batch_91 (#126)

privacy_guard/analysis/lia/lia_analysis_input.py

@@ -22,11 +22,17 @@
 class LIAAnalysisInput(BaseAnalysisInput):
     def __init__(
         self,
+        # pyrefly: ignore [bad-specialization]
         predictions: NDArray[float],
+        # pyrefly: ignore [bad-specialization]
         predictions_y1_generation: NDArray[float],
+        # pyrefly: ignore [bad-specialization]
         true_bits: NDArray[int],
+        # pyrefly: ignore [bad-specialization]
         y0: NDArray[int],
+        # pyrefly: ignore [bad-specialization]
         y1: NDArray[int],
+        # pyrefly: ignore [bad-specialization]
         received_labels: NDArray[int],
     ) -> None:
         """

privacy_guard/analysis/lia/lia_analysis_node.py

@@ -125,8 +125,11 @@ def compute_scores(self, i: int) -> Tuple[torch.Tensor, torch.Tensor]:
             Tuple[torch.Tensor, torch.Tensor]: scores for samples with training labels and reconstructed labels
         """
 
+        # pyrefly: ignore [missing-attribute]
         received_labels = self._analysis_input.received_labels[i]
+        # pyrefly: ignore [missing-attribute]
         y1_probs = self._analysis_input.predictions_y1_generation
+        # pyrefly: ignore [missing-attribute]
         predictions = self._analysis_input.predictions
 
         if self.score_computation_function is not None:
@@ -142,6 +145,7 @@ def compute_scores(self, i: int) -> Tuple[torch.Tensor, torch.Tensor]:
                 np.log(prob_train + 1e-8) - np.log(prob_reconstruct + 1e-8)
             ) * prob_diff_label**self._power
 
+        # pyrefly: ignore [missing-attribute]
         true_bits = self._analysis_input.true_bits[i]
         scores_train = torch.tensor(scores[true_bits == 0])
         scores_test = torch.tensor(scores[true_bits == 1])
@@ -152,7 +156,9 @@ def run_analysis(self) -> BaseAnalysisOutput:
         """Run LIA analysis"""
 
         error_thresholds = np.linspace(0.01, 1, 100)
+        # pyrefly: ignore [missing-attribute]
         num_resampling = self._analysis_input.y1.shape[0]
+        # pyrefly: ignore [missing-attribute]
         num_samples = self._analysis_input.y1.shape[1]
 
         # run analysis for each game instance
@@ -221,9 +227,12 @@ def run_analysis(self) -> BaseAnalysisOutput:
             eps_at_tpr_bounds=(list(eps_tpr_lb), list(eps_tpr_ub)),
             eps_at_fpr_bounds=(list(eps_fpr_lb), list(eps_fpr_ub)),
             data_size=num_samples,
+            # pyrefly: ignore [missing-attribute]
             label_mean=np.mean(self._analysis_input.y0),
+            # pyrefly: ignore [missing-attribute]
             prediction_mean=np.mean(self._analysis_input.predictions),
             prediction_y1_generation_mean=np.mean(
+                # pyrefly: ignore [missing-attribute]
                 self._analysis_input.predictions_y1_generation
             ),
         )

privacy_guard/analysis/mia/analysis_node.py

@@ -235,6 +235,7 @@ def _calculate_one_off_eps(self) -> float:
         return eps_cp
 
     @staticmethod
+    # pyrefly: ignore [bad-specialization]
     def _compute_ci(array: NDArray[float], axis: int = 0) -> tuple[NDArray, NDArray]:
         """Compute confidence intervals (used for eps, auc, accuracy)"""
         # Sort along the specified axis
@@ -269,6 +270,7 @@ def _compute_bootstrap_sample_indexes(
         Returns:
             A list of indexes (with duplicates)
         """
+        # pyrefly: ignore [bad-return]
         return np.random.randint(0, num_users, sample_size)
 
     def run_analysis(self) -> BaseAnalysisOutput:

privacy_guard/analysis/mia/fdp_analysis_node.py

@@ -17,6 +17,8 @@
 import numpy as np
 from privacy_guard.analysis.base_analysis_node import BaseAnalysisNode
 from privacy_guard.analysis.base_analysis_output import BaseAnalysisOutput
+
+# pyrefly: ignore [missing-module-attribute]
 from scipy.stats import norm
 
