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simple_sqrt_reposts.py
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simple_sqrt_reposts.py
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"""
This is basically the autoregressive (AR) algorithm,
but based on reposts instead of LBC. The softening is much
weaker, hence sqrt instead of the fourth root in the spike_height
function.
I based the code on delayed_ar because I like the symmetry thing in it,
but I set the delay to 0 so there is no actual delayed effect.
"""
import copy
import math
import time
# Half life in blocks
HALF_LIFE = 134
# Decay coefficient per block
DECAY = 0.5**(1.0/HALF_LIFE)
# How frequently to write trending values to the db
SAVE_INTERVAL = 10
# Renormalisation interval
RENORM_INTERVAL = 1000
# Assertion
assert RENORM_INTERVAL % SAVE_INTERVAL == 0
# Decay coefficient per renormalisation interval
DECAY_PER_RENORM = DECAY**(RENORM_INTERVAL)
# Log trending calculations?
TRENDING_LOG = True
def install(connection):
"""
Install the AR trending algorithm.
"""
check_trending_values(connection)
if TRENDING_LOG:
f = open("trending_ar.log", "a")
f.close()
# Stub
CREATE_TREND_TABLE = ""
def check_trending_values(connection):
"""
If the trending values appear to be based on the zscore algorithm,
reset them. This will allow resyncing from a standard snapshot.
"""
c = connection.cursor()
needs_reset = False
for row in c.execute("SELECT COUNT(*) num FROM claim WHERE trending_global <> 0;"):
if row[0] != 0:
needs_reset = True
break
if needs_reset:
print("Resetting some columns. This might take a while...", flush=True, end="")
c.execute(""" BEGIN;
UPDATE claim SET trending_group = 0;
UPDATE claim SET trending_mixed = 0;
UPDATE claim SET trending_global = 0;
UPDATE claim SET trending_local = 0;
COMMIT;""")
print("done.")
def spike_size(trending_score, x, x_old):
"""
Compute the size of a trending spike.
"""
return x**0.5 - x_old**0.5
def get_time_boost(height):
"""
Return the time boost at a given height.
"""
return 1.0/DECAY**(height % RENORM_INTERVAL)
def trending_log(s):
"""
Log a string.
"""
if TRENDING_LOG:
fout = open("trending_ar.log", "a")
fout.write(s)
fout.flush()
fout.close()
class TrendingData:
"""
An object of this class holds trending data
"""
def __init__(self):
# Dict from claim id to some trending info.
# Units are TIME VARIABLE in here
self.claims = {}
# Have all claims been read from db yet?
self.initialised = False
# List of pending spikes.
# Units are CONSTANT in here
self.pending_spikes = []
def insert_claim_from_load(self, claim_hash, trending_score, reposted):
assert not self.initialised
self.claims[claim_hash] = {"trending_score": trending_score,
"reposted": reposted,
"changed": False}
def apply_spikes(self, height):
"""
Apply all pending spikes that are due at this height.
Apply with time boost ON.
"""
time_boost = get_time_boost(height)
for spike in self.pending_spikes:
if spike["height"] > height:
# Ignore
pass
if spike["height"] == height:
# Apply
self.claims[spike["claim_hash"]]["trending_score"] += time_boost*spike["size"]
self.claims[spike["claim_hash"]]["changed"] = True
# Keep only future spikes
self.pending_spikes = [s for s in self.pending_spikes \
if s["height"] > height]
def update_claim(self, height, claim_hash, reposted):
"""
Update trending data for a claim, given its new total amount.
"""
assert self.initialised
# Extract existing total amount and trending score
# or use starting values if the claim is new
if claim_hash in self.claims:
old_state = copy.deepcopy(self.claims[claim_hash])
else:
old_state = {"trending_score": 0.0,
"reposted": 0.0,
"changed": False}
# Calculate LBC change
change = reposted - old_state["reposted"]
# Modify data if there was an LBC change
if change != 0.0:
spike = spike_size(old_state["trending_score"]/get_time_boost(height),
reposted,
old_state["reposted"])
delay = 0 #min(int(math.sqrt(reposted + 1E-8)), 1000)
if change < 0.0:
