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6
7 """ functionality for generating an image showing the results of a composite model voting on a data set
8
9 Uses *Numeric* and *PIL*
10
11 """
12 from __future__ import print_function
13 import numpy
14 from PIL import Image, ImageDraw
15
17 """ collects the votes from _composite_ for the examples in _data_
18
19 **Arguments**
20
21 - composite: a composite model
22
23 - data: a list of examples to run through _composite_
24
25 - badOnly: if set only bad (misclassified) examples will be kept
26
27 **Returns**
28
29 a 4-tuple containing:
30
31 1) the expanded list of vote details (see below)
32
33 2) the list of predicted results
34
35 3) the list of true results
36
37 4) the number of miscounted examples
38
39
40 **Notes**
41
42 pp - the expanded list of vote details consists of:
43
44 '[ vote1, vote2, ... voteN, 0, res, trueRes]'
45
46 where _res_ is the predicted results and _trueRes_ is the actual result.
47 The extra zero is included to allow a line to be drawn between the votes
48 and the results.
49
50 """
51 res = []
52 values = []
53 trueValues = []
54 misCount = 0
55 for pt in data:
56 val,err = composite.ClassifyExample(pt)
57 predict = pt[-1]
58 if not badOnly or val != predict:
59 values.append(val)
60 trueValues.append(predict)
61 if val != predict:
62 misCount = misCount + 1
63 res.append(composite.GetVoteDetails()+[0,val,pt[-1]])
64 return res,values,trueValues,misCount
65
66 -def BuildVoteImage(nModels,data,values,trueValues=[],
67 sortTrueVals=0,xScale=10,yScale=2,
68 addLine=1):
69 """ constructs the actual image
70
71 **Arguments**
72
73 - nModels: the number of models in the composite
74
75 - data: the results of voting
76
77 - values: predicted values for each example
78
79 - trueValues: true values for each example
80
81 - sortTrueVals: if nonzero the votes will be sorted so
82 that the _trueValues_ are in order, otherwise the sort
83 is by _values_
84
85 - xScale: number of pixels per vote in the x direction
86
87 - yScale: number of pixels per example in the y direction
88
89 - addLine: if nonzero, a purple line is drawn separating
90 the votes from the examples
91
92 **Returns**
93
94 a PIL image
95
96 """
97 nData = len(data)
98 data = numpy.array(data,numpy.integer)
99 if sortTrueVals and trueValues != []:
100 order = numpy.argsort(trueValues)
101 else:
102 order = numpy.argsort(values)
103 data = [data[x] for x in order]
104 maxVal = max(numpy.ravel(data))
105 data = data * 255 / maxVal
106 img = Image.fromstring('L',(nModels,nData),data.astype('B').tostring())
107
108 if addLine:
109 img = img.convert('RGB')
110 canvas = ImageDraw.Draw(img)
111 if trueValues != []:
112 canvas.line([(nModels-3,0),(nModels-3,nData)],fill=(128,0,128))
113 else:
114 canvas.line([(nModels-2,0),(nModels-2,nData)],fill=(128,0,128))
115 img = img.resize((nModels*xScale,nData*yScale))
116 return img
117
118
119 -def VoteAndBuildImage(composite,data,badOnly=0,sortTrueVals=0,
120 xScale=10,yScale=2,addLine=1):
121 """ collects votes on the examples and constructs an image
122
123 **Arguments**
124
125 - composte: a composite model
126
127 - data: the examples to be voted upon
128
129 - badOnly: if nonzero only the incorrect votes will be shown
130
131 - sortTrueVals: if nonzero the votes will be sorted so
132 that the _trueValues_ are in order, otherwise the sort
133 is by _values_
134
135 - xScale: number of pixels per vote in the x direction
136
137 - yScale: number of pixels per example in the y direction
138
139 - addLine: if nonzero, a purple line is drawn separating
140 the votes from the examples
141
142 **Returns**
143
144 a PIL image
145
146
147 """
148 nModels = len(composite)+3
149 print('nModels:',nModels-3)
150
151 res,values,trueValues,misCount = CollectVotes(composite,data,badOnly)
152 print('%d examples were misclassified'%misCount)
153 img = BuildVoteImage(nModels,res,values,trueValues,sortTrueVals,
154 xScale,yScale,addLine)
155 return img
156
158 """ provides a list of arguments for when this is used from the command line
159
160 """
161 import sys
162
163 print('Usage: VoteImg.py [optional arguments] <modelfile.pkl> <datafile.qdat>')
164 print('Optional Arguments:')
165 print('\t-o outfilename: the name of the output image file.')
166 print('\t The extension determines the type of image saved.')
167 print('\t-b: only include bad (misclassified) examples')
168 print('\t-t: sort the results by the true (input) classification')
169 print('\t-x scale: scale the image along the x axis (default: 10)')
170 print('\t-y scale: scale the image along the y axis (default: 2)')
171 print('\t-d databasename: instead of using a qdat file, pull the data from')
172 print('\t a database. In this case the filename argument')
173 print('\t is used to indicate the name of the table in the database.')
174
175 sys.exit(-1)
176
177 if __name__ == '__main__':
178 import sys,getopt
179 from rdkit.six.moves import cPickle
180 from rdkit.ML.Data import DataUtils
181
182 args,extra = getopt.getopt(sys.argv[1:],'o:bthx:y:d:')
183 if len(extra) < 2:
184 Usage()
185 badOnly = 0
186 sortTrueVals = 0
187 xScale=10
188 yScale=2
189 dbName = ''
190 outFileName='foo.png'
191 for arg,val in args:
192 if arg == '-b':
193 badOnly = 1
194 elif arg == '-t':
195 sortTrueVals = 1
196 elif arg == '-o':
197 outFileName = val
198 elif arg == '-x':
199 xScale = int(val)
200 elif arg == '-y':
201 yScale = int(val)
202 elif arg == '-d':
203 dbName = val
204 elif arg == '-h':
205 Usage()
206 else:
207 Usage()
208 modelFile=open(extra[0],'rb')
209 model = cPickle.load(modelFile)
210
211 fName= extra[1]
212 if dbName == '':
213 data = DataUtils.BuildQuantDataSet(fName)
214 else:
215 data = DataUtils.DBToQuantData(dbName,fName)
216
217 dataSet = data.GetNamedData()
218
219 img = VoteAndBuildImage(model,dataSet,badOnly=badOnly,sortTrueVals=sortTrueVals,
220 xScale=xScale,yScale=yScale)
221 img.save(outFileName)
222