forked from LBRYCommunity/lbry-sdk
Merge pull request #2857 from eggplantbren/master
Trending algorithm with time delay and variable decay rate
This commit is contained in:
Lex Berezhny
GitHub
commit
6814f2e38c
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@ -1,8 +1,9 @@
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from . import zscore
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from . import ar
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from . import variable_decay
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TRENDING_ALGORITHMS = {
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'zscore': zscore,
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'ar': ar
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'ar': ar,
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'variable_decay': variable_decay
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}
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431
lbry/wallet/server/db/trending/variable_decay.py
Normal file
431
lbry/wallet/server/db/trending/variable_decay.py
Normal file
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@ -0,0 +1,431 @@
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"""
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Delayed AR with variable decay rate.
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The spike height function is also simpler.
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"""
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import copy
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import time
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import apsw
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# Half life in blocks *for lower LBC claims* (it's shorter for whale claims)
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HALF_LIFE = 200
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# Whale threshold (higher -> less DB writing)
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WHALE_THRESHOLD = 3.0
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# Decay coefficient per block
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DECAY = 0.5**(1.0/HALF_LIFE)
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# How frequently to write trending values to the db
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SAVE_INTERVAL = 10
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# Renormalisation interval
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RENORM_INTERVAL = 1000
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# Assertion
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assert RENORM_INTERVAL % SAVE_INTERVAL == 0
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# Decay coefficient per renormalisation interval
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DECAY_PER_RENORM = DECAY**(RENORM_INTERVAL)
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# Log trending calculations?
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TRENDING_LOG = True
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def install(connection):
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"""
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Install the trending algorithm.
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"""
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check_trending_values(connection)
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if TRENDING_LOG:
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f = open("trending_variable_decay.log", "a")
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f.close()
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# Stub
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CREATE_TREND_TABLE = ""
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def check_trending_values(connection):
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"""
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If the trending values appear to be based on the zscore algorithm,
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reset them. This will allow resyncing from a standard snapshot.
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"""
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c = connection.cursor()
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needs_reset = False
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for row in c.execute("SELECT COUNT(*) num FROM claim WHERE trending_global <> 0;"):
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if row[0] != 0:
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needs_reset = True
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break
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if needs_reset:
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print("Resetting some columns. This might take a while...", flush=True, end="")
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c.execute(""" BEGIN;
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UPDATE claim SET trending_group = 0;
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UPDATE claim SET trending_mixed = 0;
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UPDATE claim SET trending_global = 0;
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UPDATE claim SET trending_local = 0;
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COMMIT;""")
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print("done.")
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def spike_height(x, x_old):
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"""
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Compute the size of a trending spike (normed - constant units).
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"""
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# Sign of trending spike
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sign = 1.0
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if x < x_old:
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sign = -1.0
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# Magnitude
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mag = abs(x**0.25 - x_old**0.25)
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# Minnow boost
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mag *= 1.0 + 2E4/(x + 100.0)**2
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return sign*mag
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def get_time_boost(height):
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"""
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Return the time boost at a given height.
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"""
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return 1.0/DECAY**(height % RENORM_INTERVAL)
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def trending_log(s):
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"""
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Log a string.
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"""
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if TRENDING_LOG:
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fout = open("trending_variable_decay.log", "a")
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fout.write(s)
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fout.flush()
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fout.close()
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class TrendingData:
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"""
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An object of this class holds trending data
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"""
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def __init__(self):
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# Dict from claim id to some trending info.
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# Units are TIME VARIABLE in here
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self.claims = {}
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# Claims with >= WHALE_THRESHOLD LBC total amount
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self.whales = set([])
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# Have all claims been read from db yet?
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self.initialised = False
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# List of pending spikes.
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# Units are CONSTANT in here
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self.pending_spikes = []
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def insert_claim_from_load(self, height, claim_hash, trending_score, total_amount):
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assert not self.initialised
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self.claims[claim_hash] = {"trending_score": trending_score,
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"total_amount": total_amount,
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"changed": False}
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if trending_score >= WHALE_THRESHOLD*get_time_boost(height):
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self.add_whale(claim_hash)
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def add_whale(self, claim_hash):
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self.whales.add(claim_hash)
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def apply_spikes(self, height):
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"""
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Apply all pending spikes that are due at this height.
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Apply with time boost ON.
