lbry-sdk/lbry/wallet/server/db/trending/variable_decay.py
2020-11-05 21:15:55 -05:00

485 lines
15 KiB
Python

"""
AR-like trending with a delayed effect and a faster
decay rate for high valued claims.
"""
import math
import time
import apsw
# Half life in blocks *for lower LBC claims* (it's shorter for whale claims)
HALF_LIFE = 200
# Whale threshold, in LBC (higher -> less DB writing)
WHALE_THRESHOLD = 10000.0
# 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 trending algorithm.
"""
check_trending_values(connection)
trending_data.initialise(connection.cursor())
if TRENDING_LOG:
f = open("trending_variable_decay.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;
COMMIT;""")
print("done.")
def trending_log(s):
"""
Log a string to the log file
"""
if TRENDING_LOG:
fout = open("trending_variable_decay.log", "a")
fout.write(s)
fout.flush()
fout.close()
def trending_unit(height):
"""
Return the trending score unit at a given height.
"""
# Round to the beginning of a SAVE_INTERVAL batch of blocks.
_height = height - (height % SAVE_INTERVAL)
return 1.0/DECAY**(height % RENORM_INTERVAL)
class TrendingDB:
"""
An in-memory database of trending scores
"""
def __init__(self):
self.conn = apsw.Connection(":memory:")
self.cursor = self.conn.cursor()
self.initialised = False
self.write_needed = set()
def execute(self, query, *args, **kwargs):
return self.cursor.execute(query, *args, **kwargs)
def executemany(self, query, *args, **kwargs):
return self.cursor.executemany(query, *args, **kwargs)
def begin(self):
self.execute("BEGIN;")
def commit(self):
self.execute("COMMIT;")
def initialise(self, db):
"""
Pass in claims.db
"""
if self.initialised:
return
trending_log("Initialising trending database...")
# The need for speed
self.execute("PRAGMA JOURNAL_MODE=OFF;")
self.execute("PRAGMA SYNCHRONOUS=0;")
self.begin()
# Create the tables
self.execute("""
CREATE TABLE IF NOT EXISTS claims
(claim_hash BYTES PRIMARY KEY,
lbc REAL NOT NULL DEFAULT 0.0,
trending_score REAL NOT NULL DEFAULT 0.0)
WITHOUT ROWID;""")
self.execute("""
CREATE TABLE IF NOT EXISTS spikes
(id INTEGER PRIMARY KEY,
claim_hash BYTES NOT NULL,
height INTEGER NOT NULL,
mass REAL NOT NULL,
FOREIGN KEY (claim_hash)
REFERENCES claims (claim_hash));""")
# Clear out any existing data
self.execute("DELETE FROM claims;")
self.execute("DELETE FROM spikes;")
# Create indexes
self.execute("CREATE INDEX idx1 ON spikes (claim_hash, height, mass);")
self.execute("CREATE INDEX idx2 ON spikes (claim_hash, height, mass DESC);")
self.execute("CREATE INDEX idx3 on claims (lbc DESC, claim_hash, trending_score);")
# Import data from claims.db
for row in db.execute("""
SELECT claim_hash,
1E-8*(amount + support_amount) AS lbc,
trending_mixed
FROM claim;
"""):
self.execute("INSERT INTO claims VALUES (?, ?, ?);", row)
self.commit()
self.initialised = True
trending_log("done.\n")
def apply_spikes(self, height):
"""
Apply spikes that are due. This occurs inside a transaction.
"""
spikes = []
unit = trending_unit(height)
for row in self.execute("""
SELECT SUM(mass), claim_hash FROM spikes
WHERE height = ?
GROUP BY claim_hash;
""", (height, )):
spikes.append((row[0]*unit, row[1]))
self.write_needed.add(row[1])
self.executemany("""
UPDATE claims
SET trending_score = (trending_score + ?)
WHERE claim_hash = ?;
""", spikes)
self.execute("DELETE FROM spikes WHERE height = ?;", (height, ))
def decay_whales(self, height):
"""
Occurs inside transaction.
