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Add verbose parameter to StructureBoost class #7

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Add verbose parameter to StructureBoost class #7

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25 changes: 16 additions & 9 deletions structureboost/structure_gb.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -107,7 +107,10 @@ class StructureBoost(object):

random_state : int default=42
A random seed that can be fixed for replicability purposes.


verbose : int default=1
Controls the verbosity when fitting and predicting. To suppress all output,
set verbose=0. Otherwise, set verbose to any nonzero int.

References
----------
Expand All @@ -123,7 +126,7 @@ class StructureBoost(object):
replace=True, min_size_split=2, max_depth=3,
gamma=0, reg_lambda=1, feat_sample_by_tree=1,
feat_sample_by_node=1, learning_rate=.02,
random_seed=0, na_unseen_action='weighted_random'):
random_seed=0, verbose=1, na_unseen_action='weighted_random'):
self.num_trees = num_trees
self.num_trees_for_prediction = num_trees
self.dec_tree_list = []
Expand All @@ -141,6 +144,7 @@ class StructureBoost(object):
self.subsample = subsample
self.replace = replace
self.random_seed = random_seed
self.verbose = verbose
self.na_unseen_action = na_unseen_action
self.mode = mode
if mode not in ['classification', 'regression']:
Expand Down Expand Up @@ -245,7 +249,7 @@ class StructureBoost(object):
y_valid = eval_set[1].astype(float)
curr_valid_answer = self.initial_pred * np.ones(len(y_valid))
curr_valid_loss = _output_loss(y_valid, curr_valid_answer,
0, self.mode)
0, self.mode, verbose=self.verbose)

# Main loop to build trees
for i in range(self.num_trees):
Expand All @@ -263,7 +267,7 @@ class StructureBoost(object):
X_valid))
if ((i+1) % self.eval_freq == 1):
curr_loss = _output_loss(y_valid, curr_valid_answer,
i, self.mode)
i, self.mode, verbose=self.verbose)
curr_step = np.floor((i+1) /
self.eval_freq).astype(int)-1
self.eval_results[curr_step] = curr_loss
Expand All @@ -272,9 +276,10 @@ class StructureBoost(object):
curr_step-early_stop_past_steps+1)])
if (curr_loss > compare_loss):
stop_now = True
print("""Stopping early: curr_loss of {}
if self.verbose:
print("""Stopping early: curr_loss of {}
exceeds compare_loss of {}"""
.format(curr_loss, compare_loss))
.format(curr_loss, compare_loss))
if stop_now:
if choose_best_eval:
self.num_trees_for_prediction = ((
Expand Down Expand Up @@ -432,13 +437,15 @@ def _get_rows_for_tree(num_rows, subsample, replace):
return np.random.choice(num_rows, rows_to_return, replace=False)


def _output_loss(y_true, pred, ind, mode):
def _output_loss(y_true, pred, ind, mode, verbose=1):
if mode == 'classification':
curr_loss = my_log_loss(y_true, 1/(1+np.exp(-pred)))
print("i={}, eval_set_log_loss = {}".format(ind, curr_loss))
if verbose:
print("i={}, eval_set_log_loss = {}".format(ind, curr_loss))
else:
curr_loss = my_mean_squared_error(y_true, pred)
print("i={}, eval_set_mse = {}".format(ind, curr_loss))
if verbose:
print("i={}, eval_set_mse = {}".format(ind, curr_loss))
return curr_loss


Expand Down