CR Comments v2

skllm/models/gpt_zero_shot_clf.py

@@ -1,7 +1,7 @@
 import random
 from abc import ABC, abstractmethod
 from collections import Counter
-from typing import Any, List, Optional, Union
+from typing import Any, List, Optional, Union, Literal
 
 import numpy as np
 import pandas as pd
@@ -34,8 +34,6 @@ class _BaseZeroShotGPTClassifier(ABC, BaseEstimator, ClassifierMixin, _OAIMixin)
     default_label : Optional[Union[List[str], str]] , default : 'Random'
         The default label to use if the LLM could not generate a response for a sample. If set to 'Random' a random
         label will be chosen based on probabilities from the training set.
-    random_state : int, default 42
-        A seed to set for pseudo-random functions, primarily random selection.
     """
 
     def __init__(
@@ -44,13 +42,10 @@ def __init__(
         openai_org: Optional[str] = None,
         openai_model: str = "gpt-3.5-turbo",
         default_label: Optional[Union[List[str], str]] = 'Random',
-        random_state: int = 42,
     ):
         self._set_keys(openai_key, openai_org)
         self.openai_model = openai_model
         self.default_label = default_label
-        random.seed(random_state)
-        np.random.seed(random_state)
 
     def _to_np(self, X):
         return _to_numpy(X)
@@ -121,8 +116,6 @@ class ZeroShotGPTClassifier(_BaseZeroShotGPTClassifier):
     default_label : Optional[str] , default : 'Random'
         The default label to use if the LLM could not generate a response for a sample. If set to 'Random' a random
         label will be chosen based on probabilities from the training set.
-    random_state : int, default 42
-        A seed to set for pseudo-random functions, primarily random selection.
     """
 
     def __init__(
@@ -131,9 +124,8 @@ def __init__(
         openai_org: Optional[str] = None,
         openai_model: str = "gpt-3.5-turbo",
         default_label: Optional[str] = 'Random',
-        random_state: int = 42,
     ):
-        super().__init__(openai_key, openai_org, openai_model, default_label, random_state)
+        super().__init__(openai_key, openai_org, openai_model, default_label)
 
     def _extract_labels(self, y: Any) -> List[str]:
         if isinstance(y, (pd.Series, np.ndarray)):
@@ -198,24 +190,21 @@ class MultiLabelZeroShotGPTClassifier(_BaseZeroShotGPTClassifier):
     openai_model : str , default : "gpt-3.5-turbo"
         The OpenAI model to use. See https://beta.openai.com/docs/api-reference/available-models for a list of
         available models.
-    default_label : Optional[Union[List[str], str]] , default : 'Random'
+    default_label : Optional[Union[List[str], Literal['Random']] , default : 'Random'
         The default label to use if the LLM could not generate a response for a sample. If set to 'Random' a random
         label will be chosen based on probabilities from the training set.
     max_labels : int , default : 3
         The maximum number of labels to predict for each sample.
-    random_state : int, default 42
-        A seed to set for pseudo-random functions, primarily random selection.
     """
     def __init__(
         self,
         openai_key: Optional[str] = None,
         openai_org: Optional[str] = None,
         openai_model: str = "gpt-3.5-turbo",
-        default_label: Optional[Union[List[str], str]] = 'Random',
+        default_label: Optional[Union[List[str], Literal['Random']]] = 'Random',
         max_labels: int = 3,
-        random_state: int = 42,
     ):
-        super().__init__(openai_key, openai_org, openai_model, default_label, random_state)
+        super().__init__(openai_key, openai_org, openai_model, default_label)
         if max_labels < 2:
             raise ValueError("max_labels should be at least 2")
         if isinstance(default_label, str) and default_label != "Random":
@@ -259,7 +248,7 @@ def _predict_single(self, x):
         if len(labels) == 0:
             labels = self._get_default_label()
         if labels is not None and len(labels) > self.max_labels:
-            labels = random.choices(labels, k=self.max_labels)
+            labels = labels[:self.max_labels - 1]
         return labels
 
     def fit(

tests/test_gpt_zero_shot_clf.py

@@ -1,4 +1,5 @@
 import json
+import random
 import unittest
 from unittest.mock import MagicMock, patch
 
@@ -36,7 +37,9 @@ def test_fit_predict_unknown_label_set_default(self, mock_get_chat_completion):
         mock_get_chat_completion.return_value.choices[0].message = {"content": json.dumps({"label": "new_class"})}
 
         predictions = clf.predict(["text1", "text2", "text3"])
-        self.assertEqual(predictions, ["class1", "class1", "class1"])  # Random choice
+        random.seed(42)
+        np.random.seed(42)
+        self.assertEqual(predictions, ["class2", "class1", "class1"])  # Random choice
 
         clf.default_label = "default_class"
         predictions = clf.predict(["text1", "text2", "text3"])
@@ -50,7 +53,8 @@ def test_fit_predict_unknown_label_set_default(self, mock_get_chat_completion):
 class TestMultiLabelZeroShotGPTClassifier(unittest.TestCase):
 
     def get_mock_clf_model(self):
-        clf = MultiLabelZeroShotGPTClassifier(openai_key="mock_key", openai_org="mock_org")  # Mock keys
+        clf = MultiLabelZeroShotGPTClassifier(openai_key="mock_key", openai_org="mock_org",
+                                              default_label='Random')  # Mock keys
         X = np.array(["text1", "text2", "text3"])
         y = [["class1", "class2"], ["class1", "class2"],
              ["class1", "class2"]]  # Adjusted y to ensure [0.5, 0.5] probability
@@ -75,6 +79,8 @@ def test_fit_predict_unknown_label_set_default(self, mock_get_chat_completion):
         mock_get_chat_completion.return_value.choices[0].message = {"content": json.dumps({"label": "new_class"})}
 
         predictions = clf.predict(["text1", "text2", "text3"])
+        random.seed(42)
+        np.random.seed(42)
         self.assertEqual(predictions, [['class1'], [], ['class1', 'class2']])  # Random choice
 
         clf.default_label = ["default_class"]