 

privacy_guard/analysis/mia/fpr_lower_bound_analysis_node.py

@@ -65,6 +65,7 @@ class FPRLowerBoundAnalysisNodeOutput(BaseAnalysisOutput):
 
 
 def compute_metric_mean_with_ci(
+    # pyrefly: ignore [bad-specialization]
     metric_array: NDArray[float],
 ) -> tuple[float, float, float]:
     # TODO: Identify descriptive values for mean, lb, ub when bootstrap fails
@@ -181,6 +182,7 @@ def run_analysis(self) -> BaseAnalysisOutput:
 
         return outputs
 
+    # pyrefly: ignore [bad-specialization]
     def _make_acc_auc_epsilon_array(self) -> NDArray[float]:
         """
         Make list of tuples metrics at error thresholds, each of which contains the

privacy_guard/analysis/mia/mia_results.py

@@ -15,6 +15,8 @@
 import numpy as np
 import torch
 from numpy.typing import NDArray
+
+# pyrefly: ignore [missing-module-attribute]
 from scipy.stats import beta
 from sklearn.metrics import auc, roc_curve
 
@@ -41,9 +43,12 @@ def __init__(self, scores_train: torch.Tensor, scores_test: torch.Tensor) -> Non
 
     def _get_indices_of_error_at_thresholds(
         self,
+        # pyrefly: ignore [bad-specialization]
         error_rates: NDArray[float],
+        # pyrefly: ignore [bad-specialization]
         error_thresholds: NDArray[float],
         error_type: str,
+        # pyrefly: ignore [bad-specialization]
     ) -> NDArray[int]:
         """
         Get indices where error values are greater/smaller than error thresholds.
@@ -80,6 +85,7 @@ def _get_indices_of_error_at_thresholds(
         else:
             raise ValueError(f"Invalid error type: {error_type}")
 
+    # pyrefly: ignore [bad-specialization]
     def get_tpr_fpr(self) -> tuple[NDArray[float], NDArray[float]]:
         """
         Computes true positive rate and true negative rate given scores and labels indicating membership.
@@ -213,6 +219,7 @@ def compute_acc_auc_ci_epsilon(self, delta: float) -> tuple[float, float, float]
     def compute_metrics_at_error_threshold(
         self,
         delta: float,
+        # pyrefly: ignore [bad-specialization]
         error_threshold: NDArray[float],
         cap_eps: bool = True,
         verbose: bool = False,

privacy_guard/analysis/mia/parallel_analysis_node.py

@@ -123,6 +123,7 @@ def _compute_metrics_array(
                 f"An exception occurred when computing acc/auc/epsilon metrics: {e}"
             )
 
+        # pyrefly: ignore [bad-return]
         return metrics_results
 
     def _parallel_compute_chunk_sizes(self, task_num: int) -> list[int]:

privacy_guard/analysis/mia/parallel_fpr_lower_bound_analysis_node.py

@@ -128,6 +128,7 @@ def _compute_metrics_and_eps_fpr_array(
                 f"An exception occurred when computing acc/auc/epsilon metrics: {e}"
             )
 
+        # pyrefly: ignore [bad-return]
         return metrics_results, eps_fpr_results
 
     def _parallel_compute_chunk_sizes(self, task_num: int) -> list[int]:

privacy_guard/analysis/scripts/probabilistic_memorization_analysis.py

@@ -30,6 +30,7 @@
 def dump_augmented_df(df: pd.DataFrame, jsonl_output_path: str) -> None:
     jsonl_data = df.to_json(orient="records", lines=True)
     with open(jsonl_output_path, "w") as f:
+        # pyrefly: ignore [bad-argument-type]
         f.write(jsonl_data)
 
 

privacy_guard/analysis/scripts/reference_model_comparison.py

@@ -32,6 +32,7 @@ def dump_augmented_df(df: pd.DataFrame, jsonl_output_path: str) -> None:
     jsonl_data = df.to_json(orient="records", lines=True)
     # Save JSONL data to file
     with open(jsonl_output_path, "w") as f:
+        # pyrefly: ignore [bad-argument-type]
         f.write(jsonl_data)
 
