1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
|
local dt = require "decisiontree._env"
local TreeState = torch.class("dt.TreeState", dt)
-- Holds the state of a subtree during decision tree training.
-- Also, manages the state of candidate splits
function TreeState:__init(exampleIds)
assert(torch.type(exampleIds) == 'torch.LongTensor')
self.exampleIds = exampleIds
self.nExampleInLeftBranch = 0
self.nExampleInRightBranch = 0
end
-- computes and returns the score of the node based on its examples
function TreeState:score(dataset)
error"NotImplemented"
end
-- Initializes the split-state-updater. Initially all examples are in the left branch.
-- exampleIdsWithFeature is list of examples to split (those having a particular feature)
function TreeState:initialize(exampleIdsWithFeature, dataset)
error"NotImplemented"
end
-- Update the split state. This call has the effect of shifting the example from the left to the right branch.
function TreeState:update(exampleId, dataset)
error"NotImplemented"
end
-- Computes the SplitInfo determined by the current split state
-- @param splitFeatureId the feature id of the split feature
-- @param splitFeatureValue the feature value of the split feature
-- @return the SplitInfo determined by the current split state
function TreeState:computeSplitInfo(splitFeatureId, splitFeatureValue)
error"NotImplemented"
end
-- bottleneck
function TreeState:findBestFeatureSplit(dataset, featureId, minLeafSize)
local dt = require "decisiontree"
assert(torch.isTypeOf(dataset, 'dt.DataSet'))
assert(torch.type(featureId) == 'number')
assert(torch.type(minLeafSize) == 'number')
-- all dataset example having this feature, sorted by value
local featureExampleIds = dataset:getSortedFeature(featureId)
local buffer = dt.getBufferTable('TreeState')
buffer.longtensor = buffer.longtensor or torch.LongTensor()
local exampleIdsWithFeature = buffer.longtensor
-- map and tensor of examples containing feature:
local exampleMap = {}
local getExampleFeatureValue
local j = 0
if torch.type(dataset.input) == 'table' then
exampleIdsWithFeature:resize(self.exampleIds:size())
self.exampleIds:apply(function(exampleId)
local input = dataset.input[exampleId]
input:buildIndex()-- only builds index first time
if input[featureId] then
j = j + 1
exampleIdsWithFeature[j] = exampleId
exampleMap[exampleId] = j
end
end)
if j == 0 then
return
end
exampleIdsWithFeature:resize(j)
getExampleFeatureValue = function(exampleId) return dataset.input[exampleId][featureId] end
else
exampleIdsWithFeature = self.exampleIds
self.exampleIds:apply(function(exampleId)
j = j + 1
exampleMap[exampleId] = j
end)
local featureValues = dataset.input:select(2,featureId)
getExampleFeatureValue = function(exampleId) return featureValues[exampleId] end
end
self:initialize(exampleIdsWithFeature, dataset)
-- bottleneck
local bestSplit, previousSplitValue, _tictoc
for i=featureExampleIds:size(1),1,-1 do -- loop over examples sorted (desc) by feature value
local exampleId = featureExampleIds[i]
local exampleIdx = exampleMap[exampleId]
if exampleIdx then
local splitValue = getExampleFeatureValue(exampleId)
if previousSplitValue and math.abs(splitValue - previousSplitValue) > dt.EPSILON then
local splitInfo = self:computeSplitInfo(featureId, previousSplitValue, _tictoc)
if (splitInfo.leftChildSize >= minLeafSize) and (splitInfo.rightChildSize >= minLeafSize) then
if (not bestSplit) or (splitInfo.splitGain < bestSplit.splitGain) then
_tictoc = bestSplit or {} -- reuse table
bestSplit = splitInfo
end
end
end
previousSplitValue = splitValue
-- bottleneck
self:update(exampleId, dataset, exampleIdx)
end
end
return bestSplit
end
-- finds the best split of examples in treeState among featureIds
function TreeState:findBestSplit(dataset, featureIds, minLeafSize, shardId, nShard)
assert(torch.isTypeOf(dataset, 'dt.DataSet'))
assert(torch.type(featureIds) == 'torch.LongTensor')
assert(torch.type(minLeafSize) == 'number')
assert(torch.type(shardId) == 'number')
assert(torch.type(nShard) == 'number')
local bestSplit
for i=1,featureIds:size(1) do
local featureId = featureIds[i]
if (nShard <= 1) or ( (featureId % nShard) + 1 == shardId ) then -- feature sharded
local splitCandidate = self:findBestFeatureSplit(dataset, featureId, minLeafSize)
if splitCandidate and ((not bestSplit) or (splitCandidate.splitGain < bestSplit.splitGain)) then
bestSplit = splitCandidate
end
end
end
return bestSplit
end
-- Partitions self given a splitInfo table, producing a pair of exampleIds corresponding to the left and right subtrees.
function TreeState:_branch(splitInfo, dataset)
local leftIdx, rightIdx = 0, 0
local nExample = self.exampleIds:size(1)
local splitExampleIds = torch.LongTensor(nExample)
for i=1,self.exampleIds:size(1) do
local exampleId = self.exampleIds[i]
local input = dataset.input[exampleId]
local val = input[splitInfo.splitId]
-- Note: when the feature is not present in the example, the example is droped from all sub-trees.
-- Which means that for most sparse data, a tree cannot reach 100% accuracy...
if val then
if val < splitInfo.splitValue then
leftIdx = leftIdx + 1
splitExampleIds[leftIdx] = exampleId
else
rightIdx = rightIdx + 1
splitExampleIds[nExample-rightIdx+1] = exampleId
end
end
end
local leftExampleIds = splitExampleIds:narrow(1,1,leftIdx)
local rightExampleIds = splitExampleIds:narrow(1,nExample-rightIdx+1,rightIdx)
assert(leftExampleIds:size(1) + rightExampleIds:size(1) <= self.exampleIds:size(1), "Left and right branches contain more data than the parent!")
return leftExampleIds, rightExampleIds
end
-- calls _branch and encapsulates the left and right exampleIds into a TreeStates
function TreeState:branch(splitInfo, dataset)
local leftExampleIds, rightExampleIds = self:_branch(splitInfo, dataset)
return self.new(leftExampleIds), self.new(rightExampleIds)
end
function TreeState:size()
return self.exampleIds:size(1)
end
function TreeState:contains(exampleId)
local found = false
self.exampleIds:apply(function(x)
if x == exampleId then
found = true
end
end)
return found
end
|