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"""
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time_boost = get_time_boost(height)
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for spike in self.pending_spikes:
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if spike["height"] > height:
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# Ignore
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pass
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if spike["height"] == height:
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# Apply
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self.claims[spike["claim_hash"]]["trending_score"] += time_boost*spike["size"]
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self.claims[spike["claim_hash"]]["changed"] = True
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if self.claims[spike["claim_hash"]]["trending_score"] >= WHALE_THRESHOLD*time_boost:
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self.add_whale(spike["claim_hash"])
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if spike["claim_hash"] in self.whales and \
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self.claims[spike["claim_hash"]]["trending_score"] < WHALE_THRESHOLD*time_boost:
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self.whales.remove(spike["claim_hash"])
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# Keep only future spikes
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self.pending_spikes = [s for s in self.pending_spikes \
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if s["height"] > height]
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def update_claim(self, height, claim_hash, total_amount):
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"""
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Update trending data for a claim, given its new total amount.
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"""
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assert self.initialised
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# Extract existing total amount and trending score
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# or use starting values if the claim is new
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if claim_hash in self.claims:
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old_state = copy.deepcopy(self.claims[claim_hash])
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else:
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old_state = {"trending_score": 0.0,
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"total_amount": 0.0,
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"changed": False}
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# Calculate LBC change
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change = total_amount - old_state["total_amount"]
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# Modify data if there was an LBC change
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if change != 0.0:
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spike = spike_height(total_amount,
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old_state["total_amount"])
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delay = min(int((total_amount + 1E-8)**0.4), HALF_LIFE)
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if change < 0.0:
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# How big would the spike be for the inverse movement?
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reverse_spike = spike_height(old_state["total_amount"], total_amount)
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# Remove that much spike from future pending ones
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for future_spike in self.pending_spikes:
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if future_spike["claim_hash"] == claim_hash:
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if reverse_spike >= future_spike["size"]:
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reverse_spike -= future_spike["size"]
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future_spike["size"] = 0.0
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elif reverse_spike > 0.0:
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future_spike["size"] -= reverse_spike
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reverse_spike = 0.0
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delay = 0
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spike = -reverse_spike
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self.pending_spikes.append({"height": height + delay,
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"claim_hash": claim_hash,
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"size": spike})
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self.claims[claim_hash] = {"total_amount": total_amount,
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"trending_score": old_state["trending_score"],
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"changed": False}
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def process_whales(self, height):
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"""
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Whale claims decay faster.
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"""
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if height % SAVE_INTERVAL != 0:
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return
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for claim_hash in self.whales:
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trending_normed = self.claims[claim_hash]["trending_score"]/get_time_boost(height)
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# Overall multiplication factor for decay rate
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decay_rate_factor = trending_normed/WHALE_THRESHOLD
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# The -1 is because this is just the *extra* part being applied
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factor = (DECAY**SAVE_INTERVAL)**(decay_rate_factor - 1.0)
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# print(claim_hash, trending_normed, decay_rate_factor)
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self.claims[claim_hash]["trending_score"] *= factor
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self.claims[claim_hash]["changed"] = True
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def test_trending():
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"""
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Quick trending test for claims with different support patterns.
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Actually use the run() function.
