fix: several graphs reiterated, todo: extrapolation to prove CNN will not scale well
This commit is contained in:
Robin Meersman
parent
079e867763
commit
46872274f4
19 changed files with 144 additions and 53 deletions
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155
make_graphs.py
155
make_graphs.py
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@ -1,5 +1,6 @@
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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if __name__ == "__main__":
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@ -9,63 +10,40 @@ if __name__ == "__main__":
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for dataset_type in df["dataset_type"].unique():
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for model_type in df["model_type"].unique():
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dataset_df = df[df["dataset_type"] == dataset_type]
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model_df = dataset_df[dataset_df["model_type"] == model_type]
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model_df = dataset_df[dataset_df["model_type"] == model_type].copy()
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# execution time
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plt.figure()
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grouped = model_df.groupby("context_length")["compression_time"].mean() / 1e9
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labels = grouped.index.astype(str) # "128", "256"
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x = np.arange(len(labels)) # [0, 1]
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plt.bar(x, grouped.values, width=0.6)
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plt.title(f"{model_type.capitalize()} mean compression time")
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plt.xticks(x, labels)
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plt.xlabel("Context length")
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plt.ylabel("Mean compression time [s]")
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_compression_time.png")
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plt.figure()
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grouped = model_df.groupby("context_length")["decompression_time"].mean() / 1e9
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labels = grouped.index.astype(str) # "128", "256"
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x = np.arange(len(labels)) # [0, 1]
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plt.bar(x, grouped.values, width=0.6)
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plt.title(f"{model_type.capitalize()} mean decompression time")
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plt.xticks(x, labels)
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plt.xlabel("Context length")
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plt.ylabel("Mean decompression time [s]")
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_decompression_time.png")
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# loss
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plt.figure(figsize=(10, 4))
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bar_height = 0.25
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files = model_df["input_file_name"].unique()
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y = np.arange(len(files))
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c256 = model_df[model_df["context_length"] == 256]
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c128 = model_df[model_df["context_length"] == 128]
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plt.barh(
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y - bar_height / 2,
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c256["mse_loss"],
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height=bar_height,
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label="256"
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model_df["original_file_size_mb"] = model_df["original_file_size"] / 1e6
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# compression
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sns.lineplot(
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data=model_df,
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x="original_file_size_mb",
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y="compression_time",
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hue="context_length",
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palette="Set1",
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markers=True,
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legend="brief",
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linestyle="-"
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)
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plt.barh(
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y + bar_height / 2,
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c128["mse_loss"],
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height=bar_height,
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label="128"
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# decompression
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sns.lineplot(
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data=model_df,
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x="original_file_size_mb",
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y="decompression_time",
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hue="context_length",
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palette="Set1",
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markers=True,
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legend=False,
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linestyle="--"
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)
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plt.yticks(y, files, rotation=45, ha="right")
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plt.title(f"{model_type.capitalize()} MSE loss for different context lengths")
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plt.xlabel("MSE loss")
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plt.ylabel("Filename")
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plt.legend()
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plt.title(f"{model_type.capitalize()} compression and decompression time: {dataset_type}")
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plt.xlabel("file size [MB]")
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plt.ylabel("Time [s]")
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plt.yscale("log")
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plt.legend([f"{style}, {c_type}" for style, c_type in zip(["Solid", "Dashed"], ["compression", "decompression"])])
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_accuracy.png")
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_execution_time.png")
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# compression ratio
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plt.figure()
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@ -74,8 +52,79 @@ if __name__ == "__main__":
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plt.plot(c256["original_file_size"] / 1e6, c256["compressed_file_size"] / 1e6, label="256")
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plt.plot(c128["original_file_size"] / 1e6, c128["compressed_file_size"] / 1e6, label="128")
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plt.title(f"{model_type.capitalize()} compressed file evolution")
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plt.title(f"{model_type.capitalize()} compressed file evolution: {dataset_type}")
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plt.xlabel("Original file size [MB]")
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plt.ylabel("Compressed file size [MB]")
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plt.legend()
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_compression_ratio.png")
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plt.savefig(f"./graphs/{model_type}_{dataset_type}_compression_ratio.png")
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# if model_type == "cnn":
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# import numpy as np
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#
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# plt.figure()
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# for length, linestyle in [(128, '-'), (256, '--')]:
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# # extrapolate execution time to larger files
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# x = model_df[model_df["context_length"] == length]["original_file_size"] / 1e6
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# y = model_df[model_df["context_length"] == length]["compression_time"]
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# y_decom = model_df[model_df["context_length"] == length]["decompression_time"]
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#
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# b1, loga1 = np.polyfit(x, np.log(y), 1)
