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+ name : CML
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+ on : [push]
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+ jobs :
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+ train-and-report :
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+ runs-on : ubuntu-latest
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+ container : docker://ghcr.io/iterative/cml:0-dvc2-base1
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+ steps :
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+ - uses : actions/checkout@v3
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+ - name : Train model
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+ env :
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+ REPO_TOKEN : ${{ secrets.GITHUB_TOKEN }}
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+ run : |
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+ pip install -r requirements.txt
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+ python train.py # generate plot.png
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+
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+ # Create CML report
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+ cat metrics.txt >> report.md
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+ echo '' >> report.md
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+ cml comment create report.md
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y_test = np .genfromtxt ("data/test_labels.csv" )
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# Fit a model
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- depth = 2
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+ depth = 5
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clf = RandomForestClassifier (max_depth = depth )
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clf .fit (X_train , y_train )
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