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Models created without a machine learning framework.
$ python3 -m pip install --user dffml-model-scratchIf we have a dataset of years of experience in a job and the Salary (in thousands) at that job we can use the Simple Linear Regression model to predict a salary given the years of experience (or the other way around).
First we create the file containing the dataset. Then we train the model, get its accuracy. And using echo pipe a new csv file of data to predict into the model, and it will give us it prediction of the Salary.
$ cat > dataset.csv << EOF
Years,Salary
1,40
2,50
3,60
4,70
5,80
EOF
$ dffml train -model scratchslr -model-features Years:int:1 -model-predict Salary -model-location tempdir -sources f=csv -source-filename dataset.csv -source-readonly -log debug
$ dffml accuracy -model scratchslr -model-features Years:int:1 -model-predict Salary -model-location tempdir -features Salary -sources f=csv -source-filename dataset.csv -source-readonly -log debug
1.0
$ echo -e 'Years,Salary\n6,0\n' | dffml predict all -model scratchslr -model-features Years:int:1 -model-predict Salary -model-location tempdir -sources f=csv -source-filename /dev/stdin -source-readonly -log debug
[
{
"extra": {},
"features": {
"Salary": 0,
"Years": 6
},
"last_updated": "2019-07-19T09:46:45Z",
"prediction": {
"confidence": 1.0,
"value": 90.0
},
"key": "0"
}
]Scratch Models are distributed under the terms of the MIT License.
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