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import os
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import math
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import matplotlib.pyplot as plt
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files = [f"models/{x}" for x in os.listdir("models") if x.endswith(".csv")]
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train_loss = {}
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eval_loss = {}
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def process_lines(lines):
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global train_loss
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global eval_loss
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name = fp.split("/")[1]
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vals = [x.split(",") for x in lines]
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train_loss[name] = (
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[int(x[0]) for x in vals],
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[math.log(float(x[1])) for x in vals],
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)
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if len(vals[0]) >= 3:
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eval_loss[name] = (
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[int(x[0]) for x in vals],
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[math.log(float(x[2])) for x in vals],
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)
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def smooth(scalars, weight):
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last = scalars[0]
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smoothed = list()
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for point in scalars:
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smoothed_val = last * weight + (1 - weight) * point
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smoothed.append(smoothed_val)
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last = smoothed_val
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return smoothed
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def plot(data, fname):
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fig, ax = plt.subplots()
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ax.grid()
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for name, val in data.items():
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ax.plot(val[0], smooth(val[1], 0.9), label=name)
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plt.legend(loc="upper right")
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plt.savefig(fname, dpi=300, bbox_inches='tight')
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for fp in files:
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with open(fp) as f:
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lines = f.readlines()
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process_lines(lines)
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plot(train_loss, "loss.png")
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plot(eval_loss, "loss-eval.png")
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