def initialize_dictionaries(self, p_set):
        """
        Initialize dictionaries with the textual inputs in the PredictorSet object
        p_set - PredictorSet object that has had data fed in
        """
        success = False
        if not (hasattr(p_set, '_type')):
            error_message = "needs to be an essay set of the train type."
            log.exception(error_message)
            raise util_functions.InputError(p_set, error_message)

        if not (p_set._type == "train"):
            error_message = "needs to be an essay set of the train type."
            log.exception(error_message)
            raise util_functions.InputError(p_set, error_message)

        div_length=len(p_set._essay_sets)
        if div_length==0:
            div_length=1

        #Ensures that even with a large amount of input textual features, training time stays reasonable
        max_feats2 = int(math.floor(200/div_length))
        for i in xrange(0,len(p_set._essay_sets)):
            self._extractors.append(FeatureExtractor())
            self._extractors[i].initialize_dictionaries(p_set._essay_sets[i], max_feats2=max_feats2)
            self._initialized = True
            success = True
        return success
def initialize_dictionaries(self, p_set):
        """
        Initialize dictionaries with the textual inputs in the PredictorSet object
        p_set - PredictorSet object that has had data fed in
        """
        success = False
        if not (hasattr(p_set, '_type')):
            error_message = "needs to be an essay set of the train type."
            log.exception(error_message)
            raise util_functions.InputError(p_set, error_message)

        if not (p_set._type == "train"):
            error_message = "needs to be an essay set of the train type."
            log.exception(error_message)
            raise util_functions.InputError(p_set, error_message)

        div_length=len(p_set._essay_sets)
        if div_length==0:
            div_length=1

        #Ensures that even with a large amount of input textual features, training time stays reasonable
        max_feats2 = int(math.floor(200/div_length))
        for i in xrange(0,len(p_set._essay_sets)):
            self._extractors.append(FeatureExtractor())
            self._extractors[i].initialize_dictionaries(p_set._essay_sets[i], max_feats2=max_feats2)
            self._initialized = True
            success = True
        return success
def initialize_dictionaries ( self , p_set ) : success = False if not ( hasattr ( p_set , '_type' ) ) : error_message = "needs to be an essay set of the train type." log . exception ( error_message ) raise util_functions . InputError ( p_set , error_message ) if not ( p_set . _type == "train" ) : error_message = "needs to be an essay set of the train type." log . exception ( error_message ) raise util_functions . InputError ( p_set , error_message ) div_length = len ( p_set . _essay_sets ) if div_length == 0 : div_length = 1 #Ensures that even with a large amount of input textual features, training time stays reasonable max_feats2 = int ( math . floor ( 200 / div_length ) ) for i in xrange ( 0 , len ( p_set . _essay_sets ) ) : self . _extractors . append ( FeatureExtractor ( ) ) self . _extractors [ i ] . initialize_dictionaries ( p_set . _essay_sets [ i ] , max_feats2 = max_feats2 ) self . _initialized = True success = True return success
Initialize dictionaries with the textual inputs in the PredictorSet object
        p_set - PredictorSet object that has had data fed in
Initialize dictionaries with the textual inputs in the PredictorSet object p_set - PredictorSet object that has had data fed in