# How big would the spike be for the inverse movement?
reverse_spike = spike_size(old_state["trending_score"]/get_time_boost(height),
old_state["reposted"], reposted)
# Remove that much spike from future pending ones
for future_spike in self.pending_spikes:
if future_spike["claim_hash"] == claim_hash:
if reverse_spike >= future_spike["size"]:
reverse_spike -= future_spike["size"]
future_spike["size"] = 0.0
elif reverse_spike > 0.0:
future_spike["size"] -= reverse_spike
reverse_spike = 0.0
delay = 0
spike -= reverse_spike
self.pending_spikes.append({"height": height + delay,
"claim_hash": claim_hash,
"size": spike})
self.claims[claim_hash] = {"reposted": reposted,
"trending_score": old_state["trending_score"],
"changed": False}
def test_trending():
"""
Quick trending test for something receiving 10 LBC per block
"""
data = TrendingData()
data.initialised = True
import random
height = 0
data.update_claim(height, "whale_claim_1", 0.01)
data.update_claim(height, "whale_claim_2", 0.01)
data.update_claim(height, "popular_minnow_claim", 0.01)
data.update_claim(height, "whale_claim_1",
data.claims["whale_claim_1"]["reposted"] + 5E5)
data.update_claim(height, "random_claim", 10.0**random.gauss(2.0, 2.0))
data.apply_spikes(height)
for height in range(1, 5000):
if height % RENORM_INTERVAL == 0:
for key in data.claims:
data.claims[key]["trending_score"] *= DECAY_PER_RENORM
# The random claim
if random.uniform(0.0, 1.0) <= 0.003:
data.update_claim(height, "random_claim", 10.0**random.gauss(2.0, 2.0))
# Add new supports
if height <= 500:
data.update_claim(height, "whale_claim_2",
data.claims["whale_claim_2"]["reposted"] + 5E5/500)
data.update_claim(height, "popular_minnow_claim",
data.claims["popular_minnow_claim"]["reposted"] + 1.0)
# Abandon all supports
if height == 100:
for key in data.claims:
data.update_claim(height, key, 0.01)
data.apply_spikes(height)
print(height, end=" ")
for key in data.claims:
print(data.claims[key]["trending_score"]/get_time_boost(height),
end=" ")
print("")
# One global instance
# pylint: disable=C0103
trending_data = TrendingData()
def run(db, height, final_height, recalculate_claim_hashes):
if height < final_height - 5*HALF_LIFE:
trending_log("Skipping AR trending at block {h}.\n".format(h=height))
return
start = time.time()
trending_log("Calculating AR trending at block {h}.\n".format(h=height))
trending_log(" Length of trending data = {l}.\n"\
.format(l=len(trending_data.claims)))
# Renormalise trending scores and mark all as having changed
if height % RENORM_INTERVAL == 0:
trending_log(" Renormalising trending scores...")
keys = trending_data.claims.keys()
for key in keys:
if trending_data.claims[key]["trending_score"] != 0.0:
trending_data.claims[key]["trending_score"] *= DECAY_PER_RENORM
trending_data.claims[key]["changed"] = True
# Tiny becomes zero
if abs(trending_data.claims[key]["trending_score"]) < 1E-9:
trending_data.claims[key]["trending_score"] = 0.0
trending_log("done.\n")
# Regular message.
trending_log(" Reading repost counts from db and updating"\
+ " trending scores in RAM...")
# Update claims from db
if not trending_data.initialised:
# On fresh launch
for row in db.execute("""
SELECT claim_hash, trending_mixed,
reposted
FROM claim;
"""):
trending_data.insert_claim_from_load(row[0], row[1], row[2])
trending_data.initialised = True
else:
for row in db.execute(f"""
SELECT claim_hash,
reposted
FROM claim
WHERE claim_hash IN
({','.join('?' for _ in recalculate_claim_hashes)});
""", recalculate_claim_hashes):
trending_data.update_claim(height, row[0], row[1])
# Apply pending spikes
trending_data.apply_spikes(height)
trending_log("done.\n")
# Write trending scores to DB
if height % SAVE_INTERVAL == 0:
trending_log(" Writing trending scores to db...")
the_list = []
keys = trending_data.claims.keys()
for key in keys:
if trending_data.claims[key]["changed"]:
the_list.append((trending_data.claims[key]["trending_score"], key))
trending_data.claims[key]["changed"] = False
trending_log("{n} scores to write...".format(n=len(the_list)))
db.executemany("UPDATE claim SET trending_mixed=? WHERE claim_hash=?;",
the_list)
trending_log("done.\n")
trending_log("Trending operations took {time} seconds.\n\n"\
.format(time=time.time() - start))
if __name__ == "__main__":
test_trending()