SoS Workflow Report

Command Line	`sos run Process_Oriented.sos -p Process_Oriented_Report.html -d`
Project directory	`/home/gaow/Documents/GIT/wiki/sos-docs/src/examples`
Worklow Name	`default`
Worklow ID	`d107c1ed414121b3`
Worklow Start Time	2018-06-13 20:08:13 (1528938493.7977116)
Worklow End Time	2018-06-13 20:08:55
Worklow Duration	41 sec
Completed steps	7
Ignored steps	1
Completed substeps	27
Ignored substeps	5

Command Line

sos run Process_Oriented.sos -p Process_Oriented_Report.html -d

Project directory

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

Worklow Name

default

Worklow ID

d107c1ed414121b3

Worklow Start Time

2018-06-13 20:08:13 (1528938493.7977116)

Worklow End Time

2018-06-13 20:08:55

Worklow Duration

41 sec

Completed steps

Ignored steps

Completed substeps

Ignored substeps

Steps

Workflow	Step	Input	Output	Start Time	Duration	Steps	Substeps
default				2018-06-13 20:08:13	41 sec	7 completed 1 ignored	27 completed 5 ignored
	default_1			2018-06-13 20:08:15	39 sec	1 completed	1 completed
	default_2	data_1.lasso.mse.csv (10) data_1.lasso.mse.csv 28 Bytes data_1.ridge.mse.csv 33 Bytes data_2.lasso.mse.csv 28 Bytes data_2.ridge.mse.csv 33 Bytes data_3.lasso.mse.csv 28 Bytes data_3.ridge.mse.csv 33 Bytes data_4.lasso.mse.csv 27 Bytes data_4.ridge.mse.csv 35 Bytes data_5.lasso.mse.csv 28 Bytes data_5.ridge.mse.csv 34 Bytes		2018-06-13 20:08:55	0 sec	1 completed	1 completed
ridge				2018-06-13 20:08:16	27 sec	3 completed	15 completed
	ridge_1		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	2018-06-13 20:08:20	3 sec	1 completed	5 completed
	ridge_2	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 143 Bytes data_2.ridge.predicted.csv 3.5 kB data_2.ridge.coef.csv 145 Bytes data_3.ridge.predicted.csv 3.5 kB data_3.ridge.coef.csv 145 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 145 Bytes	2018-06-13 20:08:23	16 sec	1 completed	5 completed
	ridge_3	data_1.ridge.pred... (10) data_1.ridge.predicted.csv 3.5 kB data_1.ridge.coef.csv 143 Bytes data_2.ridge.predicted.csv 3.5 kB data_2.ridge.coef.csv 145 Bytes data_3.ridge.predicted.csv 3.5 kB data_3.ridge.coef.csv 145 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 145 Bytes	data_1.ridge.mse.csv (5) data_1.ridge.mse.csv 33 Bytes data_2.ridge.mse.csv 33 Bytes data_3.ridge.mse.csv 33 Bytes data_4.ridge.mse.csv 35 Bytes data_5.ridge.mse.csv 34 Bytes	2018-06-13 20:08:40	3 sec	1 completed	5 completed
lasso				2018-06-13 20:08:44	10 sec	2 completed 1 ignored	10 completed 5 ignored
	lasso_1		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	2018-06-13 20:08:44	1 sec	1 ignored	5 ignored
	lasso_2	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 20:08:45	6 sec	1 completed	5 completed
	lasso_3	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 20:08:51	3 sec	1 completed	5 completed

Workflow

Step

Input

Output

Start Time

Duration

Timeline

Steps

Substeps

Tasks

default

2018-06-13 20:08:13

41 sec

7 completed 1 ignored

27 completed 5 ignored

default_1

2018-06-13 20:08:15

39 sec

1 completed

default_2

2018-06-13 20:08:55

0 sec

1 completed

ridge

2018-06-13 20:08:16

27 sec

3 completed

15 completed

ridge_1

2018-06-13 20:08:20

3 sec

1 completed

5 completed

ridge_2

2018-06-13 20:08:23

16 sec

1 completed

5 completed

ridge_3

2018-06-13 20:08:40

3 sec

1 completed

5 completed

lasso

2018-06-13 20:08:44

10 sec

2 completed 1 ignored

10 completed 5 ignored

lasso_1

2018-06-13 20:08:44

1 sec

1 ignored

5 ignored

lasso_2

2018-06-13 20:08:45

6 sec

1 completed

5 completed

lasso_3

2018-06-13 20:08:51

3 sec

1 completed

5 completed

Script

105 lines 4.4 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 "forward" flavor

parameter: beta = [3, 1.5, 0, 0, 2, 0, 0, 0]

[lasso_1, ridge_1: shared = {'data_files': 'step_output'}]
# Simulate sparse data-sets
depends: R_library("MASS")
parameter: N = (40, 200) # training and testing samples
parameter: rstd = 3
parameter: replicate = [x+1 for x in range(5)]
input: None, for_each = ['replicate'], concurrent = True
output: f"data_{_replicate}.train.csv", f"data_{_replicate}.test.csv"
R: expand = "${ }"
  set.seed(${_replicate})
  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_2]
# Ridge regression model implemented in R
# Build predictor via cross-validation and make prediction
depends: R_library("glmnet")
parameter: nfolds = 5
input: 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_2]
# LASSO model implemented in Python
# Build predictor via cross-validation and make prediction
depends: Py_Module("sklearn")
parameter: nfolds = 5
input: 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_)

[lasso_3, ridge_3]
# 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: sos_variable('data_files')
parameter: replicates = list(zip(*(iter(data_files),) * 2))
input: group_by = 2, group_with = 'replicates', concurrent = True
output: f"{_input[0]:nn}.mse.csv"
R: expand = "${ }", stderr = False
  b = c(${paths(beta):,})
  Ytruth = as.matrix(read.csv(${_replicates[1]:r}, header = F)[,-1]) %*% b
  Ypred = scan(${_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_1]
sos_run('ridge')
sos_run('lasso')

[default_2]
depends: executable("pandoc")
input: dynamic("*.mse.csv")
import numpy as np
ridge_summary = np.mean(np.matrix([sum([x.strip().split() for x in open(f).readlines()], []) for f in _input if 'ridge' in str(f)], 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 _input if 'lasso' in str(f)], dtype = float).T, axis = 1).tolist()

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

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

download:
  https://vatlab.github.io/sos-docs/css/pandoc.css

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

         

SoS Workflow Report

Steps

Execution DAG

Script