_ip), **robot_kwargs)
stack_res = []
logging.info(" >>> DGH >>> Cross-val-stacking")
for train3_idx, stack_idx in KFold(len(x_train2),
n_folds=robot_cv_stack,
shuffle=True):
y_probas = Cvs.stack_that(x_train2, y_train2, x_valid_num,
train3_idx, stack_idx, rf_rp, ext_rp,
xgb_rp)
stack_res.append(y_probas)
res.append(stack_res)
nb_auto += 1
if nb_auto == robot_nb_auto_max:
return res
return res
def robot(x_train, y_train, x_valid, rf_ip, ext_ip, xgb_ip, **robot_kwargs):
robot_cv_feat = robot_kwargs.get('robot_cv_feat', 6)
robot_cv_hopt = robot_kwargs.get('robot_cv_hopt', 6)
robot_cv_stack = robot_kwargs.get('robot_cv_stack', 5)
robot_nb_auto_max = robot_kwargs.get('robot_nb_auto_max', -1)
robot_rand_state = robot_kwargs.get('robot_rand_state', 42)
res = []
nb_auto = 0
nb_samples = len(x_train)
for train1_idx, feat_idx in KFold(nb_samples, n_folds=robot_cv_feat, shuffle=True,
random_state=robot_rand_state):
x_train1 = x_train.iloc[train1_idx]
y_train1 = y_train.iloc[train1_idx]
x_feat = x_train.iloc[feat_idx]
y_feat = y_train.iloc[feat_idx]
logging.info("Chaos feature generation")
x_train1, x_valid = chaosize(x_feat, x_train1, x_valid, y_feat, **robot_kwargs)
logging.info("Feature cleaning")
x_train_num, x_valid_num = numerize(x_train1, x_valid, **robot_kwargs)
for train2_idx, hopt_idx in KFold(len(x_train_num), n_folds=robot_cv_hopt, shuffle=True,
random_state=robot_rand_state):
x_train2 = x_train_num.iloc[train2_idx]
y_train2 = y_train1.iloc[train2_idx]
x_hopt = x_train_num.iloc[hopt_idx]
y_hopt = y_train1.iloc[hopt_idx]
logging.info("Looking for hopt parameters")
rf_rp = Misc.enhance_param(Cvs.get_best_sklopt(x_hopt, y_hopt, rf_ip), **robot_kwargs)
ext_rp = Misc.enhance_param(Cvs.get_best_etopt(x_hopt, y_hopt, ext_ip), **robot_kwargs)
xgb_rp = Misc.enhance_param(Cvs.get_best_xgbopt(x_hopt, y_hopt, xgb_ip), **robot_kwargs)
stack_res = []
logging.info("Starting cross-val-stacking")
for train3_idx, stack_idx in KFold(len(x_train2), n_folds=robot_cv_stack, shuffle=True):
y_probas = Cvs.stack_that(x_train2, y_train2, x_valid_num, train3_idx, stack_idx,
rf_rp, ext_rp, xgb_rp)
stack_res.append(y_probas)
res.append(stack_res)
nb_auto += 1
if nb_auto == robot_nb_auto_max:
return res
return res
def small_robot(x_train, y_train, x_valid, rf_rp, ext_rp, xgb_rp,
**robot_kwargs):
robot_cv_feat = robot_kwargs.get('robot_cv_feat', 6)
robot_cv_stack = robot_kwargs.get('robot_cv_stack', 5)
robot_nb_auto_max = robot_kwargs.get('robot_nb_auto_max', -1)
robot_rand_state = robot_kwargs.get('robot_rand_state', 42)
res = []
nb_auto = 0
nb_samples = len(x_train)
for train1_idx, feat_idx in tqdm(KFold(nb_samples,
n_folds=robot_cv_feat,
shuffle=True,
random_state=robot_rand_state),
desc='cv1'):
x_train1 = x_train.iloc[train1_idx]
y_train1 = y_train.iloc[train1_idx]
x_feat = x_train.iloc[feat_idx]
y_feat = y_train.iloc[feat_idx]
logging.info(" >>> DGH >>> Chaos feature generation")
x_train1, x_valid = chaosize(x_feat, x_train1, x_valid, y_feat,
**robot_kwargs)
logging.info(" >>> DGH >>> Feature cleaning")
x_train_num, x_valid_num = numerize(x_train1, x_valid, **robot_kwargs)
stack_res = []
logging.info(" >>> DGH >>> Cross-val-stacking")
for train2_idx, stack_idx in tqdm(KFold(len(x_train_num),
n_folds=robot_cv_stack,
shuffle=True),
nested=True,
desc='cv2'):
y_probas = Cvs.stack_that(x_train_num, y_train1, x_valid_num,
train2_idx, stack_idx, rf_rp, ext_rp,
xgb_rp)
stack_res.append(y_probas)
res.append(stack_res)
nb_auto += 1
if nb_auto == robot_nb_auto_max:
return res
return res
def small_robot(x_train, y_train, x_valid, rf_rp, ext_rp, xgb_rp, **robot_kwargs):
robot_cv_feat = robot_kwargs.get('robot_cv_feat', 6)
robot_cv_stack = robot_kwargs.get('robot_cv_stack', 5)
robot_nb_auto_max = robot_kwargs.get('robot_nb_auto_max', -1)
robot_rand_state = robot_kwargs.get('robot_rand_state', 42)
res = []
nb_auto = 0
nb_samples = len(x_train)
for train1_idx, feat_idx in KFold(nb_samples, n_folds=robot_cv_feat, shuffle=True,
random_state=robot_rand_state):
x_train1 = x_train.iloc[train1_idx]
y_train1 = y_train.iloc[train1_idx]
x_feat = x_train.iloc[feat_idx]
y_feat = y_train.iloc[feat_idx]
logging.info("Chaos feature generation")
x_train1, x_valid = chaosize(x_feat, x_train1, x_valid, y_feat, **robot_kwargs)
logging.info("Feature cleaning")
x_train_num, x_valid_num = numerize(x_train1, x_valid, **robot_kwargs)
stack_res = []
logging.info("Starting cross-val-stacking")
for train2_idx, stack_idx in KFold(len(x_train_num), n_folds=robot_cv_stack, shuffle=True):
y_probas = Cvs.stack_that(x_train_num, y_train1, x_valid_num, train2_idx, stack_idx,
rf_rp, ext_rp, xgb_rp)
stack_res.append(y_probas)
res.append(stack_res)
nb_auto += 1
if nb_auto == robot_nb_auto_max:
return res
return res