"""
if height % SAVE_INTERVAL != 0:
return
whales = self.execute("""
SELECT trending_score, lbc, claim_hash
FROM claims
WHERE lbc >= ?;
""", (WHALE_THRESHOLD, )).fetchall()
whales2 = []
for whale in whales:
trending, lbc, claim_hash = whale
# Overall multiplication factor for decay rate
# At WHALE_THRESHOLD, this is 1
# At 10*WHALE_THRESHOLD, it is 3
decay_rate_factor = 1.0 + 2.0*math.log10(lbc/WHALE_THRESHOLD)
# The -1 is because this is just the *extra* part being applied
factor = (DECAY**SAVE_INTERVAL)**(decay_rate_factor - 1.0)
# Decay
trending *= factor
whales2.append((trending, claim_hash))
self.write_needed.add(claim_hash)
self.executemany("UPDATE claims SET trending_score=? WHERE claim_hash=?;",
whales2)
def renorm(self, height):
"""
Renormalise trending scores. Occurs inside a transaction.
"""
if height % RENORM_INTERVAL == 0:
threshold = 1.0E-3/DECAY_PER_RENORM
for row in self.execute("""SELECT claim_hash FROM claims
WHERE ABS(trending_score) >= ?;""",
(threshold, )):
self.write_needed.add(row[0])
self.execute("""UPDATE claims SET trending_score = ?*trending_score
WHERE ABS(trending_score) >= ?;""",
(DECAY_PER_RENORM, threshold))
def write_to_claims_db(self, db, height):
"""
Write changed trending scores to claims.db.
"""
if height % SAVE_INTERVAL != 0:
return
rows = self.execute(f"""
SELECT trending_score, claim_hash
FROM claims
WHERE claim_hash IN
({','.join('?' for _ in self.write_needed)});
""", self.write_needed).fetchall()
db.executemany("""UPDATE claim SET trending_mixed = ?
WHERE claim_hash = ?;""", rows)
# Clear list of claims needing to be written to claims.db
self.write_needed = set()
def update(self, db, height, recalculate_claim_hashes):
"""
Update trending scores.
Input is a cursor to claims.db, the block height, and the list of
claims that changed.
"""
assert self.initialised
self.begin()
self.renorm(height)
# Fetch changed/new claims from claims.db
for row in db.execute(f"""
SELECT claim_hash,
1E-8*(amount + support_amount) AS lbc
FROM claim
WHERE claim_hash IN
({','.join('?' for _ in recalculate_claim_hashes)});
""", recalculate_claim_hashes):
claim_hash, lbc = row
# Insert into trending db if it does not exist
self.execute("""
INSERT INTO claims (claim_hash)
VALUES (?)
ON CONFLICT (claim_hash) DO NOTHING;""",
(claim_hash, ))
# See if it was an LBC change
old = self.execute("SELECT * FROM claims WHERE claim_hash=?;",
(claim_hash, )).fetchone()
lbc_old = old[1]
# Save new LBC value into trending db
self.execute("UPDATE claims SET lbc = ? WHERE claim_hash = ?;",
(lbc, claim_hash))
if lbc > lbc_old:
# Schedule a future spike
delay = min(int((lbc + 1E-8)**0.4), HALF_LIFE)
spike = (claim_hash, height + delay, spike_mass(lbc, lbc_old))
self.execute("""INSERT INTO spikes
(claim_hash, height, mass)
VALUES (?, ?, ?);""", spike)
elif lbc < lbc_old:
# Subtract from future spikes
penalty = spike_mass(lbc_old, lbc)
spikes = self.execute("""
SELECT * FROM spikes
WHERE claim_hash = ?
