def get_distributions(tree, instances):
"""
Calculate piecewise distributions of good, bad, and unlabeled instances.
Parameters
----------
tree : TreeNode
The decision tree.
instances : DataFrame
Labeled instances used to train the decision tree. Must have a column
for each attribute in the tree, and a 'class' column with values 'good',
'bad', or NaN (unlabeled).
Returns
-------
dict
key: parameter name
value: list of tuples, each of which describes a range of values for the
parameter, delimited by a pair of split points in the tree:
(high, low, good_count, bad_count, unlabeled_count)
"""
# key: parameter name
# value: list of split values
splits = {}
for node in tree.get_internal_nodes():
if node.split_attribute not in splits:
splits[node.split_attribute] = [node.split_value]
else:
splits[node.split_attribute].append(node.split_value)
# key: parameter name
# value: list of tuples: (high, low, good_count, bad_count, unlabeled_count)
distributions = {}
for param, split_values in splits.items():
split_values.sort()
param_base_name = util.remove_trailing_digits(param)
min_param_value = instances[param].min()
max_param_value = instances[param].max()
split_values.insert(0, min_param_value)
split_values.append(max_param_value)
# FIXME: There must be a more efficient way to use pandas for this
df = instances
segments = []
for i in range(len(split_values) - 1):
low = split_values[i]
high = split_values[i+1]
if i == 0:
df2 = df[df[param] <= high]
else:
df2 = df[(df[param] > low) & (df[param] <= high)]
counts = df2['class'].value_counts(dropna=False)
good = counts.loc['good'] if 'good' in counts.index else 0
bad = counts.loc['bad'] if 'bad' in counts.index else 0
unlabeled = counts.loc[np.nan] if np.nan in counts.index else 0
segments.append((low, high, int(good), int(bad), int(unlabeled)))
distributions[param] = segments
return distributions
def get_distributions(tree, instances):
"""
Calculate piecewise distributions of good, bad, and unlabeled instances.
Parameters
----------
tree : TreeNode
The decision tree.
instances : DataFrame
Labeled instances used to train the decision tree. Must have a column
for each attribute in the tree, and a 'label' column with values 'good',
'bad', or NaN (unlabeled).
Returns
-------
dict
key: parameter name
value: list of tuples, each of which describes a range of values for the
parameter, delimited by a pair of split points in the tree:
(high, low, good_count, bad_count, unlabeled_count)
"""
# key: parameter name
# value: list of split values
splits = {}
for node in tree.get_internal_nodes():
if node.split_attribute not in splits:
splits[node.split_attribute] = [node.split_value]
else:
splits[node.split_attribute].append(node.split_value)
# key: parameter name
# value: list of tuples: (high, low, good_count, bad_count, unlabeled_count)
distributions = {}
for param, split_values in splits.items():
split_values.sort()
param_base_name = util.remove_trailing_digits(param)
min_param_value = instances[param].min()
max_param_value = instances[param].max()
split_values.insert(0, min_param_value)
split_values.append(max_param_value)
# FIXME: There must be a more efficient way to use pandas for this
df = instances
segments = []
for i in range(len(split_values) - 1):
low = split_values[i]
high = split_values[i+1]
if i == 0:
df2 = df[df[param] <= high]
else:
df2 = df[(df[param] > low) & (df[param] <= high)]
counts = df2['label'].value_counts(dropna=False)
good = counts.loc['good'] if 'good' in counts.index else 0
bad = counts.loc['bad'] if 'bad' in counts.index else 0
unlabeled = counts.loc[np.nan] if np.nan in counts.index else 0
segments.append((low, high, int(good), int(bad), int(unlabeled)))
distributions[param] = segments
return distributions
def get_parameter_distributions(tree):
"""
Calculate parameter distributions based on the given decision tree.
For each parameter/attribute, a piecewise distribution is computed. Each
segment of this distribution has two endpoints (on the 'x' axis) and a
constant weight (on the 'y' axis'). The weight of a segment is the sum of
the number of "good" instances within its range, minus the number of "bad"
instances. Each leaf of the tree may contribute a segment of this
distribution, based on the attribute split points along the path from the
leaf to the root.
Parameters
----------
tree : TreeNode
The root node of the tree.
Returns
-------
dict
key: parameter name
value: list of non-overlapping weighted segments, each one of which is a
tuple in the form (low, high, weight)
"""
# upper and lower limits for parameter ranges
# key: parameter name
# value: (low, high)
limits = {}
# Assemble the parameter ranges for each leaf into a list of weighted
# segments for each parameter.
# key: parameter name
# value: list of (low, high, weight) segments for the parameter
segments = {}
for leaf in tree.get_leaves():
weight = leaf.instance_count - leaf.misclassified_count
if leaf.class_label == 'bad':
weight = -weight
for param, (low, high) in get_ranges_for_leaf(leaf).items():
# Ranges can have None for low or high, if there is no lower or
# upper bound, respectively. Replace None with the lowest or highest
# valid value for the parameter.
if param not in limits:
param_base_name = util.remove_trailing_digits(param)
limits[param] = valid_param_ranges[param_base_name]
low = low or limits[param][0]
high = high or limits[param][1]
seg = (low, high, weight)
if param not in segments:
segments[param] = [seg]
else:
segments[param].append(seg)
# For each parameter, combine the weighted segments.
# key: parameter name
# value: list of combined segments for the parameter, none overlapping
combined_segments = {}
for param, param_segments in segments.items():
combined_segments[param] = combine_weighted_segments(segments[param])
return combined_segments
def get_parameter_distributions(tree):
"""
Calculate parameter distributions based on the given decision tree.
For each parameter/attribute, a piecewise distribution is computed. Each
segment of this distribution has two endpoints (on the 'x' axis) and a
constant weight (on the 'y' axis'). The weight of a segment is the sum of
the number of "good" instances within its range, minus the number of "bad"
instances. Each leaf of the tree may contribute a segment of this
distribution, based on the attribute split points along the path from the
leaf to the root.
Parameters
----------
tree : TreeNode
The root node of the tree.
Returns
-------
dict
key: parameter name
value: list of non-overlapping weighted segments, each one of which is a
tuple in the form (low, high, weight)
"""
# upper and lower limits for parameter ranges
# key: parameter name
# value: (low, high)
limits = {}
# Assemble the parameter ranges for each leaf into a list of weighted
# segments for each parameter.
# key: parameter name
# value: list of (low, high, weight) segments for the parameter
segments = {}
for leaf in tree.get_leaves():
weight = leaf.instance_count - leaf.misclassified_count
if leaf.class_label == 'bad':
weight = -weight
for param, (low, high) in get_ranges_for_leaf(leaf).items():
# Ranges can have None for low or high, if there is no lower or
# upper bound, respectively. Replace None with the lowest or highest
# valid value for the parameter.
if param not in limits:
param_base_name = util.remove_trailing_digits(param)
limits[param] = valid_param_ranges[param_base_name]
low = low or limits[param][0]
high = high or limits[param][1]
seg = (low, high, weight)
if param not in segments:
segments[param] = [seg]
else:
segments[param].append(seg)
# For each parameter, combine the weighted segments.
# key: parameter name
# value: list of combined segments for the parameter, none overlapping
combined_segments = {}
for param, param_segments in segments.items():
combined_segments[param] = combine_weighted_segments(segments[param])
return combined_segments