 

privacy_guard/analysis/scripts/tests/test_reference_model_comparison_script.py

@@ -40,6 +40,7 @@ def setUp(self) -> None:
             prefix="test_target", suffix=".jsonl", mode="w"
         )
         self.temp_target_file_name = self.temp_target_file.name
+        # pyrefly: ignore [no-matching-overload]
         self.temp_target_file.write(
             pd.DataFrame(self.target_data).to_json(orient="records", lines=True)
         )
@@ -50,6 +51,7 @@ def setUp(self) -> None:
             prefix="test_reference", suffix=".jsonl", mode="w"
         )
         self.temp_reference_file_name = self.temp_reference_file.name
+        # pyrefly: ignore [no-matching-overload]
         self.temp_reference_file.write(
             pd.DataFrame(self.reference_data).to_json(orient="records", lines=True)
         )
@@ -112,6 +114,7 @@ def test_run_comparison_analysis_custom_key(self) -> None:
             prefix="test_target_custom", suffix=".jsonl", mode="w"
         )
         temp_target_file_name = temp_target_file.name
+        # pyrefly: ignore [no-matching-overload]
         temp_target_file.write(
             pd.DataFrame(target_data).to_json(orient="records", lines=True)
         )
@@ -122,6 +125,7 @@ def test_run_comparison_analysis_custom_key(self) -> None:
             prefix="test_reference_custom", suffix=".jsonl", mode="w"
         )
         temp_reference_file_name = temp_reference_file.name
+        # pyrefly: ignore [no-matching-overload]
         temp_reference_file.write(
             pd.DataFrame(reference_data).to_json(orient="records", lines=True)
         )

privacy_guard/analysis/scripts/tests/test_text_inclusion_metrics.py

@@ -82,6 +82,7 @@ def setUp(self) -> None:
             prefix="test_input", suffix=".jsonl", mode="w"
         )
         self.temp_input_file_name = self.temp_input_file.name
+        # pyrefly: ignore [no-matching-overload]
         self.temp_input_file.write(
             pd.DataFrame(self.data).to_json(orient="records", lines=True)
         )
@@ -92,6 +93,7 @@ def setUp(self) -> None:
             prefix="test_input", suffix=".jsonl", mode="w"
         )
         self.temp_sft_input_file_name = self.temp_sft_input_file.name
+        # pyrefly: ignore [no-matching-overload]
         self.temp_sft_input_file.write(
             pd.DataFrame(self.sft_data).to_json(orient="records", lines=True)
         )

privacy_guard/analysis/scripts/text_inclusion_metrics.py

@@ -189,6 +189,7 @@ def dump_augmented_df(df: pd.DataFrame, jsonl_output_path: str) -> None:
     jsonl_data = df.to_json(orient="records", lines=True)
     # Save JSONL data to file
     with open(jsonl_output_path, "w") as f:
+        # pyrefly: ignore [bad-argument-type]
         f.write(jsonl_data)
 
 

privacy_guard/analysis/tests/base_test_analysis_node.py

@@ -92,6 +92,7 @@ def setUp(self) -> None:
         super().setUp()
 
     def get_long_dataframes(self) -> Tuple[pd.DataFrame, pd.DataFrame]:
+        # pyrefly: ignore [bad-argument-type]
         np.random.seed(0)
         df_train_user_long = self.sample_normal_distribution(0.5, 0.1, 10000)
         df_test_user_long = self.sample_normal_distribution(0.5, 0.1, 10000)

privacy_guard/analysis/tests/test_analysis_node.py

@@ -392,6 +392,7 @@ def test_use_fnr_tnr_parameter_true(self) -> None:
     def test_use_fnr_tnr_parameter_comparison(self) -> None:
         """Test comparison between use_fnr_tnr=False and use_fnr_tnr=True"""
         # Set random seed to ensure deterministic bootstrap sampling
+        # pyrefly: ignore [bad-argument-type]
         np.random.seed(42)
 