ORDER BY height ASC, mass DESC;
""", (claim_hash, )).fetchall()
for spike in spikes:
spike_id, mass = spike[0], spike[3]
if mass > penalty:
# The entire penalty merely reduces this spike
self.execute("UPDATE spikes SET mass=? WHERE id=?;",
(mass - penalty, spike_id))
penalty = 0.0
else:
# Removing this spike entirely accounts for some (or
# all) of the penalty, then move on to other spikes
self.execute("DELETE FROM spikes WHERE id=?;",
(spike_id, ))
penalty -= mass
# If penalty remains, that's a negative spike to be applied
# immediately.
if penalty > 0.0:
self.execute("""
INSERT INTO spikes (claim_hash, height, mass)
VALUES (?, ?, ?);""",
(claim_hash, height, -penalty))
self.apply_spikes(height)
self.decay_whales(height)
self.commit()
self.write_to_claims_db(db, height)
# The "global" instance to work with
# pylint: disable=C0103
trending_data = TrendingDB()
def spike_mass(x, x_old):
"""
Compute the mass of a trending spike (normed - constant units).
x_old = old LBC value
x = new LBC value
"""
# Sign of trending spike
sign = 1.0
if x < x_old:
sign = -1.0
# Magnitude
mag = abs(x**0.25 - x_old**0.25)
# Minnow boost
mag *= 1.0 + 2E4/(x + 100.0)**2
return sign*mag
def run(db, height, final_height, recalculate_claim_hashes):
if height < final_height - 5*HALF_LIFE:
trending_log(f"Skipping trending calculations at block {height}.\n")
return
start = time.time()
trending_log(f"Calculating variable_decay trending at block {height}.\n")
trending_data.update(db, height, recalculate_claim_hashes)
end = time.time()
trending_log(f"Trending operations took {end - start} seconds.\n\n")
def test_trending():
"""
Quick trending test for claims with different support patterns.
Actually use the run() function.
"""
# Create a fake "claims.db" for testing
# pylint: disable=I1101
dbc = apsw.Connection(":memory:")
db = dbc.cursor()
# Create table
db.execute("""
BEGIN;
CREATE TABLE claim (claim_hash TEXT PRIMARY KEY,
amount REAL NOT NULL DEFAULT 0.0,
support_amount REAL NOT NULL DEFAULT 0.0,
trending_mixed REAL NOT NULL DEFAULT 0.0);
COMMIT;
""")
# Initialise trending data before anything happens with the claims
trending_data.initialise(db)
# Insert initial states of claims
everything = {"huge_whale": 0.01, "medium_whale": 0.01, "small_whale": 0.01,
"huge_whale_botted": 0.01, "minnow": 0.01}
def to_list_of_tuples(stuff):
l = []
for key in stuff:
l.append((key, stuff[key]))
return l
db.executemany("""
INSERT INTO claim (claim_hash, amount) VALUES (?, 1E8*?);
""", to_list_of_tuples(everything))
# Process block zero
height = 0
run(db, height, height, everything.keys())
# Save trajectories for plotting
trajectories = {}
for row in trending_data.execute("""
SELECT claim_hash, trending_score
FROM claims;
"""):
trajectories[row[0]] = [row[1]/trending_unit(height)]
# Main loop
for height in range(1, 1000):
# One-off supports
if height == 1:
everything["huge_whale"] += 5E5
everything["medium_whale"] += 5E4
everything["small_whale"] += 5E3
# Every block
if height < 500:
everything["huge_whale_botted"] += 5E5/500
everything["minnow"] += 1
# Remove supports
if height == 500:
for key in everything:
everything[key] = 0.01
# Whack into the db
db.executemany("""
UPDATE claim SET amount = 1E8*? WHERE claim_hash = ?;
""", [(y, x) for (x, y) in to_list_of_tuples(everything)])
# Call run()
run(db, height, height, everything.keys())
# Append current trending scores to trajectories
for row in db.execute("""
SELECT claim_hash, trending_mixed
FROM claim;
"""):
trajectories[row[0]].append(row[1]/trending_unit(height))
dbc.close()
# pylint: disable=C0415
import matplotlib.pyplot as plt
for key in trajectories:
plt.plot(trajectories[key], label=key)
plt.legend()
plt.show()
if __name__ == "__main__":
test_trending()