def __init__(self,name): self.metric=name self.make_name() #self.param=0 self.simCalc=SimCalculator() #self.reverse=False self.param=(0,False) #flag for negating values to swap direction of inequality - false is >, true is <
class ClassifierUP(): def __init__(self,name): self.metric=name self.make_name() #self.param=0 self.simCalc=SimCalculator() #self.reverse=False self.param=(0,False) #flag for negating values to swap direction of inequality - false is >, true is < def make_name(self): self.name=self.metric+"_UP" def fit(self,pairs,term_map,target): logging.info("Baseline fit: Ignoring training data as unsupervised classifier: "+self.name) def predict(self,pairs,term_map): #term_map is dictionary from terms (in pairs) to vectors #print "Baseline prediction: "+self.name #print "Generating width_map from "+str(len(term_map.keys()))+" keys" tags=[] for pair in pairs: wd = self.simCalc.compute_score(pair,term_map,self.metric) if self.param[1]: wd=-wd if wd > self.param[0]: tags.append(1) else: tags.append(0) return np.array(tags,dtype=int) def get_params(self): return str(self.param)
class ClassifierUP(): def __init__(self,name): self.metric=name self.make_name() self.param=0 self.simCalc=SimCalculator() def make_name(self): self.name=self.metric+"_UP" def fit(self,pairs,term_map,target): logging.info("Baseline fit: Ignoring training data as unsupervised classifier: "+self.name) def predict(self,pairs,term_map): #term_map is dictionary from terms (in pairs) to vectors #print "Baseline prediction: "+self.name #print "Generating width_map from "+str(len(term_map.keys()))+" keys" tags=[] for pair in pairs: wd = self.simCalc.compute_score(pair,term_map,self.metric) if wd > self.param: tags.append(1) else: tags.append(0) return np.array(tags,dtype=int)
def __init__(self,name): self.metric=name self.make_name() self.param=0 self.simCalc=SimCalculator()