def testDFCComputeComparedToExternalExample(self):
"""Tests `compute_dfc` compared to external example (regression).
Example from http://blog.datadive.net/interpreting-random-forests.
"""
# DIS:3, RM: 2, LSTAT:1, NOX:0
num_bucketized_features = 4
e1_feature_ids = (2, 1, 0)
e2_feature_ids = (2, 2, 2)
e3_feature_ids = (2, 2, 0)
bias_logit = 22.60 # Root node of tree_0.
activation = boosted_trees_utils._identity
f_bias = activation(bias_logit)
# Logits before and after, as is outputed from
# boosted_trees_ops.example_debug_outputs
e1_logits_path = (bias_logit, 19.96, 14.91, 18.11)
e2_logits_path = (bias_logit, 37.42, 45.10, 45.90)
e3_logits_path = (bias_logit, 37.42, 32.30, 33.58)
e1_expected_dfcs = {0: 3.20, 1: -5.05, 2: -2.64, 3: 0}
e2_expected_dfcs = {0: 0, 1: 0, 2: 23.3, 3: 0}
e3_expected_dfcs = {0: 1.28, 1: 0, 2: 9.7, 3: 0}
# Check feature contributions are correct for both examples.
# Example 1.
# pylint: disable=protected-access
# pylint: disable=line-too-long
e1_bias, e1_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e1_feature_ids, e1_logits_path, activation,
num_bucketized_features)
self.assertAllClose(e1_bias, f_bias)
self.assertAllClose(e1_dfc, e1_expected_dfcs)
# Example 2.
e2_bias, e2_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e2_feature_ids, e2_logits_path, activation,
num_bucketized_features)
self.assertAllClose(e2_bias, f_bias)
self.assertAllClose(e2_dfc, e2_expected_dfcs)
# Example 3.
e3_bias, e3_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e3_feature_ids, e3_logits_path, activation,
num_bucketized_features)
# pylint: enable=line-too-long
self.assertAllClose(e3_bias, f_bias)
self.assertAllClose(e3_dfc, e3_expected_dfcs)
# pylint: enable=protected-access
# Check if contributions sum to final prediction.
# For each tree, get leaf of last tree.
expected_logits = (18.11, 45.90, 33.58)
# Predictions should be the sum of contributions + bias.
expected_preds = [activation(logit) for logit in expected_logits]
e1_pred = e1_bias + sum(e1_dfc.values())
e2_pred = e2_bias + sum(e2_dfc.values())
e3_pred = e3_bias + sum(e3_dfc.values())
preds = [e1_pred, e2_pred, e3_pred]
self.assertAllClose(preds, expected_preds)
def testDFCGroupByFeatureColNameAndSumUsingExternalExample(self):
"""Tests grouping by feature column name and summing contributions.
Example modified from http://blog.datadive.net/interpreting-random-forests.
"""
# Use features from testDFCComputeComparedToExternalExample, but merge
# 'NOX' and 'RM' into 'NOX'.
feature_col_names = ('DIS', 'LSTAT', 'NOX', 'NOX')
e1_feature_ids = (2, 1, 2)
e2_feature_ids = (2, 2, 2)
e3_feature_ids = (2, 2, 2)
bias_logit = 22.60 # Root node of tree_0.
activation = boosted_trees_utils._identity
f_bias = activation(bias_logit)
# Logits before and after, as is outputed from
# boosted_trees_ops.example_debug_outputs
e1_logits_path = (bias_logit, 19.96, 14.91, 18.11)
e2_logits_path = (bias_logit, 37.42, 45.10, 45.90)
e3_logits_path = (bias_logit, 37.42, 32.30, 33.58)
e1_expected_dfcs = {'NOX': 3.20 - 2.64, 'LSTAT': -5.05, 'DIS': 0}
e2_expected_dfcs = {'NOX': 0 + 23.3, 'LSTAT': 0, 'DIS': 0}
e3_expected_dfcs = {'NOX': 1.28 + 9.7, 'LSTAT': 0, 'DIS': 0}
# Check feature contributions are correct for both examples.
# Example 1.
# pylint: disable=protected-access
# pylint: disable=line-too-long
e1_bias, e1_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e1_feature_ids, e1_logits_path, activation, feature_col_names)
self.assertAllClose(e1_bias, f_bias)
self.assertAllClose(e1_dfc, e1_expected_dfcs)
# Example 2.
e2_bias, e2_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e2_feature_ids, e2_logits_path, activation, feature_col_names)
self.assertAllClose(e2_bias, f_bias)
self.assertAllClose(e2_dfc, e2_expected_dfcs)
# Example 3.
e3_bias, e3_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e3_feature_ids, e3_logits_path, activation, feature_col_names)
# pylint: enable=line-too-long
self.assertAllClose(e3_bias, f_bias)
self.assertAllClose(e3_dfc, e3_expected_dfcs)
