Fix linting errors (#63)

Author: apd10

vcache/vcache_policy/strategies/benchmark_iid_verified.py

@@ -1,17 +1,13 @@
 import logging
 import os
 import queue
-import random
 import threading
 from concurrent.futures import ThreadPoolExecutor
 from enum import Enum
-from typing import Dict, List, Optional, Tuple
+from typing import Optional
 
 import numpy as np
-import statsmodels.api as sm
-from scipy.special import expit
 from scipy.stats import norm
-from sklearn.linear_model import LogisticRegression
 from typing_extensions import override
 
 from vcache.config import VCacheConfig
@@ -497,23 +493,45 @@ def select_action(
         negative_samples = similarities[labels==0].reshape(-1,1,1)
         labels = labels.reshape(-1,1,1)
         tholds = self.thold_grid.reshape(1,-1,1)
-        deltap = self.delta * (num_negative_samples + num_positive_samples)/num_negative_samples
+        deltap = (
+            self.delta * (num_negative_samples + num_positive_samples)
+        ) / num_negative_samples
 
         epsilon = self.epsilon_grid[self.epsilon_grid < deltap].reshape(1,1,-1)
 
-        cdf_estimate = np.sum(negative_samples < tholds, axis=0, keepdims=True) / num_negative_samples # (1, tholds, 1)
-        cdf_ci_lower, cdf_ci_upper = self.wilson_proportion_ci(cdf_estimate, num_negative_samples, confidence=1-epsilon) # (1, tholds, epsilon)
-
-        pc_adjusted = 1 - (deltap - epsilon) / (1 - epsilon) # adjust for positive samples (1,1,epsilon)
+        cdf_estimate = (
+            np.sum(negative_samples < tholds, axis=0, keepdims=True) /
+            num_negative_samples
+        )  # (1, tholds, 1)
+        cdf_ci_lower, cdf_ci_upper = self.wilson_proportion_ci(
+            cdf_estimate, num_negative_samples, confidence=1 - epsilon
+        )  # (1, tholds, epsilon)
+        
+        # adjust for positive samples (1,1,epsilon)
+        pc_adjusted = 1 - (deltap - epsilon) / (1 - epsilon)  
 
 
-        t_hats = (np.sum(cdf_estimate > pc_adjusted, axis=1, keepdims=True) == 0) * 1.0 + (1 - (np.sum(cdf_estimate > pc_adjusted, axis=1, keepdims=True) == 0)) * self.thold_grid[np.argmax(cdf_estimate > pc_adjusted, axis=1, keepdims=True)]
-        t_primes = (np.sum(cdf_ci_lower > pc_adjusted, axis=1, keepdims=True) == 0) * 1.0 + (1 - (np.sum(cdf_ci_lower > pc_adjusted, axis=1, keepdims=True) == 0)) * self.thold_grid[np.argmax(cdf_ci_lower > pc_adjusted, axis=1, keepdims=True)]
+        t_hats = (
+            (np.sum(cdf_estimate > pc_adjusted, axis=1, keepdims=True) == 0) * 1.0
+            + (
+                1 - (np.sum(cdf_estimate > pc_adjusted, axis=1, keepdims=True) == 0)
+            )
+            * self.thold_grid[
+                np.argmax(cdf_estimate > pc_adjusted, axis=1, keepdims=True)
+            ]
+        )
+        t_primes = (
+            (np.sum(cdf_ci_lower > pc_adjusted, axis=1, keepdims=True) == 0) * 1.0
+            + (
+                1 - (np.sum(cdf_ci_lower > pc_adjusted, axis=1, keepdims=True) == 0)
+            )
+            * self.thold_grid[
+                np.argmax(cdf_ci_lower > pc_adjusted, axis=1, keepdims=True)
+            ]
+        )
 
         t_hat = np.min(t_hats)
         t_prime = np.min(t_primes)
-        # if t_prime < 1.0:
-        #     print(f"t_hat: {t_hat}, t_prime: {t_prime} num_positive_samples: {num_positive_samples} num_negative_samples: {num_negative_samples}")
         metadata.t_prime = t_prime
         metadata.t_hat = t_hat
         metadata.var_t = -1 # not computed