MLFlow connection added
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@@ -15,7 +15,6 @@ from src.preprocessing import build_preprocessor
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from src.models import make_model
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from src.stability import compute_shap_matrix
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def save_checkpoint(path, gen, pop, seed):
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state = {
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"gen": gen,
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45
runs.txt
Normal file
45
runs.txt
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@@ -0,0 +1,45 @@
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Cross validation baseline:
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python3 -m src.protocols_methodology.exp_cv --dataset adult --algo rf --n-folds 5 --seeds 0 1 2 3 4
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Bootstrap baseline:
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python3 -m src.protocols_methodology.exp_bootstrap --dataset adult --algo rf --n-bootstrap 30 --seeds 0 1 2 3 4
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Noise baseline:
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python3 -m src.protocols_methodology.exp_noise --dataset adult --algo rf --n-replicates 30 --noise-std 0.01 --seeds 0 1 2 3 4
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Small run: compute 0
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tmux new -s small
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python3 -m src.protocols_methodology.run_nsga_protocols --dataset adult --generations 3 --pop-size 8 --seed 0
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tmux attach-session -t small
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Medium run: compute 1
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tmux new -s medium
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python3 -m src.protocols_methodology.run_nsga_protocols --dataset adult --generations 10 --pop-size 24 --seed 0
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tmux attach-session -t medium
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Larger run: compute 3
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tmux new -s larger
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python3 -m src.protocols_methodology.run_nsga_protocols --dataset adult --generations 20 --pop-size 40 --seed 0
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tmux attach-session -t larger
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Send back:
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The three baseline summary csv files:
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runs/protocol_cv/__cv_summary.csv
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runs/protocol_bootstrap/__bootstrap_summary.csv
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runs/protocol_noise/__noise_summary.csv
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The three AutoML Pareto fronts:
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runs/_protocol_study/cv/pareto_front.csv
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runs/_protocol_study/bootstrap/pareto_front.csv
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runs/_protocol_study/noise/pareto_front.csv
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@@ -18,6 +18,12 @@ from src.protocols_methodology.automl_protocol_adapters import (
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noise_protocol,
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)
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# Main network
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# mlflow.set_tracking_uri("http://192.168.2.169:5000")
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# Cluster Subnet
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mlflow.set_tracking_uri("http://10.10.0.5:5000")
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# Network with DNS resolution (specified hosts or Tailnet)
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#mlflow.set_tracking_uri("http://medea:5000")
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def save_checkpoint(path, gen, pop, seed):
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state = {
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