# pylint: enable=protected-access
# Check if contributions sum to final prediction.
# For each tree, get leaf of last tree.
expected_logits = (18.11, 45.90, 33.58)
# Predictions should be the sum of contributions + bias.
expected_preds = [activation(logit) for logit in expected_logits]
e1_pred = e1_bias + sum(e1_dfc.values())
e2_pred = e2_bias + sum(e2_dfc.values())
e3_pred = e3_bias + sum(e3_dfc.values())
preds = [e1_pred, e2_pred, e3_pred]
self.assertAllClose(preds, expected_preds)
def testDirectionalFeatureContributionsCompute(self):
"""Tests logic to compute DFCs given feature ids and logits paths."""
num_bucketized_features = 3 # Includes one unused feature.
examples_feature_ids = ((2, 2, 0, 0), (2, 2, 0))
e1_feature_ids, e2_feature_ids = examples_feature_ids
# DFCs are computed by traversing the prediction path and subtracting each
# child prediction from its parent prediction and associating the change in
# prediction with the respective feature id used for the split.
# For each activation function, f, (currently identity or sigmoid), DFCs are
# calculated for the two examples as:
# example 1:
# feature_0 = (f(1.114) - f(1.214)) + (f(6.114) - f(1.114))
# feature_1 = 0 # Feature not in ensemble, thus zero contrib.
# feature_2 = (f(0.114) - bias_pred) + (f(1.214) - f(0.114))
# example 2:
# feature_0 = f(-5.486) - f(1.514)
# feature_1 = 0 # Feature not in ensemble, thus zero contrib.
# feature_2 = (f(0.114) - bias_pred) + (f(1.514) - f(0.114))
# where bias_pred is = f(0) or f(0.21), with center_bias = {True, False},
# respectively.
# Keys are center_bias.
expected_dfcs_identity = {
False: ({
0: 4.9,
1: 0,
2: 1.214
}, {
0: -7.0,
1: 0,
2: 1.514
}),
True: ({
0: 4.9,
1: 0,
2: 1.0039999999999998
}, {
0: -7.0,
1: 0,
2: 1.3039999999999998
})
}
expected_dfcs_sigmoid = {
False: ({
0: 0.22678725678805578,
1: 0,
2: 0.2710059376234506
}, {
0: -0.81552596670046507,
1: 0,
2: 0.319653250251275
}),
True: ({
0: 0.22678725678805578,
1: 0,
2: 0.2186980280491253
}, {
0: -0.81552596670046507,
1: 0,
2: 0.26734534067694971
})
}
# pylint: disable=protected-access
for f, expected_dfcs in zip(
(boosted_trees_utils._identity, boosted_trees_utils._sigmoid),
(expected_dfcs_identity, expected_dfcs_sigmoid)):
for center_bias in [False, True]:
# If not center_bias, the bias after activation is 0.
if center_bias:
bias_logit = 0.21 # Root node of tree_0.
else:
bias_logit = 0 # 0 is default value when there is no original_leaf.
f_bias = f(bias_logit)
# Logits before and after, as is outputed from
# boosted_trees_ops.example_debug_outputs
examples_logits_paths = ((bias_logit, 0.114, 1.214, 1.114,
6.114), (bias_logit, 0.114, 1.514,
-5.486))
e1_logits_path, e2_logits_path = examples_logits_paths
e1_expected_dfcs, e2_expected_dfcs = expected_dfcs[center_bias]
# Check feature contributions are correct for both examples.
# Example 1.
# pylint:disable=line-too-long
e1_bias, e1_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e1_feature_ids, e1_logits_path, f, num_bucketized_features)
self.assertAllClose(e1_bias, f_bias)
self.assertAllClose(e1_dfc, e1_expected_dfcs)
# Example 2.
e2_bias, e2_dfc = boosted_trees_utils._compute_directional_feature_contributions(
e2_feature_ids, e2_logits_path, f, num_bucketized_features)
# pylint:enable=line-too-long
self.assertAllClose(e2_bias, f_bias)
self.assertAllClose(e2_dfc, e2_expected_dfcs)
# Check if contributions sum to final prediction.
# For each tree, get leaf of last tree.
expected_logits = (e1_logits_path[-1], e2_logits_path[-1])
# Predictions should be the sum of contributions + bias.
expected_preds = [f(logit) for logit in expected_logits]
e1_pred = e1_bias + sum(e1_dfc.values())
e2_pred = e2_bias + sum(e2_dfc.values())
preds = [e1_pred, e2_pred]
self.assertAllClose(preds, expected_preds)