From 67536f500525f1280763d009ea9bf3af9f8b0f2a Mon Sep 17 00:00:00 2001 From: jinzhenfan Date: Tue, 15 Oct 2013 01:18:08 -0700 Subject: [PATCH 1/5] Update my_twitterstream.py --- Lecture_Code/Twitter/my_twitterstream.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Lecture_Code/Twitter/my_twitterstream.py b/Lecture_Code/Twitter/my_twitterstream.py index bfc8466c..a1967087 100644 --- a/Lecture_Code/Twitter/my_twitterstream.py +++ b/Lecture_Code/Twitter/my_twitterstream.py @@ -1,4 +1,3 @@ - # # Based on the excellent UW Coursera Data Science Course Code: # https://github.com/uwescience/datasci_course_materials/blob/master/assignment1/twitterstream.py @@ -9,8 +8,8 @@ ########################################################################### ## Fill these in using the README instructions -access_token_key = "zzz999" -access_token_secret = "zzz999" +access_token_key = "1962147962-cT85dOFlRa57RdElAox5Fn5sfgKt3NFVG7L2Lo1" +access_token_secret = "nKGWdb3czLqYmWlR9CJVHnU6NkIv0QufzHbBOnBcrs" consumer_key = "zzz999" consumer_secret = "zzz999" ########################################################################### From f0f8721c925f89c0251ac74f963b146b45394bd4 Mon Sep 17 00:00:00 2001 From: jinzhenfna Date: Tue, 22 Oct 2013 22:35:12 -0700 Subject: [PATCH 2/5] my data --- HW1/BayesLogit/data/.README.md.swo | Bin 0 -> 12288 bytes HW1/BayesLogit/data/.README.md.swp | Bin 0 -> 12288 bytes HW1/BayesLogit/data/.blr_pars_1035.csv.swp | Bin 0 -> 12288 bytes HW1/BayesLogit/data/.blr_pars_1200.csv.swp | Bin 0 -> 12288 bytes HW1/BayesLogit/data/blr_data_1001.csv | 101 ++++++++++++++++++ HW1/BayesLogit/data/blr_data_1002.csv | 101 ++++++++++++++++++ HW1/BayesLogit/data/blr_data_1003.csv | 101 ++++++++++++++++++ HW1/BayesLogit/data/blr_data_1004.csv | 101 ++++++++++++++++++ HW1/BayesLogit/data/blr_data_1005.csv | 101 ++++++++++++++++++ HW1/BayesLogit/data/blr_data_1006.csv | 101 ++++++++++++++++++ HW1/BayesLogit/data/blr_data_1007.csv | 101 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create mode 100644 HW1/BayesLogit/results/blr_res_1192.csv create mode 100644 HW1/BayesLogit/results/blr_res_1193.csv create mode 100644 HW1/BayesLogit/results/blr_res_1194.csv create mode 100644 HW1/BayesLogit/results/blr_res_1195.csv create mode 100644 HW1/BayesLogit/results/blr_res_1196.csv create mode 100644 HW1/BayesLogit/results/blr_res_1197.csv create mode 100644 HW1/BayesLogit/results/blr_res_1198.csv create mode 100644 HW1/BayesLogit/results/blr_res_1199.csv create mode 100644 HW1/BayesLogit/results/blr_res_1200.csv create mode 100644 HW1/coverage_line_plot.pdf diff --git a/HW1/BayesLogit/BLR_fit.py b/HW1/BayesLogit/BLR_fit.py new file mode 100644 index 00000000..77cbee37 --- /dev/null +++ b/HW1/BayesLogit/BLR_fit.py @@ -0,0 +1,195 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Oct 28 12:14:56 2013 + +@author: jinzhenfan +""" + +## +# +# Logistic regression +# +# Y_{i} | \beta \sim \textrm{Bin}\left(n_{i},e^{x_{i}^{T}\beta}/(1+e^{x_{i}^{T}\beta})\right) +# \beta \sim N\left(\beta_{0},\Sigma_{0}\right) +# +## + +import sys +import numpy as np +from scipy import stats +import matplotlib.pyplot as plt +import scipy as sp +import csv +import pandas as pd +import math + +######################################################################################## +## Handle batch job arguments: + +nargs = len(sys.argv) +print 'Command line arguments: ' + str(sys.argv) + +#### +# sim_start ==> Lowest simulation number to be analyzed by this particular batch job +### + +####################### +sim_start = 1000 +length_datasets = 200 +####################### + +# Note: this only sets the random seed for numpy, so if you intend +# on using other modules to generate random numbers, then be sure +# to set the appropriate RNG here + +if (nargs==0): + sim_num = sim_start + 1 + np.random.seed(1330931) +else: + # Decide on the job number, usually start at 1000: + sim_num = sim_start + int(sys.argv[2]) + # Set a different random seed for every job number!!! + np.random.seed(762*sim_num + 1330931) + +# Simulation datasets numbered 1001-1200 + +######################################################################################## +######################################################################################## + + + +def log_density(m,y,x,beta,beta_0,Sigma_0_inv): + n=len(m) + m=np.matrix(m) + y=np.matrix(y) + x=np.matrix(x) + beta_0=np.matrix(beta_0) + beta=np.matrix(beta) + Sigma_0_inv=np.matrix(Sigma_0_inv) + s=0 + for i in range(n): +# print(x[i]) +# print(y[i]) +# print(beta) +# print(m[i]) + s=s+y[i]*(x[i]*beta)-m[i]*math.log(1+math.exp(x[i]*beta)) +# print beta.shape +# print beta_0.T.shape +# print Sigma_0_inv.shape +# print s.shape + log_post=s-(beta-beta_0).T*Sigma_0_inv*[(beta-beta_0)/2][0] + + return log_post + +def metropolis_hasting(trial,beta_init,v,m,y,x,beta_0,Sigma_0_inv): + acpt=0.0 + acpt_rate=0.0 + beta_curr=beta_init + beta_track=[0,beta_init] + for t in range(trial): + beta_prosal=np.random.multivariate_normal([beta_curr[0][0],beta_curr[1][0]], v, 1).T + log_alpha=log_density(m,y,x,beta_prosal,beta_0,Sigma_0_inv)-log_density(m,y,x,beta_curr,beta_0,Sigma_0_inv)[0][0] + log_u=math.log(np.random.uniform(low=0.0, high=1.0, size=1)) +# print log_alpha + # print log_u + if log_u < log_alpha[0][0]: + beta_curr=beta_prosal + acpt+=1 + else: + beta_curr=beta_curr + + beta_track.append(beta_curr.T) + acpt_rate=acpt/trial +# print("Acceptance is",acpt) +# print("Acceptance Rate is",str(acpt_rate)) + beta_track[0]=acpt_rate + return beta_track + +#def confidence_interval(data, confidence=0.95): +# a = 1.0*np.array(data) +# n = len(a) +# m, se = np.mean(a), sp.stats.stderr(a) +# h = se * sp.stats.t._ppf((1+confidence)/2., n-1) +# return m, m-h, m+h + +def bayes_logreg(m,y,x,beta_0,Sigma_0_inv,niter=4000,burnin=1000,print_every=1000,retune=100,verbose=True): + + v=np.diag([1,1]) + beta_init=beta_0 + acpt_rate=0 + +# tuning the covariace + while acpt_rate<0.3 or acpt_rate>0.7: + acpt_rate=metropolis_hasting(retune,beta_init,v,m,y,x,beta_0,Sigma_0_inv)[0] + # print acpt_rate + if acpt_rate<0.3: + v=v/math.exp(1) + elif acpt_rate>0.7: + v=v*math.exp(1) +# print("v is",v) + + print("The covariance after tuning is",v,",","with an acceptance rate of",acpt_rate) + +#sampling + beta_curr=beta_0 + beta_track=metropolis_hasting(niter+burnin,beta_init,v,m,y,x,beta_0,Sigma_0_inv) + beta_samples=beta_track[(burnin+1):(niter+burnin+1)] + print("The acceptance rate is",beta_track[0]) + # print(beta_samples) +# print(beta_samples[0]) +# print(beta_samples[0][0][0]) +# print(beta_samples[0][0][1]) +# print(beta_samples[1]) +# print(beta_samples[2]) +# print(len(beta_samples)) + beta1=np.zeros(niter) +# print len(beta1) + beta2=np.zeros(niter) + for i in range(niter): + beta1[i]=beta_samples[i][0][0] + beta2[i]=beta_samples[i][0][1] +# print beta1 + +# plt.plot[beta_samples[1,]] +# effectiveSize(beta1) +# effectiveSize(beta2) + low_margin1=np.percentile(beta1,5) + high_margin1=np.percentile(beta1,95) + low_margin2=np.percentile(beta2,5) + high_margin2=np.percentile(beta2,95) +# print(low_margin1,high_margin1,low_margin2,high_margin2) +# print