Merge pull request #61 from Sturdy-Subnet/release/1.5.2

Release/1.5.2

README.md

@@ -131,7 +131,7 @@ There are three core files.
   contracts of user-defined pools on the Ethereum Network to calculate the miners' allocation's
   yields. The scores of miners are determined based on their relative aggregate
   yields, and miners which have similar allocations to other miners will be penalized if they are
-  not perceived as being original. If miners fail to respond in ~10 seconds after receiving the
+  not perceived as being original. If miners fail to respond in ~45 seconds after receiving the
   request they are scored
   poorly.
   The best allocating miner will receive the most emissions. For more information on how

min_compute.yml

@@ -10,7 +10,7 @@
 # Even then - storage isn't utilized very often - so disk performance won't really be a bottleneck vs, CPU, RAM, 
 # and network bandwidth.
 
-version: '1.5.1' # update this version key as needed, ideally should match your release version
+version: '1.5.2' # update this version key as needed, ideally should match your release version
 
 compute_spec:
 

sturdy/__init__.py

@@ -17,7 +17,7 @@
 # DEALINGS IN THE SOFTWARE.
 
 # Define the version of the template module.
-__version__ = "1.5.1"
+__version__ = "1.5.2"
 version_split = __version__.split(".")
 __spec_version__ = (1000 * int(version_split[0])) + (10 * int(version_split[1])) + (1 * int(version_split[2]))
 

sturdy/constants.py

@@ -32,12 +32,12 @@
 QUERY_TIMEOUT = 45  # timeout (seconds)
 
 TOTAL_ALLOC_THRESHOLD = 0.98
+SIMILARITY_THRESHOLD = 0.0075  # similarity threshold for plagiarism checking
 
 # The following constants are for different pool models
 # Aave
 RESERVE_FACTOR_START_BIT_POSITION = 64
 RESERVE_FACTOR_MASK = 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF0000FFFFFFFFFFFFFFFF
-SIMILARITY_THRESHOLD = 0.1  # similarity threshold for plagiarism checking
 
 # yearn finance
 APR_ORACLE = (

tests/unit/validator/test_reward_helpers.py

@@ -798,7 +798,7 @@ def test_adjust_rewards_for_plagiarism(self) -> None:
         rewards_apy = torch.Tensor([0.05 / 0.05, 0.04 / 0.05, 0.03 / 0.05])
         apys_and_allocations = {
             "0": {"apy": 0.05, "allocations": {"asset_1": 200, "asset_2": 300}},
-            "1": {"apy": 0.04, "allocations": {"asset_1": 210, "asset_2": 310}},
+            "1": {"apy": 0.04, "allocations": {"asset_1": 202, "asset_2": 303}},
             "2": {"apy": 0.03, "allocations": {"asset_1": 200, "asset_2": 400}},
         }
         assets_and_pools = {