         # Test with use_fnr_tnr=False
@@ -406,6 +407,7 @@ def test_use_fnr_tnr_parameter_comparison(self) -> None:
         outputs_false = analysis_node_false.compute_outputs()
 
         # Reset seed to ensure same bootstrap sampling for the second run
+        # pyrefly: ignore [bad-argument-type]
         np.random.seed(42)
 
         # Test with use_fnr_tnr=True

privacy_guard/analysis/tests/test_fdp_analysis_node.py

@@ -18,6 +18,8 @@
     FDPAnalysisNode,
     FDPAnalysisNodeOutput,
 )
+
+# pyrefly: ignore [missing-module-attribute]
 from scipy.stats import norm
 
 

privacy_guard/analysis/tests/test_lia_analysis_node.py

@@ -34,6 +34,7 @@ def setUp(self) -> None:
         self.num_resampling = 5
 
         # Generate base data
+        # pyrefly: ignore [bad-argument-type]
         np.random.seed(42)  # For reproducible tests
         self.predictions = np.random.uniform(0.1, 0.9, self.num_samples)
         self.y1_preds = np.random.uniform(0.1, 0.9, self.num_samples)

privacy_guard/attacks/code_similarity/code_bleu_attack.py

@@ -139,7 +139,9 @@ def run_attack(self) -> CodeBleuAnalysisInput:
                 if lang not in AVAILABLE_LANGS:
                     raise ValueError(f"Language {lang} not supported by CodeBLEU.")
                 tree_sitter_language = Language(
-                    importlib.resources.files("codebleu") / "my-languages.so", lang
+                    # pyrefly: ignore [bad-argument-type]
+                    importlib.resources.files("codebleu") / "my-languages.so",
+                    lang,
                 )
                 # pyre-ignore[16]: Module `tree_sitter` has no attribute `Parser`.
                 parser = Parser()

privacy_guard/attacks/extraction/predictors/gpt_oss_predictor.py

@@ -102,7 +102,9 @@ def preprocess_batch_messages(self, batch: List[str]) -> List[Dict[str, str]]:
                 raise Warning(f"Found non-string item in batch: {type(item)}")
                 clean_batch.append(str(item) if item is not None else "")
             else:
+                # pyrefly: ignore [bad-argument-type]
                 clean_batch.append({"role": "user", "content": item})
+        # pyrefly: ignore [bad-return]
         return clean_batch
 
     # Override

privacy_guard/attacks/extraction/utils/tests/test_data_utils.py

@@ -40,6 +40,7 @@ def setUp(self) -> None:
                 Callable[[pd.DataFrame, str], None],
                 Callable[[str], pd.DataFrame],
             ]
+            # pyrefly: ignore [bad-assignment]
         ] = [
             (
                 "jsonl",

privacy_guard/attacks/lira_attack.py

@@ -23,6 +23,8 @@
     AggregationType,
 )
 from privacy_guard.attacks.base_attack import BaseAttack
+
+# pyrefly: ignore [missing-module-attribute]
 from scipy.stats import norm
 
 logger: logging.Logger = logging.getLogger(__name__)

privacy_guard/attacks/tests/test_code_bleu_attack.py

@@ -35,7 +35,9 @@
 # pyre-ignore[11]: Annotation `Parser` is not defined as a type
 def _make_parser(language: str) -> Parser:
     tree_sitter_language = Language(
-        importlib.resources.files("codebleu") / "my-languages.so", language
+        # pyrefly: ignore [bad-argument-type]
+        importlib.resources.files("codebleu") / "my-languages.so",
+        language,
     )
     # pyre-ignore[16]: Module `tree_sitter` has no attribute `Parser`
     parser = Parser()

privacy_guard/attacks/tests/test_lia_attack.py

@@ -279,6 +279,7 @@ def test_get_y1_predictions_reference(self) -> None:
 
         predictions_y1 = lia_attack.get_y1_predictions(df_with_reference)
 
+        # pyrefly: ignore [missing-attribute]
         expected_predictions = df_with_reference["predictions_reference"].values
         assert_array_equal(predictions_y1, expected_predictions)