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import glob
import tqdm
import pickle
import os
import datetime
import torch
import bittensor as bt
import pandas as pd
import plotly.express as px
def trust(W, S, threshold=0):
"""Trust vector for subnets with variable threshold"""
# assert (S.sum()-1).abs()<1e-4, f'Stake must sum to 1, got {S.sum()}'
Wn = (W > 0).float()
return Wn.T @ S
# return ( (W > threshold)*S.reshape(-1,1) ).sum(axis=0)
def rank(W, S):
"""Rank vector for subnets"""
# assert (S.sum()-1).abs()<1e-4, f'Stake must sum to 1, got {S.sum()}'
R = W.T @ S
return R / R.sum()
def emission(C, R):
"""Emission vector for subnets"""
E = C*R
return E / E.sum()
def YC1(T, a=0.5, b=10):
"""Yuma Consensus 1"""
return torch.sigmoid( b * (T - a) )
def load_metagraphs(root_dir, netuid):
metagraphs = []
match_path = os.path.join(root_dir, str(netuid), '*.pkl')
files = glob.glob(match_path)
print(f'Found {len(files)} metagraphs in {match_path}')
for path in tqdm.tqdm(files):
with open(path, 'rb') as f:
metagraph = pickle.load(f)
metagraphs.append(metagraph)
return metagraphs
# TODO: can calculate the emission trend using each subnet or just using root subnet
def plot_emission_trend(metagraphs, netuid, max_uids=32):
df = pd.DataFrame()
max_uids = max_uids or max(m.W.shape[1] for m in metagraphs)
for metagraph in metagraphs:
E = m.W.mean(axis=0)
df = pd.concat([df, pd.DataFrame({'emission':E}).assign(block=metagraph.block)])
df.sort_values(by='block', inplace=True)
fig = px.line(df, x=df.index, y='emission',line_group='',
title='Emission Trend',
width=800, height=600, template='plotly_white')
fig.update_xaxes(title_text='Block Height')
fig.update_yaxes(title_text='Emission')
fig.show()
return fig
def block_to_time(blocks):
if not isinstance(blocks, pd.Series):
blocks = pd.Series(blocks)
block_time_500k = datetime.datetime(2023, 5, 29, 5, 29, 0)
block_time_800k = datetime.datetime(2023, 7, 9, 21, 32, 48)
dt = (pd.Timestamp(block_time_800k)-pd.Timestamp(block_time_500k))/(800_000-500_000)
return block_time_500k + dt*(blocks-500_000)
root_dir = os.path.expanduser('~/Desktop/py/opentensor/metagraph/subnets/')
metagraphs = load_metagraphs(root_dir, 0)
metagraphs
def make_dataframe_old(metagraphs, netuid):
df = pd.DataFrame()
# max_uids=max(m.W.shape[1] for m in metagraphs)
for metagraph in sorted(metagraphs, key=lambda m: m.block):
if metagraph.n.item() == 0:
print(f'Block {metagraph.block} has no nodes, skipping')
continue
if netuid == 0:
W = metagraph.W.float()
Sn = (metagraph.S/metagraph.S.sum()).clone().float()
T = trust(W, Sn)
R = rank(W, Sn)
C = YC1(T)
E = emission(C, R)
else:
T = metagraph.T
R = metagraph.R
C = metagraph.C
E = metagraph.E
frame = pd.DataFrame({'Trust':T, 'Rank':R, 'Consensus':C, 'Emission':E, 'uid':range(len(E))}).assign(block=metagraph.block.item(), netuid=netuid)
df = pd.concat([df, frame])
df['alive'] = df.groupby('netuid')['Emission'].transform(lambda x: x > 0)
df['owner_take'] = df['Emission'] * 7200 * 0.18
df['timestamp'] = block_to_time(df['block'])
df['day'] = df['timestamp'].dt.dayofyear
df.sort_values(by=['block','netuid'], inplace=True)
return df
def make_dataframe(root_dir, netuid, cols=None):
if cols is None:
cols = ['stake','emission','trust','validator_trust','dividends','incentive','R', 'consensus','validator_permit']
frames = []
metagraphs = load_metagraphs(root_dir, netuid)
print(f'Loaded {len(metagraphs)} metagraphs for netuid {netuid}')
for m in metagraphs:
frame = pd.DataFrame({k: getattr(m, k) for k in cols})
frame['block'] = m.block.item()
frame['timestamp'] = block_to_time(frame['block'])
frame['netuid'] = netuid
frame['uid'] = range(len(frame))
frame['hotkey'] = [axon.hotkey for axon in m.axons]
frame['coldkey'] = [axon.coldkey for axon in m.axons]
frames.append(frame)
return pd.concat(frames).sort_values(by=['timestamp','block','uid'])
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