beta1 +# print beta2 + return (beta1,beta2) + + + + +df = pd.read_csv('./data/blr_data_'+str(sim_num+1).zfill(3)+'.csv') +#df = pd.read_csv('blr_data_1001.csv') +y,m,x= np.matrix(df.y).T,np.matrix(df.n).T,np.matrix([df.X1,df.X2]).T +beta_0=np.array([[0,0]]).T +Sigma_0_inv=np.matrix(np.diag([1,1])) +beta1,beta2=bayes_logreg(m,y,x,beta_0,Sigma_0_inv,niter=4000,burnin=1000,print_every=1000,retune=100,verbose=True) +# print beta1 +# print beta2 +with open("./results/blr_res_" + str(sim_num+1).zfill(3)+".csv", 'w') as out:# output file + writer = csv.writer(out, dialect='excel') + for row in range(99): + # print np.percentile(beta1,row),np.percentile(beta2,row),"\n" + writer.writerow((round(np.percentile(beta1,row),7),round(np.percentile(beta2,row),7))) +out.closed + + +# Read data corresponding to appropriate sim_num: + +# Extract X and y: + +# Fit the Bayesian model: + +# Extract posterior quantiles... + +# Write results to a (99 x p) csv file... + +# Go celebrate. + diff --git a/HW1/BayesLogit/BLR_fit_py.sh b/HW1/BayesLogit/BLR_fit_py.sh index 5e959c79..2b019fba 100644 --- a/HW1/BayesLogit/BLR_fit_py.sh +++ b/HW1/BayesLogit/BLR_fit_py.sh @@ -42,3 +42,4 @@ module load pymods/2.7 srun python BLR_fit.py -i ${SLURM_ARRAYID} + diff --git a/HW1/BayesLogit/BLR_fit_skeleton.py b/HW1/BayesLogit/BLR_fit_skeleton.py index f7ce3dfb..8c960360 100644 --- a/HW1/BayesLogit/BLR_fit_skeleton.py +++ b/HW1/BayesLogit/BLR_fit_skeleton.py @@ -1,3 +1,9 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Oct 28 12:14:56 2013 + +@author: jinzhenfan +""" ## # @@ -10,6 +16,12 @@ import sys import numpy as np +from scipy import stats +import matplotlib.pyplot as plt +import scipy as sp +import csv +import pandas as pd +import math ######################################################################################## ## Handle batch job arguments: @@ -46,15 +58,128 @@ -def bayes_logreg(n,y,X,beta_0,Sigma_0_inv,niter=10000,burnin=1000,print_every=1000,retune=100,verbose=False): - # Stuff... - return None +def log_density(m,y,x,beta,beta_0,Sigma_0_inv): + n=len(m) + m=np.matrix(m) + y=np.matrix(y) + x=np.matrix(x) + beta_0=np.matrix(beta_0) + beta=np.matrix(beta) + Sigma_0_inv=np.matrix(Sigma_0_inv) + s=0 + for i in range(n): +# print(x[i]) +# print(y[i]) +# print(beta) +# print(m[i]) + s=s+y[i]*(x[i]*beta)-m[i]*math.log(1+exp(x[i]*beta)) +# print beta.shape +# print beta_0.T.shape +# print Sigma_0_inv.shape +# print s.shape + log_post=s-(beta-beta_0).T*Sigma_0_inv*[(beta-beta_0)/2][0] + + return log_post + +def metropolis_hasting(trial,beta_init,v,m,y,x,beta_0,Sigma_0_inv): + acpt=0.0 + acpt_rate=0.0 + beta_curr=beta_init + beta_track=[0,beta_init] + for t in range(trial): + beta_prosal=np.random.multivariate_normal([0,0], v, 1).T + log_alpha=log_density(m,y,x,beta_prosal,beta_0,Sigma_0_inv)-log_density(m,y,x,beta_curr,beta_0,Sigma_0_inv)[0][0] + log_u=math.log(np.random.uniform(low=0.0, high=1.0, size=1)) +# print log_alpha + # print log_u + if log_u < log_alpha[0][0]: + beta_curr=beta_prosal + acpt+=1 + else: + beta_curr=beta_curr + + beta_track.append(beta_curr.T) + acpt_rate=acpt/trial +# print("Acceptance is",acpt) +# print("Acceptance Rate is",str(acpt_rate)) + beta_track[0]=acpt_rate + return beta_track + +def confidence_interval(data, confidence=0.95): + a = 1.0*np.array(data) + n = len(a) + m, se = np.mean(a), sp.stats.stderr(a) + h = se * sp.stats.t._ppf((1+confidence)/2., n-1) + return m, m-h, m+h + +def bayes_logreg(m,y,x,beta_0,Sigma_0_inv,niter=10000,burnin=1000,print_every=1000,retune=100,verbose=True): + + v=np.diag([1,1]) + beta_init=beta_0 + acpt_rate=0 + +# tuning the covariace + while acpt_rate<0.4 or acpt_rate>0.6: + acpt_rate=metropolis_hasting(retune,beta_init,v,m,y,x,beta_0,Sigma_0_inv)[0] + # print acpt_rate + if acpt_rate<0.4: + v=v/exp(1) + elif acpt_rate>0.6: + v=v*exp(1) +# print("v is",v) + + print("The covariance after tuning is",v,",","with an acceptance rate of",acpt_rate) + +#sampling + beta_curr=beta_0 + beta_track=metropolis_hasting(niter+burnin,beta_init,v,m,y,x,beta_0,Sigma_0_inv) + beta_samples=beta_track[(burnin+1):(niter+burnin+1)] + print("The acceptance rate is",beta_track[0]) + # print(beta_samples) +# print(beta_samples[0]) +# print(beta_samples[0][0][0]) +# print(beta_samples[0][0][1]) +# print(beta_samples[1]) +# print(beta_samples[2]) +# print(len(beta_samples)) + beta1=np.zeros(niter) +# print len(beta1) + beta2=np.zeros(niter) + for i in range(niter): + beta1[i]=beta_samples[i][0][0] + beta2[i]=beta_samples[i][0][1] +# print beta1 + +# plt.plot[beta_samples[1,]] +# effectiveSize(beta1) +# effectiveSize(beta2) + low_margin1=np.percentile(beta1,5) + high_margin1=np.percentile(beta1,95) + low_margin2=np.percentile(beta2,5) + high_margin2=np.percentile(beta2,95) +# print(low_margin1,high_margin1,low_margin2,high_margin2) +# print beta1 +# print beta2 + return (beta1,beta2) + + + +for i in range(200): + df = pd.read_csv('./data/blr_data_1'+str(i+1).zfill(3)+'.csv') + #df = pd.read_csv('blr_data_1001.csv') + y,m,x= np.matrix(df.y).T,np.matrix(df.n).T,np.matrix([df.X1,df.X2]).T + beta_0=np.matrix([0,0]).T + Sigma_0_inv=np.matrix(np.diag([1,1])) + beta1,beta2=bayes_logreg(m,y,x,beta_0,Sigma_0_inv,niter=10000,burnin=1000,print_every=1000,retune=100,verbose=True) +# print beta1 +# print beta2 + with open("./results/out_1" + str(i+1).zfill(3)+".csv", 'w') as out:# output file + writer = csv.writer(out, dialect='excel') + for row in range(100): + # print np.percentile(beta1,row),np.percentile(beta2,row),"\n" + writer.writerow((round(np.percentile(beta1,row),7),round(np.percentile(beta2,row),7))) + out.closed -################################################# -beta_0 = np.zeros(2) -Sigma_0_inv = np.diag(np.ones(p)) -# More stuff... -################################################# # Read data corresponding to appropriate sim_num: diff --git a/HW1/BayesLogit/BLR_post_process.sh b/HW1/BayesLogit/BLR_post_process.sh index 13c24e97..5be7b9e7 100644 --- a/HW1/BayesLogit/BLR_post_process.sh +++ b/HW1/BayesLogit/BLR_post_process.sh @@ -1,6 +1,6 @@ #!/bin/bash -l -module load R/3.0.0 +module load R/3.0.2 #SBATCH --job-name=blr_post_process #SBATCH --output=dump/blr_post_process.out diff --git a/HW1/BayesLogit/coverage_line_plot.pdf b/HW1/BayesLogit/coverage_line_plot.pdf new file mode 100644 index 0000000000000000000000000000000000000000..510caa15e728ae76b859dd7928cd881d34bb3a35 GIT binary patch literal 6698 zcmb7}c{tSV_xNpDM%g1p#;(Gc!C11B5y={|lxYk`voMP-W6z#Q5z3mSBqSjrWh>dW zh{zHmWGlw@eUInq`8?0(`Tf4v^?hI0ea(HJ``qW8`^-PDb8cx%BVz@qq6$blY&>it 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generated in R 3.0.1 by xtable 1.7-1 package +% Mon Oct 28 21:46:58 2013 +\begin{table}[ht] +\centering +\begin{tabular}{rrr} + \hline + & beta\_0 & beta\_1 \\ + \hline +p\_01 & 0.01 & 0.00 \\ + p\_05 & 0.06 & 0.02 \\ + p\_10 & 0.10 & 0.06 \\ + p\_25 & 0.23 & 0.20 \\ + p\_50 & 0.51 & 0.47 \\ + p\_75 & 0.73 & 0.72 \\ + p\_90 & 0.88 & 0.85 \\ + p\_95 & 0.94 & 0.94 \\ + p\_99 & 0.99 & 0.97 \\ + \hline +\end{tabular} +\end{table} diff --git a/HW1/BayesLogit/coverage_summaries.txt b/HW1/BayesLogit/coverage_summaries.txt new file mode 100644 index 00000000..b74161c7 --- /dev/null +++ b/HW1/BayesLogit/coverage_summaries.txt @@ -0,0 +1,10 @@ + beta_0 beta_1 +p_01 0.005 0.000 +p_05 0.055 0.020 +p_10 0.105 0.055 +p_25 0.235 0.195 +p_50 0.505 0.470 +p_75 0.730 0.725 +p_90 0.875 0.855 +p_95 0.935 0.945 +p_99 0.990 0.975 diff --git a/HW1/BayesLogit/data/blr_fit.py b/HW1/BayesLogit/data/blr_fit.py new file mode 100644 index 00000000..6851449f --- /dev/null +++ b/HW1/BayesLogit/data/blr_fit.