Command Line	`sos run Outcome_Oriented.sos -p Outcome_Oriented_Report.html -d`
Project directory	`/home/gaow/Documents/GIT/wiki/sos-docs/src/examples`
Worklow Name	`default`
Worklow ID	`78af6d76b1f2987c`
Worklow Start Time	2018-06-13 20:09:17 (1528938557.5805972)
Worklow End Time	2018-06-13 20:09:43
Worklow Duration	25 sec
Completed steps	26
Completed substeps	26

Workflow	Step	Output	Start Time	Duration	Steps	Substeps
default			2018-06-13 20:09:17	25 sec	26 completed	26 completed
	simulation	data_1.train.csv (2) data_1.train.csv 6.5 kB data_1.test.csv 32.6 kB	2018-06-13 20:09:25	0 sec	1 completed	1 completed
	simulation	data_2.train.csv (2) data_2.train.csv 6.5 kB data_2.test.csv 32.5 kB	2018-06-13 20:09:25	0 sec	1 completed	1 completed
	simulation	data_3.train.csv (2) data_3.train.csv 6.5 kB data_3.test.csv 32.6 kB	2018-06-13 20:09:25	0 sec	1 completed	1 completed
	simulation	data_4.train.csv (2) data_4.train.csv 6.6 kB data_4.test.csv 32.6 kB	2018-06-13 20:09:25	0 sec	1 completed	1 completed
	ridge	data_2.ridge.pred... (2) data_2.ridge.predicted.csv 3.5 kB data_2.ridge.coef.csv 146 Bytes	2018-06-13 20:09:26	5 sec	1 completed	1 completed
	ridge	data_1.ridge.pred... (2) data_1.ridge.predicted.csv 3.5 kB data_1.ridge.coef.csv 145 Bytes	2018-06-13 20:09:26	6 sec	1 completed	1 completed
	ridge	data_3.ridge.pred... (2) data_3.ridge.predicted.csv 3.5 kB data_3.ridge.coef.csv 144 Bytes	2018-06-13 20:09:26	6 sec	1 completed	1 completed
	ridge	data_4.ridge.pred... (2) data_4.ridge.predicted.csv 3.5 kB data_4.ridge.coef.csv 144 Bytes	2018-06-13 20:09:27	6 sec	1 completed	1 completed
	evaluation	data_2.ridge.mse.csv data_2.ridge.mse.csv 34 Bytes	2018-06-13 20:09:33	0 sec	1 completed	1 completed
	evaluation	data_1.ridge.mse.csv data_1.ridge.mse.csv 33 Bytes	2018-06-13 20:09:33	0 sec	1 completed	1 completed
	evaluation	data_3.ridge.mse.csv data_3.ridge.mse.csv 34 Bytes	2018-06-13 20:09:33	0 sec	1 completed	1 completed
	evaluation	data_4.ridge.mse.csv data_4.ridge.mse.csv 35 Bytes	2018-06-13 20:09:33	0 sec	1 completed	1 completed
	simulation	data_5.train.csv (2) data_5.train.csv 6.5 kB data_5.test.csv 32.6 kB	2018-06-13 20:09:34	0 sec	1 completed	1 completed
	lasso	data_1.lasso.pred... (2) data_1.lasso.predicted.csv 5.1 kB data_1.lasso.coef.csv 204 Bytes	2018-06-13 20:09:34	2 sec	1 completed	1 completed
	lasso	data_2.lasso.pred... (2) data_2.lasso.predicted.csv 5.1 kB data_2.lasso.coef.csv 200 Bytes	2018-06-13 20:09:35	2 sec	1 completed	1 completed
	lasso	data_3.lasso.pred... (2) data_3.lasso.predicted.csv 5.1 kB data_3.lasso.coef.csv 203 Bytes	2018-06-13 20:09:35	2 sec	1 completed	1 completed
	evaluation	data_1.lasso.mse.csv data_1.lasso.mse.csv 28 Bytes	2018-06-13 20:09:37	0 sec	1 completed	1 completed
	evaluation	data_2.lasso.mse.csv data_2.lasso.mse.csv 28 Bytes	2018-06-13 20:09:37	0 sec	1 completed	1 completed
	evaluation	data_3.lasso.mse.csv data_3.lasso.mse.csv 28 Bytes	2018-06-13 20:09:37	0 sec	1 completed	1 completed
	lasso	data_4.lasso.pred... (2) data_4.lasso.predicted.csv 5.1 kB data_4.lasso.coef.csv 200 Bytes	2018-06-13 20:09:37	2 sec	1 completed	1 completed
	lasso	data_5.lasso.pred... (2) data_5.lasso.predicted.csv 5.1 kB data_5.lasso.coef.csv 203 Bytes	2018-06-13 20:09:38	2 sec	1 completed	1 completed
	evaluation	data_4.lasso.mse.csv data_4.lasso.mse.csv 27 Bytes	2018-06-13 20:09:40	0 sec	1 completed	1 completed
	ridge	data_5.ridge.pred... (2) data_5.ridge.predicted.csv 3.5 kB data_5.ridge.coef.csv 143 Bytes	2018-06-13 20:09:35	5 sec	1 completed	1 completed
	evaluation	data_5.lasso.mse.csv data_5.lasso.mse.csv 28 Bytes	2018-06-13 20:09:40	0 sec	1 completed	1 completed
	evaluation	data_5.ridge.mse.csv data_5.ridge.mse.csv 34 Bytes	2018-06-13 20:09:41	0 sec	1 completed	1 completed
	default		2018-06-13 20:09:42	0 sec	1 completed	1 completed

