File size: 1,754 Bytes
e471495 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import marimo
__generated_with = "0.14.17"
app = marimo.App(width="medium")
@app.cell
def _():
import os
import subprocess
import sys
from pathlib import Path
import marimo as mo
return Path, mo, os, subprocess, sys
@app.cell
def _(Path, os):
ROOT = Path(__file__).resolve().parent
SCRIPT = ROOT / "scripts" / "decode_reimplementation.py"
OUTPUT = ROOT / "decode_reimplementation_outputs"
TRAIN_ENV = os.environ.copy()
TRAIN_ENV["DECODE_DISABLE_TENSORFLOW"] = "1"
return OUTPUT, SCRIPT, TRAIN_ENV
@app.cell
def _(mo):
mode = mo.ui.dropdown(["paper_buildings", "meters"], value="paper_buildings", label="Scope")
model_case = mo.ui.dropdown(["baselines", "lstm", "cnn", "tcn", "timesnet"], value="baselines", label="Model case")
run = mo.ui.run_button(label="Train full dataset")
mo.vstack([mo.md("# DECODE full-data experiments"), mode, model_case, run])
return mode, model_case, run
@app.cell
def _(SCRIPT, TRAIN_ENV, mode, model_case, run, subprocess, sys):
if run.value:
args = [sys.executable, str(SCRIPT), "--mode", mode.value]
if model_case.value == "baselines":
args += ["--skip-lstm"]
else:
args += ["--dl-models", model_case.value, "--epochs", "20", "--batch-size", "64"]
if model_case.value == "timesnet":
args += ["--lookback", "144"]
completed = subprocess.run(args, check=True, env=TRAIN_ENV, text=True)
else:
completed = None
completed
return
@app.cell
def _(OUTPUT, mo):
result_files = sorted(OUTPUT.glob("results_*.csv"))
mo.md("## Outputs\n" + "\n".join(f"- `{p}`" for p in result_files))
return
if __name__ == "__main__":
app.run()
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