SoS Workflow Report

Command Line	`sos run Mixed_Style.sos -p Mixed_Style_Report.html -d`
Project directory	`/home/gaow/Documents/GIT/wiki/sos-docs/src/examples`
Worklow Name	`default`
Worklow ID	`32c470172baa0b00`
Worklow Start Time	2018-06-13 20:15:29 (1528938929.2518892)
Worklow End Time	2018-06-13 20:15:59
Worklow Duration	30 sec
Completed steps	10
Completed substeps	26

Command Line

sos run Mixed_Style.sos -p Mixed_Style_Report.html -d

Project directory

/home/gaow/Documents/GIT/wiki/sos-docs/src/examples

Worklow Name

default

Worklow ID

32c470172baa0b00

Worklow Start Time

2018-06-13 20:15:29 (1528938929.2518892)

Worklow End Time

2018-06-13 20:15:59

Worklow Duration

30 sec

Completed steps

Completed substeps

Steps

Workflow	Step	Input	Output	Start Time	Duration	Steps	Substeps
default				2018-06-13 20:15:29	30 sec	10 completed	26 completed
	default			2018-06-13 20:15:31	28 sec	1 completed	1 completed
ridge				2018-06-13 20:15:31	19 sec	7 completed	15 completed
	simulation		data_1.train.csv (2) data_1.train.csv 6.5 kB data_1.test.csv 32.6 kB	2018-06-13 20:15:31	1 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 20:15:32	1 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 20:15:32	1 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 20:15:32	1 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 20:15:33	0 sec	1 completed	1 completed
	model fitting	data_1.train.csv (10) data_1.train.csv 6.5 kB data_1.test.csv 32.6 kB data_2.train.csv 6.5 kB data_2.test.csv 32.5 kB data_3.train.csv 6.5 kB data_3.test.csv 32.6 kB data_4.train.csv 6.6 kB data_4.test.csv 32.6 kB data_5.train.csv 6.5 kB data_5.test.csv 32.6 kB	data_1.ridge.pred... (10) data_1.ridge.predicted.csv 3.5 kB data_1.ridge.coef.csv 142 Bytes data_2.ridge.predicted.csv 3.5 kB data_2.ridge.coef.csv 146 Bytes data_3.ridge.predicted.csv 3.5 kB data_3.ridge.coef.csv 143 Bytes data_4.ridge.predicted.csv 3.5 kB data_4.ridge.coef.csv 142 Bytes data_5.ridge.predicted.csv 3.5 kB data_5.ridge.coef.csv 142 Bytes	2018-06-13 20:15:34	13 sec	1 completed	5 completed
	ridge_2	data_1.ridge.pred... (10) data_1.ridge.predicted.csv 3.5 kB data_1.ridge.coef.csv 142 Bytes data_2.ridge.predicted.csv 3.5 kB data_2.ridge.coef.csv 146 Bytes data_3.ridge.predicted.csv 3.5 kB data_3.ridge.coef.csv 143 Bytes data_4.ridge.predicted.csv 3.5 kB data_4.ridge.coef.csv 142 Bytes data_5.ridge.predicted.csv 3.5 kB data_5.ridge.coef.csv 142 Bytes	data_1.ridge.mse.csv (5) data_1.ridge.mse.csv 34 Bytes data_2.ridge.mse.csv 34 Bytes data_3.ridge.mse.csv 34 Bytes data_4.ridge.mse.csv 34 Bytes data_5.ridge.mse.csv 33 Bytes	2018-06-13 20:15:47	3 sec	1 completed	5 completed
lasso				2018-06-13 20:15:50	8 sec	2 completed	10 completed
	model fitting	data_1.train.csv (10) data_1.train.csv 6.5 kB data_1.test.csv 32.6 kB data_2.train.csv 6.5 kB data_2.test.csv 32.5 kB data_3.train.csv 6.5 kB data_3.test.csv 32.6 kB data_4.train.csv 6.6 kB data_4.test.csv 32.6 kB data_5.train.csv 6.5 kB data_5.test.csv 32.6 kB	data_1.lasso.pred... (10) data_1.lasso.predicted.csv 5.1 kB data_1.lasso.coef.csv 204 Bytes data_2.lasso.predicted.csv 5.1 kB data_2.lasso.coef.csv 200 Bytes data_3.lasso.predicted.csv 5.1 kB data_3.lasso.coef.csv 203 Bytes data_4.lasso.predicted.csv 5.1 kB data_4.lasso.coef.csv 200 Bytes data_5.lasso.predicted.csv 5.1 kB data_5.lasso.coef.csv 203 Bytes	2018-06-13 20:15:51	5 sec	1 completed	5 completed
	evaluate	data_1.lasso.pred... (10) data_1.lasso.predicted.csv 5.1 kB data_1.lasso.coef.csv 204 Bytes data_2.lasso.predicted.csv 5.1 kB data_2.lasso.coef.csv 200 Bytes data_3.lasso.predicted.csv 5.1 kB data_3.lasso.coef.csv 203 Bytes data_4.lasso.predicted.csv 5.1 kB data_4.lasso.coef.csv 200 Bytes data_5.lasso.predicted.csv 5.1 kB data_5.lasso.coef.csv 203 Bytes	data_1.lasso.mse.csv (5) data_1.lasso.mse.csv 28 Bytes data_2.lasso.mse.csv 28 Bytes data_3.lasso.mse.csv 28 Bytes data_4.lasso.mse.csv 27 Bytes data_5.lasso.mse.csv 28 Bytes	2018-06-13 20:15:56	3 sec	1 completed	5 completed