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Oct 22 23:20:19 2013 + +@author: jinzhenfan +""" +from scipy import stats +import matplotlib.pyplot as plt +import numpy as np +import scipy as sp +import csv +import pandas as pd +import math + + +print("Hello") + +def log_density(m,y,x,beta,beta_0,Sigma_0_inv): + n=len(m) + m=np.matrix(m) + y=np.matrix(y) + x=np.matrix(x) + beta_0=np.matrix(beta_0) + beta=np.matrix(beta) + Sigma_0_inv=np.matrix(Sigma_0_inv) + s=0 + for i in range(n): +# print(x[i]) +# print(y[i]) +# print(beta) +# print(m[i]) + s=s+y[i]*(x[i]*beta)-m[i]*math.log(1+exp(x[i]*beta)) +# print beta.shape +# print beta_0.T.shape +# print Sigma_0_inv.shape +# print s.shape + log_post=s-(beta-beta_0).T*Sigma_0_inv*[(beta-beta_0)/2][0] + + return log_post + +def metropolis_hasting(trial,beta_init,v,m,y,x,beta_0,Sigma_0_inv): + acpt=0.0 + acpt_rate=0.0 + beta_curr=beta_init + beta_track=[0,beta_init] + for t in range(trial): + beta_prosal=np.random.multivariate_normal([0,0], v, 1).T + log_alpha=log_density(m,y,x,beta_prosal,beta_0,Sigma_0_inv)-log_density(m,y,x,beta_curr,beta_0,Sigma_0_inv)[0][0] + log_u=math.log(np.random.uniform(low=0.0, high=1.0, size=1)) +# print log_alpha + # print log_u + if log_u < log_alpha[0][0]: + beta_curr=beta_prosal + acpt+=1 + else: + beta_curr=beta_curr + beta_track.append(beta_curr) + acpt_rate=acpt/trial +# print("Acceptance is",acpt) +# print("Acceptance Rate is",str(acpt_rate)) + beta_track[0]=acpt_rate + return beta_track + +def confidence_interval(data, confidence=0.95): + a = 1.0*np.array(data) + n = len(a) + m, se = np.mean(a), sp.stats.stderr(a) + h = se * sp.stats.t._ppf((1+confidence)/2., n-1) + return m, m-h, m+h + +def bayes_logreg(m,y,x,beta_0,Sigma_0_inv,niter=10000,burnin=1000,print_every=1000,retune=100,verbose=True): + + v=np.diag([1,1]) + beta_init=beta_0 + acpt_rate=0 + +# tuning the covariace + while acpt_rate<0.4 or acpt_rate>0.6: + acpt_rate=metropolis_hasting(retune,beta_init,v,m,y,x,beta_0,Sigma_0_inv)[0] + # print acpt_rate + if acpt_rate<0.4: + v=v/exp(1) + elif acpt_rate>0.6: + v=v*exp(1) + print("v is",v) + + print("The covariance after tuning is",v,",","with an acceptance rate of",acpt_rate) + +#sampling + beta_curr=beta_0 + beta_track=metropolis_hasting(niter+burnin,beta_init,v,m,y,x,beta_0,Sigma_0_inv) + beta_samples=beta_track[(burnin+2):(niter+burnin)] + print("The acceptance rate is",beta_track[0]) +# beta1=beta_samples[1,] +# beta2=beta_samples[2,] +# plt.plot[beta_samples[1,]] +# effectiveSize(beta1) +# effectiveSize(beta2) +# low_margin=np.percentile(beta1,5) +# high_margin=np.percentile(beta1,95) + +#for i in the range(1:200) +# df = pd.read_csv('blr_data_1'+str(i).zfill(3)'.csv') +df = pd.read_csv('blr_data_1001.csv') +y,m,x= np.matrix(df.y).T,np.matrix(df.n).T,np.matrix([df.X1,df.X2]).T +beta_0=np.matrix([0,0]).T +Sigma_0_inv=np.matrix(np.diag([1,1])) 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21:40:05 2013 + +@author: jinzhen +""" +from __future__ import division +import math +import sys +import numpy as np +import csv + + +print "==============================" +print "Python version:" +print str(sys.version) +print "Numpy version:" +print str(np.version.version) +print "==============================" + +mini = False +verbose = False + +datadir = "/home/pdbaines/data/" +outpath = "output/" + +# mini or full? +if mini: + rootfilename = "blb_lin_reg_mini.txt" +else: + rootfilename = "blb_lin_reg_data.txt" + +######################################################################################## +## Handle batch job arguments: + +nargs = len(sys.argv) +print 'Command line arguments: ' + str(sys.argv) + +#### +# sim_start ==> Lowest simulation number to be analyzed by this particular batch job +### + +####################### +sim_start = 1000 +length_datasets = 250 +####################### + +# Note: this only sets the random seed for numpy, so if you intend +# on using other modules to generate random numbers, then be sure +# to set the appropriate RNG here + +if (nargs==1): + sim_num = sim_start + 1 + sim_seed = 1330931 +else: + # Decide on the job number, usually start at 1000: + sim_num = sim_start + int(sys.argv[nargs-1]) + # Set a different random seed for every job number!!! + sim_seed = 762*sim_num + 1330931 +s=5 +r=50 + +job_num=int(sys.argv[nargs-1]) +# Find r and s indices: +s_index=job_num//50+1 +r_index=job_num%50 + +if r_index==0: + r_index=50 + +if r_index==50: + s_index=s_index-1 +# Reset random seed so things differ: +np.random.seed(s_index) + +# Bootstrap datasets numbered 1001-1250 + +######################################################################################## +######################################################################################## +def read_some_lines(filename,indices,nr,nc,print_every=1000,verbose=True): + # Storage: + subset = np.empty(nr*nc) + subset.shape = (nr,nc) + # Read file and extract selected rows: + row_num = 0 + sampled_so_far = 0 + # utility stuff: + next_ix = indices[0] + # append value to avoid oob: + n=0 + indices = np.append(indices,n+1) + with open(filename, 'rb') as csvfile: + datareader = csv.reader(csvfile, delimiter=',') + for current_row in datareader: + # Check if needed: + if row_num == next_ix: + # Yup: + if verbose: + print "Sampled row " + str(row_num) + ":" + print current_row + # Store values: + subset[sampled_so_far,:] = current_row[:] + # Increment indices: + sampled_so_far += 1 + next_ix = indices[sampled_so_far] + # Increment row counter: + row_num += 1 + # Print progress? + if verbose and ((row_num % print_every) == 0): + print "Finished row " + str(row_num) + " (sampled " + str(sampled_so_far) + "/" + str(nr) + " rows so far)" + return subset + + +# (Weighted) Least Squares Code: + +def wls(y, X, w, verbose=False): + # + # Fits: + # y_i = (x_i)^{T} \beta + \epsilon_{i}, + # where \epsilon_{i} \iid N(0,\sigma^{2}/w_{i}). + # i.e., each y_{i} represents the mean of w_{i} + # data points. Setting w=1 corresponds to each + # data point occuring once. + # + # Computes; \hat{\beta} = (X'W^{-1}X)^{-1}X'W^{-1}y + if verbose: + print "y (shape = " + str(y.shape) + "):" + print y + print "X (shape = " + str(X.shape) + "):" + print X + print "w (shape = " + str(w.shape) + "):" + print w + + Winv = np.diag(1.0/w) + Winv_y = np.dot(Winv,y) + Xp = np.transpose(X) + XpWinv_y = np.dot(Xp,Winv_y) + XpWinvX = np.dot(Xp,np.dot(Winv,X)) + beta_hat = np.linalg.solve(XpWinvX,XpWinv_y) + + if verbose: + print "Winv:" + print Winv + print "Winv_y:" + print Winv_y + print "Xp:" + print Xp + print "XpWinv_y:" + print