Source Code for Module TEES.Classifiers.AllTrueClassifier

 1  from Classifier import Classifier 
 2  import Core.ExampleUtils as Example 
 3  import sys, os, shutil, types 
 4   
 5 -class AllTrueClassifier(Classifier): 
 6 -    def __init__(self, workDir=None): 
 7          self._makeTempDir(workDir)         
 8       
 9 -    def __del__(self): 
10          self.debugFile.close() 
11          if self._workDir == None and os.path.exists(self.tempDir): 
12              print >> sys.stderr, "Removing temporary classifier work directory", self.tempDir 
13              shutil.rmtree(self.tempDir) 
14   
15      @classmethod 
16 -    def train(cls, examples, parameters, outputFile=None, timeout=None): 
17          return 0 
18       
19      @classmethod 
20 -    def test(cls, examples, modelPath, output=None, parameters=None, timeout=None): 
21          if type(examples) == types.ListType: 
22              print >> sys.stderr, "Classifying", len(examples), "with All-True Classifier" 
23              examples, predictions = self.filterClassificationSet(examples, False) 
24              testPath = self.tempDir+"/test.dat" 
25              Example.writeExamples(examples, testPath) 
26          else: 
27              print >> sys.stderr, "Classifying file", examples, "with All-True Classifier" 
28              testPath = examples 
29              examples = Example.readExamples(examples,False) 
30          print >> sys.stderr, "Note! Classification must be binary" 
31          #examples, predictions = self.filterClassificationSet(examples, True) 
32          predictions = [] 
33          for example in examples: 
34              #predictions.append( (example, example[1]) ) 
35              predictions.append( [2] ) #[example[1]] ) 
36           
37          if output == None: 
38              output = "predictions" 
39          f = open(output, "wt") 
40          for p in predictions: 
41              f.write(str(p[0])+"\n") 
42          f.close() 
43               
44          return predictions 
45