Workflow

Step

Input

Output

Start Time

Duration

Timeline

Steps

Substeps

Tasks

default

2018-06-13 20:15:29

30 sec

10 completed

26 completed

default

2018-06-13 20:15:31

28 sec

1 completed

ridge

2018-06-13 20:15:31

19 sec

7 completed

15 completed

simulation

2018-06-13 20:15:31

1 sec

1 completed

simulation

2018-06-13 20:15:32

1 sec

1 completed

simulation

2018-06-13 20:15:32

1 sec

1 completed

simulation

2018-06-13 20:15:32

1 sec

1 completed

simulation

2018-06-13 20:15:33

0 sec

1 completed

model fitting

2018-06-13 20:15:34

13 sec

1 completed

5 completed

ridge_2

2018-06-13 20:15:47

3 sec

1 completed

5 completed

lasso

2018-06-13 20:15:50

8 sec

2 completed

10 completed

model fitting

2018-06-13 20:15:51

5 sec

1 completed

5 completed

evaluate

2018-06-13 20:15:56

3 sec

1 completed

5 completed

Script

84 lines 3.5 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 "mixed" style that
# takes advantage of both "forward" and "backward" style
# workflows

parameter: beta = [3, 1.5, 0, 0, 2, 0, 0, 0]
data_files = [("data_{}.train.csv".format(x + 1), "data_{}.test.csv".format(x + 1)) 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_1 (model fitting)]
# Ridge regression model implemented in R
# Build predictor via cross-validation and make prediction
parameter: nfolds = 5
input: data_files, group_by = 2, concurrent = True
output: f"{_input[0]:nn}.ridge.predicted.csv", f"{_input[0]:nn}.ridge.coef.csv"
R: expand = "${ }"
  train = read.csv(${_input[0]:r}, header = F)
  test = read.csv(${_input[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_1 (model fitting)]
# LASSO model implemented in Python
# Build predictor via cross-validation and make prediction
parameter: nfolds = 5
input: data_files, group_by = 2, concurrent = True
output: f"{_input[0]:nn}.lasso.predicted.csv", f"{_input[0]:nn}.lasso.coef.csv"
python: expand = "${ }"
  import numpy as np
  from sklearn.linear_model import LassoCV
  train = np.genfromtxt(${_input[0]:r}, delimiter = ",")
  test = np.genfromtxt(${_input[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_)

[ridge_2, lasso_2 (evaluate)]
# 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: data_files
input: group_by = 2, group_with = "data_files", concurrent = True
output: f"{_input[0]:nn}.mse.csv"
R: expand = "${ }", stderr = False
  b = c(${paths(beta):,})
  Ytruth = as.matrix(read.csv(${path(_data_files[1]):r}, header = F)[,-1]) %*% b
  Ypred = scan(${_input[0]:r})
  prediction_mse = mean((Ytruth - Ypred)^2)
  betahat = scan(${_input[1]:r})
  estimation_mse = mean((betahat - b) ^ 2)
  cat(paste(prediction_mse, estimation_mse), file = ${_output:r})
 
[default]
sos_run('ridge')
sos_run('lasso')

         

SoS Workflow Report

Steps

Execution DAG

Script