XpWinv_y + print "XpWinvX:" + print XpWinvX + print "beta_hat:" + print beta_hat + return beta_hat + + + +# dataset specs: +data_cov=1000 +data_col=data_cov+1 +data_row=1000000 +gamma=0.6 +sample_row=int(math.floor(data_row**gamma)) + +#sset parameter of subsets +subset = np.empty(sample_row*data_col) +subset.shape = (sample_row,data_col) +#set beta +beta_hat = np.empty(data_cov) + +# sample indices: + +indices=sorted(np.random.choice(data_row,sample_row,replace=False)) +subset=read_some_lines(datadir+rootfilename,indices,sample_row,data_col,print_every=1000,verbose=False) + +#reset seed +np.random.seed(sim_num) +# draw a bootstrap sample of size n from each subset +entry=np.random.multinomial(data_row,[1/sample_row]*sample_row,size=1)[0] +y=subset[:,-1] +X=subset[:,:-1] +# Fit linear model with weights: +beta_hat=wls(y, X, entry, verbose=False) + +# Output file: +outfile = "output/coef_%02d_%02d.txt" % (s_index,r_index) + +# Write to file: +with open(outfile,'w') as out: + + output = csv.writer(out, delimiter='\n') + output.writerow(beta_hat) + +out.closed + + +#============================================================================== +# +# print "=========================" +# print "sim_num = " + str(sim_num) +# print "s_index = " + str(s_index) +# print "r_index = " + str(r_index) +# print "=========================" +#============================================================================== + + + + + + + + + diff --git a/HW2/BLB/blb_lin_reg_job_orig.py b/HW2/BLB/blb_lin_reg_job_orig.py new file mode 100644 index 00000000..eb1fc92f --- /dev/null +++ b/HW2/BLB/blb_lin_reg_job_orig.py @@ -0,0 +1,149 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 6 21:40:05 2013 + +@author: jinzhen +""" +from __future__ import division +import numpy as np +import csv +import math +#import sys + + + + + +## +# filename: name of csv file +# indices: array of row numbers to select +# nr: number of rows to sample i.e., len(indices) +# nc: number of cols in file +# print_every: number of lines per status update +# verbose: True/False, level of verbosity +## + +def read_some_lines_csv(filename,indices,nr,nc,print_every=1000,verbose=True): + # Storage: + subset = np.empty(nr*nc) + subset.shape = (nr,nc) + # Read file and extract selected rows: + row_num = 0 + sampled_so_far = 0 + # utility stuff: + next_ix = indices[0] + # append value to avoid oob: + n=0 + indices = np.append(indices,n+1) + with open(filename, 'rb') as csvfile: + datareader = csv.reader(csvfile, delimiter=',') + for current_row in datareader: + # Check if needed: + if row_num == next_ix: + # Yup: + if verbose: + print "Sampled row " + str(row_num) + ":" + print current_row + # Store values: + subset[sampled_so_far,:] = current_row[:] + # Increment indices: + sampled_so_far += 1 + next_ix = indices[sampled_so_far] + # Increment row counter: + row_num += 1 + # Print progress? + if verbose and ((row_num % print_every) == 0): + print "Finished row " + str(row_num) + " (sampled " + str(sampled_so_far) + "/" + str(nr) + " rows so far)" + return subset + + +# (Weighted) Least Squares Code: + +def wls(y, X, w, verbose=False): + # + # Fits: + # y_i = (x_i)^{T} \beta + \epsilon_{i}, + # where \epsilon_{i} \iid N(0,\sigma^{2}/w_{i}). + # i.e., each y_{i} represents the mean of w_{i} + # data points. Setting w=1 corresponds to each + # data point occuring once. + # + # Computes; \hat{\beta} = (X'W^{-1}X)^{-1}X'W^{-1}y + if verbose: + print "y (shape = " + str(y.shape) + "):" + print y + print "X (shape = " + str(X.shape) + "):" + print