<jupyter_start><jupyter_text>Load Data<jupyter_code>%load_ext autoreload
%autoreload 2
from __future__ import division
import random
import numpy as np
import numpy.linalg as alg
import scipy as spy
import networkx as nx

import time
from itertools import *
import sys
import numpy.linalg as LA
import pickle

from src.models.GibbsSampler_networkx import GibbsSampler
from src.models.ADMM_fully_distributed import ADMM_fully_distributed
cd data/
X = pickle.load( open( "X.p", "rb" ) )
y = pickle.load( open( "y.p", "rb" ) )
b = pickle.load( open( "b.p", "rb" ) )
temp = pickle.load( open( "temp.p", "rb" ) )
pos_node = pickle.load( open( "pos_node.p", "rb" ) )
# set hyperparameter Lambda and Rho
Lambda = 0.1
Rho = 1
A = ADMM_fully_distributed(X, y, b,pos_node,temp,Lambda, Rho)
import time

start = time.time()
A.runADMM_Grid(20)<jupyter_output>iteration =  0 objective =  29.4731496198
iteration =  1 objective =  29.3168729163
iteration =  2 objective =  27.7711427593
iteration =  3 objective =  26.4592165221
iteration =  4 objective =  25.3513519807
iteration =  5 objective =  24.4142601313
iteration =  6 objective =  23.6183370122
iteration =  7 objective =  22.9387226418
iteration =  8 objective =  22.355114756
iteration =  9 objective =  21.8511140042
iteration =  10 objective =  21.4134993411
iteration =  11 objective =  21.0315910336
iteration =  12 objective =  20.6967170458
iteration =  13 objective =  20.4017913693
iteration =  14 objective =  20.1409899231
iteration =  15 objective =  19.9094925996
iteration =  16 objective =  19.7032837319
iteration =  17 objective =  19.5189991863
iteration =  18 objective =  19.3538026593
iteration =  19 objective =  19.2052904822
{'fraction_non_alphanumeric': 0.09321533923303835, 'fraction_numerical': 0.16224188790560473, 'mean_word_length': 4.350157728706624, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '316138', 'license': 'MIT', 'n_tokens_mistral': 782, 'n_tokens_neox': 623, 'n_words': 173, 'path': 'notebook/.ipynb_checkpoints/example_ADMM-checkpoint.ipynb', 'repo_name': 'DanqingZ/social-DCM', 'size': '1695'}