add MFI strategy

PranavUikey · Jan 19, 2021 · 755527c · 755527c
1 parent ed2212f
commit 755527c
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Showing 5 changed files with 57 additions and 30 deletions.
diff --git a/CAT/model/IRT.py b/CAT/model/IRT.py
@@ -6,16 +6,11 @@
 import torch.nn as nn
 import numpy as np
 import torch.utils.data as data
+from math import exp as exp
 from sklearn.metrics import roc_auc_score
 
-try:
-    # for python module
-    from .abstract_model import AbstractModel
-    from ..dataset import AdapTestDataset, TrainDataset, Dataset
-except (ImportError, SystemError):  # pragma: no cover
-    # for python script
-    from abstract_model import AbstractModel
-    from dataset import AdapTestDataset, TrainDataset, Dataset
+from CAT.model.abstract_model import AbstractModel
+from CAT.dataset import AdapTestDataset, TrainDataset, Dataset
 
 
 class IRT(nn.Module):
@@ -41,10 +36,6 @@ def forward(self, student_ids, question_ids):
         pred = torch.sigmoid(pred)
         return pred
 
-    def get_knowledge_status(self, stu_ids):
-        stu_emb = self.theta(stu_ids)
-        return stu_emb.data
-
 
 class IRTModel(AbstractModel):
 
@@ -181,4 +172,20 @@ def get_beta(self, question_id):
         return self.model.beta.weight.data.numpy()[question_id]
 
     def get_theta(self, student_id):
-        return self.model.theta.weight.data.numpy()[student_id]
+        return self.model.theta.weight.data.numpy()[student_id]
+
+    def get_iif(self, student_id, question_id):
+        device = self.config['device']
+        sid = torch.LongTensor([student_id]).to(device)
+        qid = torch.LongTensor([question_id]).to(device)
+        theta = self.model.theta(sid).clone().detach().requires_grad_(True)
+        alpha = self.model.alpha(qid).clone().detach()
+        beta = self.model.beta(qid).clone().detach()
+        pred = (alpha * theta).sum(dim=1, keepdim=True) + beta
+        pred = torch.sigmoid(pred)
+        pred.backward()
+        pred = pred.data.numpy()[0][0]
+        grad = theta.grad.data.numpy()[0][0]
+        return grad ** 2 / (pred * (1 - pred))
+
+
diff --git a/CAT/model/abstract_model.py b/CAT/model/abstract_model.py
@@ -1,10 +1,5 @@
 from abc import ABC, abstractmethod
-try:
-    # for python module
-    from ..dataset import AdapTestDataset, TrainDataset, Dataset
-except (ImportError, SystemError):  # pragma: no cover
-    # for python script
-    from dataset import AdapTestDataset, TrainDataset, Dataset
+from CAT.dataset import AdapTestDataset, TrainDataset, Dataset
 
 
 class AbstractModel(ABC):

diff --git a/CAT/strategy/MFI_strategy.py b/CAT/strategy/MFI_strategy.py
@@ -0,0 +1,31 @@
+import numpy as np
+
+from CAT.strategy.abstract_strategy import AbstractStrategy
+from CAT.model import AbstractModel
+from CAT.dataset import AdapTestDataset
+
+
+class MFIStrategy(AbstractStrategy):
+
+    def __init__(self):
+        super().__init__()
+
+    @property
+    def name(self):
+        return 'Maximum Fisher Information Strategy'
+
+    def adaptest_select(self, model: AbstractModel, adaptest_data: AdapTestDataset):
+        assert hasattr(model, 'get_theta'), \
+            'the models must implement get_theta method'
+        assert hasattr(model, 'get_iif'), \
+            'the models must implement get_iif method'
+        selection = {}
+        for sid in range(adaptest_data.num_students):
+            theta = model.get_theta(sid)
+            untested_questions = np.array(list(adaptest_data.untested[sid]))
+            untested_iif = []
+            for qid in untested_questions:
+                untested_iif.append(model.get_iif(sid, qid))
+            j = np.argmax(untested_iif)
+            selection[sid] = untested_questions[j]
+        return selection
diff --git a/CAT/strategy/__init__.py b/CAT/strategy/__init__.py
@@ -1,2 +1,3 @@
 from .abstract_strategy import AbstractStrategy
-from .random_strategy import RandomStrategy
+from .random_strategy import RandomStrategy
+from .MFI_strategy import MFIStrategy
diff --git a/CAT/strategy/random_strategy.py b/CAT/strategy/random_strategy.py
@@ -1,15 +1,8 @@
 import numpy as np
 
-try:
-    # for python module
-    from .abstract_strategy import AbstractStrategy
-    from ..model import AbstractModel
-    from ..dataset import AdapTestDataset
-except (ImportError, SystemError):  # pragma: no cover
-    # for python script
-    from abstract_strategy import AbstractStrategy
-    from model import AbstractModel
-    from dataset import AdapTestDataset
+from CAT.strategy.abstract_strategy import AbstractStrategy
+from CAT.model import AbstractModel
+from CAT.dataset import AdapTestDataset
 
 
 class RandomStrategy(AbstractStrategy):