X + print "w (shape = " + str(w.shape) + "):" + print w + + Winv = np.diag(1.0/w) + Winv_y = np.dot(Winv,y) + Xp = np.transpose(X) + XpWinv_y = np.dot(Xp,Winv_y) + XpWinvX = np.dot(Xp,np.dot(Winv,X)) + beta_hat = np.linalg.solve(XpWinvX,XpWinv_y) + + if verbose: + print "Winv:" + print Winv + print "Winv_y:" + print Winv_y + print "Xp:" + print Xp + print "XpWinv_y:" + print XpWinv_y + print "XpWinvX:" + print XpWinvX + print "beta_hat:" + print beta_hat + return beta_hat + + + +#parameters +data_cov=40 +data_col=data_cov+1 +#n +data_row=10000 +gamma=0.6 +#b +sample_row=int(math.floor(data_row**gamma)) +s=5 +r=50 +#sample bag of bootstrap subsets +subset = np.empty(data_col*sample_row*s) +subset.shape = (s,sample_row,data_col) +sub_SE=np.empty(s*data_cov) +sub_SE.shape=(s,data_cov) + +for j in range(s): + +# bootstrap = np.empty(data_col*data_row*s) +# bootstrap.shape = (s,data_row,data_col) + beta_hat = np.empty(r*data_cov) + beta_hat.shape= (r,data_cov) + indices=sorted(np.random.choice(data_row,sample_row,replace=False)) + subset[j]=read_some_lines_csv('blb_lin_reg_mini.txt',indices,sample_row,data_col,print_every=1000,verbose=False) + + for k in range(r): + # draw a bootstrap sample of size n from each subset + entry=np.random.multinomial(data_row,[1/sample_row]*sample_row,size=1)[0] +# bootstrap[j]=np.repeat(subset[j],entry) + y=subset[j][:,-1] + X=subset[j][:,:-1] + beta_hat[k]=wls(y, X, entry, verbose=False) + + #calculate standard deviation of each bootstrap subsets + + sub_SE[j]=np.std(beta_hat,axis=0) + +# calculate the standard deviation of all +SE=np.mean(sub_SE,axis=0) +print SE + + + + + + diff --git a/HW2/BLB/blb_lin_reg_mini.txt b/HW2/BLB/blb_lin_reg_mini.txt new file mode 100644 index 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+1.0179734201129786 +1.3199404177326184 +1.32774316566343 +0.90021536797630075 +1.0283496457494374 +0.81901948390003765 +0.95584667117822375 +1.1968680447827649 +1.0746076971421763 +1.3001014159399793 +1.0227656485136347 diff --git a/HW2/BLB/read_some_lines.py b/HW2/BLB/read_some_lines.py index 4689d93a..98d5cc53 100644 --- a/HW2/BLB/read_some_lines.py +++ b/HW2/BLB/read_some_lines.py @@ -1,6 +1,6 @@ import csv import sys - +import numpy as np ## # filename: name of csv file # indices: array of row numbers to select @@ -10,13 +10,14 @@ # verbose: True/False, level of verbosity ## -def read_some_lines_csv(filename,indices,nr,nc,print_every=1000,verbose=False): +def read_some_lines(filename,indices,nr,nc,print_every=1000,verbose=False): # Storage: subset = np.empty(nr*nc) subset.shape = (nr,nc) # Read file and extract selected rows: row_num = 0 sampled_so_far = 0 + n=0 # utility stuff: next_ix = indices[0] # append value to avoid oob: diff --git a/HW2/BLB/read_some_lines.pyc 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z|9?!!Z>j%Se%}8xF#q}e&p!nJvi|=M>G&PzKP)VN?*IP~{6qf#U(oTJ@;^JvkND5T z_~+;Ue+Yi*|NmP$etZ4L{OA7f55oVJ&;Js?-TxWc*#C_Gf8tO4i9hkbivJ4)16s@g Gc#Hr8cP07& literal 0 HcmV?d00001 From 2c2830a5639ee6fc81826ce6a16e063d5a3eb942 Mon Sep 17 00:00:00 2001 From: jinzhenfna Date: Wed, 27 Nov 2013 00:32:28 -0800 Subject: [PATCH 5/5] hw3 --- HW3/EM1.R | 55 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 HW3/EM1.R diff --git a/HW3/EM1.R b/HW3/EM1.R new file mode 100644 index 00000000..72a9b87b --- /dev/null +++ b/HW3/EM1.R @@ -0,0 +1,55 @@ +"bisection" <- function(func,l,u,epsilon,iteration,verbose=T){ + converged <- F + count=0 + while(!converged){ + c<-(l+u)/2 + if (abs(func(c))

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