DFFML Scratch Models

About

Models created without a machine learning framework.

Install

$ python3 -m pip install --user dffml-model-scratch

Usage

If 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-directory 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-directory tempdir -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-directory 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"
    }
]

License

Scratch Models are distributed under the terms of the MIT License.