Merge branch 'develop' into feature/QUANT-3102

app/lib/irt/item_information_function.py

@@ -1,6 +1,7 @@
 import logging
 
 from lib.irt.models.three_parameter_logistic import ThreeParameterLogistic
+from lib.irt.models.rasch import Rasch
 from lib.errors.item_generation_error import ItemGenerationError
 
 
@@ -20,6 +21,10 @@ class ItemInformationFunction():
                 return (self.model_data.a_param * q *
                         (p - self.model_data.c_param)**2) / (p * (
                             (1 - self.model_data.c_param)**2))
+            elif self.model_data.model == 'rasch':
+                p = Rasch(self.model_data, kwargs).result()
+                q = 1 - p
+                return p * q
             else:
                 # potentially error out
                 raise ItemGenerationError(

app/lib/irt/item_response_function.py

@@ -1,4 +1,5 @@
 from lib.irt.models.three_parameter_logistic import ThreeParameterLogistic
+from lib.irt.models.rasch import Rasch
 from lib.errors.item_generation_error import ItemGenerationError
 
 
@@ -10,5 +11,7 @@ class ItemResponseFunction():
     def calculate(self, **kwargs):
         if self.model_data.model == '3PL':
             return ThreeParameterLogistic(self.model_data, kwargs).result()
+        elif self.model_data.model == 'rasch':
+            return Rasch(self.model_data, kwargs).result()
         else:
             raise ItemGenerationError("irt model not supported or provided")

app/lib/irt/models/rasch.py

@@ -5,7 +5,10 @@ from lib.irt.models.base import *
 class Rasch(Base):
 
   def result(self):
-    return 0.0
+    # contains the primary Rasch function, determining the probably of an inidividual
+    # that an individual at a certain theta would get a particular question correct
+    # https://edres.org/irt/baker/chapter6.pdf
+    return (1 / (1 + self.e(-1 * (self.theta - self.b_param))))
 
   @classmethod
   def ability_estimate(self, items) -> float:

app/models/irt_model.py

@@ -1,11 +1,11 @@
 from pydantic import BaseModel
-from typing import Dict
+from typing import Dict, Optional
 
 
 class IRTModel(BaseModel):
-    a_param: float
+    a_param: Optional[float] = None
     b_param: Dict = {"schema_bson_id": str, "field_bson_id": str}
-    c_param: float
+    c_param: Optional[float] = None
     model: str
 
     def formatted_b_param(self):

app/models/item.py

@@ -13,7 +13,7 @@ class Item(BaseModel):
     workflow_state: Optional[str] = None
     attributes: List[Attribute] = None
     b_param: float = 0.00
-    response: Optional[str] = None
+    response: Optional[int] = None
 
     def iif(self, solver_run, theta):
         return ItemInformationFunction(solver_run.irt_model).calculate(b_param=self.b_param, theta=theta)

app/services/form_generation_service.py

@@ -1,6 +1,6 @@
 import json, random, io, logging
 
-from pulp import LpProblem, LpVariable, LpMinimize, LpMaximize, LpAffineExpression, LpConstraint, LpStatus, lpSum
+from pulp import LpProblem, LpVariable, LpMinimize, LpStatus, lpSum
 
 from lib.application_configs import ApplicationConfigs
 from helpers import aws_helper, tar_helper, csv_helper, service_helper, solver_helper
@@ -72,6 +72,7 @@ class FormGenerationService(Base):
             current_drift = 0  # FF Tokyo Drift
 
             # adding an element of randomness to the items and bundles used
+            # may need to change impl based on limit of items available
             selected_items = self.solver_run.select_items_by_percent(30)
             selected_bundles = self.solver_run.select_bundles_by_percent(
                     30)
@@ -88,7 +89,7 @@ class FormGenerationService(Base):
                                        upBound=1,
                                        cat='Binary')
 
-            logging.info(f'Generating Solution for Form {form_number}')
+            logging.info(f'Generating Solution for Form {form_number} using the {self.solver_run.irt_model.model} IRT model')
 
             while current_drift <= Target.max_drift():
                 drift_percent = current_drift / 100