def testQuantileRegression(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 3
learner_config.growing_mode = learner_pb2.LearnerConfig.WHOLE_TREE
learner_config.constraints.min_node_weight = 1 / _QUANTILE_REGRESSION_SIZE
learner_config.regularization.l2 = 1.0 / _QUANTILE_REGRESSION_SIZE
learner_config.regularization.l1 = 1.0 / _QUANTILE_REGRESSION_SIZE
learner_config.regularization.tree_complexity = (
1.0 / _QUANTILE_REGRESSION_SIZE)
train_input_fn, test_input_fn, y = _quantile_regression_input_fns()
# 95% percentile.
model_upper = estimator.GradientBoostedDecisionTreeQuantileRegressor(
quantiles=[0.95],
learner_config=learner_config,
num_trees=100,
examples_per_layer=_QUANTILE_REGRESSION_SIZE,
center_bias=False)
model_upper.fit(input_fn=train_input_fn, steps=1000)
result_iter = model_upper.predict(input_fn=test_input_fn)
upper = []
for prediction_dict in result_iter:
upper.append(prediction_dict["scores"])
frac_below_upper = round(1. * np.count_nonzero(upper > y) / len(y), 3)
# +/- 3%
self.assertTrue(frac_below_upper >= 0.92)
self.assertTrue(frac_below_upper <= 0.98)
train_input_fn, test_input_fn, _ = _quantile_regression_input_fns()
model_lower = estimator.GradientBoostedDecisionTreeQuantileRegressor(
quantiles=[0.05],
learner_config=learner_config,
num_trees=100,
examples_per_layer=_QUANTILE_REGRESSION_SIZE,
center_bias=False)
model_lower.fit(input_fn=train_input_fn, steps=1000)
result_iter = model_lower.predict(input_fn=test_input_fn)
lower = []
for prediction_dict in result_iter:
lower.append(prediction_dict["scores"])
frac_above_lower = round(1. * np.count_nonzero(lower < y) / len(y), 3)
# +/- 3%
self.assertTrue(frac_above_lower >= 0.92)
self.assertTrue(frac_above_lower <= 0.98)
def testQuantileRegressionMultiDimLabel(self):
learner_config = learner_pb2.LearnerConfig()
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 6
learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
learner_config.constraints.min_node_weight = 1 / _QUANTILE_REGRESSION_SIZE
learner_config.regularization.l2 = 1.0 / _QUANTILE_REGRESSION_SIZE
learner_config.regularization.l1 = 1.0 / _QUANTILE_REGRESSION_SIZE
learner_config.regularization.tree_complexity = (
1.0 / _QUANTILE_REGRESSION_SIZE)
train_input_fn, test_input_fn, y = _quantile_regression_input_fns(
two_dimension=True)
# 95% percentile.
model_upper = estimator.GradientBoostedDecisionTreeQuantileRegressor(
quantiles=[0.95],
learner_config=learner_config,
label_dimension=2,
num_trees=18,
examples_per_layer=_QUANTILE_REGRESSION_SIZE,
center_bias=False)
model_upper.fit(input_fn=train_input_fn, steps=1000)
result_iter = model_upper.predict(input_fn=test_input_fn)
upper = []
for prediction_dict in result_iter:
upper.append(prediction_dict["scores"])
count_below_upper = np.count_nonzero(upper > y, axis=0)
count_both_below_upper = np.count_nonzero(np.prod(upper > y, axis=1))
frac_below_upper_0 = round(1. * count_below_upper[0] / len(y), 3)
frac_below_upper_1 = round(1. * count_below_upper[1] / len(y), 3)
frac_both_below_upper = round(1. * count_both_below_upper / len(y), 3)
# +/- 3%
self.assertTrue(frac_below_upper_0 >= 0.92)
self.assertTrue(frac_below_upper_0 <= 0.98)
self.assertTrue(frac_below_upper_1 >= 0.92)
self.assertTrue(frac_below_upper_1 <= 0.98)
self.assertTrue(frac_both_below_upper >= 0.91)
self.assertTrue(frac_both_below_upper <= 0.99)