98 lines 4.5 kB

            #!/usr/bin/env sos-runner
# fileformat=SOS1.0
# Author: Gao Wang and Bo Peng

#
# Linear-model based prediction
#
# This script fits linear models
# using Lasso and Ridge regression
# and summarizes their prediction performance
# This script is written in a "backward" flavor
# aka the "Make Style"

parameter: beta = [3, 1.5, 0, 0, 2, 0, 0, 0]
ridge_result = [f'data_{x+1}.ridge.mse.csv' for x in range(5)]
lasso_result = [f'data_{x+1}.lasso.mse.csv' for x in range(5)]

[simulation: provides = ["data_{id}.train.csv", "data_{id}.test.csv"]]
# Simulate sparse data-sets
depends: R_library("MASS")
parameter: N = (40, 200) # training and testing samples
parameter: rstd = 3
output: f"data_{id}.train.csv", f"data_{id}.test.csv"
R: expand = "${ }"
  set.seed(${id})
  N = sum(c(${paths(N):,}))
  p = length(c(${paths(beta):,}))
  X = MASS::mvrnorm(n = N, rep(0, p), 0.5^abs(outer(1:p, 1:p, FUN = "-")))
  Y = X %*% c(${paths(beta):,}) + rnorm(N, mean = 0, sd = ${rstd})
  Xtrain = X[1:${N[0]},]; Xtest = X[(${N[0]}+1):(${N[0]}+${N[1]}),]
  Ytrain = Y[1:${N[0]}]; Ytest = Y[(${N[0]}+1):(${N[0]}+${N[1]})]
  write.table(cbind(Ytrain, Xtrain), ${_output[0]:r}, row.names = F, col.names = F, sep = ',')
  write.table(cbind(Ytest, Xtest), ${_output[1]:r}, row.names = F, col.names = F, sep = ',')

[ridge: provides = ["data_{id}.ridge.predicted.csv", "data_{id}.ridge.coef.csv"]]
# Ridge regression model implemented in R
# Build predictor via cross-validation and make prediction
parameter: nfolds = 5
depends: f"data_{id}.train.csv", f"data_{id}.test.csv", R_library("glmnet")
output: f"data_{id}.ridge.predicted.csv", f"data_{id}.ridge.coef.csv"
R: expand = "${ }"
  train = read.csv(${_depends[0]:r}, header = F)
  test = read.csv(${_depends[1]:r}, header = F)
  model = glmnet::cv.glmnet(as.matrix(train[,-1]), train[,1], family = "gaussian", alpha = 0, nfolds = ${nfolds}, intercept = F)
  betahat = as.vector(coef(model, s = "lambda.min")[-1])
  Ypred = predict(model, as.matrix(test[,-1]), s = "lambda.min")
  write.table(Ypred, ${_output[0]:r}, row.names = F, col.names = F, sep = ',')
  write.table(betahat, ${_output[1]:r}, row.names = F, col.names = F, sep = ',')

[lasso: provides = ["data_{id}.lasso.predicted.csv", "data_{id}.lasso.coef.csv"]]
# LASSO model implemented in Python
# Build predictor via cross-validation and make prediction
parameter: nfolds = 5
depends: f"data_{id}.train.csv", f"data_{id}.test.csv", Py_Module("sklearn")
output: f"data_{id}.lasso.predicted.csv", f"data_{id}.lasso.coef.csv"
python: expand = "${ }"
  import numpy as np
  from sklearn.linear_model import LassoCV
  train = np.genfromtxt(${_depends[0]:r}, delimiter = ",")
  test = np.genfromtxt(${_depends[1]:r}, delimiter = ",")
  model = LassoCV(cv = ${nfolds}, fit_intercept = False).fit(train[:,1:], train[:,1])
  Ypred = model.predict(test[:,1:])
  np.savetxt(${_output[0]:r}, Ypred)
  np.savetxt(${_output[1]:r}, model.coef_)

[evaluation: provides = 'data_{id}.{method}.mse.csv']
# Evaluate predictors by calculating mean squared error
# of prediction vs truth (first line of output)
# and of betahat vs truth (2nd line of output)
depends: f"data_{id}.test.csv", f"data_{id}.{method}.predicted.csv",
         f"data_{id}.{method}.coef.csv"
R: expand = "${ }", stderr = False
  b = c(${paths(beta):,})
  Ytruth = as.matrix(read.csv(${_depends[0]:r}, header = F)[,-1]) %*% b
  Ypred = scan(${_depends[1]:r})
  prediction_mse = mean((Ytruth - Ypred)^2)
  betahat = scan(${_depends[2]:r})
  estimation_mse = mean((betahat - b) ^ 2)
  cat(paste(prediction_mse, estimation_mse), file = ${_output:r})

[get-pandoc-css: provides = 'pandoc.css']
download:
  https://raw.githubusercontent.com/vatlab/sos-docs/master/css/pandoc.css

[default]
depends: ridge_result, lasso_result, "pandoc.css", executable('pandoc')
import numpy as np
ridge_summary = np.mean(np.matrix([sum([x.strip().split() for x in open(f).readlines()], []) for f in ridge_result], dtype = float).T, axis = 1).tolist()
lasso_summary = np.mean(np.matrix([sum([x.strip().split() for x in open(f).readlines()], []) for f in lasso_result], dtype = float).T, axis = 1).tolist()

report: expand = "${ }", output = "report.md"

| Method | Avg. Estimation Error | Avg. Prediction Error |
|:------:|:-------:|:-------:|
| LASSO | ${lasso_summary[1]} | ${lasso_summary[0]} |
| Ridge | ${ridge_summary[1]} | ${ridge_summary[0]} |

pandoc: input = "report.md", output = "report.html", args = '{input:q} --css pandoc.css --output {output:q}'

         

SoS Workflow Report

Steps

Execution DAG

Script