Add flexible maximum for feature counts

feature_extractor.py

@@ -32,7 +32,7 @@ class FeatureExtractor(object):
         self._spell_errors_per_character=0
         self._grammar_errors_per_character=0
 
-    def initialize_dictionaries(self, e_set):
+    def initialize_dictionaries(self, e_set, max_feats2 = 200):
         """
         Initializes dictionaries from an essay set object
         Dictionaries must be initialized prior to using this to extract features
@@ -41,8 +41,8 @@ class FeatureExtractor(object):
         """
         if(hasattr(e_set, '_type')):
             if(e_set._type == "train"):
-                nvocab = util_functions.get_vocab(e_set._text, e_set._score)
-                svocab = util_functions.get_vocab(e_set._clean_stem_text, e_set._score)
+                nvocab = util_functions.get_vocab(e_set._text, e_set._score, max_feats2 = max_feats2)
+                svocab = util_functions.get_vocab(e_set._clean_stem_text, e_set._score, max_feats2 = max_feats2)
                 self._normal_dict = CountVectorizer(ngram_range=(1,2), vocabulary=nvocab)
                 self._stem_dict = CountVectorizer(ngram_range=(1,2), vocabulary=svocab)
                 self.dict_initialized = True

predictor_extractor.py

@@ -9,6 +9,8 @@ from itertools import chain
 import copy
 import operator
 import logging
+import math
+from feature_extractor import FeatureExtractor
 
 base_path = os.path.dirname(__file__)
 sys.path.append(base_path)
@@ -23,6 +25,26 @@ log = logging.getLogger(__name__)
 
 class PredictorExtractor(object):
     def __init__(self):
-        pass
+        self._extractors = []
 
-    
+    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)
+
+        max_feats2 = math.floor(200/len(p_set._essay_sets))
+        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)
+
+        return success
+
+    def gen_feats(self, p_set):
+        

predictor_set.py

@@ -36,7 +36,7 @@ class PredictorSet(object):
         self._target=[]
         self._textual_features=[]
         self._numeric_features=[]
-        self.essay_sets=[]
+        self._essay_sets=[]
 
     def add_row(self, numeric_features, textual_features, target):
 
@@ -95,7 +95,7 @@ class PredictorSet(object):
         #Create essay sets for textual features if needed
         if len(self._textual_features)==0:
             for i in xrange(0,len(textual_features)):
-                self.essay_sets.append(EssaySet(type=self._type))
+                self._essay_sets.append(EssaySet(type=self._type))
 
         #Add numeric and textual features
         self._numeric_features.append(numeric_features)
@@ -106,5 +106,5 @@ class PredictorSet(object):
 
         #Add textual features to essay sets
         for i in xrange(0,len(textual_features)):
-            self.essay_sets[i].add_essay(textual_features[i], target[i])
+            self._essay_sets[i].add_essay(textual_features[i], target[i])