def make_cross_validation(
data, solution, cv_folds, params_begin, params_mult_factor, params_add_factor, params_num_iter
):
# params = GBT_params(params_begin.n_iterations,params_begin.depth, params_begin.learning_rate, params_begin.subsample_part, params_begin.n_max_features)
params = GBT_params()
cv_iterations = (
params_num_iter.n_iterations
* params_num_iter.depth
* params_num_iter.learning_rate
* params_num_iter.subsample_part
* params_num_iter.n_max_features
)
cv_res = np.zeros(cv_iterations)
cv_times = np.zeros(cv_iterations)
cur_iter = 0
params.n_iterations = params_begin.n_iterations
for n_iterations in range(params_num_iter.n_iterations):
params.learning_rate = params_begin.learning_rate
for n_learning_rate in range(params_num_iter.learning_rate):
params.depth = params_begin.depth
for n_max_depth in range(params_num_iter.depth):
params.subsample_part = params_begin.subsample_part
for subsample_part in range(params_num_iter.subsample_part):
params.n_max_features = params_begin.n_max_features
for n_max_features in range(params_num_iter.n_max_features):
start_time = time.time()
params.print_params()
clf = ensemble.GradientBoostingClassifier(
n_estimators=params.n_iterations,
learning_rate=params.learning_rate,
max_depth=params.depth,
subsample=params.subsample_part,
max_features=int(params.n_max_features),
)
cv_res[cur_iter] = Calc_CV_ERROR(clf, data, solution, cv_folds)
print ("CV score = %1.5", cv_res[cur_iter])
cv_times[cur_iter] = time.time() - start_time
print ("CV time = %d", cv_times[cur_iter])
params.n_max_features *= params_mult_factor.n_max_features
params.n_max_features += params_add_factor.n_max_features
cur_iter += 1
params.subsample_part *= params_mult_factor.subsample_part
params.subsample_part += params_add_factor.subsample_part
params.depth *= params_mult_factor.depth
params.depth += params_add_factor.depth
params.learning_rate *= params_mult_factor.learning_rate
params.learning_rate += params_add_factor.learning_rate
params.n_iterations *= params_mult_factor.n_iterations
params.n_iterations += params_add_factor.n_iterations
return (cv_res, cv_times)
