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Response Rate Scoring Surface #46

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merged 4 commits into from
Jan 25, 2025
Merged

Response Rate Scoring Surface #46

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402479d
feat: draft - response rate scoring surface
Shr1ftyy Jan 23, 2025
5112303
fix: update rate score formula
Shr1ftyy Jan 24, 2025
2f98c69
fix: increase score scaling factor
Shr1ftyy Jan 25, 2025
37dc8a1
chore: gitignore PEM files and remove TODO comment
Shr1ftyy Jan 25, 2025
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3 changes: 3 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -1,3 +1,6 @@
# keys
*.pem

.DS_Store
.ruff_cache
.vscode
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3 changes: 3 additions & 0 deletions storb/constants.py
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Expand Up @@ -13,6 +13,9 @@
PIECE_LENGTH_SCALING = 0.5
PIECE_LENGTH_OFFSET = 8.39

# Scaling factor for scoring
SCORE_SCALING = 1000

MAX_UPLOAD_SIZE = 1 * 1024 * 1024 * 1024 * 1024 # 1 TiB

# Error correction encoding parameters
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21 changes: 18 additions & 3 deletions storb/validator/reward.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
import numpy as np

from storb.constants import SCORE_SCALING


def get_response_rate_scores(
self,
Expand Down Expand Up @@ -28,9 +30,22 @@ def get_response_rate_scores(
uids.append(uid)
ret_attempts = max(miner_stats.get("retrieval_attempts", 1), 1)
store_attempts = max(miner_stats.get("store_attempts", 1), 1)
retrieval_rate = abs(miner_stats.get("retrieval_successes", 0) / ret_attempts)
store_rate = abs(miner_stats.get("store_successes", 0) / store_attempts)
weighted_rate_sum = (retrieval_rate / 2) + (store_rate / 2)
retrieval_success_rate = abs(
miner_stats.get("retrieval_successes", 0) / ret_attempts
)
store_success_rate = abs(miner_stats.get("store_successes", 0) / store_attempts)

retrieval_rate_score = (ret_attempts / SCORE_SCALING) * np.log(
retrieval_success_rate
) + 1
store_rate_score = (store_attempts / SCORE_SCALING) * np.log(
store_success_rate
) + 1

retrieval_rate_score = np.clip(retrieval_rate_score, 0, 1)
store_rate_score = np.clip(store_rate_score, 0, 1)

weighted_rate_sum = (retrieval_rate_score / 2) + (store_rate_score / 2)
weighted_rate_sums.append(weighted_rate_sum)

uids = np.array(uids)
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