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"""
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# Create a fake "claims.db" for testing
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# pylint: disable=I1101
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dbc = apsw.Connection(":memory:")
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db = dbc.cursor()
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# Create table
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db.execute("""
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BEGIN;
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CREATE TABLE claim (claim_hash TEXT PRIMARY KEY,
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amount REAL NOT NULL DEFAULT 0.0,
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support_amount REAL NOT NULL DEFAULT 0.0,
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trending_mixed REAL NOT NULL DEFAULT 0.0);
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COMMIT;
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""")
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# Insert initial states of claims
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everything = {"huge_whale": 0.01,
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"huge_whale_botted": 0.01,
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"medium_whale": 0.01,
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"small_whale": 0.01,
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"minnow": 0.01}
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def to_list_of_tuples(stuff):
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l = []
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for key in stuff:
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l.append((key, stuff[key]))
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return l
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db.executemany("""
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INSERT INTO claim (claim_hash, amount) VALUES (?, 1E8*?);
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""", to_list_of_tuples(everything))
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height = 0
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run(db, height, height, everything.keys())
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# Save trajectories for plotting
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trajectories = {}
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for key in trending_data.claims:
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trajectories[key] = [trending_data.claims[key]["trending_score"]]
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# Main loop
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for height in range(1, 1000):
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# One-off supports
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if height == 1:
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everything["huge_whale"] += 5E5
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everything["medium_whale"] += 5E4
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everything["small_whale"] += 5E3
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# Every block
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if height < 500:
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everything["huge_whale_botted"] += 5E5/500
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everything["minnow"] += 1
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# Remove supports
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if height == 500:
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for key in everything:
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everything[key] = 0.01
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# Whack into the db
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db.executemany("""
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UPDATE claim SET amount = 1E8*? WHERE claim_hash = ?;
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""", [(y, x) for (x, y) in to_list_of_tuples(everything)])
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# Call run()
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run(db, height, height, everything.keys())
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for key in trending_data.claims:
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trajectories[key].append(trending_data.claims[key]["trending_score"]\
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/get_time_boost(height))
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dbc.close()
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# pylint: disable=C0415
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import matplotlib.pyplot as plt
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for key in trending_data.claims:
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plt.plot(trajectories[key], label=key)
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plt.legend()
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plt.show()
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# One global instance
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# pylint: disable=C0103
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trending_data = TrendingData()
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def run(db, height, final_height, recalculate_claim_hashes):
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if height < final_height - 5*HALF_LIFE:
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trending_log("Skipping variable_decay trending at block {h}.\n".format(h=height))
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return
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start = time.time()
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trending_log("Calculating variable_decay trending at block {h}.\n".format(h=height))
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trending_log(" Length of trending data = {l}.\n"\
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.format(l=len(trending_data.claims)))
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# Renormalise trending scores and mark all as having changed
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if height % RENORM_INTERVAL == 0:
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trending_log(" Renormalising trending scores...")
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keys = trending_data.claims.keys()
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trending_data.whales = set([])
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for key in keys:
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if trending_data.claims[key]["trending_score"] != 0.0:
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trending_data.claims[key]["trending_score"] *= DECAY_PER_RENORM
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trending_data.claims[key]["changed"] = True
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# Tiny becomes zero
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if abs(trending_data.claims[key]["trending_score"]) < 1E-3:
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trending_data.claims[key]["trending_score"] = 0.0
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# Re-mark whales
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if trending_data.claims[key]["trending_score"] >= WHALE_THRESHOLD*get_time_boost(height):
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trending_data.add_whale(key)
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trending_log("done.\n")
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# Regular message.
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trending_log(" Reading total_amounts from db and updating"\
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+ " trending scores in RAM...")
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# Update claims from db
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if not trending_data.initialised:
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trending_log("initial load...")
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# On fresh launch
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for row in db.execute("""
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SELECT claim_hash, trending_mixed,
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(amount + support_amount)
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AS total_amount
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FROM claim;
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"""):
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trending_data.insert_claim_from_load(height, row[0], row[1], 1E-8*row[2])
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trending_data.initialised = True
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else:
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for row in db.execute(f"""
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SELECT claim_hash,
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(amount + support_amount)
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AS total_amount
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FROM claim
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WHERE claim_hash IN
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({','.join('?' for _ in recalculate_claim_hashes)});
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""", recalculate_claim_hashes):
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trending_data.update_claim(height, row[0], 1E-8*row[1])
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# Apply pending spikes
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trending_data.apply_spikes(height)
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trending_log("done.\n")
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# Write trending scores to DB
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if height % SAVE_INTERVAL == 0:
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trending_log(" Finding and processing whales...")
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trending_log(str(len(trending_data.whales)) + " whales found...")
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trending_data.process_whales(height)
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trending_log("done.\n")
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trending_log(" Writing trending scores to db...")
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the_list = []
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keys = trending_data.claims.keys()
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for key in keys:
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if trending_data.claims[key]["changed"]:
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the_list.append((trending_data.claims[key]["trending_score"], key))
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trending_data.claims[key]["changed"] = False
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trending_log("{n} scores to write...".format(n=len(the_list)))
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db.executemany("UPDATE claim SET trending_mixed=? WHERE claim_hash=?;",
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the_list)
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trending_log("done.\n")
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trending_log("Trending operations took {time} seconds.\n\n"\
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.format(time=time.time() - start))
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if __name__ == "__main__":
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test_trending()
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