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# b2, loga2 = np.polyfit(x, np.log(y_decom), 1)
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#
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# x_comp = np.linspace(0, 40, 1000)
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# x_decomp = np.linspace(0, 40, 1000)
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# a1 = np.exp(loga1)
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# a2 = np.exp(loga2)
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#
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#
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# plt.plot(
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# x_comp, a1 * np.exp(x_comp),
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# label=f"{length} compression",
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# linestyle=linestyle
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# )
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# plt.plot(
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# x_decomp, a2 * np.exp(x_decomp),
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# label=f"{length} decompression",
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# linestyle=linestyle
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# )
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#
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#
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#
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# plt.legend()
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# plt.title(f"Extrapolated execution time for CNN compression and decompression")
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# plt.xlabel("File size [MB]")
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# plt.ylabel("Time [s]")
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# plt.tight_layout()
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# plt.savefig(f"./graphs/{model_type}_{dataset_type}_extrapolated_execution_time.png")
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for model_type in df["model_type"].unique():
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model_df = df[df["model_type"] == model_type]
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plt.figure(figsize=(10, 4))
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bar_height = 0.25
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files = model_df["input_file_name"].unique()
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y = np.arange(len(files))
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c256 = model_df[model_df["context_length"] == 256]
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c128 = model_df[model_df["context_length"] == 128]
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plt.barh(
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y - bar_height / 2,
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c256["mse_loss"],
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height=bar_height,
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label="256"
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)
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plt.barh(
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y + bar_height / 2,
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c128["mse_loss"],
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height=bar_height,
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label="128"
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)
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plt.yticks(y, files, rotation=45, ha="right")
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plt.title(f"MSE loss for different context lengths")
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plt.xlabel("MSE loss")
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plt.ylabel("Filename")
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plt.legend()
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plt.tight_layout()
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plt.savefig(f"./graphs/{model_type}_loss.png")
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@ -9,6 +9,8 @@ dependencies = [
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"fsspec==2024.9.0",
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"lorem>=0.1.1",
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"arithmeticencodingpython",
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"pandas-stubs~=2.3.3",
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"seaborn>=0.13.2",
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]
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[project.optional-dependencies]
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40
uv.lock
generated
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uv.lock
generated
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@ -1613,6 +1613,19 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/70/44/5191d2e4026f86a2a109053e194d3ba7a31a2d10a9c2348368c63ed4e85a/pandas-2.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87", size = 13202175, upload-time = "2025-09-29T23:31:59.173Z" },
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]
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[[package]]
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name = "pandas-stubs"
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version = "2.3.3.251201"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "numpy" },
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{ name = "types-pytz" },
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sdist = { url = "https://files.pythonhosted.org/packages/ee/a6/491b2af2cb3ee232765a73fb273a44cc1ac33b154f7745b2df2ee1dc4d01/pandas_stubs-2.3.3.251201.tar.gz", hash = "sha256:7a980f4f08cff2a6d7e4c6d6d26f4c5fcdb82a6f6531489b2f75c81567fe4536", size = 107787, upload-time = "2025-12-01T18:29:22.403Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/e2/68/78a3c253f146254b8e2c19f4a4768f272e12ef11001d9b45ec7b165db054/pandas_stubs-2.3.3.251201-py3-none-any.whl", hash = "sha256:eb5c9b6138bd8492fd74a47b09c9497341a278fcfbc8633ea4b35b230ebf4be5", size = 164638, upload-time = "2025-12-01T18:29:21.006Z" },
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[[package]]
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name = "pillow"
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version = "12.0.0"
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@ -1718,6 +1731,8 @@ dependencies = [
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{ name = "datasets" },
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{ name = "fsspec" },
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{ name = "lorem" },
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{ name = "pandas-stubs" },
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{ name = "seaborn" },
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]
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[package.optional-dependencies]
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@ -1746,7 +1761,9 @@ requires-dist = [
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{ name = "matplotlib", marker = "extra == 'dev'", specifier = ">=3.10.7" },
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{ name = "memray", marker = "extra == 'dev'", specifier = ">=1.19.1" },
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{ name = "optuna", marker = "extra == 'dev'", specifier = "==4.5.0" },
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{ name = "pandas-stubs", specifier = "~=2.3.3" },
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{ name = "regex", marker = "extra == 'dataset'", specifier = ">=2025.11.3" },
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{ name = "seaborn", specifier = ">=0.13.2" },
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{ name = "torch", marker = "extra == 'dev'", specifier = "==2.9.0" },
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{ name = "torchdata", marker = "extra == 'dev'", specifier = "==0.7.1" },
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{ name = "torchvision", marker = "extra == 'dev'", specifier = "==0.24.0" },
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@ -2116,6 +2133,20 @@ wheels = [
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[[package]]
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name = "seaborn"
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version = "0.13.2"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "matplotlib" },
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{ name = "numpy" },
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{ name = "pandas" },
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wheels = [
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[[package]]
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name = "setuptools"
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@ -2361,6 +2392,15 @@ wheels = [
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[[package]]
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[[package]]
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