Adding generic ML algo

predictor_set.py

@@ -4,6 +4,7 @@ import sys
 import random
 import os
 import logging
+import essay_set
 
 base_path = os.path.dirname(__file__)
 sys.path.append(base_path)
@@ -38,4 +39,63 @@ class PredictorSet(object):
         self.essay_sets=[]
 
     def add_row(self, numeric_features, textual_features, target):
-        pass
+
+        #Basic input checking
+        if not isinstance(target, (int, long, float)):
+            error_message = "Target is not a numeric value."
+            log.exception(error_message)
+            raise util_functions.InputError(target, error_message)
+
+        if not isinstance(numeric_features, list):
+            error_message = "Numeric features are not a list."
+            log.exception(error_message)
+            raise util_functions.InputError(numeric_features, error_message)
+
+        if not isinstance(textual_features, list):
+            error_message = "Textual features are not a list."
+            log.exception(error_message)
+            raise util_functions.InputError(textual_features, error_message)
+
+        #Do some length checking for parameters
+        if len(self._numeric_features)>0:
+            numeric_length  = len(self._numeric_features[-1])
+            current_numeric_length = len(numeric_features)
+            if numeric_length != current_numeric_length:
+                error_message = "Numeric features are an improper length."
+                log.exception(error_message)
+                raise util_functions.InputError(numeric_features, error_message)
+
+        if len(self._textual_features)>0:
+            textual_length  = len(self._textual_features[-1])
+            current_textual_length = len(textual_features)
+            if textual_length != current_textual_length:
+                error_message = "Textual features are an improper length."
+                log.exception(error_message)
+                raise util_functions.InputError(textual_features, error_message)
+
+        #Now check to see if text features and numeric features are individually correct
+
+        for i in xrange(0,len(numeric_features)):
+            try:
+                numeric_features[i] = float(numeric_features[i])
+            except:
+                error_message = "Numeric feature {0} not numeric.".format(numeric_features[i])
+                log.exception(error_message)
+                raise util_functions.InputError(numeric_features, error_message)
+
+
+        for i in xrange(0,len(textual_features)):
+            try:
+                textual_features[i] = str(textual_features[i].encode('ascii', 'ignore'))
+            except:
+                error_message = "Textual feature {0} not string.".format(textual_features[i])
+                log.exception(error_message)
+                raise util_functions.InputError(textual_features, error_message)
+
+        if len(self._textual_features)==0:
+            for i in xrange(0,len(textual_features)):
+                self.essay_sets.append()
+
+        self._numeric_features.append(numeric_features)
+        self._textual_features.append(textual_features)
+